From 07f29bb1844476100eaabc1895ed319b922c73b7 Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Fri, 25 Mar 2022 06:13:22 +0000 Subject: [PATCH 01/88] =?UTF-8?q?=E6=96=B0=E5=BB=BA=20SVD=5FID2019=5Ffor?= =?UTF-8?q?=5FTensorflow?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/.keep | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/.keep diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/.keep b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/.keep new file mode 100644 index 000000000..e69de29bb -- Gitee From 38c9b100a9567ee2db2b91813cf9260f8e65ca09 Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Fri, 25 Mar 2022 06:20:24 +0000 Subject: [PATCH 02/88] =?UTF-8?q?=E6=96=B0=E5=BB=BA=20GPU?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/.keep | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/.keep diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/.keep b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/.keep new file mode 100644 index 000000000..e69de29bb -- Gitee From 5cf6d84ed7bc5de07171a87063a11ef90e0ecc70 Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Fri, 25 Mar 2022 06:21:31 +0000 Subject: [PATCH 03/88] =?UTF-8?q?=E7=AC=AC=E4=B8=80=E6=AC=A1=E6=8F=90?= =?UTF-8?q?=E4=BA=A4?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../SVD_ID2019_for_Tensorflow/GPU/README.md | 183 ++++++++ .../GPU/gen_pt_test_data_gpu.py | 124 +++++ .../SVD_ID2019_for_Tensorflow/GPU/license.txt | 28 ++ .../GPU/main_point_cloud_gpu.py | 423 ++++++++++++++++++ .../GPU/requirements.txt | 35 ++ .../GPU/utils_gpu.py | 144 ++++++ 6 files changed, 937 insertions(+) create mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/README.md create mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/gen_pt_test_data_gpu.py create mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/license.txt create mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/main_point_cloud_gpu.py create mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/requirements.txt create mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/utils_gpu.py diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/README.md b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/README.md new file mode 100644 index 000000000..c7be6f5ba --- /dev/null +++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/README.md @@ -0,0 +1,183 @@ +

基本信息

+ +**发布者（Publisher）：Huawei** + +**应用领域（Application Domain）：CV** + +**版本（Version）：** + +**修改时间（Modified） ：2022.3.25** + +**框架（Framework）：TensorFlow 1.15.0** + +**处理器（Processor）：昇腾910** + +**描述（Description）：使用训练好的SVD模型，评估对称正交化在点云对准中的应用效果。** + +

概述

+ +给定两个三维点云图，利用SVD正交化过程SVDO+(M)将其投射到SO(3)上，要求网络预测最佳对齐它们的3D旋转。 + +- 开源代码： + + https://github.com/google-research/google-research/tree/master/special_orthogonalization。 + +- 参考论文： + + [An Analysis of SVD for Deep Rotation Estimation](https://arxiv.org/abs/2006.14616) + +- 参考实现： + + obs://cann-id2019/gpu/ + + +- 通过Git获取对应commit\_id的代码方法如下： + + ``` + git clone {repository_url} # 克隆仓库的代码 + cd {repository_name} # 切换到模型的代码仓目录 + git checkout {branch} # 切换到对应分支 + git reset --hard ｛commit_id｝ # 代码设置到对应的commit_id + cd ｛code_path｝ # 切换到模型代码所在路径，若仓库下只有该模型，则无需切换 + ``` + +## 默认配置 + +- 数据集获取：包含在dataset里。 + +- 训练超参 + + - log_step_count=200 + - save_summaries_steps=25000 + - train_steps=2200000 + - save_checkpoints_steps=100000 + - eval_examples=39900 + + + +

训练环境准备

+ +1. 硬件环境准备请参见各硬件产品文档"[驱动和固件安装升级指南]( https://support.huawei.com/enterprise/zh/category/ai-computing-platform-pid-1557196528909)"。需要在硬件设备上安装与CANN版本配套的固件与驱动。 +2. 宿主机上需要安装Docker并登录[Ascend Hub中心](https://ascendhub.huawei.com/#/detail?name=ascend-tensorflow-arm)获取镜像。 + + 当前模型支持的镜像列表如[表1](#zh-cn_topic_0000001074498056_table1519011227314)所示。 + + **表 1** 镜像列表 + + + + + + + + + + + + +

镜像名称

+

镜像版本

+

配套CANN版本

+
+

20.2.0

+

20.2

+
+ + + +## 脚本参数 + +``` +--input_test_files1 原始输入测试文件的正则表达式 +--input_test_files2 原始输入测试文件的正则表达式 +--output_directory1 将存储新测试文件的输出目录 +--output_directory2 将存储新测试文件的输出目录 +--num_rotations_per_file 每个测试点云的随机旋转增加数。默认为100 +--random_rotation_axang 如果为真，则使用该方法从原始基准代码中对随机旋转进行采样，否则样本采用哈尔测量。默认为真 +--method 指定用于预测旋转的方式。选项为"svd", "svd-inf", or "gs"。默认为“svd” +--checkpoint_dir 保存检查点位置 +--train_steps 训练迭代的次数。默认为2600000 +--save_checkpoints_steps 保存检查点的频率。默认为10000 +--log_step_count 日志记录一次的步数。默认为500 +--save_summaries_steps 保存一次summary的步数。默认为5000 +--learning_rate 默认为1e-5 +--lr_decay 如果为真，则衰减learning rate。默认为假 +--lr_decay_steps learning rate衰减步数。默认为35000 +--lr_decay_rate learning rate衰减速率。默认为0.95 +--predict_all_test 如果为真，则在最新的检查点上运行eval作业，并打印每个输入的错误。默认为假 +--eval_examples 测试样本的数量。默认为0 +--print_variable_names 打印模型变量名。默认为假 +--num_train_augmentations 增加每个输入点云的随机旋转数。默认为10 +--pt_cloud_train_files 匹配所有训练点文件的表达式 +--pt_cloud_test_files 匹配所有修改的测试点文件的表达式 + +``` + + + +## 运行 + +GPU运行命令如下： + +**修改原始测试数据** + +注：生成的文件points_test_modified、points0已包含在dataset文件夹中。 +```bash +# 将路径设置到训练点云图文件 +IN_FILES1=/shapenet/data/pc_plane/points/*.pts +IN_FILES2=/shapenet/data/pc_plane/points_test/*.pts + +# 设置新生成文件的路径 +NEW_TEST_FILES_DIR1=/shapenet/data/pc_plane/points0 +NEW_TEST_FILES_DIR2=/shapenet/data/pc_plane/points_test_modified + +# 决定旋转轴角的分布 +AXANG_SAMPLING=True + +python -m special_orthogonalization.gen_pt_test_data --input_test_files1=$IN_FILES1 --input_test_files2=$IN_FILES2 --output_directory1=$NEW_TEST_FILES_DIR1 --output_directory2=$NEW_TEST_FILES_DIR2 --random_rotation_axang=$AXANG_SAMPLING +``` + +**训练与评价** +```bash +# 将路径设置到原始训练数据 +TRAIN_FILES=/shapenet/data/pc_plane/points0/*.pts + +#将路径设置到旋转后的训练数据 +TEST_FILES=$NEW_TEST_FILES_DIR2/*.pts + +# 指定旋转预测方式 +METHOD=svd + +# 指定ckpt、summaries、评价结果等的存储路径 +OUT_DIR=/path/to/model + +python -m special_orthogonalization.main_point_cloud --method=$METHOD --checkpoint_dir=$OUT_DIR --log_step_count=200 --save_summaries_steps=25000 --pt_cloud_train_files=$TRAIN_FILES --pt_cloud_test_files=$TEST_FILES --train_steps=2200000 --save_checkpoints_steps=100000 --eval_examples=39900 +``` + +**从所有训练样本中生成统计数据** +```bash +# 打印均值、中位数、标准差和分位数 +python -m special_orthogonalization.main_point_cloud --method=$METHOD --checkpoint_dir=$OUT_DIR --pt_cloud_test_files=$TEST_FILES --predict_all_test=True +``` + +## 训练结果 +**精度对比：** + +由于弹性云服务器上Tesla V100的GPU训练环境，选用矩池云GPU的运行结果进行精度对比。 + + +| 测地线误差（°） | 论文发布 | GPU实测 | NPU实测 | +| ------------------------ | ------- | ----- | ------- | +| 平均值 | 1.63 | 5.55 | 待测 | +| 中值 | 0.89 | 3.65 | 待测 | +| 标准偏差 | 6.70 | 10.68 | 待测 | + +**性能对比：** + +取弹性云GPU运行的前2600步的global_step/sec平均值和NPU运行的前2600步的global_step/sec平均值进行对比，以达到性能对比的目的。 + +| 性能指标项 | 论文发布 | GPU实测 | NPU实测 | +| ------------------- | ------- | ------ | ------ | +| global_step/sec| 无 | 79.48 | 66.08 | + + diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/gen_pt_test_data_gpu.py b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/gen_pt_test_data_gpu.py new file mode 100644 index 000000000..ccef0b62f --- /dev/null +++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/gen_pt_test_data_gpu.py @@ -0,0 +1,124 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ + +# coding=utf-8 +# Copyright 2021 The Google Research Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Generate modified point cloud test data with fixed random rotations.""" +import glob +import os +import pathlib + +import utils_gpu +from absl import app +from absl import flags +import numpy as np +from scipy.stats import special_ortho_group + +FLAGS = flags.FLAGS +flags.DEFINE_string('input_test_files1', '', + 'Regular expression for the original input points files.') +flags.DEFINE_string('input_test_files2', '', + 'Regular expression for the original input test files.') +flags.DEFINE_string('output_directory1', '', + 'Output directory where new points files will be stored.') +flags.DEFINE_string('output_directory2', '', + 'Output directory where new test files will be stored.') +flags.DEFINE_integer('num_rotations_per_file', 100, + 'Number of random rotation augmentations per test point ' + 'cloud.') +flags.DEFINE_boolean('random_rotation_axang', True, + 'If true, samples random rotations using the method ' + 'from the original benchmark code. Otherwise samples ' + 'by Haar measure.') + +def gen_test_data(): + """Generate the new (modified) test data.""" + # Create output directory. + os.makedirs(FLAGS.output_directory1, exist_ok=True) + os.makedirs(FLAGS.output_directory2, exist_ok=True) + + # Get all test point cloud files in the original dataset. + input_test_files1 = glob.glob(FLAGS.input_test_files1) + input_test_files2 = glob.glob(FLAGS.input_test_files2) + + n = 1 + for in_file in input_test_files1: + pts = np.loadtxt(in_file) + m = pts.shape[0] + if n < m: + n = m + print("over") + + for in_file in input_test_files1: + out_file_prefix = pathlib.Path(in_file).stem + pts = np.loadtxt(in_file) # N x 3 + data1 = np.random.rand(n-pts.shape[0], 3) + pts = np.vstack((pts, data1)) + out_file1 = os.path.join( + FLAGS.output_directory1, '%s.pts' % (out_file_prefix)) + np.savetxt(out_file1, pts) + print("over") + + for in_file in input_test_files2: + out_file_prefix = pathlib.Path(in_file).stem + pts = np.loadtxt(in_file) # N x 3 + num_pts_to_keep = pts.shape[0] // 2 + pts = pts[:num_pts_to_keep, :] # N//2 x 3. + + for k in range(FLAGS.num_rotations_per_file): + if FLAGS.random_rotation_axang: + r = utils_gpu.random_rotation_benchmark_np(1) + r = r[0] + else: + r = special_ortho_group.rvs(3) + joined = np.float32(np.concatenate((r, pts), axis=0)) # (N//2+3) x 3. + out_file = os.path.join( + FLAGS.output_directory2, '%s_r%03d.pts' % (out_file_prefix, k)) + np.savetxt(out_file, joined) + print("over") + + +def main(unused_argv): + gen_test_data() + +if __name__ == '__main__': + app.run(main) \ No newline at end of file diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/license.txt b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/license.txt new file mode 100644 index 000000000..0384a2533 --- /dev/null +++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/license.txt @@ -0,0 +1,28 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/main_point_cloud_gpu.py b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/main_point_cloud_gpu.py new file mode 100644 index 000000000..a27b51b95 --- /dev/null +++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/main_point_cloud_gpu.py @@ -0,0 +1,423 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ + +# coding=utf-8 +# Copyright 2021 The Google Research Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Training and evaluation for the point cloud alignment experiment.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import utils_gpu +import time +from absl import flags +import numpy as np +import tensorflow as tf +import logging +FLAGS = flags.FLAGS +log = logging.getLogger('tensorflow') +log.setLevel(logging.DEBUG) + +# General flags. +flags.DEFINE_string('method', 'svd', + 'Specifies the method to use for predicting rotations. ' + 'Choices are "svd", "svd-inf", or "gs".') +flags.DEFINE_string('checkpoint_dir', '', + 'Locations for checkpoints, summaries, etc.') +flags.DEFINE_integer('train_steps', 2600000, 'Number of training iterations.') +flags.DEFINE_integer('save_checkpoints_steps', 10000, + 'How often to save checkpoints') +flags.DEFINE_integer('log_step_count', 500, 'How often to log the step count') +flags.DEFINE_integer('save_summaries_steps', 5000, + 'How often to save summaries.') +flags.DEFINE_float('learning_rate', 1e-5, 'Learning rate') +flags.DEFINE_boolean('lr_decay', False, 'Decay the learning rate if True.') +flags.DEFINE_integer('lr_decay_steps', 35000, + 'Learning rate decays steps.') +flags.DEFINE_float('lr_decay_rate', 0.95, + 'Learning rate decays rate.') +flags.DEFINE_boolean('predict_all_test', False, + 'If true, runs an eval job on latest checkpoint and ' + 'prints the error for each input.') +flags.DEFINE_integer('eval_examples', 0, 'Number of test examples.') +flags.DEFINE_boolean('print_variable_names', False, + 'Print model variable names.') + +# Flags only used in the point cloud alignment experiment. +flags.DEFINE_integer('num_train_augmentations', 10, + 'Number of random rotations for augmenting each input ' + 'point cloud.') +flags.DEFINE_string('pt_cloud_train_files', '', + 'Expression matching all training point files, e.g. ' + '/path/to/files/pc_plane/points/*.pts') +flags.DEFINE_string('pt_cloud_test_files', '', + 'Expression matching all modified test point files, e.g. ' + '/path/to/files/pc_plane/points_test/*.pts') +flags.DEFINE_boolean('random_rotation_axang', True, + 'If true, samples random rotations using the method ' + 'from the original benchmark code. Otherwise samples ' + 'by Haar measure.') + + +def pt_features(batch_pts): + """Input shape: [B, N, 3], output shape: [B, 1024].""" + with tf.variable_scope('ptenc', reuse=tf.AUTO_REUSE): + f1 = tf.layers.conv1d(inputs=batch_pts, filters=64, kernel_size=1) + f1 = tf.nn.leaky_relu(f1) + f2 = tf.layers.conv1d(inputs=f1, filters=128, kernel_size=1) + f2 = tf.nn.leaky_relu(f2) + f3 = tf.layers.conv1d(inputs=f2, filters=1024, kernel_size=1) + + f = tf.reduce_max(f3, axis=1, keep_dims=False) + return f + + +def regress_from_features(batch_features, out_dim): + """Regress to a rotation representation from point cloud encodings. + + In Zhou et al, CVPR19, the paper describes this regression network as an MLP + mapping 2048->512->512->out_dim, but the associated code implements it with + one less layer: 2048->512->out_dim. We mimic the code. + + Args: + batch_features: [batch_size, in_dim]. + out_dim: desired output dimensionality. + + Returns: + A [batch_size, out_dim] tensor. + """ + f1 = tf.layers.dense(batch_features, 512) + f1 = tf.nn.leaky_relu(f1) + f2 = tf.layers.dense(f1, out_dim) + return f2 + + +def net_point_cloud(points1, points2, mode): + """Predict a relative rotation given two point clouds. + + Args: + points1: [batch_size, N, 3] float tensor. + points2: [batch_size, N, 3] float tensor. + mode: tf.estimator.ModeKeys. + + Returns: + [batch_size, 3, 3] matrices. + """ + f1 = pt_features(points1) + f2 = pt_features(points2) + f = tf.concat([f1, f2], axis=-1) + + if FLAGS.method == 'svd': + p = regress_from_features(f, 9) + return utils_gpu.symmetric_orthogonalization(p) + + if FLAGS.method == 'svd-inf': + p = regress_from_features(f, 9) + if mode == tf.estimator.ModeKeys.TRAIN: + return tf.reshape(p, (-1, 3, 3)) + else: + return utils_gpu.symmetric_orthogonalization(p) + + if FLAGS.method == 'gs': + p = regress_from_features(f, 6) + return utils_gpu.gs_orthogonalization(p) + + +def model_fn(features, labels, mode, params): + """The model_fn used to construct the tf.Estimator.""" + del labels, params # Unused. + if mode == tf.estimator.ModeKeys.TRAIN: + # Training data has point cloud of size [1, N, 3] and random rotations + # of size [1, FLAGS.num_train_augmentations, 3, 3] + rot = features['rot'][0] + num_rot = FLAGS.num_train_augmentations + batch_pts1 = tf.tile(features['data'], [num_rot, 1, 1]) + # In this experiment it does not matter if we pre or post-multiply the + # rotation as long as we are consistent between training and eval. + batch_pts2 = tf.matmul(batch_pts1, rot) # post-multiplying! + else: + # Test data has point cloud of size [1, N, 3] and a single random + # rotation of size [1, 3, 3] + batch_pts1 = features['data'] + rot = features['rot'] + batch_pts2 = tf.matmul(batch_pts1, rot) + rot = tf.reshape(rot, (-1, 3, 3)) + + # Predict the rotation. + r = net_point_cloud(batch_pts1, batch_pts2, mode) + + # Compute the loss. + loss = tf.nn.l2_loss(rot - r) + + # Compute the relative angle in radians. + theta = utils_gpu.relative_angle(r, rot) + + # Mean angle error over the batch. + mean_theta = tf.reduce_mean(theta) + mean_theta_deg = mean_theta * 180.0 / np.pi + + # Train, eval, or predict depending on mode. + if mode == tf.estimator.ModeKeys.TRAIN: + tf.summary.scalar('train/loss', loss) + tf.summary.scalar('train/theta', mean_theta_deg) + global_step = tf.train.get_or_create_global_step() + + if FLAGS.lr_decay: + learning_rate = tf.train.exponential_decay( + FLAGS.learning_rate, + global_step, + FLAGS.lr_decay_steps, + FLAGS.lr_decay_rate) + else: + learning_rate = FLAGS.learning_rate + + tf.summary.scalar('train/learning_rate', learning_rate) + optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate) + update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) + with tf.control_dependencies(update_ops): + train_op = optimizer.minimize(loss, global_step=global_step) + return tf.estimator.EstimatorSpec( + mode=mode, + loss=loss, + train_op=train_op) + + if mode == tf.estimator.ModeKeys.EVAL: + if FLAGS.predict_all_test: + print_error_op = tf.print('error:', mean_theta_deg) + with tf.control_dependencies([print_error_op]): + eval_metric_ops = { + 'mean_degree_err': tf.metrics.mean(mean_theta_deg), + } + else: + eval_metric_ops = { + 'mean_degree_err': tf.metrics.mean(mean_theta_deg), + } + + return tf.estimator.EstimatorSpec( + mode=mode, + loss=loss, + eval_metric_ops=eval_metric_ops) + + if mode == tf.estimator.ModeKeys.PREDICT: + pred = {'error': mean_theta_deg} + return tf.estimator.EstimatorSpec( + mode=mode, + predictions=pred) + + +def train_input_fn(): + """Generate training data iterator from the .pts files.""" + def _file_to_matrix(pts_path): + """Read Nx3 point cloud from a .pts file.""" + file_buffer = tf.read_file(pts_path) + lines = tf.string_split([file_buffer], delimiter='\n') + lines1 = tf.string_split(lines.values, delimiter='\r') + values = tf.stack(tf.decode_csv(lines1.values, + record_defaults=[[0.0], [0.0], [0.0]], + field_delim=' ')) + values = tf.transpose(values)# 3xN --> Nx3. + # The experiment code in + # github.com/papagina/RotationContinuity/.../shapenet/code/train_pointnet.py + # only used the first half of the points in each file. + return values[:(tf.shape(values)[0]// 2), :] + + + + def _random_rotation(pts): + """Attach N random rotations to a point cloud.""" + if FLAGS.random_rotation_axang: + rotations = utils_gpu.random_rotation_benchmark(FLAGS.num_train_augmentations) + else: + rotations = utils_gpu.random_rotation(FLAGS.num_train_augmentations) + return pts, rotations + + pts_paths = tf.gfile.Glob(FLAGS.pt_cloud_train_files) + dataset = tf.data.Dataset.from_tensor_slices(pts_paths) + dataset = dataset.map(_file_to_matrix) + dataset = dataset.cache() # Comment out if memory cannot hold all the data. + dataset = dataset.shuffle(buffer_size=50, reshuffle_each_iteration=True) + dataset = dataset.repeat() + dataset = dataset.map(_random_rotation) + dataset = dataset.batch(1) + iterator = tf.data.make_one_shot_iterator(dataset) + batch_data, batch_rot = iterator.get_next() + features_dict = {'data': batch_data, 'rot': batch_rot} + batch_size = tf.shape(batch_data)[0] + batch_labels_dummy = tf.zeros(shape=(batch_size, 1)) + return (features_dict, batch_labels_dummy) + + + + +def eval_input_fn(): + """Generate test data from *modified* .pts files. + + See README and comments below for details on how the data is modified. + + Returns: + A tuple of features and associated labels. + """ + def _file_to_matrix(pts_path): + """Read Nx3 point cloud and 3x3 rotation matrix from a .pts file. + + The test data is a modified version of the original files. For each .pts + file we have (1) added a 3x3 rotation matrix for testing, and (2) removed + the second half of the point cloud since it is not used at all. + + Args: + pts_path: path to a .pts file. + + Returns: + A Nx3 point cloud. + A 3x3 rotation matrix. + """ + file_buffer = tf.read_file(pts_path) + lines = tf.string_split([file_buffer], delimiter='\n') + lines1 = tf.string_split(lines.values, delimiter='\r') + values = tf.stack(tf.decode_csv(lines1.values, + record_defaults=[[0.0], [0.0], [0.0]], field_delim=' ')) + values = tf.transpose(values) # 3xN --> Nx3. + # First three rows are the rotation matrix, remaining rows the point cloud. + rot = values[:3, :] + return values[4:, :], rot + + pts_paths = tf.gfile.Glob(FLAGS.pt_cloud_test_files) + dataset = tf.data.Dataset.from_tensor_slices(pts_paths) + dataset = dataset.map(_file_to_matrix) + dataset = dataset.batch(1) + iterator = tf.data.make_one_shot_iterator(dataset) + batch_data, batch_rot = iterator.get_next() + features_dict = {'data': batch_data, 'rot': batch_rot} + batch_size = tf.shape(batch_data)[0] + batch_labels_dummy = tf.zeros(shape=(batch_size, 1)) + return (features_dict, batch_labels_dummy) + + +def print_variable_names(): + """Print variable names in a model.""" + params = {'dummy': 0} + estimator = tf.estimator.Estimator( + model_fn=model_fn, + model_dir=FLAGS.checkpoint_dir, + params=params) + names = estimator.get_variable_names() + for name in names: + print(name) + + +def predict_all_test(): + """Print error statistics for the test dataset.""" + params = {'dummy': 0} + estimator = tf.estimator.Estimator( + model_fn=model_fn, + model_dir=FLAGS.checkpoint_dir, + params=params) + evals = estimator.predict(input_fn=eval_input_fn, yield_single_examples=False) + + # Print error statistics. + all_errors = [x['error'] for x in evals] + errors = np.array(all_errors) + print('Evaluated %d examples'%np.size(errors)) + print('Mean error: %f degrees', np.mean(errors)) + print('Median error: %f degrees', np.median(errors)) + print('Std: %f degrees', np.std(errors)) + sorted_errors = np.sort(errors) + n = np.size(sorted_errors) + print('\nPercentiles:') + for perc in range(1, 101): + index = np.int32(np.float32(n * perc) / 100.0) - 1 + print('%3d%%: %f'%(perc, sorted_errors[index])) + + +def train_and_eval(): + """Train and evaluate a model.""" + # create formatter and add it to the handlers + formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') + + # create file handler which logs even debug messages + fh = logging.FileHandler('loss.log') + fh.setLevel(logging.DEBUG) + fh.setFormatter(formatter) + log.addHandler(fh) + + save_summary_steps = FLAGS.save_summaries_steps + save_checkpoints_steps = FLAGS.save_checkpoints_steps + log_step_count = FLAGS.log_step_count + + config = tf.estimator.RunConfig( + save_summary_steps=save_summary_steps, + save_checkpoints_steps=save_checkpoints_steps, + log_step_count_steps=log_step_count, + keep_checkpoint_max=None) + + params = {'dummy': 0} + estimator = tf.estimator.Estimator( + model_fn=model_fn, + model_dir=FLAGS.checkpoint_dir, + config=config, + params=params) + + train_spec = tf.estimator.TrainSpec( + input_fn=train_input_fn, + max_steps=FLAGS.train_steps) + + eval_spec = tf.estimator.EvalSpec(input_fn=eval_input_fn, + start_delay_secs=60, + steps=FLAGS.eval_examples, + throttle_secs=60) + + tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec) + + +def main(argv=None): # pylint: disable=unused-argument + if FLAGS.print_variable_names: + print_variable_names() + return + + if FLAGS.predict_all_test: + predict_all_test() + else: + train_and_eval() + + +if __name__ == '__main__': + tf.app.run() diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/requirements.txt b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/requirements.txt new file mode 100644 index 000000000..1f2a5e41d --- /dev/null +++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/requirements.txt @@ -0,0 +1,35 @@ +absl-py==0.11.0 +astunparse==1.6.3 +cachetools==4.1.1 +certifi==2020.6.20 +chardet==3.0.4 +gast==0.3.3 +google-auth==1.23.0 +google-auth-oauthlib==0.4.2 +google-pasta==0.2.0 +grpcio==1.33.2 +h5py==2.10.0 +idna==2.10 +importlib-metadata==2.0.0 +Keras-Preprocessing==1.1.2 +Markdown==3.3.3 +numpy==1.18.5 +oauthlib==3.1.0 +opt-einsum==3.3.0 +protobuf==3.13.0 +pyasn1==0.4.8 +pyasn1-modules==0.2.8 +requests==2.24.0 +requests-oauthlib==1.3.0 +rsa==4.6 +scipy==1.5.3 +six==1.15.0 +tensorboard==2.3.0 +tensorboard-plugin-wit==1.7.0 +tensorflow==2.3.1 +tensorflow-estimator==2.3.0 +termcolor==1.1.0 +urllib3==1.25.11 +Werkzeug==1.0.1 +wrapt==1.12.1 +zipp==3.4.0 diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/utils_gpu.py b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/utils_gpu.py new file mode 100644 index 000000000..a08b4fa97 --- /dev/null +++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/utils_gpu.py @@ -0,0 +1,144 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ + +# coding=utf-8 +# Copyright 2021 The Google Research Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Utility functions.""" +import numpy as np +from scipy.stats import special_ortho_group +import tensorflow as tf + + +def relative_angle(r1, r2): + """Relative angle (radians) between 3D rotation matrices.""" + rel_rot = tf.matmul(tf.transpose(r1, perm=[0, 2, 1]), r2) + trace = rel_rot[:, 0, 0] + rel_rot[:, 1, 1] + rel_rot[:, 2, 2] + cos_theta = (trace - 1.0) / 2.0 + cos_theta = tf.minimum(cos_theta, tf.ones_like(cos_theta)) + cos_theta = tf.maximum(cos_theta, (-1.0) * tf.ones_like(cos_theta)) + theta = tf.acos(cos_theta) + return theta + + +def random_rotation_benchmark_np(n): + """Sample a random 3D rotation by method used in Zhou et al, CVPR19. + + This numpy function is a copy of the PyTorch function + get_sampled_rotation_matrices_by_axisAngle() in the code made available + for Zhou et al, CVPR19, at https://github.com/papagina/RotationContinuity/. + + Args: + n: the number of rotation matrices to return. + + Returns: + [n, 3, 3] np array. + """ + theta = np.random.uniform(-1, 1, n) * np.pi + sin = np.sin(theta) + axis = np.random.randn(n, 3) + axis = axis / np.maximum(np.linalg.norm(axis, axis=-1, keepdims=True), 1e-7) + qw = np.cos(theta) + qx = axis[:, 0] * sin + qy = axis[:, 1] * sin + qz = axis[:, 2] * sin + + xx = qx*qx + yy = qy*qy + zz = qz*qz + xy = qx*qy + xz = qx*qz + yz = qy*qz + xw = qx*qw + yw = qy*qw + zw = qz*qw + + row0 = np.stack((1-2*yy-2*zz, 2*xy-2*zw, 2*xz+2*yw), axis=-1) + row1 = np.stack((2*xy+2*zw, 1-2*xx-2*zz, 2*yz-2*xw), axis=-1) + row2 = np.stack((2*xz-2*yw, 2*yz+2*xw, 1-2*xx-2*yy), axis=-1) + matrix = np.stack((row0, row1, row2), axis=1) + + return matrix + + +def random_rotation_benchmark(n): + """A TF wrapper for random_rotation_benchmark_np().""" + mat = tf.py_func( + func=lambda t: np.float32(random_rotation_benchmark_np(t)), + inp=[n], + Tout=tf.float32, + stateful=True) + return tf.reshape(mat, (n, 3, 3)) + + +def random_rotation(n): + """Sample rotations from a uniform distribution on SO(3).""" + mat = tf.py_func( + func=lambda t: np.float32(special_ortho_group.rvs(3, size=t)), + inp=[n], + Tout=tf.float32, + stateful=True) + return tf.reshape(mat, (n, 3, 3)) + + +def symmetric_orthogonalization(x): + """Maps 9D input vectors onto SO(3) via symmetric orthogonalization.""" + # Innner dimensions of the input should be 3x3 matrices. + m = tf.reshape(x, (-1, 3, 3)) + _, u, v = tf.svd(m) + det = tf.linalg.det(tf.matmul(u, v, transpose_b=True)) + r = tf.matmul( + tf.concat([u[:, :, :-1], u[:, :, -1:] * tf.reshape(det, [-1, 1, 1])], 2), + v, transpose_b=True) + return r + + +def gs_orthogonalization(p6): + """Gram-Schmidt orthogonalization from 6D input.""" + # Input should be [batch_size, 6] + x = p6[:, 0:3] + y = p6[:, 3:6] + xn = tf.math.l2_normalize(x, axis=-1) + z = tf.linalg.cross(xn, y) + zn = tf.math.l2_normalize(z, axis=-1) + y = tf.linalg.cross(zn, xn) + r = tf.stack([xn, y, zn], -1) + return r -- Gitee From 0d047ec68e673dd19eb10b16c349b703fed1a4a4 Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Fri, 25 Mar 2022 06:23:10 +0000 Subject: [PATCH 04/88] =?UTF-8?q?=E6=96=B0=E5=BB=BA=20dataset?= MIME-Version: 1.0 Content-Type: text/plain; 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基本信息

- -**发布者（Publisher）：Huawei** - -**应用领域（Application Domain）：CV** - -**版本（Version）：** - -**修改时间（Modified） ：2022.3.25** - -**框架（Framework）：TensorFlow 1.15.0** - -**处理器（Processor）：昇腾910** - -**描述（Description）：使用训练好的SVD模型，评估对称正交化在点云对准中的应用效果。** - -

概述

- -给定两个三维点云图，利用SVD正交化过程SVDO+(M)将其投射到SO(3)上，要求网络预测最佳对齐它们的3D旋转。 - -- 开源代码： - - https://github.com/google-research/google-research/tree/master/special_orthogonalization。 - -- 参考论文： - - [An Analysis of SVD for Deep Rotation Estimation](https://arxiv.org/abs/2006.14616) - -- 参考实现： - - obs://cann-id2019/gpu/ - - -- 通过Git获取对应commit\_id的代码方法如下： - - ``` - git clone {repository_url} # 克隆仓库的代码 - cd {repository_name} # 切换到模型的代码仓目录 - git checkout {branch} # 切换到对应分支 - git reset --hard ｛commit_id｝ # 代码设置到对应的commit_id - cd ｛code_path｝ # 切换到模型代码所在路径，若仓库下只有该模型，则无需切换 - ``` - -## 默认配置 - -- 数据集获取：包含在dataset里。 - -- 训练超参 - - - log_step_count=200 - - save_summaries_steps=25000 - - train_steps=2200000 - - save_checkpoints_steps=100000 - - eval_examples=39900 - - - -

训练环境准备

- -1. 硬件环境准备请参见各硬件产品文档"[驱动和固件安装升级指南]( https://support.huawei.com/enterprise/zh/category/ai-computing-platform-pid-1557196528909)"。需要在硬件设备上安装与CANN版本配套的固件与驱动。 -2. 宿主机上需要安装Docker并登录[Ascend Hub中心](https://ascendhub.huawei.com/#/detail?name=ascend-tensorflow-arm)获取镜像。 - - 当前模型支持的镜像列表如[表1](#zh-cn_topic_0000001074498056_table1519011227314)所示。 - - **表 1** 镜像列表 - - - - - - - - - - - - -

镜像名称

-

镜像版本

-

配套CANN版本

-
-

20.2.0

-

20.2

-
- - - -## 脚本参数 - -``` ---input_test_files1 原始输入测试文件的正则表达式 ---input_test_files2 原始输入测试文件的正则表达式 ---output_directory1 将存储新测试文件的输出目录 ---output_directory2 将存储新测试文件的输出目录 ---num_rotations_per_file 每个测试点云的随机旋转增加数。默认为100 ---random_rotation_axang 如果为真，则使用该方法从原始基准代码中对随机旋转进行采样，否则样本采用哈尔测量。默认为真 ---method 指定用于预测旋转的方式。选项为"svd", "svd-inf", or "gs"。默认为“svd” ---checkpoint_dir 保存检查点位置 ---train_steps 训练迭代的次数。默认为2600000 ---save_checkpoints_steps 保存检查点的频率。默认为10000 ---log_step_count 日志记录一次的步数。默认为500 ---save_summaries_steps 保存一次summary的步数。默认为5000 ---learning_rate 默认为1e-5 ---lr_decay 如果为真，则衰减learning rate。默认为假 ---lr_decay_steps learning rate衰减步数。默认为35000 ---lr_decay_rate learning rate衰减速率。默认为0.95 ---predict_all_test 如果为真，则在最新的检查点上运行eval作业，并打印每个输入的错误。默认为假 ---eval_examples 测试样本的数量。默认为0 ---print_variable_names 打印模型变量名。默认为假 ---num_train_augmentations 增加每个输入点云的随机旋转数。默认为10 ---pt_cloud_train_files 匹配所有训练点文件的表达式 ---pt_cloud_test_files 匹配所有修改的测试点文件的表达式 - -``` - - - -## 运行 - -GPU运行命令如下： - -**修改原始测试数据** - -注：生成的文件points_test_modified、points0已包含在dataset文件夹中。 -```bash -# 将路径设置到训练点云图文件 -IN_FILES1=/shapenet/data/pc_plane/points/*.pts -IN_FILES2=/shapenet/data/pc_plane/points_test/*.pts - -# 设置新生成文件的路径 -NEW_TEST_FILES_DIR1=/shapenet/data/pc_plane/points0 -NEW_TEST_FILES_DIR2=/shapenet/data/pc_plane/points_test_modified - -# 决定旋转轴角的分布 -AXANG_SAMPLING=True - -python -m special_orthogonalization.gen_pt_test_data --input_test_files1=$IN_FILES1 --input_test_files2=$IN_FILES2 --output_directory1=$NEW_TEST_FILES_DIR1 --output_directory2=$NEW_TEST_FILES_DIR2 --random_rotation_axang=$AXANG_SAMPLING -``` - -**训练与评价** -```bash -# 将路径设置到原始训练数据 -TRAIN_FILES=/shapenet/data/pc_plane/points0/*.pts - -#将路径设置到旋转后的训练数据 -TEST_FILES=$NEW_TEST_FILES_DIR2/*.pts - -# 指定旋转预测方式 -METHOD=svd - -# 指定ckpt、summaries、评价结果等的存储路径 -OUT_DIR=/path/to/model - -python -m special_orthogonalization.main_point_cloud --method=$METHOD --checkpoint_dir=$OUT_DIR --log_step_count=200 --save_summaries_steps=25000 --pt_cloud_train_files=$TRAIN_FILES --pt_cloud_test_files=$TEST_FILES --train_steps=2200000 --save_checkpoints_steps=100000 --eval_examples=39900 -``` - -**从所有训练样本中生成统计数据** -```bash -# 打印均值、中位数、标准差和分位数 -python -m special_orthogonalization.main_point_cloud --method=$METHOD --checkpoint_dir=$OUT_DIR --pt_cloud_test_files=$TEST_FILES --predict_all_test=True -``` - -## 训练结果 -**精度对比：** - -由于弹性云服务器上Tesla V100的GPU训练环境，选用矩池云GPU的运行结果进行精度对比。 - - -| 测地线误差（°） | 论文发布 | GPU实测 | NPU实测 | -| ------------------------ | ------- | ----- | ------- | -| 平均值 | 1.63 | 5.55 | 待测 | -| 中值 | 0.89 | 3.65 | 待测 | -| 标准偏差 | 6.70 | 10.68 | 待测 | - -**性能对比：** - -取弹性云GPU运行的前2600步的global_step/sec平均值和NPU运行的前2600步的global_step/sec平均值进行对比，以达到性能对比的目的。 - -| 性能指标项 | 论文发布 | GPU实测 | NPU实测 | -| ------------------- | ------- | ------ | ------ | -| global_step/sec| 无 | 79.48 | 66.08 | - - -- Gitee From 20e70abf79c1d98dbb488afe1f3952bd362baf03 Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Fri, 25 Mar 2022 06:32:18 +0000 Subject: [PATCH 06/88] =?UTF-8?q?=E7=AC=AC=E4=B8=80=E6=AC=A1=E6=8F=90?= =?UTF-8?q?=E4=BA=A4?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../cv/SVD_ID2019_for_Tensorflow/README.md | 183 ++++++++++++++++++ 1 file changed, 183 insertions(+) create mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md new file mode 100644 index 000000000..03da5e24d --- /dev/null +++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md @@ -0,0 +1,183 @@ +

基本信息

+ +**发布者（Publisher）：Huawei** + +**应用领域（Application Domain）：CV** + +**版本（Version）：** + +**修改时间（Modified） ：2022.3.25** + +**框架（Framework）：TensorFlow 1.15.0** + +**处理器（Processor）：昇腾910** + +**描述（Description）：使用训练好的SVD模型，评估对称正交化在点云对准中的应用效果。** + +

概述

+ +给定两个三维点云图，利用SVD正交化过程SVDO+(M)将其投射到SO(3)上，要求网络预测最佳对齐它们的3D旋转。 + +- 开源代码： + + https://github.com/google-research/google-research/tree/master/special_orthogonalization。 + +- 参考论文： + + [An Analysis of SVD for Deep Rotation Estimation](https://arxiv.org/abs/2006.14616) + +- 参考实现： + + obs://cann-id2019/gpu/ + + +- 通过Git获取对应commit\_id的代码方法如下： + + ``` + git clone {repository_url} # 克隆仓库的代码 + cd {repository_name} # 切换到模型的代码仓目录 + git checkout {branch} # 切换到对应分支 + git reset --hard ｛commit_id｝ # 代码设置到对应的commit_id + cd ｛code_path｝ # 切换到模型代码所在路径，若仓库下只有该模型，则无需切换 + ``` + +## 默认配置 + +- 数据集获取：包含在dataset里，需要解压缩。 + +- 训练超参 + + - log_step_count=200 + - save_summaries_steps=25000 + - train_steps=2200000 + - save_checkpoints_steps=100000 + - eval_examples=39900 + + + +

训练环境准备

+ +1. 硬件环境准备请参见各硬件产品文档"[驱动和固件安装升级指南]( https://support.huawei.com/enterprise/zh/category/ai-computing-platform-pid-1557196528909)"。需要在硬件设备上安装与CANN版本配套的固件与驱动。 +2. 宿主机上需要安装Docker并登录[Ascend Hub中心](https://ascendhub.huawei.com/#/detail?name=ascend-tensorflow-arm)获取镜像。 + + 当前模型支持的镜像列表如[表1](#zh-cn_topic_0000001074498056_table1519011227314)所示。 + + **表 1** 镜像列表 + + + + + + + + + + + + +

镜像名称

+

镜像版本

+

配套CANN版本

+
+

20.2.0

+

20.2

+
+ + + +## 脚本参数 + +``` +--input_test_files1 原始输入测试文件的正则表达式 +--input_test_files2 原始输入测试文件的正则表达式 +--output_directory1 将存储新测试文件的输出目录 +--output_directory2 将存储新测试文件的输出目录 +--num_rotations_per_file 每个测试点云的随机旋转增加数。默认为100 +--random_rotation_axang 如果为真，则使用该方法从原始基准代码中对随机旋转进行采样，否则样本采用哈尔测量。默认为真 +--method 指定用于预测旋转的方式。选项为"svd", "svd-inf", or "gs"。默认为“svd” +--checkpoint_dir 保存检查点位置 +--train_steps 训练迭代的次数。默认为2600000 +--save_checkpoints_steps 保存检查点的频率。默认为10000 +--log_step_count 日志记录一次的步数。默认为500 +--save_summaries_steps 保存一次summary的步数。默认为5000 +--learning_rate 默认为1e-5 +--lr_decay 如果为真，则衰减learning rate。默认为假 +--lr_decay_steps learning rate衰减步数。默认为35000 +--lr_decay_rate learning rate衰减速率。默认为0.95 +--predict_all_test 如果为真，则在最新的检查点上运行eval作业，并打印每个输入的错误。默认为假 +--eval_examples 测试样本的数量。默认为0 +--print_variable_names 打印模型变量名。默认为假 +--num_train_augmentations 增加每个输入点云的随机旋转数。默认为10 +--pt_cloud_train_files 匹配所有训练点文件的表达式 +--pt_cloud_test_files 匹配所有修改的测试点文件的表达式 + +``` + + + +## 运行 + +GPU运行命令如下： + +**修改原始测试数据** + +注：生成的文件points_test_modified、points0已包含在dataset文件夹中。 +```bash +# 将路径设置到训练点云图文件 +IN_FILES1=/shapenet/data/pc_plane/points/*.pts +IN_FILES2=/shapenet/data/pc_plane/points_test/*.pts + +# 设置新生成文件的路径 +NEW_TEST_FILES_DIR1=/shapenet/data/pc_plane/points0 +NEW_TEST_FILES_DIR2=/shapenet/data/pc_plane/points_test_modified + +# 决定旋转轴角的分布 +AXANG_SAMPLING=True + +python -m special_orthogonalization.gen_pt_test_data --input_test_files1=$IN_FILES1 --input_test_files2=$IN_FILES2 --output_directory1=$NEW_TEST_FILES_DIR1 --output_directory2=$NEW_TEST_FILES_DIR2 --random_rotation_axang=$AXANG_SAMPLING +``` + +**训练与评价** +```bash +# 将路径设置到原始训练数据 +TRAIN_FILES=/shapenet/data/pc_plane/points0/*.pts + +#将路径设置到旋转后的训练数据 +TEST_FILES=$NEW_TEST_FILES_DIR2/*.pts + +# 指定旋转预测方式 +METHOD=svd + +# 指定ckpt、summaries、评价结果等的存储路径 +OUT_DIR=/path/to/model + +python -m special_orthogonalization.main_point_cloud --method=$METHOD --checkpoint_dir=$OUT_DIR --log_step_count=200 --save_summaries_steps=25000 --pt_cloud_train_files=$TRAIN_FILES --pt_cloud_test_files=$TEST_FILES --train_steps=2200000 --save_checkpoints_steps=100000 --eval_examples=39900 +``` + +**从所有训练样本中生成统计数据** +```bash +# 打印均值、中位数、标准差和分位数 +python -m special_orthogonalization.main_point_cloud --method=$METHOD --checkpoint_dir=$OUT_DIR --pt_cloud_test_files=$TEST_FILES --predict_all_test=True +``` + +## 训练结果 +**精度对比：** + +由于弹性云服务器上Tesla V100的GPU训练环境，选用矩池云GPU的运行结果进行精度对比。 + + +| 测地线误差（°） | 论文发布 | GPU实测 | NPU实测 | +| ------------------------ | ------- | ----- | ------- | +| 平均值 | 1.63 | 5.55 | 待测 | +| 中值 | 0.89 | 3.65 | 待测 | +| 标准偏差 | 6.70 | 10.68 | 待测 | + +**性能对比：** + +取弹性云GPU运行的前2600步的global_step/sec平均值和NPU运行的前2600步的global_step/sec平均值进行对比，以达到性能对比的目的。 + +| 性能指标项 | 论文发布 | GPU实测 | NPU实测 | +| ------------------- | ------- | ------ | ------ | +| global_step/sec| 无 | 79.48 | 66.08 | + + -- Gitee From 3961005204d28a09f3924470fd5cea7d24013e7e Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Fri, 25 Mar 2022 06:35:31 +0000 Subject: [PATCH 07/88] =?UTF-8?q?=E5=88=A0=E9=99=A4=E6=96=87=E4=BB=B6=20Te?= =?UTF-8?q?nsorFlow/contrib/cv/SVD=5FID2019=5Ffor=5FTensorflow/dataset?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/dataset/.keep | 0 1 file changed, 0 insertions(+), 0 deletions(-) delete mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/dataset/.keep diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/dataset/.keep b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/dataset/.keep deleted file mode 100644 index e69de29bb..000000000 -- Gitee From 9ad40c4cd4dbb6cec1559d0967a4588b4d0dbd6d Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Fri, 25 Mar 2022 06:35:38 +0000 Subject: [PATCH 08/88] =?UTF-8?q?=E5=88=A0=E9=99=A4=E6=96=87=E4=BB=B6=20Te?= =?UTF-8?q?nsorFlow/contrib/cv/SVD=5FID2019=5Ffor=5FTensorflow/README.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../cv/SVD_ID2019_for_Tensorflow/README.md | 183 ------------------ 1 file changed, 183 deletions(-) delete mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md deleted file mode 100644 index 03da5e24d..000000000 --- a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md +++ /dev/null @@ -1,183 +0,0 @@ -

基本信息

- -**发布者（Publisher）：Huawei** - -**应用领域（Application Domain）：CV** - -**版本（Version）：** - -**修改时间（Modified） ：2022.3.25** - -**框架（Framework）：TensorFlow 1.15.0** - -**处理器（Processor）：昇腾910** - -**描述（Description）：使用训练好的SVD模型，评估对称正交化在点云对准中的应用效果。** - -

概述

- -给定两个三维点云图，利用SVD正交化过程SVDO+(M)将其投射到SO(3)上，要求网络预测最佳对齐它们的3D旋转。 - -- 开源代码： - - https://github.com/google-research/google-research/tree/master/special_orthogonalization。 - -- 参考论文： - - [An Analysis of SVD for Deep Rotation Estimation](https://arxiv.org/abs/2006.14616) - -- 参考实现： - - obs://cann-id2019/gpu/ - - -- 通过Git获取对应commit\_id的代码方法如下： - - ``` - git clone {repository_url} # 克隆仓库的代码 - cd {repository_name} # 切换到模型的代码仓目录 - git checkout {branch} # 切换到对应分支 - git reset --hard ｛commit_id｝ # 代码设置到对应的commit_id - cd ｛code_path｝ # 切换到模型代码所在路径，若仓库下只有该模型，则无需切换 - ``` - -## 默认配置 - -- 数据集获取：包含在dataset里，需要解压缩。 - -- 训练超参 - - - log_step_count=200 - - save_summaries_steps=25000 - - train_steps=2200000 - - save_checkpoints_steps=100000 - - eval_examples=39900 - - - -

训练环境准备

- -1. 硬件环境准备请参见各硬件产品文档"[驱动和固件安装升级指南]( https://support.huawei.com/enterprise/zh/category/ai-computing-platform-pid-1557196528909)"。需要在硬件设备上安装与CANN版本配套的固件与驱动。 -2. 宿主机上需要安装Docker并登录[Ascend Hub中心](https://ascendhub.huawei.com/#/detail?name=ascend-tensorflow-arm)获取镜像。 - - 当前模型支持的镜像列表如[表1](#zh-cn_topic_0000001074498056_table1519011227314)所示。 - - **表 1** 镜像列表 - - - - - - - - - - - - -

镜像名称

-

镜像版本

-

配套CANN版本

-
-

20.2.0

-

20.2

-
- - - -## 脚本参数 - -``` ---input_test_files1 原始输入测试文件的正则表达式 ---input_test_files2 原始输入测试文件的正则表达式 ---output_directory1 将存储新测试文件的输出目录 ---output_directory2 将存储新测试文件的输出目录 ---num_rotations_per_file 每个测试点云的随机旋转增加数。默认为100 ---random_rotation_axang 如果为真，则使用该方法从原始基准代码中对随机旋转进行采样，否则样本采用哈尔测量。默认为真 ---method 指定用于预测旋转的方式。选项为"svd", "svd-inf", or "gs"。默认为“svd” ---checkpoint_dir 保存检查点位置 ---train_steps 训练迭代的次数。默认为2600000 ---save_checkpoints_steps 保存检查点的频率。默认为10000 ---log_step_count 日志记录一次的步数。默认为500 ---save_summaries_steps 保存一次summary的步数。默认为5000 ---learning_rate 默认为1e-5 ---lr_decay 如果为真，则衰减learning rate。默认为假 ---lr_decay_steps learning rate衰减步数。默认为35000 ---lr_decay_rate learning rate衰减速率。默认为0.95 ---predict_all_test 如果为真，则在最新的检查点上运行eval作业，并打印每个输入的错误。默认为假 ---eval_examples 测试样本的数量。默认为0 ---print_variable_names 打印模型变量名。默认为假 ---num_train_augmentations 增加每个输入点云的随机旋转数。默认为10 ---pt_cloud_train_files 匹配所有训练点文件的表达式 ---pt_cloud_test_files 匹配所有修改的测试点文件的表达式 - -``` - - - -## 运行 - -GPU运行命令如下： - -**修改原始测试数据** - -注：生成的文件points_test_modified、points0已包含在dataset文件夹中。 -```bash -# 将路径设置到训练点云图文件 -IN_FILES1=/shapenet/data/pc_plane/points/*.pts -IN_FILES2=/shapenet/data/pc_plane/points_test/*.pts - -# 设置新生成文件的路径 -NEW_TEST_FILES_DIR1=/shapenet/data/pc_plane/points0 -NEW_TEST_FILES_DIR2=/shapenet/data/pc_plane/points_test_modified - -# 决定旋转轴角的分布 -AXANG_SAMPLING=True - -python -m special_orthogonalization.gen_pt_test_data --input_test_files1=$IN_FILES1 --input_test_files2=$IN_FILES2 --output_directory1=$NEW_TEST_FILES_DIR1 --output_directory2=$NEW_TEST_FILES_DIR2 --random_rotation_axang=$AXANG_SAMPLING -``` - -**训练与评价** -```bash -# 将路径设置到原始训练数据 -TRAIN_FILES=/shapenet/data/pc_plane/points0/*.pts - -#将路径设置到旋转后的训练数据 -TEST_FILES=$NEW_TEST_FILES_DIR2/*.pts - -# 指定旋转预测方式 -METHOD=svd - -# 指定ckpt、summaries、评价结果等的存储路径 -OUT_DIR=/path/to/model - -python -m special_orthogonalization.main_point_cloud --method=$METHOD --checkpoint_dir=$OUT_DIR --log_step_count=200 --save_summaries_steps=25000 --pt_cloud_train_files=$TRAIN_FILES --pt_cloud_test_files=$TEST_FILES --train_steps=2200000 --save_checkpoints_steps=100000 --eval_examples=39900 -``` - -**从所有训练样本中生成统计数据** -```bash -# 打印均值、中位数、标准差和分位数 -python -m special_orthogonalization.main_point_cloud --method=$METHOD --checkpoint_dir=$OUT_DIR --pt_cloud_test_files=$TEST_FILES --predict_all_test=True -``` - -## 训练结果 -**精度对比：** - -由于弹性云服务器上Tesla V100的GPU训练环境，选用矩池云GPU的运行结果进行精度对比。 - - -| 测地线误差（°） | 论文发布 | GPU实测 | NPU实测 | -| ------------------------ | ------- | ----- | ------- | -| 平均值 | 1.63 | 5.55 | 待测 | -| 中值 | 0.89 | 3.65 | 待测 | -| 标准偏差 | 6.70 | 10.68 | 待测 | - -**性能对比：** - -取弹性云GPU运行的前2600步的global_step/sec平均值和NPU运行的前2600步的global_step/sec平均值进行对比，以达到性能对比的目的。 - -| 性能指标项 | 论文发布 | GPU实测 | NPU实测 | -| ------------------- | ------- | ------ | ------ | -| global_step/sec| 无 | 79.48 | 66.08 | - - -- Gitee From 0940de357325f452d1e7b736ec270eb89c374270 Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Fri, 25 Mar 2022 06:37:09 +0000 Subject: [PATCH 09/88] =?UTF-8?q?=E7=AC=AC=E4=B8=80=E6=AC=A1=E6=8F=90?= =?UTF-8?q?=E4=BA=A4?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../cv/SVD_ID2019_for_Tensorflow/README.md | 183 ++++++++++++++++++ 1 file changed, 183 insertions(+) create mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md new file mode 100644 index 000000000..c58caaef9 --- /dev/null +++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md @@ -0,0 +1,183 @@ +

基本信息

+ +**发布者（Publisher）：Huawei** + +**应用领域（Application Domain）：CV** + +**版本（Version）：** + +**修改时间（Modified） ：2022.3.25** + +**框架（Framework）：TensorFlow 1.15.0** + +**处理器（Processor）：昇腾910** + +**描述（Description）：使用训练好的SVD模型，评估对称正交化在点云对准中的应用效果。** + +

概述

+ +给定两个三维点云图，利用SVD正交化过程SVDO+(M)将其投射到SO(3)上，要求网络预测最佳对齐它们的3D旋转。 + +- 开源代码： + + https://github.com/google-research/google-research/tree/master/special_orthogonalization。 + +- 参考论文： + + [An Analysis of SVD for Deep Rotation Estimation](https://arxiv.org/abs/2006.14616) + +- 参考实现： + + obs://cann-id2019/gpu/ + + +- 通过Git获取对应commit\_id的代码方法如下： + + ``` + git clone {repository_url} # 克隆仓库的代码 + cd {repository_name} # 切换到模型的代码仓目录 + git checkout {branch} # 切换到对应分支 + git reset --hard ｛commit_id｝ # 代码设置到对应的commit_id + cd ｛code_path｝ # 切换到模型代码所在路径，若仓库下只有该模型，则无需切换 + ``` + +## 默认配置 + +- 数据集获取：obs://cann-id2019/dataset/ + +- 训练超参 + + - log_step_count=200 + - save_summaries_steps=25000 + - train_steps=2200000 + - save_checkpoints_steps=100000 + - eval_examples=39900 + + + +

训练环境准备

+ +1. 硬件环境准备请参见各硬件产品文档"[驱动和固件安装升级指南]( https://support.huawei.com/enterprise/zh/category/ai-computing-platform-pid-1557196528909)"。需要在硬件设备上安装与CANN版本配套的固件与驱动。 +2. 宿主机上需要安装Docker并登录[Ascend Hub中心](https://ascendhub.huawei.com/#/detail?name=ascend-tensorflow-arm)获取镜像。 + + 当前模型支持的镜像列表如[表1](#zh-cn_topic_0000001074498056_table1519011227314)所示。 + + **表 1** 镜像列表 + + + + + + + + + + + + +

镜像名称

+

镜像版本

+

配套CANN版本

+
+

20.2.0

+

20.2

+
+ + + +## 脚本参数 + +``` +--input_test_files1 原始输入测试文件的正则表达式 +--input_test_files2 原始输入测试文件的正则表达式 +--output_directory1 将存储新测试文件的输出目录 +--output_directory2 将存储新测试文件的输出目录 +--num_rotations_per_file 每个测试点云的随机旋转增加数。默认为100 +--random_rotation_axang 如果为真，则使用该方法从原始基准代码中对随机旋转进行采样，否则样本采用哈尔测量。默认为真 +--method 指定用于预测旋转的方式。选项为"svd", "svd-inf", or "gs"。默认为“svd” +--checkpoint_dir 保存检查点位置 +--train_steps 训练迭代的次数。默认为2600000 +--save_checkpoints_steps 保存检查点的频率。默认为10000 +--log_step_count 日志记录一次的步数。默认为500 +--save_summaries_steps 保存一次summary的步数。默认为5000 +--learning_rate 默认为1e-5 +--lr_decay 如果为真，则衰减learning rate。默认为假 +--lr_decay_steps learning rate衰减步数。默认为35000 +--lr_decay_rate learning rate衰减速率。默认为0.95 +--predict_all_test 如果为真，则在最新的检查点上运行eval作业，并打印每个输入的错误。默认为假 +--eval_examples 测试样本的数量。默认为0 +--print_variable_names 打印模型变量名。默认为假 +--num_train_augmentations 增加每个输入点云的随机旋转数。默认为10 +--pt_cloud_train_files 匹配所有训练点文件的表达式 +--pt_cloud_test_files 匹配所有修改的测试点文件的表达式 + +``` + + + +## 运行 + +GPU运行命令如下： + +**修改原始测试数据** + +注：生成的文件points_test_modified、points0已包含在dataset文件夹中。 +```bash +# 将路径设置到训练点云图文件 +IN_FILES1=/shapenet/data/pc_plane/points/*.pts +IN_FILES2=/shapenet/data/pc_plane/points_test/*.pts + +# 设置新生成文件的路径 +NEW_TEST_FILES_DIR1=/shapenet/data/pc_plane/points0 +NEW_TEST_FILES_DIR2=/shapenet/data/pc_plane/points_test_modified + +# 决定旋转轴角的分布 +AXANG_SAMPLING=True + +python -m special_orthogonalization.gen_pt_test_data --input_test_files1=$IN_FILES1 --input_test_files2=$IN_FILES2 --output_directory1=$NEW_TEST_FILES_DIR1 --output_directory2=$NEW_TEST_FILES_DIR2 --random_rotation_axang=$AXANG_SAMPLING +``` + +**训练与评价** +```bash +# 将路径设置到原始训练数据 +TRAIN_FILES=/shapenet/data/pc_plane/points0/*.pts + +#将路径设置到旋转后的训练数据 +TEST_FILES=$NEW_TEST_FILES_DIR2/*.pts + +# 指定旋转预测方式 +METHOD=svd + +# 指定ckpt、summaries、评价结果等的存储路径 +OUT_DIR=/path/to/model + +python -m special_orthogonalization.main_point_cloud --method=$METHOD --checkpoint_dir=$OUT_DIR --log_step_count=200 --save_summaries_steps=25000 --pt_cloud_train_files=$TRAIN_FILES --pt_cloud_test_files=$TEST_FILES --train_steps=2200000 --save_checkpoints_steps=100000 --eval_examples=39900 +``` + +**从所有训练样本中生成统计数据** +```bash +# 打印均值、中位数、标准差和分位数 +python -m special_orthogonalization.main_point_cloud --method=$METHOD --checkpoint_dir=$OUT_DIR --pt_cloud_test_files=$TEST_FILES --predict_all_test=True +``` + +## 训练结果 +**精度对比：** + +由于弹性云服务器上Tesla V100的GPU训练环境，选用矩池云GPU的运行结果进行精度对比。 + + +| 测地线误差（°） | 论文发布 | GPU实测 | NPU实测 | +| ------------------------ | ------- | ----- | ------- | +| 平均值 | 1.63 | 5.55 | 待测 | +| 中值 | 0.89 | 3.65 | 待测 | +| 标准偏差 | 6.70 | 10.68 | 待测 | + +**性能对比：** + +取弹性云GPU运行的前2600步的global_step/sec平均值和NPU运行的前2600步的global_step/sec平均值进行对比，以达到性能对比的目的。 + +| 性能指标项 | 论文发布 | GPU实测 | NPU实测 | +| ------------------- | ------- | ------ | ------ | +| global_step/sec| 无 | 79.48 | 66.08 | + + -- Gitee From 2324365ad615302ae50772abf0bdd6a9983bf03f Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Fri, 25 Mar 2022 06:38:52 +0000 Subject: [PATCH 10/88] =?UTF-8?q?=E5=88=A0=E9=99=A4=E6=96=87=E4=BB=B6=20Te?= =?UTF-8?q?nsorFlow/contrib/cv/SVD=5FID2019=5Ffor=5FTensorflow/README.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../cv/SVD_ID2019_for_Tensorflow/README.md | 183 ------------------ 1 file changed, 183 deletions(-) delete mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md deleted file mode 100644 index c58caaef9..000000000 --- a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md +++ /dev/null @@ -1,183 +0,0 @@ -

基本信息

- -**发布者（Publisher）：Huawei** - -**应用领域（Application Domain）：CV** - -**版本（Version）：** - -**修改时间（Modified） ：2022.3.25** - -**框架（Framework）：TensorFlow 1.15.0** - -**处理器（Processor）：昇腾910** - -**描述（Description）：使用训练好的SVD模型，评估对称正交化在点云对准中的应用效果。** - -

概述

- -给定两个三维点云图，利用SVD正交化过程SVDO+(M)将其投射到SO(3)上，要求网络预测最佳对齐它们的3D旋转。 - -- 开源代码： - - https://github.com/google-research/google-research/tree/master/special_orthogonalization。 - -- 参考论文： - - [An Analysis of SVD for Deep Rotation Estimation](https://arxiv.org/abs/2006.14616) - -- 参考实现： - - obs://cann-id2019/gpu/ - - -- 通过Git获取对应commit\_id的代码方法如下： - - ``` - git clone {repository_url} # 克隆仓库的代码 - cd {repository_name} # 切换到模型的代码仓目录 - git checkout {branch} # 切换到对应分支 - git reset --hard ｛commit_id｝ # 代码设置到对应的commit_id - cd ｛code_path｝ # 切换到模型代码所在路径，若仓库下只有该模型，则无需切换 - ``` - -## 默认配置 - -- 数据集获取：obs://cann-id2019/dataset/ - -- 训练超参 - - - log_step_count=200 - - save_summaries_steps=25000 - - train_steps=2200000 - - save_checkpoints_steps=100000 - - eval_examples=39900 - - - -

训练环境准备

- -1. 硬件环境准备请参见各硬件产品文档"[驱动和固件安装升级指南]( https://support.huawei.com/enterprise/zh/category/ai-computing-platform-pid-1557196528909)"。需要在硬件设备上安装与CANN版本配套的固件与驱动。 -2. 宿主机上需要安装Docker并登录[Ascend Hub中心](https://ascendhub.huawei.com/#/detail?name=ascend-tensorflow-arm)获取镜像。 - - 当前模型支持的镜像列表如[表1](#zh-cn_topic_0000001074498056_table1519011227314)所示。 - - **表 1** 镜像列表 - - - - - - - - - - - - -

镜像名称

-

镜像版本

-

配套CANN版本

-
-

20.2.0

-

20.2

-
- - - -## 脚本参数 - -``` ---input_test_files1 原始输入测试文件的正则表达式 ---input_test_files2 原始输入测试文件的正则表达式 ---output_directory1 将存储新测试文件的输出目录 ---output_directory2 将存储新测试文件的输出目录 ---num_rotations_per_file 每个测试点云的随机旋转增加数。默认为100 ---random_rotation_axang 如果为真，则使用该方法从原始基准代码中对随机旋转进行采样，否则样本采用哈尔测量。默认为真 ---method 指定用于预测旋转的方式。选项为"svd", "svd-inf", or "gs"。默认为“svd” ---checkpoint_dir 保存检查点位置 ---train_steps 训练迭代的次数。默认为2600000 ---save_checkpoints_steps 保存检查点的频率。默认为10000 ---log_step_count 日志记录一次的步数。默认为500 ---save_summaries_steps 保存一次summary的步数。默认为5000 ---learning_rate 默认为1e-5 ---lr_decay 如果为真，则衰减learning rate。默认为假 ---lr_decay_steps learning rate衰减步数。默认为35000 ---lr_decay_rate learning rate衰减速率。默认为0.95 ---predict_all_test 如果为真，则在最新的检查点上运行eval作业，并打印每个输入的错误。默认为假 ---eval_examples 测试样本的数量。默认为0 ---print_variable_names 打印模型变量名。默认为假 ---num_train_augmentations 增加每个输入点云的随机旋转数。默认为10 ---pt_cloud_train_files 匹配所有训练点文件的表达式 ---pt_cloud_test_files 匹配所有修改的测试点文件的表达式 - -``` - - - -## 运行 - -GPU运行命令如下： - -**修改原始测试数据** - -注：生成的文件points_test_modified、points0已包含在dataset文件夹中。 -```bash -# 将路径设置到训练点云图文件 -IN_FILES1=/shapenet/data/pc_plane/points/*.pts -IN_FILES2=/shapenet/data/pc_plane/points_test/*.pts - -# 设置新生成文件的路径 -NEW_TEST_FILES_DIR1=/shapenet/data/pc_plane/points0 -NEW_TEST_FILES_DIR2=/shapenet/data/pc_plane/points_test_modified - -# 决定旋转轴角的分布 -AXANG_SAMPLING=True - -python -m special_orthogonalization.gen_pt_test_data --input_test_files1=$IN_FILES1 --input_test_files2=$IN_FILES2 --output_directory1=$NEW_TEST_FILES_DIR1 --output_directory2=$NEW_TEST_FILES_DIR2 --random_rotation_axang=$AXANG_SAMPLING -``` - -**训练与评价** -```bash -# 将路径设置到原始训练数据 -TRAIN_FILES=/shapenet/data/pc_plane/points0/*.pts - -#将路径设置到旋转后的训练数据 -TEST_FILES=$NEW_TEST_FILES_DIR2/*.pts - -# 指定旋转预测方式 -METHOD=svd - -# 指定ckpt、summaries、评价结果等的存储路径 -OUT_DIR=/path/to/model - -python -m special_orthogonalization.main_point_cloud --method=$METHOD --checkpoint_dir=$OUT_DIR --log_step_count=200 --save_summaries_steps=25000 --pt_cloud_train_files=$TRAIN_FILES --pt_cloud_test_files=$TEST_FILES --train_steps=2200000 --save_checkpoints_steps=100000 --eval_examples=39900 -``` - -**从所有训练样本中生成统计数据** -```bash -# 打印均值、中位数、标准差和分位数 -python -m special_orthogonalization.main_point_cloud --method=$METHOD --checkpoint_dir=$OUT_DIR --pt_cloud_test_files=$TEST_FILES --predict_all_test=True -``` - -## 训练结果 -**精度对比：** - -由于弹性云服务器上Tesla V100的GPU训练环境，选用矩池云GPU的运行结果进行精度对比。 - - -| 测地线误差（°） | 论文发布 | GPU实测 | NPU实测 | -| ------------------------ | ------- | ----- | ------- | -| 平均值 | 1.63 | 5.55 | 待测 | -| 中值 | 0.89 | 3.65 | 待测 | -| 标准偏差 | 6.70 | 10.68 | 待测 | - -**性能对比：** - -取弹性云GPU运行的前2600步的global_step/sec平均值和NPU运行的前2600步的global_step/sec平均值进行对比，以达到性能对比的目的。 - -| 性能指标项 | 论文发布 | GPU实测 | NPU实测 | -| ------------------- | ------- | ------ | ------ | -| global_step/sec| 无 | 79.48 | 66.08 | - - -- Gitee From 7a687a80bcd52c4a90cf30ed419378fb2e42568b Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Fri, 25 Mar 2022 06:41:14 +0000 Subject: [PATCH 11/88] =?UTF-8?q?=E7=AC=AC=E4=B8=80=E6=AC=A1=E6=8F=90?= =?UTF-8?q?=E4=BA=A4?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../cv/SVD_ID2019_for_Tensorflow/README.md | 183 ++++++++++++++++++ 1 file changed, 183 insertions(+) create mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md new file mode 100644 index 000000000..2c4d94f72 --- /dev/null +++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md @@ -0,0 +1,183 @@ +

基本信息

+ +**发布者（Publisher）：Huawei** + +**应用领域（Application Domain）：CV** + +**版本（Version）：** + +**修改时间（Modified） ：2022.3.25** + +**框架（Framework）：TensorFlow 1.15.0** + +**处理器（Processor）：昇腾910** + +**描述（Description）：使用训练好的SVD模型，评估对称正交化在点云对准中的应用效果。** + +

概述

+ +给定两个三维点云图，利用SVD正交化过程SVDO+(M)将其投射到SO(3)上，要求网络预测最佳对齐它们的3D旋转。 + +- 开源代码： + + https://github.com/google-research/google-research/tree/master/special_orthogonalization。 + +- 参考论文： + + [An Analysis of SVD for Deep Rotation Estimation](https://arxiv.org/abs/2006.14616) + +- 参考实现： + + obs://cann-id2019/gpu/ + + +- 通过Git获取对应commit\_id的代码方法如下： + + ``` + git clone {repository_url} # 克隆仓库的代码 + cd {repository_name} # 切换到模型的代码仓目录 + git checkout {branch} # 切换到对应分支 + git reset --hard ｛commit_id｝ # 代码设置到对应的commit_id + cd ｛code_path｝ # 切换到模型代码所在路径，若仓库下只有该模型，则无需切换 + ``` + +## 默认配置 + +- 数据集获取：obs://cann-id2019/dataset/ + +- 训练超参 + + - log_step_count=200 + - save_summaries_steps=25000 + - train_steps=2600000 + - save_checkpoints_steps=100000 + - eval_examples=39900 + + + +

训练环境准备

+ +1. 硬件环境准备请参见各硬件产品文档"[驱动和固件安装升级指南]( https://support.huawei.com/enterprise/zh/category/ai-computing-platform-pid-1557196528909)"。需要在硬件设备上安装与CANN版本配套的固件与驱动。 +2. 宿主机上需要安装Docker并登录[Ascend Hub中心](https://ascendhub.huawei.com/#/detail?name=ascend-tensorflow-arm)获取镜像。 + + 当前模型支持的镜像列表如[表1](#zh-cn_topic_0000001074498056_table1519011227314)所示。 + + **表 1** 镜像列表 + + + + + + + + + + + + +

镜像名称

+

镜像版本

+

配套CANN版本

+
+

20.2.0

+

20.2

+
+ + + +## 脚本参数 + +``` +--input_test_files1 原始输入测试文件的正则表达式 +--input_test_files2 原始输入测试文件的正则表达式 +--output_directory1 将存储新测试文件的输出目录 +--output_directory2 将存储新测试文件的输出目录 +--num_rotations_per_file 每个测试点云的随机旋转增加数。默认为100 +--random_rotation_axang 如果为真，则使用该方法从原始基准代码中对随机旋转进行采样，否则样本采用哈尔测量。默认为真 +--method 指定用于预测旋转的方式。选项为"svd", "svd-inf", or "gs"。默认为“svd” +--checkpoint_dir 保存检查点位置 +--train_steps 训练迭代的次数。默认为2600000 +--save_checkpoints_steps 保存检查点的频率。默认为10000 +--log_step_count 日志记录一次的步数。默认为500 +--save_summaries_steps 保存一次summary的步数。默认为5000 +--learning_rate 默认为1e-5 +--lr_decay 如果为真，则衰减learning rate。默认为假 +--lr_decay_steps learning rate衰减步数。默认为35000 +--lr_decay_rate learning rate衰减速率。默认为0.95 +--predict_all_test 如果为真，则在最新的检查点上运行eval作业，并打印每个输入的错误。默认为假 +--eval_examples 测试样本的数量。默认为0 +--print_variable_names 打印模型变量名。默认为假 +--num_train_augmentations 增加每个输入点云的随机旋转数。默认为10 +--pt_cloud_train_files 匹配所有训练点文件的表达式 +--pt_cloud_test_files 匹配所有修改的测试点文件的表达式 + +``` + + + +## 运行 + +GPU运行命令如下： + +**修改原始测试数据** + +注：生成的文件points_test_modified、points0已包含在dataset文件夹中。 +```bash +# 将路径设置到训练点云图文件 +IN_FILES1=/shapenet/data/pc_plane/points/*.pts +IN_FILES2=/shapenet/data/pc_plane/points_test/*.pts + +# 设置新生成文件的路径 +NEW_TEST_FILES_DIR1=/shapenet/data/pc_plane/points0 +NEW_TEST_FILES_DIR2=/shapenet/data/pc_plane/points_test_modified + +# 决定旋转轴角的分布 +AXANG_SAMPLING=True + +python -m special_orthogonalization.gen_pt_test_data --input_test_files1=$IN_FILES1 --input_test_files2=$IN_FILES2 --output_directory1=$NEW_TEST_FILES_DIR1 --output_directory2=$NEW_TEST_FILES_DIR2 --random_rotation_axang=$AXANG_SAMPLING +``` + +**训练与评价** +```bash +# 将路径设置到原始训练数据 +TRAIN_FILES=/shapenet/data/pc_plane/points0/*.pts + +#将路径设置到旋转后的训练数据 +TEST_FILES=$NEW_TEST_FILES_DIR2/*.pts + +# 指定旋转预测方式 +METHOD=svd + +# 指定ckpt、summaries、评价结果等的存储路径 +OUT_DIR=/path/to/model + +python -m special_orthogonalization.main_point_cloud --method=$METHOD --checkpoint_dir=$OUT_DIR --log_step_count=200 --save_summaries_steps=25000 --pt_cloud_train_files=$TRAIN_FILES --pt_cloud_test_files=$TEST_FILES --train_steps=2200000 --save_checkpoints_steps=100000 --eval_examples=39900 +``` + +**从所有训练样本中生成统计数据** +```bash +# 打印均值、中位数、标准差和分位数 +python -m special_orthogonalization.main_point_cloud --method=$METHOD --checkpoint_dir=$OUT_DIR --pt_cloud_test_files=$TEST_FILES --predict_all_test=True +``` + +## 训练结果 +**精度对比：** + +由于弹性云服务器上Tesla V100的GPU训练环境无法支持运行超过100万步，选用矩池云GPU的运行结果进行精度对比。 + + +| 测地线误差（°） | 论文发布 | GPU实测 | NPU实测 | +| ------------------------ | ------- | ----- | ------- | +| 平均值 | 1.63 | 5.55 | 待测 | +| 中值 | 0.89 | 3.65 | 待测 | +| 标准偏差 | 6.70 | 10.68 | 待测 | + +**性能对比：** + +取弹性云GPU运行的前2600步的global_step/sec平均值和NPU运行的前2600步的global_step/sec平均值进行对比，以达到性能对比的目的。 + +| 性能指标项 | 论文发布 | GPU实测 | NPU实测 | +| ------------------- | ------- | ------ | ------ | +| global_step/sec| 无 | 79.48 | 66.08 | + + -- Gitee From 57e8f10a0ebe807e8573213028500018220831a0 Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Fri, 25 Mar 2022 06:42:18 +0000 Subject: [PATCH 12/88] =?UTF-8?q?=E5=88=A0=E9=99=A4=E6=96=87=E4=BB=B6=20Te?= =?UTF-8?q?nsorFlow/contrib/cv/SVD=5FID2019=5Ffor=5FTensorflow/.keep?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/.keep | 0 1 file changed, 0 insertions(+), 0 deletions(-) delete mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/.keep diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/.keep b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/.keep deleted file mode 100644 index e69de29bb..000000000 -- Gitee From 35672d9132bcb809c4ff4d5f05f2fdfcdf732b83 Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Fri, 25 Mar 2022 06:43:39 +0000 Subject: [PATCH 13/88] =?UTF-8?q?=E7=AC=AC=E4=B8=80=E6=AC=A1=E6=8F=90?= =?UTF-8?q?=E4=BA=A4?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../GPU/GPU\347\262\276\345\272\246.txt" | 106 ++++++++ .../cv/SVD_ID2019_for_Tensorflow/GPU/loss.log | 246 ++++++++++++++++++ ...2\346\226\207\347\262\276\345\272\246.jpg" | Bin 0 -> 30086 bytes 3 files changed, 352 insertions(+) create mode 100644 "TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/GPU\347\262\276\345\272\246.txt" create mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/loss.log create mode 100644 "TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/\350\256\272\346\226\207\347\262\276\345\272\246.jpg" diff --git "a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/GPU\347\262\276\345\272\246.txt" "b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/GPU\347\262\276\345\272\246.txt" new file mode 100644 index 000000000..de31ae6b3 --- /dev/null +++ "b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/GPU\347\262\276\345\272\246.txt" @@ -0,0 +1,106 @@ +Evaluated 39900 examples +Mean error: %f degrees 5.551389 +Median error: %f degrees 3.6492655 +Std: %f degrees 10.683777 + +Percentiles: + 1%: 0.703878 + 2%: 0.889114 + 3%: 1.011817 + 4%: 1.117676 + 5%: 1.203335 + 6%: 1.290285 + 7%: 1.362000 + 8%: 1.423815 + 9%: 1.484244 + 10%: 1.540022 + 11%: 1.593973 + 12%: 1.645681 + 13%: 1.698696 + 14%: 1.743833 + 15%: 1.792095 + 16%: 1.841431 + 17%: 1.893619 + 18%: 1.939569 + 19%: 1.985442 + 20%: 2.029315 + 21%: 2.077730 + 22%: 2.125596 + 23%: 2.174029 + 24%: 2.223344 + 25%: 2.272967 + 26%: 2.323889 + 27%: 2.370916 + 28%: 2.424628 + 29%: 2.477176 + 30%: 2.526464 + 31%: 2.579367 + 32%: 2.632026 + 33%: 2.682996 + 34%: 2.733374 + 35%: 2.784669 + 36%: 2.837522 + 37%: 2.893403 + 38%: 2.948823 + 39%: 3.002961 + 40%: 3.058895 + 41%: 3.115458 + 42%: 3.170274 + 43%: 3.233495 + 44%: 3.287655 + 45%: 3.344627 + 46%: 3.402717 + 47%: 3.463511 + 48%: 3.525312 + 49%: 3.588614 + 50%: 3.649158 + 51%: 3.710984 + 52%: 3.777194 + 53%: 3.840634 + 54%: 3.912314 + 55%: 3.978772 + 56%: 4.050912 + 57%: 4.124833 + 58%: 4.205145 + 59%: 4.269850 + 60%: 4.350731 + 61%: 4.429609 + 62%: 4.506891 + 63%: 4.590645 + 64%: 4.673866 + 65%: 4.769208 + 66%: 4.860751 + 67%: 4.957645 + 68%: 5.045778 + 69%: 5.145709 + 70%: 5.248520 + 71%: 5.356171 + 72%: 5.463465 + 73%: 5.576540 + 74%: 5.700179 + 75%: 5.826519 + 76%: 5.954271 + 77%: 6.075412 + 78%: 6.217035 + 79%: 6.359586 + 80%: 6.509334 + 81%: 6.681930 + 82%: 6.855579 + 83%: 7.045702 + 84%: 7.237360 + 85%: 7.455194 + 86%: 7.683445 + 87%: 7.928701 + 88%: 8.226621 + 89%: 8.539159 + 90%: 8.927838 + 91%: 9.329886 + 92%: 9.826632 + 93%: 10.504982 + 94%: 11.362451 + 95%: 12.505600 + 96%: 14.257383 + 97%: 17.200018 + 98%: 22.386629 + 99%: 37.106316 +100%: 179.459671 diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/loss.log b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/loss.log new file mode 100644 index 000000000..dc3dfb67d --- /dev/null +++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/loss.log @@ -0,0 +1,246 @@ +2022-03-19 18:03:26,273 - tensorflow - INFO - Using config: {'_model_dir': '/root/RotationContinuity-master2/shapenet/data/pc_plane/to/model1', '_tf_random_seed': None, '_save_summary_steps': 25, '_save_checkpoints_steps': 100, '_save_checkpoints_secs': None, '_session_config': allow_soft_placement: true +graph_options { + rewrite_options { + meta_optimizer_iterations: ONE + } +} +, '_keep_checkpoint_max': None, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 200, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_service': None, '_cluster_spec': , '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1} +2022-03-19 18:03:26,273 - tensorflow - INFO - Not using Distribute Coordinator. +2022-03-19 18:03:26,273 - tensorflow - INFO - Running training and evaluation locally (non-distributed). +2022-03-19 18:03:26,274 - tensorflow - INFO - Start train and evaluate loop. The evaluate will happen after every checkpoint. Checkpoint frequency is determined based on RunConfig arguments: save_checkpoints_steps 100 or save_checkpoints_secs None. +2022-03-19 18:03:26,277 - tensorflow - INFO - Skipping training since max_steps has already saved. +2022-03-19 18:04:19,049 - tensorflow - INFO - Using config: {'_model_dir': '/root/RotationContinuity-master2/shapenet/data/pc_plane/to/model1', '_tf_random_seed': None, '_save_summary_steps': 25, '_save_checkpoints_steps': 100, '_save_checkpoints_secs': None, '_session_config': allow_soft_placement: true +graph_options { + rewrite_options { + meta_optimizer_iterations: ONE + } +} +, '_keep_checkpoint_max': None, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 200, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_service': None, '_cluster_spec': , '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1} +2022-03-19 18:04:19,050 - tensorflow - INFO - Not using Distribute Coordinator. +2022-03-19 18:04:19,050 - tensorflow - INFO - Running training and evaluation locally (non-distributed). +2022-03-19 18:04:19,050 - tensorflow - INFO - Start train and evaluate loop. The evaluate will happen after every checkpoint. Checkpoint frequency is determined based on RunConfig arguments: save_checkpoints_steps 100 or save_checkpoints_secs None. +2022-03-19 18:04:19,054 - tensorflow - INFO - Skipping training since max_steps has already saved. +2022-03-19 18:04:49,280 - tensorflow - INFO - Using config: {'_model_dir': '/root/RotationContinuity-master2/shapenet/data/pc_plane/to/model2', '_tf_random_seed': None, '_save_summary_steps': 25, '_save_checkpoints_steps': 100, '_save_checkpoints_secs': None, '_session_config': allow_soft_placement: true +graph_options { + rewrite_options { + meta_optimizer_iterations: ONE + } +} +, '_keep_checkpoint_max': None, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 200, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_service': None, '_cluster_spec': , '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1} +2022-03-19 18:04:49,281 - tensorflow - INFO - Not using Distribute Coordinator. +2022-03-19 18:04:49,281 - tensorflow - INFO - Running training and evaluation locally (non-distributed). +2022-03-19 18:04:49,281 - tensorflow - INFO - Start train and evaluate loop. The evaluate will happen after every checkpoint. Checkpoint frequency is determined based on RunConfig arguments: save_checkpoints_steps 100 or save_checkpoints_secs None. +2022-03-19 18:04:49,286 - tensorflow - WARNING - From /root/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow_core/python/training/training_util.py:236: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version. +Instructions for updating: +Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts. +2022-03-19 18:04:49,292 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:244: The name tf.gfile.Glob is deprecated. Please use tf.io.gfile.glob instead. + +2022-03-19 18:04:49,683 - tensorflow - WARNING - From /root/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow_core/python/autograph/converters/directives.py:119: The name tf.read_file is deprecated. Please use tf.io.read_file instead. + +2022-03-19 18:04:49,683 - tensorflow - WARNING - From /root/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow_core/python/autograph/converters/directives.py:119: The name tf.decode_csv is deprecated. Please use tf.io.decode_csv instead. + +2022-03-19 18:04:49,766 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:223: calling string_split (from tensorflow.python.ops.ragged.ragged_string_ops) with delimiter is deprecated and will be removed in a future version. +Instructions for updating: +delimiter is deprecated, please use sep instead. +2022-03-19 18:04:49,932 - tensorflow - WARNING - From /root/special_orthogonalization2/utils_gpu.py:75: py_func (from tensorflow.python.ops.script_ops) is deprecated and will be removed in a future version. +Instructions for updating: +tf.py_func is deprecated in TF V2. Instead, there are two + options available in V2. + - tf.py_function takes a python function which manipulates tf eager + tensors instead of numpy arrays. It's easy to convert a tf eager tensor to + an ndarray (just call tensor.numpy()) but having access to eager tensors + means `tf.py_function`s can use accelerators such as GPUs as well as + being differentiable using a gradient tape. + - tf.numpy_function maintains the semantics of the deprecated tf.py_func + (it is not differentiable, and manipulates numpy arrays). It drops the + stateful argument making all functions stateful. + +2022-03-19 18:04:49,938 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:252: The name tf.data.make_one_shot_iterator is deprecated. Please use tf.compat.v1.data.make_one_shot_iterator instead. + +2022-03-19 18:04:49,961 - tensorflow - INFO - Calling model_fn. +2022-03-19 18:04:49,966 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:74: The name tf.variable_scope is deprecated. Please use tf.compat.v1.variable_scope instead. + +2022-03-19 18:04:49,966 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:74: The name tf.AUTO_REUSE is deprecated. Please use tf.compat.v1.AUTO_REUSE instead. + +2022-03-19 18:04:49,966 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:75: conv1d (from tensorflow.python.layers.convolutional) is deprecated and will be removed in a future version. +Instructions for updating: +Use `tf.keras.layers.Conv1D` instead. +2022-03-19 18:04:49,967 - tensorflow - WARNING - From /root/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow_core/python/layers/convolutional.py:218: Layer.apply (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version. +Instructions for updating: +Please use `layer.__call__` method instead. +2022-03-19 18:04:50,011 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:81: calling reduce_max_v1 (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version. +Instructions for updating: +keep_dims is deprecated, use keepdims instead +2022-03-19 18:04:50,033 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:99: dense (from tensorflow.python.layers.core) is deprecated and will be removed in a future version. +Instructions for updating: +Use keras.layers.Dense instead. +2022-03-19 18:04:50,055 - tensorflow - WARNING - From /root/special_orthogonalization2/utils_gpu.py:97: The name tf.svd is deprecated. Please use tf.linalg.svd instead. + +2022-03-19 18:04:50,079 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:171: The name tf.summary.scalar is deprecated. Please use tf.compat.v1.summary.scalar instead. + +2022-03-19 18:04:50,082 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:173: The name tf.train.get_or_create_global_step is deprecated. Please use tf.compat.v1.train.get_or_create_global_step instead. + +2022-03-19 18:04:50,083 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:185: The name tf.train.AdamOptimizer is deprecated. Please use tf.compat.v1.train.AdamOptimizer instead. + +2022-03-19 18:04:50,083 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:186: The name tf.get_collection is deprecated. Please use tf.compat.v1.get_collection instead. + +2022-03-19 18:04:50,083 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:186: The name tf.GraphKeys is deprecated. Please use tf.compat.v1.GraphKeys instead. + +2022-03-19 18:04:50,323 - tensorflow - INFO - Done calling model_fn. +2022-03-19 18:04:50,325 - tensorflow - INFO - Create CheckpointSaverHook. +2022-03-19 18:04:50,418 - tensorflow - WARNING - From /root/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow_core/python/ops/array_ops.py:1475: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version. +Instructions for updating: +Use tf.where in 2.0, which has the same broadcast rule as np.where +2022-03-19 18:04:50,485 - tensorflow - INFO - Graph was finalized. +2022-03-19 18:04:52,089 - tensorflow - INFO - Running local_init_op. +2022-03-19 18:04:52,106 - tensorflow - INFO - Done running local_init_op. +2022-03-19 18:04:52,544 - tensorflow - INFO - Saving checkpoints for 0 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model2/model.ckpt. +2022-03-19 18:04:52,608 - tensorflow - INFO - model.ckpt-0 is not in all_model_checkpoint_paths. Manually adding it. +2022-03-19 18:04:55,512 - tensorflow - INFO - loss = 32.202156, step = 0 +2022-03-19 18:04:57,008 - tensorflow - INFO - Saving checkpoints for 100 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model2/model.ckpt. +2022-03-19 18:04:57,026 - tensorflow - INFO - model.ckpt-100 is not in all_model_checkpoint_paths. Manually adding it. +2022-03-19 18:04:57,484 - tensorflow - INFO - Calling model_fn. +2022-03-19 18:04:57,596 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:203: The name tf.metrics.mean is deprecated. Please use tf.compat.v1.metrics.mean instead. + +2022-03-19 18:04:57,606 - tensorflow - INFO - Done calling model_fn. +2022-03-19 18:04:57,621 - tensorflow - INFO - Starting evaluation at 2022-03-19T18:04:57Z +2022-03-19 18:04:57,684 - tensorflow - INFO - Graph was finalized. +2022-03-19 18:04:57,687 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model2/model.ckpt-100 +2022-03-19 18:04:57,883 - tensorflow - INFO - Running local_init_op. +2022-03-19 18:04:57,949 - tensorflow - INFO - Done running local_init_op. +2022-03-19 18:04:59,509 - tensorflow - INFO - Evaluation [39/399] +2022-03-19 18:05:00,470 - tensorflow - INFO - Evaluation [78/399] +2022-03-19 18:05:00,986 - tensorflow - INFO - Evaluation [117/399] +2022-03-19 18:05:01,473 - tensorflow - INFO - Evaluation [156/399] +2022-03-19 18:05:01,813 - tensorflow - INFO - Evaluation [195/399] +2022-03-19 18:05:02,217 - tensorflow - INFO - Evaluation [234/399] +2022-03-19 18:05:02,537 - tensorflow - INFO - Evaluation [273/399] +2022-03-19 18:05:02,756 - tensorflow - INFO - Evaluation [312/399] +2022-03-19 18:05:02,876 - tensorflow - INFO - Evaluation [351/399] +2022-03-19 18:05:03,157 - tensorflow - INFO - Evaluation [390/399] +2022-03-19 18:05:03,209 - tensorflow - INFO - Evaluation [399/399] +2022-03-19 18:05:03,291 - tensorflow - INFO - Finished evaluation at 2022-03-19-18:05:03 +2022-03-19 18:05:03,292 - tensorflow - INFO - Saving dict for global step 100: global_step = 100, loss = 2.0213404, mean_degree_err = 90.8583 +2022-03-19 18:05:03,399 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 100: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model2/model.ckpt-100 +2022-03-19 18:05:04,530 - tensorflow - INFO - Saving checkpoints for 200 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model2/model.ckpt. +2022-03-19 18:05:04,547 - tensorflow - INFO - model.ckpt-200 is not in all_model_checkpoint_paths. Manually adding it. +2022-03-19 18:05:04,586 - tensorflow - INFO - Skip the current checkpoint eval due to throttle secs (60 secs). +2022-03-19 18:05:04,600 - tensorflow - INFO - global_step/sec: 22.0056 +2022-03-19 18:05:04,601 - tensorflow - INFO - loss = 18.108334, step = 200 (9.089 sec) +2022-03-19 18:05:05,711 - tensorflow - INFO - Saving checkpoints for 300 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model2/model.ckpt. +2022-03-19 18:05:05,728 - tensorflow - INFO - model.ckpt-300 is not in all_model_checkpoint_paths. Manually adding it. +2022-03-19 18:05:05,765 - tensorflow - INFO - Skip the current checkpoint eval due to throttle secs (60 secs). +2022-03-19 18:05:06,893 - tensorflow - INFO - Saving checkpoints for 400 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model2/model.ckpt. +2022-03-19 18:05:06,911 - tensorflow - INFO - model.ckpt-400 is not in all_model_checkpoint_paths. Manually adding it. +2022-03-19 18:05:06,948 - tensorflow - INFO - Skip the current checkpoint eval due to throttle secs (60 secs). +2022-03-19 18:05:06,962 - tensorflow - INFO - global_step/sec: 84.6573 +2022-03-19 18:05:06,963 - tensorflow - INFO - loss = 19.658773, step = 400 (2.362 sec) +2022-03-19 18:05:08,073 - tensorflow - INFO - Saving checkpoints for 500 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model2/model.ckpt. +2022-03-19 18:05:08,089 - tensorflow - INFO - model.ckpt-500 is not in all_model_checkpoint_paths. Manually adding it. +2022-03-19 18:05:08,126 - tensorflow - INFO - Skip the current checkpoint eval due to throttle secs (60 secs). +2022-03-19 18:05:09,261 - tensorflow - INFO - Saving checkpoints for 600 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model2/model.ckpt. +2022-03-19 18:05:09,276 - tensorflow - INFO - model.ckpt-600 is not in all_model_checkpoint_paths. Manually adding it. +2022-03-19 18:05:09,314 - tensorflow - INFO - Skip the current checkpoint eval due to throttle secs (60 secs). +2022-03-19 18:05:09,329 - tensorflow - INFO - global_step/sec: 84.4893 +2022-03-19 18:05:09,330 - tensorflow - INFO - loss = 15.302707, step = 600 (2.367 sec) +2022-03-19 18:05:10,443 - tensorflow - INFO - Saving checkpoints for 700 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model2/model.ckpt. +2022-03-19 18:05:10,461 - tensorflow - INFO - model.ckpt-700 is not in all_model_checkpoint_paths. Manually adding it. +2022-03-19 18:05:10,498 - tensorflow - INFO - Skip the current checkpoint eval due to throttle secs (60 secs). +2022-03-19 18:05:11,624 - tensorflow - INFO - Saving checkpoints for 800 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model2/model.ckpt. +2022-03-19 18:05:11,643 - tensorflow - INFO - model.ckpt-800 is not in all_model_checkpoint_paths. Manually adding it. +2022-03-19 18:05:11,680 - tensorflow - INFO - Skip the current checkpoint eval due to throttle secs (60 secs). +2022-03-19 18:05:11,695 - tensorflow - INFO - global_step/sec: 84.5516 +2022-03-19 18:05:11,696 - tensorflow - INFO - loss = 18.000507, step = 800 (2.365 sec) +2022-03-19 18:05:12,807 - tensorflow - INFO - Saving checkpoints for 900 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model2/model.ckpt. +2022-03-19 18:05:12,822 - tensorflow - INFO - model.ckpt-900 is not in all_model_checkpoint_paths. Manually adding it. +2022-03-19 18:05:12,860 - tensorflow - INFO - Skip the current checkpoint eval due to throttle secs (60 secs). +2022-03-19 18:05:13,986 - tensorflow - INFO - Saving checkpoints for 1000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model2/model.ckpt. +2022-03-19 18:05:14,002 - tensorflow - INFO - model.ckpt-1000 is not in all_model_checkpoint_paths. Manually adding it. +2022-03-19 18:05:14,039 - tensorflow - INFO - Skip the current checkpoint eval due to throttle secs (60 secs). +2022-03-19 18:05:14,054 - tensorflow - INFO - global_step/sec: 84.7931 +2022-03-19 18:05:14,054 - tensorflow - INFO - loss = 20.527103, step = 1000 (2.359 sec) +2022-03-19 18:05:15,176 - tensorflow - INFO - Saving checkpoints for 1100 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model2/model.ckpt. +2022-03-19 18:05:15,192 - tensorflow - INFO - model.ckpt-1100 is not in all_model_checkpoint_paths. Manually adding it. +2022-03-19 18:05:15,229 - tensorflow - INFO - Skip the current checkpoint eval due to throttle secs (60 secs). +2022-03-19 18:05:16,360 - tensorflow - INFO - Saving checkpoints for 1200 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model2/model.ckpt. +2022-03-19 18:05:16,378 - tensorflow - INFO - model.ckpt-1200 is not in all_model_checkpoint_paths. Manually adding it. +2022-03-19 18:05:16,415 - tensorflow - INFO - Skip the current checkpoint eval due to throttle secs (60 secs). +2022-03-19 18:05:16,430 - tensorflow - INFO - global_step/sec: 84.1547 +2022-03-19 18:05:16,431 - tensorflow - INFO - loss = 16.00374, step = 1200 (2.377 sec) +2022-03-19 18:05:17,536 - tensorflow - INFO - Saving checkpoints for 1300 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model2/model.ckpt. +2022-03-19 18:05:17,554 - tensorflow - INFO - model.ckpt-1300 is not in all_model_checkpoint_paths. Manually adding it. +2022-03-19 18:05:17,591 - tensorflow - INFO - Skip the current checkpoint eval due to throttle secs (60 secs). +2022-03-19 18:05:18,715 - tensorflow - INFO - Saving checkpoints for 1400 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model2/model.ckpt. +2022-03-19 18:05:18,731 - tensorflow - INFO - model.ckpt-1400 is not in all_model_checkpoint_paths. Manually adding it. +2022-03-19 18:05:18,768 - tensorflow - INFO - Skip the current checkpoint eval due to throttle secs (60 secs). +2022-03-19 18:05:18,782 - tensorflow - INFO - global_step/sec: 85.0196 +2022-03-19 18:05:18,783 - tensorflow - INFO - loss = 8.673609, step = 1400 (2.352 sec) +2022-03-19 18:05:19,894 - tensorflow - INFO - Saving checkpoints for 1500 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model2/model.ckpt. +2022-03-19 18:05:19,909 - tensorflow - INFO - model.ckpt-1500 is not in all_model_checkpoint_paths. Manually adding it. +2022-03-19 18:05:19,947 - tensorflow - INFO - Skip the current checkpoint eval due to throttle secs (60 secs). +2022-03-19 18:05:21,070 - tensorflow - INFO - Saving checkpoints for 1600 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model2/model.ckpt. +2022-03-19 18:05:21,092 - tensorflow - INFO - model.ckpt-1600 is not in all_model_checkpoint_paths. Manually adding it. +2022-03-19 18:05:21,129 - tensorflow - INFO - Skip the current checkpoint eval due to throttle secs (60 secs). +2022-03-19 18:05:21,144 - tensorflow - INFO - global_step/sec: 84.7018 +2022-03-19 18:05:21,144 - tensorflow - INFO - loss = 12.576811, step = 1600 (2.361 sec) +2022-03-19 18:05:22,260 - tensorflow - INFO - Saving checkpoints for 1700 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model2/model.ckpt. +2022-03-19 18:05:22,276 - tensorflow - INFO - model.ckpt-1700 is not in all_model_checkpoint_paths. Manually adding it. +2022-03-19 18:05:22,313 - tensorflow - INFO - Skip the current checkpoint eval due to throttle secs (60 secs). +2022-03-19 18:05:23,442 - tensorflow - INFO - Saving checkpoints for 1800 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model2/model.ckpt. +2022-03-19 18:05:23,458 - tensorflow - INFO - model.ckpt-1800 is not in all_model_checkpoint_paths. Manually adding it. +2022-03-19 18:05:23,496 - tensorflow - INFO - Skip the current checkpoint eval due to throttle secs (60 secs). +2022-03-19 18:05:23,510 - tensorflow - INFO - global_step/sec: 84.5082 +2022-03-19 18:05:23,511 - tensorflow - INFO - loss = 25.950819, step = 1800 (2.367 sec) +2022-03-19 18:05:24,627 - tensorflow - INFO - Saving checkpoints for 1900 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model2/model.ckpt. +2022-03-19 18:05:24,642 - tensorflow - INFO - model.ckpt-1900 is not in all_model_checkpoint_paths. Manually adding it. +2022-03-19 18:05:24,679 - tensorflow - INFO - Skip the current checkpoint eval due to throttle secs (60 secs). +2022-03-19 18:05:25,810 - tensorflow - INFO - Saving checkpoints for 2000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model2/model.ckpt. +2022-03-19 18:05:25,825 - tensorflow - INFO - model.ckpt-2000 is not in all_model_checkpoint_paths. Manually adding it. +2022-03-19 18:05:25,863 - tensorflow - INFO - Skip the current checkpoint eval due to throttle secs (60 secs). +2022-03-19 18:05:25,877 - tensorflow - INFO - global_step/sec: 84.5026 +2022-03-19 18:05:25,878 - tensorflow - INFO - loss = 10.249855, step = 2000 (2.367 sec) +2022-03-19 18:05:26,991 - tensorflow - INFO - Saving checkpoints for 2100 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model2/model.ckpt. +2022-03-19 18:05:27,007 - tensorflow - INFO - model.ckpt-2100 is not in all_model_checkpoint_paths. Manually adding it. +2022-03-19 18:05:27,044 - tensorflow - INFO - Skip the current checkpoint eval due to throttle secs (60 secs). +2022-03-19 18:05:28,175 - tensorflow - INFO - Saving checkpoints for 2200 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model2/model.ckpt. +2022-03-19 18:05:28,190 - tensorflow - INFO - model.ckpt-2200 is not in all_model_checkpoint_paths. Manually adding it. +2022-03-19 18:05:28,227 - tensorflow - INFO - Skip the current checkpoint eval due to throttle secs (60 secs). +2022-03-19 18:05:28,241 - tensorflow - INFO - global_step/sec: 84.5981 +2022-03-19 18:05:28,242 - tensorflow - INFO - loss = 10.412505, step = 2200 (2.364 sec) +2022-03-19 18:05:29,344 - tensorflow - INFO - Saving checkpoints for 2300 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model2/model.ckpt. +2022-03-19 18:05:29,360 - tensorflow - INFO - model.ckpt-2300 is not in all_model_checkpoint_paths. Manually adding it. +2022-03-19 18:05:29,397 - tensorflow - INFO - Skip the current checkpoint eval due to throttle secs (60 secs). +2022-03-19 18:05:30,505 - tensorflow - INFO - Saving checkpoints for 2400 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model2/model.ckpt. +2022-03-19 18:05:30,520 - tensorflow - INFO - model.ckpt-2400 is not in all_model_checkpoint_paths. Manually adding it. +2022-03-19 18:05:30,558 - tensorflow - INFO - Skip the current checkpoint eval due to throttle secs (60 secs). +2022-03-19 18:05:30,572 - tensorflow - INFO - global_step/sec: 85.8075 +2022-03-19 18:05:30,573 - tensorflow - INFO - loss = 24.763023, step = 2400 (2.331 sec) +2022-03-19 18:05:31,663 - tensorflow - INFO - Saving checkpoints for 2500 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model2/model.ckpt. +2022-03-19 18:05:31,679 - tensorflow - INFO - model.ckpt-2500 is not in all_model_checkpoint_paths. Manually adding it. +2022-03-19 18:05:31,716 - tensorflow - INFO - Skip the current checkpoint eval due to throttle secs (60 secs). +2022-03-19 18:05:32,818 - tensorflow - INFO - Saving checkpoints for 2600 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model2/model.ckpt. +2022-03-19 18:05:32,834 - tensorflow - INFO - model.ckpt-2600 is not in all_model_checkpoint_paths. Manually adding it. +2022-03-19 18:05:32,871 - tensorflow - INFO - Skip the current checkpoint eval due to throttle secs (60 secs). +2022-03-19 18:05:33,174 - tensorflow - INFO - Calling model_fn. +2022-03-19 18:05:33,301 - tensorflow - INFO - Done calling model_fn. +2022-03-19 18:05:33,316 - tensorflow - INFO - Starting evaluation at 2022-03-19T18:05:33Z +2022-03-19 18:05:33,379 - tensorflow - INFO - Graph was finalized. +2022-03-19 18:05:33,382 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model2/model.ckpt-2600 +2022-03-19 18:05:33,562 - tensorflow - INFO - Running local_init_op. +2022-03-19 18:05:33,627 - tensorflow - INFO - Done running local_init_op. +2022-03-19 18:05:34,142 - tensorflow - INFO - Evaluation [39/399] +2022-03-19 18:05:34,232 - tensorflow - INFO - Evaluation [78/399] +2022-03-19 18:05:34,318 - tensorflow - INFO - Evaluation [117/399] +2022-03-19 18:05:34,406 - tensorflow - INFO - Evaluation [156/399] +2022-03-19 18:05:34,493 - tensorflow - INFO - Evaluation [195/399] +2022-03-19 18:05:34,581 - tensorflow - INFO - Evaluation [234/399] +2022-03-19 18:05:34,669 - tensorflow - INFO - Evaluation [273/399] +2022-03-19 18:05:34,757 - tensorflow - INFO - Evaluation [312/399] +2022-03-19 18:05:34,844 - tensorflow - INFO - Evaluation [351/399] +2022-03-19 18:05:34,930 - tensorflow - INFO - Evaluation [390/399] +2022-03-19 18:05:34,951 - tensorflow - INFO - Evaluation [399/399] +2022-03-19 18:05:35,034 - tensorflow - INFO - Finished evaluation at 2022-03-19-18:05:35 +2022-03-19 18:05:35,035 - tensorflow - INFO - Saving dict for global step 2600: global_step = 2600, loss = 1.1534922, mean_degree_err = 59.039207 +2022-03-19 18:05:35,035 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 2600: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model2/model.ckpt-2600 +2022-03-19 18:05:35,035 - tensorflow - DEBUG - Calling exporter with the `is_the_final_export=True`. 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基本信息

- -**发布者（Publisher）：Huawei** - -**应用领域（Application Domain）：CV** - -**版本（Version）：** - -**修改时间（Modified） ：2022.3.25** - -**框架（Framework）：TensorFlow 1.15.0** - -**处理器（Processor）：昇腾910** - -**描述（Description）：使用训练好的SVD模型，评估对称正交化在点云对准中的应用效果。** - -

概述

- -给定两个三维点云图，利用SVD正交化过程SVDO+(M)将其投射到SO(3)上，要求网络预测最佳对齐它们的3D旋转。 - -- 开源代码： - - https://github.com/google-research/google-research/tree/master/special_orthogonalization。 - -- 参考论文： - - [An Analysis of SVD for Deep Rotation Estimation](https://arxiv.org/abs/2006.14616) - -- 参考实现： - - obs://cann-id2019/gpu/ - - -- 通过Git获取对应commit\_id的代码方法如下： - - ``` - git clone {repository_url} # 克隆仓库的代码 - cd {repository_name} # 切换到模型的代码仓目录 - git checkout {branch} # 切换到对应分支 - git reset --hard ｛commit_id｝ # 代码设置到对应的commit_id - cd ｛code_path｝ # 切换到模型代码所在路径，若仓库下只有该模型，则无需切换 - ``` - -## 默认配置 - -- 数据集获取：obs://cann-id2019/dataset/ - -- 训练超参 - - - log_step_count=200 - - save_summaries_steps=25000 - - train_steps=2600000 - - save_checkpoints_steps=100000 - - eval_examples=39900 - - - -

训练环境准备

- -1. 硬件环境准备请参见各硬件产品文档"[驱动和固件安装升级指南]( https://support.huawei.com/enterprise/zh/category/ai-computing-platform-pid-1557196528909)"。需要在硬件设备上安装与CANN版本配套的固件与驱动。 -2. 宿主机上需要安装Docker并登录[Ascend Hub中心](https://ascendhub.huawei.com/#/detail?name=ascend-tensorflow-arm)获取镜像。 - - 当前模型支持的镜像列表如[表1](#zh-cn_topic_0000001074498056_table1519011227314)所示。 - - **表 1** 镜像列表 - - - - - - - - - - - - -

镜像名称

-

镜像版本

-

配套CANN版本

-
-

20.2.0

-

20.2

-
- - - -## 脚本参数 - -``` ---input_test_files1 原始输入测试文件的正则表达式 ---input_test_files2 原始输入测试文件的正则表达式 ---output_directory1 将存储新测试文件的输出目录 ---output_directory2 将存储新测试文件的输出目录 ---num_rotations_per_file 每个测试点云的随机旋转增加数。默认为100 ---random_rotation_axang 如果为真，则使用该方法从原始基准代码中对随机旋转进行采样，否则样本采用哈尔测量。默认为真 ---method 指定用于预测旋转的方式。选项为"svd", "svd-inf", or "gs"。默认为“svd” ---checkpoint_dir 保存检查点位置 ---train_steps 训练迭代的次数。默认为2600000 ---save_checkpoints_steps 保存检查点的频率。默认为10000 ---log_step_count 日志记录一次的步数。默认为500 ---save_summaries_steps 保存一次summary的步数。默认为5000 ---learning_rate 默认为1e-5 ---lr_decay 如果为真，则衰减learning rate。默认为假 ---lr_decay_steps learning rate衰减步数。默认为35000 ---lr_decay_rate learning rate衰减速率。默认为0.95 ---predict_all_test 如果为真，则在最新的检查点上运行eval作业，并打印每个输入的错误。默认为假 ---eval_examples 测试样本的数量。默认为0 ---print_variable_names 打印模型变量名。默认为假 ---num_train_augmentations 增加每个输入点云的随机旋转数。默认为10 ---pt_cloud_train_files 匹配所有训练点文件的表达式 ---pt_cloud_test_files 匹配所有修改的测试点文件的表达式 - -``` - - - -## 运行 - -GPU运行命令如下： - -**修改原始测试数据** - -注：生成的文件points_test_modified、points0已包含在dataset文件夹中。 -```bash -# 将路径设置到训练点云图文件 -IN_FILES1=/shapenet/data/pc_plane/points/*.pts -IN_FILES2=/shapenet/data/pc_plane/points_test/*.pts - -# 设置新生成文件的路径 -NEW_TEST_FILES_DIR1=/shapenet/data/pc_plane/points0 -NEW_TEST_FILES_DIR2=/shapenet/data/pc_plane/points_test_modified - -# 决定旋转轴角的分布 -AXANG_SAMPLING=True - -python -m special_orthogonalization.gen_pt_test_data --input_test_files1=$IN_FILES1 --input_test_files2=$IN_FILES2 --output_directory1=$NEW_TEST_FILES_DIR1 --output_directory2=$NEW_TEST_FILES_DIR2 --random_rotation_axang=$AXANG_SAMPLING -``` - -**训练与评价** -```bash -# 将路径设置到原始训练数据 -TRAIN_FILES=/shapenet/data/pc_plane/points0/*.pts - -#将路径设置到旋转后的训练数据 -TEST_FILES=$NEW_TEST_FILES_DIR2/*.pts - -# 指定旋转预测方式 -METHOD=svd - -# 指定ckpt、summaries、评价结果等的存储路径 -OUT_DIR=/path/to/model - -python -m special_orthogonalization.main_point_cloud --method=$METHOD --checkpoint_dir=$OUT_DIR --log_step_count=200 --save_summaries_steps=25000 --pt_cloud_train_files=$TRAIN_FILES --pt_cloud_test_files=$TEST_FILES --train_steps=2200000 --save_checkpoints_steps=100000 --eval_examples=39900 -``` - -**从所有训练样本中生成统计数据** -```bash -# 打印均值、中位数、标准差和分位数 -python -m special_orthogonalization.main_point_cloud --method=$METHOD --checkpoint_dir=$OUT_DIR --pt_cloud_test_files=$TEST_FILES --predict_all_test=True -``` - -## 训练结果 -**精度对比：** - -由于弹性云服务器上Tesla V100的GPU训练环境无法支持运行超过100万步，选用矩池云GPU的运行结果进行精度对比。 - - -| 测地线误差（°） | 论文发布 | GPU实测 | NPU实测 | -| ------------------------ | ------- | ----- | ------- | -| 平均值 | 1.63 | 5.55 | 待测 | -| 中值 | 0.89 | 3.65 | 待测 | -| 标准偏差 | 6.70 | 10.68 | 待测 | - -**性能对比：** - -取弹性云GPU运行的前2600步的global_step/sec平均值和NPU运行的前2600步的global_step/sec平均值进行对比，以达到性能对比的目的。 - -| 性能指标项 | 论文发布 | GPU实测 | NPU实测 | -| ------------------- | ------- | ------ | ------ | -| global_step/sec| 无 | 79.48 | 66.08 | - - -- Gitee From f353bdabf5627ce2ca8bb9fc8d019e04b7f5202d Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Sat, 7 May 2022 13:49:32 +0000 Subject: [PATCH 16/88] =?UTF-8?q?=E6=9B=B4=E6=96=B0=E5=90=8E=E7=9A=84readm?= =?UTF-8?q?e=EF=BC=8C=E5=8A=A0=E5=85=A5=E4=BA=86NPU=E4=B8=8A=E8=BF=90?= =?UTF-8?q?=E8=A1=8C=E7=9A=84=E6=9C=80=E6=96=B0=E7=BB=93=E6=9E=9C?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../cv/SVD_ID2019_for_Tensorflow/README.md | 229 ++++++++++++++++++ 1 file changed, 229 insertions(+) create mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md new file mode 100644 index 000000000..a4ccebb34 --- /dev/null +++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md @@ -0,0 +1,229 @@ +

基本信息

+ +**发布者（Publisher）：Huawei** + +**应用领域（Application Domain）：CV** + +**版本（Version）：** + +**修改时间（Modified） ：2022.05.05** + +**框架（Framework）：TensorFlow 1.15.0** + +**处理器（Processor）：昇腾910** + +**描述（Description）：使用训练好的SVD模型，评估对称正交化在点云对准中的应用效果。** + +

概述

+ +给定两个三维点云图，利用SVD正交化过程SVDO+(M)将其投射到SO(3)上，要求网络预测最佳对齐它们的3D旋转。 + +- 开源代码： + + https://github.com/google-research/google-research/tree/master/special_orthogonalization。 + +- 参考论文： + + [An Analysis of SVD for Deep Rotation Estimation](https://arxiv.org/abs/2006.14616) + +- 参考实现： + + obs://cann-id2019/gpu/ + +- 相关迁移的工作： + 在进行代码迁移到NPU上时，输入的训练数据为点云数据，点云数据的shape为(N,3)，其中N并不是固定的，因此在NPU上存在动态shape的问题，导致模型训练无法正常进行。我们为此想了三个解决方法：1、找出所有点云数据中最小的N，对于大于N的点云数据，仅取前N行的数据输入训练。2、找到所有点云数据中最大的N，对于小于N的点云进行补0操作，将所有数据固定为最大的N后，输入网络进行训练。3、找到所有点云数据中最大的N，对小于N的点云数据，从原数据中选择一个点云进行填补至行数为N，再将数据输入网络进行训练。该三种方法均成功解决了NPU上的动态shape问题，但是第一种方法删除了样本点，因此导致最后训练出的模型精度很差；第二种方法虽然并没有丢失样本信息，但是向数据中填入大量的0，改变了本来的代码逻辑，导致最后训练出的模型精度也并不高。对于第三种方法，即没有丢失样本信息，对每个点云数据中的某一个点云样本点进行重复操作，没有改变原始的代码逻辑，最后也获得了不错的精度表现。 + +- 通过Git获取对应commit\_id的代码方法如下： + + ``` + git clone {repository_url} # 克隆仓库的代码 + cd {repository_name} # 切换到模型的代码仓目录 + git checkout {branch} # 切换到对应分支 + git reset --hard ｛commit_id｝ # 代码设置到对应的commit_id + cd ｛code_path｝ # 切换到模型代码所在路径，若仓库下只有该模型，则无需切换 + ``` + +## 默认配置 + +- 数据集获取：obs://cann-id2019/dataset/ + +- 训练超参 + + - log_step_count=200 + - save_summaries_steps=25000 + - train_steps=2600000 + - save_checkpoints_steps=100000 + - eval_examples=39900 + + + +

训练环境准备

+ +1. 硬件环境准备请参见各硬件产品文档"[驱动和固件安装升级指南]( https://support.huawei.com/enterprise/zh/category/ai-computing-platform-pid-1557196528909)"。需要在硬件设备上安装与CANN版本配套的固件与驱动。 +2. 宿主机上需要安装Docker并登录[Ascend Hub中心](https://ascendhub.huawei.com/#/detail?name=ascend-tensorflow-arm)获取镜像。 + + 当前模型支持的镜像列表如[表1](#zh-cn_topic_0000001074498056_table1519011227314)所示。 + + **表 1** 镜像列表 + + + + + + + + + + + + +

镜像名称

+

镜像版本

+

配套CANN版本

+
+

20.2.0

+

20.2

+
+ + + +## 脚本参数 + +``` +gen_pt_test_data_gpu.py 中的参数 + +--input_test_files 传入需要进行旋转的点云数据集 +--output_directory 存储旋转后点云集的路径 +--random_rotation_axang boole型，若为真将会对传入的数据集采用指定方法旋转，默认为真 +--num_rotations_per_file 每个测试点云的随机旋转增加数。默认为100 + + +main_point_cloud_gpu.py 中的参数 + +--pt_cloud_test_files 测试数据集路径 +--pt_cloud_train_files 熟练数据集路径 +--method 指定用于预测旋转的方式。选项为"svd", "svd-inf", or "gs"。默认为“svd” +--checkpoint_dir 训练模型的存放位置 +--train_steps 训练迭代的次数。默认为2600000 +--save_checkpoints_steps 保存检查点的频率。默认为10000 +--log_step_count 日志记录一次的步数。默认为200 +--save_summaries_steps 保存一次summary的步数。默认为5000 +--learning_rate 默认为1e-5 +--lr_decay 如果为真，则衰减learning rate。默认为假 +--lr_decay_steps learning rate衰减步数。默认为35000 +--lr_decay_rate learning rate衰减速率。默认为0.95 +--predict_all_test 如果为真，则在最新的检查点上运行eval作业，并打印每个输入的误差信息。默认为假 +--eval_examples 测试样本的数量。默认为0 +--print_variable_names 打印模型变量名。默认为假 +--num_train_augmentations 增加每个输入点云的随机旋转数。默认为10 + +``` + + + +## 运行 + +GPU运行命令如下： + +**生成测试数据** + +注：生成的文件points_test_modified、points已包含在dataset文件夹中。 +```bash +# 将路径设置到训练点云图文件 +IN_FILES=/shapenet/data/pc_plane/points_test/*.pts + +NEW_TEST_FILES_DIR=/shapenet/data/pc_plane/points_test_modified + +AXANG_SAMPLING=True + +# 决定旋转轴角的分布 +AXANG_SAMPLING=True + +python -m special_orthogonalization.gen_pt_test_data_gpu --input_test_files=$IN_FILES --output_directory=$NEW_TEST_FILES_DIR --random_rotation_axang=$AXANG_SAMPLING +``` + +**训练与评价** +```bash +# 将路径设置到原始训练数据 +TRAIN_FILES=/shapenet/data/pc_plane/points/*.pts + +#将路径设置到旋转后的训练数据 +TEST_FILES=$NEW_TEST_FILES_DIR/*.pts + +# 指定旋转预测方式 +METHOD=svd + +# 指定ckpt、summaries、评价结果等的存储路径 +OUT_DIR=/path/to/model + +python -m special_orthogonalization.main_point_cloud_gpu --method=$METHOD --checkpoint_dir=$OUT_DIR --log_step_count=200 --save_summaries_steps=25000 --pt_cloud_train_files=$TRAIN_FILES --pt_cloud_test_files=$TEST_FILES --train_steps=2600000 --save_checkpoints_steps=100000 --eval_examples=39900 +``` + +**从所有训练样本中生成统计数据** +```bash +# 打印均值、中位数、标准差和分位数 +python -m special_orthogonalization.main_point_cloud_gpu --method=$METHOD --checkpoint_dir=$OUT_DIR --pt_cloud_test_files=$TEST_FILES --predict_all_test=True +``` +## 运行 + +NPU运行命令方式如下: + +对于所有的三个步骤程序来说，modelarts插件obs桶中的数据路径均要写到真正包含数据的那一个路径 +如在pc_plane文件夹中含有points、points_test等包含数据的文件夹 +modelarts插件中的数据路径写为 obs://cann-id2019/data/pc_palne/ + +**生成测试数据** + +运行这一步我们需要的程序文件为gen_pt_test_data.py、modelarts_entry_Axang.py、genTestData.sh +这三个文件中的代码均不需要修改 +最后生成的旋转后的数据文件存放在obs桶当次程序文件的output路径中，文件名为test_points_modified， +为进行第二步模型训练，需要将生成旋转后的文件转移至obs桶中存放data的路径 + +注：需要确保的是存在obs桶里的data文件名为points_test + +**训练与评价** + +运行这一步我们需要的程序文件为main_point_cloud.py、modelarts_entry_acc_train.py、train_full_1p.sh +这三个文件中的代码均不需要修改 +最后生成的旋转后的数据文件存放在obs桶当次程序文件的output路径中，文件名为test_points_modified， +为进行第三步，需要将生成的output文件转移至obs桶中存放data的路径 + +注意：该次训练的模型保存在该次的obs文件夹中，进行第三步时又需要重启一次新的modelarts，因此我们需要将output文件中的 +checkpoint文件中最新模型的路径修改 +"/home/ma-user/modelarts/inputs/data_url_0/output" +这样第三步才能跑出正确的精度指标 + +**从所有训练样本中生成统计数据** + +运行这一步我们需要的程序文件为main_point_cloud.py、modelarts_entry_stat.py、genStatistical.sh +这三个文件的代码均不需要修改 +运行成功后将会在屏幕上打印出关于精度相应的统计量值 + +注意：由于加入了混合精度提高训练性能，导致该步出现未知错误，输出的误差均为0，因此建议将训练后的结果下载到本地 +修改checkpoint文件中模型的路径后，利用本地的GPU进行运行，实际上该步骤在运行时NPU成功启动但也未曾利用。 + +## 训练结果 +**精度对比：** + + + +| 测地线误差（°） | 论文发布 | GPU(初始代码未改动版本) | GPU实测|NPU实测 | +| ------------------------ | ------- | ----- | --------- |----------| +| 平均值 | 1.63 | 2.58 | 3.98 | 2.92 | +| 中值 | 0.89 | 1.68 | 2.6 | 1.7 | +| 标准差 | 6.70 | 6.93 | 9.36 | 8.45 | + +相比于论文中的精度，我们NPU迁移后实测差距依然较大，但是与我们未对代码任何改动初始的版本在GPU上跑出来的精度相差较小， +且对于相同的代码的代码改动，NPU上的精度优于GPU上的精度。需要注意的是，在NPU上运行程序时，我们采用混合精度来提升训练 +的性能，但是其中产生了未知的错误，导致代表的精度指标mean_degree_err在整个训练过程中始终为0，因此我们无法得知在NPU训练的 +260w步的过程中，精度指标是下降的过程是怎样的。值得庆幸的是通过NPU训练出的模型，能够在GPU上计算出精度，并且精度还不错。 + +**性能对比：** + +取弹性云GPU运行的前2600步的global_step/sec平均值和NPU运行的前2600步的global_step/sec平均值进行对比，以达到性能对比的目的。 + +| 性能指标项 | 论文发布 | GPU实测 | NPU实测 | +| ------------------- | ------- | ------ | ------ | +| global_step/sec| 无 | 87.64 | 101.24 | + + -- Gitee From 909bf473bee586a14649f7634c8796108836d851 Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Sat, 7 May 2022 13:55:26 +0000 Subject: [PATCH 17/88] =?UTF-8?q?=E5=88=A0=E9=99=A4=E6=96=87=E4=BB=B6=20Te?= =?UTF-8?q?nsorFlow/contrib/cv/SVD=5FID2019=5Ffor=5FTensorflow/README.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../cv/SVD_ID2019_for_Tensorflow/README.md | 229 ------------------ 1 file changed, 229 deletions(-) delete mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md deleted file mode 100644 index a4ccebb34..000000000 --- a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md +++ /dev/null @@ -1,229 +0,0 @@ -

基本信息

- -**发布者（Publisher）：Huawei** - -**应用领域（Application Domain）：CV** - -**版本（Version）：** - -**修改时间（Modified） ：2022.05.05** - -**框架（Framework）：TensorFlow 1.15.0** - -**处理器（Processor）：昇腾910** - -**描述（Description）：使用训练好的SVD模型，评估对称正交化在点云对准中的应用效果。** - -

概述

- -给定两个三维点云图，利用SVD正交化过程SVDO+(M)将其投射到SO(3)上，要求网络预测最佳对齐它们的3D旋转。 - -- 开源代码： - - https://github.com/google-research/google-research/tree/master/special_orthogonalization。 - -- 参考论文： - - [An Analysis of SVD for Deep Rotation Estimation](https://arxiv.org/abs/2006.14616) - -- 参考实现： - - obs://cann-id2019/gpu/ - -- 相关迁移的工作： - 在进行代码迁移到NPU上时，输入的训练数据为点云数据，点云数据的shape为(N,3)，其中N并不是固定的，因此在NPU上存在动态shape的问题，导致模型训练无法正常进行。我们为此想了三个解决方法：1、找出所有点云数据中最小的N，对于大于N的点云数据，仅取前N行的数据输入训练。2、找到所有点云数据中最大的N，对于小于N的点云进行补0操作，将所有数据固定为最大的N后，输入网络进行训练。3、找到所有点云数据中最大的N，对小于N的点云数据，从原数据中选择一个点云进行填补至行数为N，再将数据输入网络进行训练。该三种方法均成功解决了NPU上的动态shape问题，但是第一种方法删除了样本点，因此导致最后训练出的模型精度很差；第二种方法虽然并没有丢失样本信息，但是向数据中填入大量的0，改变了本来的代码逻辑，导致最后训练出的模型精度也并不高。对于第三种方法，即没有丢失样本信息，对每个点云数据中的某一个点云样本点进行重复操作，没有改变原始的代码逻辑，最后也获得了不错的精度表现。 - -- 通过Git获取对应commit\_id的代码方法如下： - - ``` - git clone {repository_url} # 克隆仓库的代码 - cd {repository_name} # 切换到模型的代码仓目录 - git checkout {branch} # 切换到对应分支 - git reset --hard ｛commit_id｝ # 代码设置到对应的commit_id - cd ｛code_path｝ # 切换到模型代码所在路径，若仓库下只有该模型，则无需切换 - ``` - -## 默认配置 - -- 数据集获取：obs://cann-id2019/dataset/ - -- 训练超参 - - - log_step_count=200 - - save_summaries_steps=25000 - - train_steps=2600000 - - save_checkpoints_steps=100000 - - eval_examples=39900 - - - -

训练环境准备

- -1. 硬件环境准备请参见各硬件产品文档"[驱动和固件安装升级指南]( https://support.huawei.com/enterprise/zh/category/ai-computing-platform-pid-1557196528909)"。需要在硬件设备上安装与CANN版本配套的固件与驱动。 -2. 宿主机上需要安装Docker并登录[Ascend Hub中心](https://ascendhub.huawei.com/#/detail?name=ascend-tensorflow-arm)获取镜像。 - - 当前模型支持的镜像列表如[表1](#zh-cn_topic_0000001074498056_table1519011227314)所示。 - - **表 1** 镜像列表 - - - - - - - - - - - - -

镜像名称

-

镜像版本

-

配套CANN版本

-
-

20.2.0

-

20.2

-
- - - -## 脚本参数 - -``` -gen_pt_test_data_gpu.py 中的参数 - ---input_test_files 传入需要进行旋转的点云数据集 ---output_directory 存储旋转后点云集的路径 ---random_rotation_axang boole型，若为真将会对传入的数据集采用指定方法旋转，默认为真 ---num_rotations_per_file 每个测试点云的随机旋转增加数。默认为100 - - -main_point_cloud_gpu.py 中的参数 - ---pt_cloud_test_files 测试数据集路径 ---pt_cloud_train_files 熟练数据集路径 ---method 指定用于预测旋转的方式。选项为"svd", "svd-inf", or "gs"。默认为“svd” ---checkpoint_dir 训练模型的存放位置 ---train_steps 训练迭代的次数。默认为2600000 ---save_checkpoints_steps 保存检查点的频率。默认为10000 ---log_step_count 日志记录一次的步数。默认为200 ---save_summaries_steps 保存一次summary的步数。默认为5000 ---learning_rate 默认为1e-5 ---lr_decay 如果为真，则衰减learning rate。默认为假 ---lr_decay_steps learning rate衰减步数。默认为35000 ---lr_decay_rate learning rate衰减速率。默认为0.95 ---predict_all_test 如果为真，则在最新的检查点上运行eval作业，并打印每个输入的误差信息。默认为假 ---eval_examples 测试样本的数量。默认为0 ---print_variable_names 打印模型变量名。默认为假 ---num_train_augmentations 增加每个输入点云的随机旋转数。默认为10 - -``` - - - -## 运行 - -GPU运行命令如下： - -**生成测试数据** - -注：生成的文件points_test_modified、points已包含在dataset文件夹中。 -```bash -# 将路径设置到训练点云图文件 -IN_FILES=/shapenet/data/pc_plane/points_test/*.pts - -NEW_TEST_FILES_DIR=/shapenet/data/pc_plane/points_test_modified - -AXANG_SAMPLING=True - -# 决定旋转轴角的分布 -AXANG_SAMPLING=True - -python -m special_orthogonalization.gen_pt_test_data_gpu --input_test_files=$IN_FILES --output_directory=$NEW_TEST_FILES_DIR --random_rotation_axang=$AXANG_SAMPLING -``` - -**训练与评价** -```bash -# 将路径设置到原始训练数据 -TRAIN_FILES=/shapenet/data/pc_plane/points/*.pts - -#将路径设置到旋转后的训练数据 -TEST_FILES=$NEW_TEST_FILES_DIR/*.pts - -# 指定旋转预测方式 -METHOD=svd - -# 指定ckpt、summaries、评价结果等的存储路径 -OUT_DIR=/path/to/model - -python -m special_orthogonalization.main_point_cloud_gpu --method=$METHOD --checkpoint_dir=$OUT_DIR --log_step_count=200 --save_summaries_steps=25000 --pt_cloud_train_files=$TRAIN_FILES --pt_cloud_test_files=$TEST_FILES --train_steps=2600000 --save_checkpoints_steps=100000 --eval_examples=39900 -``` - -**从所有训练样本中生成统计数据** -```bash -# 打印均值、中位数、标准差和分位数 -python -m special_orthogonalization.main_point_cloud_gpu --method=$METHOD --checkpoint_dir=$OUT_DIR --pt_cloud_test_files=$TEST_FILES --predict_all_test=True -``` -## 运行 - -NPU运行命令方式如下: - -对于所有的三个步骤程序来说，modelarts插件obs桶中的数据路径均要写到真正包含数据的那一个路径 -如在pc_plane文件夹中含有points、points_test等包含数据的文件夹 -modelarts插件中的数据路径写为 obs://cann-id2019/data/pc_palne/ - -**生成测试数据** - -运行这一步我们需要的程序文件为gen_pt_test_data.py、modelarts_entry_Axang.py、genTestData.sh -这三个文件中的代码均不需要修改 -最后生成的旋转后的数据文件存放在obs桶当次程序文件的output路径中，文件名为test_points_modified， -为进行第二步模型训练，需要将生成旋转后的文件转移至obs桶中存放data的路径 - -注：需要确保的是存在obs桶里的data文件名为points_test - -**训练与评价** - -运行这一步我们需要的程序文件为main_point_cloud.py、modelarts_entry_acc_train.py、train_full_1p.sh -这三个文件中的代码均不需要修改 -最后生成的旋转后的数据文件存放在obs桶当次程序文件的output路径中，文件名为test_points_modified， -为进行第三步，需要将生成的output文件转移至obs桶中存放data的路径 - -注意：该次训练的模型保存在该次的obs文件夹中，进行第三步时又需要重启一次新的modelarts，因此我们需要将output文件中的 -checkpoint文件中最新模型的路径修改 -"/home/ma-user/modelarts/inputs/data_url_0/output" -这样第三步才能跑出正确的精度指标 - -**从所有训练样本中生成统计数据** - -运行这一步我们需要的程序文件为main_point_cloud.py、modelarts_entry_stat.py、genStatistical.sh -这三个文件的代码均不需要修改 -运行成功后将会在屏幕上打印出关于精度相应的统计量值 - -注意：由于加入了混合精度提高训练性能，导致该步出现未知错误，输出的误差均为0，因此建议将训练后的结果下载到本地 -修改checkpoint文件中模型的路径后，利用本地的GPU进行运行，实际上该步骤在运行时NPU成功启动但也未曾利用。 - -## 训练结果 -**精度对比：** - - - -| 测地线误差（°） | 论文发布 | GPU(初始代码未改动版本) | GPU实测|NPU实测 | -| ------------------------ | ------- | ----- | --------- |----------| -| 平均值 | 1.63 | 2.58 | 3.98 | 2.92 | -| 中值 | 0.89 | 1.68 | 2.6 | 1.7 | -| 标准差 | 6.70 | 6.93 | 9.36 | 8.45 | - -相比于论文中的精度，我们NPU迁移后实测差距依然较大，但是与我们未对代码任何改动初始的版本在GPU上跑出来的精度相差较小， -且对于相同的代码的代码改动，NPU上的精度优于GPU上的精度。需要注意的是，在NPU上运行程序时，我们采用混合精度来提升训练 -的性能，但是其中产生了未知的错误，导致代表的精度指标mean_degree_err在整个训练过程中始终为0，因此我们无法得知在NPU训练的 -260w步的过程中，精度指标是下降的过程是怎样的。值得庆幸的是通过NPU训练出的模型，能够在GPU上计算出精度，并且精度还不错。 - -**性能对比：** - -取弹性云GPU运行的前2600步的global_step/sec平均值和NPU运行的前2600步的global_step/sec平均值进行对比，以达到性能对比的目的。 - -| 性能指标项 | 论文发布 | GPU实测 | NPU实测 | -| ------------------- | ------- | ------ | ------ | -| global_step/sec| 无 | 87.64 | 101.24 | - - -- Gitee From 6f1937bb19df074af7fbc1ca8087d2c290080142 Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Sat, 7 May 2022 13:55:50 +0000 Subject: [PATCH 18/88] =?UTF-8?q?=E6=9B=B4=E6=96=B0=E5=90=8E=E7=9A=84readm?= =?UTF-8?q?e=EF=BC=8C=E5=8A=A0=E5=85=A5=E4=BA=86NPU=E4=B8=8A=E8=BF=90?= =?UTF-8?q?=E8=A1=8C=E7=9A=84=E6=9C=80=E6=96=B0=E7=BB=93=E6=9E=9C?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../cv/SVD_ID2019_for_Tensorflow/README.md | 229 ++++++++++++++++++ 1 file changed, 229 insertions(+) create mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md new file mode 100644 index 000000000..e75cc15c5 --- /dev/null +++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md @@ -0,0 +1,229 @@ +

基本信息

+ +**发布者（Publisher）：Huawei** + +**应用领域（Application Domain）：CV** + +**版本（Version）：** + +**修改时间（Modified） ：2022.05.05** + +**框架（Framework）：TensorFlow 1.15.0** + +**处理器（Processor）：昇腾910** + +**描述（Description）：使用训练好的SVD模型，评估对称正交化在点云对准中的应用效果。** + +

概述

+ +给定两个三维点云图，利用SVD正交化过程SVDO+(M)将其投射到SO(3)上，要求网络预测最佳对齐它们的3D旋转。代码的训练逻辑是每训练10w步保存一个模型，并且在测试集上检验该模型的精度，最后比较的都是260w步的模型精度 + +- 开源代码： + + https://github.com/google-research/google-research/tree/master/special_orthogonalization。 + +- 参考论文： + + [An Analysis of SVD for Deep Rotation Estimation](https://arxiv.org/abs/2006.14616) + +- 参考实现： + + obs://cann-id2019/gpu/ + +- 相关迁移的工作： + 在进行代码迁移到NPU上时，输入的训练数据为点云数据，点云数据的shape为(N,3)，其中N并不是固定的，因此在NPU上存在动态shape的问题，导致模型训练无法正常进行。我们为此想了三个解决方法：1、找出所有点云数据中最小的N，对于大于N的点云数据，仅取前N行的数据输入训练。2、找到所有点云数据中最大的N，对于小于N的点云进行补0操作，将所有数据固定为最大的N后，输入网络进行训练。3、找到所有点云数据中最大的N，对小于N的点云数据，从原数据中选择一个点云进行填补至行数为N，再将数据输入网络进行训练。该三种方法均成功解决了NPU上的动态shape问题，但是第一种方法删除了样本点，因此导致最后训练出的模型精度很差；第二种方法虽然并没有丢失样本信息，但是向数据中填入大量的0，改变了本来的代码逻辑，导致最后训练出的模型精度也并不高。对于第三种方法，即没有丢失样本信息，对每个点云数据中的某一个点云样本点进行重复操作，没有改变原始的代码逻辑，最后也获得了不错的精度表现。 + +- 通过Git获取对应commit\_id的代码方法如下： + + ``` + git clone {repository_url} # 克隆仓库的代码 + cd {repository_name} # 切换到模型的代码仓目录 + git checkout {branch} # 切换到对应分支 + git reset --hard ｛commit_id｝ # 代码设置到对应的commit_id + cd ｛code_path｝ # 切换到模型代码所在路径，若仓库下只有该模型，则无需切换 + ``` + +## 默认配置 + +- 数据集获取：obs://cann-id2019/dataset/ + +- 训练超参 + + - log_step_count=200 + - save_summaries_steps=25000 + - train_steps=2600000 + - save_checkpoints_steps=100000 + - eval_examples=39900 + + + +

训练环境准备

+ +1. 硬件环境准备请参见各硬件产品文档"[驱动和固件安装升级指南]( https://support.huawei.com/enterprise/zh/category/ai-computing-platform-pid-1557196528909)"。需要在硬件设备上安装与CANN版本配套的固件与驱动。 +2. 宿主机上需要安装Docker并登录[Ascend Hub中心](https://ascendhub.huawei.com/#/detail?name=ascend-tensorflow-arm)获取镜像。 + + 当前模型支持的镜像列表如[表1](#zh-cn_topic_0000001074498056_table1519011227314)所示。 + + **表 1** 镜像列表 + + + + + + + + + + + + +

镜像名称

+

镜像版本

+

配套CANN版本

+
+

20.2.0

+

20.2

+
+ + + +## 脚本参数 + +``` +gen_pt_test_data_gpu.py 中的参数 + +--input_test_files 传入需要进行旋转的点云数据集 +--output_directory 存储旋转后点云集的路径 +--random_rotation_axang boole型，若为真将会对传入的数据集采用指定方法旋转，默认为真 +--num_rotations_per_file 每个测试点云的随机旋转增加数。默认为100 + + +main_point_cloud_gpu.py 中的参数 + +--pt_cloud_test_files 测试数据集路径 +--pt_cloud_train_files 熟练数据集路径 +--method 指定用于预测旋转的方式。选项为"svd", "svd-inf", or "gs"。默认为“svd” +--checkpoint_dir 训练模型的存放位置 +--train_steps 训练迭代的次数。默认为2600000 +--save_checkpoints_steps 保存检查点的频率。默认为10000 +--log_step_count 日志记录一次的步数。默认为200 +--save_summaries_steps 保存一次summary的步数。默认为5000 +--learning_rate 默认为1e-5 +--lr_decay 如果为真，则衰减learning rate。默认为假 +--lr_decay_steps learning rate衰减步数。默认为35000 +--lr_decay_rate learning rate衰减速率。默认为0.95 +--predict_all_test 如果为真，则在最新的检查点上运行eval作业，并打印每个输入的误差信息。默认为假 +--eval_examples 测试样本的数量。默认为0 +--print_variable_names 打印模型变量名。默认为假 +--num_train_augmentations 增加每个输入点云的随机旋转数。默认为10 + +``` + + + +## 运行 + +GPU运行命令如下： + +**生成测试数据** + +注：生成的文件points_test_modified、points已包含在dataset文件夹中。 +```bash +# 将路径设置到训练点云图文件 +IN_FILES=/shapenet/data/pc_plane/points_test/*.pts + +NEW_TEST_FILES_DIR=/shapenet/data/pc_plane/points_test_modified + +AXANG_SAMPLING=True + +# 决定旋转轴角的分布 +AXANG_SAMPLING=True + +python -m special_orthogonalization.gen_pt_test_data_gpu --input_test_files=$IN_FILES --output_directory=$NEW_TEST_FILES_DIR --random_rotation_axang=$AXANG_SAMPLING +``` + +**训练与评价** +```bash +# 将路径设置到原始训练数据 +TRAIN_FILES=/shapenet/data/pc_plane/points/*.pts + +#将路径设置到旋转后的训练数据 +TEST_FILES=$NEW_TEST_FILES_DIR/*.pts + +# 指定旋转预测方式 +METHOD=svd + +# 指定ckpt、summaries、评价结果等的存储路径 +OUT_DIR=/path/to/model + +python -m special_orthogonalization.main_point_cloud_gpu --method=$METHOD --checkpoint_dir=$OUT_DIR --log_step_count=200 --save_summaries_steps=25000 --pt_cloud_train_files=$TRAIN_FILES --pt_cloud_test_files=$TEST_FILES --train_steps=2600000 --save_checkpoints_steps=100000 --eval_examples=39900 +``` + +**从所有训练样本中生成统计数据** +```bash +# 打印均值、中位数、标准差和分位数 +python -m special_orthogonalization.main_point_cloud_gpu --method=$METHOD --checkpoint_dir=$OUT_DIR --pt_cloud_test_files=$TEST_FILES --predict_all_test=True +``` +## 运行 + +NPU运行命令方式如下: + +对于所有的三个步骤程序来说，modelarts插件obs桶中的数据路径均要写到真正包含数据的那一个路径 +如在pc_plane文件夹中含有points、points_test等包含数据的文件夹 +modelarts插件中的数据路径写为 obs://cann-id2019/data/pc_palne/ + +**生成测试数据** + +运行这一步我们需要的程序文件为gen_pt_test_data.py、modelarts_entry_Axang.py、genTestData.sh +这三个文件中的代码均不需要修改 +最后生成的旋转后的数据文件存放在obs桶当次程序文件的output路径中，文件名为test_points_modified， +为进行第二步模型训练，需要将生成旋转后的文件转移至obs桶中存放data的路径 + +注：需要确保的是存在obs桶里的data文件名为points_test + +**训练与评价** + +运行这一步我们需要的程序文件为main_point_cloud.py、modelarts_entry_acc_train.py、train_full_1p.sh +这三个文件中的代码均不需要修改 +最后生成的旋转后的数据文件存放在obs桶当次程序文件的output路径中，文件名为test_points_modified， +为进行第三步，需要将生成的output文件转移至obs桶中存放data的路径 + +注意：该次训练的模型保存在该次的obs文件夹中，进行第三步时又需要重启一次新的modelarts，因此我们需要将output文件中的 +checkpoint文件中最新模型的路径修改 +"/home/ma-user/modelarts/inputs/data_url_0/output" +这样第三步才能跑出正确的精度指标 + +**从所有训练样本中生成统计数据** + +运行这一步我们需要的程序文件为main_point_cloud.py、modelarts_entry_stat.py、genStatistical.sh +这三个文件的代码均不需要修改 +运行成功后将会在屏幕上打印出关于精度相应的统计量值 + +注意：由于加入了混合精度提高训练性能，导致该步出现未知错误，输出的误差均为0，因此建议将训练后的结果下载到本地 +修改checkpoint文件中模型的路径后，利用本地的GPU进行运行，实际上该步骤在运行时NPU成功启动但也未曾利用。 + +## 训练结果 +**精度对比：** + + + +| 测地线误差（°） | 论文发布 | GPU(初始代码未改动版本) | GPU实测|NPU实测 | +| ------------------------ | ------- | ----- | --------- |----------| +| 平均值 | 1.63 | 2.58 | 3.98 | 2.92 | +| 中值 | 0.89 | 1.68 | 2.6 | 1.7 | +| 标准差 | 6.70 | 6.93 | 9.36 | 8.45 | + +相比于论文中的精度，我们NPU迁移后实测差距依然较大，但是与我们未对代码任何改动初始的版本在GPU上跑出来的精度相差较小， +且对于相同的代码的代码改动，NPU上的精度优于GPU上的精度。需要注意的是，在NPU上运行程序时，我们采用混合精度来提升训练 +的性能，但是其中产生了未知的错误，导致代表的精度指标mean_degree_err在整个训练过程中始终为0，因此我们无法得知在NPU训练的 +260w步的过程中，精度指标是下降的过程是怎样的。值得庆幸的是通过NPU训练出的模型，能够在GPU上计算出精度，并且精度还不错。 + +**性能对比：** + +取弹性云GPU运行的前2600步的global_step/sec平均值和NPU运行的前2600步的global_step/sec平均值进行对比，以达到性能对比的目的。 + +| 性能指标项 | 论文发布 | GPU实测 | NPU实测 | +| ------------------- | ------- | ------ | ------ | +| global_step/sec| 无 | 87.64 | 101.24 | + + -- Gitee From 4d6502db3806624a34ca18ac2700c54c8eb2deee Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Sat, 7 May 2022 13:56:49 +0000 Subject: [PATCH 19/88] =?UTF-8?q?=E5=88=A0=E9=99=A4=E6=96=87=E4=BB=B6=20Te?= =?UTF-8?q?nsorFlow/contrib/cv/SVD=5FID2019=5Ffor=5FTensorflow/README.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../cv/SVD_ID2019_for_Tensorflow/README.md | 229 ------------------ 1 file changed, 229 deletions(-) delete mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md deleted file mode 100644 index e75cc15c5..000000000 --- a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md +++ /dev/null @@ -1,229 +0,0 @@ -

基本信息

- -**发布者（Publisher）：Huawei** - -**应用领域（Application Domain）：CV** - -**版本（Version）：** - -**修改时间（Modified） ：2022.05.05** - -**框架（Framework）：TensorFlow 1.15.0** - -**处理器（Processor）：昇腾910** - -**描述（Description）：使用训练好的SVD模型，评估对称正交化在点云对准中的应用效果。** - -

概述

- -给定两个三维点云图，利用SVD正交化过程SVDO+(M)将其投射到SO(3)上，要求网络预测最佳对齐它们的3D旋转。代码的训练逻辑是每训练10w步保存一个模型，并且在测试集上检验该模型的精度，最后比较的都是260w步的模型精度 - -- 开源代码： - - https://github.com/google-research/google-research/tree/master/special_orthogonalization。 - -- 参考论文： - - [An Analysis of SVD for Deep Rotation Estimation](https://arxiv.org/abs/2006.14616) - -- 参考实现： - - obs://cann-id2019/gpu/ - -- 相关迁移的工作： - 在进行代码迁移到NPU上时，输入的训练数据为点云数据，点云数据的shape为(N,3)，其中N并不是固定的，因此在NPU上存在动态shape的问题，导致模型训练无法正常进行。我们为此想了三个解决方法：1、找出所有点云数据中最小的N，对于大于N的点云数据，仅取前N行的数据输入训练。2、找到所有点云数据中最大的N，对于小于N的点云进行补0操作，将所有数据固定为最大的N后，输入网络进行训练。3、找到所有点云数据中最大的N，对小于N的点云数据，从原数据中选择一个点云进行填补至行数为N，再将数据输入网络进行训练。该三种方法均成功解决了NPU上的动态shape问题，但是第一种方法删除了样本点，因此导致最后训练出的模型精度很差；第二种方法虽然并没有丢失样本信息，但是向数据中填入大量的0，改变了本来的代码逻辑，导致最后训练出的模型精度也并不高。对于第三种方法，即没有丢失样本信息，对每个点云数据中的某一个点云样本点进行重复操作，没有改变原始的代码逻辑，最后也获得了不错的精度表现。 - -- 通过Git获取对应commit\_id的代码方法如下： - - ``` - git clone {repository_url} # 克隆仓库的代码 - cd {repository_name} # 切换到模型的代码仓目录 - git checkout {branch} # 切换到对应分支 - git reset --hard ｛commit_id｝ # 代码设置到对应的commit_id - cd ｛code_path｝ # 切换到模型代码所在路径，若仓库下只有该模型，则无需切换 - ``` - -## 默认配置 - -- 数据集获取：obs://cann-id2019/dataset/ - -- 训练超参 - - - log_step_count=200 - - save_summaries_steps=25000 - - train_steps=2600000 - - save_checkpoints_steps=100000 - - eval_examples=39900 - - - -

训练环境准备

- -1. 硬件环境准备请参见各硬件产品文档"[驱动和固件安装升级指南]( https://support.huawei.com/enterprise/zh/category/ai-computing-platform-pid-1557196528909)"。需要在硬件设备上安装与CANN版本配套的固件与驱动。 -2. 宿主机上需要安装Docker并登录[Ascend Hub中心](https://ascendhub.huawei.com/#/detail?name=ascend-tensorflow-arm)获取镜像。 - - 当前模型支持的镜像列表如[表1](#zh-cn_topic_0000001074498056_table1519011227314)所示。 - - **表 1** 镜像列表 - - - - - - - - - - - - -

镜像名称

-

镜像版本

-

配套CANN版本

-
-

20.2.0

-

20.2

-
- - - -## 脚本参数 - -``` -gen_pt_test_data_gpu.py 中的参数 - ---input_test_files 传入需要进行旋转的点云数据集 ---output_directory 存储旋转后点云集的路径 ---random_rotation_axang boole型，若为真将会对传入的数据集采用指定方法旋转，默认为真 ---num_rotations_per_file 每个测试点云的随机旋转增加数。默认为100 - - -main_point_cloud_gpu.py 中的参数 - ---pt_cloud_test_files 测试数据集路径 ---pt_cloud_train_files 熟练数据集路径 ---method 指定用于预测旋转的方式。选项为"svd", "svd-inf", or "gs"。默认为“svd” ---checkpoint_dir 训练模型的存放位置 ---train_steps 训练迭代的次数。默认为2600000 ---save_checkpoints_steps 保存检查点的频率。默认为10000 ---log_step_count 日志记录一次的步数。默认为200 ---save_summaries_steps 保存一次summary的步数。默认为5000 ---learning_rate 默认为1e-5 ---lr_decay 如果为真，则衰减learning rate。默认为假 ---lr_decay_steps learning rate衰减步数。默认为35000 ---lr_decay_rate learning rate衰减速率。默认为0.95 ---predict_all_test 如果为真，则在最新的检查点上运行eval作业，并打印每个输入的误差信息。默认为假 ---eval_examples 测试样本的数量。默认为0 ---print_variable_names 打印模型变量名。默认为假 ---num_train_augmentations 增加每个输入点云的随机旋转数。默认为10 - -``` - - - -## 运行 - -GPU运行命令如下： - -**生成测试数据** - -注：生成的文件points_test_modified、points已包含在dataset文件夹中。 -```bash -# 将路径设置到训练点云图文件 -IN_FILES=/shapenet/data/pc_plane/points_test/*.pts - -NEW_TEST_FILES_DIR=/shapenet/data/pc_plane/points_test_modified - -AXANG_SAMPLING=True - -# 决定旋转轴角的分布 -AXANG_SAMPLING=True - -python -m special_orthogonalization.gen_pt_test_data_gpu --input_test_files=$IN_FILES --output_directory=$NEW_TEST_FILES_DIR --random_rotation_axang=$AXANG_SAMPLING -``` - -**训练与评价** -```bash -# 将路径设置到原始训练数据 -TRAIN_FILES=/shapenet/data/pc_plane/points/*.pts - -#将路径设置到旋转后的训练数据 -TEST_FILES=$NEW_TEST_FILES_DIR/*.pts - -# 指定旋转预测方式 -METHOD=svd - -# 指定ckpt、summaries、评价结果等的存储路径 -OUT_DIR=/path/to/model - -python -m special_orthogonalization.main_point_cloud_gpu --method=$METHOD --checkpoint_dir=$OUT_DIR --log_step_count=200 --save_summaries_steps=25000 --pt_cloud_train_files=$TRAIN_FILES --pt_cloud_test_files=$TEST_FILES --train_steps=2600000 --save_checkpoints_steps=100000 --eval_examples=39900 -``` - -**从所有训练样本中生成统计数据** -```bash -# 打印均值、中位数、标准差和分位数 -python -m special_orthogonalization.main_point_cloud_gpu --method=$METHOD --checkpoint_dir=$OUT_DIR --pt_cloud_test_files=$TEST_FILES --predict_all_test=True -``` -## 运行 - -NPU运行命令方式如下: - -对于所有的三个步骤程序来说，modelarts插件obs桶中的数据路径均要写到真正包含数据的那一个路径 -如在pc_plane文件夹中含有points、points_test等包含数据的文件夹 -modelarts插件中的数据路径写为 obs://cann-id2019/data/pc_palne/ - -**生成测试数据** - -运行这一步我们需要的程序文件为gen_pt_test_data.py、modelarts_entry_Axang.py、genTestData.sh -这三个文件中的代码均不需要修改 -最后生成的旋转后的数据文件存放在obs桶当次程序文件的output路径中，文件名为test_points_modified， -为进行第二步模型训练，需要将生成旋转后的文件转移至obs桶中存放data的路径 - -注：需要确保的是存在obs桶里的data文件名为points_test - -**训练与评价** - -运行这一步我们需要的程序文件为main_point_cloud.py、modelarts_entry_acc_train.py、train_full_1p.sh -这三个文件中的代码均不需要修改 -最后生成的旋转后的数据文件存放在obs桶当次程序文件的output路径中，文件名为test_points_modified， -为进行第三步，需要将生成的output文件转移至obs桶中存放data的路径 - -注意：该次训练的模型保存在该次的obs文件夹中，进行第三步时又需要重启一次新的modelarts，因此我们需要将output文件中的 -checkpoint文件中最新模型的路径修改 -"/home/ma-user/modelarts/inputs/data_url_0/output" -这样第三步才能跑出正确的精度指标 - -**从所有训练样本中生成统计数据** - -运行这一步我们需要的程序文件为main_point_cloud.py、modelarts_entry_stat.py、genStatistical.sh -这三个文件的代码均不需要修改 -运行成功后将会在屏幕上打印出关于精度相应的统计量值 - -注意：由于加入了混合精度提高训练性能，导致该步出现未知错误，输出的误差均为0，因此建议将训练后的结果下载到本地 -修改checkpoint文件中模型的路径后，利用本地的GPU进行运行，实际上该步骤在运行时NPU成功启动但也未曾利用。 - -## 训练结果 -**精度对比：** - - - -| 测地线误差（°） | 论文发布 | GPU(初始代码未改动版本) | GPU实测|NPU实测 | -| ------------------------ | ------- | ----- | --------- |----------| -| 平均值 | 1.63 | 2.58 | 3.98 | 2.92 | -| 中值 | 0.89 | 1.68 | 2.6 | 1.7 | -| 标准差 | 6.70 | 6.93 | 9.36 | 8.45 | - -相比于论文中的精度，我们NPU迁移后实测差距依然较大，但是与我们未对代码任何改动初始的版本在GPU上跑出来的精度相差较小， -且对于相同的代码的代码改动，NPU上的精度优于GPU上的精度。需要注意的是，在NPU上运行程序时，我们采用混合精度来提升训练 -的性能，但是其中产生了未知的错误，导致代表的精度指标mean_degree_err在整个训练过程中始终为0，因此我们无法得知在NPU训练的 -260w步的过程中，精度指标是下降的过程是怎样的。值得庆幸的是通过NPU训练出的模型，能够在GPU上计算出精度，并且精度还不错。 - -**性能对比：** - -取弹性云GPU运行的前2600步的global_step/sec平均值和NPU运行的前2600步的global_step/sec平均值进行对比，以达到性能对比的目的。 - -| 性能指标项 | 论文发布 | GPU实测 | NPU实测 | -| ------------------- | ------- | ------ | ------ | -| global_step/sec| 无 | 87.64 | 101.24 | - - -- Gitee From 33b4df92491e137319c41788355526a0ed74e796 Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Sat, 7 May 2022 13:57:05 +0000 Subject: [PATCH 20/88] =?UTF-8?q?=E6=9B=B4=E6=96=B0=E5=90=8E=E7=9A=84readm?= =?UTF-8?q?e=EF=BC=8C=E5=8A=A0=E5=85=A5=E4=BA=86NPU=E4=B8=8A=E8=BF=90?= =?UTF-8?q?=E8=A1=8C=E7=9A=84=E6=9C=80=E6=96=B0=E7=BB=93=E6=9E=9C?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../cv/SVD_ID2019_for_Tensorflow/README.md | 229 ++++++++++++++++++ 1 file changed, 229 insertions(+) create mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md new file mode 100644 index 000000000..5724c3409 --- /dev/null +++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md @@ -0,0 +1,229 @@ +

基本信息

+ +**发布者（Publisher）：Huawei** + +**应用领域（Application Domain）：CV** + +**版本（Version）：** + +**修改时间（Modified） ：2022.05.05** + +**框架（Framework）：TensorFlow 1.15.0** + +**处理器（Processor）：昇腾910** + +**描述（Description）：使用训练好的SVD模型，评估对称正交化在点云对准中的应用效果。** + +

概述

+ +给定两个三维点云图，利用SVD正交化过程SVDO+(M)将其投射到SO(3)上，要求网络预测最佳对齐它们的3D旋转。代码的训练逻辑是每训练10w步保存一个模型，并且在测试集上检验该模型的精度，最后比较的都是260w步的模型精度 + +- 开源代码： + + https://github.com/google-research/google-research/tree/master/special_orthogonalization。 + +- 参考论文： + + [An Analysis of SVD for Deep Rotation Estimation](https://arxiv.org/abs/2006.14616) + +- 参考实现： + + obs://cann-id2019/gpu/ + +- 相关迁移的工作： + 在进行代码迁移到NPU上时，输入的训练数据为点云数据，点云数据的shape为(N,3)，其中N并不是固定的，因此在NPU上存在动态shape的问题，导致模型训练无法正常进行。我们为此想了三个解决方法：1、找出所有点云数据中最小的N，对于大于N的点云数据，仅取前N行的数据输入训练。2、找到所有点云数据中最大的N，对于小于N的点云进行补0操作，将所有数据固定为最大的N后，输入网络进行训练。3、找到所有点云数据中最大的N，对小于N的点云数据，从原数据中选择一个点云进行填补至行数为N，再将数据输入网络进行训练。该三种方法均成功解决了NPU上的动态shape问题，但是第一种方法删除了样本点，因此导致最后训练出的模型精度很差；第二种方法虽然并没有丢失样本信息，但是向数据中填入大量的0，改变了本来的代码逻辑，导致最后训练出的模型精度也并不高。对于第三种方法，即没有丢失样本信息，对每个点云数据中的某一个点云样本点进行重复操作，没有改变原始的代码逻辑，最后也获得了不错的精度表现。 + +- 通过Git获取对应commit\_id的代码方法如下： + + ``` + git clone {repository_url} # 克隆仓库的代码 + cd {repository_name} # 切换到模型的代码仓目录 + git checkout {branch} # 切换到对应分支 + git reset --hard ｛commit_id｝ # 代码设置到对应的commit_id + cd ｛code_path｝ # 切换到模型代码所在路径，若仓库下只有该模型，则无需切换 + ``` + +## 默认配置 + +- 数据集获取：obs://cann-id2019/dataset/ + +- 训练超参 + + - log_step_count=200 + - save_summaries_steps=25000 + - train_steps=2600000 + - save_checkpoints_steps=100000 + - eval_examples=39900 + + + +

训练环境准备

+ +1. 硬件环境准备请参见各硬件产品文档"[驱动和固件安装升级指南]( https://support.huawei.com/enterprise/zh/category/ai-computing-platform-pid-1557196528909)"。需要在硬件设备上安装与CANN版本配套的固件与驱动。 +2. 宿主机上需要安装Docker并登录[Ascend Hub中心](https://ascendhub.huawei.com/#/detail?name=ascend-tensorflow-arm)获取镜像。 + + 当前模型支持的镜像列表如[表1](#zh-cn_topic_0000001074498056_table1519011227314)所示。 + + **表 1** 镜像列表 + + + + + + + + + + + + +

镜像名称

+

镜像版本

+

配套CANN版本

+
+

20.2.0

+

20.2

+
+ + + +## 脚本参数 + +``` +gen_pt_test_data_gpu.py 中的参数 + +--input_test_files 传入需要进行旋转的点云数据集 +--output_directory 存储旋转后点云集的路径 +--random_rotation_axang boole型，若为真将会对传入的数据集采用指定方法旋转，默认为真 +--num_rotations_per_file 每个测试点云的随机旋转增加数。默认为100 + + +main_point_cloud_gpu.py 中的参数 + +--pt_cloud_test_files 测试数据集路径 +--pt_cloud_train_files 熟练数据集路径 +--method 指定用于预测旋转的方式。选项为"svd", "svd-inf", or "gs"。默认为“svd” +--checkpoint_dir 训练模型的存放位置 +--train_steps 训练迭代的次数。默认为2600000 +--save_checkpoints_steps 保存检查点的频率。默认为10000 +--log_step_count 日志记录一次的步数。默认为200 +--save_summaries_steps 保存一次summary的步数。默认为5000 +--learning_rate 默认为1e-5 +--lr_decay 如果为真，则衰减learning rate。默认为假 +--lr_decay_steps learning rate衰减步数。默认为35000 +--lr_decay_rate learning rate衰减速率。默认为0.95 +--predict_all_test 如果为真，则在最新的检查点上运行eval作业，并打印每个输入的误差信息。默认为假 +--eval_examples 测试样本的数量。默认为0 +--print_variable_names 打印模型变量名。默认为假 +--num_train_augmentations 增加每个输入点云的随机旋转数。默认为10 + +``` + + + +## 运行 + +GPU运行命令如下： + +**生成测试数据** + +注：生成的文件points_test_modified、points已包含在dataset文件夹中。 +```bash +# 将路径设置到训练点云图文件 +IN_FILES=/shapenet/data/pc_plane/points_test/*.pts + +NEW_TEST_FILES_DIR=/shapenet/data/pc_plane/points_test_modified + +AXANG_SAMPLING=True + +# 决定旋转轴角的分布 +AXANG_SAMPLING=True + +python -m special_orthogonalization.gen_pt_test_data_gpu --input_test_files=$IN_FILES --output_directory=$NEW_TEST_FILES_DIR --random_rotation_axang=$AXANG_SAMPLING +``` + +**训练与评价** +```bash +# 将路径设置到原始训练数据 +TRAIN_FILES=/shapenet/data/pc_plane/points/*.pts + +#将路径设置到旋转后的训练数据 +TEST_FILES=$NEW_TEST_FILES_DIR/*.pts + +# 指定旋转预测方式 +METHOD=svd + +# 指定ckpt、summaries、评价结果等的存储路径 +OUT_DIR=/path/to/model + +python -m special_orthogonalization.main_point_cloud_gpu --method=$METHOD --checkpoint_dir=$OUT_DIR --log_step_count=200 --save_summaries_steps=25000 --pt_cloud_train_files=$TRAIN_FILES --pt_cloud_test_files=$TEST_FILES --train_steps=2600000 --save_checkpoints_steps=100000 --eval_examples=39900 +``` + +**从所有训练样本中生成统计数据** +```bash +# 打印均值、中位数、标准差和分位数 +python -m special_orthogonalization.main_point_cloud_gpu --method=$METHOD --checkpoint_dir=$OUT_DIR --pt_cloud_test_files=$TEST_FILES --predict_all_test=True +``` +## 运行 + +NPU运行命令方式如下: + +对于所有的三个步骤程序来说，modelarts插件obs桶中的数据路径均要写到真正包含数据的那一个路径 +如在pc_plane文件夹中含有points、points_test等包含数据的文件夹 +modelarts插件中的数据路径写为 obs://cann-id2019/data/pc_palne/ + +**生成测试数据** + +运行这一步我们需要的程序文件为gen_pt_test_data.py、modelarts_entry_Axang.py、genTestData.sh +这三个文件中的代码均不需要修改 +最后生成的旋转后的数据文件存放在obs桶当次程序文件的output路径中，文件名为test_points_modified， +为进行第二步模型训练，需要将生成旋转后的文件转移至obs桶中存放data的路径 + +注：需要确保的是存在obs桶里的data文件名为points_test + +**训练与评价** + +运行这一步我们需要的程序文件为main_point_cloud.py、modelarts_entry_acc_train.py、train_full_1p.sh +这三个文件中的代码均不需要修改 +最后生成的旋转后的数据文件存放在obs桶当次程序文件的output路径中，文件名为test_points_modified， +为进行第三步，需要将生成的output文件转移至obs桶中存放data的路径 + +注意：该次训练的模型保存在该次的obs文件夹中，进行第三步时又需要重启一次新的modelarts，因此我们需要将output文件中的 +checkpoint文件中最新模型的路径修改 +"/home/ma-user/modelarts/inputs/data_url_0/output" +这样第三步才能跑出正确的精度指标 + +**从所有训练样本中生成统计数据** + +运行这一步我们需要的程序文件为main_point_cloud.py、modelarts_entry_stat.py、genStatistical.sh +这三个文件的代码均不需要修改 +运行成功后将会在屏幕上打印出关于精度相应的统计量值 + +注意：由于加入了混合精度提高训练性能，导致该步出现未知错误，输出的误差均为0，因此建议将训练后的结果下载到本地 +修改checkpoint文件中模型的路径后，利用本地的GPU进行运行，实际上该步骤在运行时NPU成功启动但也未曾利用。 + +## 训练结果 +**精度对比：** + + + +| 测地线误差（°） | 论文发布 | GPU(初始代码未改动版本) | GPU实测|NPU实测 | +| ------------------------ | ------- | ----- | --------- |----------| +| 平均值 | 1.63 | 2.58 | 3.98 | 2.92 | +| 中值 | 0.89 | 1.68 | 2.6 | 1.7 | +| 标准差 | 6.70 | 6.93 | 9.36 | 8.45 | + +相比于论文中的精度，我们NPU迁移后实测差距依然较大，但是与我们未对代码任何改动初始的版本在GPU上跑出来的精度相差较小， +且对于相同的代码的代码改动，NPU上的精度优于GPU上的精度。需要注意的是，在NPU上运行程序时，我们采用混合精度来提升训练 +的性能，但是其中产生了未知的错误，导致代表的精度指标mean_degree_err在整个训练过程中始终为0，因此我们无法得知在NPU训练的 +260w步的过程中，精度指标是下降的过程是怎样的。值得庆幸的是通过NPU训练出的模型，能够在GPU上计算出精度，并且精度还不错。 + +**性能对比：** + +取华为v100上GPU运行的前2w步的global_step/sec平均值和NPU运行的前2w步的global_step/sec平均值进行对比，以达到性能对比的目的。 + +| 性能指标项 | 论文发布 | GPU实测 | NPU实测 | +| ------------------- | ------- | ------ | ------ | +| global_step/sec| 无 | 87.64 | 101.24 | + + -- Gitee From 4656d8ab0fbafd5db83906dfeeb2d4fa02bcd75f Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Sat, 7 May 2022 13:57:31 +0000 Subject: [PATCH 21/88] =?UTF-8?q?=E5=88=A0=E9=99=A4=E6=96=87=E4=BB=B6=20Te?= =?UTF-8?q?nsorFlow/contrib/cv/SVD=5FID2019=5Ffor=5FTensorflow/modelzoo=5F?= =?UTF-8?q?level.txt?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../contrib/cv/SVD_ID2019_for_Tensorflow/modelzoo_level.txt | 3 --- 1 file changed, 3 deletions(-) delete mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/modelzoo_level.txt diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/modelzoo_level.txt b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/modelzoo_level.txt deleted file mode 100644 index d648fdf46..000000000 --- a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/modelzoo_level.txt +++ /dev/null @@ -1,3 +0,0 @@ -FuncStatus:NOK -PerfStatus:NOK 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TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/.keep delete mode 100644 "TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/GPU\347\262\276\345\272\246.txt" delete mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/gen_pt_test_data_gpu.py delete mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/license.txt delete mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/loss.log delete mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/main_point_cloud_gpu.py delete mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/requirements.txt delete mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/utils_gpu.py delete mode 100644 "TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/\350\256\272\346\226\207\347\262\276\345\272\246.jpg" diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/.keep b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/.keep deleted file mode 100644 index e69de29bb..000000000 diff --git "a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/GPU\347\262\276\345\272\246.txt" "b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/GPU\347\262\276\345\272\246.txt" deleted file mode 100644 index de31ae6b3..000000000 --- "a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/GPU\347\262\276\345\272\246.txt" +++ /dev/null @@ -1,106 +0,0 @@ -Evaluated 39900 examples -Mean error: %f degrees 5.551389 -Median error: %f degrees 3.6492655 -Std: %f degrees 10.683777 - -Percentiles: - 1%: 0.703878 - 2%: 0.889114 - 3%: 1.011817 - 4%: 1.117676 - 5%: 1.203335 - 6%: 1.290285 - 7%: 1.362000 - 8%: 1.423815 - 9%: 1.484244 - 10%: 1.540022 - 11%: 1.593973 - 12%: 1.645681 - 13%: 1.698696 - 14%: 1.743833 - 15%: 1.792095 - 16%: 1.841431 - 17%: 1.893619 - 18%: 1.939569 - 19%: 1.985442 - 20%: 2.029315 - 21%: 2.077730 - 22%: 2.125596 - 23%: 2.174029 - 24%: 2.223344 - 25%: 2.272967 - 26%: 2.323889 - 27%: 2.370916 - 28%: 2.424628 - 29%: 2.477176 - 30%: 2.526464 - 31%: 2.579367 - 32%: 2.632026 - 33%: 2.682996 - 34%: 2.733374 - 35%: 2.784669 - 36%: 2.837522 - 37%: 2.893403 - 38%: 2.948823 - 39%: 3.002961 - 40%: 3.058895 - 41%: 3.115458 - 42%: 3.170274 - 43%: 3.233495 - 44%: 3.287655 - 45%: 3.344627 - 46%: 3.402717 - 47%: 3.463511 - 48%: 3.525312 - 49%: 3.588614 - 50%: 3.649158 - 51%: 3.710984 - 52%: 3.777194 - 53%: 3.840634 - 54%: 3.912314 - 55%: 3.978772 - 56%: 4.050912 - 57%: 4.124833 - 58%: 4.205145 - 59%: 4.269850 - 60%: 4.350731 - 61%: 4.429609 - 62%: 4.506891 - 63%: 4.590645 - 64%: 4.673866 - 65%: 4.769208 - 66%: 4.860751 - 67%: 4.957645 - 68%: 5.045778 - 69%: 5.145709 - 70%: 5.248520 - 71%: 5.356171 - 72%: 5.463465 - 73%: 5.576540 - 74%: 5.700179 - 75%: 5.826519 - 76%: 5.954271 - 77%: 6.075412 - 78%: 6.217035 - 79%: 6.359586 - 80%: 6.509334 - 81%: 6.681930 - 82%: 6.855579 - 83%: 7.045702 - 84%: 7.237360 - 85%: 7.455194 - 86%: 7.683445 - 87%: 7.928701 - 88%: 8.226621 - 89%: 8.539159 - 90%: 8.927838 - 91%: 9.329886 - 92%: 9.826632 - 93%: 10.504982 - 94%: 11.362451 - 95%: 12.505600 - 96%: 14.257383 - 97%: 17.200018 - 98%: 22.386629 - 99%: 37.106316 -100%: 179.459671 diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/gen_pt_test_data_gpu.py b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/gen_pt_test_data_gpu.py deleted file mode 100644 index ccef0b62f..000000000 --- a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/gen_pt_test_data_gpu.py +++ /dev/null @@ -1,124 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ - -# coding=utf-8 -# Copyright 2021 The Google Research Authors. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""Generate modified point cloud test data with fixed random rotations.""" -import glob -import os -import pathlib - -import utils_gpu -from absl import app -from absl import flags -import numpy as np -from scipy.stats import special_ortho_group - -FLAGS = flags.FLAGS -flags.DEFINE_string('input_test_files1', '', - 'Regular expression for the original input points files.') -flags.DEFINE_string('input_test_files2', '', - 'Regular expression for the original input test files.') -flags.DEFINE_string('output_directory1', '', - 'Output directory where new points files will be stored.') -flags.DEFINE_string('output_directory2', '', - 'Output directory where new test files will be stored.') -flags.DEFINE_integer('num_rotations_per_file', 100, - 'Number of random rotation augmentations per test point ' - 'cloud.') -flags.DEFINE_boolean('random_rotation_axang', True, - 'If true, samples random rotations using the method ' - 'from the original benchmark code. Otherwise samples ' - 'by Haar measure.') - -def gen_test_data(): - """Generate the new (modified) test data.""" - # Create output directory. - os.makedirs(FLAGS.output_directory1, exist_ok=True) - os.makedirs(FLAGS.output_directory2, exist_ok=True) - - # Get all test point cloud files in the original dataset. - input_test_files1 = glob.glob(FLAGS.input_test_files1) - input_test_files2 = glob.glob(FLAGS.input_test_files2) - - n = 1 - for in_file in input_test_files1: - pts = np.loadtxt(in_file) - m = pts.shape[0] - if n < m: - n = m - print("over") - - for in_file in input_test_files1: - out_file_prefix = pathlib.Path(in_file).stem - pts = np.loadtxt(in_file) # N x 3 - data1 = np.random.rand(n-pts.shape[0], 3) - pts = np.vstack((pts, data1)) - out_file1 = os.path.join( - FLAGS.output_directory1, '%s.pts' % (out_file_prefix)) - np.savetxt(out_file1, pts) - print("over") - - for in_file in input_test_files2: - out_file_prefix = pathlib.Path(in_file).stem - pts = np.loadtxt(in_file) # N x 3 - num_pts_to_keep = pts.shape[0] // 2 - pts = pts[:num_pts_to_keep, :] # N//2 x 3. - - for k in range(FLAGS.num_rotations_per_file): - if FLAGS.random_rotation_axang: - r = utils_gpu.random_rotation_benchmark_np(1) - r = r[0] - else: - r = special_ortho_group.rvs(3) - joined = np.float32(np.concatenate((r, pts), axis=0)) # (N//2+3) x 3. - out_file = os.path.join( - FLAGS.output_directory2, '%s_r%03d.pts' % (out_file_prefix, k)) - np.savetxt(out_file, joined) - print("over") - - -def main(unused_argv): - gen_test_data() - -if __name__ == '__main__': - app.run(main) \ No newline at end of file diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/license.txt b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/license.txt deleted file mode 100644 index 0384a2533..000000000 --- a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/license.txt +++ /dev/null @@ -1,28 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/loss.log b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/loss.log deleted file mode 100644 index dc3dfb67d..000000000 --- a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/loss.log +++ /dev/null @@ -1,246 +0,0 @@ -2022-03-19 18:03:26,273 - tensorflow - INFO - Using config: {'_model_dir': '/root/RotationContinuity-master2/shapenet/data/pc_plane/to/model1', '_tf_random_seed': None, '_save_summary_steps': 25, '_save_checkpoints_steps': 100, '_save_checkpoints_secs': None, '_session_config': allow_soft_placement: true -graph_options { - rewrite_options { - meta_optimizer_iterations: ONE - } -} -, '_keep_checkpoint_max': None, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 200, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_service': None, '_cluster_spec': , '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1} -2022-03-19 18:03:26,273 - tensorflow - INFO - Not using Distribute Coordinator. -2022-03-19 18:03:26,273 - tensorflow - INFO - Running training and evaluation locally (non-distributed). -2022-03-19 18:03:26,274 - tensorflow - INFO - Start train and evaluate loop. The evaluate will happen after every checkpoint. Checkpoint frequency is determined based on RunConfig arguments: save_checkpoints_steps 100 or save_checkpoints_secs None. -2022-03-19 18:03:26,277 - tensorflow - INFO - Skipping training since max_steps has already saved. -2022-03-19 18:04:19,049 - tensorflow - INFO - Using config: {'_model_dir': '/root/RotationContinuity-master2/shapenet/data/pc_plane/to/model1', '_tf_random_seed': None, '_save_summary_steps': 25, '_save_checkpoints_steps': 100, '_save_checkpoints_secs': None, '_session_config': allow_soft_placement: true -graph_options { - rewrite_options { - meta_optimizer_iterations: ONE - } -} -, '_keep_checkpoint_max': None, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 200, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_service': None, '_cluster_spec': , '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1} -2022-03-19 18:04:19,050 - tensorflow - INFO - Not using Distribute Coordinator. -2022-03-19 18:04:19,050 - tensorflow - INFO - Running training and evaluation locally (non-distributed). -2022-03-19 18:04:19,050 - tensorflow - INFO - Start train and evaluate loop. The evaluate will happen after every checkpoint. Checkpoint frequency is determined based on RunConfig arguments: save_checkpoints_steps 100 or save_checkpoints_secs None. -2022-03-19 18:04:19,054 - tensorflow - INFO - Skipping training since max_steps has already saved. -2022-03-19 18:04:49,280 - tensorflow - INFO - Using config: {'_model_dir': '/root/RotationContinuity-master2/shapenet/data/pc_plane/to/model2', '_tf_random_seed': None, '_save_summary_steps': 25, '_save_checkpoints_steps': 100, '_save_checkpoints_secs': None, '_session_config': allow_soft_placement: true -graph_options { - rewrite_options { - meta_optimizer_iterations: ONE - } -} -, '_keep_checkpoint_max': None, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 200, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_service': None, '_cluster_spec': , '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1} -2022-03-19 18:04:49,281 - tensorflow - INFO - Not using Distribute Coordinator. -2022-03-19 18:04:49,281 - tensorflow - INFO - Running training and evaluation locally (non-distributed). -2022-03-19 18:04:49,281 - tensorflow - INFO - Start train and evaluate loop. The evaluate will happen after every checkpoint. Checkpoint frequency is determined based on RunConfig arguments: save_checkpoints_steps 100 or save_checkpoints_secs None. -2022-03-19 18:04:49,286 - tensorflow - WARNING - From /root/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow_core/python/training/training_util.py:236: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version. -Instructions for updating: -Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts. -2022-03-19 18:04:49,292 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:244: The name tf.gfile.Glob is deprecated. Please use tf.io.gfile.glob instead. - -2022-03-19 18:04:49,683 - tensorflow - WARNING - From /root/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow_core/python/autograph/converters/directives.py:119: The name tf.read_file is deprecated. Please use tf.io.read_file instead. - -2022-03-19 18:04:49,683 - tensorflow - WARNING - From /root/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow_core/python/autograph/converters/directives.py:119: The name tf.decode_csv is deprecated. Please use tf.io.decode_csv instead. - -2022-03-19 18:04:49,766 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:223: calling string_split (from tensorflow.python.ops.ragged.ragged_string_ops) with delimiter is deprecated and will be removed in a future version. -Instructions for updating: -delimiter is deprecated, please use sep instead. -2022-03-19 18:04:49,932 - tensorflow - WARNING - From /root/special_orthogonalization2/utils_gpu.py:75: py_func (from tensorflow.python.ops.script_ops) is deprecated and will be removed in a future version. -Instructions for updating: -tf.py_func is deprecated in TF V2. Instead, there are two - options available in V2. - - tf.py_function takes a python function which manipulates tf eager - tensors instead of numpy arrays. It's easy to convert a tf eager tensor to - an ndarray (just call tensor.numpy()) but having access to eager tensors - means `tf.py_function`s can use accelerators such as GPUs as well as - being differentiable using a gradient tape. - - tf.numpy_function maintains the semantics of the deprecated tf.py_func - (it is not differentiable, and manipulates numpy arrays). It drops the - stateful argument making all functions stateful. - -2022-03-19 18:04:49,938 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:252: The name tf.data.make_one_shot_iterator is deprecated. Please use tf.compat.v1.data.make_one_shot_iterator instead. - -2022-03-19 18:04:49,961 - tensorflow - INFO - Calling model_fn. -2022-03-19 18:04:49,966 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:74: The name tf.variable_scope is deprecated. Please use tf.compat.v1.variable_scope instead. - -2022-03-19 18:04:49,966 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:74: The name tf.AUTO_REUSE is deprecated. Please use tf.compat.v1.AUTO_REUSE instead. - -2022-03-19 18:04:49,966 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:75: conv1d (from tensorflow.python.layers.convolutional) is deprecated and will be removed in a future version. -Instructions for updating: -Use `tf.keras.layers.Conv1D` instead. -2022-03-19 18:04:49,967 - tensorflow - WARNING - From /root/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow_core/python/layers/convolutional.py:218: Layer.apply (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version. -Instructions for updating: -Please use `layer.__call__` method instead. -2022-03-19 18:04:50,011 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:81: calling reduce_max_v1 (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version. -Instructions for updating: -keep_dims is deprecated, use keepdims instead -2022-03-19 18:04:50,033 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:99: dense (from tensorflow.python.layers.core) is deprecated and will be removed in a future version. -Instructions for updating: -Use keras.layers.Dense instead. -2022-03-19 18:04:50,055 - tensorflow - WARNING - From /root/special_orthogonalization2/utils_gpu.py:97: The name tf.svd is deprecated. Please use tf.linalg.svd instead. - -2022-03-19 18:04:50,079 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:171: The name tf.summary.scalar is deprecated. Please use tf.compat.v1.summary.scalar instead. - -2022-03-19 18:04:50,082 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:173: The name tf.train.get_or_create_global_step is deprecated. Please use tf.compat.v1.train.get_or_create_global_step instead. - -2022-03-19 18:04:50,083 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:185: The name tf.train.AdamOptimizer is deprecated. Please use tf.compat.v1.train.AdamOptimizer instead. - -2022-03-19 18:04:50,083 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:186: The name tf.get_collection is deprecated. Please use tf.compat.v1.get_collection instead. - -2022-03-19 18:04:50,083 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:186: The name tf.GraphKeys is deprecated. Please use tf.compat.v1.GraphKeys instead. - -2022-03-19 18:04:50,323 - tensorflow - INFO - Done calling model_fn. -2022-03-19 18:04:50,325 - tensorflow - INFO - Create CheckpointSaverHook. -2022-03-19 18:04:50,418 - tensorflow - WARNING - From /root/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow_core/python/ops/array_ops.py:1475: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version. -Instructions for updating: -Use tf.where in 2.0, which has the same broadcast rule as np.where -2022-03-19 18:04:50,485 - tensorflow - INFO - Graph was finalized. -2022-03-19 18:04:52,089 - tensorflow - INFO - Running local_init_op. -2022-03-19 18:04:52,106 - tensorflow - INFO - Done running local_init_op. -2022-03-19 18:04:52,544 - tensorflow - INFO - Saving checkpoints for 0 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model2/model.ckpt. -2022-03-19 18:04:52,608 - tensorflow - INFO - model.ckpt-0 is not in all_model_checkpoint_paths. Manually adding it. -2022-03-19 18:04:55,512 - tensorflow - INFO - loss = 32.202156, step = 0 -2022-03-19 18:04:57,008 - tensorflow - INFO - Saving checkpoints for 100 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model2/model.ckpt. -2022-03-19 18:04:57,026 - tensorflow - INFO - model.ckpt-100 is not in all_model_checkpoint_paths. Manually adding it. -2022-03-19 18:04:57,484 - tensorflow - INFO - Calling model_fn. -2022-03-19 18:04:57,596 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:203: The name tf.metrics.mean is deprecated. Please use tf.compat.v1.metrics.mean instead. - -2022-03-19 18:04:57,606 - tensorflow - INFO - Done calling model_fn. -2022-03-19 18:04:57,621 - tensorflow - INFO - Starting evaluation at 2022-03-19T18:04:57Z -2022-03-19 18:04:57,684 - tensorflow - INFO - Graph was finalized. -2022-03-19 18:04:57,687 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model2/model.ckpt-100 -2022-03-19 18:04:57,883 - tensorflow - INFO - Running local_init_op. -2022-03-19 18:04:57,949 - tensorflow - INFO - Done running local_init_op. -2022-03-19 18:04:59,509 - tensorflow - INFO - Evaluation [39/399] -2022-03-19 18:05:00,470 - tensorflow - INFO - Evaluation [78/399] -2022-03-19 18:05:00,986 - tensorflow - INFO - Evaluation [117/399] -2022-03-19 18:05:01,473 - tensorflow - INFO - Evaluation [156/399] -2022-03-19 18:05:01,813 - tensorflow - INFO - Evaluation [195/399] -2022-03-19 18:05:02,217 - tensorflow - INFO - Evaluation [234/399] -2022-03-19 18:05:02,537 - tensorflow - INFO - Evaluation [273/399] -2022-03-19 18:05:02,756 - tensorflow - INFO - Evaluation [312/399] -2022-03-19 18:05:02,876 - tensorflow - INFO - Evaluation [351/399] -2022-03-19 18:05:03,157 - tensorflow - INFO - Evaluation [390/399] -2022-03-19 18:05:03,209 - tensorflow - INFO - Evaluation [399/399] -2022-03-19 18:05:03,291 - tensorflow - INFO - Finished evaluation at 2022-03-19-18:05:03 -2022-03-19 18:05:03,292 - tensorflow - INFO - Saving dict for global step 100: global_step = 100, loss = 2.0213404, mean_degree_err = 90.8583 -2022-03-19 18:05:03,399 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 100: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model2/model.ckpt-100 -2022-03-19 18:05:04,530 - tensorflow - INFO - Saving checkpoints for 200 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model2/model.ckpt. -2022-03-19 18:05:04,547 - tensorflow - INFO - model.ckpt-200 is not in all_model_checkpoint_paths. Manually adding it. -2022-03-19 18:05:04,586 - tensorflow - INFO - Skip the current checkpoint eval due to throttle secs (60 secs). -2022-03-19 18:05:04,600 - tensorflow - INFO - global_step/sec: 22.0056 -2022-03-19 18:05:04,601 - tensorflow - INFO - loss = 18.108334, step = 200 (9.089 sec) -2022-03-19 18:05:05,711 - tensorflow - INFO - Saving checkpoints for 300 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model2/model.ckpt. -2022-03-19 18:05:05,728 - tensorflow - INFO - model.ckpt-300 is not in all_model_checkpoint_paths. Manually adding it. -2022-03-19 18:05:05,765 - tensorflow - INFO - Skip the current checkpoint eval due to throttle secs (60 secs). -2022-03-19 18:05:06,893 - tensorflow - INFO - Saving checkpoints for 400 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model2/model.ckpt. -2022-03-19 18:05:06,911 - tensorflow - INFO - model.ckpt-400 is not in all_model_checkpoint_paths. Manually adding it. -2022-03-19 18:05:06,948 - tensorflow - INFO - Skip the current checkpoint eval due to throttle secs (60 secs). -2022-03-19 18:05:06,962 - tensorflow - INFO - global_step/sec: 84.6573 -2022-03-19 18:05:06,963 - tensorflow - INFO - loss = 19.658773, step = 400 (2.362 sec) -2022-03-19 18:05:08,073 - tensorflow - INFO - Saving checkpoints for 500 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model2/model.ckpt. -2022-03-19 18:05:08,089 - tensorflow - INFO - model.ckpt-500 is not in all_model_checkpoint_paths. Manually adding it. -2022-03-19 18:05:08,126 - tensorflow - INFO - Skip the current checkpoint eval due to throttle secs (60 secs). -2022-03-19 18:05:09,261 - tensorflow - INFO - Saving checkpoints for 600 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model2/model.ckpt. -2022-03-19 18:05:09,276 - tensorflow - INFO - model.ckpt-600 is not in all_model_checkpoint_paths. Manually adding it. -2022-03-19 18:05:09,314 - tensorflow - INFO - Skip the current checkpoint eval due to throttle secs (60 secs). -2022-03-19 18:05:09,329 - tensorflow - INFO - global_step/sec: 84.4893 -2022-03-19 18:05:09,330 - tensorflow - INFO - loss = 15.302707, step = 600 (2.367 sec) -2022-03-19 18:05:10,443 - tensorflow - INFO - Saving checkpoints for 700 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model2/model.ckpt. -2022-03-19 18:05:10,461 - tensorflow - INFO - model.ckpt-700 is not in all_model_checkpoint_paths. Manually adding it. -2022-03-19 18:05:10,498 - tensorflow - INFO - Skip the current checkpoint eval due to throttle secs (60 secs). -2022-03-19 18:05:11,624 - tensorflow - INFO - Saving checkpoints for 800 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model2/model.ckpt. -2022-03-19 18:05:11,643 - tensorflow - INFO - model.ckpt-800 is not in all_model_checkpoint_paths. Manually adding it. -2022-03-19 18:05:11,680 - tensorflow - INFO - Skip the current checkpoint eval due to throttle secs (60 secs). -2022-03-19 18:05:11,695 - tensorflow - INFO - global_step/sec: 84.5516 -2022-03-19 18:05:11,696 - tensorflow - INFO - loss = 18.000507, step = 800 (2.365 sec) -2022-03-19 18:05:12,807 - tensorflow - INFO - Saving checkpoints for 900 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model2/model.ckpt. -2022-03-19 18:05:12,822 - tensorflow - INFO - model.ckpt-900 is not in all_model_checkpoint_paths. Manually adding it. -2022-03-19 18:05:12,860 - tensorflow - INFO - Skip the current checkpoint eval due to throttle secs (60 secs). -2022-03-19 18:05:13,986 - tensorflow - INFO - Saving checkpoints for 1000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model2/model.ckpt. -2022-03-19 18:05:14,002 - tensorflow - INFO - model.ckpt-1000 is not in all_model_checkpoint_paths. Manually adding it. -2022-03-19 18:05:14,039 - tensorflow - INFO - Skip the current checkpoint eval due to throttle secs (60 secs). -2022-03-19 18:05:14,054 - tensorflow - INFO - global_step/sec: 84.7931 -2022-03-19 18:05:14,054 - tensorflow - INFO - loss = 20.527103, step = 1000 (2.359 sec) -2022-03-19 18:05:15,176 - tensorflow - INFO - Saving checkpoints for 1100 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model2/model.ckpt. -2022-03-19 18:05:15,192 - tensorflow - INFO - model.ckpt-1100 is not in all_model_checkpoint_paths. Manually adding it. -2022-03-19 18:05:15,229 - tensorflow - INFO - Skip the current checkpoint eval due to throttle secs (60 secs). -2022-03-19 18:05:16,360 - tensorflow - INFO - Saving checkpoints for 1200 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model2/model.ckpt. -2022-03-19 18:05:16,378 - tensorflow - INFO - model.ckpt-1200 is not in all_model_checkpoint_paths. Manually adding it. -2022-03-19 18:05:16,415 - tensorflow - INFO - Skip the current checkpoint eval due to throttle secs (60 secs). -2022-03-19 18:05:16,430 - tensorflow - INFO - global_step/sec: 84.1547 -2022-03-19 18:05:16,431 - tensorflow - INFO - loss = 16.00374, step = 1200 (2.377 sec) -2022-03-19 18:05:17,536 - tensorflow - INFO - Saving checkpoints for 1300 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model2/model.ckpt. -2022-03-19 18:05:17,554 - tensorflow - INFO - model.ckpt-1300 is not in all_model_checkpoint_paths. Manually adding it. -2022-03-19 18:05:17,591 - tensorflow - INFO - Skip the current checkpoint eval due to throttle secs (60 secs). -2022-03-19 18:05:18,715 - tensorflow - INFO - Saving checkpoints for 1400 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model2/model.ckpt. -2022-03-19 18:05:18,731 - tensorflow - INFO - model.ckpt-1400 is not in all_model_checkpoint_paths. Manually adding it. -2022-03-19 18:05:18,768 - tensorflow - INFO - Skip the current checkpoint eval due to throttle secs (60 secs). -2022-03-19 18:05:18,782 - tensorflow - INFO - global_step/sec: 85.0196 -2022-03-19 18:05:18,783 - tensorflow - INFO - loss = 8.673609, step = 1400 (2.352 sec) -2022-03-19 18:05:19,894 - tensorflow - INFO - Saving checkpoints for 1500 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model2/model.ckpt. -2022-03-19 18:05:19,909 - tensorflow - INFO - model.ckpt-1500 is not in all_model_checkpoint_paths. Manually adding it. -2022-03-19 18:05:19,947 - tensorflow - INFO - Skip the current checkpoint eval due to throttle secs (60 secs). -2022-03-19 18:05:21,070 - tensorflow - INFO - Saving checkpoints for 1600 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model2/model.ckpt. -2022-03-19 18:05:21,092 - tensorflow - INFO - model.ckpt-1600 is not in all_model_checkpoint_paths. Manually adding it. -2022-03-19 18:05:21,129 - tensorflow - INFO - Skip the current checkpoint eval due to throttle secs (60 secs). -2022-03-19 18:05:21,144 - tensorflow - INFO - global_step/sec: 84.7018 -2022-03-19 18:05:21,144 - tensorflow - INFO - loss = 12.576811, step = 1600 (2.361 sec) -2022-03-19 18:05:22,260 - tensorflow - INFO - Saving checkpoints for 1700 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model2/model.ckpt. -2022-03-19 18:05:22,276 - tensorflow - INFO - model.ckpt-1700 is not in all_model_checkpoint_paths. Manually adding it. -2022-03-19 18:05:22,313 - tensorflow - INFO - Skip the current checkpoint eval due to throttle secs (60 secs). -2022-03-19 18:05:23,442 - tensorflow - INFO - Saving checkpoints for 1800 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model2/model.ckpt. -2022-03-19 18:05:23,458 - tensorflow - INFO - model.ckpt-1800 is not in all_model_checkpoint_paths. Manually adding it. -2022-03-19 18:05:23,496 - tensorflow - INFO - Skip the current checkpoint eval due to throttle secs (60 secs). -2022-03-19 18:05:23,510 - tensorflow - INFO - global_step/sec: 84.5082 -2022-03-19 18:05:23,511 - tensorflow - INFO - loss = 25.950819, step = 1800 (2.367 sec) -2022-03-19 18:05:24,627 - tensorflow - INFO - Saving checkpoints for 1900 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model2/model.ckpt. -2022-03-19 18:05:24,642 - tensorflow - INFO - model.ckpt-1900 is not in all_model_checkpoint_paths. Manually adding it. -2022-03-19 18:05:24,679 - tensorflow - INFO - Skip the current checkpoint eval due to throttle secs (60 secs). -2022-03-19 18:05:25,810 - tensorflow - INFO - Saving checkpoints for 2000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model2/model.ckpt. -2022-03-19 18:05:25,825 - tensorflow - INFO - model.ckpt-2000 is not in all_model_checkpoint_paths. Manually adding it. -2022-03-19 18:05:25,863 - tensorflow - INFO - Skip the current checkpoint eval due to throttle secs (60 secs). -2022-03-19 18:05:25,877 - tensorflow - INFO - global_step/sec: 84.5026 -2022-03-19 18:05:25,878 - tensorflow - INFO - loss = 10.249855, step = 2000 (2.367 sec) -2022-03-19 18:05:26,991 - tensorflow - INFO - Saving checkpoints for 2100 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model2/model.ckpt. -2022-03-19 18:05:27,007 - tensorflow - INFO - model.ckpt-2100 is not in all_model_checkpoint_paths. Manually adding it. -2022-03-19 18:05:27,044 - tensorflow - INFO - Skip the current checkpoint eval due to throttle secs (60 secs). -2022-03-19 18:05:28,175 - tensorflow - INFO - Saving checkpoints for 2200 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model2/model.ckpt. -2022-03-19 18:05:28,190 - tensorflow - INFO - model.ckpt-2200 is not in all_model_checkpoint_paths. Manually adding it. -2022-03-19 18:05:28,227 - tensorflow - INFO - Skip the current checkpoint eval due to throttle secs (60 secs). -2022-03-19 18:05:28,241 - tensorflow - INFO - global_step/sec: 84.5981 -2022-03-19 18:05:28,242 - tensorflow - INFO - loss = 10.412505, step = 2200 (2.364 sec) -2022-03-19 18:05:29,344 - tensorflow - INFO - Saving checkpoints for 2300 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model2/model.ckpt. -2022-03-19 18:05:29,360 - tensorflow - INFO - model.ckpt-2300 is not in all_model_checkpoint_paths. Manually adding it. -2022-03-19 18:05:29,397 - tensorflow - INFO - Skip the current checkpoint eval due to throttle secs (60 secs). -2022-03-19 18:05:30,505 - tensorflow - INFO - Saving checkpoints for 2400 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model2/model.ckpt. -2022-03-19 18:05:30,520 - tensorflow - INFO - model.ckpt-2400 is not in all_model_checkpoint_paths. Manually adding it. -2022-03-19 18:05:30,558 - tensorflow - INFO - Skip the current checkpoint eval due to throttle secs (60 secs). -2022-03-19 18:05:30,572 - tensorflow - INFO - global_step/sec: 85.8075 -2022-03-19 18:05:30,573 - tensorflow - INFO - loss = 24.763023, step = 2400 (2.331 sec) -2022-03-19 18:05:31,663 - tensorflow - INFO - Saving checkpoints for 2500 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model2/model.ckpt. -2022-03-19 18:05:31,679 - tensorflow - INFO - model.ckpt-2500 is not in all_model_checkpoint_paths. Manually adding it. -2022-03-19 18:05:31,716 - tensorflow - INFO - Skip the current checkpoint eval due to throttle secs (60 secs). -2022-03-19 18:05:32,818 - tensorflow - INFO - Saving checkpoints for 2600 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model2/model.ckpt. -2022-03-19 18:05:32,834 - tensorflow - INFO - model.ckpt-2600 is not in all_model_checkpoint_paths. Manually adding it. -2022-03-19 18:05:32,871 - tensorflow - INFO - Skip the current checkpoint eval due to throttle secs (60 secs). -2022-03-19 18:05:33,174 - tensorflow - INFO - Calling model_fn. -2022-03-19 18:05:33,301 - tensorflow - INFO - Done calling model_fn. -2022-03-19 18:05:33,316 - tensorflow - INFO - Starting evaluation at 2022-03-19T18:05:33Z -2022-03-19 18:05:33,379 - tensorflow - INFO - Graph was finalized. -2022-03-19 18:05:33,382 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model2/model.ckpt-2600 -2022-03-19 18:05:33,562 - tensorflow - INFO - Running local_init_op. -2022-03-19 18:05:33,627 - tensorflow - INFO - Done running local_init_op. -2022-03-19 18:05:34,142 - tensorflow - INFO - Evaluation [39/399] -2022-03-19 18:05:34,232 - tensorflow - INFO - Evaluation [78/399] -2022-03-19 18:05:34,318 - tensorflow - INFO - Evaluation [117/399] -2022-03-19 18:05:34,406 - tensorflow - INFO - Evaluation [156/399] -2022-03-19 18:05:34,493 - tensorflow - INFO - Evaluation [195/399] -2022-03-19 18:05:34,581 - tensorflow - INFO - Evaluation [234/399] -2022-03-19 18:05:34,669 - tensorflow - INFO - Evaluation [273/399] -2022-03-19 18:05:34,757 - tensorflow - INFO - Evaluation [312/399] -2022-03-19 18:05:34,844 - tensorflow - INFO - Evaluation [351/399] -2022-03-19 18:05:34,930 - tensorflow - INFO - Evaluation [390/399] -2022-03-19 18:05:34,951 - tensorflow - INFO - Evaluation [399/399] -2022-03-19 18:05:35,034 - tensorflow - INFO - Finished evaluation at 2022-03-19-18:05:35 -2022-03-19 18:05:35,035 - tensorflow - INFO - Saving dict for global step 2600: global_step = 2600, loss = 1.1534922, mean_degree_err = 59.039207 -2022-03-19 18:05:35,035 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 2600: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model2/model.ckpt-2600 -2022-03-19 18:05:35,035 - tensorflow - DEBUG - Calling exporter with the `is_the_final_export=True`. -2022-03-19 18:05:35,053 - tensorflow - INFO - Loss for final step: 4.256054. diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/main_point_cloud_gpu.py b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/main_point_cloud_gpu.py deleted file mode 100644 index a27b51b95..000000000 --- a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/main_point_cloud_gpu.py +++ /dev/null @@ -1,423 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ - -# coding=utf-8 -# Copyright 2021 The Google Research Authors. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""Training and evaluation for the point cloud alignment experiment.""" -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import utils_gpu -import time -from absl import flags -import numpy as np -import tensorflow as tf -import logging -FLAGS = flags.FLAGS -log = logging.getLogger('tensorflow') -log.setLevel(logging.DEBUG) - -# General flags. -flags.DEFINE_string('method', 'svd', - 'Specifies the method to use for predicting rotations. ' - 'Choices are "svd", "svd-inf", or "gs".') -flags.DEFINE_string('checkpoint_dir', '', - 'Locations for checkpoints, summaries, etc.') -flags.DEFINE_integer('train_steps', 2600000, 'Number of training iterations.') -flags.DEFINE_integer('save_checkpoints_steps', 10000, - 'How often to save checkpoints') -flags.DEFINE_integer('log_step_count', 500, 'How often to log the step count') -flags.DEFINE_integer('save_summaries_steps', 5000, - 'How often to save summaries.') -flags.DEFINE_float('learning_rate', 1e-5, 'Learning rate') -flags.DEFINE_boolean('lr_decay', False, 'Decay the learning rate if True.') -flags.DEFINE_integer('lr_decay_steps', 35000, - 'Learning rate decays steps.') -flags.DEFINE_float('lr_decay_rate', 0.95, - 'Learning rate decays rate.') -flags.DEFINE_boolean('predict_all_test', False, - 'If true, runs an eval job on latest checkpoint and ' - 'prints the error for each input.') -flags.DEFINE_integer('eval_examples', 0, 'Number of test examples.') -flags.DEFINE_boolean('print_variable_names', False, - 'Print model variable names.') - -# Flags only used in the point cloud alignment experiment. -flags.DEFINE_integer('num_train_augmentations', 10, - 'Number of random rotations for augmenting each input ' - 'point cloud.') -flags.DEFINE_string('pt_cloud_train_files', '', - 'Expression matching all training point files, e.g. ' - '/path/to/files/pc_plane/points/*.pts') -flags.DEFINE_string('pt_cloud_test_files', '', - 'Expression matching all modified test point files, e.g. ' - '/path/to/files/pc_plane/points_test/*.pts') -flags.DEFINE_boolean('random_rotation_axang', True, - 'If true, samples random rotations using the method ' - 'from the original benchmark code. Otherwise samples ' - 'by Haar measure.') - - -def pt_features(batch_pts): - """Input shape: [B, N, 3], output shape: [B, 1024].""" - with tf.variable_scope('ptenc', reuse=tf.AUTO_REUSE): - f1 = tf.layers.conv1d(inputs=batch_pts, filters=64, kernel_size=1) - f1 = tf.nn.leaky_relu(f1) - f2 = tf.layers.conv1d(inputs=f1, filters=128, kernel_size=1) - f2 = tf.nn.leaky_relu(f2) - f3 = tf.layers.conv1d(inputs=f2, filters=1024, kernel_size=1) - - f = tf.reduce_max(f3, axis=1, keep_dims=False) - return f - - -def regress_from_features(batch_features, out_dim): - """Regress to a rotation representation from point cloud encodings. - - In Zhou et al, CVPR19, the paper describes this regression network as an MLP - mapping 2048->512->512->out_dim, but the associated code implements it with - one less layer: 2048->512->out_dim. We mimic the code. - - Args: - batch_features: [batch_size, in_dim]. - out_dim: desired output dimensionality. - - Returns: - A [batch_size, out_dim] tensor. - """ - f1 = tf.layers.dense(batch_features, 512) - f1 = tf.nn.leaky_relu(f1) - f2 = tf.layers.dense(f1, out_dim) - return f2 - - -def net_point_cloud(points1, points2, mode): - """Predict a relative rotation given two point clouds. - - Args: - points1: [batch_size, N, 3] float tensor. - points2: [batch_size, N, 3] float tensor. - mode: tf.estimator.ModeKeys. - - Returns: - [batch_size, 3, 3] matrices. - """ - f1 = pt_features(points1) - f2 = pt_features(points2) - f = tf.concat([f1, f2], axis=-1) - - if FLAGS.method == 'svd': - p = regress_from_features(f, 9) - return utils_gpu.symmetric_orthogonalization(p) - - if FLAGS.method == 'svd-inf': - p = regress_from_features(f, 9) - if mode == tf.estimator.ModeKeys.TRAIN: - return tf.reshape(p, (-1, 3, 3)) - else: - return utils_gpu.symmetric_orthogonalization(p) - - if FLAGS.method == 'gs': - p = regress_from_features(f, 6) - return utils_gpu.gs_orthogonalization(p) - - -def model_fn(features, labels, mode, params): - """The model_fn used to construct the tf.Estimator.""" - del labels, params # Unused. - if mode == tf.estimator.ModeKeys.TRAIN: - # Training data has point cloud of size [1, N, 3] and random rotations - # of size [1, FLAGS.num_train_augmentations, 3, 3] - rot = features['rot'][0] - num_rot = FLAGS.num_train_augmentations - batch_pts1 = tf.tile(features['data'], [num_rot, 1, 1]) - # In this experiment it does not matter if we pre or post-multiply the - # rotation as long as we are consistent between training and eval. - batch_pts2 = tf.matmul(batch_pts1, rot) # post-multiplying! - else: - # Test data has point cloud of size [1, N, 3] and a single random - # rotation of size [1, 3, 3] - batch_pts1 = features['data'] - rot = features['rot'] - batch_pts2 = tf.matmul(batch_pts1, rot) - rot = tf.reshape(rot, (-1, 3, 3)) - - # Predict the rotation. - r = net_point_cloud(batch_pts1, batch_pts2, mode) - - # Compute the loss. - loss = tf.nn.l2_loss(rot - r) - - # Compute the relative angle in radians. - theta = utils_gpu.relative_angle(r, rot) - - # Mean angle error over the batch. - mean_theta = tf.reduce_mean(theta) - mean_theta_deg = mean_theta * 180.0 / np.pi - - # Train, eval, or predict depending on mode. - if mode == tf.estimator.ModeKeys.TRAIN: - tf.summary.scalar('train/loss', loss) - tf.summary.scalar('train/theta', mean_theta_deg) - global_step = tf.train.get_or_create_global_step() - - if FLAGS.lr_decay: - learning_rate = tf.train.exponential_decay( - FLAGS.learning_rate, - global_step, - FLAGS.lr_decay_steps, - FLAGS.lr_decay_rate) - else: - learning_rate = FLAGS.learning_rate - - tf.summary.scalar('train/learning_rate', learning_rate) - optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate) - update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) - with tf.control_dependencies(update_ops): - train_op = optimizer.minimize(loss, global_step=global_step) - return tf.estimator.EstimatorSpec( - mode=mode, - loss=loss, - train_op=train_op) - - if mode == tf.estimator.ModeKeys.EVAL: - if FLAGS.predict_all_test: - print_error_op = tf.print('error:', mean_theta_deg) - with tf.control_dependencies([print_error_op]): - eval_metric_ops = { - 'mean_degree_err': tf.metrics.mean(mean_theta_deg), - } - else: - eval_metric_ops = { - 'mean_degree_err': tf.metrics.mean(mean_theta_deg), - } - - return tf.estimator.EstimatorSpec( - mode=mode, - loss=loss, - eval_metric_ops=eval_metric_ops) - - if mode == tf.estimator.ModeKeys.PREDICT: - pred = {'error': mean_theta_deg} - return tf.estimator.EstimatorSpec( - mode=mode, - predictions=pred) - - -def train_input_fn(): - """Generate training data iterator from the .pts files.""" - def _file_to_matrix(pts_path): - """Read Nx3 point cloud from a .pts file.""" - file_buffer = tf.read_file(pts_path) - lines = tf.string_split([file_buffer], delimiter='\n') - lines1 = tf.string_split(lines.values, delimiter='\r') - values = tf.stack(tf.decode_csv(lines1.values, - record_defaults=[[0.0], [0.0], [0.0]], - field_delim=' ')) - values = tf.transpose(values)# 3xN --> Nx3. - # The experiment code in - # github.com/papagina/RotationContinuity/.../shapenet/code/train_pointnet.py - # only used the first half of the points in each file. - return values[:(tf.shape(values)[0]// 2), :] - - - - def _random_rotation(pts): - """Attach N random rotations to a point cloud.""" - if FLAGS.random_rotation_axang: - rotations = utils_gpu.random_rotation_benchmark(FLAGS.num_train_augmentations) - else: - rotations = utils_gpu.random_rotation(FLAGS.num_train_augmentations) - return pts, rotations - - pts_paths = tf.gfile.Glob(FLAGS.pt_cloud_train_files) - dataset = tf.data.Dataset.from_tensor_slices(pts_paths) - dataset = dataset.map(_file_to_matrix) - dataset = dataset.cache() # Comment out if memory cannot hold all the data. - dataset = dataset.shuffle(buffer_size=50, reshuffle_each_iteration=True) - dataset = dataset.repeat() - dataset = dataset.map(_random_rotation) - dataset = dataset.batch(1) - iterator = tf.data.make_one_shot_iterator(dataset) - batch_data, batch_rot = iterator.get_next() - features_dict = {'data': batch_data, 'rot': batch_rot} - batch_size = tf.shape(batch_data)[0] - batch_labels_dummy = tf.zeros(shape=(batch_size, 1)) - return (features_dict, batch_labels_dummy) - - - - -def eval_input_fn(): - """Generate test data from *modified* .pts files. - - See README and comments below for details on how the data is modified. - - Returns: - A tuple of features and associated labels. - """ - def _file_to_matrix(pts_path): - """Read Nx3 point cloud and 3x3 rotation matrix from a .pts file. - - The test data is a modified version of the original files. For each .pts - file we have (1) added a 3x3 rotation matrix for testing, and (2) removed - the second half of the point cloud since it is not used at all. - - Args: - pts_path: path to a .pts file. - - Returns: - A Nx3 point cloud. - A 3x3 rotation matrix. - """ - file_buffer = tf.read_file(pts_path) - lines = tf.string_split([file_buffer], delimiter='\n') - lines1 = tf.string_split(lines.values, delimiter='\r') - values = tf.stack(tf.decode_csv(lines1.values, - record_defaults=[[0.0], [0.0], [0.0]], field_delim=' ')) - values = tf.transpose(values) # 3xN --> Nx3. - # First three rows are the rotation matrix, remaining rows the point cloud. - rot = values[:3, :] - return values[4:, :], rot - - pts_paths = tf.gfile.Glob(FLAGS.pt_cloud_test_files) - dataset = tf.data.Dataset.from_tensor_slices(pts_paths) - dataset = dataset.map(_file_to_matrix) - dataset = dataset.batch(1) - iterator = tf.data.make_one_shot_iterator(dataset) - batch_data, batch_rot = iterator.get_next() - features_dict = {'data': batch_data, 'rot': batch_rot} - batch_size = tf.shape(batch_data)[0] - batch_labels_dummy = tf.zeros(shape=(batch_size, 1)) - return (features_dict, batch_labels_dummy) - - -def print_variable_names(): - """Print variable names in a model.""" - params = {'dummy': 0} - estimator = tf.estimator.Estimator( - model_fn=model_fn, - model_dir=FLAGS.checkpoint_dir, - params=params) - names = estimator.get_variable_names() - for name in names: - print(name) - - -def predict_all_test(): - """Print error statistics for the test dataset.""" - params = {'dummy': 0} - estimator = tf.estimator.Estimator( - model_fn=model_fn, - model_dir=FLAGS.checkpoint_dir, - params=params) - evals = estimator.predict(input_fn=eval_input_fn, yield_single_examples=False) - - # Print error statistics. - all_errors = [x['error'] for x in evals] - errors = np.array(all_errors) - print('Evaluated %d examples'%np.size(errors)) - print('Mean error: %f degrees', np.mean(errors)) - print('Median error: %f degrees', np.median(errors)) - print('Std: %f degrees', np.std(errors)) - sorted_errors = np.sort(errors) - n = np.size(sorted_errors) - print('\nPercentiles:') - for perc in range(1, 101): - index = np.int32(np.float32(n * perc) / 100.0) - 1 - print('%3d%%: %f'%(perc, sorted_errors[index])) - - -def train_and_eval(): - """Train and evaluate a model.""" - # create formatter and add it to the handlers - formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') - - # create file handler which logs even debug messages - fh = logging.FileHandler('loss.log') - fh.setLevel(logging.DEBUG) - fh.setFormatter(formatter) - log.addHandler(fh) - - save_summary_steps = FLAGS.save_summaries_steps - save_checkpoints_steps = FLAGS.save_checkpoints_steps - log_step_count = FLAGS.log_step_count - - config = tf.estimator.RunConfig( - save_summary_steps=save_summary_steps, - save_checkpoints_steps=save_checkpoints_steps, - log_step_count_steps=log_step_count, - keep_checkpoint_max=None) - - params = {'dummy': 0} - estimator = tf.estimator.Estimator( - model_fn=model_fn, - model_dir=FLAGS.checkpoint_dir, - config=config, - params=params) - - train_spec = tf.estimator.TrainSpec( - input_fn=train_input_fn, - max_steps=FLAGS.train_steps) - - eval_spec = tf.estimator.EvalSpec(input_fn=eval_input_fn, - start_delay_secs=60, - steps=FLAGS.eval_examples, - throttle_secs=60) - - tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec) - - -def main(argv=None): # pylint: disable=unused-argument - if FLAGS.print_variable_names: - print_variable_names() - return - - if FLAGS.predict_all_test: - predict_all_test() - else: - train_and_eval() - - -if __name__ == '__main__': - tf.app.run() diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/requirements.txt b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/requirements.txt deleted file mode 100644 index 1f2a5e41d..000000000 --- a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/requirements.txt +++ /dev/null @@ -1,35 +0,0 @@ -absl-py==0.11.0 -astunparse==1.6.3 -cachetools==4.1.1 -certifi==2020.6.20 -chardet==3.0.4 -gast==0.3.3 -google-auth==1.23.0 -google-auth-oauthlib==0.4.2 -google-pasta==0.2.0 -grpcio==1.33.2 -h5py==2.10.0 -idna==2.10 -importlib-metadata==2.0.0 -Keras-Preprocessing==1.1.2 -Markdown==3.3.3 -numpy==1.18.5 -oauthlib==3.1.0 -opt-einsum==3.3.0 -protobuf==3.13.0 -pyasn1==0.4.8 -pyasn1-modules==0.2.8 -requests==2.24.0 -requests-oauthlib==1.3.0 -rsa==4.6 -scipy==1.5.3 -six==1.15.0 -tensorboard==2.3.0 -tensorboard-plugin-wit==1.7.0 -tensorflow==2.3.1 -tensorflow-estimator==2.3.0 -termcolor==1.1.0 -urllib3==1.25.11 -Werkzeug==1.0.1 -wrapt==1.12.1 -zipp==3.4.0 diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/utils_gpu.py b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/utils_gpu.py deleted file mode 100644 index a08b4fa97..000000000 --- a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/utils_gpu.py +++ /dev/null @@ -1,144 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ - -# coding=utf-8 -# Copyright 2021 The Google Research Authors. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""Utility functions.""" -import numpy as np -from scipy.stats import special_ortho_group -import tensorflow as tf - - -def relative_angle(r1, r2): - """Relative angle (radians) between 3D rotation matrices.""" - rel_rot = tf.matmul(tf.transpose(r1, perm=[0, 2, 1]), r2) - trace = rel_rot[:, 0, 0] + rel_rot[:, 1, 1] + rel_rot[:, 2, 2] - cos_theta = (trace - 1.0) / 2.0 - cos_theta = tf.minimum(cos_theta, tf.ones_like(cos_theta)) - cos_theta = tf.maximum(cos_theta, (-1.0) * tf.ones_like(cos_theta)) - theta = tf.acos(cos_theta) - return theta - - -def random_rotation_benchmark_np(n): - """Sample a random 3D rotation by method used in Zhou et al, CVPR19. - - This numpy function is a copy of the PyTorch function - get_sampled_rotation_matrices_by_axisAngle() in the code made available - for Zhou et al, CVPR19, at https://github.com/papagina/RotationContinuity/. - - Args: - n: the number of rotation matrices to return. - - Returns: - [n, 3, 3] np array. - """ - theta = np.random.uniform(-1, 1, n) * np.pi - sin = np.sin(theta) - axis = np.random.randn(n, 3) - axis = axis / np.maximum(np.linalg.norm(axis, axis=-1, keepdims=True), 1e-7) - qw = np.cos(theta) - qx = axis[:, 0] * sin - qy = axis[:, 1] * sin - qz = axis[:, 2] * sin - - xx = qx*qx - yy = qy*qy - zz = qz*qz - xy = qx*qy - xz = qx*qz - yz = qy*qz - xw = qx*qw - yw = qy*qw - zw = qz*qw - - row0 = np.stack((1-2*yy-2*zz, 2*xy-2*zw, 2*xz+2*yw), axis=-1) - row1 = np.stack((2*xy+2*zw, 1-2*xx-2*zz, 2*yz-2*xw), axis=-1) - row2 = np.stack((2*xz-2*yw, 2*yz+2*xw, 1-2*xx-2*yy), axis=-1) - matrix = np.stack((row0, row1, row2), axis=1) - - return matrix - - -def random_rotation_benchmark(n): - """A TF wrapper for random_rotation_benchmark_np().""" - mat = tf.py_func( - func=lambda t: np.float32(random_rotation_benchmark_np(t)), - inp=[n], - Tout=tf.float32, - stateful=True) - return tf.reshape(mat, (n, 3, 3)) - - -def random_rotation(n): - """Sample rotations from a uniform distribution on SO(3).""" - mat = tf.py_func( - func=lambda t: np.float32(special_ortho_group.rvs(3, size=t)), - inp=[n], - Tout=tf.float32, - stateful=True) - return tf.reshape(mat, (n, 3, 3)) - - -def symmetric_orthogonalization(x): - """Maps 9D input vectors onto SO(3) via symmetric orthogonalization.""" - # Innner dimensions of the input should be 3x3 matrices. - m = tf.reshape(x, (-1, 3, 3)) - _, u, v = tf.svd(m) - det = tf.linalg.det(tf.matmul(u, v, transpose_b=True)) - r = tf.matmul( - tf.concat([u[:, :, :-1], u[:, :, -1:] * tf.reshape(det, [-1, 1, 1])], 2), - v, transpose_b=True) - return r - - -def gs_orthogonalization(p6): - """Gram-Schmidt orthogonalization from 6D input.""" - # Input should be [batch_size, 6] - x = p6[:, 0:3] - y = p6[:, 3:6] - xn = tf.math.l2_normalize(x, axis=-1) - z = tf.linalg.cross(xn, y) - zn = tf.math.l2_normalize(z, axis=-1) - y = tf.linalg.cross(zn, xn) - r = tf.stack([xn, y, zn], -1) - return r diff --git 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839baf6895e7c749223f261dd2e6fd963092d021 Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Sat, 7 May 2022 14:06:18 +0000 Subject: [PATCH 23/88] =?UTF-8?q?=E6=96=B0=E5=BB=BA=20GPU?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/.keep | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/.keep diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/.keep b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/.keep new file mode 100644 index 000000000..e69de29bb -- Gitee From d5c5e0737860ff99f31dfe0d5cb6dcbfec03dbec Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Sat, 7 May 2022 14:07:35 +0000 Subject: [PATCH 24/88] =?UTF-8?q?GPU=E7=9B=B8=E5=85=B3=E6=9B=B4=E6=96=B0?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...\346\224\271\347\211\210\346\234\254).txt" | 106 + ...5\220\214NPU\347\211\210\346\234\254).txt" | 105 + .../gen_pt_test_data_gpu.py | 99 + .../cv/SVD_ID2019_for_Tensorflow/log_gpu.log | 26651 ++++++++++++++++ .../main_point_cloud_gpu.py | 431 + .../requirements.txt | 69 + .../cv/SVD_ID2019_for_Tensorflow/utils_gpu.py | 144 + 7 files changed, 27605 insertions(+) create mode 100644 "TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU\347\262\276\345\272\246(github\345\216\237\344\273\243\347\240\201\346\234\252\346\233\264\346\224\271\347\211\210\346\234\254).txt" create mode 100644 "TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU\347\262\276\345\272\246(\344\273\243\347\240\201\351\200\273\350\276\221\345\220\214NPU\347\211\210\346\234\254).txt" create mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/gen_pt_test_data_gpu.py create mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/log_gpu.log create mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/main_point_cloud_gpu.py create mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/requirements.txt create mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/utils_gpu.py diff --git "a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU\347\262\276\345\272\246(github\345\216\237\344\273\243\347\240\201\346\234\252\346\233\264\346\224\271\347\211\210\346\234\254).txt" "b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU\347\262\276\345\272\246(github\345\216\237\344\273\243\347\240\201\346\234\252\346\233\264\346\224\271\347\211\210\346\234\254).txt" new file mode 100644 index 000000000..e8a33b3ef --- /dev/null +++ "b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU\347\262\276\345\272\246(github\345\216\237\344\273\243\347\240\201\346\234\252\346\233\264\346\224\271\347\211\210\346\234\254).txt" @@ -0,0 +1,106 @@ +Evaluated 39900 examples +Mean error: %f degrees 2.582546 +Median error: %f degrees 1.6805115 +Std: %f degrees 6.938891 + +Percentiles: + 1%: 0.300014 + 2%: 0.380522 + 3%: 0.437902 + 4%: 0.487788 + 5%: 0.526747 + 6%: 0.566482 + 7%: 0.602634 + 8%: 0.632420 + 9%: 0.662343 + 10%: 0.689838 + 11%: 0.714364 + 12%: 0.740193 + 13%: 0.766683 + 14%: 0.791300 + 15%: 0.816133 + 16%: 0.840466 + 17%: 0.863887 + 18%: 0.886470 + 19%: 0.909567 + 20%: 0.933351 + 21%: 0.955725 + 22%: 0.978388 + 23%: 1.002101 + 24%: 1.026220 + 25%: 1.049225 + 26%: 1.074472 + 27%: 1.098249 + 28%: 1.120823 + 29%: 1.143979 + 30%: 1.165668 + 31%: 1.189761 + 32%: 1.215631 + 33%: 1.238436 + 34%: 1.261760 + 35%: 1.286031 + 36%: 1.308956 + 37%: 1.333835 + 38%: 1.358547 + 39%: 1.383949 + 40%: 1.407643 + 41%: 1.433814 + 42%: 1.458442 + 43%: 1.485167 + 44%: 1.513619 + 45%: 1.540785 + 46%: 1.567729 + 47%: 1.595813 + 48%: 1.623171 + 49%: 1.651379 + 50%: 1.680511 + 51%: 1.709835 + 52%: 1.740463 + 53%: 1.769457 + 54%: 1.803091 + 55%: 1.837601 + 56%: 1.872417 + 57%: 1.909058 + 58%: 1.944507 + 59%: 1.979321 + 60%: 2.016059 + 61%: 2.052808 + 62%: 2.091812 + 63%: 2.131297 + 64%: 2.175648 + 65%: 2.219467 + 66%: 2.265031 + 67%: 2.307493 + 68%: 2.354846 + 69%: 2.401918 + 70%: 2.450324 + 71%: 2.507802 + 72%: 2.558491 + 73%: 2.610895 + 74%: 2.670931 + 75%: 2.737095 + 76%: 2.795334 + 77%: 2.862317 + 78%: 2.931846 + 79%: 3.004461 + 80%: 3.083880 + 81%: 3.159387 + 82%: 3.250103 + 83%: 3.346382 + 84%: 3.453207 + 85%: 3.562164 + 86%: 3.686474 + 87%: 3.808481 + 88%: 3.965510 + 89%: 4.142220 + 90%: 4.348930 + 91%: 4.576029 + 92%: 4.841852 + 93%: 5.135875 + 94%: 5.504097 + 95%: 5.996492 + 96%: 6.660559 + 97%: 7.634066 + 98%: 9.250069 + 99%: 12.710493 +100%: 178.697479 \ No newline at end of file diff --git "a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU\347\262\276\345\272\246(\344\273\243\347\240\201\351\200\273\350\276\221\345\220\214NPU\347\211\210\346\234\254).txt" "b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU\347\262\276\345\272\246(\344\273\243\347\240\201\351\200\273\350\276\221\345\220\214NPU\347\211\210\346\234\254).txt" new file mode 100644 index 000000000..15a4f409e --- /dev/null +++ "b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU\347\262\276\345\272\246(\344\273\243\347\240\201\351\200\273\350\276\221\345\220\214NPU\347\211\210\346\234\254).txt" @@ -0,0 +1,105 @@ +Mean error: %f degrees 3.9795678 +Median error: %f degrees 2.5962727 +Std: %f degrees 9.361983 + +Percentiles: + 1%: 0.615485 + 2%: 0.765661 + 3%: 0.859573 + 4%: 0.933770 + 5%: 0.991698 + 6%: 1.044177 + 7%: 1.096108 + 8%: 1.139179 + 9%: 1.178358 + 10%: 1.218525 + 11%: 1.255853 + 12%: 1.294827 + 13%: 1.331633 + 14%: 1.368879 + 15%: 1.406948 + 16%: 1.441844 + 17%: 1.474588 + 18%: 1.508179 + 19%: 1.543069 + 20%: 1.576939 + 21%: 1.608395 + 22%: 1.644729 + 23%: 1.676781 + 24%: 1.711550 + 25%: 1.743160 + 26%: 1.777402 + 27%: 1.811321 + 28%: 1.847797 + 29%: 1.880135 + 30%: 1.913359 + 31%: 1.947926 + 32%: 1.980112 + 33%: 2.014214 + 34%: 2.047557 + 35%: 2.079143 + 36%: 2.111832 + 37%: 2.144206 + 38%: 2.178525 + 39%: 2.212224 + 40%: 2.245766 + 41%: 2.279673 + 42%: 2.314099 + 43%: 2.347187 + 44%: 2.381295 + 45%: 2.418161 + 46%: 2.452399 + 47%: 2.488370 + 48%: 2.523829 + 49%: 2.562771 + 50%: 2.596235 + 51%: 2.636707 + 52%: 2.675837 + 53%: 2.713537 + 54%: 2.753353 + 55%: 2.791760 + 56%: 2.830061 + 57%: 2.869558 + 58%: 2.909864 + 59%: 2.951013 + 60%: 2.995325 + 61%: 3.037444 + 62%: 3.085022 + 63%: 3.137624 + 64%: 3.186842 + 65%: 3.234585 + 66%: 3.286463 + 67%: 3.345505 + 68%: 3.400300 + 69%: 3.454171 + 70%: 3.511901 + 71%: 3.568425 + 72%: 3.631408 + 73%: 3.698511 + 74%: 3.770657 + 75%: 3.847002 + 76%: 3.918766 + 77%: 4.001153 + 78%: 4.092133 + 79%: 4.175701 + 80%: 4.280524 + 81%: 4.383471 + 82%: 4.505587 + 83%: 4.643044 + 84%: 4.795996 + 85%: 4.951040 + 86%: 5.116305 + 87%: 5.308140 + 88%: 5.553438 + 89%: 5.796063 + 90%: 6.108622 + 91%: 6.445337 + 92%: 6.875063 + 93%: 7.384479 + 94%: 7.999909 + 95%: 8.852993 + 96%: 10.022167 + 97%: 12.029458 + 98%: 15.299019 + 99%: 22.932156 +100%: 179.803162 \ No newline at end of file diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/gen_pt_test_data_gpu.py b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/gen_pt_test_data_gpu.py new file mode 100644 index 000000000..96b2770d7 --- /dev/null +++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/gen_pt_test_data_gpu.py @@ -0,0 +1,99 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ + +# coding=utf-8 +# Copyright 2021 The Google Research Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Generate modified point cloud test data with fixed random rotations.""" +import glob +import os +import pathlib + +import utils_gpu +from absl import app +from absl import flags +import numpy as np +from scipy.stats import special_ortho_group + +FLAGS = flags.FLAGS +flags.DEFINE_string('input_test_files', '/cache/data_modelarts/points_test/*.pts', + 'Regular expression for the original input test files.') +flags.DEFINE_string('output_directory', '/cache/data_modelarts/test_modified', + 'Output directory where new test files will be stored.') +flags.DEFINE_integer('num_rotations_per_file', 100, + 'Number of random rotation augmentations per test point ' + 'cloud.') +flags.DEFINE_boolean('random_rotation_axang', True, + 'If true, samples random rotations using the method ' + 'from the original benchmark code. Otherwise samples ' + 'by Haar measure.') + + +def gen_test_data(): + """Generate the new (modified) test data.""" + # Create output directory. + os.makedirs(FLAGS.output_directory, exist_ok=True) + + # Get all test point cloud files in the original dataset. + input_test_files = glob.glob(FLAGS.input_test_files) + + for in_file in input_test_files: + out_file_prefix = pathlib.Path(in_file).stem + pts = np.loadtxt(in_file) # N x 3 + num_pts_to_keep = pts.shape[0] // 2 + pts = pts[:num_pts_to_keep, :] # N//2 x 3. + + for k in range(FLAGS.num_rotations_per_file): + if FLAGS.random_rotation_axang: + r = utils_gpu.random_rotation_benchmark_np(1) + r = r[0] + else: + r = special_ortho_group.rvs(3) + joined = np.float32(np.concatenate((r, pts), axis=0)) # (N//2+3) x 3. + out_file = os.path.join( + FLAGS.output_directory, '%s_r%03d.pts'%(out_file_prefix, k)) + np.savetxt(out_file, joined) + + +def main(unused_argv): + gen_test_data() + +if __name__ == '__main__': + app.run(main) diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/log_gpu.log b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/log_gpu.log new file mode 100644 index 000000000..60f26f4b1 --- /dev/null +++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/log_gpu.log @@ -0,0 +1,26651 @@ +2022-05-01 14:07:22,858 - tensorflow - INFO - Using config: {'_model_dir': '/root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51', '_tf_random_seed': None, '_save_summary_steps': 25000, '_save_checkpoints_steps': 100000, '_save_checkpoints_secs': None, '_session_config': allow_soft_placement: true +graph_options { + rewrite_options { + meta_optimizer_iterations: ONE + } +} +, '_keep_checkpoint_max': None, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 200, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_service': None, '_cluster_spec': , '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1} +2022-05-01 14:07:22,859 - tensorflow - INFO - Not using Distribute Coordinator. +2022-05-01 14:07:22,859 - tensorflow - INFO - Running training and evaluation locally (non-distributed). +2022-05-01 14:07:22,860 - tensorflow - INFO - Start train and evaluate loop. The evaluate will happen after every checkpoint. Checkpoint frequency is determined based on RunConfig arguments: save_checkpoints_steps 100000 or save_checkpoints_secs None. +2022-05-01 14:07:22,871 - tensorflow - WARNING - From /root/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow_core/python/training/training_util.py:236: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version. +Instructions for updating: +Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts. +2022-05-01 14:07:22,878 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:249: The name tf.gfile.Glob is deprecated. Please use tf.io.gfile.glob instead. + +2022-05-01 14:07:24,110 - tensorflow - WARNING - From /root/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow_core/python/autograph/converters/directives.py:119: The name tf.read_file is deprecated. Please use tf.io.read_file instead. + +2022-05-01 14:07:24,110 - tensorflow - WARNING - From /root/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow_core/python/autograph/converters/directives.py:119: The name tf.decode_csv is deprecated. Please use tf.io.decode_csv instead. + +2022-05-01 14:07:24,230 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:224: calling string_split (from tensorflow.python.ops.ragged.ragged_string_ops) with delimiter is deprecated and will be removed in a future version. +Instructions for updating: +delimiter is deprecated, please use sep instead. +2022-05-01 14:07:24,412 - tensorflow - WARNING - From /root/special_orthogonalization2/utils_gpu.py:75: py_func (from tensorflow.python.ops.script_ops) is deprecated and will be removed in a future version. +Instructions for updating: +tf.py_func is deprecated in TF V2. Instead, there are two + options available in V2. + - tf.py_function takes a python function which manipulates tf eager + tensors instead of numpy arrays. It's easy to convert a tf eager tensor to + an ndarray (just call tensor.numpy()) but having access to eager tensors + means `tf.py_function`s can use accelerators such as GPUs as well as + being differentiable using a gradient tape. + - tf.numpy_function maintains the semantics of the deprecated tf.py_func + (it is not differentiable, and manipulates numpy arrays). It drops the + stateful argument making all functions stateful. + +2022-05-01 14:07:24,420 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:257: The name tf.data.make_one_shot_iterator is deprecated. Please use tf.compat.v1.data.make_one_shot_iterator instead. + +2022-05-01 14:07:24,443 - tensorflow - INFO - Calling model_fn. +2022-05-01 14:07:24,448 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:74: The name tf.variable_scope is deprecated. Please use tf.compat.v1.variable_scope instead. + +2022-05-01 14:07:24,448 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:74: The name tf.AUTO_REUSE is deprecated. Please use tf.compat.v1.AUTO_REUSE instead. + +2022-05-01 14:07:24,448 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:75: conv1d (from tensorflow.python.layers.convolutional) is deprecated and will be removed in a future version. +Instructions for updating: +Use `tf.keras.layers.Conv1D` instead. +2022-05-01 14:07:24,449 - tensorflow - WARNING - From /root/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow_core/python/layers/convolutional.py:218: Layer.apply (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version. +Instructions for updating: +Please use `layer.__call__` method instead. +2022-05-01 14:07:24,498 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:81: calling reduce_max_v1 (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version. +Instructions for updating: +keep_dims is deprecated, use keepdims instead +2022-05-01 14:07:24,521 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:99: dense (from tensorflow.python.layers.core) is deprecated and will be removed in a future version. +Instructions for updating: +Use keras.layers.Dense instead. +2022-05-01 14:07:24,545 - tensorflow - WARNING - From /root/special_orthogonalization2/utils_gpu.py:97: The name tf.svd is deprecated. Please use tf.linalg.svd instead. + +2022-05-01 14:07:24,570 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:172: The name tf.summary.scalar is deprecated. Please use tf.compat.v1.summary.scalar instead. + +2022-05-01 14:07:24,572 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:174: The name tf.train.get_or_create_global_step is deprecated. Please use tf.compat.v1.train.get_or_create_global_step instead. + +2022-05-01 14:07:24,573 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:186: The name tf.train.AdamOptimizer is deprecated. Please use tf.compat.v1.train.AdamOptimizer instead. + +2022-05-01 14:07:24,573 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:187: The name tf.get_collection is deprecated. Please use tf.compat.v1.get_collection instead. + +2022-05-01 14:07:24,574 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:187: The name tf.GraphKeys is deprecated. Please use tf.compat.v1.GraphKeys instead. + +2022-05-01 14:07:24,856 - tensorflow - INFO - Done calling model_fn. +2022-05-01 14:07:24,857 - tensorflow - INFO - Create CheckpointSaverHook. +2022-05-01 14:07:25,039 - tensorflow - WARNING - From /root/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow_core/python/ops/array_ops.py:1475: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version. +Instructions for updating: +Use tf.where in 2.0, which has the same broadcast rule as np.where +2022-05-01 14:07:25,108 - tensorflow - INFO - Graph was finalized. +2022-05-01 14:07:30,102 - tensorflow - INFO - Running local_init_op. +2022-05-01 14:07:30,119 - tensorflow - INFO - Done running local_init_op. +2022-05-01 14:07:30,573 - tensorflow - INFO - Saving checkpoints for 0 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. +2022-05-01 14:07:30,648 - tensorflow - INFO - model.ckpt-0 is not in all_model_checkpoint_paths. 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tensorflow - INFO - global_step/sec: 87.5264 +2022-05-01 14:25:39,832 - tensorflow - INFO - loss = 0.15828839, step = 94800 (2.285 sec) +2022-05-01 14:25:42,126 - tensorflow - INFO - global_step/sec: 87.182 +2022-05-01 14:25:42,126 - tensorflow - INFO - loss = 0.35003784, step = 95000 (2.294 sec) +2022-05-01 14:25:44,421 - tensorflow - INFO - global_step/sec: 87.1344 +2022-05-01 14:25:44,422 - tensorflow - INFO - loss = 0.47904184, step = 95200 (2.295 sec) +2022-05-01 14:25:46,720 - tensorflow - INFO - global_step/sec: 86.983 +2022-05-01 14:25:46,721 - tensorflow - INFO - loss = 0.21531895, step = 95400 (2.299 sec) +2022-05-01 14:25:49,022 - tensorflow - INFO - global_step/sec: 86.885 +2022-05-01 14:25:49,023 - tensorflow - INFO - loss = 0.15111923, step = 95600 (2.302 sec) +2022-05-01 14:25:51,318 - tensorflow - INFO - global_step/sec: 87.116 +2022-05-01 14:25:51,319 - tensorflow - INFO - loss = 0.9417725, step = 95800 (2.296 sec) +2022-05-01 14:25:53,611 - tensorflow - INFO - global_step/sec: 87.2297 +2022-05-01 14:25:53,611 - tensorflow - INFO - loss = 0.08343916, step = 96000 (2.293 sec) +2022-05-01 14:25:55,906 - tensorflow - INFO - global_step/sec: 87.1625 +2022-05-01 14:25:55,906 - tensorflow - INFO - loss = 0.11967882, step = 96200 (2.295 sec) +2022-05-01 14:25:58,207 - tensorflow - INFO - global_step/sec: 86.9043 +2022-05-01 14:25:58,207 - tensorflow - INFO - loss = 0.2613772, step = 96400 (2.301 sec) +2022-05-01 14:26:00,505 - tensorflow - INFO - global_step/sec: 87.0403 +2022-05-01 14:26:00,505 - tensorflow - INFO - loss = 0.3371205, step = 96600 (2.298 sec) +2022-05-01 14:26:02,802 - tensorflow - INFO - global_step/sec: 87.0751 +2022-05-01 14:26:02,802 - tensorflow - INFO - loss = 0.28187898, step = 96800 (2.297 sec) +2022-05-01 14:26:05,100 - tensorflow - INFO - global_step/sec: 87.0297 +2022-05-01 14:26:05,100 - tensorflow - INFO - loss = 0.42920962, step = 97000 (2.298 sec) +2022-05-01 14:26:07,396 - tensorflow - INFO - global_step/sec: 87.1099 +2022-05-01 14:26:07,396 - tensorflow - INFO - loss = 0.056796335, step = 97200 (2.296 sec) +2022-05-01 14:26:09,684 - tensorflow - INFO - global_step/sec: 87.3879 +2022-05-01 14:26:09,685 - tensorflow - INFO - loss = 0.09578763, step = 97400 (2.289 sec) +2022-05-01 14:26:11,974 - tensorflow - INFO - global_step/sec: 87.3522 +2022-05-01 14:26:11,974 - tensorflow - INFO - loss = 2.4198983, step = 97600 (2.290 sec) +2022-05-01 14:26:14,263 - tensorflow - INFO - global_step/sec: 87.3526 +2022-05-01 14:26:14,264 - tensorflow - INFO - loss = 0.13744435, step = 97800 (2.290 sec) +2022-05-01 14:26:16,549 - tensorflow - INFO - global_step/sec: 87.4993 +2022-05-01 14:26:16,550 - tensorflow - INFO - loss = 0.20825619, step = 98000 (2.286 sec) +2022-05-01 14:26:18,837 - tensorflow - INFO - global_step/sec: 87.4067 +2022-05-01 14:26:18,838 - tensorflow - INFO - loss = 0.19770706, step = 98200 (2.288 sec) +2022-05-01 14:26:21,124 - tensorflow - INFO - global_step/sec: 87.4774 +2022-05-01 14:26:21,124 - tensorflow - INFO - loss = 0.31616545, step = 98400 (2.286 sec) +2022-05-01 14:26:23,411 - tensorflow - INFO - global_step/sec: 87.4225 +2022-05-01 14:26:23,412 - tensorflow - INFO - loss = 24.242685, step = 98600 (2.288 sec) +2022-05-01 14:26:25,696 - tensorflow - INFO - global_step/sec: 87.5488 +2022-05-01 14:26:25,696 - tensorflow - INFO - loss = 0.5029043, step = 98800 (2.284 sec) +2022-05-01 14:26:27,967 - tensorflow - INFO - global_step/sec: 88.0532 +2022-05-01 14:26:27,968 - tensorflow - INFO - loss = 0.74712604, step = 99000 (2.271 sec) +2022-05-01 14:26:30,226 - tensorflow - INFO - global_step/sec: 88.5269 +2022-05-01 14:26:30,227 - tensorflow - INFO - loss = 0.21298502, step = 99200 (2.259 sec) +2022-05-01 14:26:32,511 - tensorflow - INFO - global_step/sec: 87.5423 +2022-05-01 14:26:32,511 - tensorflow - INFO - loss = 0.21085733, step = 99400 (2.285 sec) +2022-05-01 14:26:34,811 - tensorflow - INFO - global_step/sec: 86.9667 +2022-05-01 14:26:34,811 - tensorflow - INFO - loss = 0.32836705, step = 99600 (2.300 sec) +2022-05-01 14:26:37,093 - tensorflow - INFO - global_step/sec: 87.6294 +2022-05-01 14:26:37,093 - tensorflow - INFO - loss = 0.24103443, step = 99800 (2.282 sec) +2022-05-01 14:26:39,372 - tensorflow - INFO - Saving checkpoints for 100000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. +2022-05-01 14:26:39,390 - tensorflow - INFO - model.ckpt-100000 is not in all_model_checkpoint_paths. Manually adding it. +2022-05-01 14:26:40,405 - tensorflow - INFO - Calling model_fn. +2022-05-01 14:26:40,530 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:204: The name tf.metrics.mean is deprecated. Please use tf.compat.v1.metrics.mean instead. + +2022-05-01 14:26:40,541 - tensorflow - INFO - Done calling model_fn. +2022-05-01 14:26:40,556 - tensorflow - INFO - Starting evaluation at 2022-05-01T14:26:40Z +2022-05-01 14:26:40,620 - tensorflow - INFO - Graph was finalized. +2022-05-01 14:26:40,623 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-100000 +2022-05-01 14:26:40,936 - tensorflow - INFO - Running local_init_op. +2022-05-01 14:26:41,050 - tensorflow - INFO - Done running local_init_op. +2022-05-01 14:26:56,055 - tensorflow - INFO - Evaluation [3990/39900] +2022-05-01 14:27:10,591 - tensorflow - INFO - Evaluation [7980/39900] +2022-05-01 14:27:24,889 - tensorflow - INFO - Evaluation [11970/39900] +2022-05-01 14:27:39,136 - tensorflow - INFO - Evaluation [15960/39900] +2022-05-01 14:27:53,364 - tensorflow - INFO - Evaluation [19950/39900] +2022-05-01 14:28:07,812 - tensorflow - INFO - Evaluation [23940/39900] +2022-05-01 14:28:22,113 - tensorflow - INFO - Evaluation [27930/39900] +2022-05-01 14:28:36,293 - tensorflow - INFO - Evaluation [31920/39900] +2022-05-01 14:28:50,471 - tensorflow - INFO - Evaluation [35910/39900] +2022-05-01 14:29:04,661 - tensorflow - INFO - Evaluation [39900/39900] +2022-05-01 14:29:04,800 - tensorflow - INFO - Finished evaluation at 2022-05-01-14:29:04 +2022-05-01 14:29:04,800 - tensorflow - INFO - Saving dict for global step 100000: global_step = 100000, loss = 0.18664514, mean_degree_err = 15.5360365 +2022-05-01 14:29:04,973 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 100000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-100000 +2022-05-01 14:29:04,987 - tensorflow - INFO - global_step/sec: 1.35232 +2022-05-01 14:29:04,988 - tensorflow - INFO - loss = 0.09665474, step = 100000 (147.894 sec) +2022-05-01 14:29:07,296 - tensorflow - INFO - global_step/sec: 86.6181 +2022-05-01 14:29:07,296 - tensorflow - INFO - loss = 0.11935344, step = 100200 (2.309 sec) +2022-05-01 14:29:09,600 - tensorflow - INFO - global_step/sec: 86.7917 +2022-05-01 14:29:09,601 - tensorflow - INFO - loss = 0.23293598, step = 100400 (2.304 sec) +2022-05-01 14:29:11,905 - tensorflow - INFO - global_step/sec: 86.7663 +2022-05-01 14:29:11,906 - tensorflow - INFO - loss = 13.853834, step = 100600 (2.305 sec) +2022-05-01 14:29:14,200 - tensorflow - INFO - global_step/sec: 87.1497 +2022-05-01 14:29:14,200 - tensorflow - INFO - loss = 0.28267854, step = 100800 (2.295 sec) +2022-05-01 14:29:16,493 - tensorflow - INFO - global_step/sec: 87.2407 +2022-05-01 14:29:16,493 - tensorflow - INFO - loss = 0.13818274, step = 101000 (2.292 sec) +2022-05-01 14:29:18,789 - tensorflow - INFO - global_step/sec: 87.1046 +2022-05-01 14:29:18,789 - tensorflow - INFO - loss = 0.191855, step = 101200 (2.296 sec) +2022-05-01 14:29:21,092 - tensorflow - INFO - global_step/sec: 86.8156 +2022-05-01 14:29:21,093 - tensorflow - INFO - loss = 0.09758256, step = 101400 (2.304 sec) +2022-05-01 14:29:23,394 - tensorflow - INFO - global_step/sec: 86.8777 +2022-05-01 14:29:23,395 - tensorflow - INFO - loss = 0.23463662, step = 101600 (2.302 sec) +2022-05-01 14:29:25,696 - tensorflow - INFO - global_step/sec: 86.9051 +2022-05-01 14:29:25,696 - tensorflow - INFO - loss = 0.09981724, step = 101800 (2.301 sec) +2022-05-01 14:29:27,995 - tensorflow - INFO - global_step/sec: 86.9773 +2022-05-01 14:29:27,996 - tensorflow - INFO - loss = 0.3753961, step = 102000 (2.299 sec) +2022-05-01 14:29:30,294 - tensorflow - INFO - global_step/sec: 87.017 +2022-05-01 14:29:30,294 - tensorflow - INFO - loss = 0.16868381, step = 102200 (2.298 sec) +2022-05-01 14:29:32,592 - tensorflow - INFO - global_step/sec: 87.0017 +2022-05-01 14:29:32,593 - tensorflow - INFO - loss = 8.778584, step = 102400 (2.299 sec) +2022-05-01 14:29:34,886 - tensorflow - INFO - global_step/sec: 87.2183 +2022-05-01 14:29:34,886 - tensorflow - INFO - loss = 0.090705186, step = 102600 (2.293 sec) +2022-05-01 14:29:37,177 - tensorflow - INFO - global_step/sec: 87.2853 +2022-05-01 14:29:37,177 - tensorflow - INFO - loss = 0.0820912, step = 102800 (2.291 sec) +2022-05-01 14:29:39,464 - tensorflow - INFO - global_step/sec: 87.4505 +2022-05-01 14:29:39,464 - tensorflow - INFO - loss = 0.6370168, step = 103000 (2.287 sec) +2022-05-01 14:29:41,745 - tensorflow - INFO - global_step/sec: 87.6902 +2022-05-01 14:29:41,745 - tensorflow - INFO - loss = 0.043927055, step = 103200 (2.281 sec) +2022-05-01 14:29:44,025 - tensorflow - INFO - global_step/sec: 87.704 +2022-05-01 14:29:44,026 - tensorflow - INFO - loss = 0.7123804, step = 103400 (2.280 sec) +2022-05-01 14:29:46,318 - tensorflow - INFO - global_step/sec: 87.2407 +2022-05-01 14:29:46,318 - tensorflow - INFO - loss = 0.06848099, step = 103600 (2.292 sec) +2022-05-01 14:29:48,611 - tensorflow - INFO - global_step/sec: 87.2065 +2022-05-01 14:29:48,611 - tensorflow - INFO - loss = 0.16801745, step = 103800 (2.293 sec) +2022-05-01 14:29:50,907 - tensorflow - INFO - global_step/sec: 87.0944 +2022-05-01 14:29:50,908 - tensorflow - INFO - loss = 0.18619172, step = 104000 (2.296 sec) +2022-05-01 14:29:53,209 - tensorflow - INFO - global_step/sec: 86.8939 +2022-05-01 14:29:53,209 - tensorflow - INFO - loss = 4.6792035, step = 104200 (2.302 sec) +2022-05-01 14:29:55,497 - tensorflow - INFO - global_step/sec: 87.3975 +2022-05-01 14:29:55,498 - tensorflow - INFO - loss = 5.7233157, step = 104400 (2.288 sec) +2022-05-01 14:29:57,797 - tensorflow - INFO - global_step/sec: 86.9713 +2022-05-01 14:29:57,797 - tensorflow - INFO - loss = 0.21849597, step = 104600 (2.300 sec) +2022-05-01 14:30:00,091 - tensorflow - INFO - global_step/sec: 87.165 +2022-05-01 14:30:00,092 - tensorflow - INFO - loss = 4.5911975, step = 104800 (2.294 sec) +2022-05-01 14:30:02,394 - tensorflow - INFO - global_step/sec: 86.8542 +2022-05-01 14:30:02,395 - tensorflow - INFO - loss = 10.481457, step = 105000 (2.303 sec) +2022-05-01 14:30:04,688 - tensorflow - INFO - global_step/sec: 87.1941 +2022-05-01 14:30:04,688 - tensorflow - INFO - loss = 0.26965386, step = 105200 (2.294 sec) +2022-05-01 14:30:07,005 - tensorflow - INFO - global_step/sec: 86.3277 +2022-05-01 14:30:07,005 - tensorflow - INFO - loss = 0.17812374, step = 105400 (2.317 sec) +2022-05-01 14:30:09,328 - tensorflow - INFO - global_step/sec: 86.0889 +2022-05-01 14:30:09,328 - tensorflow - INFO - loss = 0.38415286, step = 105600 (2.323 sec) +2022-05-01 14:30:11,632 - tensorflow - INFO - global_step/sec: 86.8154 +2022-05-01 14:30:11,632 - tensorflow - INFO - loss = 0.18962869, step = 105800 (2.304 sec) +2022-05-01 14:30:13,928 - tensorflow - INFO - global_step/sec: 87.0754 +2022-05-01 14:30:13,929 - tensorflow - INFO - loss = 0.104067475, step = 106000 (2.297 sec) +2022-05-01 14:30:16,219 - tensorflow - INFO - global_step/sec: 87.3113 +2022-05-01 14:30:16,220 - tensorflow - INFO - loss = 0.09176713, step = 106200 (2.291 sec) +2022-05-01 14:30:18,518 - tensorflow - INFO - global_step/sec: 86.9935 +2022-05-01 14:30:18,519 - tensorflow - INFO - loss = 0.08783853, step = 106400 (2.299 sec) +2022-05-01 14:30:20,805 - tensorflow - INFO - global_step/sec: 87.4484 +2022-05-01 14:30:20,806 - tensorflow - INFO - loss = 0.055572137, step = 106600 (2.287 sec) +2022-05-01 14:30:23,090 - tensorflow - INFO - global_step/sec: 87.549 +2022-05-01 14:30:23,090 - tensorflow - INFO - loss = 0.18945137, step = 106800 (2.284 sec) +2022-05-01 14:30:25,371 - tensorflow - INFO - global_step/sec: 87.6611 +2022-05-01 14:30:25,372 - tensorflow - INFO - loss = 0.1006712, step = 107000 (2.281 sec) +2022-05-01 14:30:27,661 - tensorflow - INFO - global_step/sec: 87.343 +2022-05-01 14:30:27,661 - tensorflow - INFO - loss = 0.41802037, step = 107200 (2.290 sec) +2022-05-01 14:30:29,968 - tensorflow - INFO - global_step/sec: 86.6983 +2022-05-01 14:30:29,968 - tensorflow - INFO - loss = 0.14229545, step = 107400 (2.307 sec) +2022-05-01 14:30:32,264 - tensorflow - INFO - global_step/sec: 87.1013 +2022-05-01 14:30:32,264 - tensorflow - INFO - loss = 0.79536784, step = 107600 (2.296 sec) +2022-05-01 14:30:34,556 - tensorflow - INFO - global_step/sec: 87.2567 +2022-05-01 14:30:34,557 - tensorflow - INFO - loss = 0.09977777, step = 107800 (2.292 sec) +2022-05-01 14:30:36,850 - tensorflow - INFO - global_step/sec: 87.1778 +2022-05-01 14:30:36,851 - tensorflow - INFO - loss = 0.26099685, step = 108000 (2.294 sec) +2022-05-01 14:30:39,144 - tensorflow - INFO - global_step/sec: 87.1819 +2022-05-01 14:30:39,145 - tensorflow - INFO - loss = 0.30809918, step = 108200 (2.294 sec) +2022-05-01 14:30:41,440 - tensorflow - INFO - global_step/sec: 87.1208 +2022-05-01 14:30:41,440 - tensorflow - INFO - loss = 0.22293222, step = 108400 (2.296 sec) +2022-05-01 14:30:43,730 - tensorflow - INFO - global_step/sec: 87.3257 +2022-05-01 14:30:43,731 - tensorflow - INFO - loss = 0.04886747, step = 108600 (2.290 sec) +2022-05-01 14:30:46,017 - tensorflow - INFO - global_step/sec: 87.4719 +2022-05-01 14:30:46,017 - tensorflow - INFO - loss = 0.23293927, step = 108800 (2.286 sec) +2022-05-01 14:30:48,304 - tensorflow - INFO - global_step/sec: 87.4261 +2022-05-01 14:30:48,305 - tensorflow - INFO - loss = 0.3128304, step = 109000 (2.288 sec) +2022-05-01 14:30:50,599 - tensorflow - INFO - global_step/sec: 87.162 +2022-05-01 14:30:50,599 - tensorflow - INFO - loss = 0.034860868, step = 109200 (2.295 sec) +2022-05-01 14:30:52,890 - tensorflow - INFO - global_step/sec: 87.2961 +2022-05-01 14:30:52,890 - tensorflow - INFO - loss = 0.2239242, step = 109400 (2.291 sec) +2022-05-01 14:30:55,186 - tensorflow - INFO - global_step/sec: 87.1116 +2022-05-01 14:30:55,186 - tensorflow - INFO - loss = 0.06228984, step = 109600 (2.296 sec) +2022-05-01 14:30:57,474 - tensorflow - INFO - global_step/sec: 87.4197 +2022-05-01 14:30:57,474 - tensorflow - INFO - loss = 0.19710249, step = 109800 (2.288 sec) +2022-05-01 14:30:59,766 - tensorflow - INFO - global_step/sec: 87.2504 +2022-05-01 14:30:59,766 - tensorflow - INFO - loss = 0.1518274, step = 110000 (2.292 sec) +2022-05-01 14:31:02,062 - tensorflow - INFO - global_step/sec: 87.0991 +2022-05-01 14:31:02,063 - tensorflow - INFO - loss = 0.13968316, step = 110200 (2.296 sec) +2022-05-01 14:31:04,365 - 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tensorflow - INFO - global_step/sec: 87.0813 +2022-05-01 14:47:04,323 - tensorflow - INFO - loss = 0.12372404, step = 194200 (2.297 sec) +2022-05-01 14:47:06,624 - tensorflow - INFO - global_step/sec: 86.9004 +2022-05-01 14:47:06,624 - tensorflow - INFO - loss = 4.9969735, step = 194400 (2.302 sec) +2022-05-01 14:47:08,917 - tensorflow - INFO - global_step/sec: 87.2197 +2022-05-01 14:47:08,917 - tensorflow - INFO - loss = 0.46680868, step = 194600 (2.293 sec) +2022-05-01 14:47:11,216 - tensorflow - INFO - global_step/sec: 86.9929 +2022-05-01 14:47:11,216 - tensorflow - INFO - loss = 0.21966107, step = 194800 (2.299 sec) +2022-05-01 14:47:13,512 - tensorflow - INFO - global_step/sec: 87.0903 +2022-05-01 14:47:13,513 - tensorflow - INFO - loss = 1.4392605, step = 195000 (2.296 sec) +2022-05-01 14:47:15,796 - tensorflow - INFO - global_step/sec: 87.5799 +2022-05-01 14:47:15,796 - tensorflow - INFO - loss = 2.9109988, step = 195200 (2.284 sec) +2022-05-01 14:47:18,088 - tensorflow - INFO - global_step/sec: 87.2639 +2022-05-01 14:47:18,088 - tensorflow - INFO - loss = 0.06750238, step = 195400 (2.292 sec) +2022-05-01 14:47:20,377 - tensorflow - INFO - global_step/sec: 87.3571 +2022-05-01 14:47:20,378 - tensorflow - INFO - loss = 0.13745224, step = 195600 (2.289 sec) +2022-05-01 14:47:22,658 - tensorflow - INFO - global_step/sec: 87.6826 +2022-05-01 14:47:22,659 - tensorflow - INFO - loss = 0.22367324, step = 195800 (2.281 sec) +2022-05-01 14:47:24,952 - tensorflow - INFO - global_step/sec: 87.1776 +2022-05-01 14:47:24,953 - tensorflow - INFO - loss = 0.29652923, step = 196000 (2.294 sec) +2022-05-01 14:47:27,238 - tensorflow - INFO - global_step/sec: 87.5058 +2022-05-01 14:47:27,238 - tensorflow - INFO - loss = 0.0928004, step = 196200 (2.286 sec) +2022-05-01 14:47:29,522 - tensorflow - INFO - global_step/sec: 87.5486 +2022-05-01 14:47:29,523 - tensorflow - INFO - loss = 7.4939165, step = 196400 (2.284 sec) +2022-05-01 14:47:31,823 - tensorflow - INFO - global_step/sec: 86.9363 +2022-05-01 14:47:31,823 - tensorflow - INFO - loss = 0.436599, step = 196600 (2.301 sec) +2022-05-01 14:47:34,107 - tensorflow - INFO - global_step/sec: 87.5778 +2022-05-01 14:47:34,107 - tensorflow - INFO - loss = 0.024931233, step = 196800 (2.284 sec) +2022-05-01 14:47:36,397 - tensorflow - INFO - global_step/sec: 87.3185 +2022-05-01 14:47:36,398 - tensorflow - INFO - loss = 0.07194897, step = 197000 (2.291 sec) +2022-05-01 14:47:38,684 - tensorflow - INFO - global_step/sec: 87.4662 +2022-05-01 14:47:38,684 - tensorflow - INFO - loss = 23.8871, step = 197200 (2.287 sec) +2022-05-01 14:47:40,978 - tensorflow - INFO - global_step/sec: 87.1584 +2022-05-01 14:47:40,979 - tensorflow - INFO - loss = 0.10765213, step = 197400 (2.295 sec) +2022-05-01 14:47:43,279 - tensorflow - INFO - global_step/sec: 86.9478 +2022-05-01 14:47:43,279 - tensorflow - INFO - loss = 0.07624713, step = 197600 (2.300 sec) +2022-05-01 14:47:45,570 - tensorflow - INFO - global_step/sec: 87.2936 +2022-05-01 14:47:45,570 - tensorflow - INFO - loss = 9.676068, step = 197800 (2.291 sec) +2022-05-01 14:47:47,857 - tensorflow - INFO - global_step/sec: 87.4508 +2022-05-01 14:47:47,857 - tensorflow - INFO - loss = 0.14174747, step = 198000 (2.287 sec) +2022-05-01 14:47:50,144 - tensorflow - INFO - global_step/sec: 87.4219 +2022-05-01 14:47:50,145 - tensorflow - INFO - loss = 0.090489924, step = 198200 (2.288 sec) +2022-05-01 14:47:52,427 - tensorflow - INFO - global_step/sec: 87.6189 +2022-05-01 14:47:52,427 - tensorflow - INFO - loss = 0.074647754, step = 198400 (2.283 sec) +2022-05-01 14:47:54,723 - tensorflow - INFO - global_step/sec: 87.1252 +2022-05-01 14:47:54,723 - tensorflow - INFO - loss = 0.0646258, step = 198600 (2.296 sec) +2022-05-01 14:47:57,018 - tensorflow - INFO - global_step/sec: 87.1412 +2022-05-01 14:47:57,018 - tensorflow - INFO - loss = 0.46771583, step = 198800 (2.295 sec) +2022-05-01 14:47:59,300 - tensorflow - INFO - global_step/sec: 87.6377 +2022-05-01 14:47:59,300 - tensorflow - INFO - loss = 0.3469265, step = 199000 (2.282 sec) +2022-05-01 14:48:01,586 - tensorflow - INFO - global_step/sec: 87.4839 +2022-05-01 14:48:01,586 - tensorflow - INFO - loss = 1.0086424, step = 199200 (2.286 sec) +2022-05-01 14:48:03,872 - tensorflow - INFO - global_step/sec: 87.4938 +2022-05-01 14:48:03,872 - tensorflow - INFO - loss = 1.1862116, step = 199400 (2.286 sec) +2022-05-01 14:48:06,165 - tensorflow - INFO - global_step/sec: 87.2047 +2022-05-01 14:48:06,166 - tensorflow - INFO - loss = 0.15060207, step = 199600 (2.293 sec) +2022-05-01 14:48:08,463 - tensorflow - INFO - global_step/sec: 87.0599 +2022-05-01 14:48:08,463 - tensorflow - INFO - loss = 0.055810157, step = 199800 (2.297 sec) +2022-05-01 14:48:10,743 - tensorflow - INFO - Saving checkpoints for 200000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. +2022-05-01 14:48:10,758 - tensorflow - INFO - model.ckpt-200000 is not in all_model_checkpoint_paths. Manually adding it. +2022-05-01 14:48:11,312 - tensorflow - INFO - Calling model_fn. +2022-05-01 14:48:11,443 - tensorflow - INFO - Done calling model_fn. +2022-05-01 14:48:11,458 - tensorflow - INFO - Starting evaluation at 2022-05-01T14:48:11Z +2022-05-01 14:48:11,523 - tensorflow - INFO - Graph was finalized. +2022-05-01 14:48:11,526 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-200000 +2022-05-01 14:48:11,811 - tensorflow - INFO - Running local_init_op. +2022-05-01 14:48:11,921 - tensorflow - INFO - Done running local_init_op. +2022-05-01 14:48:22,202 - tensorflow - INFO - Evaluation [3990/39900] +2022-05-01 14:48:31,752 - tensorflow - INFO - Evaluation [7980/39900] +2022-05-01 14:48:41,342 - tensorflow - INFO - Evaluation [11970/39900] +2022-05-01 14:48:50,922 - tensorflow - INFO - Evaluation [15960/39900] +2022-05-01 14:49:00,471 - tensorflow - INFO - Evaluation [19950/39900] +2022-05-01 14:49:10,047 - tensorflow - INFO - Evaluation [23940/39900] +2022-05-01 14:49:19,631 - tensorflow - INFO - Evaluation [27930/39900] +2022-05-01 14:49:29,222 - tensorflow - INFO - Evaluation [31920/39900] +2022-05-01 14:49:38,795 - tensorflow - INFO - Evaluation [35910/39900] +2022-05-01 14:49:48,375 - tensorflow - INFO - Evaluation [39900/39900] +2022-05-01 14:49:48,512 - tensorflow - INFO - Finished evaluation at 2022-05-01-14:49:48 +2022-05-01 14:49:48,512 - tensorflow - INFO - Saving dict for global step 200000: global_step = 200000, loss = 0.100737154, mean_degree_err = 10.012244 +2022-05-01 14:49:48,513 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 200000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-200000 +2022-05-01 14:49:48,526 - tensorflow - INFO - global_step/sec: 1.99873 +2022-05-01 14:49:48,527 - tensorflow - INFO - loss = 0.12662719, step = 200000 (100.064 sec) +2022-05-01 14:49:50,832 - tensorflow - INFO - global_step/sec: 86.7276 +2022-05-01 14:49:50,833 - tensorflow - INFO - loss = 0.8001151, step = 200200 (2.306 sec) +2022-05-01 14:49:53,113 - tensorflow - INFO - global_step/sec: 87.6802 +2022-05-01 14:49:53,114 - tensorflow - INFO - loss = 0.045994636, step = 200400 (2.281 sec) +2022-05-01 14:49:55,390 - tensorflow - INFO - global_step/sec: 87.8451 +2022-05-01 14:49:55,391 - tensorflow - INFO - loss = 4.106056, step = 200600 (2.277 sec) +2022-05-01 14:49:57,661 - tensorflow - INFO - global_step/sec: 88.0574 +2022-05-01 14:49:57,662 - tensorflow - INFO - loss = 0.4528215, step = 200800 (2.271 sec) +2022-05-01 14:49:59,923 - tensorflow - INFO - global_step/sec: 88.4209 +2022-05-01 14:49:59,924 - tensorflow - INFO - loss = 0.07186222, step = 201000 (2.262 sec) +2022-05-01 14:50:02,210 - tensorflow - INFO - global_step/sec: 87.4507 +2022-05-01 14:50:02,211 - tensorflow - INFO - loss = 0.20265803, step = 201200 (2.287 sec) +2022-05-01 14:50:04,500 - tensorflow - INFO - global_step/sec: 87.3595 +2022-05-01 14:50:04,500 - tensorflow - INFO - loss = 0.03260638, step = 201400 (2.289 sec) +2022-05-01 14:50:06,787 - tensorflow - INFO - global_step/sec: 87.4457 +2022-05-01 14:50:06,787 - tensorflow - INFO - loss = 0.11148474, step = 201600 (2.287 sec) +2022-05-01 14:50:09,072 - tensorflow - INFO - global_step/sec: 87.5177 +2022-05-01 14:50:09,072 - tensorflow - INFO - loss = 1.6456022, step = 201800 (2.285 sec) +2022-05-01 14:50:11,361 - tensorflow - INFO - global_step/sec: 87.3907 +2022-05-01 14:50:11,361 - tensorflow - INFO - loss = 0.17919509, step = 202000 (2.289 sec) +2022-05-01 14:50:13,661 - tensorflow - INFO - global_step/sec: 86.9405 +2022-05-01 14:50:13,662 - tensorflow - INFO - loss = 0.09502393, step = 202200 (2.300 sec) +2022-05-01 14:50:15,963 - tensorflow - INFO - global_step/sec: 86.8866 +2022-05-01 14:50:15,963 - tensorflow - INFO - loss = 0.05594068, step = 202400 (2.302 sec) +2022-05-01 14:50:18,261 - tensorflow - INFO - global_step/sec: 87.0316 +2022-05-01 14:50:18,261 - tensorflow - INFO - loss = 0.09115332, step = 202600 (2.298 sec) +2022-05-01 14:50:20,561 - tensorflow - INFO - global_step/sec: 86.9407 +2022-05-01 14:50:20,562 - tensorflow - INFO - loss = 10.594648, step = 202800 (2.300 sec) +2022-05-01 14:50:22,854 - tensorflow - INFO - global_step/sec: 87.2173 +2022-05-01 14:50:22,855 - tensorflow - INFO - loss = 0.09624572, step = 203000 (2.293 sec) +2022-05-01 14:50:25,156 - tensorflow - INFO - global_step/sec: 86.8796 +2022-05-01 14:50:25,157 - tensorflow - INFO - loss = 0.03879582, step = 203200 (2.302 sec) +2022-05-01 14:50:27,458 - tensorflow - INFO - global_step/sec: 86.9109 +2022-05-01 14:50:27,458 - tensorflow - INFO - loss = 1.6161735, step = 203400 (2.301 sec) +2022-05-01 14:50:29,753 - tensorflow - INFO - global_step/sec: 87.1401 +2022-05-01 14:50:29,753 - tensorflow - INFO - loss = 0.12163468, step = 203600 (2.295 sec) +2022-05-01 14:50:32,061 - tensorflow - INFO - global_step/sec: 86.646 +2022-05-01 14:50:32,062 - tensorflow - INFO - loss = 11.411995, step = 203800 (2.308 sec) +2022-05-01 14:50:34,362 - tensorflow - INFO - global_step/sec: 86.9162 +2022-05-01 14:50:34,363 - tensorflow - INFO - loss = 0.72658515, step = 204000 (2.301 sec) +2022-05-01 14:50:36,659 - tensorflow - INFO - global_step/sec: 87.0901 +2022-05-01 14:50:36,659 - tensorflow - INFO - loss = 0.3619779, step = 204200 (2.296 sec) +2022-05-01 14:50:38,961 - tensorflow - INFO - global_step/sec: 86.8832 +2022-05-01 14:50:38,961 - tensorflow - INFO - loss = 5.773318, step = 204400 (2.302 sec) +2022-05-01 14:50:41,262 - tensorflow - INFO - global_step/sec: 86.9034 +2022-05-01 14:50:41,262 - tensorflow - INFO - loss = 0.10278251, step = 204600 (2.301 sec) +2022-05-01 14:50:43,554 - tensorflow - INFO - global_step/sec: 87.2608 +2022-05-01 14:50:43,554 - tensorflow - INFO - loss = 0.21095583, step = 204800 (2.292 sec) +2022-05-01 14:50:45,840 - tensorflow - INFO - global_step/sec: 87.4786 +2022-05-01 14:50:45,841 - tensorflow - INFO - loss = 0.046599954, step = 205000 (2.286 sec) +2022-05-01 14:50:48,127 - tensorflow - INFO - global_step/sec: 87.466 +2022-05-01 14:50:48,127 - tensorflow - INFO - loss = 0.34111094, step = 205200 (2.287 sec) +2022-05-01 14:50:50,406 - tensorflow - INFO - global_step/sec: 87.7639 +2022-05-01 14:50:50,406 - tensorflow - INFO - loss = 0.14029244, step = 205400 (2.279 sec) +2022-05-01 14:50:52,706 - tensorflow - INFO - global_step/sec: 86.946 +2022-05-01 14:50:52,706 - tensorflow - INFO - loss = 0.115294844, step = 205600 (2.300 sec) +2022-05-01 14:50:54,996 - tensorflow - INFO - global_step/sec: 87.3392 +2022-05-01 14:50:54,996 - tensorflow - INFO - loss = 1.6688011, step = 205800 (2.290 sec) +2022-05-01 14:50:57,284 - tensorflow - INFO - global_step/sec: 87.426 +2022-05-01 14:50:57,284 - tensorflow - INFO - loss = 1.0651115, step = 206000 (2.288 sec) +2022-05-01 14:50:59,574 - tensorflow - INFO - global_step/sec: 87.3021 +2022-05-01 14:50:59,575 - tensorflow - INFO - loss = 0.05911135, step = 206200 (2.291 sec) +2022-05-01 14:51:01,863 - tensorflow - INFO - global_step/sec: 87.3795 +2022-05-01 14:51:01,864 - tensorflow - INFO - loss = 3.0845604, step = 206400 (2.289 sec) +2022-05-01 14:51:04,155 - tensorflow - INFO - global_step/sec: 87.2606 +2022-05-01 14:51:04,156 - tensorflow - INFO - loss = 1.0244889, step = 206600 (2.292 sec) +2022-05-01 14:51:06,450 - tensorflow - INFO - global_step/sec: 87.1581 +2022-05-01 14:51:06,450 - tensorflow - INFO - loss = 0.06411409, step = 206800 (2.295 sec) +2022-05-01 14:51:08,742 - tensorflow - INFO - global_step/sec: 87.2733 +2022-05-01 14:51:08,742 - tensorflow - INFO - loss = 0.093899466, step = 207000 (2.292 sec) +2022-05-01 14:51:11,032 - tensorflow - INFO - global_step/sec: 87.3387 +2022-05-01 14:51:11,032 - tensorflow - INFO - loss = 0.057339266, step = 207200 (2.290 sec) +2022-05-01 14:51:13,323 - tensorflow - INFO - global_step/sec: 87.2977 +2022-05-01 14:51:13,323 - tensorflow - INFO - loss = 0.079025224, step = 207400 (2.291 sec) +2022-05-01 14:51:15,614 - tensorflow - INFO - global_step/sec: 87.2702 +2022-05-01 14:51:15,615 - tensorflow - INFO - loss = 0.08560538, step = 207600 (2.292 sec) +2022-05-01 14:51:17,904 - tensorflow - INFO - global_step/sec: 87.3517 +2022-05-01 14:51:17,904 - tensorflow - INFO - loss = 0.61651057, step = 207800 (2.290 sec) +2022-05-01 14:51:20,189 - tensorflow - INFO - global_step/sec: 87.5134 +2022-05-01 14:51:20,190 - tensorflow - INFO - loss = 0.050484687, step = 208000 (2.285 sec) +2022-05-01 14:51:22,480 - tensorflow - INFO - global_step/sec: 87.3092 +2022-05-01 14:51:22,480 - tensorflow - INFO - loss = 0.12937605, step = 208200 (2.291 sec) +2022-05-01 14:51:24,772 - tensorflow - INFO - global_step/sec: 87.273 +2022-05-01 14:51:24,772 - tensorflow - INFO - loss = 0.10451312, step = 208400 (2.292 sec) +2022-05-01 14:51:27,042 - tensorflow - INFO - global_step/sec: 88.1056 +2022-05-01 14:51:27,042 - tensorflow - INFO - loss = 0.25230247, step = 208600 (2.270 sec) +2022-05-01 14:51:29,322 - tensorflow - INFO - global_step/sec: 87.6994 +2022-05-01 14:51:29,323 - tensorflow - INFO - loss = 0.11170903, step = 208800 (2.281 sec) +2022-05-01 14:51:31,588 - tensorflow - INFO - global_step/sec: 88.2585 +2022-05-01 14:51:31,589 - tensorflow - INFO - loss = 0.29632747, step = 209000 (2.266 sec) +2022-05-01 14:51:33,873 - tensorflow - INFO - global_step/sec: 87.5372 +2022-05-01 14:51:33,873 - tensorflow - INFO - loss = 0.06806228, step = 209200 (2.285 sec) +2022-05-01 14:51:36,165 - tensorflow - INFO - global_step/sec: 87.2485 +2022-05-01 14:51:36,166 - tensorflow - INFO - loss = 0.14363146, step = 209400 (2.292 sec) +2022-05-01 14:51:38,456 - tensorflow - INFO - global_step/sec: 87.2922 +2022-05-01 14:51:38,457 - tensorflow - INFO - loss = 0.1182349, step = 209600 (2.291 sec) +2022-05-01 14:51:40,745 - tensorflow - INFO - global_step/sec: 87.4044 +2022-05-01 14:51:40,745 - tensorflow - INFO - loss = 0.19589159, step = 209800 (2.288 sec) +2022-05-01 14:51:43,044 - tensorflow - INFO - global_step/sec: 86.9773 +2022-05-01 14:51:43,045 - tensorflow - INFO - loss = 0.19596815, step = 210000 (2.299 sec) +2022-05-01 14:51:45,334 - tensorflow - INFO - global_step/sec: 87.3312 +2022-05-01 14:51:45,335 - 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global_step/sec: 87.165 +2022-05-01 15:07:45,696 - tensorflow - INFO - loss = 0.07792872, step = 294000 (2.295 sec) +2022-05-01 15:07:47,998 - tensorflow - INFO - global_step/sec: 86.8976 +2022-05-01 15:07:47,998 - tensorflow - INFO - loss = 0.022944633, step = 294200 (2.302 sec) +2022-05-01 15:07:50,299 - tensorflow - INFO - global_step/sec: 86.8999 +2022-05-01 15:07:50,299 - tensorflow - INFO - loss = 0.08060293, step = 294400 (2.302 sec) +2022-05-01 15:07:52,601 - tensorflow - INFO - global_step/sec: 86.8839 +2022-05-01 15:07:52,601 - tensorflow - INFO - loss = 0.18352179, step = 294600 (2.302 sec) +2022-05-01 15:07:54,902 - tensorflow - INFO - global_step/sec: 86.9161 +2022-05-01 15:07:54,902 - tensorflow - INFO - loss = 4.4136095, step = 294800 (2.301 sec) +2022-05-01 15:07:57,200 - tensorflow - INFO - global_step/sec: 87.0263 +2022-05-01 15:07:57,201 - tensorflow - INFO - loss = 0.062210653, step = 295000 (2.298 sec) +2022-05-01 15:07:59,502 - tensorflow - INFO - global_step/sec: 86.8744 +2022-05-01 15:07:59,503 - tensorflow - INFO - loss = 1.1744771, step = 295200 (2.302 sec) +2022-05-01 15:08:01,806 - tensorflow - INFO - global_step/sec: 86.8037 +2022-05-01 15:08:01,807 - tensorflow - INFO - loss = 0.07121699, step = 295400 (2.304 sec) +2022-05-01 15:08:04,117 - tensorflow - INFO - global_step/sec: 86.5692 +2022-05-01 15:08:04,117 - tensorflow - INFO - loss = 0.027087681, step = 295600 (2.310 sec) +2022-05-01 15:08:06,425 - tensorflow - INFO - global_step/sec: 86.6403 +2022-05-01 15:08:06,426 - tensorflow - INFO - loss = 0.6950201, step = 295800 (2.308 sec) +2022-05-01 15:08:08,710 - tensorflow - INFO - global_step/sec: 87.5187 +2022-05-01 15:08:08,711 - tensorflow - INFO - loss = 0.18592377, step = 296000 (2.285 sec) +2022-05-01 15:08:11,004 - tensorflow - INFO - global_step/sec: 87.2033 +2022-05-01 15:08:11,004 - tensorflow - INFO - loss = 0.05259351, step = 296200 (2.294 sec) +2022-05-01 15:08:13,291 - tensorflow - INFO - global_step/sec: 87.4529 +2022-05-01 15:08:13,291 - tensorflow - INFO - loss = 0.026855329, step = 296400 (2.287 sec) +2022-05-01 15:08:15,593 - tensorflow - INFO - global_step/sec: 86.8636 +2022-05-01 15:08:15,594 - tensorflow - INFO - loss = 0.038337093, step = 296600 (2.302 sec) +2022-05-01 15:08:17,881 - tensorflow - INFO - global_step/sec: 87.4079 +2022-05-01 15:08:17,882 - tensorflow - INFO - loss = 16.317427, step = 296800 (2.288 sec) +2022-05-01 15:08:20,174 - tensorflow - INFO - global_step/sec: 87.2457 +2022-05-01 15:08:20,174 - tensorflow - INFO - loss = 0.073704466, step = 297000 (2.292 sec) +2022-05-01 15:08:22,468 - tensorflow - INFO - global_step/sec: 87.1762 +2022-05-01 15:08:22,468 - tensorflow - INFO - loss = 0.03904153, step = 297200 (2.294 sec) +2022-05-01 15:08:24,754 - tensorflow - INFO - global_step/sec: 87.4825 +2022-05-01 15:08:24,754 - tensorflow - INFO - loss = 0.027742313, step = 297400 (2.286 sec) +2022-05-01 15:08:27,032 - tensorflow - INFO - global_step/sec: 87.7926 +2022-05-01 15:08:27,033 - tensorflow - INFO - loss = 0.20203239, step = 297600 (2.278 sec) +2022-05-01 15:08:29,305 - tensorflow - INFO - global_step/sec: 88.0037 +2022-05-01 15:08:29,305 - tensorflow - INFO - loss = 2.553576, step = 297800 (2.273 sec) +2022-05-01 15:08:31,566 - tensorflow - INFO - global_step/sec: 88.4544 +2022-05-01 15:08:31,566 - tensorflow - INFO - loss = 0.0866318, step = 298000 (2.261 sec) +2022-05-01 15:08:33,855 - tensorflow - INFO - global_step/sec: 87.3581 +2022-05-01 15:08:33,856 - tensorflow - INFO - loss = 0.067000255, step = 298200 (2.289 sec) +2022-05-01 15:08:36,143 - tensorflow - INFO - global_step/sec: 87.4357 +2022-05-01 15:08:36,143 - tensorflow - INFO - loss = 0.102808684, step = 298400 (2.287 sec) +2022-05-01 15:08:38,430 - tensorflow - INFO - global_step/sec: 87.4537 +2022-05-01 15:08:38,430 - tensorflow - INFO - loss = 0.031817265, step = 298600 (2.287 sec) +2022-05-01 15:08:40,730 - tensorflow - INFO - global_step/sec: 86.9441 +2022-05-01 15:08:40,730 - tensorflow - INFO - loss = 0.14091843, step = 298800 (2.300 sec) +2022-05-01 15:08:43,027 - tensorflow - INFO - global_step/sec: 87.0573 +2022-05-01 15:08:43,028 - tensorflow - INFO - loss = 0.042238776, step = 299000 (2.297 sec) +2022-05-01 15:08:45,310 - tensorflow - INFO - global_step/sec: 87.6187 +2022-05-01 15:08:45,310 - tensorflow - INFO - loss = 0.03812917, step = 299200 (2.283 sec) +2022-05-01 15:08:47,597 - tensorflow - INFO - global_step/sec: 87.4589 +2022-05-01 15:08:47,597 - tensorflow - INFO - loss = 0.11857946, step = 299400 (2.287 sec) +2022-05-01 15:08:49,888 - tensorflow - INFO - global_step/sec: 87.2812 +2022-05-01 15:08:49,889 - tensorflow - INFO - loss = 0.037918597, step = 299600 (2.291 sec) +2022-05-01 15:08:52,184 - tensorflow - INFO - global_step/sec: 87.0992 +2022-05-01 15:08:52,185 - tensorflow - INFO - loss = 0.12851797, step = 299800 (2.296 sec) +2022-05-01 15:08:54,471 - tensorflow - INFO - Saving checkpoints for 300000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. +2022-05-01 15:08:54,488 - tensorflow - INFO - model.ckpt-300000 is not in all_model_checkpoint_paths. Manually adding it. +2022-05-01 15:08:55,028 - tensorflow - INFO - Calling model_fn. +2022-05-01 15:08:55,159 - tensorflow - INFO - Done calling model_fn. +2022-05-01 15:08:55,174 - tensorflow - INFO - Starting evaluation at 2022-05-01T15:08:55Z +2022-05-01 15:08:55,239 - tensorflow - INFO - Graph was finalized. +2022-05-01 15:08:55,242 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-300000 +2022-05-01 15:08:55,530 - tensorflow - INFO - Running local_init_op. +2022-05-01 15:08:55,640 - tensorflow - INFO - Done running local_init_op. +2022-05-01 15:09:05,911 - tensorflow - INFO - Evaluation [3990/39900] +2022-05-01 15:09:15,477 - tensorflow - INFO - Evaluation [7980/39900] +2022-05-01 15:09:25,029 - tensorflow - INFO - Evaluation [11970/39900] +2022-05-01 15:09:34,594 - tensorflow - INFO - Evaluation [15960/39900] +2022-05-01 15:09:44,157 - tensorflow - INFO - Evaluation [19950/39900] +2022-05-01 15:09:53,715 - tensorflow - INFO - Evaluation [23940/39900] +2022-05-01 15:10:03,243 - tensorflow - INFO - Evaluation [27930/39900] +2022-05-01 15:10:12,772 - tensorflow - INFO - Evaluation [31920/39900] +2022-05-01 15:10:22,332 - tensorflow - INFO - Evaluation [35910/39900] +2022-05-01 15:10:31,903 - tensorflow - INFO - Evaluation [39900/39900] +2022-05-01 15:10:32,041 - tensorflow - INFO - Finished evaluation at 2022-05-01-15:10:32 +2022-05-01 15:10:32,042 - tensorflow - INFO - Saving dict for global step 300000: global_step = 300000, loss = 0.05468036, mean_degree_err = 7.492244 +2022-05-01 15:10:32,042 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 300000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-300000 +2022-05-01 15:10:32,056 - tensorflow - INFO - global_step/sec: 2.00257 +2022-05-01 15:10:32,057 - tensorflow - INFO - loss = 0.40683693, step = 300000 (99.872 sec) +2022-05-01 15:10:34,365 - tensorflow - INFO - global_step/sec: 86.6192 +2022-05-01 15:10:34,365 - tensorflow - INFO - loss = 0.07048734, step = 300200 (2.308 sec) +2022-05-01 15:10:36,657 - tensorflow - INFO - global_step/sec: 87.2609 +2022-05-01 15:10:36,657 - tensorflow - INFO - loss = 0.06908274, step = 300400 (2.292 sec) +2022-05-01 15:10:38,956 - tensorflow - INFO - global_step/sec: 86.9731 +2022-05-01 15:10:38,957 - tensorflow - INFO - loss = 0.031588264, step = 300600 (2.300 sec) +2022-05-01 15:10:41,244 - tensorflow - INFO - global_step/sec: 87.4289 +2022-05-01 15:10:41,244 - tensorflow - INFO - loss = 0.059713908, step = 300800 (2.288 sec) +2022-05-01 15:10:43,538 - tensorflow - INFO - global_step/sec: 87.1703 +2022-05-01 15:10:43,539 - tensorflow - INFO - loss = 0.06908436, step = 301000 (2.294 sec) +2022-05-01 15:10:45,839 - tensorflow - INFO - global_step/sec: 86.9113 +2022-05-01 15:10:45,840 - tensorflow - INFO - loss = 0.058627427, step = 301200 (2.301 sec) +2022-05-01 15:10:48,130 - tensorflow - INFO - global_step/sec: 87.3322 +2022-05-01 15:10:48,130 - tensorflow - INFO - loss = 0.22693896, step = 301400 (2.290 sec) +2022-05-01 15:10:50,416 - tensorflow - INFO - global_step/sec: 87.459 +2022-05-01 15:10:50,417 - tensorflow - INFO - loss = 0.033071175, step = 301600 (2.287 sec) +2022-05-01 15:10:52,714 - tensorflow - INFO - global_step/sec: 87.0478 +2022-05-01 15:10:52,714 - tensorflow - INFO - loss = 0.20088597, step = 301800 (2.298 sec) +2022-05-01 15:10:55,012 - tensorflow - INFO - global_step/sec: 87.0374 +2022-05-01 15:10:55,012 - tensorflow - INFO - loss = 0.11038767, step = 302000 (2.298 sec) +2022-05-01 15:10:57,312 - tensorflow - INFO - global_step/sec: 86.9659 +2022-05-01 15:10:57,312 - tensorflow - INFO - loss = 0.024178479, step = 302200 (2.300 sec) +2022-05-01 15:10:59,607 - tensorflow - INFO - global_step/sec: 87.1475 +2022-05-01 15:10:59,607 - tensorflow - INFO - loss = 0.051052075, step = 302400 (2.295 sec) +2022-05-01 15:11:01,904 - tensorflow - INFO - global_step/sec: 87.058 +2022-05-01 15:11:01,904 - tensorflow - INFO - loss = 0.12262677, step = 302600 (2.297 sec) +2022-05-01 15:11:04,191 - tensorflow - INFO - global_step/sec: 87.4306 +2022-05-01 15:11:04,192 - tensorflow - INFO - loss = 0.051911946, step = 302800 (2.288 sec) +2022-05-01 15:11:06,482 - tensorflow - INFO - global_step/sec: 87.3025 +2022-05-01 15:11:06,483 - tensorflow - INFO - loss = 0.017697304, step = 303000 (2.291 sec) +2022-05-01 15:11:08,782 - tensorflow - INFO - global_step/sec: 86.971 +2022-05-01 15:11:08,782 - tensorflow - INFO - loss = 0.07305166, step = 303200 (2.300 sec) +2022-05-01 15:11:11,086 - tensorflow - INFO - global_step/sec: 86.7852 +2022-05-01 15:11:11,087 - tensorflow - INFO - loss = 0.03269926, step = 303400 (2.305 sec) +2022-05-01 15:11:13,393 - tensorflow - INFO - global_step/sec: 86.7138 +2022-05-01 15:11:13,393 - tensorflow - INFO - loss = 0.046545926, step = 303600 (2.306 sec) +2022-05-01 15:11:15,698 - tensorflow - INFO - global_step/sec: 86.7779 +2022-05-01 15:11:15,698 - tensorflow - INFO - loss = 0.06280299, step = 303800 (2.305 sec) +2022-05-01 15:11:17,991 - tensorflow - INFO - global_step/sec: 87.2206 +2022-05-01 15:11:17,991 - tensorflow - INFO - loss = 0.04077121, step = 304000 (2.293 sec) +2022-05-01 15:11:20,283 - tensorflow - INFO - global_step/sec: 87.2438 +2022-05-01 15:11:20,283 - tensorflow - INFO - loss = 0.10791534, step = 304200 (2.292 sec) +2022-05-01 15:11:22,591 - tensorflow - INFO - global_step/sec: 86.6598 +2022-05-01 15:11:22,591 - tensorflow - INFO - loss = 0.7236721, step = 304400 (2.308 sec) +2022-05-01 15:11:24,899 - tensorflow - INFO - global_step/sec: 86.6611 +2022-05-01 15:11:24,899 - tensorflow - INFO - loss = 0.13847843, step = 304600 (2.308 sec) +2022-05-01 15:11:27,201 - tensorflow - INFO - global_step/sec: 86.8704 +2022-05-01 15:11:27,202 - tensorflow - INFO - loss = 0.107650064, step = 304800 (2.302 sec) +2022-05-01 15:11:29,502 - tensorflow - INFO - global_step/sec: 86.9391 +2022-05-01 15:11:29,502 - tensorflow - INFO - loss = 1.1622391, step = 305000 (2.300 sec) +2022-05-01 15:11:31,809 - tensorflow - INFO - global_step/sec: 86.6717 +2022-05-01 15:11:31,810 - tensorflow - INFO - loss = 0.07405165, step = 305200 (2.308 sec) +2022-05-01 15:11:34,110 - tensorflow - INFO - global_step/sec: 86.9039 +2022-05-01 15:11:34,111 - tensorflow - INFO - loss = 0.39629376, step = 305400 (2.301 sec) +2022-05-01 15:11:36,404 - tensorflow - INFO - global_step/sec: 87.2114 +2022-05-01 15:11:36,404 - tensorflow - INFO - loss = 0.03248347, step = 305600 (2.293 sec) +2022-05-01 15:11:38,697 - tensorflow - INFO - global_step/sec: 87.2072 +2022-05-01 15:11:38,698 - tensorflow - INFO - loss = 0.19235176, step = 305800 (2.293 sec) +2022-05-01 15:11:40,976 - tensorflow - INFO - global_step/sec: 87.7727 +2022-05-01 15:11:40,976 - tensorflow - INFO - loss = 0.23082805, step = 306000 (2.279 sec) +2022-05-01 15:11:43,245 - tensorflow - INFO - global_step/sec: 88.1357 +2022-05-01 15:11:43,245 - tensorflow - INFO - loss = 0.09423758, step = 306200 (2.269 sec) +2022-05-01 15:11:45,528 - tensorflow - INFO - global_step/sec: 87.5988 +2022-05-01 15:11:45,529 - tensorflow - INFO - loss = 0.09137006, step = 306400 (2.283 sec) +2022-05-01 15:11:47,824 - tensorflow - INFO - global_step/sec: 87.1219 +2022-05-01 15:11:47,824 - tensorflow - INFO - loss = 0.03408081, step = 306600 (2.296 sec) +2022-05-01 15:11:50,124 - tensorflow - INFO - global_step/sec: 86.9651 +2022-05-01 15:11:50,124 - tensorflow - INFO - loss = 0.20070125, step = 306800 (2.300 sec) +2022-05-01 15:11:52,421 - tensorflow - INFO - global_step/sec: 87.0634 +2022-05-01 15:11:52,421 - tensorflow - INFO - loss = 0.44823924, step = 307000 (2.297 sec) +2022-05-01 15:11:54,716 - tensorflow - INFO - global_step/sec: 87.1318 +2022-05-01 15:11:54,716 - tensorflow - INFO - loss = 0.090255864, step = 307200 (2.295 sec) +2022-05-01 15:11:57,013 - tensorflow - INFO - global_step/sec: 87.0697 +2022-05-01 15:11:57,014 - tensorflow - INFO - loss = 0.6353988, step = 307400 (2.297 sec) +2022-05-01 15:11:59,302 - tensorflow - INFO - global_step/sec: 87.3928 +2022-05-01 15:11:59,302 - tensorflow - INFO - loss = 0.0418145, step = 307600 (2.288 sec) +2022-05-01 15:12:01,594 - tensorflow - INFO - global_step/sec: 87.2546 +2022-05-01 15:12:01,594 - tensorflow - INFO - loss = 0.4108369, step = 307800 (2.292 sec) +2022-05-01 15:12:03,899 - tensorflow - INFO - global_step/sec: 86.7711 +2022-05-01 15:12:03,899 - tensorflow - INFO - loss = 0.15063372, step = 308000 (2.305 sec) +2022-05-01 15:12:06,199 - tensorflow - INFO - global_step/sec: 86.9332 +2022-05-01 15:12:06,200 - tensorflow - INFO - loss = 0.2462475, step = 308200 (2.301 sec) +2022-05-01 15:12:08,500 - tensorflow - INFO - global_step/sec: 86.9282 +2022-05-01 15:12:08,500 - tensorflow - INFO - loss = 0.040768966, step = 308400 (2.301 sec) +2022-05-01 15:12:10,794 - tensorflow - INFO - global_step/sec: 87.1863 +2022-05-01 15:12:10,794 - tensorflow - INFO - loss = 0.2742311, step = 308600 (2.294 sec) +2022-05-01 15:12:13,092 - tensorflow - INFO - global_step/sec: 87.039 +2022-05-01 15:12:13,092 - tensorflow - INFO - loss = 0.1378581, step = 308800 (2.298 sec) +2022-05-01 15:12:15,382 - tensorflow - INFO - global_step/sec: 87.3243 +2022-05-01 15:12:15,383 - tensorflow - INFO - loss = 10.739632, step = 309000 (2.290 sec) +2022-05-01 15:12:17,679 - tensorflow - INFO - global_step/sec: 87.0808 +2022-05-01 15:12:17,679 - tensorflow - INFO - loss = 0.054495342, step = 309200 (2.297 sec) +2022-05-01 15:12:19,972 - tensorflow - INFO - global_step/sec: 87.2328 +2022-05-01 15:12:19,972 - tensorflow - INFO - loss = 0.085411735, step = 309400 (2.293 sec) +2022-05-01 15:12:22,263 - tensorflow - INFO - global_step/sec: 87.2891 +2022-05-01 15:12:22,263 - tensorflow - INFO - loss = 0.14313415, step = 309600 (2.291 sec) +2022-05-01 15:12:24,548 - tensorflow - INFO - global_step/sec: 87.5073 +2022-05-01 15:12:24,549 - tensorflow - INFO - loss = 0.032904863, step = 309800 (2.285 sec) +2022-05-01 15:12:26,839 - tensorflow - INFO - global_step/sec: 87.3072 +2022-05-01 15:12:26,840 - tensorflow - INFO - loss = 0.033875167, step = 310000 (2.291 sec) +2022-05-01 15:12:29,139 - tensorflow - INFO - global_step/sec: 86.9747 +2022-05-01 15:12:29,139 - 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tensorflow - INFO - global_step/sec: 87.1993 +2022-05-01 15:28:15,844 - tensorflow - INFO - loss = 0.1180383, step = 392800 (2.294 sec) +2022-05-01 15:28:18,132 - tensorflow - INFO - global_step/sec: 87.4058 +2022-05-01 15:28:18,132 - tensorflow - INFO - loss = 0.020066151, step = 393000 (2.288 sec) +2022-05-01 15:28:20,414 - tensorflow - INFO - global_step/sec: 87.6439 +2022-05-01 15:28:20,414 - tensorflow - INFO - loss = 0.04308885, step = 393200 (2.282 sec) +2022-05-01 15:28:22,713 - tensorflow - INFO - global_step/sec: 86.9714 +2022-05-01 15:28:22,714 - tensorflow - INFO - loss = 0.08658816, step = 393400 (2.300 sec) +2022-05-01 15:28:25,006 - tensorflow - INFO - global_step/sec: 87.2448 +2022-05-01 15:28:25,006 - tensorflow - INFO - loss = 0.07704064, step = 393600 (2.292 sec) +2022-05-01 15:28:27,300 - tensorflow - INFO - global_step/sec: 87.1794 +2022-05-01 15:28:27,300 - tensorflow - INFO - loss = 0.09365944, step = 393800 (2.294 sec) +2022-05-01 15:28:29,581 - tensorflow - INFO - global_step/sec: 87.6698 +2022-05-01 15:28:29,582 - tensorflow - INFO - loss = 0.120907575, step = 394000 (2.281 sec) +2022-05-01 15:28:31,857 - tensorflow - INFO - global_step/sec: 87.9 +2022-05-01 15:28:31,857 - tensorflow - INFO - loss = 0.2553339, step = 394200 (2.275 sec) +2022-05-01 15:28:34,119 - tensorflow - INFO - global_step/sec: 88.3854 +2022-05-01 15:28:34,120 - tensorflow - INFO - loss = 0.20861246, step = 394400 (2.263 sec) +2022-05-01 15:28:36,389 - tensorflow - INFO - global_step/sec: 88.1201 +2022-05-01 15:28:36,389 - tensorflow - INFO - loss = 0.024497438, step = 394600 (2.270 sec) +2022-05-01 15:28:38,690 - tensorflow - INFO - global_step/sec: 86.9248 +2022-05-01 15:28:38,690 - tensorflow - INFO - loss = 0.2303257, step = 394800 (2.301 sec) +2022-05-01 15:28:40,987 - tensorflow - INFO - global_step/sec: 87.0582 +2022-05-01 15:28:40,988 - tensorflow - INFO - loss = 6.556267, step = 395000 (2.297 sec) +2022-05-01 15:28:43,289 - tensorflow - INFO - global_step/sec: 86.8876 +2022-05-01 15:28:43,289 - tensorflow - INFO - loss = 0.1679577, step = 395200 (2.302 sec) +2022-05-01 15:28:45,593 - tensorflow - INFO - global_step/sec: 86.8084 +2022-05-01 15:28:45,593 - tensorflow - INFO - loss = 0.072121605, step = 395400 (2.304 sec) +2022-05-01 15:28:47,892 - tensorflow - INFO - global_step/sec: 86.9844 +2022-05-01 15:28:47,893 - tensorflow - INFO - loss = 0.27504706, step = 395600 (2.299 sec) +2022-05-01 15:28:50,194 - tensorflow - INFO - global_step/sec: 86.8971 +2022-05-01 15:28:50,194 - tensorflow - INFO - loss = 0.27844322, step = 395800 (2.302 sec) +2022-05-01 15:28:52,501 - tensorflow - INFO - global_step/sec: 86.693 +2022-05-01 15:28:52,501 - tensorflow - INFO - loss = 1.3566101, step = 396000 (2.307 sec) +2022-05-01 15:28:54,801 - tensorflow - INFO - global_step/sec: 86.9296 +2022-05-01 15:28:54,802 - tensorflow - INFO - loss = 0.06718867, step = 396200 (2.301 sec) +2022-05-01 15:28:57,104 - tensorflow - INFO - global_step/sec: 86.8481 +2022-05-01 15:28:57,105 - tensorflow - INFO - loss = 0.0668054, step = 396400 (2.303 sec) +2022-05-01 15:28:59,400 - tensorflow - INFO - global_step/sec: 87.113 +2022-05-01 15:28:59,401 - tensorflow - INFO - loss = 0.12444751, step = 396600 (2.296 sec) +2022-05-01 15:29:01,701 - tensorflow - INFO - global_step/sec: 86.9377 +2022-05-01 15:29:01,701 - tensorflow - INFO - loss = 0.03913559, step = 396800 (2.301 sec) +2022-05-01 15:29:04,007 - tensorflow - INFO - global_step/sec: 86.7135 +2022-05-01 15:29:04,008 - tensorflow - INFO - loss = 0.05213821, step = 397000 (2.306 sec) +2022-05-01 15:29:06,306 - tensorflow - INFO - global_step/sec: 87.0015 +2022-05-01 15:29:06,306 - tensorflow - INFO - loss = 0.05093122, step = 397200 (2.299 sec) +2022-05-01 15:29:08,597 - tensorflow - INFO - global_step/sec: 87.2929 +2022-05-01 15:29:08,598 - tensorflow - INFO - loss = 0.023556273, step = 397400 (2.291 sec) +2022-05-01 15:29:10,882 - tensorflow - INFO - global_step/sec: 87.5211 +2022-05-01 15:29:10,883 - tensorflow - INFO - loss = 0.04940243, step = 397600 (2.285 sec) +2022-05-01 15:29:13,174 - tensorflow - INFO - global_step/sec: 87.2662 +2022-05-01 15:29:13,175 - tensorflow - INFO - loss = 0.44391492, step = 397800 (2.292 sec) +2022-05-01 15:29:15,469 - tensorflow - INFO - global_step/sec: 87.1639 +2022-05-01 15:29:15,469 - tensorflow - INFO - loss = 0.05440904, step = 398000 (2.295 sec) +2022-05-01 15:29:17,755 - tensorflow - INFO - global_step/sec: 87.4721 +2022-05-01 15:29:17,756 - tensorflow - INFO - loss = 0.021781359, step = 398200 (2.286 sec) +2022-05-01 15:29:20,050 - tensorflow - INFO - global_step/sec: 87.1435 +2022-05-01 15:29:20,051 - tensorflow - INFO - loss = 0.1761256, step = 398400 (2.295 sec) +2022-05-01 15:29:22,337 - tensorflow - INFO - global_step/sec: 87.459 +2022-05-01 15:29:22,337 - tensorflow - INFO - loss = 0.031269975, step = 398600 (2.287 sec) +2022-05-01 15:29:24,636 - tensorflow - INFO - global_step/sec: 86.9899 +2022-05-01 15:29:24,636 - tensorflow - INFO - loss = 0.029436901, step = 398800 (2.299 sec) +2022-05-01 15:29:26,921 - tensorflow - INFO - global_step/sec: 87.5231 +2022-05-01 15:29:26,922 - tensorflow - INFO - loss = 0.036546864, step = 399000 (2.285 sec) +2022-05-01 15:29:29,216 - tensorflow - INFO - global_step/sec: 87.1351 +2022-05-01 15:29:29,217 - tensorflow - INFO - loss = 0.073827825, step = 399200 (2.295 sec) +2022-05-01 15:29:31,514 - tensorflow - INFO - global_step/sec: 87.0353 +2022-05-01 15:29:31,515 - tensorflow - INFO - loss = 0.04035163, step = 399400 (2.298 sec) +2022-05-01 15:29:33,802 - tensorflow - INFO - global_step/sec: 87.4241 +2022-05-01 15:29:33,803 - tensorflow - INFO - loss = 0.01856226, step = 399600 (2.288 sec) +2022-05-01 15:29:36,094 - tensorflow - INFO - global_step/sec: 87.2769 +2022-05-01 15:29:36,094 - tensorflow - INFO - loss = 0.58974075, step = 399800 (2.292 sec) +2022-05-01 15:29:38,371 - tensorflow - INFO - Saving checkpoints for 400000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. +2022-05-01 15:29:38,389 - tensorflow - INFO - model.ckpt-400000 is not in all_model_checkpoint_paths. Manually adding it. +2022-05-01 15:29:38,933 - tensorflow - INFO - Calling model_fn. +2022-05-01 15:29:39,066 - tensorflow - INFO - Done calling model_fn. +2022-05-01 15:29:39,082 - tensorflow - INFO - Starting evaluation at 2022-05-01T15:29:39Z +2022-05-01 15:29:39,147 - tensorflow - INFO - Graph was finalized. +2022-05-01 15:29:39,149 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-400000 +2022-05-01 15:29:39,438 - tensorflow - INFO - Running local_init_op. +2022-05-01 15:29:39,547 - tensorflow - INFO - Done running local_init_op. +2022-05-01 15:29:49,774 - tensorflow - INFO - Evaluation [3990/39900] +2022-05-01 15:29:59,312 - tensorflow - INFO - Evaluation [7980/39900] +2022-05-01 15:30:08,770 - tensorflow - INFO - Evaluation [11970/39900] +2022-05-01 15:30:18,321 - tensorflow - INFO - Evaluation [15960/39900] +2022-05-01 15:30:27,839 - tensorflow - INFO - Evaluation [19950/39900] +2022-05-01 15:30:37,374 - tensorflow - INFO - Evaluation [23940/39900] +2022-05-01 15:30:46,920 - tensorflow - INFO - Evaluation [27930/39900] +2022-05-01 15:30:56,464 - tensorflow - INFO - Evaluation [31920/39900] +2022-05-01 15:31:06,005 - tensorflow - INFO - Evaluation [35910/39900] +2022-05-01 15:31:15,544 - tensorflow - INFO - Evaluation [39900/39900] +2022-05-01 15:31:15,682 - tensorflow - INFO - Finished evaluation at 2022-05-01-15:31:15 +2022-05-01 15:31:15,682 - tensorflow - INFO - Saving dict for global step 400000: global_step = 400000, loss = 0.04516113, mean_degree_err = 6.516394 +2022-05-01 15:31:15,682 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 400000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-400000 +2022-05-01 15:31:15,696 - tensorflow - INFO - global_step/sec: 2.00798 +2022-05-01 15:31:15,697 - tensorflow - INFO - loss = 0.02225634, step = 400000 (99.603 sec) +2022-05-01 15:31:18,011 - tensorflow - INFO - global_step/sec: 86.4114 +2022-05-01 15:31:18,011 - tensorflow - INFO - loss = 0.31792247, step = 400200 (2.314 sec) +2022-05-01 15:31:20,306 - tensorflow - INFO - global_step/sec: 87.1549 +2022-05-01 15:31:20,306 - tensorflow - INFO - loss = 0.10387549, step = 400400 (2.295 sec) +2022-05-01 15:31:22,607 - tensorflow - INFO - global_step/sec: 86.9092 +2022-05-01 15:31:22,607 - tensorflow - INFO - loss = 0.12722068, step = 400600 (2.301 sec) +2022-05-01 15:31:24,903 - tensorflow - INFO - global_step/sec: 87.0888 +2022-05-01 15:31:24,904 - tensorflow - INFO - loss = 0.22603582, step = 400800 (2.297 sec) +2022-05-01 15:31:27,198 - tensorflow - INFO - global_step/sec: 87.1598 +2022-05-01 15:31:27,199 - tensorflow - INFO - loss = 0.15252268, step = 401000 (2.295 sec) +2022-05-01 15:31:29,491 - tensorflow - INFO - global_step/sec: 87.2261 +2022-05-01 15:31:29,491 - tensorflow - INFO - loss = 0.24389297, step = 401200 (2.293 sec) +2022-05-01 15:31:31,795 - tensorflow - INFO - global_step/sec: 86.8182 +2022-05-01 15:31:31,795 - tensorflow - INFO - loss = 0.015286315, step = 401400 (2.304 sec) +2022-05-01 15:31:34,096 - tensorflow - INFO - global_step/sec: 86.9031 +2022-05-01 15:31:34,096 - tensorflow - INFO - loss = 0.09249173, step = 401600 (2.301 sec) +2022-05-01 15:31:36,388 - tensorflow - INFO - global_step/sec: 87.2577 +2022-05-01 15:31:36,389 - tensorflow - INFO - loss = 0.097399205, step = 401800 (2.292 sec) +2022-05-01 15:31:38,645 - tensorflow - INFO - global_step/sec: 88.6012 +2022-05-01 15:31:38,646 - tensorflow - INFO - loss = 0.030494688, step = 402000 (2.257 sec) +2022-05-01 15:31:40,923 - tensorflow - INFO - global_step/sec: 87.8078 +2022-05-01 15:31:40,924 - tensorflow - INFO - loss = 0.111601226, step = 402200 (2.278 sec) +2022-05-01 15:31:43,212 - tensorflow - INFO - global_step/sec: 87.3939 +2022-05-01 15:31:43,212 - tensorflow - INFO - loss = 0.028065145, step = 402400 (2.288 sec) +2022-05-01 15:31:45,507 - tensorflow - INFO - global_step/sec: 87.1462 +2022-05-01 15:31:45,507 - tensorflow - INFO - loss = 0.024193406, step = 402600 (2.295 sec) +2022-05-01 15:31:47,798 - tensorflow - INFO - global_step/sec: 87.2884 +2022-05-01 15:31:47,798 - tensorflow - INFO - loss = 20.300358, step = 402800 (2.291 sec) +2022-05-01 15:31:50,098 - tensorflow - INFO - global_step/sec: 86.9538 +2022-05-01 15:31:50,098 - tensorflow - INFO - loss = 17.936117, step = 403000 (2.300 sec) +2022-05-01 15:31:52,388 - tensorflow - INFO - global_step/sec: 87.3362 +2022-05-01 15:31:52,388 - tensorflow - INFO - loss = 0.04836905, step = 403200 (2.290 sec) +2022-05-01 15:31:54,683 - tensorflow - INFO - global_step/sec: 87.148 +2022-05-01 15:31:54,683 - tensorflow - INFO - loss = 11.243692, step = 403400 (2.295 sec) +2022-05-01 15:31:56,975 - tensorflow - INFO - global_step/sec: 87.2586 +2022-05-01 15:31:56,975 - tensorflow - INFO - loss = 0.024591817, step = 403600 (2.292 sec) +2022-05-01 15:31:59,270 - tensorflow - INFO - global_step/sec: 87.1365 +2022-05-01 15:31:59,271 - tensorflow - INFO - loss = 0.10885215, step = 403800 (2.295 sec) +2022-05-01 15:32:01,569 - tensorflow - INFO - global_step/sec: 87.0194 +2022-05-01 15:32:01,569 - tensorflow - INFO - loss = 0.09319634, step = 404000 (2.298 sec) +2022-05-01 15:32:03,855 - tensorflow - INFO - global_step/sec: 87.4566 +2022-05-01 15:32:03,856 - tensorflow - INFO - loss = 0.053963237, step = 404200 (2.287 sec) +2022-05-01 15:32:06,151 - tensorflow - INFO - global_step/sec: 87.1172 +2022-05-01 15:32:06,152 - tensorflow - INFO - loss = 0.053012453, step = 404400 (2.296 sec) +2022-05-01 15:32:08,451 - tensorflow - INFO - global_step/sec: 86.9517 +2022-05-01 15:32:08,452 - tensorflow - INFO - loss = 0.12956715, step = 404600 (2.300 sec) +2022-05-01 15:32:10,755 - tensorflow - INFO - global_step/sec: 86.8279 +2022-05-01 15:32:10,755 - tensorflow - INFO - loss = 0.09968604, step = 404800 (2.303 sec) +2022-05-01 15:32:13,065 - tensorflow - INFO - global_step/sec: 86.551 +2022-05-01 15:32:13,066 - tensorflow - INFO - loss = 0.026279986, step = 405000 (2.311 sec) +2022-05-01 15:32:15,371 - tensorflow - INFO - global_step/sec: 86.7337 +2022-05-01 15:32:15,372 - tensorflow - INFO - loss = 0.09798093, step = 405200 (2.306 sec) +2022-05-01 15:32:17,675 - tensorflow - INFO - global_step/sec: 86.8323 +2022-05-01 15:32:17,675 - tensorflow - INFO - loss = 0.044201896, step = 405400 (2.303 sec) +2022-05-01 15:32:19,974 - tensorflow - INFO - global_step/sec: 86.981 +2022-05-01 15:32:19,974 - tensorflow - INFO - loss = 0.05312757, step = 405600 (2.299 sec) +2022-05-01 15:32:22,279 - tensorflow - INFO - global_step/sec: 86.7527 +2022-05-01 15:32:22,280 - tensorflow - INFO - loss = 0.10622845, step = 405800 (2.305 sec) +2022-05-01 15:32:24,581 - tensorflow - INFO - global_step/sec: 86.9053 +2022-05-01 15:32:24,581 - tensorflow - INFO - loss = 0.10170749, step = 406000 (2.301 sec) +2022-05-01 15:32:26,884 - tensorflow - INFO - global_step/sec: 86.8522 +2022-05-01 15:32:26,884 - tensorflow - INFO - loss = 0.048127078, step = 406200 (2.303 sec) +2022-05-01 15:32:29,187 - tensorflow - INFO - global_step/sec: 86.8426 +2022-05-01 15:32:29,187 - tensorflow - INFO - loss = 0.052297786, step = 406400 (2.303 sec) +2022-05-01 15:32:31,494 - tensorflow - INFO - global_step/sec: 86.6579 +2022-05-01 15:32:31,495 - tensorflow - INFO - loss = 0.08015016, step = 406600 (2.308 sec) +2022-05-01 15:32:33,796 - tensorflow - INFO - global_step/sec: 86.898 +2022-05-01 15:32:33,796 - tensorflow - INFO - loss = 0.16766617, step = 406800 (2.301 sec) +2022-05-01 15:32:36,098 - tensorflow - INFO - global_step/sec: 86.898 +2022-05-01 15:32:36,098 - tensorflow - INFO - loss = 0.8134452, step = 407000 (2.302 sec) +2022-05-01 15:32:38,395 - tensorflow - INFO - global_step/sec: 87.0599 +2022-05-01 15:32:38,395 - tensorflow - INFO - loss = 0.03777675, step = 407200 (2.297 sec) +2022-05-01 15:32:40,695 - tensorflow - INFO - global_step/sec: 86.9366 +2022-05-01 15:32:40,696 - tensorflow - INFO - loss = 0.13193761, step = 407400 (2.300 sec) +2022-05-01 15:32:42,981 - tensorflow - INFO - global_step/sec: 87.5104 +2022-05-01 15:32:42,981 - tensorflow - INFO - loss = 0.11008027, step = 407600 (2.285 sec) +2022-05-01 15:32:45,272 - tensorflow - INFO - global_step/sec: 87.3061 +2022-05-01 15:32:45,272 - tensorflow - INFO - loss = 0.0595809, step = 407800 (2.291 sec) +2022-05-01 15:32:47,562 - tensorflow - INFO - global_step/sec: 87.3225 +2022-05-01 15:32:47,562 - tensorflow - INFO - loss = 0.018416446, step = 408000 (2.290 sec) +2022-05-01 15:32:49,854 - tensorflow - INFO - global_step/sec: 87.245 +2022-05-01 15:32:49,855 - tensorflow - INFO - loss = 0.22051051, step = 408200 (2.292 sec) +2022-05-01 15:32:52,157 - tensorflow - INFO - global_step/sec: 86.8691 +2022-05-01 15:32:52,157 - tensorflow - INFO - loss = 0.5370528, step = 408400 (2.302 sec) +2022-05-01 15:32:54,452 - tensorflow - INFO - global_step/sec: 87.1151 +2022-05-01 15:32:54,453 - tensorflow - INFO - loss = 0.031772103, step = 408600 (2.296 sec) +2022-05-01 15:32:56,746 - tensorflow - INFO - global_step/sec: 87.21 +2022-05-01 15:32:56,746 - tensorflow - INFO - loss = 0.2617071, step = 408800 (2.293 sec) +2022-05-01 15:32:59,045 - tensorflow - INFO - 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loss = 0.07888447, step = 491400 (2.260 sec) +2022-05-01 15:48:45,734 - tensorflow - INFO - global_step/sec: 87.0183 +2022-05-01 15:48:45,735 - tensorflow - INFO - loss = 0.013879217, step = 491600 (2.298 sec) +2022-05-01 15:48:48,028 - tensorflow - INFO - global_step/sec: 87.1904 +2022-05-01 15:48:48,029 - tensorflow - INFO - loss = 0.19325846, step = 491800 (2.294 sec) +2022-05-01 15:48:50,324 - tensorflow - INFO - global_step/sec: 87.1239 +2022-05-01 15:48:50,324 - tensorflow - INFO - loss = 0.05001726, step = 492000 (2.296 sec) +2022-05-01 15:48:52,622 - tensorflow - INFO - global_step/sec: 87.034 +2022-05-01 15:48:52,622 - tensorflow - INFO - loss = 0.08362567, step = 492200 (2.298 sec) +2022-05-01 15:48:54,918 - tensorflow - INFO - global_step/sec: 87.1066 +2022-05-01 15:48:54,918 - tensorflow - INFO - loss = 0.045164224, step = 492400 (2.296 sec) +2022-05-01 15:48:57,213 - tensorflow - INFO - global_step/sec: 87.1404 +2022-05-01 15:48:57,213 - tensorflow - INFO - loss = 0.12752767, step = 492600 (2.295 sec) +2022-05-01 15:48:59,510 - tensorflow - INFO - global_step/sec: 87.0822 +2022-05-01 15:48:59,510 - tensorflow - INFO - loss = 0.060145747, step = 492800 (2.297 sec) +2022-05-01 15:49:01,809 - tensorflow - INFO - global_step/sec: 86.9762 +2022-05-01 15:49:01,810 - tensorflow - INFO - loss = 0.503282, step = 493000 (2.299 sec) +2022-05-01 15:49:04,097 - tensorflow - INFO - global_step/sec: 87.4125 +2022-05-01 15:49:04,098 - tensorflow - INFO - loss = 0.08907508, step = 493200 (2.288 sec) +2022-05-01 15:49:06,400 - tensorflow - INFO - global_step/sec: 86.839 +2022-05-01 15:49:06,401 - tensorflow - INFO - loss = 0.036446743, step = 493400 (2.303 sec) +2022-05-01 15:49:08,695 - tensorflow - INFO - global_step/sec: 87.1736 +2022-05-01 15:49:08,695 - tensorflow - INFO - loss = 3.1655476, step = 493600 (2.294 sec) +2022-05-01 15:49:10,991 - tensorflow - INFO - global_step/sec: 87.0857 +2022-05-01 15:49:10,992 - tensorflow - INFO - loss = 0.02401543, step = 493800 (2.297 sec) +2022-05-01 15:49:13,281 - tensorflow - INFO - global_step/sec: 87.3311 +2022-05-01 15:49:13,282 - tensorflow - INFO - loss = 0.02471529, step = 494000 (2.290 sec) +2022-05-01 15:49:15,573 - tensorflow - INFO - global_step/sec: 87.2857 +2022-05-01 15:49:15,573 - tensorflow - INFO - loss = 0.048128817, step = 494200 (2.291 sec) +2022-05-01 15:49:17,856 - tensorflow - INFO - global_step/sec: 87.5903 +2022-05-01 15:49:17,856 - tensorflow - INFO - loss = 0.3277765, step = 494400 (2.283 sec) +2022-05-01 15:49:20,153 - tensorflow - INFO - global_step/sec: 87.0611 +2022-05-01 15:49:20,154 - tensorflow - INFO - loss = 0.015861625, step = 494600 (2.297 sec) +2022-05-01 15:49:22,448 - tensorflow - INFO - global_step/sec: 87.1354 +2022-05-01 15:49:22,449 - tensorflow - INFO - loss = 0.028060673, step = 494800 (2.295 sec) +2022-05-01 15:49:24,744 - tensorflow - INFO - global_step/sec: 87.1415 +2022-05-01 15:49:24,744 - tensorflow - INFO - loss = 0.03264568, step = 495000 (2.295 sec) +2022-05-01 15:49:27,031 - tensorflow - INFO - global_step/sec: 87.4192 +2022-05-01 15:49:27,032 - tensorflow - INFO - loss = 0.45592192, step = 495200 (2.288 sec) +2022-05-01 15:49:29,313 - tensorflow - INFO - global_step/sec: 87.6582 +2022-05-01 15:49:29,313 - tensorflow - INFO - loss = 0.074402526, step = 495400 (2.282 sec) +2022-05-01 15:49:31,604 - tensorflow - INFO - global_step/sec: 87.2896 +2022-05-01 15:49:31,605 - tensorflow - INFO - loss = 0.34785342, step = 495600 (2.291 sec) +2022-05-01 15:49:33,894 - tensorflow - INFO - global_step/sec: 87.3332 +2022-05-01 15:49:33,895 - tensorflow - INFO - loss = 0.014895788, step = 495800 (2.290 sec) +2022-05-01 15:49:36,192 - tensorflow - INFO - global_step/sec: 87.0249 +2022-05-01 15:49:36,193 - tensorflow - INFO - loss = 0.039041396, step = 496000 (2.298 sec) +2022-05-01 15:49:38,480 - tensorflow - INFO - global_step/sec: 87.4465 +2022-05-01 15:49:38,480 - tensorflow - INFO - loss = 0.023041882, step = 496200 (2.287 sec) +2022-05-01 15:49:40,778 - tensorflow - INFO - global_step/sec: 87.01 +2022-05-01 15:49:40,779 - tensorflow - INFO - loss = 7.3349943, step = 496400 (2.299 sec) +2022-05-01 15:49:43,075 - tensorflow - INFO - global_step/sec: 87.0711 +2022-05-01 15:49:43,076 - tensorflow - INFO - loss = 0.690408, step = 496600 (2.297 sec) +2022-05-01 15:49:45,364 - tensorflow - INFO - global_step/sec: 87.3821 +2022-05-01 15:49:45,364 - tensorflow - INFO - loss = 0.042593334, step = 496800 (2.289 sec) +2022-05-01 15:49:47,666 - tensorflow - INFO - global_step/sec: 86.8956 +2022-05-01 15:49:47,666 - tensorflow - INFO - loss = 0.0668559, step = 497000 (2.302 sec) +2022-05-01 15:49:49,973 - tensorflow - INFO - global_step/sec: 86.6792 +2022-05-01 15:49:49,973 - tensorflow - INFO - loss = 0.011198775, step = 497200 (2.307 sec) +2022-05-01 15:49:52,277 - tensorflow - INFO - global_step/sec: 86.8116 +2022-05-01 15:49:52,277 - tensorflow - INFO - loss = 0.06476675, step = 497400 (2.304 sec) +2022-05-01 15:49:54,581 - tensorflow - INFO - global_step/sec: 86.8063 +2022-05-01 15:49:54,581 - tensorflow - INFO - loss = 0.043987837, step = 497600 (2.304 sec) +2022-05-01 15:49:56,880 - tensorflow - INFO - global_step/sec: 86.9838 +2022-05-01 15:49:56,880 - tensorflow - INFO - loss = 0.04835829, step = 497800 (2.299 sec) +2022-05-01 15:49:59,180 - tensorflow - INFO - global_step/sec: 86.9417 +2022-05-01 15:49:59,181 - tensorflow - INFO - loss = 0.044502974, step = 498000 (2.300 sec) +2022-05-01 15:50:01,478 - tensorflow - INFO - global_step/sec: 87.0622 +2022-05-01 15:50:01,478 - tensorflow - INFO - loss = 0.060919967, step = 498200 (2.297 sec) +2022-05-01 15:50:03,783 - tensorflow - INFO - global_step/sec: 86.7672 +2022-05-01 15:50:03,783 - tensorflow - INFO - loss = 0.12766704, step = 498400 (2.305 sec) +2022-05-01 15:50:06,076 - tensorflow - INFO - global_step/sec: 87.2122 +2022-05-01 15:50:06,076 - tensorflow - INFO - loss = 0.026480768, step = 498600 (2.293 sec) +2022-05-01 15:50:08,366 - tensorflow - INFO - global_step/sec: 87.3191 +2022-05-01 15:50:08,367 - tensorflow - INFO - loss = 1.5395248, step = 498800 (2.290 sec) +2022-05-01 15:50:10,651 - tensorflow - INFO - global_step/sec: 87.5458 +2022-05-01 15:50:10,651 - tensorflow - INFO - loss = 0.05769307, step = 499000 (2.285 sec) +2022-05-01 15:50:12,914 - tensorflow - INFO - global_step/sec: 88.3691 +2022-05-01 15:50:12,915 - tensorflow - INFO - loss = 0.06950209, step = 499200 (2.263 sec) +2022-05-01 15:50:15,180 - tensorflow - INFO - global_step/sec: 88.2645 +2022-05-01 15:50:15,180 - tensorflow - INFO - loss = 0.41808796, step = 499400 (2.266 sec) +2022-05-01 15:50:17,438 - tensorflow - INFO - global_step/sec: 88.5739 +2022-05-01 15:50:17,438 - tensorflow - INFO - loss = 0.16664134, step = 499600 (2.258 sec) +2022-05-01 15:50:19,734 - tensorflow - INFO - global_step/sec: 87.1192 +2022-05-01 15:50:19,734 - tensorflow - INFO - loss = 20.43417, step = 499800 (2.296 sec) +2022-05-01 15:50:22,016 - tensorflow - INFO - Saving checkpoints for 500000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. +2022-05-01 15:50:22,032 - tensorflow - INFO - model.ckpt-500000 is not in all_model_checkpoint_paths. Manually adding it. +2022-05-01 15:50:22,559 - tensorflow - INFO - Calling model_fn. +2022-05-01 15:50:22,692 - tensorflow - INFO - Done calling model_fn. +2022-05-01 15:50:22,707 - tensorflow - INFO - Starting evaluation at 2022-05-01T15:50:22Z +2022-05-01 15:50:22,772 - tensorflow - INFO - Graph was finalized. +2022-05-01 15:50:22,775 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-500000 +2022-05-01 15:50:23,062 - tensorflow - INFO - Running local_init_op. +2022-05-01 15:50:23,174 - tensorflow - INFO - Done running local_init_op. +2022-05-01 15:50:33,530 - tensorflow - INFO - Evaluation [3990/39900] +2022-05-01 15:50:43,155 - tensorflow - INFO - Evaluation [7980/39900] +2022-05-01 15:50:52,766 - tensorflow - INFO - Evaluation [11970/39900] +2022-05-01 15:51:02,371 - tensorflow - INFO - Evaluation [15960/39900] +2022-05-01 15:51:11,960 - tensorflow - INFO - Evaluation [19950/39900] +2022-05-01 15:51:21,578 - tensorflow - INFO - Evaluation [23940/39900] +2022-05-01 15:51:31,204 - tensorflow - INFO - Evaluation [27930/39900] +2022-05-01 15:51:40,848 - tensorflow - INFO - Evaluation [31920/39900] +2022-05-01 15:51:50,412 - tensorflow - INFO - Evaluation [35910/39900] +2022-05-01 15:52:00,044 - tensorflow - INFO - Evaluation [39900/39900] +2022-05-01 15:52:00,185 - tensorflow - INFO - Finished evaluation at 2022-05-01-15:52:00 +2022-05-01 15:52:00,185 - tensorflow - INFO - Saving dict for global step 500000: global_step = 500000, loss = 0.046809282, mean_degree_err = 6.3430543 +2022-05-01 15:52:00,186 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 500000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-500000 +2022-05-01 15:52:00,200 - tensorflow - INFO - global_step/sec: 1.99072 +2022-05-01 15:52:00,201 - tensorflow - INFO - loss = 0.063635156, step = 500000 (100.467 sec) +2022-05-01 15:52:02,510 - tensorflow - INFO - global_step/sec: 86.5758 +2022-05-01 15:52:02,510 - tensorflow - INFO - loss = 0.07470157, step = 500200 (2.310 sec) +2022-05-01 15:52:04,794 - tensorflow - INFO - global_step/sec: 87.5492 +2022-05-01 15:52:04,795 - tensorflow - INFO - loss = 0.31317902, step = 500400 (2.284 sec) +2022-05-01 15:52:07,086 - tensorflow - INFO - global_step/sec: 87.2645 +2022-05-01 15:52:07,087 - tensorflow - INFO - loss = 0.18935311, step = 500600 (2.292 sec) +2022-05-01 15:52:09,376 - tensorflow - INFO - global_step/sec: 87.3425 +2022-05-01 15:52:09,376 - tensorflow - INFO - loss = 0.020323459, step = 500800 (2.290 sec) +2022-05-01 15:52:11,673 - tensorflow - INFO - global_step/sec: 87.067 +2022-05-01 15:52:11,673 - tensorflow - INFO - loss = 0.024707984, step = 501000 (2.297 sec) +2022-05-01 15:52:13,966 - tensorflow - INFO - global_step/sec: 87.224 +2022-05-01 15:52:13,966 - tensorflow - INFO - loss = 0.02308048, step = 501200 (2.293 sec) +2022-05-01 15:52:16,256 - tensorflow - INFO - global_step/sec: 87.3316 +2022-05-01 15:52:16,257 - tensorflow - INFO - loss = 0.084123634, step = 501400 (2.290 sec) +2022-05-01 15:52:18,545 - tensorflow - INFO - global_step/sec: 87.3829 +2022-05-01 15:52:18,545 - tensorflow - INFO - loss = 0.9689673, step = 501600 (2.289 sec) +2022-05-01 15:52:20,830 - tensorflow - INFO - global_step/sec: 87.508 +2022-05-01 15:52:20,831 - tensorflow - INFO - loss = 0.04496465, step = 501800 (2.285 sec) +2022-05-01 15:52:23,122 - tensorflow - INFO - global_step/sec: 87.2854 +2022-05-01 15:52:23,122 - tensorflow - INFO - loss = 0.026626464, step = 502000 (2.291 sec) +2022-05-01 15:52:25,412 - tensorflow - INFO - global_step/sec: 87.3072 +2022-05-01 15:52:25,413 - tensorflow - INFO - loss = 0.086509205, step = 502200 (2.291 sec) +2022-05-01 15:52:27,707 - tensorflow - INFO - global_step/sec: 87.1582 +2022-05-01 15:52:27,708 - tensorflow - INFO - loss = 0.06746422, step = 502400 (2.295 sec) +2022-05-01 15:52:29,998 - tensorflow - INFO - global_step/sec: 87.3076 +2022-05-01 15:52:29,998 - tensorflow - INFO - loss = 0.0978373, step = 502600 (2.291 sec) +2022-05-01 15:52:32,295 - tensorflow - INFO - global_step/sec: 87.078 +2022-05-01 15:52:32,295 - tensorflow - INFO - loss = 0.104280345, step = 502800 (2.297 sec) +2022-05-01 15:52:34,591 - tensorflow - INFO - global_step/sec: 87.1093 +2022-05-01 15:52:34,591 - tensorflow - INFO - loss = 0.091107875, step = 503000 (2.296 sec) +2022-05-01 15:52:36,888 - tensorflow - INFO - global_step/sec: 87.0597 +2022-05-01 15:52:36,888 - tensorflow - INFO - loss = 0.033102345, step = 503200 (2.297 sec) +2022-05-01 15:52:39,175 - tensorflow - INFO - global_step/sec: 87.4474 +2022-05-01 15:52:39,175 - tensorflow - INFO - loss = 0.09425828, step = 503400 (2.287 sec) +2022-05-01 15:52:41,466 - tensorflow - INFO - global_step/sec: 87.2883 +2022-05-01 15:52:41,467 - tensorflow - INFO - loss = 0.061163433, step = 503600 (2.291 sec) +2022-05-01 15:52:43,758 - tensorflow - INFO - global_step/sec: 87.265 +2022-05-01 15:52:43,759 - tensorflow - INFO - loss = 0.13534966, step = 503800 (2.292 sec) +2022-05-01 15:52:46,054 - tensorflow - INFO - global_step/sec: 87.108 +2022-05-01 15:52:46,055 - tensorflow - INFO - loss = 0.06398092, step = 504000 (2.296 sec) +2022-05-01 15:52:48,353 - tensorflow - INFO - global_step/sec: 86.9983 +2022-05-01 15:52:48,354 - tensorflow - INFO - loss = 0.014031668, step = 504200 (2.299 sec) +2022-05-01 15:52:50,640 - tensorflow - INFO - global_step/sec: 87.4405 +2022-05-01 15:52:50,641 - tensorflow - INFO - loss = 0.049638722, step = 504400 (2.287 sec) +2022-05-01 15:52:52,947 - tensorflow - INFO - global_step/sec: 86.7174 +2022-05-01 15:52:52,947 - tensorflow - INFO - loss = 0.03826969, step = 504600 (2.306 sec) +2022-05-01 15:52:55,241 - tensorflow - INFO - global_step/sec: 87.1834 +2022-05-01 15:52:55,241 - tensorflow - INFO - loss = 0.08917885, step = 504800 (2.294 sec) +2022-05-01 15:52:57,543 - tensorflow - INFO - global_step/sec: 86.8621 +2022-05-01 15:52:57,544 - tensorflow - INFO - loss = 0.1652728, step = 505000 (2.303 sec) +2022-05-01 15:52:59,836 - tensorflow - INFO - global_step/sec: 87.2339 +2022-05-01 15:52:59,836 - tensorflow - INFO - loss = 0.17992705, step = 505200 (2.293 sec) +2022-05-01 15:53:02,139 - tensorflow - INFO - global_step/sec: 86.8256 +2022-05-01 15:53:02,140 - tensorflow - INFO - loss = 0.037294824, step = 505400 (2.303 sec) +2022-05-01 15:53:04,432 - tensorflow - INFO - global_step/sec: 87.2285 +2022-05-01 15:53:04,433 - tensorflow - INFO - loss = 0.15038131, step = 505600 (2.293 sec) +2022-05-01 15:53:06,724 - tensorflow - INFO - global_step/sec: 87.28 +2022-05-01 15:53:06,724 - tensorflow - INFO - loss = 0.065310985, step = 505800 (2.292 sec) +2022-05-01 15:53:09,012 - tensorflow - INFO - global_step/sec: 87.396 +2022-05-01 15:53:09,013 - tensorflow - INFO - loss = 0.034203827, step = 506000 (2.288 sec) +2022-05-01 15:53:11,312 - tensorflow - INFO - global_step/sec: 86.96 +2022-05-01 15:53:11,312 - tensorflow - INFO - loss = 0.16935429, step = 506200 (2.300 sec) +2022-05-01 15:53:13,611 - tensorflow - INFO - global_step/sec: 86.985 +2022-05-01 15:53:13,612 - tensorflow - INFO - loss = 0.035027437, step = 506400 (2.299 sec) +2022-05-01 15:53:15,902 - tensorflow - INFO - global_step/sec: 87.3028 +2022-05-01 15:53:15,903 - tensorflow - INFO - loss = 0.057028934, step = 506600 (2.291 sec) +2022-05-01 15:53:18,182 - tensorflow - INFO - global_step/sec: 87.7217 +2022-05-01 15:53:18,183 - tensorflow - INFO - loss = 0.0799098, step = 506800 (2.280 sec) +2022-05-01 15:53:20,471 - tensorflow - INFO - global_step/sec: 87.379 +2022-05-01 15:53:20,471 - tensorflow - INFO - loss = 0.03132668, step = 507000 (2.289 sec) +2022-05-01 15:53:22,772 - tensorflow - INFO - global_step/sec: 86.9129 +2022-05-01 15:53:22,773 - tensorflow - INFO - loss = 0.049004238, step = 507200 (2.301 sec) +2022-05-01 15:53:25,074 - tensorflow - INFO - global_step/sec: 86.9042 +2022-05-01 15:53:25,074 - tensorflow - INFO - loss = 0.05456598, step = 507400 (2.301 sec) +2022-05-01 15:53:27,378 - tensorflow - INFO - global_step/sec: 86.7786 +2022-05-01 15:53:27,379 - tensorflow - INFO - loss = 0.03548259, step = 507600 (2.305 sec) +2022-05-01 15:53:29,677 - 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INFO - loss = 0.012185383, step = 591400 (2.249 sec) +2022-05-01 16:09:28,952 - tensorflow - INFO - global_step/sec: 88.8107 +2022-05-01 16:09:28,953 - tensorflow - INFO - loss = 0.009116696, step = 591600 (2.252 sec) +2022-05-01 16:09:31,214 - tensorflow - INFO - global_step/sec: 88.4188 +2022-05-01 16:09:31,215 - tensorflow - INFO - loss = 0.017143156, step = 591800 (2.262 sec) +2022-05-01 16:09:33,469 - tensorflow - INFO - global_step/sec: 88.7175 +2022-05-01 16:09:33,469 - tensorflow - INFO - loss = 0.014566356, step = 592000 (2.254 sec) +2022-05-01 16:09:35,720 - tensorflow - INFO - global_step/sec: 88.8272 +2022-05-01 16:09:35,721 - tensorflow - INFO - loss = 0.13501108, step = 592200 (2.252 sec) +2022-05-01 16:09:37,975 - tensorflow - INFO - global_step/sec: 88.7008 +2022-05-01 16:09:37,975 - tensorflow - INFO - loss = 0.026087768, step = 592400 (2.255 sec) +2022-05-01 16:09:40,226 - tensorflow - INFO - global_step/sec: 88.8584 +2022-05-01 16:09:40,226 - tensorflow - INFO - loss = 0.03718528, step = 592600 (2.251 sec) +2022-05-01 16:09:42,472 - tensorflow - INFO - global_step/sec: 89.0448 +2022-05-01 16:09:42,472 - tensorflow - INFO - loss = 0.07650729, step = 592800 (2.246 sec) +2022-05-01 16:09:44,722 - tensorflow - INFO - global_step/sec: 88.8853 +2022-05-01 16:09:44,722 - tensorflow - INFO - loss = 3.1181715, step = 593000 (2.250 sec) +2022-05-01 16:09:46,975 - tensorflow - INFO - global_step/sec: 88.7733 +2022-05-01 16:09:46,975 - tensorflow - INFO - loss = 0.051136553, step = 593200 (2.253 sec) +2022-05-01 16:09:49,235 - tensorflow - INFO - global_step/sec: 88.4955 +2022-05-01 16:09:49,235 - tensorflow - INFO - loss = 0.32656357, step = 593400 (2.260 sec) +2022-05-01 16:09:51,488 - tensorflow - INFO - global_step/sec: 88.7762 +2022-05-01 16:09:51,488 - tensorflow - INFO - loss = 0.051417626, step = 593600 (2.253 sec) +2022-05-01 16:09:53,744 - tensorflow - INFO - global_step/sec: 88.6572 +2022-05-01 16:09:53,744 - tensorflow - INFO - loss = 0.0070130154, step = 593800 (2.256 sec) +2022-05-01 16:09:55,998 - tensorflow - INFO - global_step/sec: 88.7031 +2022-05-01 16:09:55,999 - tensorflow - INFO - loss = 0.012833834, step = 594000 (2.255 sec) +2022-05-01 16:09:58,251 - tensorflow - INFO - global_step/sec: 88.7779 +2022-05-01 16:09:58,252 - tensorflow - INFO - loss = 0.014024916, step = 594200 (2.253 sec) +2022-05-01 16:10:00,502 - tensorflow - INFO - global_step/sec: 88.8557 +2022-05-01 16:10:00,502 - tensorflow - INFO - loss = 0.07648599, step = 594400 (2.251 sec) +2022-05-01 16:10:02,763 - tensorflow - INFO - global_step/sec: 88.4516 +2022-05-01 16:10:02,764 - tensorflow - INFO - loss = 0.08582879, step = 594600 (2.261 sec) +2022-05-01 16:10:05,018 - tensorflow - INFO - global_step/sec: 88.6866 +2022-05-01 16:10:05,019 - tensorflow - INFO - loss = 0.04442747, step = 594800 (2.255 sec) +2022-05-01 16:10:07,268 - tensorflow - INFO - global_step/sec: 88.9089 +2022-05-01 16:10:07,268 - tensorflow - INFO - loss = 0.6102391, step = 595000 (2.249 sec) +2022-05-01 16:10:09,519 - tensorflow - INFO - global_step/sec: 88.8406 +2022-05-01 16:10:09,519 - tensorflow - INFO - loss = 0.00982367, step = 595200 (2.251 sec) +2022-05-01 16:10:11,772 - tensorflow - INFO - global_step/sec: 88.7721 +2022-05-01 16:10:11,772 - tensorflow - INFO - loss = 0.03102067, step = 595400 (2.253 sec) +2022-05-01 16:10:14,023 - tensorflow - INFO - global_step/sec: 88.8381 +2022-05-01 16:10:14,024 - tensorflow - INFO - loss = 0.020223914, step = 595600 (2.251 sec) +2022-05-01 16:10:16,277 - tensorflow - INFO - global_step/sec: 88.7384 +2022-05-01 16:10:16,277 - tensorflow - INFO - loss = 0.021211697, step = 595800 (2.254 sec) +2022-05-01 16:10:18,532 - tensorflow - INFO - global_step/sec: 88.6797 +2022-05-01 16:10:18,533 - tensorflow - INFO - loss = 0.3812176, step = 596000 (2.255 sec) +2022-05-01 16:10:20,787 - tensorflow - INFO - global_step/sec: 88.6834 +2022-05-01 16:10:20,788 - tensorflow - INFO - loss = 0.28526512, step = 596200 (2.255 sec) +2022-05-01 16:10:23,039 - tensorflow - INFO - global_step/sec: 88.8428 +2022-05-01 16:10:23,039 - tensorflow - INFO - loss = 0.016735926, step = 596400 (2.251 sec) +2022-05-01 16:10:25,288 - tensorflow - INFO - global_step/sec: 88.9144 +2022-05-01 16:10:25,288 - tensorflow - INFO - loss = 0.082976624, step = 596600 (2.249 sec) +2022-05-01 16:10:27,547 - tensorflow - INFO - global_step/sec: 88.5296 +2022-05-01 16:10:27,548 - tensorflow - INFO - loss = 0.045340955, step = 596800 (2.259 sec) +2022-05-01 16:10:29,798 - tensorflow - INFO - global_step/sec: 88.8455 +2022-05-01 16:10:29,799 - tensorflow - INFO - loss = 0.05019103, step = 597000 (2.251 sec) +2022-05-01 16:10:32,056 - tensorflow - INFO - global_step/sec: 88.5854 +2022-05-01 16:10:32,056 - tensorflow - INFO - loss = 0.2732757, step = 597200 (2.258 sec) +2022-05-01 16:10:34,310 - tensorflow - INFO - global_step/sec: 88.7338 +2022-05-01 16:10:34,310 - tensorflow - INFO - loss = 0.044302374, step = 597400 (2.254 sec) +2022-05-01 16:10:36,559 - tensorflow - INFO - global_step/sec: 88.9157 +2022-05-01 16:10:36,560 - tensorflow - INFO - loss = 0.033330336, step = 597600 (2.249 sec) +2022-05-01 16:10:38,815 - tensorflow - INFO - global_step/sec: 88.6414 +2022-05-01 16:10:38,816 - tensorflow - INFO - loss = 0.071330816, step = 597800 (2.256 sec) +2022-05-01 16:10:41,071 - tensorflow - INFO - global_step/sec: 88.6738 +2022-05-01 16:10:41,071 - tensorflow - INFO - loss = 0.03155005, step = 598000 (2.255 sec) +2022-05-01 16:10:43,331 - tensorflow - INFO - global_step/sec: 88.4949 +2022-05-01 16:10:43,331 - tensorflow - INFO - loss = 0.036724314, step = 598200 (2.260 sec) +2022-05-01 16:10:45,590 - tensorflow - INFO - global_step/sec: 88.5148 +2022-05-01 16:10:45,591 - tensorflow - INFO - loss = 0.058887675, step = 598400 (2.260 sec) +2022-05-01 16:10:47,850 - tensorflow - INFO - global_step/sec: 88.5321 +2022-05-01 16:10:47,850 - tensorflow - INFO - loss = 0.04405027, step = 598600 (2.259 sec) +2022-05-01 16:10:50,113 - tensorflow - INFO - global_step/sec: 88.3546 +2022-05-01 16:10:50,114 - tensorflow - INFO - loss = 0.026605247, step = 598800 (2.264 sec) +2022-05-01 16:10:52,375 - tensorflow - INFO - global_step/sec: 88.4125 +2022-05-01 16:10:52,376 - tensorflow - INFO - loss = 0.09087759, step = 599000 (2.262 sec) +2022-05-01 16:10:54,638 - tensorflow - INFO - global_step/sec: 88.3708 +2022-05-01 16:10:54,639 - tensorflow - INFO - loss = 0.050264105, step = 599200 (2.263 sec) +2022-05-01 16:10:56,895 - tensorflow - INFO - global_step/sec: 88.6399 +2022-05-01 16:10:56,895 - tensorflow - INFO - loss = 0.5043671, step = 599400 (2.256 sec) +2022-05-01 16:10:59,157 - tensorflow - INFO - global_step/sec: 88.41 +2022-05-01 16:10:59,157 - tensorflow - INFO - loss = 0.057621866, step = 599600 (2.262 sec) +2022-05-01 16:11:01,418 - tensorflow - INFO - global_step/sec: 88.4709 +2022-05-01 16:11:01,418 - tensorflow - INFO - loss = 0.04510246, step = 599800 (2.261 sec) +2022-05-01 16:11:03,669 - tensorflow - INFO - Saving checkpoints for 600000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. +2022-05-01 16:11:03,687 - tensorflow - INFO - model.ckpt-600000 is not in all_model_checkpoint_paths. Manually adding it. +2022-05-01 16:11:04,248 - tensorflow - INFO - Calling model_fn. +2022-05-01 16:11:04,379 - tensorflow - INFO - Done calling model_fn. +2022-05-01 16:11:04,394 - tensorflow - INFO - Starting evaluation at 2022-05-01T16:11:04Z +2022-05-01 16:11:04,458 - tensorflow - INFO - Graph was finalized. +2022-05-01 16:11:04,461 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-600000 +2022-05-01 16:11:04,742 - tensorflow - INFO - Running local_init_op. +2022-05-01 16:11:04,850 - tensorflow - INFO - Done running local_init_op. +2022-05-01 16:11:14,952 - tensorflow - INFO - Evaluation [3990/39900] +2022-05-01 16:11:24,367 - tensorflow - INFO - Evaluation [7980/39900] +2022-05-01 16:11:33,791 - tensorflow - INFO - Evaluation [11970/39900] +2022-05-01 16:11:43,194 - tensorflow - INFO - Evaluation [15960/39900] +2022-05-01 16:11:52,560 - tensorflow - INFO - Evaluation [19950/39900] +2022-05-01 16:12:01,964 - tensorflow - INFO - Evaluation [23940/39900] +2022-05-01 16:12:11,336 - tensorflow - INFO - Evaluation [27930/39900] +2022-05-01 16:12:20,741 - tensorflow - INFO - Evaluation [31920/39900] +2022-05-01 16:12:30,115 - tensorflow - INFO - Evaluation [35910/39900] +2022-05-01 16:12:39,506 - tensorflow - INFO - Evaluation [39900/39900] +2022-05-01 16:12:39,644 - tensorflow - INFO - Finished evaluation at 2022-05-01-16:12:39 +2022-05-01 16:12:39,644 - tensorflow - INFO - Saving dict for global step 600000: global_step = 600000, loss = 0.05211654, mean_degree_err = 7.7307415 +2022-05-01 16:12:39,645 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 600000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-600000 +2022-05-01 16:12:39,658 - tensorflow - INFO - global_step/sec: 2.03582 +2022-05-01 16:12:39,659 - tensorflow - INFO - loss = 1.0458663, step = 600000 (98.241 sec) +2022-05-01 16:12:41,927 - tensorflow - INFO - global_step/sec: 88.1445 +2022-05-01 16:12:41,928 - tensorflow - INFO - loss = 0.025115207, step = 600200 (2.269 sec) +2022-05-01 16:12:44,180 - tensorflow - INFO - global_step/sec: 88.7807 +2022-05-01 16:12:44,180 - tensorflow - INFO - loss = 0.103602424, step = 600400 (2.253 sec) +2022-05-01 16:12:46,441 - tensorflow - INFO - global_step/sec: 88.4632 +2022-05-01 16:12:46,441 - tensorflow - INFO - loss = 0.026233017, step = 600600 (2.261 sec) +2022-05-01 16:12:48,692 - tensorflow - INFO - global_step/sec: 88.8496 +2022-05-01 16:12:48,692 - tensorflow - INFO - loss = 0.9358847, step = 600800 (2.251 sec) +2022-05-01 16:12:50,948 - tensorflow - INFO - global_step/sec: 88.6401 +2022-05-01 16:12:50,949 - tensorflow - INFO - loss = 0.03394095, step = 601000 (2.256 sec) +2022-05-01 16:12:53,204 - tensorflow - INFO - global_step/sec: 88.6696 +2022-05-01 16:12:53,204 - tensorflow - INFO - loss = 0.020482913, step = 601200 (2.256 sec) +2022-05-01 16:12:55,460 - tensorflow - INFO - global_step/sec: 88.6249 +2022-05-01 16:12:55,461 - tensorflow - INFO - loss = 1.1575468, step = 601400 (2.257 sec) +2022-05-01 16:12:57,713 - tensorflow - INFO - global_step/sec: 88.7857 +2022-05-01 16:12:57,714 - tensorflow - INFO - loss = 0.05647299, step = 601600 (2.253 sec) +2022-05-01 16:12:59,964 - tensorflow - INFO - global_step/sec: 88.8566 +2022-05-01 16:12:59,964 - tensorflow - INFO - loss = 0.7979704, step = 601800 (2.251 sec) +2022-05-01 16:13:02,221 - tensorflow - INFO - global_step/sec: 88.6268 +2022-05-01 16:13:02,221 - tensorflow - INFO - loss = 0.042243555, step = 602000 (2.257 sec) +2022-05-01 16:13:04,476 - tensorflow - INFO - global_step/sec: 88.6789 +2022-05-01 16:13:04,476 - tensorflow - INFO - loss = 0.059275866, step = 602200 (2.255 sec) +2022-05-01 16:13:06,732 - tensorflow - INFO - global_step/sec: 88.6556 +2022-05-01 16:13:06,732 - tensorflow - INFO - loss = 0.6342204, step = 602400 (2.256 sec) +2022-05-01 16:13:08,989 - tensorflow - INFO - global_step/sec: 88.624 +2022-05-01 16:13:08,989 - tensorflow - INFO - loss = 0.03493088, step = 602600 (2.257 sec) +2022-05-01 16:13:11,246 - tensorflow - INFO - global_step/sec: 88.6143 +2022-05-01 16:13:11,246 - tensorflow - INFO - loss = 0.36002302, step = 602800 (2.257 sec) +2022-05-01 16:13:13,497 - tensorflow - INFO - global_step/sec: 88.818 +2022-05-01 16:13:13,498 - tensorflow - INFO - loss = 0.040594425, step = 603000 (2.252 sec) +2022-05-01 16:13:15,754 - tensorflow - INFO - global_step/sec: 88.6284 +2022-05-01 16:13:15,754 - tensorflow - INFO - loss = 0.10270108, step = 603200 (2.257 sec) +2022-05-01 16:13:18,007 - tensorflow - INFO - global_step/sec: 88.7484 +2022-05-01 16:13:18,008 - tensorflow - INFO - loss = 0.016271198, step = 603400 (2.254 sec) +2022-05-01 16:13:20,256 - tensorflow - INFO - global_step/sec: 88.9565 +2022-05-01 16:13:20,256 - tensorflow - INFO - loss = 0.071318746, step = 603600 (2.248 sec) +2022-05-01 16:13:22,507 - tensorflow - INFO - global_step/sec: 88.8344 +2022-05-01 16:13:22,508 - tensorflow - INFO - loss = 0.018505175, step = 603800 (2.251 sec) +2022-05-01 16:13:24,760 - tensorflow - INFO - global_step/sec: 88.7743 +2022-05-01 16:13:24,760 - tensorflow - INFO - loss = 0.015764019, step = 604000 (2.253 sec) +2022-05-01 16:13:27,018 - tensorflow - INFO - global_step/sec: 88.5653 +2022-05-01 16:13:27,019 - tensorflow - INFO - loss = 0.38297236, step = 604200 (2.258 sec) +2022-05-01 16:13:29,280 - tensorflow - INFO - global_step/sec: 88.4164 +2022-05-01 16:13:29,281 - tensorflow - INFO - loss = 0.022897664, step = 604400 (2.262 sec) +2022-05-01 16:13:31,540 - tensorflow - INFO - global_step/sec: 88.5055 +2022-05-01 16:13:31,541 - tensorflow - INFO - loss = 0.036261506, step = 604600 (2.260 sec) +2022-05-01 16:13:33,789 - tensorflow - INFO - global_step/sec: 88.9331 +2022-05-01 16:13:33,789 - tensorflow - INFO - loss = 0.026952295, step = 604800 (2.249 sec) +2022-05-01 16:13:36,043 - tensorflow - INFO - global_step/sec: 88.7405 +2022-05-01 16:13:36,043 - tensorflow - INFO - loss = 0.017694829, step = 605000 (2.254 sec) +2022-05-01 16:13:38,296 - tensorflow - INFO - global_step/sec: 88.7579 +2022-05-01 16:13:38,296 - tensorflow - INFO - loss = 0.017842809, step = 605200 (2.253 sec) +2022-05-01 16:13:40,550 - tensorflow - INFO - global_step/sec: 88.7298 +2022-05-01 16:13:40,550 - tensorflow - INFO - loss = 0.013539692, step = 605400 (2.254 sec) +2022-05-01 16:13:42,802 - tensorflow - INFO - global_step/sec: 88.7996 +2022-05-01 16:13:42,803 - tensorflow - INFO - loss = 1.015567, step = 605600 (2.252 sec) +2022-05-01 16:13:45,055 - tensorflow - INFO - global_step/sec: 88.78 +2022-05-01 16:13:45,055 - tensorflow - INFO - loss = 0.039732587, step = 605800 (2.253 sec) +2022-05-01 16:13:47,308 - tensorflow - INFO - global_step/sec: 88.7719 +2022-05-01 16:13:47,308 - tensorflow - INFO - loss = 0.052872486, step = 606000 (2.253 sec) +2022-05-01 16:13:49,564 - tensorflow - INFO - global_step/sec: 88.6697 +2022-05-01 16:13:49,564 - tensorflow - INFO - loss = 0.01597492, step = 606200 (2.256 sec) +2022-05-01 16:13:51,816 - tensorflow - INFO - global_step/sec: 88.781 +2022-05-01 16:13:51,817 - tensorflow - INFO - loss = 0.11118606, step = 606400 (2.253 sec) +2022-05-01 16:13:54,069 - tensorflow - INFO - global_step/sec: 88.7908 +2022-05-01 16:13:54,069 - tensorflow - INFO - loss = 8.458463, step = 606600 (2.253 sec) +2022-05-01 16:13:56,324 - tensorflow - INFO - global_step/sec: 88.6696 +2022-05-01 16:13:56,325 - tensorflow - INFO - loss = 0.6168818, step = 606800 (2.256 sec) +2022-05-01 16:13:58,577 - tensorflow - INFO - global_step/sec: 88.7971 +2022-05-01 16:13:58,577 - tensorflow - INFO - loss = 0.016155917, step = 607000 (2.252 sec) +2022-05-01 16:14:00,830 - tensorflow - INFO - global_step/sec: 88.7463 +2022-05-01 16:14:00,831 - tensorflow - INFO - loss = 0.025292601, step = 607200 (2.254 sec) +2022-05-01 16:14:03,089 - tensorflow - INFO - global_step/sec: 88.5512 +2022-05-01 16:14:03,089 - tensorflow - INFO - loss = 2.134423, step = 607400 (2.259 sec) +2022-05-01 16:14:05,340 - tensorflow - INFO - global_step/sec: 88.8408 +2022-05-01 16:14:05,341 - tensorflow - INFO - loss = 0.20809147, step = 607600 (2.251 sec) +2022-05-01 16:14:07,601 - 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tensorflow - INFO - loss = 0.36914372, step = 690200 (2.266 sec) +2022-05-01 16:29:39,870 - tensorflow - INFO - global_step/sec: 86.8138 +2022-05-01 16:29:39,870 - tensorflow - INFO - loss = 0.34035963, step = 690400 (2.304 sec) +2022-05-01 16:29:42,161 - tensorflow - INFO - global_step/sec: 87.2855 +2022-05-01 16:29:42,162 - tensorflow - INFO - loss = 0.051499836, step = 690600 (2.291 sec) +2022-05-01 16:29:44,441 - tensorflow - INFO - global_step/sec: 87.7261 +2022-05-01 16:29:44,442 - tensorflow - INFO - loss = 0.063221216, step = 690800 (2.280 sec) +2022-05-01 16:29:46,714 - tensorflow - INFO - global_step/sec: 88.0058 +2022-05-01 16:29:46,714 - tensorflow - INFO - loss = 0.064412564, step = 691000 (2.273 sec) +2022-05-01 16:29:48,986 - tensorflow - INFO - global_step/sec: 88.0249 +2022-05-01 16:29:48,986 - tensorflow - INFO - loss = 0.03744691, step = 691200 (2.272 sec) +2022-05-01 16:29:51,261 - tensorflow - INFO - global_step/sec: 87.8832 +2022-05-01 16:29:51,262 - tensorflow - INFO - loss = 0.12669168, step = 691400 (2.276 sec) +2022-05-01 16:29:53,539 - tensorflow - INFO - global_step/sec: 87.8175 +2022-05-01 16:29:53,539 - tensorflow - INFO - loss = 0.40596506, step = 691600 (2.278 sec) +2022-05-01 16:29:55,806 - tensorflow - INFO - global_step/sec: 88.2063 +2022-05-01 16:29:55,807 - tensorflow - INFO - loss = 0.010353914, step = 691800 (2.267 sec) +2022-05-01 16:29:58,072 - tensorflow - INFO - global_step/sec: 88.2865 +2022-05-01 16:29:58,072 - tensorflow - INFO - loss = 0.040336475, step = 692000 (2.265 sec) +2022-05-01 16:30:00,327 - tensorflow - INFO - global_step/sec: 88.669 +2022-05-01 16:30:00,328 - tensorflow - INFO - loss = 0.020247448, step = 692200 (2.256 sec) +2022-05-01 16:30:02,586 - tensorflow - INFO - global_step/sec: 88.5495 +2022-05-01 16:30:02,586 - tensorflow - INFO - loss = 0.022701023, step = 692400 (2.259 sec) +2022-05-01 16:30:04,839 - tensorflow - INFO - global_step/sec: 88.7533 +2022-05-01 16:30:04,840 - tensorflow - INFO - loss = 0.012202943, step = 692600 (2.253 sec) +2022-05-01 16:30:07,093 - tensorflow - INFO - global_step/sec: 88.7234 +2022-05-01 16:30:07,094 - tensorflow - INFO - loss = 0.05705206, step = 692800 (2.254 sec) +2022-05-01 16:30:09,352 - tensorflow - INFO - global_step/sec: 88.5445 +2022-05-01 16:30:09,353 - tensorflow - INFO - loss = 0.063872375, step = 693000 (2.259 sec) +2022-05-01 16:30:11,612 - tensorflow - INFO - global_step/sec: 88.4898 +2022-05-01 16:30:11,613 - tensorflow - INFO - loss = 0.20911783, step = 693200 (2.260 sec) +2022-05-01 16:30:13,868 - tensorflow - INFO - global_step/sec: 88.6538 +2022-05-01 16:30:13,869 - tensorflow - INFO - loss = 0.17761394, step = 693400 (2.256 sec) +2022-05-01 16:30:16,123 - tensorflow - INFO - global_step/sec: 88.7222 +2022-05-01 16:30:16,123 - tensorflow - INFO - loss = 2.785748, step = 693600 (2.254 sec) +2022-05-01 16:30:18,375 - tensorflow - INFO - global_step/sec: 88.7818 +2022-05-01 16:30:18,376 - tensorflow - INFO - loss = 0.047160655, step = 693800 (2.253 sec) +2022-05-01 16:30:20,628 - tensorflow - INFO - global_step/sec: 88.7787 +2022-05-01 16:30:20,629 - tensorflow - INFO - loss = 0.14111653, step = 694000 (2.253 sec) +2022-05-01 16:30:22,884 - tensorflow - INFO - global_step/sec: 88.645 +2022-05-01 16:30:22,885 - tensorflow - INFO - loss = 0.061342344, step = 694200 (2.256 sec) +2022-05-01 16:30:25,140 - tensorflow - INFO - global_step/sec: 88.6744 +2022-05-01 16:30:25,140 - tensorflow - INFO - loss = 0.03867026, step = 694400 (2.255 sec) +2022-05-01 16:30:27,397 - tensorflow - INFO - global_step/sec: 88.6029 +2022-05-01 16:30:27,397 - tensorflow - INFO - loss = 0.034364447, step = 694600 (2.257 sec) +2022-05-01 16:30:29,651 - tensorflow - INFO - global_step/sec: 88.7231 +2022-05-01 16:30:29,652 - tensorflow - INFO - loss = 0.022616444, step = 694800 (2.254 sec) +2022-05-01 16:30:31,917 - tensorflow - INFO - global_step/sec: 88.2733 +2022-05-01 16:30:31,917 - tensorflow - INFO - loss = 0.078567564, step = 695000 (2.266 sec) +2022-05-01 16:30:34,172 - tensorflow - INFO - global_step/sec: 88.6967 +2022-05-01 16:30:34,172 - tensorflow - INFO - loss = 0.02870323, step = 695200 (2.255 sec) +2022-05-01 16:30:36,429 - tensorflow - INFO - global_step/sec: 88.6052 +2022-05-01 16:30:36,429 - tensorflow - INFO - loss = 0.027117027, step = 695400 (2.257 sec) +2022-05-01 16:30:38,675 - tensorflow - INFO - global_step/sec: 89.0403 +2022-05-01 16:30:38,676 - tensorflow - INFO - loss = 9.146388, step = 695600 (2.246 sec) +2022-05-01 16:30:40,926 - tensorflow - INFO - global_step/sec: 88.8497 +2022-05-01 16:30:40,927 - tensorflow - INFO - loss = 0.14333989, step = 695800 (2.251 sec) +2022-05-01 16:30:43,185 - tensorflow - INFO - global_step/sec: 88.5331 +2022-05-01 16:30:43,186 - tensorflow - INFO - loss = 0.3762799, step = 696000 (2.259 sec) +2022-05-01 16:30:45,458 - tensorflow - INFO - global_step/sec: 88.0085 +2022-05-01 16:30:45,458 - tensorflow - INFO - loss = 0.025378358, step = 696200 (2.272 sec) +2022-05-01 16:30:47,735 - tensorflow - INFO - global_step/sec: 87.8242 +2022-05-01 16:30:47,735 - tensorflow - INFO - loss = 0.028789608, step = 696400 (2.277 sec) +2022-05-01 16:30:50,009 - tensorflow - INFO - global_step/sec: 87.9507 +2022-05-01 16:30:50,009 - tensorflow - INFO - loss = 0.013650784, step = 696600 (2.274 sec) +2022-05-01 16:30:52,313 - tensorflow - INFO - global_step/sec: 86.8005 +2022-05-01 16:30:52,314 - tensorflow - INFO - loss = 0.029505478, step = 696800 (2.304 sec) +2022-05-01 16:30:54,587 - tensorflow - INFO - global_step/sec: 87.9393 +2022-05-01 16:30:54,588 - tensorflow - INFO - loss = 1.0471745, step = 697000 (2.274 sec) +2022-05-01 16:30:56,858 - tensorflow - INFO - global_step/sec: 88.0994 +2022-05-01 16:30:56,858 - tensorflow - INFO - loss = 0.037719212, step = 697200 (2.270 sec) +2022-05-01 16:30:59,134 - tensorflow - INFO - global_step/sec: 87.8514 +2022-05-01 16:30:59,135 - tensorflow - INFO - loss = 0.030568294, step = 697400 (2.277 sec) +2022-05-01 16:31:01,409 - tensorflow - INFO - global_step/sec: 87.9152 +2022-05-01 16:31:01,410 - tensorflow - INFO - loss = 0.13457468, step = 697600 (2.275 sec) +2022-05-01 16:31:03,682 - tensorflow - INFO - global_step/sec: 87.9968 +2022-05-01 16:31:03,682 - tensorflow - INFO - loss = 0.08395249, step = 697800 (2.273 sec) +2022-05-01 16:31:05,955 - tensorflow - INFO - global_step/sec: 87.9794 +2022-05-01 16:31:05,956 - tensorflow - INFO - loss = 0.06530699, step = 698000 (2.273 sec) +2022-05-01 16:31:08,212 - tensorflow - INFO - global_step/sec: 88.6067 +2022-05-01 16:31:08,213 - tensorflow - INFO - loss = 0.0882684, step = 698200 (2.257 sec) +2022-05-01 16:31:10,465 - tensorflow - INFO - global_step/sec: 88.7685 +2022-05-01 16:31:10,466 - tensorflow - INFO - loss = 0.060899876, step = 698400 (2.253 sec) +2022-05-01 16:31:12,720 - tensorflow - INFO - global_step/sec: 88.7142 +2022-05-01 16:31:12,720 - tensorflow - INFO - loss = 0.18980502, step = 698600 (2.254 sec) +2022-05-01 16:31:14,972 - tensorflow - INFO - global_step/sec: 88.7923 +2022-05-01 16:31:14,973 - tensorflow - INFO - loss = 0.28167945, step = 698800 (2.252 sec) +2022-05-01 16:31:17,229 - tensorflow - INFO - global_step/sec: 88.6269 +2022-05-01 16:31:17,229 - tensorflow - INFO - loss = 0.015430516, step = 699000 (2.257 sec) +2022-05-01 16:31:19,482 - tensorflow - INFO - global_step/sec: 88.7613 +2022-05-01 16:31:19,483 - tensorflow - INFO - loss = 0.020742904, step = 699200 (2.253 sec) +2022-05-01 16:31:21,733 - tensorflow - INFO - global_step/sec: 88.8653 +2022-05-01 16:31:21,733 - tensorflow - INFO - loss = 0.020765623, step = 699400 (2.251 sec) +2022-05-01 16:31:23,993 - tensorflow - INFO - global_step/sec: 88.4763 +2022-05-01 16:31:23,994 - tensorflow - INFO - loss = 0.018228173, step = 699600 (2.261 sec) +2022-05-01 16:31:26,250 - tensorflow - INFO - global_step/sec: 88.6307 +2022-05-01 16:31:26,250 - tensorflow - INFO - loss = 0.022793582, step = 699800 (2.257 sec) +2022-05-01 16:31:28,499 - tensorflow - INFO - Saving checkpoints for 700000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. +2022-05-01 16:31:28,517 - tensorflow - INFO - model.ckpt-700000 is not in all_model_checkpoint_paths. Manually adding it. +2022-05-01 16:31:29,045 - tensorflow - INFO - Calling model_fn. +2022-05-01 16:31:29,177 - tensorflow - INFO - Done calling model_fn. +2022-05-01 16:31:29,192 - tensorflow - INFO - Starting evaluation at 2022-05-01T16:31:29Z +2022-05-01 16:31:29,256 - tensorflow - INFO - Graph was finalized. +2022-05-01 16:31:29,259 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-700000 +2022-05-01 16:31:29,540 - tensorflow - INFO - Running local_init_op. +2022-05-01 16:31:29,646 - tensorflow - INFO - Done running local_init_op. +2022-05-01 16:31:39,785 - tensorflow - INFO - Evaluation [3990/39900] +2022-05-01 16:31:49,238 - tensorflow - INFO - Evaluation [7980/39900] +2022-05-01 16:31:58,714 - tensorflow - INFO - Evaluation [11970/39900] +2022-05-01 16:32:08,192 - tensorflow - INFO - Evaluation [15960/39900] +2022-05-01 16:32:17,622 - tensorflow - INFO - Evaluation [19950/39900] +2022-05-01 16:32:27,039 - tensorflow - INFO - Evaluation [23940/39900] +2022-05-01 16:32:36,492 - tensorflow - INFO - Evaluation [27930/39900] +2022-05-01 16:32:45,931 - tensorflow - INFO - Evaluation [31920/39900] +2022-05-01 16:32:55,356 - tensorflow - INFO - Evaluation [35910/39900] +2022-05-01 16:33:04,797 - tensorflow - INFO - Evaluation [39900/39900] +2022-05-01 16:33:04,934 - tensorflow - INFO - Finished evaluation at 2022-05-01-16:33:04 +2022-05-01 16:33:04,934 - tensorflow - INFO - Saving dict for global step 700000: global_step = 700000, loss = 0.03090773, mean_degree_err = 4.9194665 +2022-05-01 16:33:04,934 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 700000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-700000 +2022-05-01 16:33:04,948 - tensorflow - INFO - global_step/sec: 2.02638 +2022-05-01 16:33:04,949 - tensorflow - INFO - loss = 0.45383355, step = 700000 (98.699 sec) +2022-05-01 16:33:07,219 - tensorflow - INFO - global_step/sec: 88.0497 +2022-05-01 16:33:07,220 - tensorflow - INFO - loss = 0.02814351, step = 700200 (2.271 sec) +2022-05-01 16:33:09,476 - tensorflow - INFO - global_step/sec: 88.6366 +2022-05-01 16:33:09,476 - tensorflow - INFO - loss = 0.031599652, step = 700400 (2.256 sec) +2022-05-01 16:33:11,728 - tensorflow - INFO - global_step/sec: 88.8244 +2022-05-01 16:33:11,728 - tensorflow - INFO - loss = 0.06685158, step = 700600 (2.252 sec) +2022-05-01 16:33:13,984 - tensorflow - INFO - global_step/sec: 88.6234 +2022-05-01 16:33:13,985 - tensorflow - INFO - loss = 0.05536495, step = 700800 (2.257 sec) +2022-05-01 16:33:16,239 - tensorflow - INFO - global_step/sec: 88.7024 +2022-05-01 16:33:16,239 - tensorflow - INFO - loss = 0.009829298, step = 701000 (2.255 sec) +2022-05-01 16:33:18,494 - tensorflow - INFO - global_step/sec: 88.679 +2022-05-01 16:33:18,495 - tensorflow - INFO - loss = 0.02428996, step = 701200 (2.255 sec) +2022-05-01 16:33:20,752 - tensorflow - INFO - global_step/sec: 88.6027 +2022-05-01 16:33:20,752 - tensorflow - INFO - loss = 0.018015191, step = 701400 (2.257 sec) +2022-05-01 16:33:23,006 - tensorflow - INFO - global_step/sec: 88.7342 +2022-05-01 16:33:23,006 - tensorflow - INFO - loss = 0.07055589, step = 701600 (2.254 sec) +2022-05-01 16:33:25,255 - tensorflow - INFO - global_step/sec: 88.92 +2022-05-01 16:33:25,255 - tensorflow - INFO - loss = 0.6384876, step = 701800 (2.249 sec) +2022-05-01 16:33:27,511 - tensorflow - INFO - global_step/sec: 88.6497 +2022-05-01 16:33:27,511 - tensorflow - INFO - loss = 0.014400528, step = 702000 (2.256 sec) +2022-05-01 16:33:29,762 - tensorflow - INFO - global_step/sec: 88.8495 +2022-05-01 16:33:29,762 - tensorflow - INFO - loss = 0.030938236, step = 702200 (2.251 sec) +2022-05-01 16:33:32,022 - tensorflow - INFO - global_step/sec: 88.5049 +2022-05-01 16:33:32,022 - tensorflow - INFO - loss = 0.05836013, step = 702400 (2.260 sec) +2022-05-01 16:33:34,280 - tensorflow - INFO - global_step/sec: 88.5689 +2022-05-01 16:33:34,280 - tensorflow - INFO - loss = 0.014873834, step = 702600 (2.258 sec) +2022-05-01 16:33:36,535 - tensorflow - INFO - global_step/sec: 88.6665 +2022-05-01 16:33:36,536 - tensorflow - INFO - loss = 0.05012783, step = 702800 (2.256 sec) +2022-05-01 16:33:38,792 - tensorflow - INFO - global_step/sec: 88.6365 +2022-05-01 16:33:38,792 - tensorflow - INFO - loss = 0.04358377, step = 703000 (2.256 sec) +2022-05-01 16:33:41,051 - tensorflow - INFO - global_step/sec: 88.5095 +2022-05-01 16:33:41,052 - tensorflow - INFO - loss = 0.028191432, step = 703200 (2.260 sec) +2022-05-01 16:33:43,301 - tensorflow - INFO - global_step/sec: 88.914 +2022-05-01 16:33:43,301 - tensorflow - INFO - loss = 0.047327407, step = 703400 (2.249 sec) +2022-05-01 16:33:45,567 - tensorflow - INFO - global_step/sec: 88.2587 +2022-05-01 16:33:45,567 - tensorflow - INFO - loss = 0.06812384, step = 703600 (2.266 sec) +2022-05-01 16:33:47,818 - tensorflow - INFO - global_step/sec: 88.8492 +2022-05-01 16:33:47,818 - tensorflow - INFO - loss = 0.033104226, step = 703800 (2.251 sec) +2022-05-01 16:33:50,072 - tensorflow - INFO - global_step/sec: 88.7064 +2022-05-01 16:33:50,073 - tensorflow - INFO - loss = 0.06181826, step = 704000 (2.255 sec) +2022-05-01 16:33:52,331 - tensorflow - INFO - global_step/sec: 88.5635 +2022-05-01 16:33:52,331 - tensorflow - INFO - loss = 2.1382723, step = 704200 (2.258 sec) +2022-05-01 16:33:54,584 - tensorflow - INFO - global_step/sec: 88.7633 +2022-05-01 16:33:54,584 - tensorflow - INFO - loss = 0.01735505, step = 704400 (2.253 sec) +2022-05-01 16:33:56,845 - tensorflow - INFO - global_step/sec: 88.4379 +2022-05-01 16:33:56,846 - tensorflow - INFO - loss = 0.030577263, step = 704600 (2.261 sec) +2022-05-01 16:33:59,103 - tensorflow - INFO - global_step/sec: 88.5835 +2022-05-01 16:33:59,104 - tensorflow - INFO - loss = 0.053564556, step = 704800 (2.258 sec) +2022-05-01 16:34:01,356 - tensorflow - INFO - global_step/sec: 88.7922 +2022-05-01 16:34:01,356 - tensorflow - INFO - loss = 0.03156396, step = 705000 (2.252 sec) +2022-05-01 16:34:03,613 - tensorflow - INFO - global_step/sec: 88.6046 +2022-05-01 16:34:03,613 - tensorflow - INFO - loss = 0.023791179, step = 705200 (2.257 sec) +2022-05-01 16:34:05,863 - tensorflow - INFO - global_step/sec: 88.8913 +2022-05-01 16:34:05,863 - tensorflow - INFO - loss = 0.2363714, step = 705400 (2.250 sec) +2022-05-01 16:34:08,119 - tensorflow - INFO - global_step/sec: 88.6522 +2022-05-01 16:34:08,119 - tensorflow - INFO - loss = 0.22751844, step = 705600 (2.256 sec) +2022-05-01 16:34:10,378 - tensorflow - INFO - global_step/sec: 88.5083 +2022-05-01 16:34:10,379 - tensorflow - INFO - loss = 0.02583364, step = 705800 (2.260 sec) +2022-05-01 16:34:12,631 - tensorflow - INFO - global_step/sec: 88.7984 +2022-05-01 16:34:12,631 - tensorflow - INFO - loss = 0.24674636, step = 706000 (2.252 sec) +2022-05-01 16:34:14,882 - tensorflow - INFO - global_step/sec: 88.851 +2022-05-01 16:34:14,882 - tensorflow - INFO - loss = 0.05409362, step = 706200 (2.251 sec) +2022-05-01 16:34:17,134 - tensorflow - INFO - global_step/sec: 88.795 +2022-05-01 16:34:17,134 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.3819 +2022-05-01 16:34:32,944 - tensorflow - INFO - loss = 0.22880709, step = 707800 (2.263 sec) +2022-05-01 16:34:35,210 - tensorflow - INFO - global_step/sec: 88.2509 +2022-05-01 16:34:35,211 - tensorflow - INFO - loss = 0.029884279, step = 708000 (2.266 sec) +2022-05-01 16:34:37,465 - tensorflow - INFO - global_step/sec: 88.7019 +2022-05-01 16:34:37,465 - tensorflow - INFO - loss = 0.043589737, step = 708200 (2.255 sec) +2022-05-01 16:34:39,720 - tensorflow - INFO - global_step/sec: 88.6972 +2022-05-01 16:34:39,720 - tensorflow - INFO - loss = 0.14944857, step = 708400 (2.255 sec) +2022-05-01 16:34:41,978 - tensorflow - INFO - global_step/sec: 88.5747 +2022-05-01 16:34:41,978 - tensorflow - INFO - loss = 0.038527794, step = 708600 (2.258 sec) +2022-05-01 16:34:44,235 - tensorflow - INFO - global_step/sec: 88.6068 +2022-05-01 16:34:44,235 - tensorflow - INFO - loss = 0.023336954, step = 708800 (2.257 sec) +2022-05-01 16:34:46,492 - tensorflow - 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INFO - loss = 0.030673733, step = 751400 (2.251 sec) +2022-05-01 16:42:47,203 - tensorflow - INFO - global_step/sec: 88.7587 +2022-05-01 16:42:47,203 - tensorflow - INFO - loss = 0.034062106, step = 751600 (2.253 sec) +2022-05-01 16:42:49,457 - tensorflow - INFO - global_step/sec: 88.7155 +2022-05-01 16:42:49,458 - tensorflow - INFO - loss = 0.020252142, step = 751800 (2.254 sec) +2022-05-01 16:42:51,714 - tensorflow - INFO - global_step/sec: 88.6465 +2022-05-01 16:42:51,714 - tensorflow - INFO - loss = 0.027839506, step = 752000 (2.256 sec) +2022-05-01 16:42:53,967 - tensorflow - INFO - global_step/sec: 88.7497 +2022-05-01 16:42:53,967 - tensorflow - INFO - loss = 0.118473224, step = 752200 (2.254 sec) +2022-05-01 16:42:56,228 - tensorflow - INFO - global_step/sec: 88.473 +2022-05-01 16:42:56,228 - tensorflow - INFO - loss = 0.03127812, step = 752400 (2.261 sec) +2022-05-01 16:42:58,484 - tensorflow - INFO - global_step/sec: 88.6423 +2022-05-01 16:42:58,484 - tensorflow - INFO - loss = 0.24062337, step = 752600 (2.256 sec) +2022-05-01 16:43:00,737 - 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tensorflow - INFO - global_step/sec: 88.7567 +2022-05-01 16:43:57,116 - tensorflow - INFO - loss = 0.09343986, step = 757800 (2.253 sec) +2022-05-01 16:43:59,372 - tensorflow - INFO - global_step/sec: 88.6528 +2022-05-01 16:43:59,372 - tensorflow - INFO - loss = 0.06432448, step = 758000 (2.256 sec) +2022-05-01 16:44:01,623 - tensorflow - INFO - global_step/sec: 88.8473 +2022-05-01 16:44:01,623 - tensorflow - INFO - loss = 0.038612396, step = 758200 (2.251 sec) +2022-05-01 16:44:03,877 - tensorflow - INFO - global_step/sec: 88.7272 +2022-05-01 16:44:03,878 - tensorflow - INFO - loss = 0.3299265, step = 758400 (2.254 sec) +2022-05-01 16:44:06,132 - tensorflow - INFO - global_step/sec: 88.7053 +2022-05-01 16:44:06,132 - tensorflow - INFO - loss = 0.034532495, step = 758600 (2.255 sec) +2022-05-01 16:44:08,386 - tensorflow - INFO - global_step/sec: 88.7216 +2022-05-01 16:44:08,386 - tensorflow - INFO - loss = 0.49787322, step = 758800 (2.254 sec) +2022-05-01 16:44:10,637 - tensorflow - INFO - 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INFO - loss = 0.016628332, step = 761400 (2.249 sec) +2022-05-01 16:44:39,942 - tensorflow - INFO - global_step/sec: 88.8153 +2022-05-01 16:44:39,942 - tensorflow - INFO - loss = 0.015344487, step = 761600 (2.252 sec) +2022-05-01 16:44:42,189 - tensorflow - INFO - global_step/sec: 88.9995 +2022-05-01 16:44:42,190 - tensorflow - INFO - loss = 0.06295501, step = 761800 (2.247 sec) +2022-05-01 16:44:44,450 - tensorflow - INFO - global_step/sec: 88.4536 +2022-05-01 16:44:44,451 - tensorflow - INFO - loss = 0.03487993, step = 762000 (2.261 sec) +2022-05-01 16:44:46,704 - tensorflow - INFO - global_step/sec: 88.7216 +2022-05-01 16:44:46,705 - tensorflow - INFO - loss = 8.466547, step = 762200 (2.254 sec) +2022-05-01 16:44:48,956 - tensorflow - INFO - global_step/sec: 88.8244 +2022-05-01 16:44:48,957 - tensorflow - INFO - loss = 0.09064051, step = 762400 (2.252 sec) +2022-05-01 16:44:51,215 - tensorflow - INFO - global_step/sec: 88.5358 +2022-05-01 16:44:51,216 - tensorflow - INFO - loss = 0.022429889, step = 762600 (2.259 sec) +2022-05-01 16:44:53,472 - 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global_step/sec: 88.8334 +2022-05-01 16:49:49,096 - tensorflow - INFO - loss = 0.014966049, step = 789000 (2.251 sec) +2022-05-01 16:49:51,351 - tensorflow - INFO - global_step/sec: 88.6844 +2022-05-01 16:49:51,352 - tensorflow - INFO - loss = 0.24684186, step = 789200 (2.255 sec) +2022-05-01 16:49:53,609 - tensorflow - INFO - global_step/sec: 88.5698 +2022-05-01 16:49:53,610 - tensorflow - INFO - loss = 0.017537221, step = 789400 (2.258 sec) +2022-05-01 16:49:55,861 - tensorflow - INFO - global_step/sec: 88.8061 +2022-05-01 16:49:55,862 - tensorflow - INFO - loss = 0.047886416, step = 789600 (2.252 sec) +2022-05-01 16:49:58,121 - tensorflow - INFO - global_step/sec: 88.5289 +2022-05-01 16:49:58,121 - tensorflow - INFO - loss = 0.03703781, step = 789800 (2.259 sec) +2022-05-01 16:50:00,378 - tensorflow - INFO - global_step/sec: 88.5943 +2022-05-01 16:50:00,379 - tensorflow - INFO - loss = 0.027382951, step = 790000 (2.258 sec) +2022-05-01 16:50:02,633 - tensorflow - INFO - global_step/sec: 88.702 +2022-05-01 16:50:02,633 - tensorflow - INFO - loss = 0.015727483, step = 790200 (2.255 sec) +2022-05-01 16:50:04,897 - tensorflow - INFO - global_step/sec: 88.3484 +2022-05-01 16:50:04,897 - tensorflow - INFO - loss = 0.019167326, step = 790400 (2.264 sec) +2022-05-01 16:50:07,149 - tensorflow - INFO - global_step/sec: 88.7865 +2022-05-01 16:50:07,150 - tensorflow - INFO - loss = 0.11715488, step = 790600 (2.253 sec) +2022-05-01 16:50:09,403 - tensorflow - INFO - global_step/sec: 88.757 +2022-05-01 16:50:09,403 - tensorflow - INFO - loss = 0.01616585, step = 790800 (2.253 sec) +2022-05-01 16:50:11,659 - tensorflow - INFO - global_step/sec: 88.6453 +2022-05-01 16:50:11,659 - tensorflow - INFO - loss = 0.021459151, step = 791000 (2.256 sec) +2022-05-01 16:50:13,914 - tensorflow - INFO - global_step/sec: 88.6707 +2022-05-01 16:50:13,915 - tensorflow - INFO - loss = 0.0305481, step = 791200 (2.256 sec) +2022-05-01 16:50:16,170 - tensorflow - INFO - global_step/sec: 88.6709 +2022-05-01 16:50:16,170 - tensorflow - INFO - loss = 0.06955601, step = 791400 (2.256 sec) +2022-05-01 16:50:18,425 - tensorflow - INFO - global_step/sec: 88.6634 +2022-05-01 16:50:18,426 - tensorflow - INFO - loss = 0.020836467, step = 791600 (2.256 sec) +2022-05-01 16:50:20,678 - tensorflow - INFO - global_step/sec: 88.7733 +2022-05-01 16:50:20,679 - tensorflow - INFO - loss = 0.032687094, step = 791800 (2.253 sec) +2022-05-01 16:50:22,932 - tensorflow - INFO - global_step/sec: 88.7638 +2022-05-01 16:50:22,932 - tensorflow - INFO - loss = 5.1432867, step = 792000 (2.253 sec) +2022-05-01 16:50:25,187 - tensorflow - INFO - global_step/sec: 88.6954 +2022-05-01 16:50:25,187 - tensorflow - INFO - loss = 0.041422613, step = 792200 (2.255 sec) +2022-05-01 16:50:27,440 - tensorflow - INFO - global_step/sec: 88.7438 +2022-05-01 16:50:27,441 - tensorflow - INFO - loss = 0.021101072, step = 792400 (2.254 sec) +2022-05-01 16:50:29,698 - tensorflow - INFO - global_step/sec: 88.5708 +2022-05-01 16:50:29,699 - tensorflow - INFO - loss = 0.048175126, step = 792600 (2.258 sec) +2022-05-01 16:50:31,957 - tensorflow - INFO - global_step/sec: 88.5379 +2022-05-01 16:50:31,958 - tensorflow - INFO - loss = 0.01844433, step = 792800 (2.259 sec) +2022-05-01 16:50:34,210 - tensorflow - INFO - global_step/sec: 88.7669 +2022-05-01 16:50:34,211 - tensorflow - INFO - loss = 0.017058585, step = 793000 (2.253 sec) +2022-05-01 16:50:36,459 - tensorflow - INFO - global_step/sec: 88.9281 +2022-05-01 16:50:36,460 - tensorflow - INFO - loss = 0.055735964, step = 793200 (2.249 sec) +2022-05-01 16:50:38,712 - tensorflow - INFO - global_step/sec: 88.7662 +2022-05-01 16:50:38,713 - tensorflow - INFO - loss = 0.016033482, step = 793400 (2.253 sec) +2022-05-01 16:50:40,966 - tensorflow - INFO - global_step/sec: 88.7285 +2022-05-01 16:50:40,967 - tensorflow - INFO - loss = 0.2633359, step = 793600 (2.254 sec) +2022-05-01 16:50:43,223 - tensorflow - INFO - global_step/sec: 88.6167 +2022-05-01 16:50:43,224 - tensorflow - INFO - loss = 0.04098193, step = 793800 (2.257 sec) +2022-05-01 16:50:45,478 - tensorflow - INFO - global_step/sec: 88.711 +2022-05-01 16:50:45,478 - tensorflow - INFO - loss = 0.0509121, step = 794000 (2.254 sec) +2022-05-01 16:50:47,736 - tensorflow - INFO - global_step/sec: 88.5622 +2022-05-01 16:50:47,737 - tensorflow - INFO - loss = 8.175721, step = 794200 (2.258 sec) +2022-05-01 16:50:49,988 - tensorflow - INFO - global_step/sec: 88.8208 +2022-05-01 16:50:49,988 - tensorflow - INFO - loss = 0.037834972, step = 794400 (2.252 sec) +2022-05-01 16:50:52,240 - tensorflow - INFO - global_step/sec: 88.7932 +2022-05-01 16:50:52,241 - tensorflow - INFO - loss = 0.13239951, step = 794600 (2.252 sec) +2022-05-01 16:50:54,501 - tensorflow - INFO - global_step/sec: 88.4647 +2022-05-01 16:50:54,502 - tensorflow - INFO - loss = 0.017206036, step = 794800 (2.261 sec) +2022-05-01 16:50:56,766 - tensorflow - INFO - global_step/sec: 88.3221 +2022-05-01 16:50:56,766 - tensorflow - INFO - loss = 0.02704255, step = 795000 (2.264 sec) +2022-05-01 16:50:59,024 - tensorflow - INFO - global_step/sec: 88.5609 +2022-05-01 16:50:59,024 - tensorflow - INFO - loss = 0.008105198, step = 795200 (2.258 sec) +2022-05-01 16:51:01,276 - tensorflow - INFO - global_step/sec: 88.8152 +2022-05-01 16:51:01,276 - tensorflow - INFO - loss = 0.09291974, step = 795400 (2.252 sec) +2022-05-01 16:51:03,537 - tensorflow - INFO - global_step/sec: 88.4481 +2022-05-01 16:51:03,537 - tensorflow - INFO - loss = 0.058347143, step = 795600 (2.261 sec) +2022-05-01 16:51:05,793 - tensorflow - INFO - global_step/sec: 88.6529 +2022-05-01 16:51:05,793 - tensorflow - INFO - loss = 0.030016731, step = 795800 (2.256 sec) +2022-05-01 16:51:08,052 - tensorflow - INFO - global_step/sec: 88.5311 +2022-05-01 16:51:08,052 - tensorflow - INFO - loss = 0.46277308, step = 796000 (2.259 sec) +2022-05-01 16:51:10,309 - tensorflow - INFO - global_step/sec: 88.6285 +2022-05-01 16:51:10,309 - tensorflow - INFO - loss = 0.10902999, step = 796200 (2.257 sec) +2022-05-01 16:51:12,566 - tensorflow - INFO - global_step/sec: 88.5918 +2022-05-01 16:51:12,567 - tensorflow - INFO - loss = 0.034445357, step = 796400 (2.258 sec) +2022-05-01 16:51:14,823 - tensorflow - INFO - global_step/sec: 88.6104 +2022-05-01 16:51:14,824 - tensorflow - INFO - loss = 0.19711475, step = 796600 (2.257 sec) +2022-05-01 16:51:17,077 - tensorflow - INFO - global_step/sec: 88.7387 +2022-05-01 16:51:17,078 - tensorflow - INFO - loss = 0.04598197, step = 796800 (2.254 sec) +2022-05-01 16:51:19,333 - tensorflow - INFO - global_step/sec: 88.6389 +2022-05-01 16:51:19,334 - tensorflow - INFO - loss = 0.02298907, step = 797000 (2.256 sec) +2022-05-01 16:51:21,592 - tensorflow - INFO - global_step/sec: 88.5418 +2022-05-01 16:51:21,593 - tensorflow - INFO - loss = 0.015141054, step = 797200 (2.259 sec) +2022-05-01 16:51:23,849 - tensorflow - INFO - global_step/sec: 88.625 +2022-05-01 16:51:23,849 - tensorflow - INFO - loss = 0.021146398, step = 797400 (2.257 sec) +2022-05-01 16:51:26,106 - tensorflow - INFO - global_step/sec: 88.6213 +2022-05-01 16:51:26,106 - tensorflow - INFO - loss = 0.084369704, step = 797600 (2.257 sec) +2022-05-01 16:51:28,365 - tensorflow - INFO - global_step/sec: 88.5264 +2022-05-01 16:51:28,365 - tensorflow - INFO - loss = 0.028809236, step = 797800 (2.259 sec) +2022-05-01 16:51:30,625 - tensorflow - INFO - global_step/sec: 88.5081 +2022-05-01 16:51:30,625 - tensorflow - INFO - loss = 0.016825045, step = 798000 (2.260 sec) +2022-05-01 16:51:32,883 - tensorflow - INFO - global_step/sec: 88.5496 +2022-05-01 16:51:32,884 - tensorflow - INFO - loss = 0.03811729, step = 798200 (2.259 sec) +2022-05-01 16:51:35,144 - tensorflow - INFO - global_step/sec: 88.453 +2022-05-01 16:51:35,145 - tensorflow - INFO - loss = 0.012519284, step = 798400 (2.261 sec) +2022-05-01 16:51:37,398 - tensorflow - INFO - global_step/sec: 88.753 +2022-05-01 16:51:37,398 - tensorflow - INFO - loss = 0.38044575, step = 798600 (2.253 sec) +2022-05-01 16:51:39,652 - tensorflow - INFO - global_step/sec: 88.7151 +2022-05-01 16:51:39,653 - tensorflow - INFO - loss = 1.1314454, step = 798800 (2.254 sec) +2022-05-01 16:51:41,902 - tensorflow - INFO - global_step/sec: 88.8894 +2022-05-01 16:51:41,903 - tensorflow - INFO - loss = 0.017083233, step = 799000 (2.250 sec) +2022-05-01 16:51:44,159 - tensorflow - INFO - global_step/sec: 88.6192 +2022-05-01 16:51:44,159 - tensorflow - INFO - loss = 0.042783007, step = 799200 (2.257 sec) +2022-05-01 16:51:46,421 - tensorflow - INFO - global_step/sec: 88.4185 +2022-05-01 16:51:46,421 - tensorflow - INFO - loss = 0.030692004, step = 799400 (2.262 sec) +2022-05-01 16:51:48,680 - tensorflow - INFO - global_step/sec: 88.5367 +2022-05-01 16:51:48,680 - tensorflow - INFO - loss = 0.041929305, step = 799600 (2.259 sec) +2022-05-01 16:51:50,932 - tensorflow - INFO - global_step/sec: 88.7945 +2022-05-01 16:51:50,933 - tensorflow - INFO - loss = 0.018519888, step = 799800 (2.252 sec) +2022-05-01 16:51:53,175 - tensorflow - INFO - Saving checkpoints for 800000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. +2022-05-01 16:51:53,191 - tensorflow - INFO - model.ckpt-800000 is not in all_model_checkpoint_paths. Manually adding it. +2022-05-01 16:51:53,709 - tensorflow - INFO - Calling model_fn. +2022-05-01 16:51:53,839 - tensorflow - INFO - Done calling model_fn. +2022-05-01 16:51:53,855 - tensorflow - INFO - Starting evaluation at 2022-05-01T16:51:53Z +2022-05-01 16:51:54,134 - tensorflow - INFO - Graph was finalized. +2022-05-01 16:51:54,137 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-800000 +2022-05-01 16:51:54,419 - tensorflow - INFO - Running local_init_op. +2022-05-01 16:51:54,527 - tensorflow - INFO - Done running local_init_op. +2022-05-01 16:52:04,691 - tensorflow - INFO - Evaluation [3990/39900] +2022-05-01 16:52:14,173 - tensorflow - INFO - Evaluation [7980/39900] +2022-05-01 16:52:23,645 - tensorflow - INFO - Evaluation [11970/39900] +2022-05-01 16:52:33,137 - tensorflow - INFO - Evaluation [15960/39900] +2022-05-01 16:52:42,626 - tensorflow - INFO - Evaluation [19950/39900] +2022-05-01 16:52:52,112 - tensorflow - INFO - Evaluation [23940/39900] +2022-05-01 16:53:01,615 - tensorflow - INFO - Evaluation [27930/39900] +2022-05-01 16:53:11,104 - tensorflow - INFO - Evaluation [31920/39900] +2022-05-01 16:53:20,576 - tensorflow - INFO - Evaluation [35910/39900] +2022-05-01 16:53:30,075 - tensorflow - INFO - Evaluation [39900/39900] +2022-05-01 16:53:30,215 - tensorflow - INFO - Finished evaluation at 2022-05-01-16:53:30 +2022-05-01 16:53:30,215 - tensorflow - INFO - Saving dict for global step 800000: global_step = 800000, loss = 0.026910517, mean_degree_err = 4.5198975 +2022-05-01 16:53:30,215 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 800000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-800000 +2022-05-01 16:53:30,229 - tensorflow - INFO - global_step/sec: 2.01416 +2022-05-01 16:53:30,230 - tensorflow - INFO - loss = 0.0899144, step = 800000 (99.297 sec) +2022-05-01 16:53:32,509 - tensorflow - INFO - global_step/sec: 87.7145 +2022-05-01 16:53:32,510 - tensorflow - INFO - loss = 0.010599776, step = 800200 (2.280 sec) +2022-05-01 16:53:34,761 - tensorflow - INFO - global_step/sec: 88.8073 +2022-05-01 16:53:34,762 - tensorflow - INFO - loss = 0.010874215, step = 800400 (2.252 sec) +2022-05-01 16:53:37,020 - tensorflow - INFO - global_step/sec: 88.5681 +2022-05-01 16:53:37,020 - tensorflow - INFO - loss = 0.041985117, step = 800600 (2.258 sec) +2022-05-01 16:53:39,271 - tensorflow - INFO - global_step/sec: 88.8123 +2022-05-01 16:53:39,272 - tensorflow - INFO - loss = 0.052029114, step = 800800 (2.252 sec) +2022-05-01 16:53:41,528 - tensorflow - INFO - global_step/sec: 88.6158 +2022-05-01 16:53:41,529 - tensorflow - INFO - loss = 0.103633836, step = 801000 (2.257 sec) +2022-05-01 16:53:43,789 - tensorflow - INFO - global_step/sec: 88.4821 +2022-05-01 16:53:43,789 - tensorflow - INFO - loss = 0.024310533, step = 801200 (2.260 sec) +2022-05-01 16:53:46,046 - tensorflow - INFO - global_step/sec: 88.6088 +2022-05-01 16:53:46,046 - tensorflow - INFO - loss = 0.07490441, step = 801400 (2.257 sec) +2022-05-01 16:53:48,299 - tensorflow - INFO - global_step/sec: 88.7689 +2022-05-01 16:53:48,299 - tensorflow - INFO - loss = 0.019317463, step = 801600 (2.253 sec) +2022-05-01 16:53:50,555 - tensorflow - INFO - global_step/sec: 88.636 +2022-05-01 16:53:50,556 - tensorflow - INFO - loss = 0.05756301, step = 801800 (2.256 sec) +2022-05-01 16:53:52,814 - tensorflow - INFO - global_step/sec: 88.5411 +2022-05-01 16:53:52,815 - tensorflow - INFO - loss = 2.24759, step = 802000 (2.259 sec) +2022-05-01 16:53:55,075 - tensorflow - INFO - global_step/sec: 88.4525 +2022-05-01 16:53:55,076 - tensorflow - INFO - loss = 0.10740603, step = 802200 (2.261 sec) +2022-05-01 16:53:57,332 - tensorflow - INFO - global_step/sec: 88.6125 +2022-05-01 16:53:57,333 - tensorflow - INFO - loss = 0.020427408, step = 802400 (2.257 sec) +2022-05-01 16:53:59,589 - tensorflow - INFO - global_step/sec: 88.6375 +2022-05-01 16:53:59,589 - tensorflow - INFO - loss = 0.019146318, step = 802600 (2.256 sec) +2022-05-01 16:54:01,845 - tensorflow - INFO - global_step/sec: 88.6373 +2022-05-01 16:54:01,845 - tensorflow - INFO - loss = 0.02906144, step = 802800 (2.256 sec) +2022-05-01 16:54:04,106 - tensorflow - INFO - global_step/sec: 88.4622 +2022-05-01 16:54:04,106 - tensorflow - INFO - loss = 0.036592133, step = 803000 (2.261 sec) +2022-05-01 16:54:06,359 - tensorflow - INFO - global_step/sec: 88.7492 +2022-05-01 16:54:06,360 - tensorflow - INFO - loss = 0.42497355, step = 803200 (2.254 sec) +2022-05-01 16:54:08,617 - tensorflow - INFO - global_step/sec: 88.5852 +2022-05-01 16:54:08,618 - tensorflow - INFO - loss = 0.1766534, step = 803400 (2.258 sec) +2022-05-01 16:54:10,872 - tensorflow - INFO - global_step/sec: 88.7036 +2022-05-01 16:54:10,872 - tensorflow - INFO - loss = 0.024735363, step = 803600 (2.255 sec) +2022-05-01 16:54:13,128 - tensorflow - INFO - global_step/sec: 88.6317 +2022-05-01 16:54:13,129 - tensorflow - INFO - loss = 0.030707162, step = 803800 (2.257 sec) +2022-05-01 16:54:15,383 - tensorflow - INFO - 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INFO - loss = 0.02205094, step = 886400 (2.253 sec) +2022-05-01 17:09:46,996 - tensorflow - INFO - global_step/sec: 88.5699 +2022-05-01 17:09:46,996 - tensorflow - INFO - loss = 0.012074009, step = 886600 (2.258 sec) +2022-05-01 17:09:49,256 - tensorflow - INFO - global_step/sec: 88.4804 +2022-05-01 17:09:49,257 - tensorflow - INFO - loss = 0.05748287, step = 886800 (2.260 sec) +2022-05-01 17:09:51,510 - tensorflow - INFO - global_step/sec: 88.7343 +2022-05-01 17:09:51,511 - tensorflow - INFO - loss = 0.0186936, step = 887000 (2.254 sec) +2022-05-01 17:09:53,766 - tensorflow - INFO - global_step/sec: 88.6744 +2022-05-01 17:09:53,766 - tensorflow - INFO - loss = 0.06944361, step = 887200 (2.255 sec) +2022-05-01 17:09:56,023 - tensorflow - INFO - global_step/sec: 88.6072 +2022-05-01 17:09:56,023 - tensorflow - INFO - loss = 0.031998947, step = 887400 (2.257 sec) +2022-05-01 17:09:58,275 - tensorflow - INFO - global_step/sec: 88.7897 +2022-05-01 17:09:58,276 - tensorflow - INFO - loss = 0.032064967, step = 887600 (2.253 sec) +2022-05-01 17:10:00,532 - tensorflow - INFO - global_step/sec: 88.6205 +2022-05-01 17:10:00,532 - tensorflow - INFO - loss = 0.05730361, step = 887800 (2.257 sec) +2022-05-01 17:10:02,792 - tensorflow - INFO - global_step/sec: 88.5096 +2022-05-01 17:10:02,792 - tensorflow - INFO - loss = 0.11221716, step = 888000 (2.260 sec) +2022-05-01 17:10:05,051 - tensorflow - INFO - global_step/sec: 88.5407 +2022-05-01 17:10:05,051 - tensorflow - INFO - loss = 0.010961418, step = 888200 (2.259 sec) +2022-05-01 17:10:07,310 - tensorflow - INFO - global_step/sec: 88.5327 +2022-05-01 17:10:07,310 - tensorflow - INFO - loss = 0.019681413, step = 888400 (2.259 sec) +2022-05-01 17:10:09,572 - tensorflow - INFO - global_step/sec: 88.4162 +2022-05-01 17:10:09,572 - tensorflow - INFO - loss = 0.023460643, step = 888600 (2.262 sec) +2022-05-01 17:10:11,833 - tensorflow - INFO - global_step/sec: 88.4609 +2022-05-01 17:10:11,833 - tensorflow - INFO - loss = 0.024240648, step = 888800 (2.261 sec) +2022-05-01 17:10:14,097 - tensorflow - INFO - global_step/sec: 88.3333 +2022-05-01 17:10:14,097 - tensorflow - INFO - loss = 0.05807086, step = 889000 (2.264 sec) +2022-05-01 17:10:16,359 - tensorflow - INFO - global_step/sec: 88.4001 +2022-05-01 17:10:16,360 - tensorflow - INFO - loss = 2.2288222, step = 889200 (2.262 sec) +2022-05-01 17:10:18,621 - tensorflow - INFO - global_step/sec: 88.4196 +2022-05-01 17:10:18,622 - tensorflow - INFO - loss = 0.01446765, step = 889400 (2.262 sec) +2022-05-01 17:10:20,886 - tensorflow - INFO - global_step/sec: 88.3086 +2022-05-01 17:10:20,886 - tensorflow - INFO - loss = 0.08652735, step = 889600 (2.265 sec) +2022-05-01 17:10:23,147 - tensorflow - INFO - global_step/sec: 88.451 +2022-05-01 17:10:23,147 - tensorflow - INFO - loss = 0.034366205, step = 889800 (2.261 sec) +2022-05-01 17:10:25,409 - tensorflow - INFO - global_step/sec: 88.4101 +2022-05-01 17:10:25,410 - tensorflow - INFO - loss = 0.025828442, step = 890000 (2.262 sec) +2022-05-01 17:10:27,672 - tensorflow - INFO - global_step/sec: 88.3771 +2022-05-01 17:10:27,673 - tensorflow - INFO - loss = 0.035792287, step = 890200 (2.263 sec) +2022-05-01 17:10:29,933 - tensorflow - INFO - global_step/sec: 88.4806 +2022-05-01 17:10:29,933 - tensorflow - INFO - loss = 0.07184704, step = 890400 (2.260 sec) +2022-05-01 17:10:32,204 - tensorflow - INFO - global_step/sec: 88.0624 +2022-05-01 17:10:32,204 - tensorflow - INFO - loss = 0.020343488, step = 890600 (2.271 sec) +2022-05-01 17:10:34,462 - tensorflow - INFO - global_step/sec: 88.5795 +2022-05-01 17:10:34,462 - tensorflow - INFO - loss = 0.14220776, step = 890800 (2.258 sec) +2022-05-01 17:10:36,715 - tensorflow - INFO - global_step/sec: 88.7423 +2022-05-01 17:10:36,716 - tensorflow - INFO - loss = 0.031225761, step = 891000 (2.254 sec) +2022-05-01 17:10:38,968 - tensorflow - INFO - global_step/sec: 88.7761 +2022-05-01 17:10:38,969 - tensorflow - INFO - loss = 0.06711307, step = 891200 (2.253 sec) +2022-05-01 17:10:41,229 - tensorflow - INFO - global_step/sec: 88.4802 +2022-05-01 17:10:41,229 - tensorflow - INFO - loss = 0.052210785, step = 891400 (2.260 sec) +2022-05-01 17:10:43,482 - tensorflow - INFO - global_step/sec: 88.7556 +2022-05-01 17:10:43,482 - tensorflow - INFO - loss = 0.017388288, step = 891600 (2.253 sec) +2022-05-01 17:10:45,740 - tensorflow - INFO - global_step/sec: 88.5706 +2022-05-01 17:10:45,740 - tensorflow - INFO - loss = 0.028012885, step = 891800 (2.258 sec) +2022-05-01 17:10:47,995 - tensorflow - INFO - global_step/sec: 88.708 +2022-05-01 17:10:47,995 - tensorflow - INFO - loss = 0.03236787, step = 892000 (2.255 sec) +2022-05-01 17:10:50,248 - tensorflow - INFO - global_step/sec: 88.7693 +2022-05-01 17:10:50,248 - tensorflow - INFO - loss = 0.07721909, step = 892200 (2.253 sec) +2022-05-01 17:10:52,506 - tensorflow - INFO - global_step/sec: 88.5659 +2022-05-01 17:10:52,506 - tensorflow - INFO - loss = 0.02358967, step = 892400 (2.258 sec) +2022-05-01 17:10:54,761 - tensorflow - INFO - global_step/sec: 88.6786 +2022-05-01 17:10:54,762 - tensorflow - INFO - loss = 0.016704123, step = 892600 (2.255 sec) +2022-05-01 17:10:57,017 - tensorflow - INFO - global_step/sec: 88.6653 +2022-05-01 17:10:57,017 - tensorflow - INFO - loss = 0.02639231, step = 892800 (2.256 sec) +2022-05-01 17:10:59,272 - tensorflow - INFO - global_step/sec: 88.7048 +2022-05-01 17:10:59,272 - tensorflow - INFO - loss = 0.58438075, step = 893000 (2.255 sec) +2022-05-01 17:11:01,525 - tensorflow - INFO - global_step/sec: 88.7504 +2022-05-01 17:11:01,525 - tensorflow - INFO - loss = 0.062124535, step = 893200 (2.254 sec) +2022-05-01 17:11:03,785 - tensorflow - INFO - global_step/sec: 88.5042 +2022-05-01 17:11:03,785 - tensorflow - INFO - loss = 0.120855875, step = 893400 (2.260 sec) +2022-05-01 17:11:06,043 - tensorflow - INFO - global_step/sec: 88.5804 +2022-05-01 17:11:06,043 - tensorflow - INFO - loss = 0.037611075, step = 893600 (2.258 sec) +2022-05-01 17:11:08,298 - tensorflow - INFO - global_step/sec: 88.6667 +2022-05-01 17:11:08,299 - tensorflow - INFO - loss = 0.025271785, step = 893800 (2.256 sec) +2022-05-01 17:11:10,548 - tensorflow - INFO - global_step/sec: 88.8843 +2022-05-01 17:11:10,549 - tensorflow - INFO - loss = 0.052211516, step = 894000 (2.250 sec) +2022-05-01 17:11:12,804 - tensorflow - INFO - global_step/sec: 88.6806 +2022-05-01 17:11:12,804 - tensorflow - INFO - loss = 0.023117388, step = 894200 (2.255 sec) +2022-05-01 17:11:15,063 - tensorflow - INFO - global_step/sec: 88.5046 +2022-05-01 17:11:15,064 - tensorflow - INFO - loss = 0.0851819, step = 894400 (2.260 sec) +2022-05-01 17:11:17,316 - tensorflow - INFO - global_step/sec: 88.7882 +2022-05-01 17:11:17,316 - tensorflow - INFO - loss = 0.03454585, step = 894600 (2.253 sec) +2022-05-01 17:11:19,576 - tensorflow - INFO - global_step/sec: 88.4988 +2022-05-01 17:11:19,576 - tensorflow - INFO - loss = 0.029106721, step = 894800 (2.260 sec) +2022-05-01 17:11:21,830 - tensorflow - INFO - global_step/sec: 88.7318 +2022-05-01 17:11:21,830 - tensorflow - INFO - loss = 0.02112896, step = 895000 (2.254 sec) +2022-05-01 17:11:24,089 - tensorflow - INFO - global_step/sec: 88.5157 +2022-05-01 17:11:24,090 - tensorflow - INFO - loss = 0.031981267, step = 895200 (2.260 sec) +2022-05-01 17:11:26,349 - tensorflow - INFO - global_step/sec: 88.495 +2022-05-01 17:11:26,350 - tensorflow - INFO - loss = 0.10848446, step = 895400 (2.260 sec) +2022-05-01 17:11:28,608 - tensorflow - INFO - global_step/sec: 88.5451 +2022-05-01 17:11:28,609 - tensorflow - INFO - loss = 0.48689383, step = 895600 (2.259 sec) +2022-05-01 17:11:30,874 - tensorflow - INFO - global_step/sec: 88.2549 +2022-05-01 17:11:30,875 - tensorflow - INFO - loss = 0.0074283807, step = 895800 (2.266 sec) +2022-05-01 17:11:33,139 - tensorflow - INFO - global_step/sec: 88.3263 +2022-05-01 17:11:33,139 - tensorflow - INFO - loss = 0.26092327, step = 896000 (2.264 sec) +2022-05-01 17:11:35,401 - tensorflow - INFO - global_step/sec: 88.4119 +2022-05-01 17:11:35,401 - tensorflow - INFO - loss = 0.026314523, step = 896200 (2.262 sec) +2022-05-01 17:11:37,650 - tensorflow - INFO - global_step/sec: 88.9025 +2022-05-01 17:11:37,651 - tensorflow - INFO - loss = 2.9744234, step = 896400 (2.250 sec) +2022-05-01 17:11:39,908 - tensorflow - INFO - global_step/sec: 88.6083 +2022-05-01 17:11:39,908 - tensorflow - INFO - loss = 0.064552605, step = 896600 (2.257 sec) +2022-05-01 17:11:42,161 - tensorflow - INFO - global_step/sec: 88.7661 +2022-05-01 17:11:42,161 - tensorflow - INFO - loss = 0.012820228, step = 896800 (2.253 sec) +2022-05-01 17:11:44,418 - tensorflow - INFO - global_step/sec: 88.6105 +2022-05-01 17:11:44,418 - tensorflow - INFO - loss = 0.19172603, step = 897000 (2.257 sec) +2022-05-01 17:11:46,673 - tensorflow - INFO - global_step/sec: 88.6802 +2022-05-01 17:11:46,674 - tensorflow - INFO - loss = 0.36441582, step = 897200 (2.255 sec) +2022-05-01 17:11:48,928 - tensorflow - INFO - global_step/sec: 88.6997 +2022-05-01 17:11:48,928 - tensorflow - INFO - loss = 0.22654104, step = 897400 (2.255 sec) +2022-05-01 17:11:51,184 - tensorflow - INFO - global_step/sec: 88.656 +2022-05-01 17:11:51,184 - tensorflow - INFO - loss = 0.014491557, step = 897600 (2.256 sec) +2022-05-01 17:11:53,435 - tensorflow - INFO - global_step/sec: 88.8386 +2022-05-01 17:11:53,435 - tensorflow - INFO - loss = 0.015789127, step = 897800 (2.251 sec) +2022-05-01 17:11:55,699 - tensorflow - INFO - global_step/sec: 88.3486 +2022-05-01 17:11:55,699 - tensorflow - INFO - loss = 0.028927404, step = 898000 (2.264 sec) +2022-05-01 17:11:57,953 - tensorflow - INFO - global_step/sec: 88.7079 +2022-05-01 17:11:57,954 - tensorflow - INFO - loss = 0.3525368, step = 898200 (2.255 sec) +2022-05-01 17:12:00,206 - tensorflow - INFO - global_step/sec: 88.8036 +2022-05-01 17:12:00,206 - tensorflow - INFO - loss = 0.094420716, step = 898400 (2.252 sec) +2022-05-01 17:12:02,463 - tensorflow - INFO - global_step/sec: 88.5853 +2022-05-01 17:12:02,464 - tensorflow - INFO - loss = 0.018449508, step = 898600 (2.258 sec) +2022-05-01 17:12:04,723 - tensorflow - INFO - global_step/sec: 88.5187 +2022-05-01 17:12:04,723 - tensorflow - INFO - loss = 0.027755884, step = 898800 (2.259 sec) +2022-05-01 17:12:06,977 - tensorflow - INFO - global_step/sec: 88.7219 +2022-05-01 17:12:06,977 - tensorflow - INFO - loss = 0.2651413, step = 899000 (2.254 sec) +2022-05-01 17:12:09,238 - tensorflow - INFO - global_step/sec: 88.4568 +2022-05-01 17:12:09,238 - tensorflow - INFO - loss = 0.113350786, step = 899200 (2.261 sec) +2022-05-01 17:12:11,490 - tensorflow - INFO - global_step/sec: 88.8042 +2022-05-01 17:12:11,490 - tensorflow - INFO - loss = 0.022238614, step = 899400 (2.252 sec) +2022-05-01 17:12:13,741 - tensorflow - INFO - global_step/sec: 88.8416 +2022-05-01 17:12:13,742 - tensorflow - INFO - loss = 0.06388148, step = 899600 (2.251 sec) +2022-05-01 17:12:15,993 - tensorflow - INFO - global_step/sec: 88.8187 +2022-05-01 17:12:15,993 - tensorflow - INFO - loss = 0.16148487, step = 899800 (2.252 sec) +2022-05-01 17:12:18,237 - tensorflow - INFO - Saving checkpoints for 900000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. +2022-05-01 17:12:18,255 - tensorflow - INFO - model.ckpt-900000 is not in all_model_checkpoint_paths. Manually adding it. +2022-05-01 17:12:18,789 - tensorflow - INFO - Calling model_fn. +2022-05-01 17:12:18,919 - tensorflow - INFO - Done calling model_fn. +2022-05-01 17:12:18,934 - tensorflow - INFO - Starting evaluation at 2022-05-01T17:12:18Z +2022-05-01 17:12:18,999 - tensorflow - INFO - Graph was finalized. +2022-05-01 17:12:19,002 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-900000 +2022-05-01 17:12:19,283 - tensorflow - INFO - Running local_init_op. +2022-05-01 17:12:19,392 - tensorflow - INFO - Done running local_init_op. +2022-05-01 17:12:29,492 - tensorflow - INFO - Evaluation [3990/39900] +2022-05-01 17:12:38,917 - tensorflow - INFO - Evaluation [7980/39900] +2022-05-01 17:12:48,325 - tensorflow - INFO - Evaluation [11970/39900] +2022-05-01 17:12:57,723 - tensorflow - INFO - Evaluation [15960/39900] +2022-05-01 17:13:07,119 - tensorflow - INFO - Evaluation [19950/39900] +2022-05-01 17:13:16,541 - tensorflow - INFO - Evaluation [23940/39900] +2022-05-01 17:13:25,933 - tensorflow - INFO - Evaluation [27930/39900] +2022-05-01 17:13:35,327 - tensorflow - INFO - Evaluation [31920/39900] +2022-05-01 17:13:44,745 - tensorflow - INFO - Evaluation [35910/39900] +2022-05-01 17:13:54,197 - tensorflow - INFO - Evaluation [39900/39900] +2022-05-01 17:13:54,334 - tensorflow - INFO - Finished evaluation at 2022-05-01-17:13:54 +2022-05-01 17:13:54,334 - tensorflow - INFO - Saving dict for global step 900000: global_step = 900000, loss = 0.035565972, mean_degree_err = 6.131773 +2022-05-01 17:13:54,336 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 900000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-900000 +2022-05-01 17:13:54,350 - tensorflow - INFO - global_step/sec: 2.03342 +2022-05-01 17:13:54,350 - tensorflow - INFO - loss = 0.013065057, step = 900000 (98.357 sec) +2022-05-01 17:13:56,636 - tensorflow - INFO - global_step/sec: 87.487 +2022-05-01 17:13:56,636 - tensorflow - INFO - loss = 0.012146402, step = 900200 (2.286 sec) +2022-05-01 17:13:58,898 - tensorflow - INFO - global_step/sec: 88.4111 +2022-05-01 17:13:58,898 - tensorflow - INFO - loss = 0.016029745, step = 900400 (2.262 sec) +2022-05-01 17:14:01,161 - tensorflow - INFO - global_step/sec: 88.3742 +2022-05-01 17:14:01,161 - tensorflow - INFO - loss = 0.031824853, step = 900600 (2.263 sec) +2022-05-01 17:14:03,421 - tensorflow - INFO - global_step/sec: 88.4699 +2022-05-01 17:14:03,422 - tensorflow - INFO - loss = 0.018616183, step = 900800 (2.261 sec) +2022-05-01 17:14:05,686 - tensorflow - INFO - global_step/sec: 88.3096 +2022-05-01 17:14:05,687 - tensorflow - INFO - loss = 0.027795551, step = 901000 (2.265 sec) +2022-05-01 17:14:07,942 - tensorflow - INFO - global_step/sec: 88.6682 +2022-05-01 17:14:07,942 - tensorflow - INFO - loss = 0.014406723, step = 901200 (2.256 sec) +2022-05-01 17:14:10,203 - tensorflow - INFO - global_step/sec: 88.4331 +2022-05-01 17:14:10,204 - tensorflow - 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INFO - global_step/sec: 88.0331 +2022-05-01 17:29:43,181 - tensorflow - INFO - loss = 0.0091108745, step = 984000 (2.272 sec) +2022-05-01 17:29:45,447 - tensorflow - INFO - global_step/sec: 88.2278 +2022-05-01 17:29:45,447 - tensorflow - INFO - loss = 0.015508783, step = 984200 (2.267 sec) +2022-05-01 17:29:47,719 - tensorflow - INFO - global_step/sec: 88.0386 +2022-05-01 17:29:47,719 - tensorflow - INFO - loss = 0.036307346, step = 984400 (2.272 sec) +2022-05-01 17:29:49,983 - tensorflow - INFO - global_step/sec: 88.3255 +2022-05-01 17:29:49,983 - tensorflow - INFO - loss = 0.07458148, step = 984600 (2.264 sec) +2022-05-01 17:29:52,249 - tensorflow - INFO - global_step/sec: 88.2767 +2022-05-01 17:29:52,249 - tensorflow - INFO - loss = 0.025107179, step = 984800 (2.266 sec) +2022-05-01 17:29:54,520 - tensorflow - INFO - global_step/sec: 88.0421 +2022-05-01 17:29:54,521 - tensorflow - INFO - loss = 0.03554614, step = 985000 (2.272 sec) +2022-05-01 17:29:56,795 - tensorflow - INFO - global_step/sec: 87.9401 +2022-05-01 17:29:56,795 - tensorflow - INFO - loss = 0.019618697, step = 985200 (2.274 sec) +2022-05-01 17:29:59,061 - tensorflow - INFO - global_step/sec: 88.2275 +2022-05-01 17:29:59,062 - tensorflow - INFO - loss = 0.008834165, step = 985400 (2.267 sec) +2022-05-01 17:30:01,329 - tensorflow - INFO - global_step/sec: 88.1823 +2022-05-01 17:30:01,330 - tensorflow - INFO - loss = 0.016060974, step = 985600 (2.268 sec) +2022-05-01 17:30:03,600 - tensorflow - INFO - global_step/sec: 88.0822 +2022-05-01 17:30:03,600 - tensorflow - INFO - loss = 0.06835969, step = 985800 (2.271 sec) +2022-05-01 17:30:05,870 - tensorflow - INFO - global_step/sec: 88.0937 +2022-05-01 17:30:05,871 - tensorflow - INFO - loss = 0.015472726, step = 986000 (2.270 sec) +2022-05-01 17:30:08,146 - tensorflow - INFO - global_step/sec: 87.8741 +2022-05-01 17:30:08,147 - tensorflow - INFO - loss = 0.052314468, step = 986200 (2.276 sec) +2022-05-01 17:30:10,413 - tensorflow - INFO - global_step/sec: 88.2481 +2022-05-01 17:30:10,413 - tensorflow - INFO - loss = 0.09791733, step = 986400 (2.266 sec) +2022-05-01 17:30:12,680 - tensorflow - INFO - global_step/sec: 88.1941 +2022-05-01 17:30:12,681 - tensorflow - INFO - loss = 0.072465055, step = 986600 (2.268 sec) +2022-05-01 17:30:14,946 - tensorflow - INFO - global_step/sec: 88.2583 +2022-05-01 17:30:14,947 - tensorflow - INFO - loss = 0.028652836, step = 986800 (2.266 sec) +2022-05-01 17:30:17,223 - tensorflow - INFO - global_step/sec: 87.8373 +2022-05-01 17:30:17,224 - tensorflow - INFO - loss = 0.20894825, step = 987000 (2.277 sec) +2022-05-01 17:30:19,492 - tensorflow - INFO - global_step/sec: 88.1752 +2022-05-01 17:30:19,492 - tensorflow - INFO - loss = 0.017675523, step = 987200 (2.268 sec) +2022-05-01 17:30:21,767 - tensorflow - INFO - global_step/sec: 87.8834 +2022-05-01 17:30:21,768 - tensorflow - INFO - loss = 0.038935244, step = 987400 (2.276 sec) +2022-05-01 17:30:24,033 - tensorflow - INFO - global_step/sec: 88.2717 +2022-05-01 17:30:24,034 - tensorflow - INFO - loss = 0.057368305, step = 987600 (2.266 sec) +2022-05-01 17:30:26,300 - tensorflow - INFO - global_step/sec: 88.2279 +2022-05-01 17:30:26,300 - tensorflow - INFO - loss = 0.04953441, step = 987800 (2.267 sec) +2022-05-01 17:30:28,574 - tensorflow - INFO - global_step/sec: 87.9615 +2022-05-01 17:30:28,574 - tensorflow - INFO - loss = 0.021212831, step = 988000 (2.274 sec) +2022-05-01 17:30:30,846 - tensorflow - INFO - global_step/sec: 88.0065 +2022-05-01 17:30:30,847 - tensorflow - INFO - loss = 0.0115836, step = 988200 (2.273 sec) +2022-05-01 17:30:33,117 - tensorflow - INFO - global_step/sec: 88.0964 +2022-05-01 17:30:33,117 - tensorflow - INFO - loss = 0.03893371, step = 988400 (2.270 sec) +2022-05-01 17:30:35,382 - tensorflow - INFO - global_step/sec: 88.2646 +2022-05-01 17:30:35,383 - tensorflow - INFO - loss = 0.011640495, step = 988600 (2.266 sec) +2022-05-01 17:30:37,643 - tensorflow - INFO - global_step/sec: 88.491 +2022-05-01 17:30:37,643 - tensorflow - INFO - loss = 0.07432926, step = 988800 (2.260 sec) +2022-05-01 17:30:39,908 - tensorflow - INFO - global_step/sec: 88.2962 +2022-05-01 17:30:39,908 - tensorflow - INFO - loss = 0.11697139, step = 989000 (2.265 sec) +2022-05-01 17:30:42,171 - tensorflow - INFO - global_step/sec: 88.3618 +2022-05-01 17:30:42,171 - tensorflow - INFO - loss = 0.28487983, step = 989200 (2.263 sec) +2022-05-01 17:30:44,441 - tensorflow - INFO - global_step/sec: 88.1261 +2022-05-01 17:30:44,441 - tensorflow - INFO - loss = 0.024115972, step = 989400 (2.270 sec) +2022-05-01 17:30:46,714 - tensorflow - INFO - global_step/sec: 87.9892 +2022-05-01 17:30:46,714 - tensorflow - INFO - loss = 0.013503012, step = 989600 (2.273 sec) +2022-05-01 17:30:48,984 - tensorflow - INFO - global_step/sec: 88.0741 +2022-05-01 17:30:48,985 - tensorflow - INFO - loss = 0.022738425, step = 989800 (2.271 sec) +2022-05-01 17:30:51,254 - tensorflow - INFO - global_step/sec: 88.1222 +2022-05-01 17:30:51,254 - tensorflow - INFO - loss = 0.0064695748, step = 990000 (2.270 sec) +2022-05-01 17:30:53,519 - tensorflow - INFO - global_step/sec: 88.2881 +2022-05-01 17:30:53,520 - tensorflow - INFO - loss = 0.021077337, step = 990200 (2.265 sec) +2022-05-01 17:30:55,789 - tensorflow - INFO - global_step/sec: 88.131 +2022-05-01 17:30:55,789 - tensorflow - INFO - loss = 0.046095267, step = 990400 (2.269 sec) +2022-05-01 17:30:58,058 - tensorflow - INFO - global_step/sec: 88.1344 +2022-05-01 17:30:58,058 - tensorflow - INFO - loss = 0.013050471, step = 990600 (2.269 sec) +2022-05-01 17:31:00,333 - tensorflow - INFO - global_step/sec: 87.9212 +2022-05-01 17:31:00,333 - tensorflow - INFO - loss = 0.04462536, step = 990800 (2.275 sec) +2022-05-01 17:31:02,605 - tensorflow - INFO - global_step/sec: 88.0144 +2022-05-01 17:31:02,605 - tensorflow - INFO - loss = 0.13494569, step = 991000 (2.272 sec) +2022-05-01 17:31:04,877 - tensorflow - INFO - global_step/sec: 88.022 +2022-05-01 17:31:04,878 - tensorflow - INFO - loss = 0.016361812, step = 991200 (2.272 sec) +2022-05-01 17:31:07,153 - tensorflow - INFO - global_step/sec: 87.8648 +2022-05-01 17:31:07,154 - tensorflow - INFO - loss = 0.14117768, step = 991400 (2.276 sec) +2022-05-01 17:31:09,430 - tensorflow - INFO - global_step/sec: 87.8558 +2022-05-01 17:31:09,430 - tensorflow - INFO - loss = 0.010469894, step = 991600 (2.276 sec) +2022-05-01 17:31:11,706 - tensorflow - INFO - global_step/sec: 87.8807 +2022-05-01 17:31:11,706 - tensorflow - INFO - loss = 0.11327028, step = 991800 (2.276 sec) +2022-05-01 17:31:13,984 - tensorflow - INFO - global_step/sec: 87.7696 +2022-05-01 17:31:13,985 - tensorflow - INFO - loss = 0.23409489, step = 992000 (2.279 sec) +2022-05-01 17:31:16,261 - tensorflow - INFO - global_step/sec: 87.862 +2022-05-01 17:31:16,261 - tensorflow - INFO - loss = 5.709214, step = 992200 (2.276 sec) +2022-05-01 17:31:18,536 - tensorflow - INFO - global_step/sec: 87.891 +2022-05-01 17:31:18,537 - tensorflow - INFO - loss = 0.02604986, step = 992400 (2.276 sec) +2022-05-01 17:31:20,809 - tensorflow - INFO - global_step/sec: 87.995 +2022-05-01 17:31:20,809 - tensorflow - INFO - loss = 0.020889275, step = 992600 (2.273 sec) +2022-05-01 17:31:23,086 - tensorflow - INFO - global_step/sec: 87.822 +2022-05-01 17:31:23,087 - tensorflow - INFO - loss = 0.041441254, step = 992800 (2.277 sec) +2022-05-01 17:31:25,366 - tensorflow - INFO - global_step/sec: 87.7491 +2022-05-01 17:31:25,366 - tensorflow - INFO - loss = 0.0638247, step = 993000 (2.279 sec) +2022-05-01 17:31:27,642 - tensorflow - INFO - global_step/sec: 87.8521 +2022-05-01 17:31:27,643 - tensorflow - INFO - loss = 0.09642565, step = 993200 (2.277 sec) +2022-05-01 17:31:29,918 - tensorflow - INFO - global_step/sec: 87.8951 +2022-05-01 17:31:29,918 - tensorflow - INFO - loss = 0.053129744, step = 993400 (2.275 sec) +2022-05-01 17:31:32,199 - tensorflow - INFO - global_step/sec: 87.6458 +2022-05-01 17:31:32,200 - tensorflow - INFO - loss = 0.06550279, step = 993600 (2.282 sec) +2022-05-01 17:31:34,469 - tensorflow - INFO - global_step/sec: 88.107 +2022-05-01 17:31:34,470 - tensorflow - INFO - loss = 0.01763028, step = 993800 (2.270 sec) +2022-05-01 17:31:36,738 - tensorflow - INFO - global_step/sec: 88.1738 +2022-05-01 17:31:36,738 - tensorflow - INFO - loss = 0.36183137, step = 994000 (2.268 sec) +2022-05-01 17:31:39,012 - tensorflow - INFO - global_step/sec: 87.9508 +2022-05-01 17:31:39,012 - tensorflow - INFO - loss = 0.048842005, step = 994200 (2.274 sec) +2022-05-01 17:31:41,284 - tensorflow - INFO - global_step/sec: 88.0298 +2022-05-01 17:31:41,284 - tensorflow - INFO - loss = 31.629017, step = 994400 (2.272 sec) +2022-05-01 17:31:43,550 - tensorflow - INFO - global_step/sec: 88.2431 +2022-05-01 17:31:43,551 - tensorflow - INFO - loss = 0.017132096, step = 994600 (2.267 sec) +2022-05-01 17:31:45,821 - tensorflow - INFO - global_step/sec: 88.0834 +2022-05-01 17:31:45,821 - tensorflow - INFO - loss = 0.04079227, step = 994800 (2.271 sec) +2022-05-01 17:31:48,087 - tensorflow - INFO - global_step/sec: 88.2552 +2022-05-01 17:31:48,087 - tensorflow - INFO - loss = 0.825424, step = 995000 (2.266 sec) +2022-05-01 17:31:50,356 - tensorflow - INFO - global_step/sec: 88.1421 +2022-05-01 17:31:50,356 - tensorflow - INFO - loss = 0.01457743, step = 995200 (2.269 sec) +2022-05-01 17:31:52,627 - tensorflow - INFO - global_step/sec: 88.0675 +2022-05-01 17:31:52,627 - tensorflow - INFO - loss = 0.08510444, step = 995400 (2.271 sec) +2022-05-01 17:31:54,894 - tensorflow - INFO - global_step/sec: 88.2252 +2022-05-01 17:31:54,894 - tensorflow - INFO - loss = 0.10075992, step = 995600 (2.267 sec) +2022-05-01 17:31:57,164 - tensorflow - INFO - global_step/sec: 88.1038 +2022-05-01 17:31:57,164 - tensorflow - INFO - loss = 0.5939552, step = 995800 (2.270 sec) +2022-05-01 17:31:59,431 - tensorflow - INFO - global_step/sec: 88.237 +2022-05-01 17:31:59,431 - tensorflow - INFO - loss = 0.023191817, step = 996000 (2.267 sec) +2022-05-01 17:32:01,698 - tensorflow - INFO - global_step/sec: 88.2096 +2022-05-01 17:32:01,698 - tensorflow - INFO - loss = 0.03370051, step = 996200 (2.267 sec) +2022-05-01 17:32:03,972 - tensorflow - INFO - global_step/sec: 87.941 +2022-05-01 17:32:03,972 - tensorflow - INFO - loss = 0.045667723, step = 996400 (2.274 sec) +2022-05-01 17:32:06,245 - tensorflow - INFO - global_step/sec: 88.0017 +2022-05-01 17:32:06,245 - tensorflow - INFO - loss = 5.847514, step = 996600 (2.273 sec) +2022-05-01 17:32:08,515 - tensorflow - INFO - global_step/sec: 88.1051 +2022-05-01 17:32:08,515 - tensorflow - INFO - loss = 0.0076303408, step = 996800 (2.270 sec) +2022-05-01 17:32:10,780 - tensorflow - INFO - global_step/sec: 88.3048 +2022-05-01 17:32:10,780 - tensorflow - INFO - loss = 0.014137331, step = 997000 (2.265 sec) +2022-05-01 17:32:13,053 - tensorflow - INFO - global_step/sec: 87.9674 +2022-05-01 17:32:13,054 - tensorflow - INFO - loss = 0.012826626, step = 997200 (2.274 sec) +2022-05-01 17:32:15,317 - tensorflow - INFO - global_step/sec: 88.3681 +2022-05-01 17:32:15,317 - tensorflow - INFO - loss = 0.027969304, step = 997400 (2.263 sec) +2022-05-01 17:32:17,589 - tensorflow - INFO - global_step/sec: 88.0105 +2022-05-01 17:32:17,589 - tensorflow - INFO - loss = 0.05405557, step = 997600 (2.272 sec) +2022-05-01 17:32:19,864 - tensorflow - INFO - global_step/sec: 87.9009 +2022-05-01 17:32:19,865 - tensorflow - INFO - loss = 0.095805936, step = 997800 (2.275 sec) +2022-05-01 17:32:22,134 - tensorflow - INFO - global_step/sec: 88.1227 +2022-05-01 17:32:22,134 - tensorflow - INFO - loss = 0.10985251, step = 998000 (2.270 sec) +2022-05-01 17:32:24,401 - tensorflow - INFO - global_step/sec: 88.229 +2022-05-01 17:32:24,401 - tensorflow - INFO - loss = 0.031816743, step = 998200 (2.267 sec) +2022-05-01 17:32:26,668 - tensorflow - INFO - global_step/sec: 88.2149 +2022-05-01 17:32:26,668 - tensorflow - INFO - loss = 0.28096992, step = 998400 (2.267 sec) +2022-05-01 17:32:28,937 - tensorflow - INFO - global_step/sec: 88.1398 +2022-05-01 17:32:28,937 - tensorflow - INFO - loss = 0.03208027, step = 998600 (2.269 sec) +2022-05-01 17:32:31,209 - tensorflow - INFO - global_step/sec: 88.0357 +2022-05-01 17:32:31,209 - tensorflow - INFO - loss = 0.11195407, step = 998800 (2.272 sec) +2022-05-01 17:32:33,473 - tensorflow - INFO - global_step/sec: 88.3346 +2022-05-01 17:32:33,473 - tensorflow - INFO - loss = 0.11846994, step = 999000 (2.264 sec) +2022-05-01 17:32:35,741 - tensorflow - INFO - global_step/sec: 88.1636 +2022-05-01 17:32:35,742 - tensorflow - INFO - loss = 0.01635918, step = 999200 (2.268 sec) +2022-05-01 17:32:38,008 - tensorflow - INFO - global_step/sec: 88.2422 +2022-05-01 17:32:38,008 - tensorflow - INFO - loss = 0.086311236, step = 999400 (2.267 sec) +2022-05-01 17:32:40,277 - tensorflow - INFO - global_step/sec: 88.1249 +2022-05-01 17:32:40,278 - tensorflow - INFO - loss = 0.028363196, step = 999600 (2.269 sec) +2022-05-01 17:32:42,543 - tensorflow - INFO - global_step/sec: 88.2577 +2022-05-01 17:32:42,544 - tensorflow - INFO - loss = 0.02159482, step = 999800 (2.266 sec) +2022-05-01 17:32:44,799 - tensorflow - INFO - Saving checkpoints for 1000000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. +2022-05-01 17:32:44,814 - tensorflow - INFO - model.ckpt-1000000 is not in all_model_checkpoint_paths. Manually adding it. +2022-05-01 17:32:45,367 - tensorflow - INFO - Calling model_fn. +2022-05-01 17:32:45,495 - tensorflow - INFO - Done calling model_fn. +2022-05-01 17:32:45,510 - tensorflow - INFO - Starting evaluation at 2022-05-01T17:32:45Z +2022-05-01 17:32:45,575 - tensorflow - INFO - Graph was finalized. +2022-05-01 17:32:45,578 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1000000 +2022-05-01 17:32:45,861 - tensorflow - INFO - Running local_init_op. +2022-05-01 17:32:45,969 - tensorflow - INFO - Done running local_init_op. +2022-05-01 17:32:56,249 - tensorflow - INFO - Evaluation [3990/39900] +2022-05-01 17:33:05,789 - tensorflow - INFO - Evaluation [7980/39900] +2022-05-01 17:33:15,364 - tensorflow - INFO - Evaluation [11970/39900] +2022-05-01 17:33:24,953 - tensorflow - INFO - Evaluation [15960/39900] +2022-05-01 17:33:34,512 - tensorflow - INFO - Evaluation [19950/39900] +2022-05-01 17:33:44,109 - tensorflow - INFO - Evaluation [23940/39900] +2022-05-01 17:33:53,662 - tensorflow - INFO - Evaluation [27930/39900] +2022-05-01 17:34:03,220 - tensorflow - INFO - Evaluation [31920/39900] +2022-05-01 17:34:12,787 - tensorflow - INFO - Evaluation [35910/39900] +2022-05-01 17:34:22,350 - tensorflow - INFO - Evaluation [39900/39900] +2022-05-01 17:34:22,488 - tensorflow - INFO - Finished evaluation at 2022-05-01-17:34:22 +2022-05-01 17:34:22,489 - tensorflow - INFO - Saving dict for global step 1000000: global_step = 1000000, loss = 0.024924062, mean_degree_err = 4.3142204 +2022-05-01 17:34:22,489 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 1000000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1000000 +2022-05-01 17:34:22,503 - tensorflow - INFO - global_step/sec: 2.00081 +2022-05-01 17:34:22,504 - tensorflow - INFO - loss = 0.026846617, step = 1000000 (99.960 sec) +2022-05-01 17:34:24,791 - tensorflow - INFO - global_step/sec: 87.4221 +2022-05-01 17:34:24,791 - tensorflow - INFO - loss = 0.062070694, step = 1000200 (2.287 sec) +2022-05-01 17:34:27,060 - tensorflow - INFO - global_step/sec: 88.1238 +2022-05-01 17:34:27,061 - tensorflow - INFO - loss = 0.008369792, step = 1000400 (2.270 sec) +2022-05-01 17:34:29,329 - tensorflow - INFO - global_step/sec: 88.1393 +2022-05-01 17:34:29,330 - tensorflow - INFO - loss = 0.012222083, step = 1000600 (2.269 sec) +2022-05-01 17:34:31,595 - tensorflow - INFO - global_step/sec: 88.2946 +2022-05-01 17:34:31,595 - tensorflow - INFO - loss = 0.17804483, step = 1000800 (2.265 sec) +2022-05-01 17:34:33,869 - tensorflow - INFO - global_step/sec: 87.9165 +2022-05-01 17:34:33,870 - tensorflow - INFO - loss = 0.070887625, step = 1001000 (2.275 sec) +2022-05-01 17:34:36,147 - tensorflow - INFO - global_step/sec: 87.8316 +2022-05-01 17:34:36,147 - tensorflow - INFO - loss = 0.099069044, step = 1001200 (2.277 sec) +2022-05-01 17:34:38,417 - tensorflow - INFO - global_step/sec: 88.087 +2022-05-01 17:34:38,417 - tensorflow - INFO - loss = 0.24533297, step = 1001400 (2.271 sec) +2022-05-01 17:34:40,688 - tensorflow - INFO - global_step/sec: 88.0499 +2022-05-01 17:34:40,689 - tensorflow - INFO - loss = 0.025633255, step = 1001600 (2.271 sec) +2022-05-01 17:34:42,965 - tensorflow - INFO - global_step/sec: 87.8484 +2022-05-01 17:34:42,966 - tensorflow - INFO - loss = 1.1007679, step = 1001800 (2.277 sec) +2022-05-01 17:34:45,238 - tensorflow - INFO - global_step/sec: 87.9866 +2022-05-01 17:34:45,239 - tensorflow - INFO - loss = 0.12545982, step = 1002000 (2.273 sec) +2022-05-01 17:34:47,510 - tensorflow - INFO - global_step/sec: 88.0195 +2022-05-01 17:34:47,511 - tensorflow - INFO - loss = 0.060006622, step = 1002200 (2.272 sec) +2022-05-01 17:34:49,789 - tensorflow - INFO - global_step/sec: 87.7842 +2022-05-01 17:34:49,789 - tensorflow - INFO - loss = 0.058975074, step = 1002400 (2.278 sec) +2022-05-01 17:34:52,063 - tensorflow - INFO - global_step/sec: 87.9484 +2022-05-01 17:34:52,063 - tensorflow - INFO - loss = 0.3131972, step = 1002600 (2.274 sec) +2022-05-01 17:34:54,336 - tensorflow - INFO - global_step/sec: 87.9905 +2022-05-01 17:34:54,336 - tensorflow - INFO - loss = 0.08788369, step = 1002800 (2.273 sec) +2022-05-01 17:34:56,611 - tensorflow - INFO - global_step/sec: 87.9106 +2022-05-01 17:34:56,611 - tensorflow - INFO - loss = 0.22353008, step = 1003000 (2.275 sec) +2022-05-01 17:34:58,886 - tensorflow - INFO - global_step/sec: 87.9241 +2022-05-01 17:34:58,886 - tensorflow - INFO - loss = 0.022893198, step = 1003200 (2.275 sec) +2022-05-01 17:35:01,154 - tensorflow - INFO - global_step/sec: 88.1526 +2022-05-01 17:35:01,155 - tensorflow - INFO - loss = 0.029260218, step = 1003400 (2.269 sec) +2022-05-01 17:35:03,426 - tensorflow - INFO - global_step/sec: 88.0377 +2022-05-01 17:35:03,426 - tensorflow - INFO - loss = 0.03587124, step = 1003600 (2.272 sec) +2022-05-01 17:35:05,693 - tensorflow - INFO - global_step/sec: 88.2093 +2022-05-01 17:35:05,694 - tensorflow - INFO - loss = 0.027963502, step = 1003800 (2.267 sec) +2022-05-01 17:35:07,958 - tensorflow - INFO - global_step/sec: 88.3015 +2022-05-01 17:35:07,959 - tensorflow - INFO - loss = 0.21320803, step = 1004000 (2.265 sec) +2022-05-01 17:35:10,224 - tensorflow - INFO - global_step/sec: 88.269 +2022-05-01 17:35:10,225 - tensorflow - INFO - loss = 0.010730086, step = 1004200 (2.266 sec) +2022-05-01 17:35:12,488 - tensorflow - INFO - global_step/sec: 88.3264 +2022-05-01 17:35:12,489 - tensorflow - INFO - loss = 0.020996207, step = 1004400 (2.264 sec) +2022-05-01 17:35:14,760 - tensorflow - INFO - global_step/sec: 88.0305 +2022-05-01 17:35:14,761 - tensorflow - INFO - loss = 0.014935322, step = 1004600 (2.272 sec) +2022-05-01 17:35:17,027 - tensorflow - INFO - global_step/sec: 88.238 +2022-05-01 17:35:17,027 - tensorflow - INFO - loss = 0.020521518, step = 1004800 (2.267 sec) +2022-05-01 17:35:19,299 - tensorflow - INFO - global_step/sec: 88.0149 +2022-05-01 17:35:19,300 - tensorflow - INFO - loss = 0.027829817, step = 1005000 (2.272 sec) +2022-05-01 17:35:21,569 - tensorflow - INFO - global_step/sec: 88.1069 +2022-05-01 17:35:21,570 - tensorflow - INFO - loss = 0.01738238, step = 1005200 (2.270 sec) +2022-05-01 17:35:23,837 - tensorflow - INFO - global_step/sec: 88.1844 +2022-05-01 17:35:23,838 - tensorflow - INFO - loss = 0.02370587, step = 1005400 (2.268 sec) +2022-05-01 17:35:26,101 - tensorflow - INFO - global_step/sec: 88.3633 +2022-05-01 17:35:26,101 - tensorflow - INFO - loss = 0.027154919, step = 1005600 (2.263 sec) +2022-05-01 17:35:28,371 - tensorflow - INFO - global_step/sec: 88.0921 +2022-05-01 17:35:28,371 - tensorflow - INFO - loss = 0.08494438, step = 1005800 (2.270 sec) +2022-05-01 17:35:30,638 - tensorflow - INFO - global_step/sec: 88.2119 +2022-05-01 17:35:30,639 - tensorflow - INFO - loss = 0.01766038, step = 1006000 (2.267 sec) +2022-05-01 17:35:32,904 - tensorflow - INFO - global_step/sec: 88.2707 +2022-05-01 17:35:32,904 - tensorflow - INFO - loss = 0.0381309, step = 1006200 (2.266 sec) +2022-05-01 17:35:35,171 - tensorflow - INFO - global_step/sec: 88.2355 +2022-05-01 17:35:35,171 - tensorflow - INFO - loss = 0.3142389, step = 1006400 (2.267 sec) +2022-05-01 17:35:37,437 - tensorflow - INFO - global_step/sec: 88.2609 +2022-05-01 17:35:37,437 - tensorflow - INFO - loss = 0.011612243, step = 1006600 (2.266 sec) +2022-05-01 17:35:39,705 - tensorflow - INFO - global_step/sec: 88.1593 +2022-05-01 17:35:39,706 - tensorflow - INFO - loss = 0.010148924, step = 1006800 (2.269 sec) +2022-05-01 17:35:41,969 - tensorflow - INFO - global_step/sec: 88.3549 +2022-05-01 17:35:41,969 - tensorflow - INFO - loss = 0.048177227, step = 1007000 (2.264 sec) +2022-05-01 17:35:44,237 - tensorflow - INFO - global_step/sec: 88.1994 +2022-05-01 17:35:44,237 - tensorflow - INFO - loss = 0.033830248, step = 1007200 (2.268 sec) +2022-05-01 17:35:46,503 - tensorflow - INFO - global_step/sec: 88.2257 +2022-05-01 17:35:46,504 - tensorflow - INFO - loss = 0.02257288, step = 1007400 (2.267 sec) +2022-05-01 17:35:48,772 - tensorflow - INFO - global_step/sec: 88.1676 +2022-05-01 17:35:48,772 - tensorflow - INFO - loss = 0.014890956, step = 1007600 (2.268 sec) +2022-05-01 17:35:51,043 - tensorflow - INFO - global_step/sec: 88.0437 +2022-05-01 17:35:51,044 - tensorflow - INFO - loss = 0.020477772, step = 1007800 (2.272 sec) +2022-05-01 17:35:53,317 - tensorflow - INFO - global_step/sec: 87.9613 +2022-05-01 17:35:53,318 - tensorflow - INFO - loss = 0.09701069, step = 1008000 (2.274 sec) +2022-05-01 17:35:55,584 - tensorflow - INFO - global_step/sec: 88.2138 +2022-05-01 17:35:55,585 - tensorflow - INFO - loss = 0.06696297, step = 1008200 (2.267 sec) +2022-05-01 17:35:57,854 - tensorflow - INFO - global_step/sec: 88.1281 +2022-05-01 17:35:57,854 - tensorflow - INFO - loss = 0.03343594, step = 1008400 (2.269 sec) +2022-05-01 17:36:00,118 - tensorflow - INFO - global_step/sec: 88.3367 +2022-05-01 17:36:00,118 - tensorflow - INFO - loss = 0.019149898, step = 1008600 (2.264 sec) +2022-05-01 17:36:02,386 - tensorflow - INFO - global_step/sec: 88.1685 +2022-05-01 17:36:02,387 - tensorflow - INFO - loss = 0.06799473, step = 1008800 (2.268 sec) +2022-05-01 17:36:04,654 - tensorflow - INFO - global_step/sec: 88.1859 +2022-05-01 17:36:04,655 - tensorflow - INFO - loss = 0.0144699365, step = 1009000 (2.268 sec) +2022-05-01 17:36:06,921 - tensorflow - INFO - global_step/sec: 88.231 +2022-05-01 17:36:06,921 - tensorflow - INFO - loss = 0.013778967, step = 1009200 (2.267 sec) +2022-05-01 17:36:09,190 - tensorflow - INFO - global_step/sec: 88.1347 +2022-05-01 17:36:09,191 - tensorflow - INFO - loss = 0.6359856, step = 1009400 (2.269 sec) +2022-05-01 17:36:11,455 - tensorflow - INFO - global_step/sec: 88.2936 +2022-05-01 17:36:11,456 - tensorflow - INFO - loss = 0.04642318, step = 1009600 (2.265 sec) +2022-05-01 17:36:13,720 - tensorflow - INFO - global_step/sec: 88.3184 +2022-05-01 17:36:13,720 - tensorflow - INFO - loss = 0.02470339, step = 1009800 (2.265 sec) +2022-05-01 17:36:15,986 - tensorflow - INFO - global_step/sec: 88.2754 +2022-05-01 17:36:15,986 - tensorflow - INFO - loss = 0.020680029, step = 1010000 (2.266 sec) +2022-05-01 17:36:18,257 - tensorflow - INFO - global_step/sec: 88.067 +2022-05-01 17:36:18,257 - tensorflow - INFO - loss = 0.3155625, step = 1010200 (2.271 sec) +2022-05-01 17:36:20,524 - tensorflow - INFO - global_step/sec: 88.211 +2022-05-01 17:36:20,524 - tensorflow - INFO - loss = 0.02587337, step = 1010400 (2.267 sec) +2022-05-01 17:36:22,793 - tensorflow - INFO - global_step/sec: 88.1232 +2022-05-01 17:36:22,794 - tensorflow - INFO - loss = 0.0048405537, step = 1010600 (2.270 sec) +2022-05-01 17:36:25,065 - tensorflow - INFO - global_step/sec: 88.043 +2022-05-01 17:36:25,066 - tensorflow - INFO - loss = 0.16252938, step = 1010800 (2.272 sec) +2022-05-01 17:36:27,337 - tensorflow - INFO - global_step/sec: 88.0333 +2022-05-01 17:36:27,337 - tensorflow - INFO - loss = 0.07259081, step = 1011000 (2.272 sec) +2022-05-01 17:36:29,613 - tensorflow - INFO - global_step/sec: 87.8746 +2022-05-01 17:36:29,613 - tensorflow - INFO - loss = 0.079295605, step = 1011200 (2.276 sec) +2022-05-01 17:36:31,889 - tensorflow - INFO - global_step/sec: 87.8681 +2022-05-01 17:36:31,889 - tensorflow - INFO - loss = 0.13707778, step = 1011400 (2.276 sec) +2022-05-01 17:36:34,155 - tensorflow - INFO - global_step/sec: 88.2678 +2022-05-01 17:36:34,155 - tensorflow - INFO - loss = 0.15011756, step = 1011600 (2.266 sec) +2022-05-01 17:36:36,418 - tensorflow - INFO - global_step/sec: 88.3756 +2022-05-01 17:36:36,418 - tensorflow - INFO - loss = 0.026561353, step = 1011800 (2.263 sec) +2022-05-01 17:36:38,686 - tensorflow - INFO - global_step/sec: 88.1862 +2022-05-01 17:36:38,686 - tensorflow - INFO - loss = 0.01753925, step = 1012000 (2.268 sec) +2022-05-01 17:36:40,950 - tensorflow - INFO - global_step/sec: 88.334 +2022-05-01 17:36:40,950 - tensorflow - INFO - loss = 0.06748611, step = 1012200 (2.264 sec) +2022-05-01 17:36:43,215 - tensorflow - INFO - global_step/sec: 88.2923 +2022-05-01 17:36:43,216 - tensorflow - INFO - loss = 0.04561261, step = 1012400 (2.265 sec) +2022-05-01 17:36:45,484 - tensorflow - INFO - global_step/sec: 88.1397 +2022-05-01 17:36:45,485 - tensorflow - INFO - loss = 0.05712227, step = 1012600 (2.269 sec) +2022-05-01 17:36:47,752 - tensorflow - INFO - global_step/sec: 88.1759 +2022-05-01 17:36:47,753 - tensorflow - INFO - loss = 0.019870777, step = 1012800 (2.268 sec) +2022-05-01 17:36:50,018 - tensorflow - INFO - global_step/sec: 88.2743 +2022-05-01 17:36:50,019 - tensorflow - INFO - loss = 0.01134631, step = 1013000 (2.266 sec) +2022-05-01 17:36:52,289 - tensorflow - INFO - global_step/sec: 88.0655 +2022-05-01 17:36:52,290 - tensorflow - INFO - loss = 0.044465177, step = 1013200 (2.271 sec) +2022-05-01 17:36:54,555 - tensorflow - INFO - global_step/sec: 88.2728 +2022-05-01 17:36:54,555 - tensorflow - INFO - loss = 0.016091332, step = 1013400 (2.266 sec) +2022-05-01 17:36:56,827 - tensorflow - INFO - global_step/sec: 88.0224 +2022-05-01 17:36:56,827 - tensorflow - INFO - loss = 0.032021984, step = 1013600 (2.272 sec) +2022-05-01 17:36:59,093 - tensorflow - INFO - global_step/sec: 88.2541 +2022-05-01 17:36:59,094 - tensorflow - INFO - loss = 0.009528428, step = 1013800 (2.266 sec) +2022-05-01 17:37:01,361 - tensorflow - INFO - global_step/sec: 88.2062 +2022-05-01 17:37:01,361 - tensorflow - INFO - loss = 0.028446853, step = 1014000 (2.267 sec) +2022-05-01 17:37:03,625 - tensorflow - INFO - global_step/sec: 88.3271 +2022-05-01 17:37:03,625 - tensorflow - INFO - loss = 0.022161685, step = 1014200 (2.264 sec) +2022-05-01 17:37:05,889 - tensorflow - INFO - global_step/sec: 88.3212 +2022-05-01 17:37:05,890 - tensorflow - INFO - loss = 0.027153911, step = 1014400 (2.264 sec) +2022-05-01 17:37:08,162 - tensorflow - INFO - global_step/sec: 88.0141 +2022-05-01 17:37:08,162 - tensorflow - INFO - loss = 0.026425365, step = 1014600 (2.272 sec) +2022-05-01 17:37:10,430 - tensorflow - INFO - global_step/sec: 88.1765 +2022-05-01 17:37:10,430 - tensorflow - INFO - loss = 0.15881792, step = 1014800 (2.268 sec) +2022-05-01 17:37:12,695 - tensorflow - INFO - global_step/sec: 88.3157 +2022-05-01 17:37:12,695 - tensorflow - INFO - loss = 0.018980812, step = 1015000 (2.265 sec) +2022-05-01 17:37:14,963 - tensorflow - INFO - global_step/sec: 88.156 +2022-05-01 17:37:14,964 - tensorflow - INFO - loss = 0.029627044, step = 1015200 (2.269 sec) +2022-05-01 17:37:17,234 - tensorflow - INFO - global_step/sec: 88.0946 +2022-05-01 17:37:17,234 - tensorflow - INFO - loss = 0.0192644, step = 1015400 (2.270 sec) +2022-05-01 17:37:19,502 - tensorflow - INFO - global_step/sec: 88.1756 +2022-05-01 17:37:19,502 - tensorflow - INFO - loss = 0.01489502, step = 1015600 (2.268 sec) +2022-05-01 17:37:21,765 - tensorflow - INFO - global_step/sec: 88.3639 +2022-05-01 17:37:21,766 - tensorflow - INFO - loss = 0.019267049, step = 1015800 (2.263 sec) +2022-05-01 17:37:24,038 - tensorflow - INFO - global_step/sec: 87.9912 +2022-05-01 17:37:24,039 - tensorflow - INFO - loss = 0.22161603, step = 1016000 (2.273 sec) +2022-05-01 17:37:26,307 - tensorflow - INFO - global_step/sec: 88.1393 +2022-05-01 17:37:26,308 - tensorflow - INFO - loss = 0.066511795, step = 1016200 (2.269 sec) +2022-05-01 17:37:28,576 - tensorflow - INFO - global_step/sec: 88.1463 +2022-05-01 17:37:28,577 - tensorflow - INFO - loss = 0.06413953, step = 1016400 (2.269 sec) +2022-05-01 17:37:30,849 - tensorflow - INFO - global_step/sec: 87.9878 +2022-05-01 17:37:30,850 - tensorflow - INFO - loss = 0.15124063, step = 1016600 (2.273 sec) +2022-05-01 17:37:33,113 - tensorflow - INFO - global_step/sec: 88.3409 +2022-05-01 17:37:33,114 - tensorflow - INFO - loss = 0.018985903, step = 1016800 (2.264 sec) +2022-05-01 17:37:35,387 - tensorflow - INFO - global_step/sec: 87.9567 +2022-05-01 17:37:35,387 - tensorflow - INFO - loss = 0.69668007, step = 1017000 (2.274 sec) +2022-05-01 17:37:37,650 - tensorflow - INFO - global_step/sec: 88.3761 +2022-05-01 17:37:37,650 - tensorflow - INFO - loss = 0.017857641, step = 1017200 (2.263 sec) +2022-05-01 17:37:39,918 - tensorflow - INFO - global_step/sec: 88.182 +2022-05-01 17:37:39,919 - tensorflow - INFO - loss = 0.1674631, step = 1017400 (2.268 sec) +2022-05-01 17:37:42,181 - tensorflow - INFO - global_step/sec: 88.3689 +2022-05-01 17:37:42,182 - tensorflow - INFO - loss = 0.0150306225, step = 1017600 (2.263 sec) +2022-05-01 17:37:44,443 - tensorflow - INFO - global_step/sec: 88.4201 +2022-05-01 17:37:44,444 - tensorflow - INFO - loss = 0.01596085, step = 1017800 (2.262 sec) +2022-05-01 17:37:46,716 - tensorflow - INFO - global_step/sec: 87.9915 +2022-05-01 17:37:46,717 - tensorflow - INFO - loss = 0.005287162, step = 1018000 (2.273 sec) +2022-05-01 17:37:48,988 - tensorflow - INFO - global_step/sec: 88.0348 +2022-05-01 17:37:48,988 - tensorflow - INFO - loss = 0.060017582, step = 1018200 (2.272 sec) +2022-05-01 17:37:51,256 - tensorflow - INFO - global_step/sec: 88.1867 +2022-05-01 17:37:51,256 - tensorflow - INFO - loss = 2.2948382, step = 1018400 (2.268 sec) +2022-05-01 17:37:53,527 - tensorflow - INFO - global_step/sec: 88.0823 +2022-05-01 17:37:53,527 - tensorflow - INFO - loss = 2.3659844, step = 1018600 (2.271 sec) +2022-05-01 17:37:55,794 - tensorflow - INFO - global_step/sec: 88.2187 +2022-05-01 17:37:55,794 - tensorflow - INFO - loss = 0.019238647, step = 1018800 (2.267 sec) +2022-05-01 17:37:58,059 - tensorflow - INFO - global_step/sec: 88.2758 +2022-05-01 17:37:58,060 - tensorflow - INFO - loss = 0.06865194, step = 1019000 (2.266 sec) +2022-05-01 17:38:00,333 - tensorflow - INFO - global_step/sec: 87.9655 +2022-05-01 17:38:00,333 - tensorflow - INFO - loss = 0.02945345, step = 1019200 (2.274 sec) +2022-05-01 17:38:02,610 - tensorflow - INFO - global_step/sec: 87.8484 +2022-05-01 17:38:02,610 - tensorflow - INFO - loss = 0.026851423, step = 1019400 (2.277 sec) +2022-05-01 17:38:04,880 - tensorflow - INFO - global_step/sec: 88.0892 +2022-05-01 17:38:04,880 - tensorflow - INFO - loss = 0.032587398, step = 1019600 (2.270 sec) +2022-05-01 17:38:07,154 - tensorflow - INFO - global_step/sec: 87.9357 +2022-05-01 17:38:07,155 - tensorflow - INFO - loss = 0.010946276, step = 1019800 (2.274 sec) +2022-05-01 17:38:09,429 - tensorflow - INFO - global_step/sec: 87.9204 +2022-05-01 17:38:09,430 - tensorflow - 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INFO - global_step/sec: 88.2866 +2022-05-01 17:49:55,223 - tensorflow - INFO - loss = 0.15574074, step = 1082200 (2.265 sec) +2022-05-01 17:49:57,493 - tensorflow - INFO - global_step/sec: 88.0623 +2022-05-01 17:49:57,494 - tensorflow - INFO - loss = 0.2386426, step = 1082400 (2.271 sec) +2022-05-01 17:49:59,763 - tensorflow - INFO - global_step/sec: 88.1404 +2022-05-01 17:49:59,763 - tensorflow - INFO - loss = 0.06196802, step = 1082600 (2.269 sec) +2022-05-01 17:50:02,031 - tensorflow - INFO - global_step/sec: 88.155 +2022-05-01 17:50:02,032 - tensorflow - INFO - loss = 0.103330344, step = 1082800 (2.269 sec) +2022-05-01 17:50:04,306 - tensorflow - INFO - global_step/sec: 87.9084 +2022-05-01 17:50:04,307 - tensorflow - INFO - loss = 0.49878764, step = 1083000 (2.275 sec) +2022-05-01 17:50:06,578 - tensorflow - INFO - global_step/sec: 88.0236 +2022-05-01 17:50:06,579 - tensorflow - INFO - loss = 0.023565196, step = 1083200 (2.272 sec) +2022-05-01 17:50:08,846 - tensorflow - INFO - global_step/sec: 88.214 +2022-05-01 17:50:08,846 - tensorflow - INFO - loss = 0.0065097404, step = 1083400 (2.267 sec) +2022-05-01 17:50:11,116 - tensorflow - INFO - global_step/sec: 88.0904 +2022-05-01 17:50:11,117 - tensorflow - INFO - loss = 0.055036277, step = 1083600 (2.270 sec) +2022-05-01 17:50:13,382 - tensorflow - INFO - global_step/sec: 88.2649 +2022-05-01 17:50:13,382 - tensorflow - INFO - loss = 0.013235767, step = 1083800 (2.266 sec) +2022-05-01 17:50:15,651 - tensorflow - INFO - global_step/sec: 88.1396 +2022-05-01 17:50:15,652 - tensorflow - INFO - loss = 0.53809595, step = 1084000 (2.269 sec) +2022-05-01 17:50:17,920 - tensorflow - INFO - global_step/sec: 88.1463 +2022-05-01 17:50:17,920 - tensorflow - INFO - loss = 0.045387957, step = 1084200 (2.269 sec) +2022-05-01 17:50:20,189 - tensorflow - INFO - global_step/sec: 88.1543 +2022-05-01 17:50:20,189 - tensorflow - INFO - loss = 0.07011947, step = 1084400 (2.269 sec) +2022-05-01 17:50:22,447 - tensorflow - INFO - global_step/sec: 88.5501 +2022-05-01 17:50:22,448 - tensorflow - INFO - loss = 0.0272142, step = 1084600 (2.259 sec) +2022-05-01 17:50:24,711 - tensorflow - INFO - global_step/sec: 88.3559 +2022-05-01 17:50:24,711 - tensorflow - INFO - loss = 0.03552171, step = 1084800 (2.264 sec) +2022-05-01 17:50:26,979 - tensorflow - INFO - global_step/sec: 88.1753 +2022-05-01 17:50:26,980 - tensorflow - INFO - loss = 0.24743606, step = 1085000 (2.268 sec) +2022-05-01 17:50:29,254 - tensorflow - INFO - global_step/sec: 87.9045 +2022-05-01 17:50:29,255 - tensorflow - INFO - loss = 0.017738506, step = 1085200 (2.275 sec) +2022-05-01 17:50:31,527 - tensorflow - INFO - global_step/sec: 88.0035 +2022-05-01 17:50:31,527 - tensorflow - INFO - loss = 0.023227235, step = 1085400 (2.273 sec) +2022-05-01 17:50:33,791 - tensorflow - INFO - global_step/sec: 88.353 +2022-05-01 17:50:33,791 - tensorflow - INFO - loss = 0.016620908, step = 1085600 (2.264 sec) +2022-05-01 17:50:36,061 - tensorflow - INFO - global_step/sec: 88.0798 +2022-05-01 17:50:36,062 - tensorflow - INFO - loss = 0.07042611, step = 1085800 (2.271 sec) +2022-05-01 17:50:38,326 - tensorflow - INFO - global_step/sec: 88.3042 +2022-05-01 17:50:38,327 - tensorflow - INFO - loss = 0.021134865, step = 1086000 (2.265 sec) +2022-05-01 17:50:40,593 - tensorflow - INFO - global_step/sec: 88.2187 +2022-05-01 17:50:40,594 - tensorflow - INFO - loss = 0.009019922, step = 1086200 (2.267 sec) +2022-05-01 17:50:42,864 - tensorflow - INFO - global_step/sec: 88.0683 +2022-05-01 17:50:42,865 - tensorflow - INFO - loss = 0.003965199, step = 1086400 (2.271 sec) +2022-05-01 17:50:45,131 - tensorflow - INFO - global_step/sec: 88.2364 +2022-05-01 17:50:45,131 - tensorflow - INFO - loss = 0.02528206, step = 1086600 (2.267 sec) +2022-05-01 17:50:47,399 - tensorflow - INFO - global_step/sec: 88.1892 +2022-05-01 17:50:47,399 - tensorflow - INFO - loss = 0.052117527, step = 1086800 (2.268 sec) +2022-05-01 17:50:49,666 - tensorflow - INFO - global_step/sec: 88.2082 +2022-05-01 17:50:49,667 - tensorflow - INFO - loss = 0.055440534, step = 1087000 (2.267 sec) +2022-05-01 17:50:51,929 - tensorflow - INFO - global_step/sec: 88.3791 +2022-05-01 17:50:51,930 - tensorflow - INFO - loss = 1.0760046, step = 1087200 (2.263 sec) +2022-05-01 17:50:54,200 - tensorflow - INFO - global_step/sec: 88.0743 +2022-05-01 17:50:54,200 - tensorflow - INFO - loss = 0.048136946, step = 1087400 (2.271 sec) +2022-05-01 17:50:56,467 - tensorflow - INFO - global_step/sec: 88.2212 +2022-05-01 17:50:56,467 - tensorflow - INFO - loss = 0.024933062, step = 1087600 (2.267 sec) +2022-05-01 17:50:58,733 - tensorflow - INFO - global_step/sec: 88.2768 +2022-05-01 17:50:58,733 - tensorflow - INFO - loss = 0.06785029, step = 1087800 (2.266 sec) +2022-05-01 17:51:00,996 - tensorflow - INFO - global_step/sec: 88.3452 +2022-05-01 17:51:00,997 - tensorflow - INFO - loss = 25.39687, step = 1088000 (2.264 sec) +2022-05-01 17:51:03,267 - tensorflow - INFO - global_step/sec: 88.0922 +2022-05-01 17:51:03,267 - tensorflow - INFO - loss = 0.18148156, step = 1088200 (2.270 sec) +2022-05-01 17:51:05,536 - tensorflow - INFO - global_step/sec: 88.1349 +2022-05-01 17:51:05,536 - tensorflow - INFO - loss = 0.026244733, step = 1088400 (2.269 sec) +2022-05-01 17:51:07,806 - tensorflow - INFO - global_step/sec: 88.089 +2022-05-01 17:51:07,807 - tensorflow - INFO - loss = 0.030630097, step = 1088600 (2.270 sec) +2022-05-01 17:51:10,075 - tensorflow - INFO - global_step/sec: 88.1674 +2022-05-01 17:51:10,075 - tensorflow - INFO - loss = 0.017277874, step = 1088800 (2.268 sec) +2022-05-01 17:51:12,335 - tensorflow - INFO - global_step/sec: 88.4842 +2022-05-01 17:51:12,336 - tensorflow - INFO - loss = 0.018339299, step = 1089000 (2.260 sec) +2022-05-01 17:51:14,605 - tensorflow - INFO - global_step/sec: 88.0965 +2022-05-01 17:51:14,606 - tensorflow - INFO - loss = 0.02243248, step = 1089200 (2.270 sec) +2022-05-01 17:51:16,869 - tensorflow - INFO - global_step/sec: 88.362 +2022-05-01 17:51:16,869 - tensorflow - INFO - loss = 0.11833146, step = 1089400 (2.263 sec) +2022-05-01 17:51:19,140 - tensorflow - INFO - global_step/sec: 88.0618 +2022-05-01 17:51:19,140 - tensorflow - INFO - loss = 0.080452554, step = 1089600 (2.271 sec) +2022-05-01 17:51:21,410 - tensorflow - INFO - global_step/sec: 88.0926 +2022-05-01 17:51:21,411 - tensorflow - INFO - loss = 0.03880716, step = 1089800 (2.270 sec) +2022-05-01 17:51:23,679 - tensorflow - INFO - global_step/sec: 88.1435 +2022-05-01 17:51:23,680 - tensorflow - INFO - loss = 0.008254628, step = 1090000 (2.269 sec) +2022-05-01 17:51:25,951 - tensorflow - INFO - global_step/sec: 88.0392 +2022-05-01 17:51:25,951 - tensorflow - INFO - loss = 0.00797873, step = 1090200 (2.272 sec) +2022-05-01 17:51:28,220 - tensorflow - INFO - global_step/sec: 88.1287 +2022-05-01 17:51:28,221 - tensorflow - INFO - loss = 0.20207301, step = 1090400 (2.269 sec) +2022-05-01 17:51:30,497 - tensorflow - INFO - global_step/sec: 87.8379 +2022-05-01 17:51:30,498 - tensorflow - INFO - loss = 0.05177628, step = 1090600 (2.277 sec) +2022-05-01 17:51:32,779 - tensorflow - INFO - global_step/sec: 87.675 +2022-05-01 17:51:32,779 - tensorflow - INFO - loss = 0.14082907, step = 1090800 (2.281 sec) +2022-05-01 17:51:35,050 - tensorflow - INFO - global_step/sec: 88.0333 +2022-05-01 17:51:35,051 - tensorflow - INFO - loss = 0.38474733, step = 1091000 (2.272 sec) +2022-05-01 17:51:37,317 - tensorflow - INFO - global_step/sec: 88.2365 +2022-05-01 17:51:37,317 - tensorflow - INFO - loss = 0.025618026, step = 1091200 (2.267 sec) +2022-05-01 17:51:39,581 - tensorflow - INFO - global_step/sec: 88.3333 +2022-05-01 17:51:39,582 - tensorflow - INFO - loss = 0.035239328, step = 1091400 (2.264 sec) +2022-05-01 17:51:41,845 - tensorflow - INFO - global_step/sec: 88.339 +2022-05-01 17:51:41,846 - tensorflow - INFO - loss = 0.026621893, step = 1091600 (2.264 sec) +2022-05-01 17:51:44,119 - tensorflow - INFO - global_step/sec: 87.9404 +2022-05-01 17:51:44,120 - tensorflow - INFO - loss = 0.012498661, step = 1091800 (2.274 sec) +2022-05-01 17:51:46,388 - tensorflow - INFO - global_step/sec: 88.1492 +2022-05-01 17:51:46,389 - tensorflow - INFO - loss = 0.07283859, step = 1092000 (2.269 sec) +2022-05-01 17:51:48,657 - tensorflow - INFO - global_step/sec: 88.1626 +2022-05-01 17:51:48,657 - tensorflow - INFO - loss = 0.023764364, step = 1092200 (2.269 sec) +2022-05-01 17:51:50,922 - tensorflow - INFO - global_step/sec: 88.2985 +2022-05-01 17:51:50,922 - tensorflow - INFO - loss = 0.013630393, step = 1092400 (2.265 sec) +2022-05-01 17:51:53,192 - tensorflow - INFO - global_step/sec: 88.1159 +2022-05-01 17:51:53,192 - tensorflow - INFO - loss = 0.05786172, step = 1092600 (2.270 sec) +2022-05-01 17:51:55,455 - tensorflow - INFO - global_step/sec: 88.3758 +2022-05-01 17:51:55,455 - tensorflow - INFO - loss = 0.019399472, step = 1092800 (2.263 sec) +2022-05-01 17:51:57,732 - tensorflow - INFO - global_step/sec: 87.8392 +2022-05-01 17:51:57,732 - tensorflow - INFO - loss = 0.008102739, step = 1093000 (2.277 sec) +2022-05-01 17:51:59,999 - tensorflow - INFO - global_step/sec: 88.2145 +2022-05-01 17:51:59,999 - tensorflow - INFO - loss = 0.014448153, step = 1093200 (2.267 sec) +2022-05-01 17:52:02,269 - tensorflow - INFO - global_step/sec: 88.1059 +2022-05-01 17:52:02,269 - tensorflow - INFO - loss = 0.07463558, step = 1093400 (2.270 sec) +2022-05-01 17:52:04,539 - tensorflow - INFO - global_step/sec: 88.094 +2022-05-01 17:52:04,540 - tensorflow - INFO - loss = 0.029204894, step = 1093600 (2.270 sec) +2022-05-01 17:52:06,810 - tensorflow - INFO - global_step/sec: 88.0569 +2022-05-01 17:52:06,811 - tensorflow - INFO - loss = 0.095928356, step = 1093800 (2.271 sec) +2022-05-01 17:52:09,082 - tensorflow - INFO - global_step/sec: 88.0486 +2022-05-01 17:52:09,082 - tensorflow - INFO - loss = 0.008705968, step = 1094000 (2.272 sec) +2022-05-01 17:52:11,356 - tensorflow - INFO - global_step/sec: 87.9548 +2022-05-01 17:52:11,356 - tensorflow - INFO - loss = 0.02741694, step = 1094200 (2.274 sec) +2022-05-01 17:52:13,631 - tensorflow - INFO - global_step/sec: 87.8973 +2022-05-01 17:52:13,632 - tensorflow - INFO - loss = 0.026683709, step = 1094400 (2.275 sec) +2022-05-01 17:52:15,904 - tensorflow - INFO - global_step/sec: 87.9759 +2022-05-01 17:52:15,905 - tensorflow - INFO - loss = 0.039245937, step = 1094600 (2.273 sec) +2022-05-01 17:52:18,182 - tensorflow - INFO - global_step/sec: 87.8312 +2022-05-01 17:52:18,182 - tensorflow - INFO - loss = 0.03418677, step = 1094800 (2.277 sec) +2022-05-01 17:52:20,458 - tensorflow - INFO - global_step/sec: 87.8518 +2022-05-01 17:52:20,459 - tensorflow - INFO - loss = 0.018989494, step = 1095000 (2.277 sec) +2022-05-01 17:52:22,733 - tensorflow - INFO - global_step/sec: 87.904 +2022-05-01 17:52:22,734 - tensorflow - INFO - loss = 0.010458937, step = 1095200 (2.275 sec) +2022-05-01 17:52:25,010 - tensorflow - INFO - global_step/sec: 87.8573 +2022-05-01 17:52:25,010 - tensorflow - INFO - loss = 0.96352756, step = 1095400 (2.276 sec) +2022-05-01 17:52:27,283 - tensorflow - INFO - global_step/sec: 87.9611 +2022-05-01 17:52:27,284 - tensorflow - INFO - loss = 0.06753484, step = 1095600 (2.274 sec) +2022-05-01 17:52:29,557 - tensorflow - INFO - global_step/sec: 87.9567 +2022-05-01 17:52:29,558 - tensorflow - INFO - loss = 0.015580781, step = 1095800 (2.274 sec) +2022-05-01 17:52:31,831 - tensorflow - INFO - global_step/sec: 87.962 +2022-05-01 17:52:31,831 - tensorflow - INFO - loss = 0.15739396, step = 1096000 (2.274 sec) +2022-05-01 17:52:34,108 - tensorflow - INFO - global_step/sec: 87.8395 +2022-05-01 17:52:34,108 - tensorflow - INFO - loss = 0.05162877, step = 1096200 (2.277 sec) +2022-05-01 17:52:36,376 - tensorflow - INFO - global_step/sec: 88.1794 +2022-05-01 17:52:36,376 - tensorflow - INFO - loss = 0.011576347, step = 1096400 (2.268 sec) +2022-05-01 17:52:38,645 - tensorflow - INFO - global_step/sec: 88.1411 +2022-05-01 17:52:38,646 - tensorflow - INFO - loss = 0.010396266, step = 1096600 (2.269 sec) +2022-05-01 17:52:40,912 - tensorflow - INFO - global_step/sec: 88.2174 +2022-05-01 17:52:40,913 - tensorflow - INFO - loss = 0.022482887, step = 1096800 (2.267 sec) +2022-05-01 17:52:43,179 - tensorflow - INFO - global_step/sec: 88.2341 +2022-05-01 17:52:43,179 - tensorflow - INFO - loss = 0.010558477, step = 1097000 (2.267 sec) +2022-05-01 17:52:45,446 - tensorflow - INFO - global_step/sec: 88.2151 +2022-05-01 17:52:45,447 - tensorflow - INFO - loss = 0.036490947, step = 1097200 (2.267 sec) +2022-05-01 17:52:47,709 - tensorflow - INFO - global_step/sec: 88.3911 +2022-05-01 17:52:47,709 - tensorflow - INFO - loss = 0.15203127, step = 1097400 (2.263 sec) +2022-05-01 17:52:49,982 - tensorflow - INFO - global_step/sec: 87.9806 +2022-05-01 17:52:49,982 - tensorflow - INFO - loss = 0.05722553, step = 1097600 (2.273 sec) +2022-05-01 17:52:52,254 - tensorflow - INFO - global_step/sec: 88.0291 +2022-05-01 17:52:52,254 - tensorflow - INFO - loss = 0.051573113, step = 1097800 (2.272 sec) +2022-05-01 17:52:54,519 - tensorflow - INFO - global_step/sec: 88.3146 +2022-05-01 17:52:54,519 - tensorflow - INFO - loss = 0.042725034, step = 1098000 (2.265 sec) +2022-05-01 17:52:56,791 - tensorflow - INFO - global_step/sec: 88.0275 +2022-05-01 17:52:56,791 - tensorflow - INFO - loss = 0.2444915, step = 1098200 (2.272 sec) +2022-05-01 17:52:59,057 - tensorflow - INFO - global_step/sec: 88.2664 +2022-05-01 17:52:59,057 - tensorflow - INFO - loss = 0.01457686, step = 1098400 (2.266 sec) +2022-05-01 17:53:01,327 - tensorflow - INFO - global_step/sec: 88.0678 +2022-05-01 17:53:01,328 - tensorflow - INFO - loss = 0.008405423, step = 1098600 (2.271 sec) +2022-05-01 17:53:03,599 - tensorflow - INFO - global_step/sec: 88.0387 +2022-05-01 17:53:03,600 - tensorflow - INFO - loss = 0.023712724, step = 1098800 (2.272 sec) +2022-05-01 17:53:05,868 - tensorflow - INFO - global_step/sec: 88.1347 +2022-05-01 17:53:05,869 - tensorflow - INFO - loss = 0.017779306, step = 1099000 (2.269 sec) +2022-05-01 17:53:08,139 - tensorflow - INFO - global_step/sec: 88.0815 +2022-05-01 17:53:08,139 - tensorflow - INFO - loss = 0.03620185, step = 1099200 (2.271 sec) +2022-05-01 17:53:10,414 - tensorflow - INFO - global_step/sec: 87.921 +2022-05-01 17:53:10,414 - tensorflow - INFO - loss = 0.00687285, step = 1099400 (2.275 sec) +2022-05-01 17:53:12,683 - tensorflow - INFO - global_step/sec: 88.141 +2022-05-01 17:53:12,683 - tensorflow - INFO - loss = 0.015671916, step = 1099600 (2.269 sec) +2022-05-01 17:53:14,957 - tensorflow - INFO - global_step/sec: 87.9438 +2022-05-01 17:53:14,958 - tensorflow - INFO - loss = 0.021432642, step = 1099800 (2.274 sec) +2022-05-01 17:53:17,213 - tensorflow - INFO - Saving checkpoints for 1100000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. +2022-05-01 17:53:17,228 - tensorflow - INFO - model.ckpt-1100000 is not in all_model_checkpoint_paths. Manually adding it. +2022-05-01 17:53:17,753 - tensorflow - INFO - Calling model_fn. +2022-05-01 17:53:17,880 - tensorflow - INFO - Done calling model_fn. +2022-05-01 17:53:17,895 - tensorflow - INFO - Starting evaluation at 2022-05-01T17:53:17Z +2022-05-01 17:53:17,959 - tensorflow - INFO - Graph was finalized. +2022-05-01 17:53:17,962 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1100000 +2022-05-01 17:53:18,242 - tensorflow - INFO - Running local_init_op. +2022-05-01 17:53:18,350 - tensorflow - INFO - Done running local_init_op. +2022-05-01 17:53:28,495 - tensorflow - INFO - Evaluation [3990/39900] +2022-05-01 17:53:37,950 - tensorflow - INFO - Evaluation [7980/39900] +2022-05-01 17:53:47,393 - tensorflow - INFO - Evaluation [11970/39900] +2022-05-01 17:53:56,838 - tensorflow - INFO - Evaluation [15960/39900] +2022-05-01 17:54:06,279 - tensorflow - INFO - Evaluation [19950/39900] +2022-05-01 17:54:15,733 - tensorflow - INFO - Evaluation [23940/39900] +2022-05-01 17:54:25,160 - tensorflow - INFO - Evaluation [27930/39900] +2022-05-01 17:54:34,614 - tensorflow - INFO - Evaluation [31920/39900] +2022-05-01 17:54:44,055 - tensorflow - INFO - Evaluation [35910/39900] +2022-05-01 17:54:53,494 - tensorflow - INFO - Evaluation [39900/39900] +2022-05-01 17:54:53,631 - tensorflow - INFO - Finished evaluation at 2022-05-01-17:54:53 +2022-05-01 17:54:53,632 - tensorflow - INFO - Saving dict for global step 1100000: global_step = 1100000, loss = 0.025317876, mean_degree_err = 4.4118333 +2022-05-01 17:54:53,632 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 1100000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1100000 +2022-05-01 17:54:53,646 - tensorflow - INFO - global_step/sec: 2.02658 +2022-05-01 17:54:53,647 - tensorflow - INFO - loss = 5.8303533, step = 1100000 (98.689 sec) +2022-05-01 17:54:55,928 - tensorflow - INFO - global_step/sec: 87.6514 +2022-05-01 17:54:55,928 - tensorflow - INFO - loss = 0.017083686, step = 1100200 (2.281 sec) +2022-05-01 17:54:58,192 - tensorflow - INFO - global_step/sec: 88.3137 +2022-05-01 17:54:58,193 - tensorflow - INFO - loss = 0.029804125, step = 1100400 (2.265 sec) +2022-05-01 17:55:00,461 - tensorflow - INFO - global_step/sec: 88.1546 +2022-05-01 17:55:00,461 - tensorflow - INFO - loss = 0.16030154, step = 1100600 (2.269 sec) +2022-05-01 17:55:02,731 - tensorflow - INFO - global_step/sec: 88.1041 +2022-05-01 17:55:02,731 - tensorflow - INFO - loss = 0.014746798, step = 1100800 (2.270 sec) +2022-05-01 17:55:05,001 - tensorflow - INFO - global_step/sec: 88.1035 +2022-05-01 17:55:05,001 - tensorflow - INFO - loss = 0.018318102, step = 1101000 (2.270 sec) +2022-05-01 17:55:07,275 - tensorflow - INFO - global_step/sec: 87.959 +2022-05-01 17:55:07,275 - tensorflow - INFO - loss = 0.026991412, step = 1101200 (2.274 sec) +2022-05-01 17:55:09,546 - tensorflow - INFO - global_step/sec: 88.0721 +2022-05-01 17:55:09,546 - tensorflow - INFO - loss = 0.03456292, step = 1101400 (2.271 sec) +2022-05-01 17:55:11,813 - tensorflow - INFO - global_step/sec: 88.2073 +2022-05-01 17:55:11,813 - tensorflow - INFO - loss = 0.007666119, step = 1101600 (2.267 sec) +2022-05-01 17:55:14,081 - tensorflow - INFO - global_step/sec: 88.1868 +2022-05-01 17:55:14,081 - tensorflow - INFO - loss = 0.019554604, step = 1101800 (2.268 sec) +2022-05-01 17:55:16,350 - tensorflow - INFO - global_step/sec: 88.1564 +2022-05-01 17:55:16,350 - tensorflow - INFO - loss = 0.013642142, step = 1102000 (2.269 sec) +2022-05-01 17:55:18,618 - tensorflow - INFO - global_step/sec: 88.17 +2022-05-01 17:55:18,618 - tensorflow - INFO - loss = 0.01439528, step = 1102200 (2.268 sec) +2022-05-01 17:55:20,890 - tensorflow - INFO - global_step/sec: 88.0217 +2022-05-01 17:55:20,891 - tensorflow - INFO - loss = 0.015880037, step = 1102400 (2.272 sec) +2022-05-01 17:55:23,160 - tensorflow - INFO - global_step/sec: 88.1192 +2022-05-01 17:55:23,160 - tensorflow - INFO - loss = 0.048466884, step = 1102600 (2.270 sec) +2022-05-01 17:55:25,429 - tensorflow - INFO - global_step/sec: 88.1244 +2022-05-01 17:55:25,430 - tensorflow - INFO - loss = 0.049483653, step = 1102800 (2.270 sec) +2022-05-01 17:55:27,694 - tensorflow - INFO - global_step/sec: 88.3191 +2022-05-01 17:55:27,694 - tensorflow - INFO - loss = 0.3012721, step = 1103000 (2.265 sec) +2022-05-01 17:55:29,964 - tensorflow - INFO - global_step/sec: 88.0985 +2022-05-01 17:55:29,964 - tensorflow - INFO - loss = 0.019714927, step = 1103200 (2.270 sec) +2022-05-01 17:55:32,245 - tensorflow - INFO - global_step/sec: 87.6843 +2022-05-01 17:55:32,245 - tensorflow - INFO - loss = 0.022536471, step = 1103400 (2.281 sec) +2022-05-01 17:55:34,522 - tensorflow - INFO - global_step/sec: 87.8411 +2022-05-01 17:55:34,522 - tensorflow - INFO - loss = 0.04687898, step = 1103600 (2.277 sec) +2022-05-01 17:55:36,798 - tensorflow - INFO - global_step/sec: 87.8777 +2022-05-01 17:55:36,798 - tensorflow - INFO - loss = 0.058065794, step = 1103800 (2.276 sec) +2022-05-01 17:55:39,072 - tensorflow - INFO - global_step/sec: 87.9228 +2022-05-01 17:55:39,073 - tensorflow - INFO - loss = 0.45963365, step = 1104000 (2.275 sec) +2022-05-01 17:55:41,350 - tensorflow - INFO - global_step/sec: 87.8175 +2022-05-01 17:55:41,350 - tensorflow - INFO - loss = 0.6161173, step = 1104200 (2.277 sec) +2022-05-01 17:55:43,623 - tensorflow - INFO - global_step/sec: 87.9808 +2022-05-01 17:55:43,623 - tensorflow - INFO - loss = 0.025974642, step = 1104400 (2.273 sec) +2022-05-01 17:55:45,900 - tensorflow - INFO - global_step/sec: 87.8351 +2022-05-01 17:55:45,901 - tensorflow - INFO - loss = 0.04511058, step = 1104600 (2.277 sec) +2022-05-01 17:55:48,179 - tensorflow - INFO - global_step/sec: 87.7429 +2022-05-01 17:55:48,180 - tensorflow - INFO - loss = 0.14503525, step = 1104800 (2.279 sec) +2022-05-01 17:55:50,453 - tensorflow - INFO - global_step/sec: 87.965 +2022-05-01 17:55:50,454 - tensorflow - INFO - loss = 0.030037086, step = 1105000 (2.274 sec) +2022-05-01 17:55:52,733 - tensorflow - INFO - global_step/sec: 87.7381 +2022-05-01 17:55:52,733 - tensorflow - INFO - loss = 0.044755362, step = 1105200 (2.279 sec) +2022-05-01 17:55:55,006 - tensorflow - INFO - global_step/sec: 87.9747 +2022-05-01 17:55:55,006 - tensorflow - INFO - loss = 0.01589223, step = 1105400 (2.273 sec) +2022-05-01 17:55:57,288 - tensorflow - INFO - global_step/sec: 87.658 +2022-05-01 17:55:57,288 - tensorflow - INFO - loss = 1.0562906, step = 1105600 (2.282 sec) +2022-05-01 17:55:59,564 - tensorflow - INFO - global_step/sec: 87.867 +2022-05-01 17:55:59,564 - tensorflow - INFO - loss = 0.12573439, step = 1105800 (2.276 sec) +2022-05-01 17:56:01,843 - tensorflow - INFO - global_step/sec: 87.747 +2022-05-01 17:56:01,843 - tensorflow - INFO - loss = 0.018375054, step = 1106000 (2.279 sec) +2022-05-01 17:56:04,112 - tensorflow - INFO - global_step/sec: 88.1355 +2022-05-01 17:56:04,113 - tensorflow - INFO - loss = 0.018151755, step = 1106200 (2.269 sec) +2022-05-01 17:56:06,383 - tensorflow - INFO - global_step/sec: 88.0759 +2022-05-01 17:56:06,383 - tensorflow - INFO - loss = 0.021065243, step = 1106400 (2.271 sec) +2022-05-01 17:56:08,650 - tensorflow - INFO - global_step/sec: 88.2391 +2022-05-01 17:56:08,650 - tensorflow - INFO - loss = 0.3877352, step = 1106600 (2.267 sec) +2022-05-01 17:56:10,919 - tensorflow - INFO - global_step/sec: 88.138 +2022-05-01 17:56:10,919 - tensorflow - INFO - loss = 0.005250844, step = 1106800 (2.269 sec) +2022-05-01 17:56:13,192 - tensorflow - INFO - global_step/sec: 87.9927 +2022-05-01 17:56:13,192 - tensorflow - INFO - loss = 0.01313768, step = 1107000 (2.273 sec) +2022-05-01 17:56:15,463 - tensorflow - INFO - global_step/sec: 88.0475 +2022-05-01 17:56:15,464 - tensorflow - INFO - loss = 0.11005046, step = 1107200 (2.271 sec) +2022-05-01 17:56:17,733 - tensorflow - INFO - global_step/sec: 88.1181 +2022-05-01 17:56:17,733 - tensorflow - INFO - loss = 0.060840447, step = 1107400 (2.270 sec) +2022-05-01 17:56:20,001 - tensorflow - INFO - global_step/sec: 88.1591 +2022-05-01 17:56:20,002 - tensorflow - INFO - loss = 0.008830101, step = 1107600 (2.269 sec) +2022-05-01 17:56:22,270 - tensorflow - INFO - global_step/sec: 88.167 +2022-05-01 17:56:22,270 - tensorflow - INFO - loss = 0.044792574, step = 1107800 (2.268 sec) +2022-05-01 17:56:24,542 - tensorflow - INFO - global_step/sec: 88.0255 +2022-05-01 17:56:24,542 - tensorflow - INFO - loss = 0.024858685, step = 1108000 (2.272 sec) +2022-05-01 17:56:26,810 - tensorflow - INFO - global_step/sec: 88.1793 +2022-05-01 17:56:26,810 - tensorflow - INFO - loss = 0.010348566, step = 1108200 (2.268 sec) +2022-05-01 17:56:29,085 - tensorflow - INFO - global_step/sec: 87.9299 +2022-05-01 17:56:29,085 - tensorflow - INFO - loss = 0.031716585, step = 1108400 (2.275 sec) +2022-05-01 17:56:31,361 - tensorflow - INFO - global_step/sec: 87.8529 +2022-05-01 17:56:31,362 - tensorflow - INFO - loss = 0.013905285, step = 1108600 (2.277 sec) +2022-05-01 17:56:33,628 - tensorflow - INFO - global_step/sec: 88.23 +2022-05-01 17:56:33,628 - tensorflow - INFO - loss = 0.020705432, step = 1108800 (2.267 sec) +2022-05-01 17:56:35,895 - tensorflow - 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tensorflow - INFO - loss = 0.6640206, step = 1110200 (2.266 sec) +2022-05-01 17:56:51,770 - tensorflow - INFO - global_step/sec: 87.9529 +2022-05-01 17:56:51,771 - tensorflow - INFO - loss = 0.96618474, step = 1110400 (2.274 sec) +2022-05-01 17:56:54,031 - tensorflow - INFO - global_step/sec: 88.4509 +2022-05-01 17:56:54,032 - tensorflow - INFO - loss = 0.021798681, step = 1110600 (2.261 sec) +2022-05-01 17:56:56,299 - tensorflow - INFO - global_step/sec: 88.1892 +2022-05-01 17:56:56,300 - tensorflow - INFO - loss = 0.027802408, step = 1110800 (2.268 sec) +2022-05-01 17:56:58,566 - tensorflow - INFO - global_step/sec: 88.2435 +2022-05-01 17:56:58,566 - tensorflow - INFO - loss = 0.99206895, step = 1111000 (2.266 sec) +2022-05-01 17:57:00,827 - tensorflow - INFO - global_step/sec: 88.4496 +2022-05-01 17:57:00,827 - tensorflow - INFO - loss = 0.085407555, step = 1111200 (2.261 sec) +2022-05-01 17:57:03,098 - tensorflow - INFO - global_step/sec: 88.045 +2022-05-01 17:57:03,099 - tensorflow - INFO - loss = 0.008278963, step = 1111400 (2.272 sec) +2022-05-01 17:57:05,372 - tensorflow - INFO - global_step/sec: 87.9661 +2022-05-01 17:57:05,372 - tensorflow - INFO - loss = 0.010176811, step = 1111600 (2.274 sec) +2022-05-01 17:57:07,641 - tensorflow - INFO - global_step/sec: 88.1519 +2022-05-01 17:57:07,641 - tensorflow - INFO - loss = 0.045207664, step = 1111800 (2.269 sec) +2022-05-01 17:57:09,909 - tensorflow - INFO - global_step/sec: 88.179 +2022-05-01 17:57:09,909 - tensorflow - INFO - loss = 2.1899543, step = 1112000 (2.268 sec) +2022-05-01 17:57:12,177 - tensorflow - INFO - global_step/sec: 88.1906 +2022-05-01 17:57:12,177 - tensorflow - INFO - loss = 0.007977364, step = 1112200 (2.268 sec) +2022-05-01 17:57:14,443 - tensorflow - INFO - global_step/sec: 88.2589 +2022-05-01 17:57:14,443 - tensorflow - INFO - loss = 0.041103326, step = 1112400 (2.266 sec) +2022-05-01 17:57:16,709 - tensorflow - INFO - global_step/sec: 88.2595 +2022-05-01 17:57:16,709 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.1861 +2022-05-01 17:57:32,576 - tensorflow - INFO - loss = 0.010891835, step = 1114000 (2.268 sec) +2022-05-01 17:57:34,841 - tensorflow - INFO - global_step/sec: 88.2696 +2022-05-01 17:57:34,842 - tensorflow - INFO - loss = 0.020776497, step = 1114200 (2.266 sec) +2022-05-01 17:57:37,107 - tensorflow - INFO - global_step/sec: 88.272 +2022-05-01 17:57:37,107 - tensorflow - INFO - loss = 0.037697658, step = 1114400 (2.266 sec) +2022-05-01 17:57:39,377 - tensorflow - INFO - global_step/sec: 88.0885 +2022-05-01 17:57:39,378 - tensorflow - INFO - loss = 0.031510655, step = 1114600 (2.270 sec) +2022-05-01 17:57:41,642 - tensorflow - INFO - global_step/sec: 88.3284 +2022-05-01 17:57:41,642 - tensorflow - INFO - loss = 0.1090098, step = 1114800 (2.264 sec) +2022-05-01 17:57:43,915 - tensorflow - INFO - global_step/sec: 87.992 +2022-05-01 17:57:43,915 - tensorflow - INFO - loss = 0.049176022, step = 1115000 (2.273 sec) +2022-05-01 17:57:46,183 - tensorflow - INFO - global_step/sec: 88.1881 +2022-05-01 17:57:46,183 - tensorflow - INFO - loss = 0.4434392, step = 1115200 (2.268 sec) +2022-05-01 17:57:48,444 - tensorflow - INFO - global_step/sec: 88.446 +2022-05-01 17:57:48,444 - tensorflow - INFO - loss = 0.015171867, step = 1115400 (2.261 sec) +2022-05-01 17:57:50,717 - tensorflow - INFO - global_step/sec: 87.9781 +2022-05-01 17:57:50,717 - tensorflow - INFO - loss = 1.0668705, step = 1115600 (2.273 sec) +2022-05-01 17:57:52,985 - tensorflow - INFO - global_step/sec: 88.2044 +2022-05-01 17:57:52,985 - tensorflow - INFO - loss = 0.009059138, step = 1115800 (2.267 sec) +2022-05-01 17:57:55,255 - tensorflow - INFO - global_step/sec: 88.1046 +2022-05-01 17:57:55,255 - tensorflow - INFO - loss = 0.011908996, step = 1116000 (2.270 sec) +2022-05-01 17:57:57,523 - tensorflow - INFO - global_step/sec: 88.1727 +2022-05-01 17:57:57,523 - tensorflow - INFO - loss = 0.046205234, step = 1116200 (2.268 sec) +2022-05-01 17:57:59,792 - tensorflow - INFO - global_step/sec: 88.1376 +2022-05-01 17:57:59,792 - tensorflow - INFO - loss = 0.005333678, step = 1116400 (2.269 sec) +2022-05-01 17:58:02,057 - tensorflow - INFO - global_step/sec: 88.3141 +2022-05-01 17:58:02,057 - tensorflow - INFO - loss = 0.017803967, step = 1116600 (2.265 sec) +2022-05-01 17:58:04,329 - tensorflow - INFO - global_step/sec: 88.0055 +2022-05-01 17:58:04,330 - tensorflow - INFO - loss = 0.010758027, step = 1116800 (2.273 sec) +2022-05-01 17:58:06,594 - tensorflow - INFO - global_step/sec: 88.2998 +2022-05-01 17:58:06,595 - tensorflow - INFO - loss = 0.008021735, step = 1117000 (2.265 sec) +2022-05-01 17:58:08,871 - tensorflow - INFO - global_step/sec: 87.848 +2022-05-01 17:58:08,871 - tensorflow - INFO - loss = 0.11568797, step = 1117200 (2.277 sec) +2022-05-01 17:58:11,137 - tensorflow - INFO - global_step/sec: 88.2549 +2022-05-01 17:58:11,138 - tensorflow - INFO - loss = 0.09385828, step = 1117400 (2.266 sec) +2022-05-01 17:58:13,407 - tensorflow - INFO - global_step/sec: 88.1012 +2022-05-01 17:58:13,408 - tensorflow - INFO - loss = 0.39304575, step = 1117600 (2.270 sec) +2022-05-01 17:58:15,673 - tensorflow - INFO - global_step/sec: 88.2546 +2022-05-01 17:58:15,674 - tensorflow - INFO - loss = 0.019489039, step = 1117800 (2.266 sec) +2022-05-01 17:58:17,943 - tensorflow - INFO - global_step/sec: 88.1359 +2022-05-01 17:58:17,943 - tensorflow - INFO - loss = 0.04016178, step = 1118000 (2.269 sec) +2022-05-01 17:58:20,208 - tensorflow - INFO - global_step/sec: 88.2896 +2022-05-01 17:58:20,208 - tensorflow - INFO - loss = 0.551699, step = 1118200 (2.265 sec) +2022-05-01 17:58:22,476 - tensorflow - INFO - global_step/sec: 88.1972 +2022-05-01 17:58:22,476 - tensorflow - INFO - loss = 0.012216413, step = 1118400 (2.268 sec) +2022-05-01 17:58:24,737 - tensorflow - INFO - global_step/sec: 88.4329 +2022-05-01 17:58:24,738 - tensorflow - INFO - loss = 0.023845818, step = 1118600 (2.262 sec) +2022-05-01 17:58:27,002 - tensorflow - INFO - global_step/sec: 88.3141 +2022-05-01 17:58:27,002 - tensorflow - INFO - loss = 0.025817413, step = 1118800 (2.265 sec) +2022-05-01 17:58:29,267 - 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INFO - global_step/sec: 88.7199 +2022-05-01 18:10:37,622 - tensorflow - INFO - loss = 7.8540344, step = 1183400 (2.254 sec) +2022-05-01 18:10:39,878 - tensorflow - INFO - global_step/sec: 88.6312 +2022-05-01 18:10:39,879 - tensorflow - INFO - loss = 0.02740361, step = 1183600 (2.257 sec) +2022-05-01 18:10:42,136 - tensorflow - INFO - global_step/sec: 88.5754 +2022-05-01 18:10:42,137 - tensorflow - INFO - loss = 0.034917794, step = 1183800 (2.258 sec) +2022-05-01 18:10:44,385 - tensorflow - INFO - global_step/sec: 88.933 +2022-05-01 18:10:44,386 - tensorflow - INFO - loss = 0.038255285, step = 1184000 (2.249 sec) +2022-05-01 18:10:46,643 - tensorflow - INFO - global_step/sec: 88.5861 +2022-05-01 18:10:46,643 - tensorflow - INFO - loss = 0.008987745, step = 1184200 (2.258 sec) +2022-05-01 18:10:48,893 - tensorflow - INFO - global_step/sec: 88.8941 +2022-05-01 18:10:48,893 - tensorflow - INFO - loss = 0.019046573, step = 1184400 (2.250 sec) +2022-05-01 18:10:51,153 - tensorflow - INFO - global_step/sec: 88.4934 +2022-05-01 18:10:51,153 - 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tensorflow - INFO - global_step/sec: 88.7232 +2022-05-01 18:11:20,484 - tensorflow - INFO - loss = 0.04313879, step = 1187200 (2.254 sec) +2022-05-01 18:11:22,742 - tensorflow - INFO - global_step/sec: 88.5613 +2022-05-01 18:11:22,743 - tensorflow - INFO - loss = 0.007858479, step = 1187400 (2.258 sec) +2022-05-01 18:11:25,003 - tensorflow - INFO - global_step/sec: 88.4462 +2022-05-01 18:11:25,004 - tensorflow - INFO - loss = 0.113287, step = 1187600 (2.261 sec) +2022-05-01 18:11:27,261 - tensorflow - INFO - global_step/sec: 88.5974 +2022-05-01 18:11:27,261 - tensorflow - INFO - loss = 0.010161064, step = 1187800 (2.257 sec) +2022-05-01 18:11:29,518 - tensorflow - INFO - global_step/sec: 88.6209 +2022-05-01 18:11:29,518 - tensorflow - INFO - loss = 0.018867392, step = 1188000 (2.257 sec) +2022-05-01 18:11:31,780 - tensorflow - INFO - global_step/sec: 88.4049 +2022-05-01 18:11:31,780 - tensorflow - INFO - loss = 0.061028652, step = 1188200 (2.262 sec) +2022-05-01 18:11:34,039 - tensorflow - INFO - global_step/sec: 88.5441 +2022-05-01 18:11:34,039 - tensorflow - INFO - loss = 0.03321492, step = 1188400 (2.259 sec) +2022-05-01 18:11:36,301 - tensorflow - INFO - global_step/sec: 88.4174 +2022-05-01 18:11:36,301 - tensorflow - INFO - loss = 0.066412434, step = 1188600 (2.262 sec) +2022-05-01 18:11:38,550 - tensorflow - INFO - global_step/sec: 88.9089 +2022-05-01 18:11:38,551 - tensorflow - INFO - loss = 0.017991543, step = 1188800 (2.250 sec) +2022-05-01 18:11:40,807 - tensorflow - INFO - global_step/sec: 88.6189 +2022-05-01 18:11:40,807 - tensorflow - INFO - loss = 0.027934484, step = 1189000 (2.257 sec) +2022-05-01 18:11:43,058 - tensorflow - INFO - global_step/sec: 88.8359 +2022-05-01 18:11:43,059 - tensorflow - INFO - loss = 0.014947686, step = 1189200 (2.251 sec) +2022-05-01 18:11:45,312 - tensorflow - INFO - global_step/sec: 88.7526 +2022-05-01 18:11:45,312 - tensorflow - INFO - loss = 0.019492371, step = 1189400 (2.253 sec) +2022-05-01 18:11:47,565 - tensorflow - INFO - global_step/sec: 88.7467 +2022-05-01 18:11:47,566 - tensorflow - INFO - loss = 0.036995653, step = 1189600 (2.254 sec) +2022-05-01 18:11:49,825 - tensorflow - INFO - global_step/sec: 88.5181 +2022-05-01 18:11:49,825 - tensorflow - INFO - loss = 0.09084523, step = 1189800 (2.259 sec) +2022-05-01 18:11:52,078 - tensorflow - INFO - global_step/sec: 88.748 +2022-05-01 18:11:52,079 - tensorflow - INFO - loss = 0.10673227, step = 1190000 (2.254 sec) +2022-05-01 18:11:54,329 - tensorflow - INFO - global_step/sec: 88.8723 +2022-05-01 18:11:54,329 - tensorflow - INFO - loss = 0.0142885335, step = 1190200 (2.250 sec) +2022-05-01 18:11:56,583 - tensorflow - INFO - global_step/sec: 88.7303 +2022-05-01 18:11:56,583 - tensorflow - INFO - loss = 0.43347728, step = 1190400 (2.254 sec) +2022-05-01 18:11:58,840 - tensorflow - INFO - global_step/sec: 88.6206 +2022-05-01 18:11:58,840 - tensorflow - INFO - loss = 0.030480046, step = 1190600 (2.257 sec) +2022-05-01 18:12:01,093 - tensorflow - INFO - global_step/sec: 88.7518 +2022-05-01 18:12:01,094 - tensorflow - INFO - loss = 0.08956727, step = 1190800 (2.253 sec) +2022-05-01 18:12:03,353 - tensorflow - INFO - global_step/sec: 88.4922 +2022-05-01 18:12:03,354 - tensorflow - INFO - loss = 0.034298472, step = 1191000 (2.260 sec) +2022-05-01 18:12:05,608 - tensorflow - INFO - global_step/sec: 88.6941 +2022-05-01 18:12:05,609 - tensorflow - INFO - loss = 0.04235449, step = 1191200 (2.255 sec) +2022-05-01 18:12:07,865 - tensorflow - INFO - global_step/sec: 88.6266 +2022-05-01 18:12:07,865 - tensorflow - INFO - loss = 0.018376255, step = 1191400 (2.257 sec) +2022-05-01 18:12:10,118 - tensorflow - INFO - global_step/sec: 88.7677 +2022-05-01 18:12:10,118 - tensorflow - INFO - loss = 0.03255583, step = 1191600 (2.253 sec) +2022-05-01 18:12:12,377 - tensorflow - INFO - global_step/sec: 88.5139 +2022-05-01 18:12:12,378 - tensorflow - INFO - loss = 0.012742184, step = 1191800 (2.260 sec) +2022-05-01 18:12:14,641 - tensorflow - INFO - global_step/sec: 88.3719 +2022-05-01 18:12:14,641 - tensorflow - INFO - loss = 0.010812651, step = 1192000 (2.263 sec) +2022-05-01 18:12:16,898 - tensorflow - INFO - global_step/sec: 88.6071 +2022-05-01 18:12:16,898 - tensorflow - INFO - loss = 0.12380754, step = 1192200 (2.257 sec) +2022-05-01 18:12:19,146 - tensorflow - INFO - global_step/sec: 88.9405 +2022-05-01 18:12:19,147 - tensorflow - INFO - loss = 0.017856931, step = 1192400 (2.249 sec) +2022-05-01 18:12:21,399 - tensorflow - INFO - global_step/sec: 88.8056 +2022-05-01 18:12:21,399 - tensorflow - INFO - loss = 0.006084865, step = 1192600 (2.252 sec) +2022-05-01 18:12:23,656 - tensorflow - INFO - global_step/sec: 88.5832 +2022-05-01 18:12:23,657 - tensorflow - INFO - loss = 0.019839931, step = 1192800 (2.258 sec) +2022-05-01 18:12:25,910 - tensorflow - INFO - global_step/sec: 88.747 +2022-05-01 18:12:25,910 - tensorflow - INFO - loss = 0.057857707, step = 1193000 (2.254 sec) +2022-05-01 18:12:28,167 - tensorflow - INFO - global_step/sec: 88.6283 +2022-05-01 18:12:28,167 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.8447 +2022-05-01 18:12:43,948 - tensorflow - INFO - loss = 0.009096233, step = 1194600 (2.251 sec) +2022-05-01 18:12:46,203 - tensorflow - INFO - global_step/sec: 88.6816 +2022-05-01 18:12:46,204 - tensorflow - INFO - loss = 0.061590552, step = 1194800 (2.255 sec) +2022-05-01 18:12:48,458 - tensorflow - INFO - global_step/sec: 88.6798 +2022-05-01 18:12:48,459 - tensorflow - INFO - loss = 0.56076455, step = 1195000 (2.255 sec) +2022-05-01 18:12:50,717 - tensorflow - INFO - global_step/sec: 88.5356 +2022-05-01 18:12:50,718 - tensorflow - INFO - loss = 0.047898788, step = 1195200 (2.259 sec) +2022-05-01 18:12:52,976 - tensorflow - INFO - global_step/sec: 88.5673 +2022-05-01 18:12:52,976 - tensorflow - INFO - loss = 0.037085086, step = 1195400 (2.258 sec) +2022-05-01 18:12:55,230 - tensorflow - INFO - global_step/sec: 88.716 +2022-05-01 18:12:55,230 - tensorflow - INFO - loss = 0.056205664, step = 1195600 (2.254 sec) +2022-05-01 18:12:57,491 - tensorflow - INFO - global_step/sec: 88.4358 +2022-05-01 18:12:57,492 - tensorflow - INFO - loss = 0.03376495, step = 1195800 (2.261 sec) +2022-05-01 18:12:59,749 - tensorflow - INFO - global_step/sec: 88.5909 +2022-05-01 18:12:59,749 - tensorflow - INFO - loss = 0.0069675804, step = 1196000 (2.258 sec) +2022-05-01 18:13:02,015 - tensorflow - INFO - global_step/sec: 88.2758 +2022-05-01 18:13:02,015 - tensorflow - INFO - loss = 0.09626718, step = 1196200 (2.266 sec) +2022-05-01 18:13:04,278 - tensorflow - INFO - global_step/sec: 88.384 +2022-05-01 18:13:04,278 - tensorflow - INFO - loss = 0.28406236, step = 1196400 (2.263 sec) +2022-05-01 18:13:06,539 - tensorflow - INFO - global_step/sec: 88.4227 +2022-05-01 18:13:06,540 - tensorflow - INFO - loss = 0.013516074, step = 1196600 (2.262 sec) +2022-05-01 18:13:08,799 - tensorflow - INFO - global_step/sec: 88.5036 +2022-05-01 18:13:08,800 - tensorflow - INFO - loss = 0.037272662, step = 1196800 (2.260 sec) +2022-05-01 18:13:11,060 - tensorflow - INFO - global_step/sec: 88.4786 +2022-05-01 18:13:11,060 - tensorflow - INFO - loss = 0.020124855, step = 1197000 (2.260 sec) +2022-05-01 18:13:13,325 - tensorflow - INFO - global_step/sec: 88.2711 +2022-05-01 18:13:13,326 - tensorflow - INFO - loss = 0.03263738, step = 1197200 (2.266 sec) +2022-05-01 18:13:15,591 - tensorflow - INFO - global_step/sec: 88.2575 +2022-05-01 18:13:15,592 - tensorflow - INFO - loss = 0.21277907, step = 1197400 (2.266 sec) +2022-05-01 18:13:17,858 - tensorflow - INFO - global_step/sec: 88.2431 +2022-05-01 18:13:17,858 - tensorflow - INFO - loss = 0.011050388, step = 1197600 (2.266 sec) +2022-05-01 18:13:20,124 - tensorflow - INFO - global_step/sec: 88.2652 +2022-05-01 18:13:20,124 - tensorflow - INFO - loss = 0.029462758, step = 1197800 (2.266 sec) +2022-05-01 18:13:22,385 - tensorflow - INFO - global_step/sec: 88.4637 +2022-05-01 18:13:22,385 - tensorflow - INFO - loss = 0.016916715, step = 1198000 (2.261 sec) +2022-05-01 18:13:24,649 - tensorflow - INFO - global_step/sec: 88.3252 +2022-05-01 18:13:24,649 - tensorflow - INFO - loss = 0.01425764, step = 1198200 (2.264 sec) +2022-05-01 18:13:26,914 - tensorflow - INFO - global_step/sec: 88.3187 +2022-05-01 18:13:26,914 - tensorflow - INFO - loss = 0.08497985, step = 1198400 (2.265 sec) +2022-05-01 18:13:29,178 - tensorflow - INFO - global_step/sec: 88.3126 +2022-05-01 18:13:29,179 - tensorflow - INFO - loss = 0.0450411, step = 1198600 (2.265 sec) +2022-05-01 18:13:31,448 - tensorflow - INFO - global_step/sec: 88.1132 +2022-05-01 18:13:31,448 - tensorflow - INFO - loss = 0.033562914, step = 1198800 (2.270 sec) +2022-05-01 18:13:33,706 - tensorflow - INFO - global_step/sec: 88.5748 +2022-05-01 18:13:33,706 - tensorflow - INFO - loss = 0.082803875, step = 1199000 (2.258 sec) +2022-05-01 18:13:35,964 - tensorflow - INFO - global_step/sec: 88.5552 +2022-05-01 18:13:35,965 - tensorflow - INFO - loss = 1.5781188, step = 1199200 (2.258 sec) +2022-05-01 18:13:38,215 - tensorflow - INFO - global_step/sec: 88.8648 +2022-05-01 18:13:38,215 - tensorflow - INFO - loss = 0.0135265775, step = 1199400 (2.251 sec) +2022-05-01 18:13:40,467 - tensorflow - INFO - global_step/sec: 88.8301 +2022-05-01 18:13:40,467 - tensorflow - INFO - loss = 0.009275254, step = 1199600 (2.252 sec) +2022-05-01 18:13:42,718 - tensorflow - INFO - global_step/sec: 88.8346 +2022-05-01 18:13:42,718 - tensorflow - INFO - loss = 0.029307913, step = 1199800 (2.251 sec) +2022-05-01 18:13:44,962 - tensorflow - INFO - Saving checkpoints for 1200000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. +2022-05-01 18:13:44,978 - tensorflow - INFO - model.ckpt-1200000 is not in all_model_checkpoint_paths. Manually adding it. +2022-05-01 18:13:45,518 - tensorflow - INFO - Calling model_fn. +2022-05-01 18:13:45,645 - tensorflow - INFO - Done calling model_fn. +2022-05-01 18:13:45,660 - tensorflow - INFO - Starting evaluation at 2022-05-01T18:13:45Z +2022-05-01 18:13:45,724 - tensorflow - INFO - Graph was finalized. +2022-05-01 18:13:45,727 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1200000 +2022-05-01 18:13:46,008 - tensorflow - INFO - Running local_init_op. +2022-05-01 18:13:46,116 - tensorflow - INFO - Done running local_init_op. +2022-05-01 18:13:56,323 - tensorflow - INFO - Evaluation [3990/39900] +2022-05-01 18:14:05,837 - tensorflow - INFO - Evaluation [7980/39900] +2022-05-01 18:14:15,350 - tensorflow - INFO - Evaluation [11970/39900] +2022-05-01 18:14:24,848 - tensorflow - INFO - Evaluation [15960/39900] +2022-05-01 18:14:34,349 - tensorflow - INFO - Evaluation [19950/39900] +2022-05-01 18:14:43,851 - tensorflow - INFO - Evaluation [23940/39900] +2022-05-01 18:14:53,368 - tensorflow - INFO - Evaluation [27930/39900] +2022-05-01 18:15:02,897 - tensorflow - INFO - Evaluation [31920/39900] +2022-05-01 18:15:12,412 - tensorflow - INFO - Evaluation [35910/39900] +2022-05-01 18:15:21,938 - tensorflow - INFO - Evaluation [39900/39900] +2022-05-01 18:15:22,073 - tensorflow - INFO - Finished evaluation at 2022-05-01-18:15:22 +2022-05-01 18:15:22,074 - tensorflow - INFO - Saving dict for global step 1200000: global_step = 1200000, loss = 0.049551684, mean_degree_err = 8.192199 +2022-05-01 18:15:22,074 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 1200000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1200000 +2022-05-01 18:15:22,088 - tensorflow - INFO - global_step/sec: 2.01268 +2022-05-01 18:15:22,089 - tensorflow - INFO - loss = 0.10029067, step = 1200000 (99.370 sec) +2022-05-01 18:15:24,354 - tensorflow - INFO - global_step/sec: 88.256 +2022-05-01 18:15:24,354 - tensorflow - INFO - loss = 0.03690188, step = 1200200 (2.266 sec) +2022-05-01 18:15:26,611 - tensorflow - INFO - global_step/sec: 88.6113 +2022-05-01 18:15:26,611 - tensorflow - INFO - loss = 0.033221673, step = 1200400 (2.257 sec) +2022-05-01 18:15:28,864 - tensorflow - INFO - global_step/sec: 88.7596 +2022-05-01 18:15:28,865 - tensorflow - INFO - loss = 0.021254009, step = 1200600 (2.253 sec) +2022-05-01 18:15:31,122 - tensorflow - INFO - global_step/sec: 88.5909 +2022-05-01 18:15:31,122 - tensorflow - INFO - loss = 0.03441968, step = 1200800 (2.258 sec) +2022-05-01 18:15:33,376 - tensorflow - INFO - global_step/sec: 88.7153 +2022-05-01 18:15:33,377 - tensorflow - INFO - loss = 0.030785479, step = 1201000 (2.254 sec) +2022-05-01 18:15:35,629 - tensorflow - INFO - global_step/sec: 88.7627 +2022-05-01 18:15:35,630 - tensorflow - INFO - loss = 0.008263586, step = 1201200 (2.253 sec) +2022-05-01 18:15:37,886 - tensorflow - INFO - global_step/sec: 88.6402 +2022-05-01 18:15:37,886 - tensorflow - INFO - loss = 0.08718412, step = 1201400 (2.256 sec) +2022-05-01 18:15:40,141 - tensorflow - INFO - global_step/sec: 88.6754 +2022-05-01 18:15:40,142 - tensorflow - INFO - loss = 0.06642119, step = 1201600 (2.255 sec) +2022-05-01 18:15:42,395 - tensorflow - INFO - global_step/sec: 88.7366 +2022-05-01 18:15:42,395 - tensorflow - INFO - loss = 0.05555537, step = 1201800 (2.254 sec) +2022-05-01 18:15:44,645 - tensorflow - INFO - global_step/sec: 88.9064 +2022-05-01 18:15:44,645 - tensorflow - INFO - loss = 1.1072648, step = 1202000 (2.250 sec) +2022-05-01 18:15:46,905 - tensorflow - INFO - global_step/sec: 88.478 +2022-05-01 18:15:46,905 - tensorflow - INFO - loss = 0.06818071, step = 1202200 (2.260 sec) +2022-05-01 18:15:49,162 - tensorflow - INFO - global_step/sec: 88.6274 +2022-05-01 18:15:49,162 - tensorflow - INFO - loss = 0.02435386, step = 1202400 (2.257 sec) +2022-05-01 18:15:51,425 - tensorflow - INFO - global_step/sec: 88.3848 +2022-05-01 18:15:51,425 - tensorflow - INFO - loss = 0.031496737, step = 1202600 (2.263 sec) +2022-05-01 18:15:53,680 - tensorflow - INFO - global_step/sec: 88.6802 +2022-05-01 18:15:53,680 - tensorflow - INFO - loss = 0.22101256, step = 1202800 (2.255 sec) +2022-05-01 18:15:55,937 - tensorflow - INFO - global_step/sec: 88.6014 +2022-05-01 18:15:55,938 - tensorflow - INFO - loss = 0.010230656, step = 1203000 (2.257 sec) +2022-05-01 18:15:58,194 - tensorflow - INFO - global_step/sec: 88.5991 +2022-05-01 18:15:58,195 - tensorflow - INFO - loss = 2.6677594, step = 1203200 (2.257 sec) +2022-05-01 18:16:00,449 - tensorflow - INFO - global_step/sec: 88.7291 +2022-05-01 18:16:00,449 - tensorflow - INFO - loss = 0.14728445, step = 1203400 (2.254 sec) +2022-05-01 18:16:02,714 - tensorflow - INFO - global_step/sec: 88.2869 +2022-05-01 18:16:02,714 - tensorflow - INFO - loss = 0.03147973, step = 1203600 (2.265 sec) +2022-05-01 18:16:04,972 - tensorflow - INFO - global_step/sec: 88.5771 +2022-05-01 18:16:04,972 - tensorflow - INFO - loss = 0.089283906, step = 1203800 (2.258 sec) +2022-05-01 18:16:07,227 - tensorflow - INFO - global_step/sec: 88.6778 +2022-05-01 18:16:07,228 - tensorflow - INFO - loss = 0.05661044, step = 1204000 (2.255 sec) +2022-05-01 18:16:09,482 - tensorflow - INFO - global_step/sec: 88.6908 +2022-05-01 18:16:09,483 - tensorflow - INFO - loss = 0.018297628, step = 1204200 (2.255 sec) +2022-05-01 18:16:11,740 - tensorflow - INFO - global_step/sec: 88.5849 +2022-05-01 18:16:11,740 - tensorflow - INFO - loss = 0.025900912, step = 1204400 (2.258 sec) +2022-05-01 18:16:14,000 - tensorflow - INFO - global_step/sec: 88.4942 +2022-05-01 18:16:14,000 - tensorflow - INFO - loss = 0.034806952, step = 1204600 (2.260 sec) +2022-05-01 18:16:16,267 - tensorflow - INFO - global_step/sec: 88.2319 +2022-05-01 18:16:16,267 - tensorflow - INFO - loss = 0.018693745, step = 1204800 (2.267 sec) +2022-05-01 18:16:18,518 - tensorflow - INFO - global_step/sec: 88.8386 +2022-05-01 18:16:18,518 - tensorflow - INFO - loss = 0.034952633, step = 1205000 (2.251 sec) +2022-05-01 18:16:20,778 - tensorflow - INFO - global_step/sec: 88.4784 +2022-05-01 18:16:20,779 - tensorflow - INFO - loss = 0.013241578, step = 1205200 (2.260 sec) +2022-05-01 18:16:23,041 - tensorflow - INFO - global_step/sec: 88.3991 +2022-05-01 18:16:23,041 - tensorflow - INFO - loss = 0.011428614, step = 1205400 (2.262 sec) +2022-05-01 18:16:25,300 - tensorflow - INFO - global_step/sec: 88.5366 +2022-05-01 18:16:25,300 - tensorflow - INFO - loss = 0.56577766, step = 1205600 (2.259 sec) +2022-05-01 18:16:27,560 - tensorflow - INFO - global_step/sec: 88.4861 +2022-05-01 18:16:27,560 - tensorflow - INFO - loss = 0.01962918, step = 1205800 (2.260 sec) +2022-05-01 18:16:29,818 - tensorflow - INFO - global_step/sec: 88.5815 +2022-05-01 18:16:29,818 - tensorflow - INFO - loss = 0.077739194, step = 1206000 (2.258 sec) +2022-05-01 18:16:32,082 - tensorflow - INFO - global_step/sec: 88.3441 +2022-05-01 18:16:32,082 - tensorflow - INFO - loss = 0.030087005, step = 1206200 (2.264 sec) +2022-05-01 18:16:34,344 - tensorflow - INFO - global_step/sec: 88.3936 +2022-05-01 18:16:34,345 - tensorflow - INFO - loss = 0.029479984, step = 1206400 (2.263 sec) +2022-05-01 18:16:36,608 - tensorflow - INFO - global_step/sec: 88.3688 +2022-05-01 18:16:36,608 - tensorflow - INFO - loss = 0.13650742, step = 1206600 (2.263 sec) +2022-05-01 18:16:38,871 - tensorflow - INFO - global_step/sec: 88.3564 +2022-05-01 18:16:38,872 - tensorflow - INFO - loss = 0.025181746, step = 1206800 (2.264 sec) +2022-05-01 18:16:41,139 - tensorflow - INFO - global_step/sec: 88.2039 +2022-05-01 18:16:41,139 - tensorflow - INFO - loss = 0.10817782, step = 1207000 (2.268 sec) +2022-05-01 18:16:43,404 - tensorflow - INFO - global_step/sec: 88.2996 +2022-05-01 18:16:43,404 - tensorflow - INFO - loss = 0.024515321, step = 1207200 (2.265 sec) +2022-05-01 18:16:45,668 - tensorflow - INFO - global_step/sec: 88.3078 +2022-05-01 18:16:45,669 - tensorflow - INFO - loss = 0.008551151, step = 1207400 (2.265 sec) +2022-05-01 18:16:47,931 - tensorflow - INFO - global_step/sec: 88.4017 +2022-05-01 18:16:47,931 - tensorflow - INFO - loss = 0.017895028, step = 1207600 (2.262 sec) +2022-05-01 18:16:50,193 - tensorflow - INFO - global_step/sec: 88.4078 +2022-05-01 18:16:50,194 - tensorflow - INFO - loss = 2.2733283, step = 1207800 (2.262 sec) +2022-05-01 18:16:52,460 - tensorflow - INFO - global_step/sec: 88.2292 +2022-05-01 18:16:52,460 - tensorflow - INFO - loss = 0.2346119, step = 1208000 (2.267 sec) +2022-05-01 18:16:54,729 - tensorflow - INFO - global_step/sec: 88.1437 +2022-05-01 18:16:54,729 - tensorflow - INFO - loss = 0.89419997, step = 1208200 (2.269 sec) +2022-05-01 18:16:57,001 - tensorflow - INFO - global_step/sec: 88.0239 +2022-05-01 18:16:57,001 - tensorflow - INFO - loss = 0.012121639, step = 1208400 (2.272 sec) +2022-05-01 18:16:59,265 - tensorflow - INFO - global_step/sec: 88.3247 +2022-05-01 18:16:59,266 - tensorflow - INFO - loss = 0.031270366, step = 1208600 (2.264 sec) +2022-05-01 18:17:01,521 - tensorflow - INFO - global_step/sec: 88.6854 +2022-05-01 18:17:01,521 - tensorflow - INFO - loss = 0.18687676, step = 1208800 (2.255 sec) +2022-05-01 18:17:03,776 - tensorflow - INFO - global_step/sec: 88.6925 +2022-05-01 18:17:03,776 - tensorflow - INFO - loss = 0.028050845, step = 1209000 (2.255 sec) +2022-05-01 18:17:06,030 - tensorflow - INFO - global_step/sec: 88.7049 +2022-05-01 18:17:06,031 - tensorflow - INFO - loss = 0.031182192, step = 1209200 (2.255 sec) +2022-05-01 18:17:08,289 - tensorflow - INFO - global_step/sec: 88.5497 +2022-05-01 18:17:08,289 - tensorflow - INFO - loss = 0.068460576, step = 1209400 (2.259 sec) +2022-05-01 18:17:10,545 - tensorflow - INFO - global_step/sec: 88.6406 +2022-05-01 18:17:10,546 - tensorflow - INFO - loss = 0.021954631, step = 1209600 (2.256 sec) +2022-05-01 18:17:12,799 - tensorflow - INFO - global_step/sec: 88.7257 +2022-05-01 18:17:12,800 - tensorflow - INFO - loss = 0.004034278, step = 1209800 (2.254 sec) +2022-05-01 18:17:15,054 - tensorflow - INFO - global_step/sec: 88.7223 +2022-05-01 18:17:15,054 - tensorflow - INFO - loss = 0.025063317, step = 1210000 (2.254 sec) +2022-05-01 18:17:17,312 - tensorflow - INFO - global_step/sec: 88.5539 +2022-05-01 18:17:17,312 - tensorflow - INFO - loss = 0.050667234, step = 1210200 (2.258 sec) +2022-05-01 18:17:19,567 - tensorflow - INFO - global_step/sec: 88.6839 +2022-05-01 18:17:19,568 - tensorflow - INFO - loss = 0.1030391, step = 1210400 (2.255 sec) +2022-05-01 18:17:21,818 - tensorflow - INFO - global_step/sec: 88.8672 +2022-05-01 18:17:21,818 - tensorflow - INFO - loss = 0.034747954, step = 1210600 (2.250 sec) +2022-05-01 18:17:24,074 - tensorflow - INFO - global_step/sec: 88.6347 +2022-05-01 18:17:24,075 - tensorflow - INFO - loss = 0.012536058, step = 1210800 (2.257 sec) +2022-05-01 18:17:26,326 - tensorflow - INFO - global_step/sec: 88.8194 +2022-05-01 18:17:26,326 - tensorflow - INFO - loss = 0.43177557, step = 1211000 (2.252 sec) +2022-05-01 18:17:28,580 - tensorflow - INFO - global_step/sec: 88.7257 +2022-05-01 18:17:28,581 - tensorflow - INFO - loss = 0.008483944, step = 1211200 (2.254 sec) +2022-05-01 18:17:30,840 - tensorflow - INFO - global_step/sec: 88.4892 +2022-05-01 18:17:30,841 - tensorflow - INFO - loss = 0.03102298, step = 1211400 (2.260 sec) +2022-05-01 18:17:33,100 - tensorflow - INFO - global_step/sec: 88.5174 +2022-05-01 18:17:33,100 - tensorflow - INFO - loss = 0.022476638, step = 1211600 (2.259 sec) +2022-05-01 18:17:35,356 - tensorflow - INFO - global_step/sec: 88.6581 +2022-05-01 18:17:35,356 - tensorflow - INFO - loss = 0.086075, step = 1211800 (2.256 sec) +2022-05-01 18:17:37,609 - tensorflow - INFO - global_step/sec: 88.7642 +2022-05-01 18:17:37,609 - tensorflow - INFO - loss = 0.019420132, step = 1212000 (2.253 sec) +2022-05-01 18:17:39,860 - tensorflow - INFO - global_step/sec: 88.8414 +2022-05-01 18:17:39,860 - tensorflow - INFO - loss = 0.023218995, step = 1212200 (2.251 sec) +2022-05-01 18:17:42,112 - tensorflow - INFO - global_step/sec: 88.8194 +2022-05-01 18:17:42,112 - tensorflow - INFO - loss = 0.019371906, step = 1212400 (2.252 sec) +2022-05-01 18:17:44,370 - tensorflow - INFO - global_step/sec: 88.5792 +2022-05-01 18:17:44,370 - tensorflow - INFO - loss = 0.011097064, step = 1212600 (2.258 sec) +2022-05-01 18:17:46,630 - tensorflow - INFO - global_step/sec: 88.4806 +2022-05-01 18:17:46,630 - tensorflow - INFO - loss = 0.010509571, step = 1212800 (2.260 sec) +2022-05-01 18:17:48,882 - tensorflow - INFO - global_step/sec: 88.7898 +2022-05-01 18:17:48,883 - tensorflow - INFO - loss = 0.010224899, step = 1213000 (2.252 sec) +2022-05-01 18:17:51,140 - tensorflow - INFO - global_step/sec: 88.5783 +2022-05-01 18:17:51,141 - tensorflow - INFO - loss = 0.022628814, step = 1213200 (2.258 sec) +2022-05-01 18:17:53,397 - tensorflow - INFO - global_step/sec: 88.6391 +2022-05-01 18:17:53,397 - tensorflow - INFO - loss = 0.048516, step = 1213400 (2.256 sec) +2022-05-01 18:17:55,656 - tensorflow - INFO - global_step/sec: 88.5166 +2022-05-01 18:17:55,657 - tensorflow - INFO - loss = 0.18198201, step = 1213600 (2.260 sec) +2022-05-01 18:17:57,917 - tensorflow - INFO - global_step/sec: 88.4749 +2022-05-01 18:17:57,917 - tensorflow - INFO - loss = 0.025691815, step = 1213800 (2.260 sec) +2022-05-01 18:18:00,169 - tensorflow - INFO - global_step/sec: 88.8108 +2022-05-01 18:18:00,169 - tensorflow - INFO - loss = 0.00946398, step = 1214000 (2.252 sec) +2022-05-01 18:18:02,425 - tensorflow - INFO - global_step/sec: 88.6339 +2022-05-01 18:18:02,426 - tensorflow - INFO - loss = 0.018743878, step = 1214200 (2.257 sec) +2022-05-01 18:18:04,688 - tensorflow - INFO - global_step/sec: 88.3704 +2022-05-01 18:18:04,689 - tensorflow - INFO - loss = 0.011845864, step = 1214400 (2.263 sec) +2022-05-01 18:18:06,942 - tensorflow - INFO - global_step/sec: 88.7471 +2022-05-01 18:18:06,942 - tensorflow - INFO - loss = 0.032631807, step = 1214600 (2.254 sec) +2022-05-01 18:18:09,198 - tensorflow - INFO - global_step/sec: 88.6552 +2022-05-01 18:18:09,198 - tensorflow - INFO - loss = 0.038079884, step = 1214800 (2.256 sec) +2022-05-01 18:18:11,462 - tensorflow - INFO - global_step/sec: 88.3451 +2022-05-01 18:18:11,462 - tensorflow - INFO - loss = 0.025871545, step = 1215000 (2.264 sec) +2022-05-01 18:18:13,719 - tensorflow - INFO - global_step/sec: 88.6184 +2022-05-01 18:18:13,719 - tensorflow - INFO - loss = 0.04004892, step = 1215200 (2.257 sec) +2022-05-01 18:18:15,978 - tensorflow - INFO - global_step/sec: 88.5162 +2022-05-01 18:18:15,978 - tensorflow - INFO - loss = 0.015076388, step = 1215400 (2.259 sec) +2022-05-01 18:18:18,227 - tensorflow - INFO - global_step/sec: 88.933 +2022-05-01 18:18:18,227 - tensorflow - INFO - loss = 0.0905877, step = 1215600 (2.249 sec) +2022-05-01 18:18:20,490 - tensorflow - INFO - global_step/sec: 88.3891 +2022-05-01 18:18:20,490 - tensorflow - INFO - loss = 0.040627148, step = 1215800 (2.263 sec) +2022-05-01 18:18:22,746 - tensorflow - INFO - global_step/sec: 88.628 +2022-05-01 18:18:22,747 - tensorflow - INFO - loss = 0.032390036, step = 1216000 (2.257 sec) +2022-05-01 18:18:25,004 - tensorflow - INFO - global_step/sec: 88.5901 +2022-05-01 18:18:25,004 - tensorflow - INFO - loss = 0.019741608, step = 1216200 (2.258 sec) +2022-05-01 18:18:27,260 - tensorflow - INFO - global_step/sec: 88.6545 +2022-05-01 18:18:27,260 - tensorflow - INFO - loss = 0.016765134, step = 1216400 (2.256 sec) +2022-05-01 18:18:29,513 - tensorflow - INFO - global_step/sec: 88.7752 +2022-05-01 18:18:29,513 - tensorflow - INFO - loss = 0.05650171, step = 1216600 (2.253 sec) +2022-05-01 18:18:31,769 - tensorflow - INFO - global_step/sec: 88.6245 +2022-05-01 18:18:31,770 - tensorflow - INFO - loss = 0.011445334, step = 1216800 (2.257 sec) +2022-05-01 18:18:34,025 - tensorflow - INFO - global_step/sec: 88.6534 +2022-05-01 18:18:34,026 - tensorflow - INFO - loss = 0.033182055, step = 1217000 (2.256 sec) +2022-05-01 18:18:36,278 - tensorflow - INFO - global_step/sec: 88.7766 +2022-05-01 18:18:36,279 - tensorflow - INFO - loss = 0.00765276, step = 1217200 (2.253 sec) +2022-05-01 18:18:38,534 - tensorflow - INFO - global_step/sec: 88.6491 +2022-05-01 18:18:38,535 - tensorflow - INFO - loss = 14.751188, step = 1217400 (2.256 sec) +2022-05-01 18:18:40,787 - tensorflow - 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INFO - loss = 0.03170078, step = 1282000 (2.258 sec) +2022-05-01 18:30:49,729 - tensorflow - INFO - global_step/sec: 88.2437 +2022-05-01 18:30:49,729 - tensorflow - INFO - loss = 0.010821324, step = 1282200 (2.267 sec) +2022-05-01 18:30:51,994 - tensorflow - INFO - global_step/sec: 88.2914 +2022-05-01 18:30:51,994 - tensorflow - INFO - loss = 0.053051386, step = 1282400 (2.265 sec) +2022-05-01 18:30:54,259 - tensorflow - INFO - global_step/sec: 88.2854 +2022-05-01 18:30:54,260 - tensorflow - INFO - loss = 0.017596355, step = 1282600 (2.265 sec) +2022-05-01 18:30:56,523 - tensorflow - INFO - global_step/sec: 88.3679 +2022-05-01 18:30:56,523 - tensorflow - INFO - loss = 0.0117900185, step = 1282800 (2.263 sec) +2022-05-01 18:30:58,779 - tensorflow - INFO - global_step/sec: 88.6545 +2022-05-01 18:30:58,779 - tensorflow - INFO - loss = 0.022554511, step = 1283000 (2.256 sec) +2022-05-01 18:31:01,027 - tensorflow - INFO - global_step/sec: 88.9362 +2022-05-01 18:31:01,028 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.5468 +2022-05-01 18:31:16,815 - tensorflow - INFO - loss = 0.03547059, step = 1284600 (2.259 sec) +2022-05-01 18:31:19,072 - tensorflow - INFO - global_step/sec: 88.581 +2022-05-01 18:31:19,073 - tensorflow - INFO - loss = 0.010466327, step = 1284800 (2.258 sec) +2022-05-01 18:31:21,332 - tensorflow - INFO - global_step/sec: 88.5187 +2022-05-01 18:31:21,332 - tensorflow - INFO - loss = 0.013831692, step = 1285000 (2.259 sec) +2022-05-01 18:31:23,589 - tensorflow - INFO - global_step/sec: 88.5789 +2022-05-01 18:31:23,590 - tensorflow - INFO - loss = 0.019581486, step = 1285200 (2.258 sec) +2022-05-01 18:31:25,847 - tensorflow - INFO - global_step/sec: 88.5897 +2022-05-01 18:31:25,847 - tensorflow - INFO - loss = 0.04874866, step = 1285400 (2.258 sec) +2022-05-01 18:31:28,107 - tensorflow - INFO - global_step/sec: 88.5069 +2022-05-01 18:31:28,107 - tensorflow - INFO - loss = 0.036323167, step = 1285600 (2.260 sec) +2022-05-01 18:31:30,366 - tensorflow - INFO - global_step/sec: 88.5367 +2022-05-01 18:31:30,366 - tensorflow - INFO - loss = 0.01787388, step = 1285800 (2.259 sec) +2022-05-01 18:31:32,626 - tensorflow - INFO - global_step/sec: 88.5028 +2022-05-01 18:31:32,626 - tensorflow - INFO - loss = 0.2014732, step = 1286000 (2.260 sec) +2022-05-01 18:31:34,886 - tensorflow - INFO - global_step/sec: 88.4615 +2022-05-01 18:31:34,887 - tensorflow - INFO - loss = 0.010692698, step = 1286200 (2.261 sec) +2022-05-01 18:31:37,139 - tensorflow - INFO - global_step/sec: 88.7775 +2022-05-01 18:31:37,140 - tensorflow - INFO - loss = 0.026695687, step = 1286400 (2.253 sec) +2022-05-01 18:31:39,391 - tensorflow - INFO - global_step/sec: 88.813 +2022-05-01 18:31:39,392 - tensorflow - INFO - loss = 0.051970016, step = 1286600 (2.252 sec) +2022-05-01 18:31:41,645 - tensorflow - INFO - global_step/sec: 88.7399 +2022-05-01 18:31:41,645 - tensorflow - INFO - loss = 0.06618852, step = 1286800 (2.254 sec) +2022-05-01 18:31:43,897 - tensorflow - INFO - global_step/sec: 88.8046 +2022-05-01 18:31:43,897 - tensorflow - INFO - loss = 0.024169106, step = 1287000 (2.252 sec) +2022-05-01 18:31:46,153 - tensorflow - INFO - global_step/sec: 88.6677 +2022-05-01 18:31:46,153 - tensorflow - INFO - loss = 0.009108191, step = 1287200 (2.256 sec) +2022-05-01 18:31:48,406 - tensorflow - INFO - global_step/sec: 88.76 +2022-05-01 18:31:48,406 - tensorflow - INFO - loss = 0.08247757, step = 1287400 (2.253 sec) +2022-05-01 18:31:50,658 - tensorflow - INFO - global_step/sec: 88.82 +2022-05-01 18:31:50,658 - tensorflow - INFO - loss = 0.010382421, step = 1287600 (2.252 sec) +2022-05-01 18:31:52,908 - tensorflow - INFO - global_step/sec: 88.8904 +2022-05-01 18:31:52,908 - tensorflow - INFO - loss = 0.4617443, step = 1287800 (2.250 sec) +2022-05-01 18:31:55,155 - tensorflow - INFO - global_step/sec: 88.9815 +2022-05-01 18:31:55,156 - tensorflow - INFO - loss = 0.018690221, step = 1288000 (2.248 sec) +2022-05-01 18:31:57,423 - tensorflow - INFO - global_step/sec: 88.1855 +2022-05-01 18:31:57,424 - tensorflow - INFO - loss = 0.006660033, step = 1288200 (2.268 sec) +2022-05-01 18:31:59,680 - tensorflow - INFO - global_step/sec: 88.626 +2022-05-01 18:31:59,680 - tensorflow - INFO - loss = 0.08108465, step = 1288400 (2.257 sec) +2022-05-01 18:32:01,939 - tensorflow - INFO - global_step/sec: 88.548 +2022-05-01 18:32:01,939 - tensorflow - INFO - loss = 0.14775956, step = 1288600 (2.259 sec) +2022-05-01 18:32:04,193 - tensorflow - INFO - global_step/sec: 88.7121 +2022-05-01 18:32:04,193 - tensorflow - INFO - loss = 0.024420854, step = 1288800 (2.254 sec) +2022-05-01 18:32:06,444 - tensorflow - INFO - global_step/sec: 88.8523 +2022-05-01 18:32:06,444 - tensorflow - INFO - loss = 2.4564295, step = 1289000 (2.251 sec) +2022-05-01 18:32:08,694 - tensorflow - INFO - global_step/sec: 88.8765 +2022-05-01 18:32:08,695 - tensorflow - INFO - loss = 0.037567124, step = 1289200 (2.250 sec) +2022-05-01 18:32:10,951 - tensorflow - INFO - global_step/sec: 88.6394 +2022-05-01 18:32:10,951 - tensorflow - INFO - loss = 0.020364668, step = 1289400 (2.256 sec) +2022-05-01 18:32:13,204 - tensorflow - INFO - global_step/sec: 88.744 +2022-05-01 18:32:13,205 - tensorflow - INFO - loss = 0.0064563705, step = 1289600 (2.254 sec) +2022-05-01 18:32:15,464 - tensorflow - INFO - global_step/sec: 88.4911 +2022-05-01 18:32:15,465 - tensorflow - INFO - loss = 0.022302406, step = 1289800 (2.260 sec) +2022-05-01 18:32:17,713 - tensorflow - INFO - global_step/sec: 88.9568 +2022-05-01 18:32:17,713 - tensorflow - INFO - loss = 0.03229746, step = 1290000 (2.248 sec) +2022-05-01 18:32:19,963 - tensorflow - INFO - global_step/sec: 88.8878 +2022-05-01 18:32:19,963 - tensorflow - INFO - loss = 0.012207605, step = 1290200 (2.250 sec) +2022-05-01 18:32:22,218 - tensorflow - INFO - global_step/sec: 88.6647 +2022-05-01 18:32:22,219 - tensorflow - INFO - loss = 2.3386588, step = 1290400 (2.256 sec) +2022-05-01 18:32:24,474 - tensorflow - INFO - global_step/sec: 88.6881 +2022-05-01 18:32:24,474 - tensorflow - INFO - loss = 0.012496609, step = 1290600 (2.255 sec) +2022-05-01 18:32:26,729 - tensorflow - INFO - global_step/sec: 88.6877 +2022-05-01 18:32:26,729 - tensorflow - INFO - loss = 0.07285924, step = 1290800 (2.255 sec) +2022-05-01 18:32:28,980 - tensorflow - INFO - global_step/sec: 88.8342 +2022-05-01 18:32:28,980 - tensorflow - INFO - loss = 0.29961962, step = 1291000 (2.251 sec) +2022-05-01 18:32:31,243 - tensorflow - INFO - global_step/sec: 88.3785 +2022-05-01 18:32:31,243 - tensorflow - INFO - loss = 0.15170337, step = 1291200 (2.263 sec) +2022-05-01 18:32:33,499 - tensorflow - INFO - global_step/sec: 88.6536 +2022-05-01 18:32:33,499 - tensorflow - INFO - loss = 0.038761783, step = 1291400 (2.256 sec) +2022-05-01 18:32:35,754 - tensorflow - INFO - global_step/sec: 88.6969 +2022-05-01 18:32:35,754 - tensorflow - INFO - loss = 0.09981961, step = 1291600 (2.255 sec) +2022-05-01 18:32:38,002 - tensorflow - INFO - global_step/sec: 88.9676 +2022-05-01 18:32:38,002 - tensorflow - INFO - loss = 0.2219835, step = 1291800 (2.248 sec) +2022-05-01 18:32:40,252 - tensorflow - INFO - global_step/sec: 88.8749 +2022-05-01 18:32:40,253 - tensorflow - INFO - loss = 0.005805756, step = 1292000 (2.250 sec) +2022-05-01 18:32:42,510 - tensorflow - INFO - global_step/sec: 88.5711 +2022-05-01 18:32:42,511 - tensorflow - INFO - loss = 0.029483391, step = 1292200 (2.258 sec) +2022-05-01 18:32:44,761 - tensorflow - INFO - global_step/sec: 88.8757 +2022-05-01 18:32:44,761 - tensorflow - INFO - loss = 0.014592943, step = 1292400 (2.250 sec) +2022-05-01 18:32:47,018 - tensorflow - INFO - global_step/sec: 88.5939 +2022-05-01 18:32:47,018 - tensorflow - INFO - loss = 0.014249931, step = 1292600 (2.258 sec) +2022-05-01 18:32:49,275 - tensorflow - INFO - global_step/sec: 88.6192 +2022-05-01 18:32:49,275 - tensorflow - INFO - loss = 0.017477775, step = 1292800 (2.257 sec) +2022-05-01 18:32:51,527 - tensorflow - INFO - global_step/sec: 88.8213 +2022-05-01 18:32:51,527 - tensorflow - INFO - loss = 0.011953859, step = 1293000 (2.252 sec) +2022-05-01 18:32:53,778 - tensorflow - INFO - global_step/sec: 88.8499 +2022-05-01 18:32:53,778 - tensorflow - INFO - loss = 0.011802371, step = 1293200 (2.251 sec) +2022-05-01 18:32:56,028 - tensorflow - INFO - global_step/sec: 88.8638 +2022-05-01 18:32:56,029 - tensorflow - INFO - loss = 0.015671462, step = 1293400 (2.251 sec) +2022-05-01 18:32:58,283 - tensorflow - INFO - global_step/sec: 88.709 +2022-05-01 18:32:58,283 - tensorflow - INFO - loss = 0.010309601, step = 1293600 (2.255 sec) +2022-05-01 18:33:00,540 - tensorflow - INFO - global_step/sec: 88.6174 +2022-05-01 18:33:00,540 - tensorflow - INFO - loss = 0.5059539, step = 1293800 (2.257 sec) +2022-05-01 18:33:02,795 - tensorflow - INFO - global_step/sec: 88.6678 +2022-05-01 18:33:02,796 - tensorflow - INFO - loss = 0.029848307, step = 1294000 (2.256 sec) +2022-05-01 18:33:05,054 - tensorflow - INFO - global_step/sec: 88.566 +2022-05-01 18:33:05,054 - tensorflow - INFO - loss = 0.008277052, step = 1294200 (2.258 sec) +2022-05-01 18:33:07,311 - tensorflow - INFO - global_step/sec: 88.5882 +2022-05-01 18:33:07,312 - tensorflow - INFO - loss = 0.20894733, step = 1294400 (2.258 sec) +2022-05-01 18:33:09,563 - tensorflow - INFO - global_step/sec: 88.8015 +2022-05-01 18:33:09,564 - tensorflow - INFO - loss = 0.15793276, step = 1294600 (2.252 sec) +2022-05-01 18:33:11,819 - tensorflow - INFO - global_step/sec: 88.6602 +2022-05-01 18:33:11,820 - tensorflow - INFO - loss = 0.010115347, step = 1294800 (2.256 sec) +2022-05-01 18:33:14,067 - tensorflow - INFO - global_step/sec: 88.9644 +2022-05-01 18:33:14,068 - tensorflow - INFO - loss = 0.01433547, step = 1295000 (2.248 sec) +2022-05-01 18:33:16,320 - tensorflow - INFO - global_step/sec: 88.7784 +2022-05-01 18:33:16,320 - tensorflow - INFO - loss = 0.05959379, step = 1295200 (2.253 sec) +2022-05-01 18:33:18,574 - tensorflow - INFO - global_step/sec: 88.7471 +2022-05-01 18:33:18,574 - tensorflow - INFO - loss = 0.009571571, step = 1295400 (2.254 sec) +2022-05-01 18:33:20,829 - tensorflow - INFO - global_step/sec: 88.688 +2022-05-01 18:33:20,829 - tensorflow - INFO - loss = 0.013059959, step = 1295600 (2.255 sec) +2022-05-01 18:33:23,086 - tensorflow - INFO - global_step/sec: 88.6207 +2022-05-01 18:33:23,086 - tensorflow - INFO - loss = 0.019144017, step = 1295800 (2.257 sec) +2022-05-01 18:33:25,348 - tensorflow - INFO - global_step/sec: 88.4099 +2022-05-01 18:33:25,348 - tensorflow - INFO - loss = 0.012158981, step = 1296000 (2.262 sec) +2022-05-01 18:33:27,601 - tensorflow - INFO - global_step/sec: 88.7429 +2022-05-01 18:33:27,602 - tensorflow - INFO - loss = 0.012564669, step = 1296200 (2.254 sec) +2022-05-01 18:33:29,853 - tensorflow - INFO - global_step/sec: 88.816 +2022-05-01 18:33:29,854 - tensorflow - INFO - loss = 6.0557833, step = 1296400 (2.252 sec) +2022-05-01 18:33:32,110 - tensorflow - INFO - global_step/sec: 88.6116 +2022-05-01 18:33:32,111 - tensorflow - INFO - loss = 0.022484478, step = 1296600 (2.257 sec) +2022-05-01 18:33:34,367 - tensorflow - INFO - global_step/sec: 88.6347 +2022-05-01 18:33:34,367 - tensorflow - INFO - loss = 0.05494872, step = 1296800 (2.256 sec) +2022-05-01 18:33:36,624 - tensorflow - INFO - global_step/sec: 88.5898 +2022-05-01 18:33:36,625 - tensorflow - INFO - loss = 0.01839782, step = 1297000 (2.258 sec) +2022-05-01 18:33:38,880 - tensorflow - INFO - global_step/sec: 88.654 +2022-05-01 18:33:38,881 - tensorflow - INFO - loss = 0.029880244, step = 1297200 (2.256 sec) +2022-05-01 18:33:41,131 - tensorflow - INFO - global_step/sec: 88.8782 +2022-05-01 18:33:41,131 - tensorflow - INFO - loss = 0.018286102, step = 1297400 (2.250 sec) +2022-05-01 18:33:43,382 - tensorflow - INFO - global_step/sec: 88.8519 +2022-05-01 18:33:43,382 - tensorflow - INFO - loss = 0.25781596, step = 1297600 (2.251 sec) +2022-05-01 18:33:45,635 - tensorflow - INFO - global_step/sec: 88.742 +2022-05-01 18:33:45,636 - tensorflow - INFO - loss = 0.016293323, step = 1297800 (2.254 sec) +2022-05-01 18:33:47,887 - tensorflow - INFO - global_step/sec: 88.8093 +2022-05-01 18:33:47,888 - tensorflow - INFO - loss = 0.0564257, step = 1298000 (2.252 sec) +2022-05-01 18:33:50,142 - tensorflow - INFO - global_step/sec: 88.7104 +2022-05-01 18:33:50,142 - tensorflow - INFO - loss = 0.01472992, step = 1298200 (2.255 sec) +2022-05-01 18:33:52,399 - tensorflow - INFO - global_step/sec: 88.6091 +2022-05-01 18:33:52,399 - tensorflow - INFO - loss = 0.21787146, step = 1298400 (2.257 sec) +2022-05-01 18:33:54,656 - tensorflow - INFO - global_step/sec: 88.6034 +2022-05-01 18:33:54,657 - tensorflow - INFO - loss = 0.030389164, step = 1298600 (2.257 sec) +2022-05-01 18:33:56,921 - tensorflow - INFO - global_step/sec: 88.3192 +2022-05-01 18:33:56,921 - tensorflow - INFO - loss = 0.014049642, step = 1298800 (2.264 sec) +2022-05-01 18:33:59,178 - tensorflow - INFO - global_step/sec: 88.611 +2022-05-01 18:33:59,178 - tensorflow - INFO - loss = 0.07121677, step = 1299000 (2.257 sec) +2022-05-01 18:34:01,441 - tensorflow - INFO - global_step/sec: 88.3786 +2022-05-01 18:34:01,441 - tensorflow - INFO - loss = 0.011796599, step = 1299200 (2.263 sec) +2022-05-01 18:34:03,706 - tensorflow - INFO - global_step/sec: 88.3047 +2022-05-01 18:34:03,706 - tensorflow - INFO - loss = 0.02658301, step = 1299400 (2.265 sec) +2022-05-01 18:34:05,964 - tensorflow - INFO - global_step/sec: 88.5573 +2022-05-01 18:34:05,964 - tensorflow - INFO - loss = 0.020927524, step = 1299600 (2.258 sec) +2022-05-01 18:34:08,224 - tensorflow - INFO - global_step/sec: 88.4939 +2022-05-01 18:34:08,225 - tensorflow - INFO - loss = 0.08239248, step = 1299800 (2.260 sec) +2022-05-01 18:34:10,473 - tensorflow - INFO - Saving checkpoints for 1300000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. +2022-05-01 18:34:10,488 - tensorflow - INFO - model.ckpt-1300000 is not in all_model_checkpoint_paths. Manually adding it. +2022-05-01 18:34:11,011 - tensorflow - INFO - Calling model_fn. +2022-05-01 18:34:11,139 - tensorflow - INFO - Done calling model_fn. +2022-05-01 18:34:11,154 - tensorflow - INFO - Starting evaluation at 2022-05-01T18:34:11Z +2022-05-01 18:34:11,218 - tensorflow - INFO - Graph was finalized. +2022-05-01 18:34:11,221 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1300000 +2022-05-01 18:34:11,503 - tensorflow - INFO - Running local_init_op. +2022-05-01 18:34:11,611 - tensorflow - INFO - Done running local_init_op. +2022-05-01 18:34:21,788 - tensorflow - INFO - Evaluation [3990/39900] +2022-05-01 18:34:31,243 - tensorflow - INFO - Evaluation [7980/39900] +2022-05-01 18:34:40,667 - tensorflow - INFO - Evaluation [11970/39900] +2022-05-01 18:34:50,117 - tensorflow - INFO - Evaluation [15960/39900] +2022-05-01 18:34:59,573 - tensorflow - INFO - Evaluation [19950/39900] +2022-05-01 18:35:09,037 - tensorflow - INFO - Evaluation [23940/39900] +2022-05-01 18:35:18,472 - tensorflow - INFO - Evaluation [27930/39900] +2022-05-01 18:35:27,938 - tensorflow - INFO - Evaluation [31920/39900] +2022-05-01 18:35:37,388 - tensorflow - INFO - Evaluation [35910/39900] +2022-05-01 18:35:46,832 - tensorflow - INFO - Evaluation [39900/39900] +2022-05-01 18:35:46,968 - tensorflow - INFO - Finished evaluation at 2022-05-01-18:35:46 +2022-05-01 18:35:46,969 - tensorflow - INFO - Saving dict for global step 1300000: global_step = 1300000, loss = 0.037376873, mean_degree_err = 5.5991697 +2022-05-01 18:35:46,969 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 1300000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1300000 +2022-05-01 18:35:46,983 - tensorflow - INFO - global_step/sec: 2.02513 +2022-05-01 18:35:46,984 - tensorflow - INFO - loss = 0.030853275, step = 1300000 (98.759 sec) +2022-05-01 18:35:49,254 - tensorflow - INFO - global_step/sec: 88.0857 +2022-05-01 18:35:49,254 - tensorflow - INFO - loss = 0.024246916, step = 1300200 (2.270 sec) +2022-05-01 18:35:51,503 - tensorflow - INFO - global_step/sec: 88.8944 +2022-05-01 18:35:51,504 - tensorflow - INFO - loss = 4.38457, step = 1300400 (2.250 sec) +2022-05-01 18:35:53,758 - tensorflow - INFO - global_step/sec: 88.7213 +2022-05-01 18:35:53,758 - tensorflow - INFO - loss = 0.03259698, step = 1300600 (2.254 sec) +2022-05-01 18:35:56,012 - tensorflow - INFO - global_step/sec: 88.7204 +2022-05-01 18:35:56,012 - tensorflow - INFO - loss = 0.026033219, step = 1300800 (2.254 sec) +2022-05-01 18:35:58,264 - tensorflow - INFO - global_step/sec: 88.8051 +2022-05-01 18:35:58,265 - tensorflow - INFO - loss = 0.03283344, step = 1301000 (2.252 sec) +2022-05-01 18:36:00,523 - tensorflow - INFO - global_step/sec: 88.5379 +2022-05-01 18:36:00,523 - tensorflow - INFO - loss = 0.012986472, step = 1301200 (2.259 sec) +2022-05-01 18:36:02,785 - tensorflow - INFO - global_step/sec: 88.4277 +2022-05-01 18:36:02,785 - tensorflow - INFO - loss = 0.078854054, step = 1301400 (2.262 sec) +2022-05-01 18:36:05,037 - tensorflow - INFO - global_step/sec: 88.801 +2022-05-01 18:36:05,037 - tensorflow - INFO - loss = 0.11142617, step = 1301600 (2.252 sec) +2022-05-01 18:36:07,299 - tensorflow - INFO - global_step/sec: 88.4314 +2022-05-01 18:36:07,299 - tensorflow - INFO - loss = 0.04029717, step = 1301800 (2.262 sec) +2022-05-01 18:36:09,551 - tensorflow - INFO - global_step/sec: 88.7994 +2022-05-01 18:36:09,551 - tensorflow - INFO - loss = 0.014742036, step = 1302000 (2.252 sec) +2022-05-01 18:36:11,808 - tensorflow - INFO - global_step/sec: 88.6242 +2022-05-01 18:36:11,808 - tensorflow - INFO - loss = 0.015955841, step = 1302200 (2.257 sec) +2022-05-01 18:36:14,065 - tensorflow - INFO - global_step/sec: 88.6135 +2022-05-01 18:36:14,065 - tensorflow - INFO - loss = 0.013542844, step = 1302400 (2.257 sec) +2022-05-01 18:36:16,323 - tensorflow - INFO - global_step/sec: 88.5507 +2022-05-01 18:36:16,324 - tensorflow - INFO - loss = 0.26178345, step = 1302600 (2.259 sec) +2022-05-01 18:36:18,576 - tensorflow - INFO - global_step/sec: 88.7931 +2022-05-01 18:36:18,576 - tensorflow - INFO - loss = 0.03694377, step = 1302800 (2.252 sec) +2022-05-01 18:36:20,827 - tensorflow - INFO - global_step/sec: 88.8155 +2022-05-01 18:36:20,828 - tensorflow - INFO - loss = 0.15881334, step = 1303000 (2.252 sec) +2022-05-01 18:36:23,081 - tensorflow - INFO - global_step/sec: 88.7524 +2022-05-01 18:36:23,081 - tensorflow - INFO - loss = 0.016012246, step = 1303200 (2.254 sec) +2022-05-01 18:36:25,341 - tensorflow - INFO - global_step/sec: 88.4896 +2022-05-01 18:36:25,342 - tensorflow - INFO - loss = 0.0563306, step = 1303400 (2.260 sec) +2022-05-01 18:36:27,600 - tensorflow - INFO - global_step/sec: 88.5233 +2022-05-01 18:36:27,601 - tensorflow - INFO - loss = 0.00582133, step = 1303600 (2.259 sec) +2022-05-01 18:36:29,857 - tensorflow - INFO - global_step/sec: 88.6167 +2022-05-01 18:36:29,858 - tensorflow - INFO - loss = 0.018234067, step = 1303800 (2.257 sec) +2022-05-01 18:36:32,120 - tensorflow - INFO - global_step/sec: 88.3989 +2022-05-01 18:36:32,120 - tensorflow - INFO - loss = 0.020625934, step = 1304000 (2.262 sec) +2022-05-01 18:36:34,377 - tensorflow - INFO - global_step/sec: 88.6045 +2022-05-01 18:36:34,377 - tensorflow - INFO - loss = 0.03280647, step = 1304200 (2.257 sec) +2022-05-01 18:36:36,635 - tensorflow - INFO - global_step/sec: 88.5891 +2022-05-01 18:36:36,635 - tensorflow - INFO - loss = 0.024327138, step = 1304400 (2.258 sec) +2022-05-01 18:36:38,889 - tensorflow - INFO - global_step/sec: 88.7012 +2022-05-01 18:36:38,890 - tensorflow - INFO - loss = 0.029814724, step = 1304600 (2.255 sec) +2022-05-01 18:36:41,148 - tensorflow - INFO - global_step/sec: 88.5416 +2022-05-01 18:36:41,149 - tensorflow - INFO - loss = 0.5983901, step = 1304800 (2.259 sec) +2022-05-01 18:36:43,405 - tensorflow - INFO - global_step/sec: 88.611 +2022-05-01 18:36:43,406 - tensorflow - INFO - loss = 0.0073133297, step = 1305000 (2.257 sec) +2022-05-01 18:36:45,661 - tensorflow - INFO - global_step/sec: 88.6585 +2022-05-01 18:36:45,662 - tensorflow - INFO - loss = 0.05105731, step = 1305200 (2.256 sec) +2022-05-01 18:36:47,915 - tensorflow - INFO - global_step/sec: 88.7485 +2022-05-01 18:36:47,915 - tensorflow - INFO - loss = 0.028995048, step = 1305400 (2.254 sec) +2022-05-01 18:36:50,163 - tensorflow - INFO - global_step/sec: 88.9523 +2022-05-01 18:36:50,163 - tensorflow - INFO - loss = 1.0717584, step = 1305600 (2.248 sec) +2022-05-01 18:36:52,420 - tensorflow - INFO - global_step/sec: 88.6202 +2022-05-01 18:36:52,420 - tensorflow - INFO - loss = 0.025184149, step = 1305800 (2.257 sec) +2022-05-01 18:36:54,675 - tensorflow - INFO - global_step/sec: 88.6934 +2022-05-01 18:36:54,675 - tensorflow - INFO - loss = 0.015993021, step = 1306000 (2.255 sec) +2022-05-01 18:36:56,927 - tensorflow - INFO - global_step/sec: 88.7944 +2022-05-01 18:36:56,928 - tensorflow - INFO - loss = 0.007527591, step = 1306200 (2.252 sec) +2022-05-01 18:36:59,180 - tensorflow - INFO - global_step/sec: 88.7991 +2022-05-01 18:36:59,180 - tensorflow - INFO - loss = 0.020365115, step = 1306400 (2.252 sec) +2022-05-01 18:37:01,433 - tensorflow - INFO - global_step/sec: 88.7458 +2022-05-01 18:37:01,434 - tensorflow - INFO - loss = 0.0067551583, step = 1306600 (2.254 sec) +2022-05-01 18:37:03,692 - tensorflow - INFO - global_step/sec: 88.5519 +2022-05-01 18:37:03,692 - tensorflow - INFO - loss = 0.05375622, step = 1306800 (2.259 sec) +2022-05-01 18:37:05,946 - tensorflow - INFO - global_step/sec: 88.7302 +2022-05-01 18:37:05,946 - tensorflow - INFO - loss = 0.011336693, step = 1307000 (2.254 sec) +2022-05-01 18:37:08,202 - tensorflow - INFO - global_step/sec: 88.6489 +2022-05-01 18:37:08,202 - tensorflow - INFO - loss = 0.1290312, step = 1307200 (2.256 sec) +2022-05-01 18:37:10,458 - tensorflow - INFO - global_step/sec: 88.6472 +2022-05-01 18:37:10,458 - tensorflow - INFO - loss = 0.024874978, step = 1307400 (2.256 sec) +2022-05-01 18:37:12,714 - tensorflow - INFO - global_step/sec: 88.6395 +2022-05-01 18:37:12,715 - tensorflow - INFO - loss = 0.049371824, step = 1307600 (2.256 sec) +2022-05-01 18:37:14,970 - tensorflow - INFO - global_step/sec: 88.6755 +2022-05-01 18:37:14,970 - tensorflow - INFO - loss = 0.11340335, step = 1307800 (2.255 sec) +2022-05-01 18:37:17,228 - tensorflow - INFO - global_step/sec: 88.5512 +2022-05-01 18:37:17,229 - tensorflow - INFO - loss = 0.031907097, step = 1308000 (2.259 sec) +2022-05-01 18:37:19,487 - tensorflow - INFO - global_step/sec: 88.5646 +2022-05-01 18:37:19,487 - tensorflow - INFO - loss = 0.011532921, step = 1308200 (2.258 sec) +2022-05-01 18:37:21,741 - tensorflow - INFO - global_step/sec: 88.7206 +2022-05-01 18:37:21,741 - tensorflow - INFO - loss = 0.04323201, step = 1308400 (2.254 sec) +2022-05-01 18:37:23,990 - tensorflow - INFO - global_step/sec: 88.901 +2022-05-01 18:37:23,991 - tensorflow - INFO - loss = 0.026521616, step = 1308600 (2.250 sec) +2022-05-01 18:37:26,247 - tensorflow - INFO - global_step/sec: 88.6133 +2022-05-01 18:37:26,248 - tensorflow - INFO - loss = 0.017270735, step = 1308800 (2.257 sec) +2022-05-01 18:37:28,510 - tensorflow - INFO - global_step/sec: 88.4004 +2022-05-01 18:37:28,510 - tensorflow - INFO - loss = 0.021364436, step = 1309000 (2.262 sec) +2022-05-01 18:37:30,776 - tensorflow - INFO - global_step/sec: 88.2497 +2022-05-01 18:37:30,777 - tensorflow - INFO - loss = 0.007567391, step = 1309200 (2.266 sec) +2022-05-01 18:37:33,040 - tensorflow - INFO - global_step/sec: 88.3285 +2022-05-01 18:37:33,041 - tensorflow - INFO - loss = 0.04614389, step = 1309400 (2.264 sec) +2022-05-01 18:37:35,302 - tensorflow - INFO - global_step/sec: 88.4257 +2022-05-01 18:37:35,303 - tensorflow - INFO - loss = 0.00833034, step = 1309600 (2.262 sec) +2022-05-01 18:37:37,568 - tensorflow - INFO - global_step/sec: 88.2544 +2022-05-01 18:37:37,569 - tensorflow - INFO - loss = 0.045131706, step = 1309800 (2.266 sec) +2022-05-01 18:37:39,826 - tensorflow - INFO - global_step/sec: 88.5956 +2022-05-01 18:37:39,826 - tensorflow - INFO - loss = 0.010167251, step = 1310000 (2.257 sec) +2022-05-01 18:37:42,090 - tensorflow - INFO - global_step/sec: 88.3233 +2022-05-01 18:37:42,091 - tensorflow - INFO - loss = 0.18503237, step = 1310200 (2.264 sec) +2022-05-01 18:37:44,354 - tensorflow - INFO - global_step/sec: 88.3432 +2022-05-01 18:37:44,355 - tensorflow - INFO - loss = 0.022806745, step = 1310400 (2.264 sec) +2022-05-01 18:37:46,613 - tensorflow - INFO - global_step/sec: 88.547 +2022-05-01 18:37:46,613 - tensorflow - INFO - loss = 0.06512076, step = 1310600 (2.259 sec) +2022-05-01 18:37:48,871 - tensorflow - INFO - global_step/sec: 88.5569 +2022-05-01 18:37:48,873 - tensorflow - INFO - loss = 0.009896301, step = 1310800 (2.259 sec) +2022-05-01 18:37:51,133 - tensorflow - INFO - global_step/sec: 88.4468 +2022-05-01 18:37:51,133 - tensorflow - INFO - loss = 0.041337546, step = 1311000 (2.260 sec) +2022-05-01 18:37:53,394 - tensorflow - INFO - global_step/sec: 88.4387 +2022-05-01 18:37:53,394 - tensorflow - INFO - loss = 0.0063431333, step = 1311200 (2.261 sec) +2022-05-01 18:37:55,655 - tensorflow - INFO - global_step/sec: 88.4545 +2022-05-01 18:37:55,655 - tensorflow - INFO - loss = 0.08382498, step = 1311400 (2.261 sec) +2022-05-01 18:37:57,922 - tensorflow - INFO - global_step/sec: 88.2091 +2022-05-01 18:37:57,923 - tensorflow - INFO - loss = 0.048825525, step = 1311600 (2.267 sec) +2022-05-01 18:38:00,178 - tensorflow - INFO - global_step/sec: 88.6561 +2022-05-01 18:38:00,179 - tensorflow - INFO - loss = 0.028192045, step = 1311800 (2.256 sec) +2022-05-01 18:38:02,434 - tensorflow - INFO - global_step/sec: 88.6844 +2022-05-01 18:38:02,434 - tensorflow - INFO - loss = 6.6153092, step = 1312000 (2.255 sec) +2022-05-01 18:38:04,685 - tensorflow - INFO - global_step/sec: 88.8115 +2022-05-01 18:38:04,686 - tensorflow - INFO - loss = 0.026785431, step = 1312200 (2.252 sec) +2022-05-01 18:38:06,942 - tensorflow - INFO - global_step/sec: 88.6361 +2022-05-01 18:38:06,942 - tensorflow - INFO - loss = 0.072804034, step = 1312400 (2.256 sec) +2022-05-01 18:38:09,197 - tensorflow - INFO - global_step/sec: 88.6924 +2022-05-01 18:38:09,197 - tensorflow - INFO - loss = 0.06181521, step = 1312600 (2.255 sec) +2022-05-01 18:38:11,457 - tensorflow - INFO - global_step/sec: 88.4754 +2022-05-01 18:38:11,458 - tensorflow - INFO - loss = 0.008904839, step = 1312800 (2.261 sec) +2022-05-01 18:38:13,713 - tensorflow - INFO - global_step/sec: 88.6783 +2022-05-01 18:38:13,713 - tensorflow - INFO - loss = 0.01900611, step = 1313000 (2.255 sec) +2022-05-01 18:38:15,968 - tensorflow - INFO - global_step/sec: 88.6909 +2022-05-01 18:38:15,968 - tensorflow - INFO - loss = 0.019653503, step = 1313200 (2.255 sec) +2022-05-01 18:38:18,222 - tensorflow - INFO - global_step/sec: 88.7306 +2022-05-01 18:38:18,222 - tensorflow - INFO - loss = 0.39353144, step = 1313400 (2.254 sec) +2022-05-01 18:38:20,472 - tensorflow - INFO - global_step/sec: 88.8621 +2022-05-01 18:38:20,473 - tensorflow - INFO - loss = 0.08586927, step = 1313600 (2.251 sec) +2022-05-01 18:38:22,728 - tensorflow - INFO - global_step/sec: 88.6664 +2022-05-01 18:38:22,728 - tensorflow - INFO - loss = 0.13145106, step = 1313800 (2.256 sec) +2022-05-01 18:38:24,987 - tensorflow - INFO - global_step/sec: 88.5386 +2022-05-01 18:38:24,987 - tensorflow - INFO - loss = 0.020194734, step = 1314000 (2.259 sec) +2022-05-01 18:38:27,238 - tensorflow - INFO - global_step/sec: 88.8625 +2022-05-01 18:38:27,238 - tensorflow - INFO - loss = 0.028168859, step = 1314200 (2.251 sec) +2022-05-01 18:38:29,495 - tensorflow - INFO - global_step/sec: 88.6107 +2022-05-01 18:38:29,495 - tensorflow - INFO - loss = 0.008614794, step = 1314400 (2.257 sec) +2022-05-01 18:38:31,750 - tensorflow - INFO - global_step/sec: 88.6865 +2022-05-01 18:38:31,750 - tensorflow - INFO - loss = 0.0250443, step = 1314600 (2.255 sec) +2022-05-01 18:38:34,001 - tensorflow - INFO - global_step/sec: 88.8416 +2022-05-01 18:38:34,001 - tensorflow - INFO - loss = 0.02536972, step = 1314800 (2.251 sec) +2022-05-01 18:38:36,255 - tensorflow - INFO - global_step/sec: 88.7452 +2022-05-01 18:38:36,255 - tensorflow - INFO - loss = 0.10148332, step = 1315000 (2.254 sec) +2022-05-01 18:38:38,510 - tensorflow - INFO - global_step/sec: 88.6834 +2022-05-01 18:38:38,510 - tensorflow - INFO - loss = 0.015541778, step = 1315200 (2.255 sec) +2022-05-01 18:38:40,764 - tensorflow - INFO - global_step/sec: 88.7077 +2022-05-01 18:38:40,765 - tensorflow - INFO - loss = 0.007116256, step = 1315400 (2.255 sec) +2022-05-01 18:38:43,018 - tensorflow - INFO - global_step/sec: 88.7318 +2022-05-01 18:38:43,019 - tensorflow - INFO - loss = 0.011176328, step = 1315600 (2.254 sec) +2022-05-01 18:38:45,273 - tensorflow - INFO - global_step/sec: 88.7104 +2022-05-01 18:38:45,273 - tensorflow - INFO - loss = 0.016587943, step = 1315800 (2.255 sec) +2022-05-01 18:38:47,529 - tensorflow - INFO - global_step/sec: 88.6377 +2022-05-01 18:38:47,530 - tensorflow - INFO - loss = 0.022784336, step = 1316000 (2.256 sec) +2022-05-01 18:38:49,787 - tensorflow - INFO - global_step/sec: 88.5991 +2022-05-01 18:38:49,787 - tensorflow - INFO - loss = 0.035682764, step = 1316200 (2.257 sec) +2022-05-01 18:38:52,041 - tensorflow - INFO - global_step/sec: 88.7257 +2022-05-01 18:38:52,041 - tensorflow - INFO - loss = 0.015324963, step = 1316400 (2.254 sec) +2022-05-01 18:38:54,291 - tensorflow - INFO - global_step/sec: 88.8863 +2022-05-01 18:38:54,291 - tensorflow - INFO - loss = 0.0121060815, step = 1316600 (2.250 sec) +2022-05-01 18:38:56,542 - tensorflow - INFO - global_step/sec: 88.8349 +2022-05-01 18:38:56,543 - tensorflow - INFO - loss = 0.03760961, step = 1316800 (2.251 sec) +2022-05-01 18:38:58,797 - tensorflow - INFO - global_step/sec: 88.6893 +2022-05-01 18:38:58,798 - tensorflow - INFO - loss = 0.042246453, step = 1317000 (2.255 sec) +2022-05-01 18:39:01,054 - tensorflow - INFO - global_step/sec: 88.6316 +2022-05-01 18:39:01,054 - tensorflow - INFO - loss = 0.028432056, step = 1317200 (2.256 sec) +2022-05-01 18:39:03,314 - tensorflow - INFO - global_step/sec: 88.4892 +2022-05-01 18:39:03,314 - tensorflow - INFO - loss = 0.015022637, step = 1317400 (2.260 sec) +2022-05-01 18:39:05,566 - 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INFO - loss = 0.065892175, step = 1383000 (2.257 sec) +2022-05-01 18:51:25,233 - tensorflow - INFO - global_step/sec: 88.5119 +2022-05-01 18:51:25,234 - tensorflow - INFO - loss = 0.023639807, step = 1383200 (2.260 sec) +2022-05-01 18:51:27,491 - tensorflow - INFO - global_step/sec: 88.5873 +2022-05-01 18:51:27,491 - tensorflow - INFO - loss = 0.09788959, step = 1383400 (2.258 sec) +2022-05-01 18:51:29,749 - tensorflow - INFO - global_step/sec: 88.5779 +2022-05-01 18:51:29,749 - tensorflow - INFO - loss = 0.31161013, step = 1383600 (2.258 sec) +2022-05-01 18:51:32,014 - tensorflow - INFO - global_step/sec: 88.2782 +2022-05-01 18:51:32,015 - tensorflow - INFO - loss = 0.018368604, step = 1383800 (2.266 sec) +2022-05-01 18:51:34,270 - tensorflow - INFO - global_step/sec: 88.6773 +2022-05-01 18:51:34,270 - tensorflow - INFO - loss = 0.03938148, step = 1384000 (2.255 sec) +2022-05-01 18:51:36,528 - tensorflow - INFO - global_step/sec: 88.5622 +2022-05-01 18:51:36,528 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.5203 +2022-05-01 18:51:52,329 - tensorflow - INFO - loss = 0.0189787, step = 1385600 (2.259 sec) +2022-05-01 18:51:54,587 - tensorflow - INFO - global_step/sec: 88.5633 +2022-05-01 18:51:54,588 - tensorflow - INFO - loss = 0.02249827, step = 1385800 (2.258 sec) +2022-05-01 18:51:56,853 - tensorflow - INFO - global_step/sec: 88.2833 +2022-05-01 18:51:56,853 - tensorflow - INFO - loss = 0.013075041, step = 1386000 (2.265 sec) +2022-05-01 18:51:59,116 - tensorflow - INFO - global_step/sec: 88.3614 +2022-05-01 18:51:59,116 - tensorflow - INFO - loss = 0.008851925, step = 1386200 (2.263 sec) +2022-05-01 18:52:01,375 - tensorflow - INFO - global_step/sec: 88.5203 +2022-05-01 18:52:01,376 - tensorflow - INFO - loss = 0.109118104, step = 1386400 (2.259 sec) +2022-05-01 18:52:03,636 - tensorflow - INFO - global_step/sec: 88.4737 +2022-05-01 18:52:03,636 - tensorflow - INFO - loss = 0.011091237, step = 1386600 (2.261 sec) +2022-05-01 18:52:05,895 - tensorflow - INFO - global_step/sec: 88.5509 +2022-05-01 18:52:05,895 - tensorflow - INFO - loss = 0.023800887, step = 1386800 (2.259 sec) +2022-05-01 18:52:08,152 - tensorflow - INFO - global_step/sec: 88.6082 +2022-05-01 18:52:08,152 - tensorflow - INFO - loss = 0.011461074, step = 1387000 (2.257 sec) +2022-05-01 18:52:10,415 - tensorflow - INFO - global_step/sec: 88.3837 +2022-05-01 18:52:10,415 - tensorflow - INFO - loss = 0.027946133, step = 1387200 (2.263 sec) +2022-05-01 18:52:12,675 - tensorflow - INFO - global_step/sec: 88.4788 +2022-05-01 18:52:12,675 - tensorflow - INFO - loss = 0.028160535, step = 1387400 (2.260 sec) +2022-05-01 18:52:14,936 - tensorflow - INFO - global_step/sec: 88.4406 +2022-05-01 18:52:14,937 - tensorflow - INFO - loss = 0.037239272, step = 1387600 (2.261 sec) +2022-05-01 18:52:17,193 - tensorflow - INFO - global_step/sec: 88.6221 +2022-05-01 18:52:17,194 - tensorflow - INFO - loss = 0.009626794, step = 1387800 (2.257 sec) +2022-05-01 18:52:19,457 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.015943076, step = 1389200 (2.255 sec) +2022-05-01 18:52:35,228 - tensorflow - INFO - global_step/sec: 88.8893 +2022-05-01 18:52:35,228 - tensorflow - INFO - loss = 0.20571089, step = 1389400 (2.250 sec) +2022-05-01 18:52:37,486 - tensorflow - INFO - global_step/sec: 88.5603 +2022-05-01 18:52:37,486 - tensorflow - INFO - loss = 0.05094683, step = 1389600 (2.258 sec) +2022-05-01 18:52:39,737 - tensorflow - INFO - global_step/sec: 88.8359 +2022-05-01 18:52:39,738 - tensorflow - INFO - loss = 0.007741022, step = 1389800 (2.251 sec) +2022-05-01 18:52:41,988 - tensorflow - INFO - global_step/sec: 88.845 +2022-05-01 18:52:41,989 - tensorflow - INFO - loss = 0.27258632, step = 1390000 (2.251 sec) +2022-05-01 18:52:44,242 - tensorflow - INFO - global_step/sec: 88.7345 +2022-05-01 18:52:44,243 - tensorflow - INFO - loss = 0.00971758, step = 1390200 (2.254 sec) +2022-05-01 18:52:46,495 - tensorflow - INFO - global_step/sec: 88.799 +2022-05-01 18:52:46,495 - tensorflow - INFO - loss = 0.020727014, step = 1390400 (2.252 sec) +2022-05-01 18:52:48,749 - tensorflow - INFO - global_step/sec: 88.7312 +2022-05-01 18:52:48,749 - tensorflow - INFO - loss = 0.03944362, step = 1390600 (2.254 sec) +2022-05-01 18:52:51,008 - tensorflow - INFO - global_step/sec: 88.5047 +2022-05-01 18:52:51,009 - tensorflow - INFO - loss = 0.041285716, step = 1390800 (2.260 sec) +2022-05-01 18:52:53,265 - tensorflow - INFO - global_step/sec: 88.6425 +2022-05-01 18:52:53,265 - tensorflow - INFO - loss = 0.010349192, step = 1391000 (2.256 sec) +2022-05-01 18:52:55,517 - tensorflow - INFO - global_step/sec: 88.8059 +2022-05-01 18:52:55,517 - tensorflow - INFO - loss = 0.012859952, step = 1391200 (2.252 sec) +2022-05-01 18:52:57,770 - tensorflow - INFO - global_step/sec: 88.7646 +2022-05-01 18:52:57,770 - tensorflow - INFO - loss = 0.017221091, step = 1391400 (2.253 sec) +2022-05-01 18:53:00,022 - tensorflow - INFO - global_step/sec: 88.8126 +2022-05-01 18:53:00,022 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.6284 +2022-05-01 18:53:15,797 - tensorflow - INFO - loss = 0.08090894, step = 1393000 (2.257 sec) +2022-05-01 18:53:18,051 - tensorflow - INFO - global_step/sec: 88.7182 +2022-05-01 18:53:18,052 - tensorflow - INFO - loss = 0.049930155, step = 1393200 (2.254 sec) +2022-05-01 18:53:20,305 - tensorflow - INFO - global_step/sec: 88.7516 +2022-05-01 18:53:20,305 - tensorflow - INFO - loss = 0.007678329, step = 1393400 (2.253 sec) +2022-05-01 18:53:22,556 - tensorflow - INFO - global_step/sec: 88.844 +2022-05-01 18:53:22,556 - tensorflow - INFO - loss = 3.3933983, step = 1393600 (2.251 sec) +2022-05-01 18:53:24,812 - tensorflow - INFO - global_step/sec: 88.6503 +2022-05-01 18:53:24,812 - tensorflow - INFO - loss = 0.50099397, step = 1393800 (2.256 sec) +2022-05-01 18:53:27,063 - tensorflow - INFO - global_step/sec: 88.8246 +2022-05-01 18:53:27,064 - tensorflow - INFO - loss = 0.016014893, step = 1394000 (2.252 sec) +2022-05-01 18:53:29,320 - tensorflow - INFO - global_step/sec: 88.627 +2022-05-01 18:53:29,321 - tensorflow - INFO - loss = 0.6599004, step = 1394200 (2.257 sec) +2022-05-01 18:53:31,580 - tensorflow - INFO - global_step/sec: 88.5192 +2022-05-01 18:53:31,580 - tensorflow - INFO - loss = 0.04345376, step = 1394400 (2.259 sec) +2022-05-01 18:53:33,829 - tensorflow - INFO - global_step/sec: 88.9041 +2022-05-01 18:53:33,830 - tensorflow - INFO - loss = 0.44049037, step = 1394600 (2.250 sec) +2022-05-01 18:53:36,081 - tensorflow - INFO - global_step/sec: 88.7955 +2022-05-01 18:53:36,082 - tensorflow - INFO - loss = 0.012496112, step = 1394800 (2.252 sec) +2022-05-01 18:53:38,329 - tensorflow - INFO - global_step/sec: 88.9786 +2022-05-01 18:53:38,330 - tensorflow - INFO - loss = 0.03480921, step = 1395000 (2.248 sec) +2022-05-01 18:53:40,590 - tensorflow - INFO - global_step/sec: 88.4503 +2022-05-01 18:53:40,591 - tensorflow - INFO - loss = 0.010541502, step = 1395200 (2.261 sec) +2022-05-01 18:53:42,839 - tensorflow - INFO - global_step/sec: 88.9279 +2022-05-01 18:53:42,840 - tensorflow - INFO - loss = 0.020792527, step = 1395400 (2.249 sec) +2022-05-01 18:53:45,087 - tensorflow - INFO - global_step/sec: 88.9803 +2022-05-01 18:53:45,088 - tensorflow - INFO - loss = 0.26173452, step = 1395600 (2.248 sec) +2022-05-01 18:53:47,335 - tensorflow - INFO - global_step/sec: 88.962 +2022-05-01 18:53:47,336 - tensorflow - INFO - loss = 0.01936509, step = 1395800 (2.248 sec) +2022-05-01 18:53:49,590 - tensorflow - INFO - global_step/sec: 88.7004 +2022-05-01 18:53:49,590 - tensorflow - INFO - loss = 0.016364845, step = 1396000 (2.255 sec) +2022-05-01 18:53:51,847 - tensorflow - INFO - global_step/sec: 88.6169 +2022-05-01 18:53:51,847 - tensorflow - INFO - loss = 0.0135764945, step = 1396200 (2.257 sec) +2022-05-01 18:53:54,100 - tensorflow - INFO - global_step/sec: 88.7631 +2022-05-01 18:53:54,101 - tensorflow - INFO - loss = 0.019440118, step = 1396400 (2.253 sec) +2022-05-01 18:53:56,351 - tensorflow - INFO - global_step/sec: 88.8551 +2022-05-01 18:53:56,351 - tensorflow - INFO - loss = 0.024875076, step = 1396600 (2.251 sec) +2022-05-01 18:53:58,600 - tensorflow - INFO - global_step/sec: 88.939 +2022-05-01 18:53:58,600 - tensorflow - INFO - loss = 0.008331047, step = 1396800 (2.249 sec) +2022-05-01 18:54:00,856 - tensorflow - INFO - global_step/sec: 88.6235 +2022-05-01 18:54:00,857 - tensorflow - INFO - loss = 0.009838301, step = 1397000 (2.257 sec) +2022-05-01 18:54:03,110 - tensorflow - INFO - global_step/sec: 88.7341 +2022-05-01 18:54:03,111 - tensorflow - INFO - loss = 0.011992238, step = 1397200 (2.254 sec) +2022-05-01 18:54:05,369 - tensorflow - INFO - global_step/sec: 88.5512 +2022-05-01 18:54:05,369 - tensorflow - INFO - loss = 0.37778002, step = 1397400 (2.259 sec) +2022-05-01 18:54:07,626 - tensorflow - INFO - global_step/sec: 88.6144 +2022-05-01 18:54:07,626 - tensorflow - INFO - loss = 0.020134173, step = 1397600 (2.257 sec) +2022-05-01 18:54:09,882 - tensorflow - INFO - global_step/sec: 88.6686 +2022-05-01 18:54:09,882 - tensorflow - INFO - loss = 0.07450393, step = 1397800 (2.256 sec) +2022-05-01 18:54:12,133 - tensorflow - INFO - global_step/sec: 88.8287 +2022-05-01 18:54:12,133 - tensorflow - INFO - loss = 0.04988601, step = 1398000 (2.252 sec) +2022-05-01 18:54:14,384 - tensorflow - INFO - global_step/sec: 88.8409 +2022-05-01 18:54:14,385 - tensorflow - INFO - loss = 0.022269156, step = 1398200 (2.251 sec) +2022-05-01 18:54:16,641 - tensorflow - INFO - global_step/sec: 88.631 +2022-05-01 18:54:16,641 - tensorflow - INFO - loss = 0.025311477, step = 1398400 (2.257 sec) +2022-05-01 18:54:18,898 - tensorflow - INFO - global_step/sec: 88.5862 +2022-05-01 18:54:18,899 - tensorflow - INFO - loss = 0.43566298, step = 1398600 (2.258 sec) +2022-05-01 18:54:21,153 - tensorflow - INFO - global_step/sec: 88.7096 +2022-05-01 18:54:21,153 - tensorflow - INFO - loss = 0.011970665, step = 1398800 (2.255 sec) +2022-05-01 18:54:23,404 - tensorflow - INFO - global_step/sec: 88.8583 +2022-05-01 18:54:23,404 - tensorflow - INFO - loss = 0.060460646, step = 1399000 (2.251 sec) +2022-05-01 18:54:25,659 - tensorflow - INFO - global_step/sec: 88.698 +2022-05-01 18:54:25,659 - tensorflow - INFO - loss = 0.024868513, step = 1399200 (2.255 sec) +2022-05-01 18:54:27,915 - tensorflow - INFO - global_step/sec: 88.6522 +2022-05-01 18:54:27,915 - tensorflow - INFO - loss = 0.11175607, step = 1399400 (2.256 sec) +2022-05-01 18:54:30,173 - tensorflow - INFO - global_step/sec: 88.5635 +2022-05-01 18:54:30,173 - tensorflow - INFO - loss = 0.008654011, step = 1399600 (2.258 sec) +2022-05-01 18:54:32,427 - tensorflow - INFO - global_step/sec: 88.7413 +2022-05-01 18:54:32,427 - tensorflow - INFO - loss = 0.053722493, step = 1399800 (2.254 sec) +2022-05-01 18:54:34,666 - tensorflow - INFO - Saving checkpoints for 1400000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. +2022-05-01 18:54:34,682 - tensorflow - INFO - model.ckpt-1400000 is not in all_model_checkpoint_paths. Manually adding it. +2022-05-01 18:54:35,205 - tensorflow - INFO - Calling model_fn. +2022-05-01 18:54:35,331 - tensorflow - INFO - Done calling model_fn. +2022-05-01 18:54:35,346 - tensorflow - INFO - Starting evaluation at 2022-05-01T18:54:35Z +2022-05-01 18:54:35,409 - tensorflow - INFO - Graph was finalized. +2022-05-01 18:54:35,412 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1400000 +2022-05-01 18:54:35,695 - tensorflow - INFO - Running local_init_op. +2022-05-01 18:54:35,802 - tensorflow - INFO - Done running local_init_op. +2022-05-01 18:54:45,951 - tensorflow - INFO - Evaluation [3990/39900] +2022-05-01 18:54:55,477 - tensorflow - INFO - Evaluation [7980/39900] +2022-05-01 18:55:04,953 - tensorflow - INFO - Evaluation [11970/39900] +2022-05-01 18:55:14,432 - tensorflow - INFO - Evaluation [15960/39900] +2022-05-01 18:55:23,916 - tensorflow - INFO - Evaluation [19950/39900] +2022-05-01 18:55:33,443 - tensorflow - INFO - Evaluation [23940/39900] +2022-05-01 18:55:42,942 - tensorflow - INFO - Evaluation [27930/39900] +2022-05-01 18:55:52,441 - tensorflow - INFO - Evaluation [31920/39900] +2022-05-01 18:56:01,914 - tensorflow - INFO - Evaluation [35910/39900] +2022-05-01 18:56:11,413 - tensorflow - INFO - Evaluation [39900/39900] +2022-05-01 18:56:11,550 - tensorflow - INFO - Finished evaluation at 2022-05-01-18:56:11 +2022-05-01 18:56:11,550 - tensorflow - INFO - Saving dict for global step 1400000: global_step = 1400000, loss = 0.022675576, mean_degree_err = 4.711262 +2022-05-01 18:56:11,551 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 1400000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1400000 +2022-05-01 18:56:11,564 - tensorflow - INFO - global_step/sec: 2.01739 +2022-05-01 18:56:11,565 - tensorflow - INFO - loss = 1.4852409, step = 1400000 (99.138 sec) +2022-05-01 18:56:13,836 - tensorflow - INFO - global_step/sec: 88.0279 +2022-05-01 18:56:13,837 - tensorflow - INFO - loss = 0.014152582, step = 1400200 (2.272 sec) +2022-05-01 18:56:16,096 - tensorflow - INFO - global_step/sec: 88.4963 +2022-05-01 18:56:16,097 - tensorflow - INFO - loss = 0.034330446, step = 1400400 (2.260 sec) +2022-05-01 18:56:18,349 - tensorflow - INFO - global_step/sec: 88.7976 +2022-05-01 18:56:18,349 - tensorflow - INFO - loss = 0.03530475, step = 1400600 (2.252 sec) +2022-05-01 18:56:20,604 - tensorflow - INFO - global_step/sec: 88.694 +2022-05-01 18:56:20,604 - tensorflow - INFO - loss = 0.029222818, step = 1400800 (2.255 sec) +2022-05-01 18:56:22,862 - tensorflow - INFO - global_step/sec: 88.5766 +2022-05-01 18:56:22,862 - tensorflow - INFO - loss = 0.11862981, step = 1401000 (2.258 sec) +2022-05-01 18:56:25,120 - tensorflow - INFO - global_step/sec: 88.5508 +2022-05-01 18:56:25,121 - tensorflow - INFO - loss = 0.037320793, step = 1401200 (2.259 sec) +2022-05-01 18:56:27,376 - tensorflow - INFO - global_step/sec: 88.6684 +2022-05-01 18:56:27,376 - tensorflow - INFO - loss = 0.3678997, step = 1401400 (2.256 sec) +2022-05-01 18:56:29,631 - tensorflow - INFO - global_step/sec: 88.6882 +2022-05-01 18:56:29,631 - tensorflow - INFO - loss = 0.16894914, step = 1401600 (2.255 sec) +2022-05-01 18:56:31,899 - tensorflow - INFO - global_step/sec: 88.1598 +2022-05-01 18:56:31,900 - tensorflow - INFO - loss = 0.02190847, step = 1401800 (2.269 sec) +2022-05-01 18:56:34,158 - tensorflow - INFO - global_step/sec: 88.5499 +2022-05-01 18:56:34,158 - tensorflow - INFO - loss = 0.0119854715, step = 1402000 (2.259 sec) +2022-05-01 18:56:36,409 - tensorflow - INFO - global_step/sec: 88.8583 +2022-05-01 18:56:36,409 - tensorflow - INFO - loss = 0.020928359, step = 1402200 (2.251 sec) +2022-05-01 18:56:38,663 - tensorflow - INFO - global_step/sec: 88.7287 +2022-05-01 18:56:38,663 - tensorflow - INFO - loss = 0.01655328, step = 1402400 (2.254 sec) +2022-05-01 18:56:40,918 - tensorflow - INFO - global_step/sec: 88.7054 +2022-05-01 18:56:40,918 - tensorflow - INFO - loss = 0.012418087, step = 1402600 (2.255 sec) +2022-05-01 18:56:43,171 - tensorflow - INFO - global_step/sec: 88.7564 +2022-05-01 18:56:43,171 - tensorflow - INFO - loss = 0.0074203396, step = 1402800 (2.253 sec) +2022-05-01 18:56:45,420 - tensorflow - INFO - global_step/sec: 88.9291 +2022-05-01 18:56:45,420 - tensorflow - INFO - loss = 0.3566626, step = 1403000 (2.249 sec) +2022-05-01 18:56:47,673 - tensorflow - INFO - global_step/sec: 88.7658 +2022-05-01 18:56:47,673 - tensorflow - INFO - loss = 0.24041605, step = 1403200 (2.253 sec) +2022-05-01 18:56:49,929 - tensorflow - INFO - global_step/sec: 88.6645 +2022-05-01 18:56:49,929 - tensorflow - INFO - loss = 0.030028937, step = 1403400 (2.256 sec) +2022-05-01 18:56:52,184 - tensorflow - INFO - global_step/sec: 88.69 +2022-05-01 18:56:52,184 - tensorflow - INFO - loss = 0.07775682, step = 1403600 (2.255 sec) +2022-05-01 18:56:54,434 - tensorflow - INFO - global_step/sec: 88.8945 +2022-05-01 18:56:54,434 - tensorflow - INFO - loss = 0.026719717, step = 1403800 (2.250 sec) +2022-05-01 18:56:56,689 - tensorflow - INFO - global_step/sec: 88.6841 +2022-05-01 18:56:56,689 - tensorflow - INFO - loss = 0.013260361, step = 1404000 (2.255 sec) +2022-05-01 18:56:58,939 - tensorflow - INFO - global_step/sec: 88.8716 +2022-05-01 18:56:58,940 - tensorflow - INFO - loss = 0.09285254, step = 1404200 (2.250 sec) +2022-05-01 18:57:01,190 - tensorflow - INFO - global_step/sec: 88.8651 +2022-05-01 18:57:01,190 - tensorflow - INFO - loss = 0.04332969, step = 1404400 (2.251 sec) +2022-05-01 18:57:03,449 - tensorflow - INFO - global_step/sec: 88.5198 +2022-05-01 18:57:03,450 - tensorflow - INFO - loss = 0.024444506, step = 1404600 (2.259 sec) +2022-05-01 18:57:05,702 - tensorflow - INFO - global_step/sec: 88.7623 +2022-05-01 18:57:05,703 - tensorflow - INFO - loss = 0.09296678, step = 1404800 (2.253 sec) +2022-05-01 18:57:07,956 - tensorflow - INFO - global_step/sec: 88.7664 +2022-05-01 18:57:07,956 - tensorflow - INFO - loss = 0.050033934, step = 1405000 (2.253 sec) +2022-05-01 18:57:10,204 - tensorflow - INFO - global_step/sec: 88.944 +2022-05-01 18:57:10,205 - tensorflow - INFO - loss = 0.1169054, step = 1405200 (2.249 sec) +2022-05-01 18:57:12,455 - tensorflow - INFO - global_step/sec: 88.8588 +2022-05-01 18:57:12,455 - tensorflow - INFO - loss = 0.0509997, step = 1405400 (2.251 sec) +2022-05-01 18:57:14,706 - tensorflow - INFO - global_step/sec: 88.8525 +2022-05-01 18:57:14,706 - tensorflow - INFO - loss = 0.036650993, step = 1405600 (2.251 sec) +2022-05-01 18:57:16,963 - tensorflow - INFO - global_step/sec: 88.6143 +2022-05-01 18:57:16,963 - tensorflow - INFO - loss = 0.018636728, step = 1405800 (2.257 sec) +2022-05-01 18:57:19,214 - tensorflow - INFO - global_step/sec: 88.846 +2022-05-01 18:57:19,214 - tensorflow - INFO - loss = 0.1015276, step = 1406000 (2.251 sec) +2022-05-01 18:57:21,467 - tensorflow - INFO - global_step/sec: 88.7726 +2022-05-01 18:57:21,467 - tensorflow - INFO - loss = 0.11122762, step = 1406200 (2.253 sec) +2022-05-01 18:57:23,717 - tensorflow - INFO - global_step/sec: 88.8702 +2022-05-01 18:57:23,718 - tensorflow - INFO - loss = 0.016882874, step = 1406400 (2.250 sec) +2022-05-01 18:57:25,962 - tensorflow - INFO - global_step/sec: 89.0965 +2022-05-01 18:57:25,963 - tensorflow - INFO - loss = 0.01265777, step = 1406600 (2.245 sec) +2022-05-01 18:57:28,214 - tensorflow - INFO - global_step/sec: 88.7964 +2022-05-01 18:57:28,215 - tensorflow - INFO - loss = 0.022421839, step = 1406800 (2.252 sec) +2022-05-01 18:57:30,473 - tensorflow - INFO - global_step/sec: 88.5704 +2022-05-01 18:57:30,473 - tensorflow - INFO - loss = 0.014182041, step = 1407000 (2.258 sec) +2022-05-01 18:57:32,728 - tensorflow - INFO - global_step/sec: 88.6894 +2022-05-01 18:57:32,728 - tensorflow - INFO - loss = 1.00102, step = 1407200 (2.255 sec) +2022-05-01 18:57:34,980 - tensorflow - INFO - global_step/sec: 88.8137 +2022-05-01 18:57:34,980 - tensorflow - INFO - loss = 0.02176574, step = 1407400 (2.252 sec) +2022-05-01 18:57:37,230 - tensorflow - INFO - global_step/sec: 88.852 +2022-05-01 18:57:37,231 - tensorflow - INFO - loss = 0.02716237, step = 1407600 (2.251 sec) +2022-05-01 18:57:39,486 - tensorflow - INFO - global_step/sec: 88.6751 +2022-05-01 18:57:39,486 - tensorflow - INFO - loss = 0.38880143, step = 1407800 (2.255 sec) +2022-05-01 18:57:41,745 - tensorflow - INFO - global_step/sec: 88.5416 +2022-05-01 18:57:41,745 - tensorflow - INFO - loss = 0.017485047, step = 1408000 (2.259 sec) +2022-05-01 18:57:44,002 - tensorflow - INFO - global_step/sec: 88.592 +2022-05-01 18:57:44,003 - tensorflow - INFO - loss = 0.022851784, step = 1408200 (2.258 sec) +2022-05-01 18:57:46,253 - tensorflow - INFO - global_step/sec: 88.8694 +2022-05-01 18:57:46,253 - tensorflow - INFO - loss = 0.0145049505, step = 1408400 (2.251 sec) +2022-05-01 18:57:48,509 - tensorflow - INFO - global_step/sec: 88.6418 +2022-05-01 18:57:48,509 - tensorflow - INFO - loss = 0.011519443, step = 1408600 (2.256 sec) +2022-05-01 18:57:50,769 - tensorflow - INFO - global_step/sec: 88.4946 +2022-05-01 18:57:50,769 - tensorflow - INFO - loss = 0.021781186, step = 1408800 (2.260 sec) +2022-05-01 18:57:53,019 - tensorflow - INFO - global_step/sec: 88.8902 +2022-05-01 18:57:53,019 - tensorflow - INFO - loss = 3.0907974, step = 1409000 (2.250 sec) +2022-05-01 18:57:55,280 - tensorflow - INFO - global_step/sec: 88.4413 +2022-05-01 18:57:55,281 - tensorflow - INFO - loss = 0.014968009, step = 1409200 (2.261 sec) +2022-05-01 18:57:57,539 - tensorflow - INFO - global_step/sec: 88.5681 +2022-05-01 18:57:57,539 - tensorflow - INFO - loss = 0.023805398, step = 1409400 (2.258 sec) +2022-05-01 18:57:59,792 - tensorflow - INFO - global_step/sec: 88.7324 +2022-05-01 18:57:59,793 - tensorflow - INFO - loss = 0.061329484, step = 1409600 (2.254 sec) +2022-05-01 18:58:02,047 - tensorflow - INFO - global_step/sec: 88.7144 +2022-05-01 18:58:02,047 - tensorflow - INFO - loss = 0.21556099, step = 1409800 (2.254 sec) +2022-05-01 18:58:04,300 - tensorflow - INFO - global_step/sec: 88.7584 +2022-05-01 18:58:04,301 - tensorflow - INFO - loss = 0.010661656, step = 1410000 (2.253 sec) +2022-05-01 18:58:06,554 - tensorflow - INFO - global_step/sec: 88.7311 +2022-05-01 18:58:06,555 - tensorflow - INFO - loss = 0.040110946, step = 1410200 (2.254 sec) +2022-05-01 18:58:08,810 - tensorflow - INFO - global_step/sec: 88.666 +2022-05-01 18:58:08,810 - tensorflow - INFO - loss = 0.30820417, step = 1410400 (2.256 sec) +2022-05-01 18:58:11,058 - tensorflow - INFO - global_step/sec: 88.9565 +2022-05-01 18:58:11,059 - tensorflow - INFO - loss = 0.14052302, step = 1410600 (2.248 sec) +2022-05-01 18:58:13,313 - tensorflow - INFO - global_step/sec: 88.7151 +2022-05-01 18:58:13,313 - tensorflow - INFO - loss = 0.0441318, step = 1410800 (2.254 sec) +2022-05-01 18:58:15,564 - tensorflow - INFO - global_step/sec: 88.8257 +2022-05-01 18:58:15,565 - tensorflow - INFO - loss = 0.012569793, step = 1411000 (2.252 sec) +2022-05-01 18:58:17,818 - tensorflow - INFO - global_step/sec: 88.7461 +2022-05-01 18:58:17,818 - tensorflow - INFO - loss = 0.02455093, step = 1411200 (2.254 sec) +2022-05-01 18:58:20,076 - tensorflow - INFO - global_step/sec: 88.5621 +2022-05-01 18:58:20,077 - tensorflow - INFO - loss = 0.02233207, step = 1411400 (2.258 sec) +2022-05-01 18:58:22,325 - tensorflow - INFO - global_step/sec: 88.9377 +2022-05-01 18:58:22,325 - tensorflow - INFO - loss = 0.034525357, step = 1411600 (2.249 sec) +2022-05-01 18:58:24,575 - tensorflow - INFO - global_step/sec: 88.8987 +2022-05-01 18:58:24,575 - tensorflow - INFO - loss = 0.007599951, step = 1411800 (2.250 sec) +2022-05-01 18:58:26,828 - tensorflow - INFO - global_step/sec: 88.7656 +2022-05-01 18:58:26,828 - tensorflow - INFO - loss = 0.025538668, step = 1412000 (2.253 sec) +2022-05-01 18:58:29,087 - tensorflow - INFO - global_step/sec: 88.5194 +2022-05-01 18:58:29,088 - tensorflow - INFO - loss = 0.028048117, step = 1412200 (2.259 sec) +2022-05-01 18:58:31,343 - tensorflow - INFO - global_step/sec: 88.6744 +2022-05-01 18:58:31,343 - tensorflow - INFO - loss = 0.096236065, step = 1412400 (2.255 sec) +2022-05-01 18:58:33,602 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.036252193, step = 1413800 (2.257 sec) +2022-05-01 18:58:49,377 - tensorflow - INFO - global_step/sec: 88.8191 +2022-05-01 18:58:49,377 - tensorflow - INFO - loss = 0.0118514225, step = 1414000 (2.252 sec) +2022-05-01 18:58:51,637 - tensorflow - INFO - global_step/sec: 88.4904 +2022-05-01 18:58:51,637 - tensorflow - INFO - loss = 0.030325359, step = 1414200 (2.260 sec) +2022-05-01 18:58:53,901 - tensorflow - INFO - global_step/sec: 88.3327 +2022-05-01 18:58:53,902 - tensorflow - INFO - loss = 0.058954038, step = 1414400 (2.264 sec) +2022-05-01 18:58:56,164 - tensorflow - INFO - global_step/sec: 88.3723 +2022-05-01 18:58:56,165 - tensorflow - INFO - loss = 0.016944539, step = 1414600 (2.263 sec) +2022-05-01 18:58:58,429 - tensorflow - INFO - global_step/sec: 88.3008 +2022-05-01 18:58:58,430 - tensorflow - INFO - loss = 0.059797004, step = 1414800 (2.265 sec) +2022-05-01 18:59:00,688 - tensorflow - INFO - global_step/sec: 88.5279 +2022-05-01 18:59:00,689 - tensorflow - INFO - loss = 0.047088657, step = 1415000 (2.259 sec) +2022-05-01 18:59:02,949 - tensorflow - INFO - global_step/sec: 88.4629 +2022-05-01 18:59:02,950 - tensorflow - INFO - loss = 0.032564018, step = 1415200 (2.261 sec) +2022-05-01 18:59:05,212 - tensorflow - INFO - global_step/sec: 88.3951 +2022-05-01 18:59:05,212 - tensorflow - INFO - loss = 0.31576952, step = 1415400 (2.263 sec) +2022-05-01 18:59:07,471 - tensorflow - INFO - global_step/sec: 88.5447 +2022-05-01 18:59:07,471 - tensorflow - INFO - loss = 0.10594467, step = 1415600 (2.259 sec) +2022-05-01 18:59:09,730 - tensorflow - INFO - global_step/sec: 88.5123 +2022-05-01 18:59:09,731 - tensorflow - INFO - loss = 0.019736312, step = 1415800 (2.260 sec) +2022-05-01 18:59:11,994 - tensorflow - INFO - global_step/sec: 88.3524 +2022-05-01 18:59:11,994 - tensorflow - INFO - loss = 0.056081526, step = 1416000 (2.264 sec) +2022-05-01 18:59:14,255 - tensorflow - INFO - global_step/sec: 88.436 +2022-05-01 18:59:14,256 - tensorflow - INFO - loss = 0.019702645, step = 1416200 (2.262 sec) +2022-05-01 18:59:16,522 - tensorflow - INFO - global_step/sec: 88.2391 +2022-05-01 18:59:16,522 - tensorflow - INFO - loss = 0.030277722, step = 1416400 (2.267 sec) +2022-05-01 18:59:18,781 - tensorflow - INFO - global_step/sec: 88.5352 +2022-05-01 18:59:18,781 - tensorflow - INFO - loss = 0.0281749, step = 1416600 (2.259 sec) +2022-05-01 18:59:21,042 - tensorflow - INFO - global_step/sec: 88.4425 +2022-05-01 18:59:21,043 - tensorflow - INFO - loss = 0.006726307, step = 1416800 (2.261 sec) +2022-05-01 18:59:23,301 - tensorflow - INFO - global_step/sec: 88.5526 +2022-05-01 18:59:23,301 - tensorflow - INFO - loss = 0.019687772, step = 1417000 (2.258 sec) +2022-05-01 18:59:25,561 - tensorflow - INFO - global_step/sec: 88.477 +2022-05-01 18:59:25,562 - tensorflow - INFO - loss = 0.08760145, step = 1417200 (2.261 sec) +2022-05-01 18:59:27,812 - tensorflow - INFO - global_step/sec: 88.8728 +2022-05-01 18:59:27,812 - tensorflow - INFO - loss = 0.010994015, step = 1417400 (2.250 sec) +2022-05-01 18:59:30,067 - 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global_step/sec: 88.5686 +2022-05-01 19:14:50,377 - tensorflow - INFO - loss = 0.009027958, step = 1499200 (2.258 sec) +2022-05-01 19:14:52,636 - tensorflow - INFO - global_step/sec: 88.5199 +2022-05-01 19:14:52,636 - tensorflow - INFO - loss = 0.017943226, step = 1499400 (2.259 sec) +2022-05-01 19:14:54,887 - tensorflow - INFO - global_step/sec: 88.83 +2022-05-01 19:14:54,888 - tensorflow - INFO - loss = 19.609617, step = 1499600 (2.252 sec) +2022-05-01 19:14:57,142 - tensorflow - INFO - global_step/sec: 88.7111 +2022-05-01 19:14:57,142 - tensorflow - INFO - loss = 0.034245685, step = 1499800 (2.254 sec) +2022-05-01 19:14:59,381 - tensorflow - INFO - Saving checkpoints for 1500000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. +2022-05-01 19:14:59,397 - tensorflow - INFO - model.ckpt-1500000 is not in all_model_checkpoint_paths. Manually adding it. +2022-05-01 19:14:59,914 - tensorflow - INFO - Calling model_fn. +2022-05-01 19:15:00,041 - tensorflow - INFO - Done calling model_fn. +2022-05-01 19:15:00,056 - tensorflow - INFO - Starting evaluation at 2022-05-01T19:15:00Z +2022-05-01 19:15:00,120 - tensorflow - INFO - Graph was finalized. +2022-05-01 19:15:00,123 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1500000 +2022-05-01 19:15:00,404 - tensorflow - INFO - Running local_init_op. +2022-05-01 19:15:00,511 - tensorflow - INFO - Done running local_init_op. +2022-05-01 19:15:10,766 - tensorflow - INFO - Evaluation [3990/39900] +2022-05-01 19:15:20,318 - tensorflow - INFO - Evaluation [7980/39900] +2022-05-01 19:15:29,860 - tensorflow - INFO - Evaluation [11970/39900] +2022-05-01 19:15:39,412 - tensorflow - INFO - Evaluation [15960/39900] +2022-05-01 19:15:48,935 - tensorflow - INFO - Evaluation [19950/39900] +2022-05-01 19:15:58,483 - tensorflow - INFO - Evaluation [23940/39900] +2022-05-01 19:16:08,019 - tensorflow - INFO - Evaluation [27930/39900] +2022-05-01 19:16:17,585 - tensorflow - INFO - Evaluation [31920/39900] +2022-05-01 19:16:27,118 - tensorflow - INFO - Evaluation [35910/39900] +2022-05-01 19:16:36,684 - tensorflow - INFO - Evaluation [39900/39900] +2022-05-01 19:16:36,821 - tensorflow - INFO - Finished evaluation at 2022-05-01-19:16:36 +2022-05-01 19:16:36,821 - tensorflow - INFO - Saving dict for global step 1500000: global_step = 1500000, loss = 0.019688489, mean_degree_err = 3.836875 +2022-05-01 19:16:36,822 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 1500000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1500000 +2022-05-01 19:16:36,835 - tensorflow - INFO - global_step/sec: 2.00614 +2022-05-01 19:16:36,836 - tensorflow - INFO - loss = 0.016405582, step = 1500000 (99.694 sec) +2022-05-01 19:16:39,110 - tensorflow - INFO - global_step/sec: 87.9407 +2022-05-01 19:16:39,110 - tensorflow - INFO - loss = 0.03980479, step = 1500200 (2.274 sec) +2022-05-01 19:16:41,368 - tensorflow - INFO - global_step/sec: 88.5673 +2022-05-01 19:16:41,368 - tensorflow - INFO - loss = 0.03160281, step = 1500400 (2.258 sec) +2022-05-01 19:16:43,626 - tensorflow - INFO - global_step/sec: 88.5718 +2022-05-01 19:16:43,626 - tensorflow - INFO - loss = 0.010400597, step = 1500600 (2.258 sec) +2022-05-01 19:16:45,885 - tensorflow - INFO - global_step/sec: 88.548 +2022-05-01 19:16:45,885 - tensorflow - INFO - loss = 0.018450955, step = 1500800 (2.259 sec) +2022-05-01 19:16:48,145 - tensorflow - INFO - global_step/sec: 88.4955 +2022-05-01 19:16:48,145 - tensorflow - INFO - loss = 0.039949965, step = 1501000 (2.260 sec) +2022-05-01 19:16:50,403 - tensorflow - INFO - global_step/sec: 88.5761 +2022-05-01 19:16:50,403 - tensorflow - INFO - loss = 0.012375185, step = 1501200 (2.258 sec) +2022-05-01 19:16:52,665 - tensorflow - INFO - global_step/sec: 88.387 +2022-05-01 19:16:52,666 - tensorflow - INFO - loss = 0.013643623, step = 1501400 (2.263 sec) +2022-05-01 19:16:54,927 - tensorflow - INFO - global_step/sec: 88.4466 +2022-05-01 19:16:54,927 - tensorflow - INFO - loss = 0.039375756, step = 1501600 (2.261 sec) +2022-05-01 19:16:57,186 - tensorflow - INFO - global_step/sec: 88.536 +2022-05-01 19:16:57,186 - tensorflow - INFO - loss = 0.0076917754, step = 1501800 (2.259 sec) +2022-05-01 19:16:59,445 - tensorflow - INFO - global_step/sec: 88.5146 +2022-05-01 19:16:59,445 - tensorflow - INFO - loss = 0.009893475, step = 1502000 (2.260 sec) +2022-05-01 19:17:01,705 - tensorflow - INFO - global_step/sec: 88.4975 +2022-05-01 19:17:01,705 - tensorflow - INFO - loss = 0.047156, step = 1502200 (2.260 sec) +2022-05-01 19:17:03,962 - tensorflow - INFO - global_step/sec: 88.5943 +2022-05-01 19:17:03,963 - tensorflow - INFO - loss = 0.021427762, step = 1502400 (2.258 sec) +2022-05-01 19:17:06,219 - tensorflow - INFO - global_step/sec: 88.6152 +2022-05-01 19:17:06,220 - tensorflow - INFO - loss = 0.0681486, step = 1502600 (2.257 sec) +2022-05-01 19:17:08,473 - tensorflow - INFO - global_step/sec: 88.751 +2022-05-01 19:17:08,473 - tensorflow - INFO - loss = 0.028135728, step = 1502800 (2.254 sec) +2022-05-01 19:17:10,724 - tensorflow - INFO - global_step/sec: 88.8541 +2022-05-01 19:17:10,724 - tensorflow - INFO - loss = 0.009688498, step = 1503000 (2.251 sec) +2022-05-01 19:17:12,973 - tensorflow - INFO - global_step/sec: 88.9314 +2022-05-01 19:17:12,973 - tensorflow - INFO - loss = 0.009663465, step = 1503200 (2.249 sec) +2022-05-01 19:17:15,228 - tensorflow - INFO - global_step/sec: 88.6792 +2022-05-01 19:17:15,228 - tensorflow - INFO - loss = 0.010690352, step = 1503400 (2.255 sec) +2022-05-01 19:17:17,479 - tensorflow - INFO - global_step/sec: 88.8556 +2022-05-01 19:17:17,479 - tensorflow - INFO - loss = 0.01446222, step = 1503600 (2.251 sec) +2022-05-01 19:17:19,730 - tensorflow - INFO - global_step/sec: 88.8299 +2022-05-01 19:17:19,731 - tensorflow - INFO - loss = 0.16363442, step = 1503800 (2.251 sec) +2022-05-01 19:17:21,983 - tensorflow - INFO - global_step/sec: 88.7991 +2022-05-01 19:17:21,983 - tensorflow - INFO - loss = 0.24157685, step = 1504000 (2.252 sec) +2022-05-01 19:17:24,237 - tensorflow - INFO - global_step/sec: 88.7346 +2022-05-01 19:17:24,237 - tensorflow - INFO - loss = 0.037840072, step = 1504200 (2.254 sec) +2022-05-01 19:17:26,496 - tensorflow - INFO - global_step/sec: 88.5264 +2022-05-01 19:17:26,496 - tensorflow - INFO - loss = 0.013430537, step = 1504400 (2.259 sec) +2022-05-01 19:17:28,747 - tensorflow - INFO - global_step/sec: 88.8359 +2022-05-01 19:17:28,748 - tensorflow - INFO - loss = 0.0102399755, step = 1504600 (2.251 sec) +2022-05-01 19:17:31,003 - tensorflow - INFO - global_step/sec: 88.6629 +2022-05-01 19:17:31,003 - tensorflow - INFO - loss = 0.47035003, step = 1504800 (2.256 sec) +2022-05-01 19:17:33,258 - tensorflow - INFO - global_step/sec: 88.6973 +2022-05-01 19:17:33,258 - tensorflow - INFO - loss = 0.035697296, step = 1505000 (2.255 sec) +2022-05-01 19:17:35,510 - tensorflow - INFO - global_step/sec: 88.8108 +2022-05-01 19:17:35,510 - tensorflow - INFO - loss = 0.01537087, step = 1505200 (2.252 sec) +2022-05-01 19:17:37,760 - tensorflow - INFO - global_step/sec: 88.86 +2022-05-01 19:17:37,761 - tensorflow - INFO - loss = 0.0061171902, step = 1505400 (2.251 sec) +2022-05-01 19:17:40,012 - tensorflow - INFO - global_step/sec: 88.8074 +2022-05-01 19:17:40,013 - tensorflow - INFO - loss = 0.008986438, step = 1505600 (2.252 sec) +2022-05-01 19:17:42,265 - tensorflow - INFO - global_step/sec: 88.7722 +2022-05-01 19:17:42,266 - tensorflow - INFO - loss = 0.0074248677, step = 1505800 (2.253 sec) +2022-05-01 19:17:44,519 - tensorflow - INFO - global_step/sec: 88.7331 +2022-05-01 19:17:44,520 - tensorflow - INFO - loss = 0.045062054, step = 1506000 (2.254 sec) +2022-05-01 19:17:46,775 - tensorflow - INFO - global_step/sec: 88.6578 +2022-05-01 19:17:46,776 - tensorflow - INFO - loss = 0.049057905, step = 1506200 (2.256 sec) +2022-05-01 19:17:49,031 - tensorflow - INFO - global_step/sec: 88.6698 +2022-05-01 19:17:49,031 - tensorflow - INFO - loss = 9.571878, step = 1506400 (2.256 sec) +2022-05-01 19:17:51,279 - tensorflow - INFO - global_step/sec: 88.9518 +2022-05-01 19:17:51,280 - tensorflow - INFO - loss = 0.010163815, step = 1506600 (2.248 sec) +2022-05-01 19:17:53,537 - tensorflow - INFO - global_step/sec: 88.5769 +2022-05-01 19:17:53,538 - tensorflow - INFO - loss = 0.037897877, step = 1506800 (2.258 sec) +2022-05-01 19:17:55,785 - tensorflow - INFO - global_step/sec: 88.9572 +2022-05-01 19:17:55,786 - tensorflow - INFO - loss = 0.011446608, step = 1507000 (2.248 sec) +2022-05-01 19:17:58,047 - tensorflow - INFO - global_step/sec: 88.4464 +2022-05-01 19:17:58,047 - tensorflow - INFO - loss = 0.030028125, step = 1507200 (2.261 sec) +2022-05-01 19:18:00,304 - tensorflow - INFO - global_step/sec: 88.5904 +2022-05-01 19:18:00,305 - tensorflow - INFO - loss = 0.37124282, step = 1507400 (2.258 sec) +2022-05-01 19:18:02,558 - tensorflow - INFO - global_step/sec: 88.7302 +2022-05-01 19:18:02,559 - tensorflow - INFO - loss = 0.0071982597, step = 1507600 (2.254 sec) +2022-05-01 19:18:04,817 - tensorflow - INFO - global_step/sec: 88.5361 +2022-05-01 19:18:04,818 - tensorflow - INFO - loss = 0.026802769, step = 1507800 (2.259 sec) +2022-05-01 19:18:07,076 - tensorflow - INFO - global_step/sec: 88.5585 +2022-05-01 19:18:07,076 - tensorflow - INFO - loss = 0.01104089, step = 1508000 (2.258 sec) +2022-05-01 19:18:09,330 - tensorflow - INFO - global_step/sec: 88.7186 +2022-05-01 19:18:09,330 - tensorflow - INFO - loss = 0.026884561, step = 1508200 (2.254 sec) +2022-05-01 19:18:11,580 - tensorflow - INFO - global_step/sec: 88.8738 +2022-05-01 19:18:11,581 - tensorflow - INFO - loss = 0.04371132, step = 1508400 (2.250 sec) +2022-05-01 19:18:13,832 - tensorflow - INFO - global_step/sec: 88.8071 +2022-05-01 19:18:13,833 - tensorflow - INFO - loss = 0.35379708, step = 1508600 (2.252 sec) +2022-05-01 19:18:16,083 - tensorflow - INFO - global_step/sec: 88.8594 +2022-05-01 19:18:16,084 - tensorflow - INFO - loss = 0.01931915, step = 1508800 (2.251 sec) +2022-05-01 19:18:18,341 - tensorflow - INFO - global_step/sec: 88.5654 +2022-05-01 19:18:18,342 - tensorflow - INFO - loss = 0.009167142, step = 1509000 (2.258 sec) +2022-05-01 19:18:20,596 - tensorflow - INFO - global_step/sec: 88.6953 +2022-05-01 19:18:20,597 - tensorflow - INFO - loss = 0.010578865, step = 1509200 (2.255 sec) +2022-05-01 19:18:22,845 - tensorflow - INFO - global_step/sec: 88.9425 +2022-05-01 19:18:22,845 - tensorflow - INFO - loss = 0.013854955, step = 1509400 (2.249 sec) +2022-05-01 19:18:25,094 - tensorflow - INFO - global_step/sec: 88.9234 +2022-05-01 19:18:25,095 - tensorflow - INFO - loss = 0.06622584, step = 1509600 (2.249 sec) +2022-05-01 19:18:27,348 - tensorflow - INFO - global_step/sec: 88.7279 +2022-05-01 19:18:27,349 - tensorflow - INFO - loss = 0.8782365, step = 1509800 (2.254 sec) +2022-05-01 19:18:29,604 - tensorflow - INFO - global_step/sec: 88.6551 +2022-05-01 19:18:29,605 - tensorflow - INFO - loss = 0.041533154, step = 1510000 (2.256 sec) +2022-05-01 19:18:31,865 - tensorflow - INFO - global_step/sec: 88.4552 +2022-05-01 19:18:31,866 - tensorflow - INFO - loss = 0.048824005, step = 1510200 (2.261 sec) +2022-05-01 19:18:34,119 - tensorflow - INFO - global_step/sec: 88.75 +2022-05-01 19:18:34,119 - tensorflow - INFO - loss = 0.06452929, step = 1510400 (2.253 sec) +2022-05-01 19:18:36,373 - tensorflow - INFO - global_step/sec: 88.6973 +2022-05-01 19:18:36,374 - tensorflow - INFO - loss = 0.021103691, step = 1510600 (2.255 sec) +2022-05-01 19:18:38,626 - tensorflow - INFO - global_step/sec: 88.7726 +2022-05-01 19:18:38,627 - tensorflow - INFO - loss = 0.03738752, step = 1510800 (2.253 sec) +2022-05-01 19:18:40,874 - tensorflow - INFO - global_step/sec: 89.0037 +2022-05-01 19:18:40,874 - tensorflow - INFO - loss = 0.020102445, step = 1511000 (2.247 sec) +2022-05-01 19:18:43,130 - tensorflow - INFO - global_step/sec: 88.6228 +2022-05-01 19:18:43,131 - tensorflow - INFO - loss = 0.014712809, step = 1511200 (2.257 sec) +2022-05-01 19:18:45,387 - tensorflow - INFO - global_step/sec: 88.6175 +2022-05-01 19:18:45,388 - tensorflow - INFO - loss = 0.115707554, step = 1511400 (2.257 sec) +2022-05-01 19:18:47,637 - tensorflow - INFO - global_step/sec: 88.8917 +2022-05-01 19:18:47,638 - tensorflow - INFO - loss = 0.022547714, step = 1511600 (2.250 sec) +2022-05-01 19:18:49,890 - tensorflow - INFO - global_step/sec: 88.7748 +2022-05-01 19:18:49,890 - tensorflow - INFO - loss = 0.04035278, step = 1511800 (2.253 sec) +2022-05-01 19:18:52,142 - tensorflow - INFO - global_step/sec: 88.8283 +2022-05-01 19:18:52,142 - tensorflow - INFO - loss = 0.011679567, step = 1512000 (2.252 sec) +2022-05-01 19:18:54,395 - tensorflow - INFO - global_step/sec: 88.7433 +2022-05-01 19:18:54,396 - tensorflow - INFO - loss = 0.026710436, step = 1512200 (2.254 sec) +2022-05-01 19:18:56,651 - tensorflow - INFO - global_step/sec: 88.6618 +2022-05-01 19:18:56,651 - tensorflow - INFO - loss = 0.017468259, step = 1512400 (2.256 sec) +2022-05-01 19:18:58,904 - tensorflow - INFO - global_step/sec: 88.7731 +2022-05-01 19:18:58,904 - tensorflow - INFO - loss = 0.010182685, step = 1512600 (2.253 sec) +2022-05-01 19:19:01,153 - tensorflow - INFO - global_step/sec: 88.9342 +2022-05-01 19:19:01,153 - tensorflow - INFO - loss = 0.015702553, step = 1512800 (2.249 sec) +2022-05-01 19:19:03,408 - tensorflow - INFO - global_step/sec: 88.6827 +2022-05-01 19:19:03,408 - tensorflow - INFO - loss = 0.088849895, step = 1513000 (2.255 sec) +2022-05-01 19:19:05,660 - tensorflow - INFO - global_step/sec: 88.7913 +2022-05-01 19:19:05,661 - tensorflow - INFO - loss = 0.010062225, step = 1513200 (2.252 sec) +2022-05-01 19:19:07,918 - tensorflow - INFO - global_step/sec: 88.5757 +2022-05-01 19:19:07,919 - tensorflow - INFO - loss = 0.055493012, step = 1513400 (2.258 sec) +2022-05-01 19:19:10,175 - tensorflow - INFO - global_step/sec: 88.6124 +2022-05-01 19:19:10,176 - tensorflow - INFO - loss = 0.011499999, step = 1513600 (2.257 sec) +2022-05-01 19:19:12,424 - tensorflow - INFO - global_step/sec: 88.9579 +2022-05-01 19:19:12,424 - tensorflow - INFO - loss = 0.961452, step = 1513800 (2.248 sec) +2022-05-01 19:19:14,675 - tensorflow - INFO - global_step/sec: 88.8292 +2022-05-01 19:19:14,676 - tensorflow - INFO - loss = 0.005127412, step = 1514000 (2.252 sec) +2022-05-01 19:19:16,930 - tensorflow - INFO - global_step/sec: 88.7034 +2022-05-01 19:19:16,930 - tensorflow - INFO - loss = 0.013860978, step = 1514200 (2.255 sec) +2022-05-01 19:19:19,186 - tensorflow - INFO - global_step/sec: 88.647 +2022-05-01 19:19:19,186 - tensorflow - INFO - loss = 0.009205536, step = 1514400 (2.256 sec) +2022-05-01 19:19:21,441 - tensorflow - INFO - global_step/sec: 88.708 +2022-05-01 19:19:21,441 - tensorflow - INFO - loss = 0.0066144904, step = 1514600 (2.255 sec) +2022-05-01 19:19:23,700 - tensorflow - INFO - global_step/sec: 88.5165 +2022-05-01 19:19:23,701 - tensorflow - INFO - loss = 0.04965475, step = 1514800 (2.259 sec) +2022-05-01 19:19:25,954 - tensorflow - INFO - global_step/sec: 88.7295 +2022-05-01 19:19:25,955 - tensorflow - INFO - loss = 0.017812291, step = 1515000 (2.254 sec) +2022-05-01 19:19:28,204 - tensorflow - INFO - global_step/sec: 88.8944 +2022-05-01 19:19:28,204 - tensorflow - INFO - loss = 0.027830694, step = 1515200 (2.250 sec) +2022-05-01 19:19:30,466 - tensorflow - INFO - global_step/sec: 88.4032 +2022-05-01 19:19:30,467 - tensorflow - INFO - loss = 0.027515944, step = 1515400 (2.262 sec) +2022-05-01 19:19:32,726 - tensorflow - INFO - global_step/sec: 88.5016 +2022-05-01 19:19:32,727 - tensorflow - INFO - loss = 0.11355199, step = 1515600 (2.260 sec) +2022-05-01 19:19:34,977 - tensorflow - INFO - global_step/sec: 88.8434 +2022-05-01 19:19:34,978 - tensorflow - INFO - loss = 0.014939457, step = 1515800 (2.251 sec) +2022-05-01 19:19:37,231 - tensorflow - INFO - global_step/sec: 88.7384 +2022-05-01 19:19:37,232 - tensorflow - INFO - loss = 0.020215426, step = 1516000 (2.254 sec) +2022-05-01 19:19:39,484 - tensorflow - INFO - global_step/sec: 88.7715 +2022-05-01 19:19:39,485 - tensorflow - INFO - 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INFO - loss = 0.02174133, step = 1598000 (2.258 sec) +2022-05-01 19:35:04,335 - tensorflow - INFO - global_step/sec: 88.5995 +2022-05-01 19:35:04,335 - tensorflow - INFO - loss = 0.034647133, step = 1598200 (2.257 sec) +2022-05-01 19:35:06,584 - tensorflow - INFO - global_step/sec: 88.8987 +2022-05-01 19:35:06,585 - tensorflow - INFO - loss = 0.048062347, step = 1598400 (2.250 sec) +2022-05-01 19:35:08,836 - tensorflow - INFO - global_step/sec: 88.808 +2022-05-01 19:35:08,837 - tensorflow - INFO - loss = 0.06000845, step = 1598600 (2.252 sec) +2022-05-01 19:35:11,090 - tensorflow - INFO - global_step/sec: 88.738 +2022-05-01 19:35:11,091 - tensorflow - INFO - loss = 0.014896672, step = 1598800 (2.254 sec) +2022-05-01 19:35:13,348 - tensorflow - INFO - global_step/sec: 88.5689 +2022-05-01 19:35:13,349 - tensorflow - INFO - loss = 0.11341639, step = 1599000 (2.258 sec) +2022-05-01 19:35:15,607 - tensorflow - INFO - global_step/sec: 88.5451 +2022-05-01 19:35:15,607 - tensorflow - INFO - loss = 0.017968118, step = 1599200 (2.259 sec) +2022-05-01 19:35:17,859 - tensorflow - INFO - global_step/sec: 88.7948 +2022-05-01 19:35:17,860 - tensorflow - INFO - loss = 0.02949712, step = 1599400 (2.252 sec) +2022-05-01 19:35:20,112 - tensorflow - INFO - global_step/sec: 88.7764 +2022-05-01 19:35:20,113 - tensorflow - INFO - loss = 0.016649557, step = 1599600 (2.253 sec) +2022-05-01 19:35:22,367 - tensorflow - INFO - global_step/sec: 88.6975 +2022-05-01 19:35:22,368 - tensorflow - INFO - loss = 0.019204346, step = 1599800 (2.255 sec) +2022-05-01 19:35:24,612 - tensorflow - INFO - Saving checkpoints for 1600000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. +2022-05-01 19:35:24,629 - tensorflow - INFO - model.ckpt-1600000 is not in all_model_checkpoint_paths. Manually adding it. +2022-05-01 19:35:25,147 - tensorflow - INFO - Calling model_fn. +2022-05-01 19:35:25,275 - tensorflow - INFO - Done calling model_fn. +2022-05-01 19:35:25,290 - tensorflow - INFO - Starting evaluation at 2022-05-01T19:35:25Z +2022-05-01 19:35:25,354 - tensorflow - INFO - Graph was finalized. +2022-05-01 19:35:25,357 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1600000 +2022-05-01 19:35:25,639 - tensorflow - INFO - Running local_init_op. +2022-05-01 19:35:25,747 - tensorflow - INFO - Done running local_init_op. +2022-05-01 19:35:35,989 - tensorflow - INFO - Evaluation [3990/39900] +2022-05-01 19:35:45,552 - tensorflow - INFO - Evaluation [7980/39900] +2022-05-01 19:35:55,109 - tensorflow - INFO - Evaluation [11970/39900] +2022-05-01 19:36:04,674 - tensorflow - INFO - Evaluation [15960/39900] +2022-05-01 19:36:14,213 - tensorflow - INFO - Evaluation [19950/39900] +2022-05-01 19:36:23,777 - tensorflow - INFO - Evaluation [23940/39900] +2022-05-01 19:36:33,343 - tensorflow - INFO - Evaluation [27930/39900] +2022-05-01 19:36:42,896 - tensorflow - INFO - Evaluation [31920/39900] +2022-05-01 19:36:52,449 - tensorflow - INFO - Evaluation [35910/39900] +2022-05-01 19:37:02,022 - tensorflow - INFO - Evaluation [39900/39900] +2022-05-01 19:37:02,157 - tensorflow - INFO - Finished evaluation at 2022-05-01-19:37:02 +2022-05-01 19:37:02,157 - tensorflow - INFO - Saving dict for global step 1600000: global_step = 1600000, loss = 0.0288886, mean_degree_err = 5.0725417 +2022-05-01 19:37:02,157 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 1600000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1600000 +2022-05-01 19:37:02,171 - tensorflow - INFO - global_step/sec: 2.00392 +2022-05-01 19:37:02,172 - tensorflow - INFO - loss = 0.036867063, step = 1600000 (99.805 sec) +2022-05-01 19:37:04,446 - tensorflow - INFO - global_step/sec: 87.9391 +2022-05-01 19:37:04,446 - tensorflow - INFO - loss = 0.040967178, step = 1600200 (2.274 sec) +2022-05-01 19:37:06,704 - tensorflow - INFO - global_step/sec: 88.5445 +2022-05-01 19:37:06,705 - tensorflow - INFO - loss = 0.22369778, step = 1600400 (2.259 sec) +2022-05-01 19:37:08,963 - tensorflow - INFO - global_step/sec: 88.5558 +2022-05-01 19:37:08,963 - tensorflow - INFO - loss = 0.04972737, step = 1600600 (2.258 sec) +2022-05-01 19:37:11,221 - tensorflow - INFO - global_step/sec: 88.5523 +2022-05-01 19:37:11,222 - tensorflow - INFO - loss = 0.012088019, step = 1600800 (2.259 sec) +2022-05-01 19:37:13,476 - tensorflow - INFO - global_step/sec: 88.7227 +2022-05-01 19:37:13,476 - tensorflow - INFO - loss = 0.027309652, step = 1601000 (2.254 sec) +2022-05-01 19:37:15,733 - tensorflow - INFO - global_step/sec: 88.6028 +2022-05-01 19:37:15,733 - tensorflow - INFO - loss = 0.010206912, step = 1601200 (2.257 sec) +2022-05-01 19:37:17,986 - tensorflow - INFO - global_step/sec: 88.7572 +2022-05-01 19:37:17,987 - tensorflow - INFO - loss = 0.005925677, step = 1601400 (2.253 sec) +2022-05-01 19:37:20,241 - tensorflow - INFO - global_step/sec: 88.712 +2022-05-01 19:37:20,241 - tensorflow - INFO - loss = 0.01557181, step = 1601600 (2.254 sec) +2022-05-01 19:37:22,502 - tensorflow - INFO - global_step/sec: 88.4272 +2022-05-01 19:37:22,503 - tensorflow - INFO - loss = 0.012669121, step = 1601800 (2.262 sec) +2022-05-01 19:37:24,760 - tensorflow - INFO - global_step/sec: 88.6035 +2022-05-01 19:37:24,760 - tensorflow - INFO - loss = 0.010645354, step = 1602000 (2.257 sec) +2022-05-01 19:37:27,014 - tensorflow - INFO - global_step/sec: 88.7156 +2022-05-01 19:37:27,014 - tensorflow - INFO - loss = 0.017084083, step = 1602200 (2.254 sec) +2022-05-01 19:37:29,268 - tensorflow - INFO - global_step/sec: 88.7194 +2022-05-01 19:37:29,269 - tensorflow - INFO - loss = 0.025699474, step = 1602400 (2.254 sec) +2022-05-01 19:37:31,527 - tensorflow - INFO - global_step/sec: 88.5463 +2022-05-01 19:37:31,527 - tensorflow - INFO - loss = 0.022008544, step = 1602600 (2.259 sec) +2022-05-01 19:37:33,783 - tensorflow - INFO - global_step/sec: 88.6539 +2022-05-01 19:37:33,783 - tensorflow - INFO - loss = 0.010230547, step = 1602800 (2.256 sec) +2022-05-01 19:37:36,036 - tensorflow - INFO - global_step/sec: 88.7676 +2022-05-01 19:37:36,037 - tensorflow - INFO - loss = 0.029947232, step = 1603000 (2.253 sec) +2022-05-01 19:37:38,289 - tensorflow - INFO - global_step/sec: 88.7648 +2022-05-01 19:37:38,290 - tensorflow - INFO - loss = 0.007518686, step = 1603200 (2.253 sec) +2022-05-01 19:37:40,547 - tensorflow - INFO - global_step/sec: 88.585 +2022-05-01 19:37:40,547 - tensorflow - INFO - loss = 0.0097355675, step = 1603400 (2.258 sec) +2022-05-01 19:37:42,809 - tensorflow - INFO - global_step/sec: 88.4294 +2022-05-01 19:37:42,809 - tensorflow - INFO - loss = 0.074278854, step = 1603600 (2.262 sec) +2022-05-01 19:37:45,073 - tensorflow - INFO - global_step/sec: 88.3324 +2022-05-01 19:37:45,073 - tensorflow - INFO - loss = 0.017599702, step = 1603800 (2.264 sec) +2022-05-01 19:37:47,338 - tensorflow - INFO - global_step/sec: 88.3053 +2022-05-01 19:37:47,338 - tensorflow - INFO - loss = 0.06725284, step = 1604000 (2.265 sec) +2022-05-01 19:37:49,600 - tensorflow - INFO - global_step/sec: 88.4228 +2022-05-01 19:37:49,600 - tensorflow - INFO - loss = 0.06788803, step = 1604200 (2.262 sec) +2022-05-01 19:37:51,865 - tensorflow - INFO - global_step/sec: 88.2895 +2022-05-01 19:37:51,865 - tensorflow - INFO - loss = 0.024817625, step = 1604400 (2.265 sec) +2022-05-01 19:37:54,130 - tensorflow - INFO - global_step/sec: 88.3108 +2022-05-01 19:37:54,130 - tensorflow - INFO - loss = 0.011394728, step = 1604600 (2.265 sec) +2022-05-01 19:37:56,394 - tensorflow - INFO - global_step/sec: 88.3047 +2022-05-01 19:37:56,395 - tensorflow - INFO - loss = 0.07237558, step = 1604800 (2.265 sec) +2022-05-01 19:37:58,658 - tensorflow - INFO - global_step/sec: 88.3465 +2022-05-01 19:37:58,659 - tensorflow - INFO - loss = 0.08910976, step = 1605000 (2.264 sec) +2022-05-01 19:38:00,919 - tensorflow - INFO - global_step/sec: 88.4754 +2022-05-01 19:38:00,919 - tensorflow - INFO - loss = 0.10602189, step = 1605200 (2.261 sec) +2022-05-01 19:38:03,187 - tensorflow - INFO - global_step/sec: 88.1663 +2022-05-01 19:38:03,188 - tensorflow - INFO - loss = 0.35692906, step = 1605400 (2.268 sec) +2022-05-01 19:38:05,451 - tensorflow - INFO - global_step/sec: 88.3521 +2022-05-01 19:38:05,451 - tensorflow - INFO - loss = 0.025508648, step = 1605600 (2.264 sec) +2022-05-01 19:38:07,710 - tensorflow - INFO - global_step/sec: 88.531 +2022-05-01 19:38:07,710 - tensorflow - INFO - loss = 0.020784743, step = 1605800 (2.259 sec) +2022-05-01 19:38:09,971 - tensorflow - INFO - global_step/sec: 88.4562 +2022-05-01 19:38:09,971 - tensorflow - INFO - loss = 0.009046527, step = 1606000 (2.261 sec) +2022-05-01 19:38:12,230 - tensorflow - INFO - global_step/sec: 88.5493 +2022-05-01 19:38:12,230 - tensorflow - INFO - loss = 0.12052645, step = 1606200 (2.259 sec) +2022-05-01 19:38:14,487 - tensorflow - INFO - global_step/sec: 88.6104 +2022-05-01 19:38:14,487 - tensorflow - INFO - loss = 0.010093826, step = 1606400 (2.257 sec) +2022-05-01 19:38:16,748 - tensorflow - INFO - global_step/sec: 88.4417 +2022-05-01 19:38:16,748 - tensorflow - INFO - loss = 0.060307868, step = 1606600 (2.261 sec) +2022-05-01 19:38:19,003 - tensorflow - INFO - global_step/sec: 88.7118 +2022-05-01 19:38:19,003 - tensorflow - INFO - loss = 0.0069079367, step = 1606800 (2.255 sec) +2022-05-01 19:38:21,254 - tensorflow - INFO - global_step/sec: 88.8224 +2022-05-01 19:38:21,255 - tensorflow - INFO - loss = 0.040427912, step = 1607000 (2.252 sec) +2022-05-01 19:38:23,512 - tensorflow - INFO - global_step/sec: 88.6025 +2022-05-01 19:38:23,512 - tensorflow - INFO - loss = 0.04088958, step = 1607200 (2.257 sec) +2022-05-01 19:38:25,767 - tensorflow - INFO - global_step/sec: 88.6759 +2022-05-01 19:38:25,767 - tensorflow - INFO - loss = 0.4712106, step = 1607400 (2.255 sec) +2022-05-01 19:38:28,028 - tensorflow - INFO - global_step/sec: 88.4476 +2022-05-01 19:38:28,029 - tensorflow - INFO - loss = 0.11803105, step = 1607600 (2.261 sec) +2022-05-01 19:38:30,286 - tensorflow - INFO - global_step/sec: 88.5953 +2022-05-01 19:38:30,286 - tensorflow - INFO - loss = 0.0077702906, step = 1607800 (2.257 sec) +2022-05-01 19:38:32,542 - tensorflow - INFO - global_step/sec: 88.6321 +2022-05-01 19:38:32,543 - tensorflow - INFO - loss = 0.07963283, step = 1608000 (2.256 sec) +2022-05-01 19:38:34,799 - tensorflow - INFO - global_step/sec: 88.6339 +2022-05-01 19:38:34,799 - tensorflow - INFO - loss = 0.28873613, step = 1608200 (2.256 sec) +2022-05-01 19:38:37,053 - tensorflow - INFO - global_step/sec: 88.7161 +2022-05-01 19:38:37,053 - tensorflow - INFO - loss = 0.05215706, step = 1608400 (2.254 sec) +2022-05-01 19:38:39,307 - tensorflow - INFO - global_step/sec: 88.7456 +2022-05-01 19:38:39,307 - tensorflow - INFO - loss = 0.0728301, step = 1608600 (2.254 sec) +2022-05-01 19:38:41,563 - tensorflow - INFO - global_step/sec: 88.6317 +2022-05-01 19:38:41,564 - tensorflow - INFO - loss = 0.05030317, step = 1608800 (2.257 sec) +2022-05-01 19:38:43,819 - tensorflow - INFO - global_step/sec: 88.6564 +2022-05-01 19:38:43,819 - tensorflow - INFO - loss = 0.013282448, step = 1609000 (2.256 sec) +2022-05-01 19:38:46,071 - tensorflow - INFO - global_step/sec: 88.8167 +2022-05-01 19:38:46,071 - tensorflow - INFO - loss = 0.019527066, step = 1609200 (2.252 sec) +2022-05-01 19:38:48,327 - tensorflow - INFO - global_step/sec: 88.6506 +2022-05-01 19:38:48,327 - tensorflow - INFO - loss = 0.009463694, step = 1609400 (2.256 sec) +2022-05-01 19:38:50,578 - tensorflow - INFO - global_step/sec: 88.849 +2022-05-01 19:38:50,578 - tensorflow - INFO - loss = 0.014914087, step = 1609600 (2.251 sec) +2022-05-01 19:38:52,834 - tensorflow - INFO - global_step/sec: 88.6385 +2022-05-01 19:38:52,835 - tensorflow - INFO - loss = 0.03069091, step = 1609800 (2.256 sec) +2022-05-01 19:38:55,093 - tensorflow - INFO - global_step/sec: 88.5442 +2022-05-01 19:38:55,093 - tensorflow - INFO - loss = 0.015568502, step = 1610000 (2.259 sec) +2022-05-01 19:38:57,350 - tensorflow - INFO - global_step/sec: 88.6284 +2022-05-01 19:38:57,350 - tensorflow - INFO - loss = 0.026304193, step = 1610200 (2.257 sec) +2022-05-01 19:38:59,600 - tensorflow - INFO - global_step/sec: 88.8798 +2022-05-01 19:38:59,600 - tensorflow - INFO - loss = 0.01577914, step = 1610400 (2.250 sec) +2022-05-01 19:39:01,853 - tensorflow - INFO - global_step/sec: 88.7517 +2022-05-01 19:39:01,854 - tensorflow - INFO - loss = 0.010001397, step = 1610600 (2.253 sec) +2022-05-01 19:39:04,111 - tensorflow - INFO - global_step/sec: 88.6022 +2022-05-01 19:39:04,111 - tensorflow - INFO - loss = 0.16669104, step = 1610800 (2.257 sec) +2022-05-01 19:39:06,372 - tensorflow - INFO - global_step/sec: 88.4573 +2022-05-01 19:39:06,372 - tensorflow - INFO - loss = 0.086048484, step = 1611000 (2.261 sec) +2022-05-01 19:39:08,626 - tensorflow - INFO - global_step/sec: 88.7357 +2022-05-01 19:39:08,626 - tensorflow - INFO - loss = 0.015150867, step = 1611200 (2.254 sec) +2022-05-01 19:39:10,874 - tensorflow - INFO - global_step/sec: 88.9594 +2022-05-01 19:39:10,874 - tensorflow - INFO - loss = 0.029532706, step = 1611400 (2.248 sec) +2022-05-01 19:39:13,123 - tensorflow - INFO - global_step/sec: 88.9277 +2022-05-01 19:39:13,123 - tensorflow - INFO - loss = 0.0062228935, step = 1611600 (2.249 sec) +2022-05-01 19:39:15,379 - tensorflow - INFO - global_step/sec: 88.631 +2022-05-01 19:39:15,380 - tensorflow - INFO - loss = 0.02429897, step = 1611800 (2.257 sec) +2022-05-01 19:39:17,634 - tensorflow - INFO - global_step/sec: 88.7114 +2022-05-01 19:39:17,634 - tensorflow - INFO - loss = 0.012944584, step = 1612000 (2.254 sec) +2022-05-01 19:39:19,890 - tensorflow - INFO - global_step/sec: 88.6536 +2022-05-01 19:39:19,890 - tensorflow - INFO - loss = 0.12568352, step = 1612200 (2.256 sec) +2022-05-01 19:39:22,144 - tensorflow - INFO - global_step/sec: 88.7101 +2022-05-01 19:39:22,145 - tensorflow - INFO - loss = 0.5938471, step = 1612400 (2.255 sec) +2022-05-01 19:39:24,396 - tensorflow - INFO - global_step/sec: 88.8153 +2022-05-01 19:39:24,397 - tensorflow - INFO - loss = 0.034583516, step = 1612600 (2.252 sec) +2022-05-01 19:39:26,652 - tensorflow - INFO - global_step/sec: 88.6671 +2022-05-01 19:39:26,652 - tensorflow - INFO - loss = 0.022513185, step = 1612800 (2.256 sec) +2022-05-01 19:39:28,906 - tensorflow - INFO - global_step/sec: 88.7386 +2022-05-01 19:39:28,906 - tensorflow - INFO - loss = 0.017508483, step = 1613000 (2.254 sec) +2022-05-01 19:39:31,167 - tensorflow - INFO - global_step/sec: 88.4312 +2022-05-01 19:39:31,168 - tensorflow - INFO - loss = 0.004814804, step = 1613200 (2.262 sec) +2022-05-01 19:39:33,421 - tensorflow - INFO - global_step/sec: 88.7257 +2022-05-01 19:39:33,422 - tensorflow - INFO - loss = 0.02977703, step = 1613400 (2.254 sec) +2022-05-01 19:39:35,669 - tensorflow - INFO - global_step/sec: 88.9721 +2022-05-01 19:39:35,670 - tensorflow - INFO - loss = 0.026092917, step = 1613600 (2.248 sec) +2022-05-01 19:39:37,918 - tensorflow - INFO - global_step/sec: 88.9591 +2022-05-01 19:39:37,918 - tensorflow - 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INFO - global_step/sec: 88.5567 +2022-05-01 19:55:00,420 - tensorflow - INFO - loss = 0.0068152975, step = 1695600 (2.258 sec) +2022-05-01 19:55:02,672 - tensorflow - INFO - global_step/sec: 88.8051 +2022-05-01 19:55:02,673 - tensorflow - INFO - loss = 0.00684587, step = 1695800 (2.252 sec) +2022-05-01 19:55:04,928 - tensorflow - INFO - global_step/sec: 88.644 +2022-05-01 19:55:04,929 - tensorflow - INFO - loss = 0.0701153, step = 1696000 (2.256 sec) +2022-05-01 19:55:07,178 - tensorflow - INFO - global_step/sec: 88.9092 +2022-05-01 19:55:07,178 - tensorflow - INFO - loss = 0.22754294, step = 1696200 (2.250 sec) +2022-05-01 19:55:09,429 - tensorflow - INFO - global_step/sec: 88.8372 +2022-05-01 19:55:09,430 - tensorflow - INFO - loss = 0.08576741, step = 1696400 (2.251 sec) +2022-05-01 19:55:11,678 - tensorflow - INFO - global_step/sec: 88.9161 +2022-05-01 19:55:11,679 - tensorflow - INFO - loss = 0.023143955, step = 1696600 (2.249 sec) +2022-05-01 19:55:13,931 - tensorflow - INFO - global_step/sec: 88.7994 +2022-05-01 19:55:13,931 - tensorflow - INFO - loss = 0.013207562, step = 1696800 (2.252 sec) +2022-05-01 19:55:16,183 - tensorflow - INFO - global_step/sec: 88.8008 +2022-05-01 19:55:16,183 - tensorflow - INFO - loss = 0.03657867, step = 1697000 (2.252 sec) +2022-05-01 19:55:18,435 - tensorflow - INFO - global_step/sec: 88.827 +2022-05-01 19:55:18,435 - tensorflow - INFO - loss = 0.0099722, step = 1697200 (2.252 sec) +2022-05-01 19:55:20,681 - tensorflow - INFO - global_step/sec: 89.0184 +2022-05-01 19:55:20,682 - tensorflow - INFO - loss = 0.21834797, step = 1697400 (2.247 sec) +2022-05-01 19:55:22,931 - tensorflow - INFO - global_step/sec: 88.9084 +2022-05-01 19:55:22,931 - tensorflow - INFO - loss = 0.026116528, step = 1697600 (2.249 sec) +2022-05-01 19:55:25,186 - tensorflow - INFO - global_step/sec: 88.6695 +2022-05-01 19:55:25,187 - tensorflow - INFO - loss = 0.01051794, step = 1697800 (2.256 sec) +2022-05-01 19:55:27,446 - tensorflow - INFO - global_step/sec: 88.5181 +2022-05-01 19:55:27,446 - tensorflow - INFO - loss = 0.009885623, step = 1698000 (2.259 sec) +2022-05-01 19:55:29,711 - tensorflow - INFO - global_step/sec: 88.2778 +2022-05-01 19:55:29,712 - tensorflow - INFO - loss = 0.02006259, step = 1698200 (2.266 sec) +2022-05-01 19:55:31,979 - tensorflow - INFO - global_step/sec: 88.188 +2022-05-01 19:55:31,980 - tensorflow - INFO - loss = 0.0032196615, step = 1698400 (2.268 sec) +2022-05-01 19:55:34,236 - tensorflow - INFO - global_step/sec: 88.6414 +2022-05-01 19:55:34,236 - tensorflow - INFO - loss = 0.010056755, step = 1698600 (2.256 sec) +2022-05-01 19:55:36,494 - tensorflow - INFO - global_step/sec: 88.5485 +2022-05-01 19:55:36,495 - tensorflow - INFO - loss = 0.053509187, step = 1698800 (2.259 sec) +2022-05-01 19:55:38,753 - tensorflow - INFO - global_step/sec: 88.5453 +2022-05-01 19:55:38,753 - tensorflow - INFO - loss = 0.08257486, step = 1699000 (2.259 sec) +2022-05-01 19:55:41,011 - tensorflow - INFO - global_step/sec: 88.5622 +2022-05-01 19:55:41,012 - tensorflow - INFO - loss = 0.037976272, step = 1699200 (2.258 sec) +2022-05-01 19:55:43,270 - tensorflow - INFO - global_step/sec: 88.554 +2022-05-01 19:55:43,270 - tensorflow - INFO - loss = 0.017978225, step = 1699400 (2.258 sec) +2022-05-01 19:55:45,528 - tensorflow - INFO - global_step/sec: 88.5821 +2022-05-01 19:55:45,528 - tensorflow - INFO - loss = 0.012758279, step = 1699600 (2.258 sec) +2022-05-01 19:55:47,786 - tensorflow - INFO - global_step/sec: 88.5669 +2022-05-01 19:55:47,786 - tensorflow - INFO - loss = 0.022172417, step = 1699800 (2.258 sec) +2022-05-01 19:55:50,034 - tensorflow - INFO - Saving checkpoints for 1700000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. +2022-05-01 19:55:50,049 - tensorflow - INFO - model.ckpt-1700000 is not in all_model_checkpoint_paths. Manually adding it. +2022-05-01 19:55:50,567 - tensorflow - INFO - Calling model_fn. +2022-05-01 19:55:50,695 - tensorflow - INFO - Done calling model_fn. +2022-05-01 19:55:50,710 - tensorflow - INFO - Starting evaluation at 2022-05-01T19:55:50Z +2022-05-01 19:55:50,773 - tensorflow - INFO - Graph was finalized. +2022-05-01 19:55:50,777 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1700000 +2022-05-01 19:55:51,056 - tensorflow - INFO - Running local_init_op. +2022-05-01 19:55:51,164 - tensorflow - INFO - Done running local_init_op. +2022-05-01 19:56:01,425 - tensorflow - INFO - Evaluation [3990/39900] +2022-05-01 19:56:10,995 - tensorflow - INFO - Evaluation [7980/39900] +2022-05-01 19:56:20,542 - tensorflow - INFO - Evaluation [11970/39900] +2022-05-01 19:56:30,094 - tensorflow - INFO - Evaluation [15960/39900] +2022-05-01 19:56:39,656 - tensorflow - INFO - Evaluation [19950/39900] +2022-05-01 19:56:49,226 - tensorflow - INFO - Evaluation [23940/39900] +2022-05-01 19:56:58,785 - tensorflow - INFO - Evaluation [27930/39900] +2022-05-01 19:57:08,350 - tensorflow - INFO - Evaluation [31920/39900] +2022-05-01 19:57:17,900 - tensorflow - INFO - Evaluation [35910/39900] +2022-05-01 19:57:27,483 - tensorflow - INFO - Evaluation [39900/39900] +2022-05-01 19:57:27,620 - tensorflow - INFO - Finished evaluation at 2022-05-01-19:57:27 +2022-05-01 19:57:27,620 - tensorflow - INFO - Saving dict for global step 1700000: global_step = 1700000, loss = 0.016528208, mean_degree_err = 3.3223944 +2022-05-01 19:57:27,621 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 1700000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1700000 +2022-05-01 19:57:27,635 - tensorflow - INFO - global_step/sec: 2.00302 +2022-05-01 19:57:27,635 - tensorflow - INFO - loss = 0.018213827, step = 1700000 (99.849 sec) +2022-05-01 19:57:29,903 - tensorflow - INFO - global_step/sec: 88.1838 +2022-05-01 19:57:29,903 - tensorflow - INFO - loss = 0.021835033, step = 1700200 (2.268 sec) +2022-05-01 19:57:32,156 - tensorflow - INFO - global_step/sec: 88.7664 +2022-05-01 19:57:32,156 - tensorflow - INFO - loss = 0.041633718, step = 1700400 (2.253 sec) +2022-05-01 19:57:34,411 - tensorflow - INFO - global_step/sec: 88.6858 +2022-05-01 19:57:34,411 - tensorflow - INFO - loss = 0.10517413, step = 1700600 (2.255 sec) +2022-05-01 19:57:36,664 - tensorflow - INFO - global_step/sec: 88.7511 +2022-05-01 19:57:36,665 - tensorflow - INFO - loss = 0.037491966, step = 1700800 (2.254 sec) +2022-05-01 19:57:38,916 - tensorflow - INFO - global_step/sec: 88.8348 +2022-05-01 19:57:38,916 - tensorflow - INFO - loss = 0.031109095, step = 1701000 (2.251 sec) +2022-05-01 19:57:41,173 - tensorflow - INFO - global_step/sec: 88.623 +2022-05-01 19:57:41,173 - tensorflow - INFO - loss = 0.040808327, step = 1701200 (2.257 sec) +2022-05-01 19:57:43,428 - tensorflow - INFO - global_step/sec: 88.6723 +2022-05-01 19:57:43,428 - tensorflow - INFO - loss = 0.47118276, step = 1701400 (2.256 sec) +2022-05-01 19:57:45,677 - tensorflow - INFO - global_step/sec: 88.942 +2022-05-01 19:57:45,677 - tensorflow - INFO - loss = 0.040171977, step = 1701600 (2.249 sec) +2022-05-01 19:57:47,929 - tensorflow - INFO - global_step/sec: 88.8062 +2022-05-01 19:57:47,929 - tensorflow - INFO - loss = 0.01732021, step = 1701800 (2.252 sec) +2022-05-01 19:57:50,183 - tensorflow - INFO - global_step/sec: 88.7345 +2022-05-01 19:57:50,183 - tensorflow - INFO - loss = 0.013448552, step = 1702000 (2.254 sec) +2022-05-01 19:57:52,438 - tensorflow - INFO - global_step/sec: 88.6815 +2022-05-01 19:57:52,438 - tensorflow - INFO - loss = 0.017138971, step = 1702200 (2.255 sec) +2022-05-01 19:57:54,692 - tensorflow - INFO - global_step/sec: 88.7224 +2022-05-01 19:57:54,693 - tensorflow - INFO - loss = 0.01174594, step = 1702400 (2.254 sec) +2022-05-01 19:57:56,946 - tensorflow - INFO - global_step/sec: 88.7225 +2022-05-01 19:57:56,947 - tensorflow - INFO - loss = 0.041543134, step = 1702600 (2.254 sec) +2022-05-01 19:57:59,199 - tensorflow - INFO - global_step/sec: 88.7761 +2022-05-01 19:57:59,200 - tensorflow - INFO - loss = 0.048404578, step = 1702800 (2.253 sec) +2022-05-01 19:58:01,454 - tensorflow - INFO - global_step/sec: 88.7005 +2022-05-01 19:58:01,454 - tensorflow - INFO - loss = 0.14215983, step = 1703000 (2.255 sec) +2022-05-01 19:58:03,707 - tensorflow - INFO - global_step/sec: 88.7618 +2022-05-01 19:58:03,708 - tensorflow - INFO - loss = 0.027555201, step = 1703200 (2.253 sec) +2022-05-01 19:58:05,972 - tensorflow - INFO - global_step/sec: 88.3134 +2022-05-01 19:58:05,972 - tensorflow - INFO - loss = 0.045765974, step = 1703400 (2.265 sec) +2022-05-01 19:58:08,230 - tensorflow - INFO - global_step/sec: 88.5631 +2022-05-01 19:58:08,231 - tensorflow - INFO - loss = 0.037620254, step = 1703600 (2.258 sec) +2022-05-01 19:58:10,487 - tensorflow - INFO - global_step/sec: 88.638 +2022-05-01 19:58:10,487 - tensorflow - INFO - loss = 0.03932178, step = 1703800 (2.256 sec) +2022-05-01 19:58:12,738 - tensorflow - INFO - global_step/sec: 88.8331 +2022-05-01 19:58:12,738 - tensorflow - INFO - loss = 0.024784284, step = 1704000 (2.251 sec) +2022-05-01 19:58:14,990 - tensorflow - INFO - global_step/sec: 88.8245 +2022-05-01 19:58:14,990 - tensorflow - INFO - loss = 0.03867591, step = 1704200 (2.252 sec) +2022-05-01 19:58:17,242 - tensorflow - INFO - global_step/sec: 88.8119 +2022-05-01 19:58:17,242 - tensorflow - INFO - loss = 0.17101954, step = 1704400 (2.252 sec) +2022-05-01 19:58:19,502 - tensorflow - INFO - global_step/sec: 88.4969 +2022-05-01 19:58:19,502 - tensorflow - INFO - loss = 0.12228063, step = 1704600 (2.260 sec) +2022-05-01 19:58:21,751 - tensorflow - INFO - global_step/sec: 88.8995 +2022-05-01 19:58:21,752 - tensorflow - INFO - loss = 0.020781545, step = 1704800 (2.250 sec) +2022-05-01 19:58:24,001 - tensorflow - INFO - global_step/sec: 88.8852 +2022-05-01 19:58:24,002 - tensorflow - INFO - loss = 0.016060911, step = 1705000 (2.250 sec) +2022-05-01 19:58:26,257 - tensorflow - INFO - global_step/sec: 88.6791 +2022-05-01 19:58:26,257 - tensorflow - INFO - loss = 0.013135418, step = 1705200 (2.255 sec) +2022-05-01 19:58:28,508 - tensorflow - INFO - global_step/sec: 88.8238 +2022-05-01 19:58:28,509 - tensorflow - INFO - loss = 0.033608403, step = 1705400 (2.252 sec) +2022-05-01 19:58:30,774 - tensorflow - INFO - global_step/sec: 88.2905 +2022-05-01 19:58:30,774 - tensorflow - INFO - loss = 0.02455812, step = 1705600 (2.265 sec) +2022-05-01 19:58:33,024 - tensorflow - INFO - global_step/sec: 88.8609 +2022-05-01 19:58:33,025 - tensorflow - INFO - loss = 0.73054725, step = 1705800 (2.251 sec) +2022-05-01 19:58:35,271 - tensorflow - INFO - global_step/sec: 89.0251 +2022-05-01 19:58:35,271 - tensorflow - INFO - loss = 0.46992564, step = 1706000 (2.247 sec) +2022-05-01 19:58:37,521 - tensorflow - INFO - global_step/sec: 88.8733 +2022-05-01 19:58:37,522 - tensorflow - INFO - loss = 0.008819725, step = 1706200 (2.250 sec) +2022-05-01 19:58:39,774 - tensorflow - INFO - global_step/sec: 88.7783 +2022-05-01 19:58:39,774 - tensorflow - INFO - loss = 0.05270659, step = 1706400 (2.253 sec) +2022-05-01 19:58:42,030 - tensorflow - INFO - global_step/sec: 88.6664 +2022-05-01 19:58:42,030 - tensorflow - INFO - loss = 0.06409759, step = 1706600 (2.256 sec) +2022-05-01 19:58:44,289 - tensorflow - INFO - global_step/sec: 88.5222 +2022-05-01 19:58:44,289 - tensorflow - INFO - loss = 0.017197635, step = 1706800 (2.259 sec) +2022-05-01 19:58:46,544 - tensorflow - INFO - global_step/sec: 88.7045 +2022-05-01 19:58:46,544 - tensorflow - INFO - loss = 0.0068109254, step = 1707000 (2.255 sec) +2022-05-01 19:58:48,793 - tensorflow - INFO - global_step/sec: 88.9021 +2022-05-01 19:58:48,794 - tensorflow - INFO - loss = 0.005135089, step = 1707200 (2.250 sec) +2022-05-01 19:58:51,047 - tensorflow - INFO - global_step/sec: 88.7387 +2022-05-01 19:58:51,048 - tensorflow - INFO - loss = 0.015555227, step = 1707400 (2.254 sec) +2022-05-01 19:58:53,301 - tensorflow - INFO - global_step/sec: 88.7398 +2022-05-01 19:58:53,301 - tensorflow - INFO - loss = 0.008056469, step = 1707600 (2.254 sec) +2022-05-01 19:58:55,556 - tensorflow - INFO - global_step/sec: 88.6893 +2022-05-01 19:58:55,556 - tensorflow - INFO - loss = 11.810549, step = 1707800 (2.255 sec) +2022-05-01 19:58:57,820 - tensorflow - INFO - global_step/sec: 88.3409 +2022-05-01 19:58:57,820 - tensorflow - INFO - loss = 0.63033855, step = 1708000 (2.264 sec) +2022-05-01 19:59:00,084 - tensorflow - INFO - global_step/sec: 88.3282 +2022-05-01 19:59:00,085 - tensorflow - INFO - loss = 0.02858428, step = 1708200 (2.264 sec) +2022-05-01 19:59:02,342 - tensorflow - INFO - global_step/sec: 88.5962 +2022-05-01 19:59:02,342 - tensorflow - INFO - loss = 0.028509991, step = 1708400 (2.257 sec) +2022-05-01 19:59:04,599 - tensorflow - INFO - global_step/sec: 88.5975 +2022-05-01 19:59:04,599 - tensorflow - INFO - loss = 0.043081976, step = 1708600 (2.257 sec) +2022-05-01 19:59:06,856 - tensorflow - INFO - global_step/sec: 88.6067 +2022-05-01 19:59:06,857 - tensorflow - INFO - loss = 0.6489007, step = 1708800 (2.257 sec) +2022-05-01 19:59:09,115 - tensorflow - INFO - global_step/sec: 88.5532 +2022-05-01 19:59:09,115 - tensorflow - INFO - loss = 0.06143674, step = 1709000 (2.259 sec) +2022-05-01 19:59:11,374 - tensorflow - INFO - global_step/sec: 88.5094 +2022-05-01 19:59:11,375 - tensorflow - INFO - loss = 0.014285991, step = 1709200 (2.260 sec) +2022-05-01 19:59:13,635 - tensorflow - INFO - global_step/sec: 88.482 +2022-05-01 19:59:13,635 - tensorflow - INFO - loss = 0.02394065, step = 1709400 (2.260 sec) +2022-05-01 19:59:15,894 - tensorflow - INFO - global_step/sec: 88.521 +2022-05-01 19:59:15,895 - tensorflow - INFO - loss = 0.040852383, step = 1709600 (2.259 sec) +2022-05-01 19:59:18,155 - tensorflow - INFO - global_step/sec: 88.4788 +2022-05-01 19:59:18,155 - tensorflow - INFO - loss = 0.020867106, step = 1709800 (2.260 sec) +2022-05-01 19:59:20,416 - tensorflow - INFO - global_step/sec: 88.444 +2022-05-01 19:59:20,416 - tensorflow - INFO - loss = 0.015351091, step = 1710000 (2.261 sec) +2022-05-01 19:59:22,678 - tensorflow - INFO - global_step/sec: 88.4161 +2022-05-01 19:59:22,678 - tensorflow - INFO - loss = 0.008525239, step = 1710200 (2.262 sec) +2022-05-01 19:59:24,939 - tensorflow - INFO - global_step/sec: 88.4628 +2022-05-01 19:59:24,939 - tensorflow - INFO - loss = 0.041885566, step = 1710400 (2.261 sec) +2022-05-01 19:59:27,199 - tensorflow - INFO - global_step/sec: 88.4711 +2022-05-01 19:59:27,200 - tensorflow - INFO - loss = 0.018942935, step = 1710600 (2.261 sec) +2022-05-01 19:59:29,452 - tensorflow - INFO - global_step/sec: 88.8002 +2022-05-01 19:59:29,452 - tensorflow - INFO - loss = 0.008305543, step = 1710800 (2.252 sec) +2022-05-01 19:59:31,714 - tensorflow - INFO - global_step/sec: 88.3904 +2022-05-01 19:59:31,715 - tensorflow - INFO - loss = 0.02622455, step = 1711000 (2.263 sec) +2022-05-01 19:59:33,966 - tensorflow - INFO - global_step/sec: 88.8314 +2022-05-01 19:59:33,966 - tensorflow - INFO - loss = 0.03469228, step = 1711200 (2.251 sec) +2022-05-01 19:59:36,219 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.0059882104, step = 1793000 (2.252 sec) +2022-05-01 20:14:58,433 - tensorflow - INFO - global_step/sec: 88.7048 +2022-05-01 20:14:58,433 - tensorflow - INFO - loss = 0.0121996235, step = 1793200 (2.255 sec) +2022-05-01 20:15:00,692 - tensorflow - INFO - global_step/sec: 88.5164 +2022-05-01 20:15:00,693 - tensorflow - INFO - loss = 0.013352605, step = 1793400 (2.259 sec) +2022-05-01 20:15:02,947 - tensorflow - INFO - global_step/sec: 88.7274 +2022-05-01 20:15:02,947 - tensorflow - INFO - loss = 0.35117516, step = 1793600 (2.254 sec) +2022-05-01 20:15:05,204 - tensorflow - INFO - global_step/sec: 88.6128 +2022-05-01 20:15:05,204 - tensorflow - INFO - loss = 0.017232269, step = 1793800 (2.257 sec) +2022-05-01 20:15:07,457 - tensorflow - INFO - global_step/sec: 88.76 +2022-05-01 20:15:07,457 - tensorflow - INFO - loss = 0.06693153, step = 1794000 (2.253 sec) +2022-05-01 20:15:09,709 - tensorflow - INFO - global_step/sec: 88.7869 +2022-05-01 20:15:09,710 - tensorflow - INFO - loss = 0.056630254, step = 1794200 (2.253 sec) +2022-05-01 20:15:11,960 - tensorflow - INFO - global_step/sec: 88.8683 +2022-05-01 20:15:11,960 - tensorflow - INFO - loss = 0.031748325, step = 1794400 (2.251 sec) +2022-05-01 20:15:14,210 - tensorflow - INFO - global_step/sec: 88.8958 +2022-05-01 20:15:14,210 - tensorflow - INFO - loss = 0.021562433, step = 1794600 (2.250 sec) +2022-05-01 20:15:16,462 - tensorflow - INFO - global_step/sec: 88.7856 +2022-05-01 20:15:16,463 - tensorflow - INFO - loss = 0.014114676, step = 1794800 (2.253 sec) +2022-05-01 20:15:18,724 - tensorflow - INFO - global_step/sec: 88.441 +2022-05-01 20:15:18,724 - tensorflow - INFO - loss = 0.017012898, step = 1795000 (2.261 sec) +2022-05-01 20:15:20,976 - tensorflow - INFO - global_step/sec: 88.7909 +2022-05-01 20:15:20,977 - tensorflow - INFO - loss = 0.3222133, step = 1795200 (2.252 sec) +2022-05-01 20:15:23,231 - tensorflow - INFO - global_step/sec: 88.7104 +2022-05-01 20:15:23,231 - tensorflow - INFO - loss = 0.019055504, step = 1795400 (2.255 sec) +2022-05-01 20:15:25,485 - tensorflow - INFO - global_step/sec: 88.7088 +2022-05-01 20:15:25,486 - tensorflow - INFO - loss = 0.009157332, step = 1795600 (2.255 sec) +2022-05-01 20:15:27,738 - tensorflow - INFO - global_step/sec: 88.8014 +2022-05-01 20:15:27,738 - tensorflow - INFO - loss = 0.014342883, step = 1795800 (2.252 sec) +2022-05-01 20:15:29,991 - tensorflow - INFO - global_step/sec: 88.7719 +2022-05-01 20:15:29,991 - tensorflow - INFO - loss = 0.013180383, step = 1796000 (2.253 sec) +2022-05-01 20:15:32,253 - tensorflow - INFO - global_step/sec: 88.4086 +2022-05-01 20:15:32,253 - tensorflow - INFO - loss = 0.0068212757, step = 1796200 (2.262 sec) +2022-05-01 20:15:34,515 - tensorflow - INFO - global_step/sec: 88.4121 +2022-05-01 20:15:34,515 - tensorflow - INFO - loss = 0.017810522, step = 1796400 (2.262 sec) +2022-05-01 20:15:36,763 - tensorflow - INFO - global_step/sec: 88.9589 +2022-05-01 20:15:36,764 - tensorflow - INFO - loss = 0.06250075, step = 1796600 (2.248 sec) +2022-05-01 20:15:39,023 - tensorflow - INFO - global_step/sec: 88.4817 +2022-05-01 20:15:39,024 - tensorflow - INFO - loss = 0.010002321, step = 1796800 (2.260 sec) +2022-05-01 20:15:41,274 - tensorflow - INFO - global_step/sec: 88.8512 +2022-05-01 20:15:41,275 - tensorflow - INFO - loss = 0.004910208, step = 1797000 (2.251 sec) +2022-05-01 20:15:43,528 - tensorflow - INFO - global_step/sec: 88.745 +2022-05-01 20:15:43,529 - tensorflow - INFO - loss = 0.96549255, step = 1797200 (2.254 sec) +2022-05-01 20:15:45,778 - tensorflow - INFO - global_step/sec: 88.9052 +2022-05-01 20:15:45,778 - tensorflow - INFO - loss = 0.012416754, step = 1797400 (2.250 sec) +2022-05-01 20:15:48,032 - tensorflow - INFO - global_step/sec: 88.7304 +2022-05-01 20:15:48,032 - tensorflow - INFO - loss = 0.25109956, step = 1797600 (2.254 sec) +2022-05-01 20:15:50,285 - tensorflow - INFO - global_step/sec: 88.7773 +2022-05-01 20:15:50,285 - tensorflow - INFO - loss = 0.017483495, step = 1797800 (2.253 sec) +2022-05-01 20:15:52,541 - tensorflow - INFO - global_step/sec: 88.6378 +2022-05-01 20:15:52,541 - tensorflow - INFO - loss = 0.028559688, step = 1798000 (2.256 sec) +2022-05-01 20:15:54,800 - tensorflow - INFO - global_step/sec: 88.5397 +2022-05-01 20:15:54,800 - tensorflow - INFO - loss = 1.5846872, step = 1798200 (2.259 sec) +2022-05-01 20:15:57,053 - tensorflow - INFO - global_step/sec: 88.7447 +2022-05-01 20:15:57,054 - tensorflow - INFO - loss = 0.008915515, step = 1798400 (2.254 sec) +2022-05-01 20:15:59,308 - tensorflow - INFO - global_step/sec: 88.7048 +2022-05-01 20:15:59,308 - tensorflow - INFO - loss = 0.01911306, step = 1798600 (2.255 sec) +2022-05-01 20:16:01,564 - tensorflow - INFO - global_step/sec: 88.6598 +2022-05-01 20:16:01,564 - tensorflow - INFO - loss = 0.0072809146, step = 1798800 (2.256 sec) +2022-05-01 20:16:03,820 - tensorflow - INFO - global_step/sec: 88.6346 +2022-05-01 20:16:03,821 - tensorflow - INFO - loss = 0.044288322, step = 1799000 (2.256 sec) +2022-05-01 20:16:06,074 - tensorflow - INFO - global_step/sec: 88.7646 +2022-05-01 20:16:06,074 - tensorflow - INFO - loss = 0.23373622, step = 1799200 (2.253 sec) +2022-05-01 20:16:08,332 - tensorflow - INFO - global_step/sec: 88.5687 +2022-05-01 20:16:08,332 - tensorflow - INFO - loss = 0.05972874, step = 1799400 (2.258 sec) +2022-05-01 20:16:10,588 - tensorflow - INFO - global_step/sec: 88.6542 +2022-05-01 20:16:10,588 - tensorflow - INFO - loss = 0.061447047, step = 1799600 (2.256 sec) +2022-05-01 20:16:12,838 - tensorflow - INFO - global_step/sec: 88.8724 +2022-05-01 20:16:12,838 - tensorflow - INFO - loss = 0.01870015, step = 1799800 (2.250 sec) +2022-05-01 20:16:15,078 - tensorflow - INFO - Saving checkpoints for 1800000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. +2022-05-01 20:16:15,094 - tensorflow - INFO - model.ckpt-1800000 is not in all_model_checkpoint_paths. Manually adding it. +2022-05-01 20:16:15,614 - tensorflow - INFO - Calling model_fn. +2022-05-01 20:16:15,741 - tensorflow - INFO - Done calling model_fn. +2022-05-01 20:16:15,756 - tensorflow - INFO - Starting evaluation at 2022-05-01T20:16:15Z +2022-05-01 20:16:15,820 - tensorflow - INFO - Graph was finalized. +2022-05-01 20:16:15,823 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1800000 +2022-05-01 20:16:16,107 - tensorflow - INFO - Running local_init_op. +2022-05-01 20:16:16,216 - tensorflow - INFO - Done running local_init_op. +2022-05-01 20:16:26,353 - tensorflow - INFO - Evaluation [3990/39900] +2022-05-01 20:16:35,826 - tensorflow - INFO - Evaluation [7980/39900] +2022-05-01 20:16:45,282 - tensorflow - INFO - Evaluation [11970/39900] +2022-05-01 20:16:54,752 - tensorflow - INFO - Evaluation [15960/39900] +2022-05-01 20:17:04,229 - tensorflow - INFO - Evaluation [19950/39900] +2022-05-01 20:17:13,709 - tensorflow - INFO - Evaluation [23940/39900] +2022-05-01 20:17:23,164 - tensorflow - INFO - Evaluation [27930/39900] +2022-05-01 20:17:32,617 - tensorflow - INFO - Evaluation [31920/39900] +2022-05-01 20:17:42,064 - tensorflow - INFO - Evaluation [35910/39900] +2022-05-01 20:17:51,527 - tensorflow - INFO - Evaluation [39900/39900] +2022-05-01 20:17:51,666 - tensorflow - INFO - Finished evaluation at 2022-05-01-20:17:51 +2022-05-01 20:17:51,666 - tensorflow - INFO - Saving dict for global step 1800000: global_step = 1800000, loss = 0.016763782, mean_degree_err = 3.5563521 +2022-05-01 20:17:51,667 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 1800000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1800000 +2022-05-01 20:17:51,681 - tensorflow - INFO - global_step/sec: 2.02342 +2022-05-01 20:17:51,682 - tensorflow - INFO - loss = 0.027914403, step = 1800000 (98.843 sec) +2022-05-01 20:17:53,950 - tensorflow - INFO - global_step/sec: 88.1532 +2022-05-01 20:17:53,950 - tensorflow - INFO - loss = 0.021869771, step = 1800200 (2.268 sec) +2022-05-01 20:17:56,207 - tensorflow - INFO - global_step/sec: 88.5779 +2022-05-01 20:17:56,208 - tensorflow - INFO - loss = 0.092984244, step = 1800400 (2.258 sec) +2022-05-01 20:17:58,460 - tensorflow - INFO - global_step/sec: 88.8035 +2022-05-01 20:17:58,460 - tensorflow - INFO - loss = 0.013734039, step = 1800600 (2.252 sec) +2022-05-01 20:18:00,715 - tensorflow - INFO - global_step/sec: 88.671 +2022-05-01 20:18:00,716 - tensorflow - INFO - loss = 0.024841797, step = 1800800 (2.256 sec) +2022-05-01 20:18:02,971 - tensorflow - INFO - global_step/sec: 88.6527 +2022-05-01 20:18:02,972 - tensorflow - INFO - loss = 0.022523532, step = 1801000 (2.256 sec) +2022-05-01 20:18:05,226 - tensorflow - INFO - global_step/sec: 88.6917 +2022-05-01 20:18:05,227 - tensorflow - INFO - loss = 0.019885017, step = 1801200 (2.255 sec) +2022-05-01 20:18:07,485 - tensorflow - INFO - global_step/sec: 88.5244 +2022-05-01 20:18:07,486 - tensorflow - INFO - loss = 0.035318907, step = 1801400 (2.259 sec) +2022-05-01 20:18:09,741 - tensorflow - INFO - global_step/sec: 88.6836 +2022-05-01 20:18:09,741 - tensorflow - INFO - loss = 0.010549978, step = 1801600 (2.255 sec) +2022-05-01 20:18:12,000 - tensorflow - INFO - global_step/sec: 88.5235 +2022-05-01 20:18:12,000 - tensorflow - INFO - loss = 0.30921438, step = 1801800 (2.259 sec) +2022-05-01 20:18:14,257 - tensorflow - INFO - global_step/sec: 88.6201 +2022-05-01 20:18:14,257 - tensorflow - INFO - loss = 0.060837887, step = 1802000 (2.257 sec) +2022-05-01 20:18:16,514 - tensorflow - INFO - global_step/sec: 88.6091 +2022-05-01 20:18:16,514 - tensorflow - INFO - loss = 0.073248446, step = 1802200 (2.257 sec) +2022-05-01 20:18:18,769 - tensorflow - INFO - global_step/sec: 88.7003 +2022-05-01 20:18:18,769 - tensorflow - INFO - loss = 0.020106304, step = 1802400 (2.255 sec) +2022-05-01 20:18:21,026 - tensorflow - INFO - global_step/sec: 88.609 +2022-05-01 20:18:21,026 - tensorflow - INFO - loss = 0.34301686, step = 1802600 (2.257 sec) +2022-05-01 20:18:23,277 - tensorflow - INFO - global_step/sec: 88.851 +2022-05-01 20:18:23,277 - tensorflow - INFO - loss = 0.03422773, step = 1802800 (2.251 sec) +2022-05-01 20:18:25,536 - tensorflow - INFO - global_step/sec: 88.5365 +2022-05-01 20:18:25,536 - tensorflow - INFO - loss = 0.010858849, step = 1803000 (2.259 sec) +2022-05-01 20:18:27,794 - tensorflow - INFO - global_step/sec: 88.5571 +2022-05-01 20:18:27,794 - tensorflow - INFO - loss = 0.28296545, step = 1803200 (2.258 sec) +2022-05-01 20:18:30,056 - tensorflow - INFO - global_step/sec: 88.4263 +2022-05-01 20:18:30,056 - tensorflow - INFO - loss = 0.04658611, step = 1803400 (2.262 sec) +2022-05-01 20:18:32,319 - tensorflow - INFO - global_step/sec: 88.3909 +2022-05-01 20:18:32,319 - tensorflow - INFO - loss = 0.048911583, step = 1803600 (2.263 sec) +2022-05-01 20:18:34,577 - tensorflow - INFO - global_step/sec: 88.5663 +2022-05-01 20:18:34,577 - tensorflow - INFO - loss = 0.01163725, step = 1803800 (2.258 sec) +2022-05-01 20:18:36,830 - tensorflow - INFO - global_step/sec: 88.7513 +2022-05-01 20:18:36,831 - tensorflow - INFO - loss = 9.297604, step = 1804000 (2.254 sec) +2022-05-01 20:18:39,083 - tensorflow - INFO - global_step/sec: 88.7746 +2022-05-01 20:18:39,084 - tensorflow - INFO - loss = 0.0068070027, step = 1804200 (2.253 sec) +2022-05-01 20:18:41,337 - tensorflow - INFO - global_step/sec: 88.7427 +2022-05-01 20:18:41,337 - tensorflow - INFO - loss = 0.015604509, step = 1804400 (2.254 sec) +2022-05-01 20:18:43,593 - tensorflow - INFO - global_step/sec: 88.6336 +2022-05-01 20:18:43,594 - tensorflow - INFO - loss = 0.0144014135, step = 1804600 (2.256 sec) +2022-05-01 20:18:45,845 - tensorflow - INFO - global_step/sec: 88.8138 +2022-05-01 20:18:45,846 - tensorflow - INFO - loss = 0.02853755, step = 1804800 (2.252 sec) +2022-05-01 20:18:48,096 - tensorflow - INFO - global_step/sec: 88.8374 +2022-05-01 20:18:48,097 - tensorflow - INFO - loss = 0.008257154, step = 1805000 (2.251 sec) +2022-05-01 20:18:50,349 - tensorflow - INFO - global_step/sec: 88.7729 +2022-05-01 20:18:50,350 - tensorflow - INFO - loss = 0.02656951, step = 1805200 (2.253 sec) +2022-05-01 20:18:52,603 - tensorflow - INFO - global_step/sec: 88.755 +2022-05-01 20:18:52,603 - tensorflow - INFO - loss = 0.01831774, step = 1805400 (2.253 sec) +2022-05-01 20:18:54,856 - tensorflow - INFO - global_step/sec: 88.7477 +2022-05-01 20:18:54,857 - tensorflow - INFO - loss = 0.023801386, step = 1805600 (2.254 sec) +2022-05-01 20:18:57,118 - tensorflow - INFO - global_step/sec: 88.4459 +2022-05-01 20:18:57,118 - tensorflow - INFO - loss = 0.65763414, step = 1805800 (2.261 sec) +2022-05-01 20:18:59,372 - tensorflow - INFO - global_step/sec: 88.7027 +2022-05-01 20:18:59,373 - tensorflow - INFO - loss = 0.101669826, step = 1806000 (2.255 sec) +2022-05-01 20:19:01,624 - tensorflow - INFO - global_step/sec: 88.82 +2022-05-01 20:19:01,625 - tensorflow - INFO - loss = 0.012000534, step = 1806200 (2.252 sec) +2022-05-01 20:19:03,880 - tensorflow - INFO - global_step/sec: 88.6715 +2022-05-01 20:19:03,880 - tensorflow - INFO - loss = 0.026554633, step = 1806400 (2.255 sec) +2022-05-01 20:19:06,133 - tensorflow - INFO - global_step/sec: 88.7724 +2022-05-01 20:19:06,133 - tensorflow - INFO - loss = 0.013799883, step = 1806600 (2.253 sec) +2022-05-01 20:19:08,392 - tensorflow - INFO - global_step/sec: 88.5263 +2022-05-01 20:19:08,392 - tensorflow - INFO - loss = 0.30768314, step = 1806800 (2.259 sec) +2022-05-01 20:19:10,646 - tensorflow - INFO - global_step/sec: 88.7053 +2022-05-01 20:19:10,647 - tensorflow - INFO - loss = 0.025185607, step = 1807000 (2.255 sec) +2022-05-01 20:19:12,902 - tensorflow - INFO - global_step/sec: 88.6882 +2022-05-01 20:19:12,902 - tensorflow - INFO - loss = 0.008936843, step = 1807200 (2.255 sec) +2022-05-01 20:19:15,156 - tensorflow - INFO - global_step/sec: 88.7325 +2022-05-01 20:19:15,156 - tensorflow - INFO - loss = 0.036230564, step = 1807400 (2.254 sec) +2022-05-01 20:19:17,411 - tensorflow - INFO - global_step/sec: 88.6543 +2022-05-01 20:19:17,412 - tensorflow - INFO - loss = 0.011483357, step = 1807600 (2.256 sec) +2022-05-01 20:19:19,671 - tensorflow - INFO - global_step/sec: 88.5007 +2022-05-01 20:19:19,672 - tensorflow - INFO - loss = 0.024457522, step = 1807800 (2.260 sec) +2022-05-01 20:19:21,933 - tensorflow - INFO - global_step/sec: 88.4353 +2022-05-01 20:19:21,933 - tensorflow - INFO - loss = 0.00525497, step = 1808000 (2.262 sec) +2022-05-01 20:19:24,185 - tensorflow - INFO - global_step/sec: 88.8016 +2022-05-01 20:19:24,186 - tensorflow - INFO - loss = 0.012021458, step = 1808200 (2.252 sec) +2022-05-01 20:19:26,438 - tensorflow - INFO - global_step/sec: 88.7816 +2022-05-01 20:19:26,438 - tensorflow - INFO - loss = 0.025556244, step = 1808400 (2.253 sec) +2022-05-01 20:19:28,695 - tensorflow - INFO - global_step/sec: 88.6239 +2022-05-01 20:19:28,695 - tensorflow - INFO - loss = 0.05274012, step = 1808600 (2.257 sec) +2022-05-01 20:19:30,950 - tensorflow - INFO - global_step/sec: 88.6846 +2022-05-01 20:19:30,950 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.8368 +2022-05-01 20:19:46,722 - tensorflow - INFO - loss = 0.016368955, step = 1810200 (2.251 sec) +2022-05-01 20:19:48,981 - tensorflow - INFO - global_step/sec: 88.5389 +2022-05-01 20:19:48,981 - tensorflow - INFO - loss = 0.007853529, step = 1810400 (2.259 sec) +2022-05-01 20:19:51,232 - tensorflow - INFO - global_step/sec: 88.8182 +2022-05-01 20:19:51,233 - tensorflow - INFO - loss = 0.0068646725, step = 1810600 (2.252 sec) +2022-05-01 20:19:53,485 - tensorflow - INFO - global_step/sec: 88.767 +2022-05-01 20:19:53,486 - tensorflow - INFO - loss = 0.21444012, step = 1810800 (2.253 sec) +2022-05-01 20:19:55,739 - tensorflow - INFO - global_step/sec: 88.7615 +2022-05-01 20:19:55,739 - tensorflow - INFO - loss = 0.049558554, step = 1811000 (2.253 sec) +2022-05-01 20:19:57,990 - tensorflow - INFO - global_step/sec: 88.8386 +2022-05-01 20:19:57,990 - tensorflow - INFO - loss = 0.0171601, step = 1811200 (2.251 sec) +2022-05-01 20:20:00,245 - tensorflow - 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global_step/sec: 88.7964 +2022-05-01 20:35:20,792 - tensorflow - INFO - loss = 0.014841132, step = 1893000 (2.252 sec) +2022-05-01 20:35:23,047 - tensorflow - INFO - global_step/sec: 88.6593 +2022-05-01 20:35:23,048 - tensorflow - INFO - loss = 0.021191655, step = 1893200 (2.256 sec) +2022-05-01 20:35:25,299 - tensorflow - INFO - global_step/sec: 88.8251 +2022-05-01 20:35:25,299 - tensorflow - INFO - loss = 0.016463384, step = 1893400 (2.252 sec) +2022-05-01 20:35:27,546 - tensorflow - INFO - global_step/sec: 89.0306 +2022-05-01 20:35:27,546 - tensorflow - INFO - loss = 0.012355963, step = 1893600 (2.246 sec) +2022-05-01 20:35:29,802 - tensorflow - INFO - global_step/sec: 88.6377 +2022-05-01 20:35:29,802 - tensorflow - INFO - loss = 2.4077692, step = 1893800 (2.256 sec) +2022-05-01 20:35:32,067 - tensorflow - INFO - global_step/sec: 88.3073 +2022-05-01 20:35:32,067 - tensorflow - INFO - loss = 0.008806356, step = 1894000 (2.265 sec) +2022-05-01 20:35:34,323 - tensorflow - INFO - global_step/sec: 88.6326 +2022-05-01 20:35:34,324 - tensorflow - INFO - loss = 0.67305064, step = 1894200 (2.256 sec) +2022-05-01 20:35:36,582 - tensorflow - INFO - global_step/sec: 88.5272 +2022-05-01 20:35:36,583 - tensorflow - INFO - loss = 0.098160475, step = 1894400 (2.259 sec) +2022-05-01 20:35:38,836 - tensorflow - INFO - global_step/sec: 88.7334 +2022-05-01 20:35:38,837 - tensorflow - INFO - loss = 0.008926311, step = 1894600 (2.254 sec) +2022-05-01 20:35:41,086 - tensorflow - INFO - global_step/sec: 88.8908 +2022-05-01 20:35:41,087 - tensorflow - INFO - loss = 0.010282107, step = 1894800 (2.250 sec) +2022-05-01 20:35:43,338 - tensorflow - INFO - global_step/sec: 88.8349 +2022-05-01 20:35:43,338 - tensorflow - INFO - loss = 0.025060002, step = 1895000 (2.251 sec) +2022-05-01 20:35:45,592 - tensorflow - INFO - global_step/sec: 88.7108 +2022-05-01 20:35:45,593 - tensorflow - INFO - loss = 0.030853648, step = 1895200 (2.254 sec) +2022-05-01 20:35:47,849 - tensorflow - INFO - global_step/sec: 88.6236 +2022-05-01 20:35:47,849 - tensorflow - INFO - loss = 0.24151732, step = 1895400 (2.257 sec) +2022-05-01 20:35:50,105 - tensorflow - INFO - global_step/sec: 88.6622 +2022-05-01 20:35:50,105 - tensorflow - INFO - loss = 0.026341088, step = 1895600 (2.256 sec) +2022-05-01 20:35:52,356 - tensorflow - INFO - global_step/sec: 88.8508 +2022-05-01 20:35:52,356 - tensorflow - INFO - loss = 0.019398818, step = 1895800 (2.251 sec) +2022-05-01 20:35:54,609 - tensorflow - INFO - global_step/sec: 88.7598 +2022-05-01 20:35:54,609 - tensorflow - INFO - loss = 0.018978981, step = 1896000 (2.253 sec) +2022-05-01 20:35:56,861 - tensorflow - INFO - global_step/sec: 88.8227 +2022-05-01 20:35:56,861 - tensorflow - INFO - loss = 0.015217569, step = 1896200 (2.252 sec) +2022-05-01 20:35:59,111 - tensorflow - INFO - global_step/sec: 88.8691 +2022-05-01 20:35:59,111 - tensorflow - INFO - loss = 0.018522281, step = 1896400 (2.251 sec) +2022-05-01 20:36:01,371 - tensorflow - INFO - global_step/sec: 88.4987 +2022-05-01 20:36:01,371 - tensorflow - INFO - loss = 0.035829056, step = 1896600 (2.260 sec) +2022-05-01 20:36:03,624 - tensorflow - INFO - global_step/sec: 88.7635 +2022-05-01 20:36:03,625 - tensorflow - INFO - loss = 0.01608836, step = 1896800 (2.253 sec) +2022-05-01 20:36:05,882 - tensorflow - INFO - global_step/sec: 88.5936 +2022-05-01 20:36:05,882 - tensorflow - INFO - loss = 0.025285728, step = 1897000 (2.258 sec) +2022-05-01 20:36:08,132 - tensorflow - INFO - global_step/sec: 88.8569 +2022-05-01 20:36:08,133 - tensorflow - INFO - loss = 0.00848588, step = 1897200 (2.251 sec) +2022-05-01 20:36:10,387 - tensorflow - INFO - global_step/sec: 88.7181 +2022-05-01 20:36:10,387 - tensorflow - INFO - loss = 0.013154034, step = 1897400 (2.254 sec) +2022-05-01 20:36:12,640 - tensorflow - INFO - global_step/sec: 88.7697 +2022-05-01 20:36:12,640 - tensorflow - INFO - loss = 0.0072760545, step = 1897600 (2.253 sec) +2022-05-01 20:36:14,900 - tensorflow - INFO - global_step/sec: 88.4853 +2022-05-01 20:36:14,901 - tensorflow - INFO - loss = 0.00759117, step = 1897800 (2.260 sec) +2022-05-01 20:36:17,156 - tensorflow - INFO - global_step/sec: 88.667 +2022-05-01 20:36:17,156 - tensorflow - INFO - loss = 0.05357273, step = 1898000 (2.256 sec) +2022-05-01 20:36:19,405 - tensorflow - INFO - global_step/sec: 88.9256 +2022-05-01 20:36:19,405 - tensorflow - INFO - loss = 0.013738202, step = 1898200 (2.249 sec) +2022-05-01 20:36:21,661 - tensorflow - INFO - global_step/sec: 88.6619 +2022-05-01 20:36:21,661 - tensorflow - INFO - loss = 0.6070651, step = 1898400 (2.256 sec) +2022-05-01 20:36:23,918 - tensorflow - INFO - global_step/sec: 88.5968 +2022-05-01 20:36:23,918 - tensorflow - INFO - loss = 0.014128826, step = 1898600 (2.257 sec) +2022-05-01 20:36:26,177 - tensorflow - INFO - global_step/sec: 88.521 +2022-05-01 20:36:26,178 - tensorflow - INFO - loss = 0.0088235345, step = 1898800 (2.259 sec) +2022-05-01 20:36:28,436 - tensorflow - INFO - global_step/sec: 88.5464 +2022-05-01 20:36:28,436 - tensorflow - INFO - loss = 0.13586804, step = 1899000 (2.259 sec) +2022-05-01 20:36:30,696 - tensorflow - INFO - global_step/sec: 88.5006 +2022-05-01 20:36:30,696 - tensorflow - INFO - loss = 0.010024523, step = 1899200 (2.260 sec) +2022-05-01 20:36:32,957 - tensorflow - INFO - global_step/sec: 88.4388 +2022-05-01 20:36:32,958 - tensorflow - INFO - loss = 0.011715906, step = 1899400 (2.261 sec) +2022-05-01 20:36:35,209 - tensorflow - INFO - global_step/sec: 88.8165 +2022-05-01 20:36:35,210 - tensorflow - INFO - loss = 0.030111665, step = 1899600 (2.252 sec) +2022-05-01 20:36:37,466 - tensorflow - INFO - global_step/sec: 88.6293 +2022-05-01 20:36:37,466 - tensorflow - INFO - loss = 0.03254773, step = 1899800 (2.257 sec) +2022-05-01 20:36:39,716 - tensorflow - INFO - Saving checkpoints for 1900000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. +2022-05-01 20:36:39,731 - tensorflow - INFO - model.ckpt-1900000 is not in all_model_checkpoint_paths. Manually adding it. +2022-05-01 20:36:40,255 - tensorflow - INFO - Calling model_fn. +2022-05-01 20:36:40,383 - tensorflow - INFO - Done calling model_fn. +2022-05-01 20:36:40,398 - tensorflow - INFO - Starting evaluation at 2022-05-01T20:36:40Z +2022-05-01 20:36:40,461 - tensorflow - INFO - Graph was finalized. +2022-05-01 20:36:40,465 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1900000 +2022-05-01 20:36:40,746 - tensorflow - INFO - Running local_init_op. +2022-05-01 20:36:40,853 - tensorflow - INFO - Done running local_init_op. +2022-05-01 20:36:50,996 - tensorflow - INFO - Evaluation [3990/39900] +2022-05-01 20:37:00,456 - tensorflow - INFO - Evaluation [7980/39900] +2022-05-01 20:37:09,895 - tensorflow - INFO - Evaluation [11970/39900] +2022-05-01 20:37:19,351 - tensorflow - INFO - Evaluation [15960/39900] +2022-05-01 20:37:28,792 - tensorflow - INFO - Evaluation [19950/39900] +2022-05-01 20:37:38,253 - tensorflow - INFO - Evaluation [23940/39900] +2022-05-01 20:37:47,688 - tensorflow - INFO - Evaluation [27930/39900] +2022-05-01 20:37:57,152 - tensorflow - INFO - Evaluation [31920/39900] +2022-05-01 20:38:06,602 - tensorflow - INFO - Evaluation [35910/39900] +2022-05-01 20:38:16,082 - tensorflow - INFO - Evaluation [39900/39900] +2022-05-01 20:38:16,222 - tensorflow - INFO - Finished evaluation at 2022-05-01-20:38:16 +2022-05-01 20:38:16,222 - tensorflow - INFO - Saving dict for global step 1900000: global_step = 1900000, loss = 0.021781396, mean_degree_err = 3.919044 +2022-05-01 20:38:16,223 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 1900000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1900000 +2022-05-01 20:38:16,236 - tensorflow - INFO - global_step/sec: 2.0249 +2022-05-01 20:38:16,237 - tensorflow - INFO - loss = 0.010318757, step = 1900000 (98.771 sec) +2022-05-01 20:38:18,518 - tensorflow - INFO - global_step/sec: 87.665 +2022-05-01 20:38:18,518 - tensorflow - INFO - loss = 0.05919086, step = 1900200 (2.281 sec) +2022-05-01 20:38:20,775 - tensorflow - INFO - global_step/sec: 88.6049 +2022-05-01 20:38:20,775 - tensorflow - INFO - loss = 0.0043990593, step = 1900400 (2.257 sec) +2022-05-01 20:38:23,037 - tensorflow - INFO - global_step/sec: 88.411 +2022-05-01 20:38:23,038 - tensorflow - INFO - loss = 0.014942108, step = 1900600 (2.262 sec) +2022-05-01 20:38:25,292 - tensorflow - INFO - global_step/sec: 88.6773 +2022-05-01 20:38:25,293 - tensorflow - INFO - loss = 0.018904645, step = 1900800 (2.255 sec) +2022-05-01 20:38:27,548 - tensorflow - INFO - global_step/sec: 88.656 +2022-05-01 20:38:27,549 - tensorflow - INFO - loss = 0.014355497, step = 1901000 (2.256 sec) +2022-05-01 20:38:29,801 - tensorflow - INFO - global_step/sec: 88.7702 +2022-05-01 20:38:29,802 - tensorflow - INFO - loss = 0.022026083, step = 1901200 (2.253 sec) +2022-05-01 20:38:32,061 - tensorflow - INFO - global_step/sec: 88.511 +2022-05-01 20:38:32,061 - tensorflow - INFO - loss = 0.026987147, step = 1901400 (2.260 sec) +2022-05-01 20:38:34,317 - tensorflow - INFO - global_step/sec: 88.64 +2022-05-01 20:38:34,318 - tensorflow - INFO - loss = 0.016082386, step = 1901600 (2.256 sec) +2022-05-01 20:38:36,573 - tensorflow - INFO - global_step/sec: 88.6813 +2022-05-01 20:38:36,573 - tensorflow - INFO - loss = 0.021116573, step = 1901800 (2.255 sec) +2022-05-01 20:38:38,827 - tensorflow - INFO - global_step/sec: 88.7107 +2022-05-01 20:38:38,828 - tensorflow - INFO - loss = 0.17873903, step = 1902000 (2.255 sec) +2022-05-01 20:38:41,079 - tensorflow - INFO - global_step/sec: 88.8149 +2022-05-01 20:38:41,079 - tensorflow - INFO - loss = 0.010280648, step = 1902200 (2.252 sec) +2022-05-01 20:38:43,339 - tensorflow - INFO - global_step/sec: 88.4946 +2022-05-01 20:38:43,339 - tensorflow - INFO - loss = 0.007372737, step = 1902400 (2.260 sec) +2022-05-01 20:38:45,596 - tensorflow - INFO - global_step/sec: 88.6306 +2022-05-01 20:38:45,596 - tensorflow - INFO - loss = 0.069900736, step = 1902600 (2.257 sec) +2022-05-01 20:38:47,853 - tensorflow - INFO - global_step/sec: 88.5961 +2022-05-01 20:38:47,853 - tensorflow - INFO - loss = 0.025056195, step = 1902800 (2.257 sec) +2022-05-01 20:38:50,112 - tensorflow - INFO - global_step/sec: 88.5546 +2022-05-01 20:38:50,112 - tensorflow - INFO - loss = 0.09052631, step = 1903000 (2.258 sec) +2022-05-01 20:38:52,368 - tensorflow - INFO - global_step/sec: 88.614 +2022-05-01 20:38:52,369 - tensorflow - INFO - loss = 0.006703171, step = 1903200 (2.257 sec) +2022-05-01 20:38:54,621 - tensorflow - INFO - global_step/sec: 88.7818 +2022-05-01 20:38:54,622 - tensorflow - INFO - loss = 1.4233687, step = 1903400 (2.253 sec) +2022-05-01 20:38:56,877 - tensorflow - INFO - global_step/sec: 88.6577 +2022-05-01 20:38:56,877 - tensorflow - INFO - loss = 0.008456467, step = 1903600 (2.256 sec) +2022-05-01 20:38:59,128 - tensorflow - INFO - global_step/sec: 88.8706 +2022-05-01 20:38:59,128 - tensorflow - INFO - loss = 0.0235586, step = 1903800 (2.250 sec) +2022-05-01 20:39:01,390 - tensorflow - INFO - global_step/sec: 88.3861 +2022-05-01 20:39:01,391 - tensorflow - INFO - loss = 0.028131448, step = 1904000 (2.263 sec) +2022-05-01 20:39:03,646 - tensorflow - INFO - global_step/sec: 88.6654 +2022-05-01 20:39:03,646 - tensorflow - INFO - loss = 0.3278071, step = 1904200 (2.256 sec) +2022-05-01 20:39:05,902 - tensorflow - INFO - global_step/sec: 88.6647 +2022-05-01 20:39:05,902 - tensorflow - INFO - loss = 0.061649084, step = 1904400 (2.256 sec) +2022-05-01 20:39:08,156 - tensorflow - INFO - global_step/sec: 88.7094 +2022-05-01 20:39:08,157 - tensorflow - INFO - loss = 0.011733083, step = 1904600 (2.255 sec) +2022-05-01 20:39:10,409 - tensorflow - INFO - global_step/sec: 88.7938 +2022-05-01 20:39:10,409 - tensorflow - INFO - loss = 0.09543198, step = 1904800 (2.252 sec) +2022-05-01 20:39:12,669 - tensorflow - INFO - global_step/sec: 88.4762 +2022-05-01 20:39:12,670 - tensorflow - INFO - loss = 0.011331211, step = 1905000 (2.261 sec) +2022-05-01 20:39:14,932 - tensorflow - INFO - global_step/sec: 88.3948 +2022-05-01 20:39:14,932 - tensorflow - INFO - loss = 0.14373742, step = 1905200 (2.262 sec) +2022-05-01 20:39:17,182 - tensorflow - INFO - global_step/sec: 88.8763 +2022-05-01 20:39:17,182 - tensorflow - INFO - loss = 0.059360716, step = 1905400 (2.250 sec) +2022-05-01 20:39:19,435 - tensorflow - INFO - global_step/sec: 88.7831 +2022-05-01 20:39:19,435 - tensorflow - INFO - loss = 0.010062706, step = 1905600 (2.253 sec) +2022-05-01 20:39:21,692 - tensorflow - INFO - global_step/sec: 88.6162 +2022-05-01 20:39:21,692 - tensorflow - INFO - loss = 0.032864153, step = 1905800 (2.257 sec) +2022-05-01 20:39:23,948 - tensorflow - INFO - global_step/sec: 88.6232 +2022-05-01 20:39:23,949 - tensorflow - INFO - loss = 0.003971881, step = 1906000 (2.257 sec) +2022-05-01 20:39:26,206 - tensorflow - INFO - global_step/sec: 88.6007 +2022-05-01 20:39:26,206 - tensorflow - INFO - loss = 0.04123966, step = 1906200 (2.257 sec) +2022-05-01 20:39:28,463 - tensorflow - INFO - global_step/sec: 88.5905 +2022-05-01 20:39:28,464 - tensorflow - INFO - loss = 0.014023264, step = 1906400 (2.258 sec) +2022-05-01 20:39:30,727 - tensorflow - INFO - global_step/sec: 88.3625 +2022-05-01 20:39:30,727 - tensorflow - INFO - loss = 0.019056926, step = 1906600 (2.263 sec) +2022-05-01 20:39:32,987 - tensorflow - INFO - global_step/sec: 88.4871 +2022-05-01 20:39:32,987 - tensorflow - INFO - loss = 0.03874336, step = 1906800 (2.260 sec) +2022-05-01 20:39:35,239 - tensorflow - INFO - global_step/sec: 88.7957 +2022-05-01 20:39:35,240 - tensorflow - INFO - loss = 0.011484627, step = 1907000 (2.252 sec) +2022-05-01 20:39:37,493 - tensorflow - INFO - global_step/sec: 88.7349 +2022-05-01 20:39:37,494 - tensorflow - INFO - loss = 0.40345308, step = 1907200 (2.254 sec) +2022-05-01 20:39:39,751 - tensorflow - INFO - global_step/sec: 88.5682 +2022-05-01 20:39:39,752 - tensorflow - INFO - loss = 0.2545933, step = 1907400 (2.258 sec) +2022-05-01 20:39:42,002 - tensorflow - INFO - global_step/sec: 88.8538 +2022-05-01 20:39:42,003 - tensorflow - INFO - loss = 0.030244552, step = 1907600 (2.251 sec) +2022-05-01 20:39:44,256 - tensorflow - INFO - global_step/sec: 88.752 +2022-05-01 20:39:44,256 - tensorflow - INFO - loss = 0.07354349, step = 1907800 (2.253 sec) +2022-05-01 20:39:46,511 - tensorflow - INFO - global_step/sec: 88.6774 +2022-05-01 20:39:46,511 - tensorflow - INFO - loss = 0.19981001, step = 1908000 (2.255 sec) +2022-05-01 20:39:48,768 - tensorflow - INFO - global_step/sec: 88.6124 +2022-05-01 20:39:48,768 - tensorflow - INFO - loss = 0.01842366, step = 1908200 (2.257 sec) +2022-05-01 20:39:51,025 - tensorflow - INFO - global_step/sec: 88.61 +2022-05-01 20:39:51,026 - tensorflow - INFO - loss = 0.029061416, step = 1908400 (2.257 sec) +2022-05-01 20:39:53,274 - tensorflow - INFO - global_step/sec: 88.9391 +2022-05-01 20:39:53,274 - tensorflow - INFO - loss = 0.49992847, step = 1908600 (2.249 sec) +2022-05-01 20:39:55,530 - tensorflow - INFO - global_step/sec: 88.6647 +2022-05-01 20:39:55,530 - tensorflow - 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INFO - global_step/sec: 88.9599 +2022-05-01 20:56:12,142 - tensorflow - INFO - loss = 0.050442714, step = 1995400 (2.248 sec) +2022-05-01 20:56:14,393 - tensorflow - INFO - global_step/sec: 88.8572 +2022-05-01 20:56:14,393 - tensorflow - INFO - loss = 0.0054912255, step = 1995600 (2.251 sec) +2022-05-01 20:56:16,642 - tensorflow - INFO - global_step/sec: 88.9007 +2022-05-01 20:56:16,643 - tensorflow - INFO - loss = 0.098836556, step = 1995800 (2.250 sec) +2022-05-01 20:56:18,894 - tensorflow - INFO - global_step/sec: 88.8068 +2022-05-01 20:56:18,895 - tensorflow - INFO - loss = 0.024129063, step = 1996000 (2.252 sec) +2022-05-01 20:56:21,153 - tensorflow - INFO - global_step/sec: 88.5339 +2022-05-01 20:56:21,154 - tensorflow - INFO - loss = 0.0049340297, step = 1996200 (2.259 sec) +2022-05-01 20:56:23,408 - tensorflow - INFO - global_step/sec: 88.7193 +2022-05-01 20:56:23,408 - tensorflow - INFO - loss = 0.0698601, step = 1996400 (2.254 sec) +2022-05-01 20:56:25,665 - tensorflow - INFO - global_step/sec: 88.5965 +2022-05-01 20:56:25,666 - tensorflow - INFO - loss = 0.012717225, step = 1996600 (2.257 sec) +2022-05-01 20:56:27,920 - tensorflow - INFO - global_step/sec: 88.6987 +2022-05-01 20:56:27,920 - tensorflow - INFO - loss = 0.13922852, step = 1996800 (2.255 sec) +2022-05-01 20:56:30,180 - tensorflow - INFO - global_step/sec: 88.5127 +2022-05-01 20:56:30,180 - tensorflow - INFO - loss = 0.10752138, step = 1997000 (2.260 sec) +2022-05-01 20:56:32,444 - tensorflow - INFO - global_step/sec: 88.3182 +2022-05-01 20:56:32,445 - tensorflow - INFO - loss = 0.0060213064, step = 1997200 (2.265 sec) +2022-05-01 20:56:34,701 - tensorflow - INFO - global_step/sec: 88.625 +2022-05-01 20:56:34,701 - tensorflow - INFO - loss = 0.0439571, step = 1997400 (2.257 sec) +2022-05-01 20:56:36,954 - tensorflow - INFO - global_step/sec: 88.751 +2022-05-01 20:56:36,955 - tensorflow - INFO - loss = 0.006770684, step = 1997600 (2.253 sec) +2022-05-01 20:56:39,209 - tensorflow - INFO - global_step/sec: 88.7202 +2022-05-01 20:56:39,209 - tensorflow - INFO - loss = 0.0121766105, step = 1997800 (2.254 sec) +2022-05-01 20:56:41,465 - tensorflow - INFO - global_step/sec: 88.6415 +2022-05-01 20:56:41,465 - tensorflow - INFO - loss = 0.047288876, step = 1998000 (2.256 sec) +2022-05-01 20:56:43,718 - tensorflow - INFO - global_step/sec: 88.7616 +2022-05-01 20:56:43,719 - tensorflow - INFO - loss = 0.062814854, step = 1998200 (2.253 sec) +2022-05-01 20:56:45,978 - tensorflow - INFO - global_step/sec: 88.4877 +2022-05-01 20:56:45,979 - tensorflow - INFO - loss = 0.020641323, step = 1998400 (2.260 sec) +2022-05-01 20:56:48,228 - tensorflow - INFO - global_step/sec: 88.8852 +2022-05-01 20:56:48,229 - tensorflow - INFO - loss = 16.469042, step = 1998600 (2.250 sec) +2022-05-01 20:56:50,483 - tensorflow - INFO - global_step/sec: 88.7054 +2022-05-01 20:56:50,483 - tensorflow - INFO - loss = 0.011845652, step = 1998800 (2.255 sec) +2022-05-01 20:56:52,740 - tensorflow - INFO - global_step/sec: 88.6063 +2022-05-01 20:56:52,741 - tensorflow - INFO - loss = 0.0357652, step = 1999000 (2.257 sec) +2022-05-01 20:56:54,994 - tensorflow - INFO - global_step/sec: 88.7291 +2022-05-01 20:56:54,995 - tensorflow - INFO - loss = 0.07935603, step = 1999200 (2.254 sec) +2022-05-01 20:56:57,256 - tensorflow - INFO - global_step/sec: 88.4201 +2022-05-01 20:56:57,257 - tensorflow - INFO - loss = 0.012287976, step = 1999400 (2.262 sec) +2022-05-01 20:56:59,509 - tensorflow - INFO - global_step/sec: 88.7909 +2022-05-01 20:56:59,509 - tensorflow - INFO - loss = 0.0061325766, step = 1999600 (2.252 sec) +2022-05-01 20:57:01,759 - tensorflow - INFO - global_step/sec: 88.8596 +2022-05-01 20:57:01,760 - tensorflow - INFO - loss = 0.0115417605, step = 1999800 (2.251 sec) +2022-05-01 20:57:04,005 - tensorflow - INFO - Saving checkpoints for 2000000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. +2022-05-01 20:57:04,023 - tensorflow - INFO - model.ckpt-2000000 is not in all_model_checkpoint_paths. Manually adding it. +2022-05-01 20:57:04,551 - tensorflow - INFO - Calling model_fn. +2022-05-01 20:57:04,678 - tensorflow - INFO - Done calling model_fn. +2022-05-01 20:57:04,693 - tensorflow - INFO - Starting evaluation at 2022-05-01T20:57:04Z +2022-05-01 20:57:05,038 - tensorflow - INFO - Graph was finalized. +2022-05-01 20:57:05,041 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-2000000 +2022-05-01 20:57:05,323 - tensorflow - INFO - Running local_init_op. +2022-05-01 20:57:05,432 - tensorflow - INFO - Done running local_init_op. +2022-05-01 20:57:15,574 - tensorflow - INFO - Evaluation [3990/39900] +2022-05-01 20:57:25,021 - tensorflow - INFO - Evaluation [7980/39900] +2022-05-01 20:57:34,462 - tensorflow - INFO - Evaluation [11970/39900] +2022-05-01 20:57:43,894 - tensorflow - INFO - Evaluation [15960/39900] +2022-05-01 20:57:53,335 - tensorflow - INFO - Evaluation [19950/39900] +2022-05-01 20:58:02,783 - tensorflow - INFO - Evaluation [23940/39900] +2022-05-01 20:58:12,227 - tensorflow - INFO - Evaluation [27930/39900] +2022-05-01 20:58:21,686 - tensorflow - INFO - Evaluation [31920/39900] +2022-05-01 20:58:31,130 - tensorflow - INFO - Evaluation [35910/39900] +2022-05-01 20:58:40,568 - tensorflow - INFO - Evaluation [39900/39900] +2022-05-01 20:58:40,705 - tensorflow - INFO - Finished evaluation at 2022-05-01-20:58:40 +2022-05-01 20:58:40,706 - tensorflow - INFO - Saving dict for global step 2000000: global_step = 2000000, loss = 0.015859876, mean_degree_err = 3.1788845 +2022-05-01 20:58:40,706 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 2000000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-2000000 +2022-05-01 20:58:40,720 - tensorflow - INFO - global_step/sec: 2.02101 +2022-05-01 20:58:40,721 - tensorflow - INFO - loss = 0.01467032, step = 2000000 (98.961 sec) +2022-05-01 20:58:42,981 - tensorflow - INFO - global_step/sec: 88.4545 +2022-05-01 20:58:42,982 - tensorflow - INFO - loss = 0.013274206, step = 2000200 (2.261 sec) +2022-05-01 20:58:45,239 - tensorflow - INFO - global_step/sec: 88.558 +2022-05-01 20:58:45,240 - tensorflow - INFO - loss = 0.026724704, step = 2000400 (2.258 sec) +2022-05-01 20:58:47,492 - tensorflow - INFO - global_step/sec: 88.7754 +2022-05-01 20:58:47,493 - tensorflow - INFO - loss = 0.007345394, step = 2000600 (2.253 sec) +2022-05-01 20:58:49,751 - tensorflow - INFO - global_step/sec: 88.5343 +2022-05-01 20:58:49,752 - tensorflow - INFO - loss = 0.008276798, step = 2000800 (2.259 sec) +2022-05-01 20:58:52,005 - tensorflow - INFO - global_step/sec: 88.7607 +2022-05-01 20:58:52,005 - tensorflow - INFO - loss = 0.037647825, step = 2001000 (2.253 sec) +2022-05-01 20:58:54,264 - tensorflow - INFO - global_step/sec: 88.5243 +2022-05-01 20:58:54,264 - tensorflow - INFO - loss = 0.014356632, step = 2001200 (2.259 sec) +2022-05-01 20:58:56,527 - tensorflow - INFO - global_step/sec: 88.3761 +2022-05-01 20:58:56,527 - tensorflow - INFO - loss = 0.018045077, step = 2001400 (2.263 sec) +2022-05-01 20:58:58,788 - tensorflow - INFO - global_step/sec: 88.4435 +2022-05-01 20:58:58,789 - tensorflow - INFO - loss = 0.25831372, step = 2001600 (2.261 sec) +2022-05-01 20:59:01,052 - tensorflow - INFO - global_step/sec: 88.3513 +2022-05-01 20:59:01,052 - tensorflow - INFO - loss = 0.0193472, step = 2001800 (2.264 sec) +2022-05-01 20:59:03,311 - tensorflow - INFO - global_step/sec: 88.5515 +2022-05-01 20:59:03,311 - tensorflow - INFO - loss = 0.039867893, step = 2002000 (2.259 sec) +2022-05-01 20:59:05,575 - tensorflow - INFO - global_step/sec: 88.3249 +2022-05-01 20:59:05,575 - tensorflow - INFO - loss = 0.021221787, step = 2002200 (2.264 sec) +2022-05-01 20:59:07,836 - tensorflow - INFO - global_step/sec: 88.4694 +2022-05-01 20:59:07,836 - tensorflow - INFO - loss = 0.04830868, step = 2002400 (2.261 sec) +2022-05-01 20:59:10,096 - tensorflow - INFO - global_step/sec: 88.4924 +2022-05-01 20:59:10,096 - tensorflow - INFO - loss = 1.0361093, step = 2002600 (2.260 sec) +2022-05-01 20:59:12,354 - tensorflow - INFO - global_step/sec: 88.5539 +2022-05-01 20:59:12,355 - tensorflow - INFO - loss = 0.018668309, step = 2002800 (2.259 sec) +2022-05-01 20:59:14,616 - tensorflow - INFO - global_step/sec: 88.4391 +2022-05-01 20:59:14,616 - tensorflow - INFO - loss = 0.014582523, step = 2003000 (2.261 sec) +2022-05-01 20:59:16,875 - tensorflow - INFO - global_step/sec: 88.5287 +2022-05-01 20:59:16,875 - tensorflow - INFO - loss = 0.34214815, step = 2003200 (2.259 sec) +2022-05-01 20:59:19,141 - tensorflow - INFO - global_step/sec: 88.2529 +2022-05-01 20:59:19,141 - tensorflow - INFO - loss = 0.027201526, step = 2003400 (2.266 sec) +2022-05-01 20:59:21,399 - tensorflow - INFO - global_step/sec: 88.5729 +2022-05-01 20:59:21,399 - tensorflow - INFO - loss = 0.017191805, step = 2003600 (2.258 sec) +2022-05-01 20:59:23,659 - tensorflow - INFO - global_step/sec: 88.5133 +2022-05-01 20:59:23,659 - tensorflow - INFO - loss = 0.040490415, step = 2003800 (2.260 sec) +2022-05-01 20:59:25,911 - tensorflow - INFO - global_step/sec: 88.8008 +2022-05-01 20:59:25,911 - tensorflow - INFO - loss = 0.019516265, step = 2004000 (2.252 sec) +2022-05-01 20:59:28,164 - tensorflow - INFO - global_step/sec: 88.7503 +2022-05-01 20:59:28,165 - tensorflow - INFO - loss = 0.025298692, step = 2004200 (2.254 sec) +2022-05-01 20:59:30,427 - tensorflow - INFO - global_step/sec: 88.3838 +2022-05-01 20:59:30,428 - tensorflow - INFO - loss = 0.02187489, step = 2004400 (2.263 sec) +2022-05-01 20:59:32,683 - tensorflow - INFO - global_step/sec: 88.6602 +2022-05-01 20:59:32,683 - tensorflow - INFO - loss = 0.008246376, step = 2004600 (2.256 sec) +2022-05-01 20:59:34,936 - tensorflow - INFO - global_step/sec: 88.7644 +2022-05-01 20:59:34,936 - tensorflow - INFO - loss = 0.00793772, step = 2004800 (2.253 sec) +2022-05-01 20:59:37,186 - tensorflow - INFO - global_step/sec: 88.9049 +2022-05-01 20:59:37,186 - tensorflow - INFO - loss = 0.009742752, step = 2005000 (2.250 sec) +2022-05-01 20:59:39,439 - tensorflow - INFO - global_step/sec: 88.7513 +2022-05-01 20:59:39,440 - tensorflow - INFO - loss = 0.019376889, step = 2005200 (2.253 sec) +2022-05-01 20:59:41,694 - tensorflow - INFO - global_step/sec: 88.6935 +2022-05-01 20:59:41,695 - tensorflow - INFO - loss = 0.0048106383, step = 2005400 (2.255 sec) +2022-05-01 20:59:43,958 - tensorflow - INFO - global_step/sec: 88.3361 +2022-05-01 20:59:43,959 - tensorflow - INFO - loss = 0.13213453, step = 2005600 (2.264 sec) +2022-05-01 20:59:46,211 - tensorflow - INFO - global_step/sec: 88.7763 +2022-05-01 20:59:46,211 - tensorflow - INFO - loss = 0.050713982, step = 2005800 (2.253 sec) +2022-05-01 20:59:48,461 - tensorflow - INFO - global_step/sec: 88.8766 +2022-05-01 20:59:48,462 - tensorflow - INFO - loss = 0.027069667, step = 2006000 (2.250 sec) +2022-05-01 20:59:50,712 - tensorflow - INFO - global_step/sec: 88.8684 +2022-05-01 20:59:50,712 - tensorflow - INFO - loss = 0.2192508, step = 2006200 (2.251 sec) +2022-05-01 20:59:52,968 - tensorflow - INFO - global_step/sec: 88.629 +2022-05-01 20:59:52,969 - tensorflow - INFO - loss = 0.02215605, step = 2006400 (2.257 sec) +2022-05-01 20:59:55,224 - tensorflow - INFO - global_step/sec: 88.688 +2022-05-01 20:59:55,224 - tensorflow - INFO - loss = 0.015829494, step = 2006600 (2.255 sec) +2022-05-01 20:59:57,483 - tensorflow - INFO - global_step/sec: 88.5101 +2022-05-01 20:59:57,484 - tensorflow - INFO - loss = 0.020551346, step = 2006800 (2.260 sec) +2022-05-01 20:59:59,737 - tensorflow - INFO - global_step/sec: 88.7463 +2022-05-01 20:59:59,737 - tensorflow - INFO - loss = 2.006907, step = 2007000 (2.254 sec) +2022-05-01 21:00:01,992 - tensorflow - INFO - global_step/sec: 88.6747 +2022-05-01 21:00:01,993 - tensorflow - INFO - loss = 0.009550948, step = 2007200 (2.255 sec) +2022-05-01 21:00:04,245 - tensorflow - INFO - global_step/sec: 88.7846 +2022-05-01 21:00:04,245 - tensorflow - INFO - loss = 0.03207432, step = 2007400 (2.253 sec) +2022-05-01 21:00:06,496 - tensorflow - INFO - global_step/sec: 88.8523 +2022-05-01 21:00:06,496 - tensorflow - INFO - loss = 0.008671386, step = 2007600 (2.251 sec) +2022-05-01 21:00:08,752 - tensorflow - INFO - global_step/sec: 88.6274 +2022-05-01 21:00:08,753 - tensorflow - INFO - loss = 0.081116244, step = 2007800 (2.257 sec) +2022-05-01 21:00:11,003 - tensorflow - INFO - global_step/sec: 88.8742 +2022-05-01 21:00:11,003 - tensorflow - INFO - loss = 0.024560489, step = 2008000 (2.250 sec) +2022-05-01 21:00:13,260 - tensorflow - INFO - global_step/sec: 88.625 +2022-05-01 21:00:13,260 - tensorflow - INFO - loss = 0.054812565, step = 2008200 (2.257 sec) +2022-05-01 21:00:15,514 - tensorflow - INFO - global_step/sec: 88.7045 +2022-05-01 21:00:15,515 - tensorflow - INFO - loss = 0.021981124, step = 2008400 (2.255 sec) +2022-05-01 21:00:17,765 - tensorflow - INFO - global_step/sec: 88.8768 +2022-05-01 21:00:17,765 - tensorflow - INFO - loss = 0.024330994, step = 2008600 (2.250 sec) +2022-05-01 21:00:20,021 - tensorflow - INFO - global_step/sec: 88.6422 +2022-05-01 21:00:20,021 - tensorflow - INFO - loss = 0.121265806, step = 2008800 (2.256 sec) +2022-05-01 21:00:22,279 - tensorflow - INFO - global_step/sec: 88.5661 +2022-05-01 21:00:22,279 - tensorflow - INFO - loss = 0.013724765, step = 2009000 (2.258 sec) +2022-05-01 21:00:24,529 - tensorflow - INFO - global_step/sec: 88.8898 +2022-05-01 21:00:24,529 - tensorflow - INFO - loss = 0.07820247, step = 2009200 (2.250 sec) +2022-05-01 21:00:26,781 - tensorflow - INFO - global_step/sec: 88.8091 +2022-05-01 21:00:26,781 - tensorflow - INFO - loss = 0.069547355, step = 2009400 (2.252 sec) +2022-05-01 21:00:29,039 - tensorflow - INFO - global_step/sec: 88.5597 +2022-05-01 21:00:29,040 - tensorflow - INFO - loss = 0.032171834, step = 2009600 (2.258 sec) +2022-05-01 21:00:31,300 - tensorflow - INFO - global_step/sec: 88.4701 +2022-05-01 21:00:31,300 - tensorflow - INFO - loss = 0.074208364, step = 2009800 (2.261 sec) +2022-05-01 21:00:33,551 - tensorflow - INFO - global_step/sec: 88.8576 +2022-05-01 21:00:33,551 - tensorflow - INFO - loss = 0.014253901, step = 2010000 (2.251 sec) +2022-05-01 21:00:35,801 - tensorflow - INFO - global_step/sec: 88.8754 +2022-05-01 21:00:35,802 - tensorflow - INFO - loss = 0.107017614, step = 2010200 (2.250 sec) +2022-05-01 21:00:38,052 - tensorflow - INFO - global_step/sec: 88.8646 +2022-05-01 21:00:38,052 - tensorflow - INFO - loss = 0.02333238, step = 2010400 (2.251 sec) +2022-05-01 21:00:40,304 - tensorflow - INFO - global_step/sec: 88.8074 +2022-05-01 21:00:40,304 - tensorflow - INFO - loss = 0.013060355, step = 2010600 (2.252 sec) +2022-05-01 21:00:42,550 - tensorflow - INFO - global_step/sec: 89.0439 +2022-05-01 21:00:42,550 - tensorflow - INFO - loss = 0.05624281, step = 2010800 (2.246 sec) +2022-05-01 21:00:44,804 - tensorflow - INFO - global_step/sec: 88.7119 +2022-05-01 21:00:44,805 - tensorflow - INFO - loss = 0.09383938, step = 2011000 (2.254 sec) +2022-05-01 21:00:47,063 - tensorflow - INFO - global_step/sec: 88.5444 +2022-05-01 21:00:47,064 - tensorflow - INFO - loss = 0.026484538, step = 2011200 (2.259 sec) +2022-05-01 21:00:49,314 - tensorflow - 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tensorflow - INFO - global_step/sec: 88.8212 +2022-05-01 21:16:11,134 - tensorflow - INFO - loss = 0.011259941, step = 2093200 (2.252 sec) +2022-05-01 21:16:13,387 - tensorflow - INFO - global_step/sec: 88.7433 +2022-05-01 21:16:13,388 - tensorflow - INFO - loss = 0.020150546, step = 2093400 (2.254 sec) +2022-05-01 21:16:15,642 - tensorflow - INFO - global_step/sec: 88.7082 +2022-05-01 21:16:15,642 - tensorflow - INFO - loss = 0.0073077874, step = 2093600 (2.255 sec) +2022-05-01 21:16:17,889 - tensorflow - INFO - global_step/sec: 88.9956 +2022-05-01 21:16:17,890 - tensorflow - INFO - loss = 0.0035933796, step = 2093800 (2.247 sec) +2022-05-01 21:16:20,144 - tensorflow - INFO - global_step/sec: 88.7031 +2022-05-01 21:16:20,144 - tensorflow - INFO - loss = 0.011978446, step = 2094000 (2.255 sec) +2022-05-01 21:16:22,396 - tensorflow - INFO - global_step/sec: 88.8286 +2022-05-01 21:16:22,396 - tensorflow - INFO - loss = 0.009533863, step = 2094200 (2.252 sec) +2022-05-01 21:16:24,655 - tensorflow - INFO - global_step/sec: 88.5093 +2022-05-01 21:16:24,656 - tensorflow - INFO - loss = 0.01545664, step = 2094400 (2.260 sec) +2022-05-01 21:16:26,912 - tensorflow - INFO - global_step/sec: 88.6031 +2022-05-01 21:16:26,913 - tensorflow - INFO - loss = 0.023175605, step = 2094600 (2.257 sec) +2022-05-01 21:16:29,174 - tensorflow - INFO - global_step/sec: 88.4327 +2022-05-01 21:16:29,174 - tensorflow - INFO - loss = 0.24491048, step = 2094800 (2.262 sec) +2022-05-01 21:16:31,445 - tensorflow - INFO - global_step/sec: 88.0511 +2022-05-01 21:16:31,446 - tensorflow - INFO - loss = 0.01627911, step = 2095000 (2.271 sec) +2022-05-01 21:16:33,716 - tensorflow - INFO - global_step/sec: 88.0973 +2022-05-01 21:16:33,716 - tensorflow - INFO - loss = 0.07918907, step = 2095200 (2.270 sec) +2022-05-01 21:16:35,974 - tensorflow - INFO - global_step/sec: 88.5521 +2022-05-01 21:16:35,975 - tensorflow - INFO - loss = 0.012776617, step = 2095400 (2.259 sec) +2022-05-01 21:16:38,239 - tensorflow - INFO - global_step/sec: 88.3085 +2022-05-01 21:16:38,239 - tensorflow - INFO - loss = 0.026352443, step = 2095600 (2.265 sec) +2022-05-01 21:16:40,497 - tensorflow - INFO - global_step/sec: 88.555 +2022-05-01 21:16:40,498 - tensorflow - INFO - loss = 0.012043992, step = 2095800 (2.259 sec) +2022-05-01 21:16:42,759 - tensorflow - INFO - global_step/sec: 88.4462 +2022-05-01 21:16:42,759 - tensorflow - INFO - loss = 0.023362797, step = 2096000 (2.261 sec) +2022-05-01 21:16:45,021 - tensorflow - INFO - global_step/sec: 88.4093 +2022-05-01 21:16:45,021 - tensorflow - INFO - loss = 0.03033696, step = 2096200 (2.262 sec) +2022-05-01 21:16:47,281 - tensorflow - INFO - global_step/sec: 88.499 +2022-05-01 21:16:47,281 - tensorflow - INFO - loss = 0.0076375394, step = 2096400 (2.260 sec) +2022-05-01 21:16:49,542 - tensorflow - INFO - global_step/sec: 88.4654 +2022-05-01 21:16:49,542 - tensorflow - INFO - loss = 0.008694179, step = 2096600 (2.261 sec) +2022-05-01 21:16:51,800 - tensorflow - INFO - global_step/sec: 88.5724 +2022-05-01 21:16:51,800 - tensorflow - INFO - loss = 0.037878785, step = 2096800 (2.258 sec) +2022-05-01 21:16:54,059 - tensorflow - INFO - global_step/sec: 88.5216 +2022-05-01 21:16:54,059 - tensorflow - INFO - loss = 0.12967177, step = 2097000 (2.259 sec) +2022-05-01 21:16:56,321 - tensorflow - INFO - global_step/sec: 88.4241 +2022-05-01 21:16:56,321 - tensorflow - INFO - loss = 0.017660087, step = 2097200 (2.262 sec) +2022-05-01 21:16:58,580 - tensorflow - INFO - global_step/sec: 88.5298 +2022-05-01 21:16:58,580 - tensorflow - INFO - loss = 0.010364544, step = 2097400 (2.259 sec) +2022-05-01 21:17:00,841 - tensorflow - INFO - global_step/sec: 88.4503 +2022-05-01 21:17:00,842 - tensorflow - INFO - loss = 0.01831447, step = 2097600 (2.261 sec) +2022-05-01 21:17:03,098 - tensorflow - INFO - global_step/sec: 88.6275 +2022-05-01 21:17:03,098 - tensorflow - INFO - loss = 0.04949817, step = 2097800 (2.257 sec) +2022-05-01 21:17:05,350 - tensorflow - INFO - global_step/sec: 88.8195 +2022-05-01 21:17:05,350 - tensorflow - INFO - loss = 0.04070931, step = 2098000 (2.252 sec) +2022-05-01 21:17:07,604 - tensorflow - INFO - global_step/sec: 88.7045 +2022-05-01 21:17:07,605 - tensorflow - INFO - loss = 0.035523713, step = 2098200 (2.255 sec) +2022-05-01 21:17:09,859 - tensorflow - INFO - global_step/sec: 88.6824 +2022-05-01 21:17:09,860 - tensorflow - INFO - loss = 0.021236856, step = 2098400 (2.255 sec) +2022-05-01 21:17:12,119 - tensorflow - INFO - global_step/sec: 88.5007 +2022-05-01 21:17:12,120 - tensorflow - INFO - loss = 0.016438108, step = 2098600 (2.260 sec) +2022-05-01 21:17:14,369 - tensorflow - INFO - global_step/sec: 88.8847 +2022-05-01 21:17:14,370 - tensorflow - INFO - loss = 0.00890751, step = 2098800 (2.250 sec) +2022-05-01 21:17:16,623 - tensorflow - INFO - global_step/sec: 88.7402 +2022-05-01 21:17:16,624 - tensorflow - INFO - loss = 0.005814164, step = 2099000 (2.254 sec) +2022-05-01 21:17:18,879 - tensorflow - INFO - global_step/sec: 88.6581 +2022-05-01 21:17:18,879 - tensorflow - INFO - loss = 0.009963096, step = 2099200 (2.256 sec) +2022-05-01 21:17:21,129 - tensorflow - INFO - global_step/sec: 88.8753 +2022-05-01 21:17:21,130 - tensorflow - INFO - loss = 0.008472566, step = 2099400 (2.250 sec) +2022-05-01 21:17:23,383 - tensorflow - INFO - global_step/sec: 88.7272 +2022-05-01 21:17:23,384 - tensorflow - INFO - loss = 0.008451465, step = 2099600 (2.254 sec) +2022-05-01 21:17:25,635 - tensorflow - INFO - global_step/sec: 88.8399 +2022-05-01 21:17:25,635 - tensorflow - INFO - loss = 0.028972732, step = 2099800 (2.251 sec) +2022-05-01 21:17:27,870 - tensorflow - INFO - Saving checkpoints for 2100000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. +2022-05-01 21:17:27,885 - tensorflow - INFO - model.ckpt-2100000 is not in all_model_checkpoint_paths. Manually adding it. +2022-05-01 21:17:28,408 - tensorflow - INFO - Calling model_fn. +2022-05-01 21:17:28,535 - tensorflow - INFO - Done calling model_fn. +2022-05-01 21:17:28,550 - tensorflow - INFO - Starting evaluation at 2022-05-01T21:17:28Z +2022-05-01 21:17:28,618 - tensorflow - INFO - Graph was finalized. +2022-05-01 21:17:28,621 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-2100000 +2022-05-01 21:17:28,903 - tensorflow - INFO - Running local_init_op. +2022-05-01 21:17:29,010 - tensorflow - INFO - Done running local_init_op. +2022-05-01 21:17:39,236 - tensorflow - INFO - Evaluation [3990/39900] +2022-05-01 21:17:48,745 - tensorflow - INFO - Evaluation [7980/39900] +2022-05-01 21:17:58,242 - tensorflow - INFO - Evaluation [11970/39900] +2022-05-01 21:18:07,766 - tensorflow - INFO - Evaluation [15960/39900] +2022-05-01 21:18:17,302 - tensorflow - INFO - Evaluation [19950/39900] +2022-05-01 21:18:26,848 - tensorflow - INFO - Evaluation [23940/39900] +2022-05-01 21:18:36,360 - tensorflow - INFO - Evaluation [27930/39900] +2022-05-01 21:18:45,842 - tensorflow - INFO - Evaluation [31920/39900] +2022-05-01 21:18:55,344 - tensorflow - INFO - Evaluation [35910/39900] +2022-05-01 21:19:04,884 - tensorflow - INFO - Evaluation [39900/39900] +2022-05-01 21:19:05,022 - tensorflow - INFO - Finished evaluation at 2022-05-01-21:19:05 +2022-05-01 21:19:05,022 - tensorflow - INFO - Saving dict for global step 2100000: global_step = 2100000, loss = 0.016458333, mean_degree_err = 3.3069553 +2022-05-01 21:19:05,022 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 2100000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-2100000 +2022-05-01 21:19:05,036 - tensorflow - INFO - global_step/sec: 2.01204 +2022-05-01 21:19:05,037 - tensorflow - INFO - loss = 0.05436354, step = 2100000 (99.402 sec) +2022-05-01 21:19:07,310 - tensorflow - INFO - global_step/sec: 87.9633 +2022-05-01 21:19:07,310 - tensorflow - INFO - loss = 0.038885124, step = 2100200 (2.273 sec) +2022-05-01 21:19:09,564 - tensorflow - INFO - global_step/sec: 88.7438 +2022-05-01 21:19:09,564 - tensorflow - INFO - loss = 0.0110758245, step = 2100400 (2.254 sec) +2022-05-01 21:19:11,816 - tensorflow - INFO - global_step/sec: 88.8034 +2022-05-01 21:19:11,816 - tensorflow - INFO - loss = 0.02910995, step = 2100600 (2.252 sec) +2022-05-01 21:19:14,067 - tensorflow - INFO - global_step/sec: 88.8318 +2022-05-01 21:19:14,068 - tensorflow - INFO - loss = 0.019992223, step = 2100800 (2.251 sec) +2022-05-01 21:19:16,325 - tensorflow - INFO - global_step/sec: 88.5787 +2022-05-01 21:19:16,325 - tensorflow - INFO - loss = 0.010165205, step = 2101000 (2.258 sec) +2022-05-01 21:19:18,576 - tensorflow - INFO - global_step/sec: 88.8618 +2022-05-01 21:19:18,576 - tensorflow - INFO - loss = 0.013112518, step = 2101200 (2.251 sec) +2022-05-01 21:19:20,828 - tensorflow - INFO - global_step/sec: 88.8129 +2022-05-01 21:19:20,828 - tensorflow - INFO - loss = 0.01900612, step = 2101400 (2.252 sec) +2022-05-01 21:19:23,079 - tensorflow - INFO - global_step/sec: 88.8353 +2022-05-01 21:19:23,079 - tensorflow - INFO - loss = 0.05495621, step = 2101600 (2.251 sec) +2022-05-01 21:19:25,329 - tensorflow - INFO - global_step/sec: 88.9022 +2022-05-01 21:19:25,329 - tensorflow - INFO - loss = 0.05240432, step = 2101800 (2.250 sec) +2022-05-01 21:19:27,584 - tensorflow - INFO - global_step/sec: 88.69 +2022-05-01 21:19:27,584 - tensorflow - INFO - loss = 0.0105040055, step = 2102000 (2.255 sec) +2022-05-01 21:19:29,834 - tensorflow - INFO - global_step/sec: 88.86 +2022-05-01 21:19:29,835 - tensorflow - INFO - loss = 0.070999, step = 2102200 (2.251 sec) +2022-05-01 21:19:32,095 - tensorflow - INFO - global_step/sec: 88.4786 +2022-05-01 21:19:32,095 - tensorflow - INFO - loss = 0.016450152, step = 2102400 (2.260 sec) +2022-05-01 21:19:34,353 - tensorflow - INFO - global_step/sec: 88.5747 +2022-05-01 21:19:34,353 - tensorflow - INFO - loss = 0.009405328, step = 2102600 (2.258 sec) +2022-05-01 21:19:36,604 - tensorflow - INFO - global_step/sec: 88.8532 +2022-05-01 21:19:36,604 - tensorflow - INFO - loss = 0.09677004, step = 2102800 (2.251 sec) +2022-05-01 21:19:38,856 - tensorflow - INFO - global_step/sec: 88.8127 +2022-05-01 21:19:38,856 - tensorflow - INFO - loss = 0.010431, step = 2103000 (2.252 sec) +2022-05-01 21:19:41,114 - tensorflow - INFO - global_step/sec: 88.5512 +2022-05-01 21:19:41,115 - tensorflow - INFO - loss = 0.009252382, step = 2103200 (2.259 sec) +2022-05-01 21:19:43,362 - tensorflow - INFO - global_step/sec: 88.9678 +2022-05-01 21:19:43,363 - tensorflow - INFO - loss = 0.010826778, step = 2103400 (2.248 sec) +2022-05-01 21:19:45,612 - tensorflow - INFO - global_step/sec: 88.8826 +2022-05-01 21:19:45,613 - tensorflow - INFO - loss = 0.042679265, step = 2103600 (2.250 sec) +2022-05-01 21:19:47,860 - tensorflow - INFO - global_step/sec: 88.9817 +2022-05-01 21:19:47,860 - tensorflow - INFO - loss = 0.00809348, step = 2103800 (2.248 sec) +2022-05-01 21:19:50,115 - tensorflow - INFO - global_step/sec: 88.6784 +2022-05-01 21:19:50,116 - tensorflow - INFO - loss = 0.0068497146, step = 2104000 (2.255 sec) +2022-05-01 21:19:52,372 - tensorflow - INFO - global_step/sec: 88.617 +2022-05-01 21:19:52,373 - tensorflow - INFO - loss = 0.010443541, step = 2104200 (2.257 sec) +2022-05-01 21:19:54,626 - tensorflow - INFO - global_step/sec: 88.7574 +2022-05-01 21:19:54,626 - tensorflow - INFO - loss = 0.02146333, step = 2104400 (2.253 sec) +2022-05-01 21:19:56,883 - tensorflow - INFO - global_step/sec: 88.6164 +2022-05-01 21:19:56,883 - tensorflow - INFO - loss = 0.006963673, step = 2104600 (2.257 sec) +2022-05-01 21:19:59,136 - tensorflow - INFO - global_step/sec: 88.7392 +2022-05-01 21:19:59,137 - tensorflow - INFO - loss = 0.05600545, step = 2104800 (2.254 sec) +2022-05-01 21:20:01,392 - tensorflow - INFO - global_step/sec: 88.6644 +2022-05-01 21:20:01,392 - tensorflow - INFO - loss = 0.07515754, step = 2105000 (2.256 sec) +2022-05-01 21:20:03,651 - tensorflow - INFO - global_step/sec: 88.5435 +2022-05-01 21:20:03,651 - tensorflow - INFO - loss = 0.010558774, step = 2105200 (2.259 sec) +2022-05-01 21:20:05,915 - tensorflow - INFO - global_step/sec: 88.3282 +2022-05-01 21:20:05,916 - tensorflow - INFO - loss = 0.014063058, step = 2105400 (2.264 sec) +2022-05-01 21:20:08,178 - tensorflow - INFO - global_step/sec: 88.3788 +2022-05-01 21:20:08,179 - tensorflow - INFO - loss = 0.037848994, step = 2105600 (2.263 sec) +2022-05-01 21:20:10,439 - tensorflow - INFO - global_step/sec: 88.4571 +2022-05-01 21:20:10,439 - tensorflow - INFO - loss = 0.009343641, step = 2105800 (2.261 sec) +2022-05-01 21:20:12,700 - tensorflow - INFO - global_step/sec: 88.4639 +2022-05-01 21:20:12,700 - tensorflow - INFO - loss = 0.020438509, step = 2106000 (2.261 sec) +2022-05-01 21:20:14,959 - tensorflow - INFO - global_step/sec: 88.5247 +2022-05-01 21:20:14,960 - tensorflow - INFO - loss = 0.0067908484, step = 2106200 (2.259 sec) +2022-05-01 21:20:17,222 - tensorflow - INFO - global_step/sec: 88.3955 +2022-05-01 21:20:17,222 - tensorflow - INFO - loss = 0.10189227, step = 2106400 (2.263 sec) +2022-05-01 21:20:19,477 - tensorflow - INFO - global_step/sec: 88.6961 +2022-05-01 21:20:19,477 - tensorflow - INFO - loss = 0.029760987, step = 2106600 (2.255 sec) +2022-05-01 21:20:21,742 - tensorflow - INFO - global_step/sec: 88.2739 +2022-05-01 21:20:21,743 - tensorflow - INFO - loss = 0.034703635, step = 2106800 (2.266 sec) +2022-05-01 21:20:24,005 - tensorflow - INFO - global_step/sec: 88.3958 +2022-05-01 21:20:24,005 - tensorflow - INFO - loss = 0.017966729, step = 2107000 (2.263 sec) +2022-05-01 21:20:26,265 - tensorflow - INFO - global_step/sec: 88.4732 +2022-05-01 21:20:26,266 - tensorflow - INFO - loss = 0.019259527, step = 2107200 (2.261 sec) +2022-05-01 21:20:28,528 - tensorflow - INFO - global_step/sec: 88.4109 +2022-05-01 21:20:28,528 - tensorflow - INFO - loss = 0.010687232, step = 2107400 (2.262 sec) +2022-05-01 21:20:30,790 - tensorflow - INFO - global_step/sec: 88.4049 +2022-05-01 21:20:30,790 - tensorflow - INFO - loss = 0.03140063, step = 2107600 (2.262 sec) +2022-05-01 21:20:33,050 - tensorflow - INFO - global_step/sec: 88.4724 +2022-05-01 21:20:33,051 - tensorflow - INFO - loss = 0.0077216662, step = 2107800 (2.261 sec) +2022-05-01 21:20:35,303 - tensorflow - INFO - global_step/sec: 88.7959 +2022-05-01 21:20:35,303 - tensorflow - INFO - loss = 0.058895633, step = 2108000 (2.252 sec) +2022-05-01 21:20:37,555 - tensorflow - INFO - global_step/sec: 88.79 +2022-05-01 21:20:37,556 - tensorflow - INFO - loss = 0.2672078, step = 2108200 (2.252 sec) +2022-05-01 21:20:39,809 - tensorflow - INFO - global_step/sec: 88.7541 +2022-05-01 21:20:39,809 - tensorflow - INFO - loss = 0.0117831575, step = 2108400 (2.253 sec) +2022-05-01 21:20:42,062 - tensorflow - INFO - global_step/sec: 88.7505 +2022-05-01 21:20:42,063 - tensorflow - INFO - loss = 0.028337851, step = 2108600 (2.254 sec) +2022-05-01 21:20:44,316 - tensorflow - INFO - global_step/sec: 88.7299 +2022-05-01 21:20:44,317 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.5985 +2022-05-01 21:21:00,090 - tensorflow - INFO - loss = 0.006488925, step = 2110200 (2.257 sec) +2022-05-01 21:21:02,341 - tensorflow - INFO - global_step/sec: 88.8574 +2022-05-01 21:21:02,341 - tensorflow - INFO - loss = 0.021128904, step = 2110400 (2.251 sec) +2022-05-01 21:21:04,590 - tensorflow - INFO - global_step/sec: 88.9032 +2022-05-01 21:21:04,591 - tensorflow - INFO - loss = 2.7757092, step = 2110600 (2.250 sec) +2022-05-01 21:21:06,843 - tensorflow - INFO - global_step/sec: 88.7744 +2022-05-01 21:21:06,844 - tensorflow - INFO - loss = 0.04770029, step = 2110800 (2.253 sec) +2022-05-01 21:21:09,096 - tensorflow - INFO - global_step/sec: 88.784 +2022-05-01 21:21:09,096 - tensorflow - INFO - loss = 0.26345018, step = 2111000 (2.253 sec) +2022-05-01 21:21:11,358 - tensorflow - INFO - global_step/sec: 88.4278 +2022-05-01 21:21:11,358 - tensorflow - INFO - loss = 0.006933829, step = 2111200 (2.262 sec) +2022-05-01 21:21:13,614 - tensorflow - 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INFO - loss = 0.007883164, step = 2190400 (2.255 sec) +2022-05-01 21:36:06,665 - tensorflow - INFO - global_step/sec: 88.7001 +2022-05-01 21:36:06,665 - tensorflow - INFO - loss = 0.01097027, step = 2190600 (2.255 sec) +2022-05-01 21:36:08,918 - tensorflow - INFO - global_step/sec: 88.7474 +2022-05-01 21:36:08,919 - tensorflow - INFO - loss = 0.037311327, step = 2190800 (2.254 sec) +2022-05-01 21:36:11,167 - tensorflow - INFO - global_step/sec: 88.9231 +2022-05-01 21:36:11,168 - tensorflow - INFO - loss = 0.019213201, step = 2191000 (2.249 sec) +2022-05-01 21:36:13,420 - tensorflow - INFO - global_step/sec: 88.7924 +2022-05-01 21:36:13,420 - tensorflow - INFO - loss = 0.013376619, step = 2191200 (2.252 sec) +2022-05-01 21:36:15,670 - tensorflow - INFO - global_step/sec: 88.8899 +2022-05-01 21:36:15,670 - tensorflow - INFO - loss = 0.009635315, step = 2191400 (2.250 sec) +2022-05-01 21:36:17,930 - tensorflow - INFO - global_step/sec: 88.4729 +2022-05-01 21:36:17,931 - tensorflow - INFO - loss = 0.015364771, step = 2191600 (2.261 sec) +2022-05-01 21:36:20,185 - tensorflow - INFO - global_step/sec: 88.6835 +2022-05-01 21:36:20,186 - tensorflow - INFO - loss = 0.13073684, step = 2191800 (2.255 sec) +2022-05-01 21:36:22,446 - tensorflow - INFO - global_step/sec: 88.4874 +2022-05-01 21:36:22,446 - tensorflow - INFO - loss = 0.05943086, step = 2192000 (2.260 sec) +2022-05-01 21:36:24,701 - tensorflow - INFO - global_step/sec: 88.6829 +2022-05-01 21:36:24,701 - tensorflow - INFO - loss = 0.010933407, step = 2192200 (2.255 sec) +2022-05-01 21:36:26,954 - tensorflow - INFO - global_step/sec: 88.7558 +2022-05-01 21:36:26,955 - tensorflow - INFO - loss = 0.005767065, step = 2192400 (2.253 sec) +2022-05-01 21:36:29,206 - tensorflow - INFO - global_step/sec: 88.8305 +2022-05-01 21:36:29,206 - tensorflow - INFO - loss = 0.07748546, step = 2192600 (2.251 sec) +2022-05-01 21:36:31,470 - tensorflow - INFO - global_step/sec: 88.3346 +2022-05-01 21:36:31,470 - tensorflow - INFO - loss = 0.030347891, step = 2192800 (2.264 sec) +2022-05-01 21:36:33,726 - tensorflow - INFO - global_step/sec: 88.6374 +2022-05-01 21:36:33,727 - tensorflow - INFO - loss = 0.0073875445, step = 2193000 (2.256 sec) +2022-05-01 21:36:35,984 - tensorflow - INFO - global_step/sec: 88.58 +2022-05-01 21:36:35,985 - tensorflow - INFO - loss = 0.020786371, step = 2193200 (2.258 sec) +2022-05-01 21:36:38,232 - tensorflow - INFO - global_step/sec: 88.9812 +2022-05-01 21:36:38,232 - tensorflow - INFO - loss = 0.047285732, step = 2193400 (2.248 sec) +2022-05-01 21:36:40,484 - tensorflow - INFO - global_step/sec: 88.8151 +2022-05-01 21:36:40,484 - tensorflow - INFO - loss = 0.014292076, step = 2193600 (2.252 sec) +2022-05-01 21:36:42,739 - tensorflow - INFO - global_step/sec: 88.6817 +2022-05-01 21:36:42,739 - tensorflow - INFO - loss = 0.025393961, step = 2193800 (2.255 sec) +2022-05-01 21:36:44,993 - tensorflow - INFO - global_step/sec: 88.7232 +2022-05-01 21:36:44,994 - tensorflow - INFO - loss = 0.023196246, step = 2194000 (2.254 sec) +2022-05-01 21:36:47,244 - tensorflow - INFO - global_step/sec: 88.8493 +2022-05-01 21:36:47,245 - tensorflow - INFO - loss = 0.016532872, step = 2194200 (2.251 sec) +2022-05-01 21:36:49,500 - tensorflow - INFO - global_step/sec: 88.6702 +2022-05-01 21:36:49,500 - tensorflow - INFO - loss = 0.005099818, step = 2194400 (2.256 sec) +2022-05-01 21:36:51,756 - tensorflow - INFO - global_step/sec: 88.6475 +2022-05-01 21:36:51,756 - tensorflow - INFO - loss = 0.014568393, step = 2194600 (2.256 sec) +2022-05-01 21:36:54,003 - tensorflow - INFO - global_step/sec: 89.0025 +2022-05-01 21:36:54,003 - tensorflow - INFO - loss = 0.00928764, step = 2194800 (2.247 sec) +2022-05-01 21:36:56,258 - tensorflow - INFO - global_step/sec: 88.6935 +2022-05-01 21:36:56,258 - tensorflow - INFO - loss = 0.0219753, step = 2195000 (2.255 sec) +2022-05-01 21:36:58,511 - tensorflow - INFO - global_step/sec: 88.779 +2022-05-01 21:36:58,511 - tensorflow - INFO - loss = 1.0612338, step = 2195200 (2.253 sec) +2022-05-01 21:37:00,761 - tensorflow - INFO - global_step/sec: 88.8767 +2022-05-01 21:37:00,761 - tensorflow - INFO - loss = 0.31271794, step = 2195400 (2.250 sec) +2022-05-01 21:37:03,013 - tensorflow - INFO - global_step/sec: 88.8259 +2022-05-01 21:37:03,013 - tensorflow - INFO - loss = 0.07092461, step = 2195600 (2.252 sec) +2022-05-01 21:37:05,266 - tensorflow - INFO - global_step/sec: 88.766 +2022-05-01 21:37:05,266 - tensorflow - INFO - loss = 0.02657806, step = 2195800 (2.253 sec) +2022-05-01 21:37:07,522 - tensorflow - INFO - global_step/sec: 88.624 +2022-05-01 21:37:07,523 - tensorflow - INFO - loss = 0.012601734, step = 2196000 (2.257 sec) +2022-05-01 21:37:09,780 - tensorflow - INFO - global_step/sec: 88.5875 +2022-05-01 21:37:09,780 - tensorflow - INFO - loss = 0.010346203, step = 2196200 (2.258 sec) +2022-05-01 21:37:12,029 - tensorflow - INFO - global_step/sec: 88.94 +2022-05-01 21:37:12,029 - tensorflow - INFO - loss = 0.0631614, step = 2196400 (2.249 sec) +2022-05-01 21:37:14,278 - tensorflow - INFO - global_step/sec: 88.9297 +2022-05-01 21:37:14,278 - tensorflow - INFO - loss = 0.02143442, step = 2196600 (2.249 sec) +2022-05-01 21:37:16,528 - tensorflow - INFO - global_step/sec: 88.8974 +2022-05-01 21:37:16,528 - tensorflow - INFO - loss = 0.013591796, step = 2196800 (2.250 sec) +2022-05-01 21:37:18,783 - tensorflow - INFO - global_step/sec: 88.6885 +2022-05-01 21:37:18,783 - tensorflow - INFO - loss = 0.009670453, step = 2197000 (2.255 sec) +2022-05-01 21:37:21,040 - tensorflow - INFO - global_step/sec: 88.618 +2022-05-01 21:37:21,040 - tensorflow - INFO - loss = 0.012457259, step = 2197200 (2.257 sec) +2022-05-01 21:37:23,288 - tensorflow - INFO - global_step/sec: 88.938 +2022-05-01 21:37:23,289 - tensorflow - INFO - loss = 0.026262462, step = 2197400 (2.249 sec) +2022-05-01 21:37:25,540 - tensorflow - INFO - global_step/sec: 88.8215 +2022-05-01 21:37:25,540 - tensorflow - INFO - loss = 0.0077347336, step = 2197600 (2.252 sec) +2022-05-01 21:37:27,791 - tensorflow - INFO - global_step/sec: 88.8591 +2022-05-01 21:37:27,791 - tensorflow - INFO - loss = 0.01425202, step = 2197800 (2.251 sec) +2022-05-01 21:37:30,051 - tensorflow - INFO - global_step/sec: 88.4816 +2022-05-01 21:37:30,052 - tensorflow - INFO - loss = 0.07255714, step = 2198000 (2.260 sec) +2022-05-01 21:37:32,311 - tensorflow - INFO - global_step/sec: 88.4938 +2022-05-01 21:37:32,312 - tensorflow - INFO - loss = 0.20931415, step = 2198200 (2.260 sec) +2022-05-01 21:37:34,571 - tensorflow - INFO - global_step/sec: 88.4917 +2022-05-01 21:37:34,572 - tensorflow - INFO - loss = 0.037890192, step = 2198400 (2.260 sec) +2022-05-01 21:37:36,839 - tensorflow - INFO - global_step/sec: 88.1986 +2022-05-01 21:37:36,839 - tensorflow - INFO - loss = 0.037103213, step = 2198600 (2.268 sec) +2022-05-01 21:37:39,100 - tensorflow - INFO - global_step/sec: 88.4457 +2022-05-01 21:37:39,101 - tensorflow - INFO - loss = 0.021963807, step = 2198800 (2.261 sec) +2022-05-01 21:37:41,359 - tensorflow - INFO - global_step/sec: 88.5571 +2022-05-01 21:37:41,359 - tensorflow - INFO - loss = 0.017868139, step = 2199000 (2.258 sec) +2022-05-01 21:37:43,620 - tensorflow - INFO - global_step/sec: 88.4404 +2022-05-01 21:37:43,620 - tensorflow - INFO - loss = 0.08600532, step = 2199200 (2.261 sec) +2022-05-01 21:37:45,884 - tensorflow - INFO - global_step/sec: 88.3505 +2022-05-01 21:37:45,884 - tensorflow - INFO - loss = 0.014971986, step = 2199400 (2.264 sec) +2022-05-01 21:37:48,149 - tensorflow - INFO - global_step/sec: 88.2993 +2022-05-01 21:37:48,149 - tensorflow - INFO - loss = 0.009885784, step = 2199600 (2.265 sec) +2022-05-01 21:37:50,409 - tensorflow - INFO - global_step/sec: 88.4647 +2022-05-01 21:37:50,410 - tensorflow - INFO - loss = 0.16105652, step = 2199800 (2.261 sec) +2022-05-01 21:37:52,664 - tensorflow - INFO - Saving checkpoints for 2200000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. +2022-05-01 21:37:52,680 - tensorflow - INFO - model.ckpt-2200000 is not in all_model_checkpoint_paths. Manually adding it. +2022-05-01 21:37:53,206 - tensorflow - INFO - Calling model_fn. +2022-05-01 21:37:53,334 - tensorflow - INFO - Done calling model_fn. +2022-05-01 21:37:53,349 - tensorflow - INFO - Starting evaluation at 2022-05-01T21:37:53Z +2022-05-01 21:37:53,417 - tensorflow - INFO - Graph was finalized. +2022-05-01 21:37:53,421 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-2200000 +2022-05-01 21:37:53,703 - tensorflow - INFO - Running local_init_op. +2022-05-01 21:37:53,812 - tensorflow - INFO - Done running local_init_op. +2022-05-01 21:38:04,130 - tensorflow - INFO - Evaluation [3990/39900] +2022-05-01 21:38:13,663 - tensorflow - INFO - Evaluation [7980/39900] +2022-05-01 21:38:23,220 - tensorflow - INFO - Evaluation [11970/39900] +2022-05-01 21:38:32,772 - tensorflow - INFO - Evaluation [15960/39900] +2022-05-01 21:38:42,320 - tensorflow - INFO - Evaluation [19950/39900] +2022-05-01 21:38:51,892 - tensorflow - INFO - Evaluation [23940/39900] +2022-05-01 21:39:01,423 - tensorflow - INFO - Evaluation [27930/39900] +2022-05-01 21:39:10,981 - tensorflow - INFO - Evaluation [31920/39900] +2022-05-01 21:39:20,545 - tensorflow - INFO - Evaluation [35910/39900] +2022-05-01 21:39:30,119 - tensorflow - INFO - Evaluation [39900/39900] +2022-05-01 21:39:30,258 - tensorflow - INFO - Finished evaluation at 2022-05-01-21:39:30 +2022-05-01 21:39:30,259 - tensorflow - INFO - Saving dict for global step 2200000: global_step = 2200000, loss = 0.015515498, mean_degree_err = 3.1324177 +2022-05-01 21:39:30,259 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 2200000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-2200000 +2022-05-01 21:39:30,273 - tensorflow - INFO - global_step/sec: 2.00273 +2022-05-01 21:39:30,274 - tensorflow - INFO - loss = 0.016591474, step = 2200000 (99.864 sec) +2022-05-01 21:39:32,548 - tensorflow - INFO - global_step/sec: 87.916 +2022-05-01 21:39:32,548 - tensorflow - INFO - loss = 0.04369278, step = 2200200 (2.274 sec) +2022-05-01 21:39:34,804 - tensorflow - INFO - global_step/sec: 88.6638 +2022-05-01 21:39:34,804 - tensorflow - INFO - loss = 0.035334364, step = 2200400 (2.256 sec) +2022-05-01 21:39:37,057 - tensorflow - INFO - global_step/sec: 88.7553 +2022-05-01 21:39:37,057 - tensorflow - INFO - loss = 0.0069875494, step = 2200600 (2.253 sec) +2022-05-01 21:39:39,309 - tensorflow - INFO - global_step/sec: 88.8105 +2022-05-01 21:39:39,309 - tensorflow - INFO - loss = 0.041061778, step = 2200800 (2.252 sec) +2022-05-01 21:39:41,567 - tensorflow - INFO - global_step/sec: 88.579 +2022-05-01 21:39:41,567 - tensorflow - INFO - loss = 0.011567708, step = 2201000 (2.258 sec) +2022-05-01 21:39:43,822 - tensorflow - INFO - global_step/sec: 88.6912 +2022-05-01 21:39:43,822 - tensorflow - INFO - loss = 0.014715085, step = 2201200 (2.255 sec) +2022-05-01 21:39:46,073 - tensorflow - INFO - global_step/sec: 88.8445 +2022-05-01 21:39:46,073 - tensorflow - INFO - loss = 0.009093689, step = 2201400 (2.251 sec) +2022-05-01 21:39:48,322 - tensorflow - INFO - global_step/sec: 88.9168 +2022-05-01 21:39:48,323 - tensorflow - INFO - loss = 1.0612983, step = 2201600 (2.249 sec) +2022-05-01 21:39:50,575 - tensorflow - INFO - global_step/sec: 88.7781 +2022-05-01 21:39:50,575 - tensorflow - INFO - loss = 0.009138137, step = 2201800 (2.253 sec) +2022-05-01 21:39:52,827 - tensorflow - INFO - global_step/sec: 88.8019 +2022-05-01 21:39:52,828 - tensorflow - INFO - loss = 0.010925783, step = 2202000 (2.252 sec) +2022-05-01 21:39:55,085 - tensorflow - INFO - global_step/sec: 88.5793 +2022-05-01 21:39:55,086 - tensorflow - INFO - loss = 0.014196128, step = 2202200 (2.258 sec) +2022-05-01 21:39:57,344 - tensorflow - INFO - global_step/sec: 88.5409 +2022-05-01 21:39:57,344 - tensorflow - INFO - loss = 0.1022256, step = 2202400 (2.259 sec) +2022-05-01 21:39:59,595 - tensorflow - INFO - global_step/sec: 88.8293 +2022-05-01 21:39:59,596 - tensorflow - INFO - loss = 0.12285152, step = 2202600 (2.252 sec) +2022-05-01 21:40:01,849 - tensorflow - INFO - global_step/sec: 88.7609 +2022-05-01 21:40:01,849 - tensorflow - INFO - loss = 0.00926359, step = 2202800 (2.253 sec) +2022-05-01 21:40:04,101 - tensorflow - INFO - global_step/sec: 88.7822 +2022-05-01 21:40:04,102 - tensorflow - INFO - loss = 0.15393083, step = 2203000 (2.253 sec) +2022-05-01 21:40:06,353 - tensorflow - INFO - global_step/sec: 88.8288 +2022-05-01 21:40:06,353 - tensorflow - INFO - loss = 0.01870555, step = 2203200 (2.252 sec) +2022-05-01 21:40:08,613 - tensorflow - INFO - global_step/sec: 88.5019 +2022-05-01 21:40:08,613 - tensorflow - INFO - loss = 0.020982817, step = 2203400 (2.260 sec) +2022-05-01 21:40:10,864 - tensorflow - INFO - global_step/sec: 88.8411 +2022-05-01 21:40:10,864 - tensorflow - INFO - loss = 0.03838037, step = 2203600 (2.251 sec) +2022-05-01 21:40:13,117 - tensorflow - INFO - global_step/sec: 88.769 +2022-05-01 21:40:13,117 - tensorflow - INFO - loss = 0.019582264, step = 2203800 (2.253 sec) +2022-05-01 21:40:15,369 - tensorflow - INFO - global_step/sec: 88.8261 +2022-05-01 21:40:15,369 - tensorflow - INFO - loss = 0.0038917055, step = 2204000 (2.252 sec) +2022-05-01 21:40:17,623 - tensorflow - INFO - global_step/sec: 88.7092 +2022-05-01 21:40:17,624 - tensorflow - INFO - loss = 0.012383769, step = 2204200 (2.255 sec) +2022-05-01 21:40:19,879 - tensorflow - INFO - global_step/sec: 88.6498 +2022-05-01 21:40:19,880 - tensorflow - INFO - loss = 0.006668681, step = 2204400 (2.256 sec) +2022-05-01 21:40:22,129 - tensorflow - INFO - global_step/sec: 88.9148 +2022-05-01 21:40:22,129 - tensorflow - INFO - loss = 0.008942205, step = 2204600 (2.249 sec) +2022-05-01 21:40:24,380 - tensorflow - INFO - global_step/sec: 88.8245 +2022-05-01 21:40:24,381 - tensorflow - INFO - loss = 0.10828018, step = 2204800 (2.252 sec) +2022-05-01 21:40:26,635 - tensorflow - INFO - global_step/sec: 88.7117 +2022-05-01 21:40:26,635 - tensorflow - INFO - loss = 0.026557386, step = 2205000 (2.255 sec) +2022-05-01 21:40:28,884 - tensorflow - INFO - global_step/sec: 88.9343 +2022-05-01 21:40:28,884 - tensorflow - INFO - loss = 0.009144509, step = 2205200 (2.249 sec) +2022-05-01 21:40:31,141 - tensorflow - INFO - global_step/sec: 88.6109 +2022-05-01 21:40:31,141 - tensorflow - INFO - loss = 0.018995715, step = 2205400 (2.257 sec) +2022-05-01 21:40:33,394 - tensorflow - INFO - global_step/sec: 88.7683 +2022-05-01 21:40:33,394 - tensorflow - INFO - loss = 0.02026128, step = 2205600 (2.253 sec) +2022-05-01 21:40:35,649 - tensorflow - INFO - global_step/sec: 88.6789 +2022-05-01 21:40:35,649 - tensorflow - INFO - loss = 0.052717946, step = 2205800 (2.255 sec) +2022-05-01 21:40:37,899 - tensorflow - INFO - global_step/sec: 88.9058 +2022-05-01 21:40:37,899 - tensorflow - INFO - loss = 0.11664444, step = 2206000 (2.250 sec) +2022-05-01 21:40:40,151 - tensorflow - INFO - global_step/sec: 88.7921 +2022-05-01 21:40:40,151 - tensorflow - INFO - loss = 0.0046029845, step = 2206200 (2.252 sec) +2022-05-01 21:40:42,401 - tensorflow - INFO - global_step/sec: 88.8739 +2022-05-01 21:40:42,402 - 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tensorflow - INFO - loss = 0.008577246, step = 2288000 (2.254 sec) +2022-05-01 21:56:04,808 - tensorflow - INFO - global_step/sec: 88.6691 +2022-05-01 21:56:04,809 - tensorflow - INFO - loss = 0.004305575, step = 2288200 (2.256 sec) +2022-05-01 21:56:07,061 - tensorflow - INFO - global_step/sec: 88.7987 +2022-05-01 21:56:07,061 - tensorflow - INFO - loss = 0.05146283, step = 2288400 (2.252 sec) +2022-05-01 21:56:09,314 - tensorflow - INFO - global_step/sec: 88.7583 +2022-05-01 21:56:09,314 - tensorflow - INFO - loss = 0.007753142, step = 2288600 (2.253 sec) +2022-05-01 21:56:11,572 - tensorflow - INFO - global_step/sec: 88.5821 +2022-05-01 21:56:11,572 - tensorflow - INFO - loss = 0.03586986, step = 2288800 (2.258 sec) +2022-05-01 21:56:13,823 - tensorflow - INFO - global_step/sec: 88.8218 +2022-05-01 21:56:13,824 - tensorflow - INFO - loss = 0.0688177, step = 2289000 (2.252 sec) +2022-05-01 21:56:16,075 - tensorflow - INFO - global_step/sec: 88.8439 +2022-05-01 21:56:16,075 - tensorflow - INFO - loss = 0.00816809, step = 2289200 (2.251 sec) +2022-05-01 21:56:18,327 - tensorflow - INFO - global_step/sec: 88.81 +2022-05-01 21:56:18,327 - tensorflow - INFO - loss = 0.07801333, step = 2289400 (2.252 sec) +2022-05-01 21:56:20,581 - tensorflow - INFO - global_step/sec: 88.718 +2022-05-01 21:56:20,581 - tensorflow - INFO - loss = 0.048330776, step = 2289600 (2.254 sec) +2022-05-01 21:56:22,843 - tensorflow - INFO - global_step/sec: 88.4233 +2022-05-01 21:56:22,843 - tensorflow - INFO - loss = 0.05577566, step = 2289800 (2.262 sec) +2022-05-01 21:56:25,099 - tensorflow - INFO - global_step/sec: 88.6608 +2022-05-01 21:56:25,099 - tensorflow - INFO - loss = 0.042707544, step = 2290000 (2.256 sec) +2022-05-01 21:56:27,358 - tensorflow - INFO - global_step/sec: 88.534 +2022-05-01 21:56:27,358 - tensorflow - INFO - loss = 0.007859388, step = 2290200 (2.259 sec) +2022-05-01 21:56:29,618 - tensorflow - INFO - global_step/sec: 88.4927 +2022-05-01 21:56:29,618 - tensorflow - INFO - loss = 0.022069693, step = 2290400 (2.260 sec) +2022-05-01 21:56:31,886 - tensorflow - INFO - global_step/sec: 88.1805 +2022-05-01 21:56:31,886 - tensorflow - INFO - loss = 0.02007738, step = 2290600 (2.268 sec) +2022-05-01 21:56:34,146 - tensorflow - INFO - global_step/sec: 88.4982 +2022-05-01 21:56:34,146 - tensorflow - INFO - loss = 0.006746944, step = 2290800 (2.260 sec) +2022-05-01 21:56:36,398 - tensorflow - INFO - global_step/sec: 88.8038 +2022-05-01 21:56:36,398 - tensorflow - INFO - loss = 0.005979812, step = 2291000 (2.252 sec) +2022-05-01 21:56:38,648 - tensorflow - INFO - global_step/sec: 88.8831 +2022-05-01 21:56:38,648 - tensorflow - INFO - loss = 0.007480228, step = 2291200 (2.250 sec) +2022-05-01 21:56:40,901 - tensorflow - INFO - global_step/sec: 88.7637 +2022-05-01 21:56:40,902 - tensorflow - INFO - loss = 0.04220979, step = 2291400 (2.253 sec) +2022-05-01 21:56:43,148 - tensorflow - INFO - global_step/sec: 89.0311 +2022-05-01 21:56:43,148 - tensorflow - INFO - loss = 0.037302103, step = 2291600 (2.246 sec) +2022-05-01 21:56:45,405 - tensorflow - INFO - global_step/sec: 88.5843 +2022-05-01 21:56:45,406 - tensorflow - INFO - loss = 0.2169662, step = 2291800 (2.258 sec) +2022-05-01 21:56:47,662 - tensorflow - INFO - global_step/sec: 88.6128 +2022-05-01 21:56:47,663 - tensorflow - INFO - loss = 0.07506015, step = 2292000 (2.257 sec) +2022-05-01 21:56:49,917 - tensorflow - INFO - global_step/sec: 88.7082 +2022-05-01 21:56:49,917 - tensorflow - INFO - loss = 0.011692755, step = 2292200 (2.255 sec) +2022-05-01 21:56:52,167 - tensorflow - INFO - global_step/sec: 88.9001 +2022-05-01 21:56:52,167 - tensorflow - INFO - loss = 0.044270314, step = 2292400 (2.250 sec) +2022-05-01 21:56:54,418 - tensorflow - INFO - global_step/sec: 88.8406 +2022-05-01 21:56:54,418 - tensorflow - INFO - loss = 0.0096690245, step = 2292600 (2.251 sec) +2022-05-01 21:56:56,671 - tensorflow - INFO - global_step/sec: 88.7609 +2022-05-01 21:56:56,671 - tensorflow - INFO - loss = 0.015168619, step = 2292800 (2.253 sec) +2022-05-01 21:56:58,929 - tensorflow - INFO - global_step/sec: 88.5879 +2022-05-01 21:56:58,929 - tensorflow - INFO - loss = 0.0038347745, step = 2293000 (2.258 sec) +2022-05-01 21:57:01,179 - tensorflow - INFO - global_step/sec: 88.8582 +2022-05-01 21:57:01,180 - tensorflow - INFO - loss = 0.014080549, step = 2293200 (2.251 sec) +2022-05-01 21:57:03,439 - tensorflow - INFO - global_step/sec: 88.5001 +2022-05-01 21:57:03,440 - tensorflow - INFO - loss = 0.0055178367, step = 2293400 (2.260 sec) +2022-05-01 21:57:05,691 - tensorflow - INFO - global_step/sec: 88.8284 +2022-05-01 21:57:05,691 - tensorflow - INFO - loss = 0.031844318, step = 2293600 (2.252 sec) +2022-05-01 21:57:07,944 - tensorflow - INFO - global_step/sec: 88.7622 +2022-05-01 21:57:07,945 - tensorflow - INFO - loss = 1.3870256, step = 2293800 (2.253 sec) +2022-05-01 21:57:10,202 - tensorflow - INFO - global_step/sec: 88.5595 +2022-05-01 21:57:10,203 - tensorflow - INFO - loss = 0.010294217, step = 2294000 (2.258 sec) +2022-05-01 21:57:12,453 - tensorflow - INFO - global_step/sec: 88.8624 +2022-05-01 21:57:12,454 - tensorflow - INFO - loss = 0.014056441, step = 2294200 (2.251 sec) +2022-05-01 21:57:14,700 - tensorflow - INFO - global_step/sec: 89.0314 +2022-05-01 21:57:14,700 - tensorflow - INFO - loss = 0.028425688, step = 2294400 (2.246 sec) +2022-05-01 21:57:16,956 - tensorflow - INFO - global_step/sec: 88.6322 +2022-05-01 21:57:16,956 - tensorflow - INFO - loss = 0.14513147, step = 2294600 (2.257 sec) +2022-05-01 21:57:19,212 - tensorflow - INFO - global_step/sec: 88.6421 +2022-05-01 21:57:19,213 - tensorflow - INFO - loss = 0.018518772, step = 2294800 (2.256 sec) +2022-05-01 21:57:21,462 - tensorflow - INFO - global_step/sec: 88.8915 +2022-05-01 21:57:21,463 - tensorflow - INFO - loss = 0.0112281665, step = 2295000 (2.250 sec) +2022-05-01 21:57:23,724 - tensorflow - INFO - global_step/sec: 88.4416 +2022-05-01 21:57:23,724 - tensorflow - INFO - loss = 0.033915814, step = 2295200 (2.261 sec) +2022-05-01 21:57:25,974 - tensorflow - INFO - global_step/sec: 88.8701 +2022-05-01 21:57:25,975 - tensorflow - INFO - loss = 0.045164473, step = 2295400 (2.250 sec) +2022-05-01 21:57:28,221 - tensorflow - INFO - global_step/sec: 88.998 +2022-05-01 21:57:28,222 - tensorflow - INFO - loss = 0.015295318, step = 2295600 (2.247 sec) +2022-05-01 21:57:30,477 - tensorflow - INFO - global_step/sec: 88.6528 +2022-05-01 21:57:30,478 - tensorflow - INFO - loss = 0.055552155, step = 2295800 (2.256 sec) +2022-05-01 21:57:32,728 - tensorflow - INFO - global_step/sec: 88.8457 +2022-05-01 21:57:32,729 - tensorflow - INFO - loss = 0.007291389, step = 2296000 (2.251 sec) +2022-05-01 21:57:34,982 - tensorflow - INFO - global_step/sec: 88.7465 +2022-05-01 21:57:34,983 - tensorflow - INFO - loss = 0.009181721, step = 2296200 (2.254 sec) +2022-05-01 21:57:37,241 - tensorflow - INFO - global_step/sec: 88.5505 +2022-05-01 21:57:37,241 - tensorflow - INFO - loss = 0.015211635, step = 2296400 (2.259 sec) +2022-05-01 21:57:39,493 - tensorflow - INFO - global_step/sec: 88.8017 +2022-05-01 21:57:39,493 - tensorflow - INFO - loss = 0.07218133, step = 2296600 (2.252 sec) +2022-05-01 21:57:41,741 - tensorflow - INFO - global_step/sec: 88.98 +2022-05-01 21:57:41,741 - tensorflow - INFO - loss = 0.2888913, step = 2296800 (2.248 sec) +2022-05-01 21:57:43,991 - tensorflow - INFO - global_step/sec: 88.8873 +2022-05-01 21:57:43,991 - tensorflow - INFO - loss = 0.011046013, step = 2297000 (2.250 sec) +2022-05-01 21:57:46,238 - tensorflow - INFO - global_step/sec: 88.9803 +2022-05-01 21:57:46,239 - tensorflow - INFO - loss = 0.14288104, step = 2297200 (2.248 sec) +2022-05-01 21:57:48,495 - tensorflow - INFO - global_step/sec: 88.6299 +2022-05-01 21:57:48,495 - tensorflow - INFO - loss = 0.010061863, step = 2297400 (2.257 sec) +2022-05-01 21:57:50,747 - tensorflow - INFO - global_step/sec: 88.7933 +2022-05-01 21:57:50,748 - tensorflow - INFO - loss = 0.011489116, step = 2297600 (2.252 sec) +2022-05-01 21:57:52,996 - tensorflow - INFO - global_step/sec: 88.9238 +2022-05-01 21:57:52,997 - tensorflow - INFO - loss = 0.004584601, step = 2297800 (2.249 sec) +2022-05-01 21:57:55,247 - tensorflow - INFO - global_step/sec: 88.8742 +2022-05-01 21:57:55,247 - tensorflow - INFO - loss = 0.010511144, step = 2298000 (2.250 sec) +2022-05-01 21:57:57,500 - tensorflow - INFO - global_step/sec: 88.7639 +2022-05-01 21:57:57,500 - tensorflow - INFO - loss = 0.0056808935, step = 2298200 (2.253 sec) +2022-05-01 21:57:59,753 - tensorflow - INFO - global_step/sec: 88.7811 +2022-05-01 21:57:59,753 - tensorflow - INFO - loss = 0.01763389, step = 2298400 (2.253 sec) +2022-05-01 21:58:02,004 - tensorflow - INFO - global_step/sec: 88.8373 +2022-05-01 21:58:02,004 - tensorflow - INFO - loss = 0.11997159, step = 2298600 (2.251 sec) +2022-05-01 21:58:04,258 - tensorflow - INFO - global_step/sec: 88.7125 +2022-05-01 21:58:04,259 - tensorflow - INFO - loss = 0.06352675, step = 2298800 (2.254 sec) +2022-05-01 21:58:06,511 - tensorflow - INFO - global_step/sec: 88.7991 +2022-05-01 21:58:06,511 - tensorflow - INFO - loss = 0.18617159, step = 2299000 (2.252 sec) +2022-05-01 21:58:08,764 - tensorflow - INFO - global_step/sec: 88.7447 +2022-05-01 21:58:08,765 - tensorflow - INFO - loss = 0.010075515, step = 2299200 (2.254 sec) +2022-05-01 21:58:11,020 - tensorflow - INFO - global_step/sec: 88.6623 +2022-05-01 21:58:11,021 - tensorflow - INFO - loss = 0.07672944, step = 2299400 (2.256 sec) +2022-05-01 21:58:13,274 - tensorflow - INFO - global_step/sec: 88.7209 +2022-05-01 21:58:13,275 - tensorflow - INFO - loss = 0.012490427, step = 2299600 (2.254 sec) +2022-05-01 21:58:15,521 - tensorflow - INFO - global_step/sec: 89.0263 +2022-05-01 21:58:15,521 - tensorflow - INFO - loss = 0.084134504, step = 2299800 (2.246 sec) +2022-05-01 21:58:17,758 - tensorflow - INFO - Saving checkpoints for 2300000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. +2022-05-01 21:58:17,774 - tensorflow - INFO - model.ckpt-2300000 is not in all_model_checkpoint_paths. Manually adding it. +2022-05-01 21:58:18,309 - tensorflow - INFO - Calling model_fn. +2022-05-01 21:58:18,436 - tensorflow - INFO - Done calling model_fn. +2022-05-01 21:58:18,451 - tensorflow - INFO - Starting evaluation at 2022-05-01T21:58:18Z +2022-05-01 21:58:18,514 - tensorflow - INFO - Graph was finalized. +2022-05-01 21:58:18,517 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-2300000 +2022-05-01 21:58:18,797 - tensorflow - INFO - Running local_init_op. +2022-05-01 21:58:18,905 - tensorflow - INFO - Done running local_init_op. +2022-05-01 21:58:29,046 - tensorflow - INFO - Evaluation [3990/39900] +2022-05-01 21:58:38,480 - tensorflow - INFO - Evaluation [7980/39900] +2022-05-01 21:58:47,929 - tensorflow - INFO - Evaluation [11970/39900] +2022-05-01 21:58:57,409 - tensorflow - INFO - Evaluation [15960/39900] +2022-05-01 21:59:06,885 - tensorflow - INFO - Evaluation [19950/39900] +2022-05-01 21:59:16,344 - tensorflow - INFO - Evaluation [23940/39900] +2022-05-01 21:59:25,817 - tensorflow - INFO - Evaluation [27930/39900] +2022-05-01 21:59:35,288 - tensorflow - INFO - Evaluation [31920/39900] +2022-05-01 21:59:44,735 - tensorflow - INFO - Evaluation [35910/39900] +2022-05-01 21:59:54,202 - tensorflow - INFO - Evaluation [39900/39900] +2022-05-01 21:59:54,339 - tensorflow - INFO - Finished evaluation at 2022-05-01-21:59:54 +2022-05-01 21:59:54,339 - tensorflow - INFO - Saving dict for global step 2300000: global_step = 2300000, loss = 0.017903524, mean_degree_err = 3.449092 +2022-05-01 21:59:54,340 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 2300000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-2300000 +2022-05-01 21:59:54,353 - tensorflow - INFO - global_step/sec: 2.02363 +2022-05-01 21:59:54,354 - tensorflow - INFO - loss = 0.0091372905, step = 2300000 (98.833 sec) +2022-05-01 21:59:56,633 - tensorflow - INFO - global_step/sec: 87.7322 +2022-05-01 21:59:56,633 - tensorflow - INFO - loss = 0.024899326, step = 2300200 (2.279 sec) +2022-05-01 21:59:58,886 - tensorflow - INFO - global_step/sec: 88.7735 +2022-05-01 21:59:58,886 - tensorflow - INFO - loss = 0.016124004, step = 2300400 (2.253 sec) +2022-05-01 22:00:01,143 - tensorflow - INFO - global_step/sec: 88.6166 +2022-05-01 22:00:01,143 - tensorflow - INFO - loss = 0.01588392, step = 2300600 (2.257 sec) +2022-05-01 22:00:03,397 - tensorflow - INFO - global_step/sec: 88.7324 +2022-05-01 22:00:03,397 - tensorflow - INFO - loss = 0.010000843, step = 2300800 (2.254 sec) +2022-05-01 22:00:05,651 - tensorflow - INFO - global_step/sec: 88.7368 +2022-05-01 22:00:05,651 - tensorflow - INFO - loss = 0.059633113, step = 2301000 (2.254 sec) +2022-05-01 22:00:07,911 - tensorflow - INFO - global_step/sec: 88.4922 +2022-05-01 22:00:07,911 - tensorflow - INFO - loss = 0.0062330198, step = 2301200 (2.260 sec) +2022-05-01 22:00:10,169 - tensorflow - INFO - global_step/sec: 88.568 +2022-05-01 22:00:10,169 - tensorflow - INFO - loss = 0.23845582, step = 2301400 (2.258 sec) +2022-05-01 22:00:12,422 - tensorflow - INFO - global_step/sec: 88.7789 +2022-05-01 22:00:12,422 - tensorflow - INFO - loss = 0.011025475, step = 2301600 (2.253 sec) +2022-05-01 22:00:14,674 - tensorflow - INFO - global_step/sec: 88.8013 +2022-05-01 22:00:14,674 - tensorflow - INFO - loss = 0.0045896634, step = 2301800 (2.252 sec) +2022-05-01 22:00:16,926 - tensorflow - INFO - global_step/sec: 88.8024 +2022-05-01 22:00:16,927 - tensorflow - INFO - loss = 0.0063096797, step = 2302000 (2.252 sec) +2022-05-01 22:00:19,178 - tensorflow - INFO - global_step/sec: 88.8262 +2022-05-01 22:00:19,178 - tensorflow - INFO - loss = 0.006863452, step = 2302200 (2.252 sec) +2022-05-01 22:00:21,436 - tensorflow - INFO - global_step/sec: 88.5556 +2022-05-01 22:00:21,437 - tensorflow - INFO - loss = 0.048675433, step = 2302400 (2.259 sec) +2022-05-01 22:00:23,687 - tensorflow - INFO - global_step/sec: 88.8532 +2022-05-01 22:00:23,687 - tensorflow - INFO - loss = 0.0055150455, step = 2302600 (2.251 sec) +2022-05-01 22:00:25,941 - tensorflow - INFO - global_step/sec: 88.7483 +2022-05-01 22:00:25,941 - tensorflow - INFO - loss = 0.01271037, step = 2302800 (2.254 sec) +2022-05-01 22:00:28,193 - tensorflow - INFO - global_step/sec: 88.7814 +2022-05-01 22:00:28,194 - tensorflow - INFO - loss = 0.009561626, step = 2303000 (2.253 sec) +2022-05-01 22:00:30,452 - tensorflow - INFO - global_step/sec: 88.5499 +2022-05-01 22:00:30,452 - tensorflow - INFO - loss = 0.10002799, step = 2303200 (2.259 sec) +2022-05-01 22:00:32,708 - tensorflow - INFO - global_step/sec: 88.6695 +2022-05-01 22:00:32,708 - tensorflow - INFO - loss = 0.005737627, step = 2303400 (2.256 sec) +2022-05-01 22:00:34,966 - tensorflow - INFO - global_step/sec: 88.5713 +2022-05-01 22:00:34,966 - tensorflow - INFO - loss = 0.04569764, step = 2303600 (2.258 sec) +2022-05-01 22:00:37,215 - tensorflow - INFO - global_step/sec: 88.9007 +2022-05-01 22:00:37,216 - tensorflow - INFO - loss = 0.004944335, step = 2303800 (2.250 sec) +2022-05-01 22:00:39,464 - 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global_step/sec: 88.7456 +2022-05-01 22:15:59,742 - tensorflow - INFO - loss = 0.016343482, step = 2385600 (2.254 sec) +2022-05-01 22:16:02,001 - tensorflow - INFO - global_step/sec: 88.5057 +2022-05-01 22:16:02,002 - tensorflow - INFO - loss = 0.008009482, step = 2385800 (2.260 sec) +2022-05-01 22:16:04,257 - tensorflow - INFO - global_step/sec: 88.6761 +2022-05-01 22:16:04,257 - tensorflow - INFO - loss = 0.0510852, step = 2386000 (2.255 sec) +2022-05-01 22:16:06,510 - tensorflow - INFO - global_step/sec: 88.7768 +2022-05-01 22:16:06,510 - tensorflow - INFO - loss = 0.058420647, step = 2386200 (2.253 sec) +2022-05-01 22:16:08,761 - tensorflow - INFO - global_step/sec: 88.8516 +2022-05-01 22:16:08,761 - tensorflow - INFO - loss = 0.018244365, step = 2386400 (2.251 sec) +2022-05-01 22:16:11,013 - tensorflow - INFO - global_step/sec: 88.8016 +2022-05-01 22:16:11,013 - tensorflow - INFO - loss = 0.07714485, step = 2386600 (2.252 sec) +2022-05-01 22:16:13,266 - tensorflow - INFO - global_step/sec: 88.7739 +2022-05-01 22:16:13,266 - tensorflow - INFO - loss = 0.1699921, step = 2386800 (2.253 sec) +2022-05-01 22:16:15,529 - tensorflow - INFO - global_step/sec: 88.3722 +2022-05-01 22:16:15,529 - tensorflow - INFO - loss = 0.03827375, step = 2387000 (2.263 sec) +2022-05-01 22:16:17,789 - tensorflow - INFO - global_step/sec: 88.5068 +2022-05-01 22:16:17,789 - tensorflow - INFO - loss = 0.046373516, step = 2387200 (2.260 sec) +2022-05-01 22:16:20,055 - tensorflow - INFO - global_step/sec: 88.2586 +2022-05-01 22:16:20,055 - tensorflow - INFO - loss = 0.013266853, step = 2387400 (2.266 sec) +2022-05-01 22:16:22,317 - tensorflow - INFO - global_step/sec: 88.3962 +2022-05-01 22:16:22,318 - tensorflow - INFO - loss = 0.021442072, step = 2387600 (2.263 sec) +2022-05-01 22:16:24,578 - tensorflow - INFO - global_step/sec: 88.4747 +2022-05-01 22:16:24,578 - tensorflow - INFO - loss = 0.10181264, step = 2387800 (2.260 sec) +2022-05-01 22:16:26,841 - tensorflow - INFO - global_step/sec: 88.3802 +2022-05-01 22:16:26,841 - tensorflow - INFO - loss = 0.05081415, step = 2388000 (2.263 sec) +2022-05-01 22:16:29,106 - tensorflow - INFO - global_step/sec: 88.3035 +2022-05-01 22:16:29,106 - tensorflow - INFO - loss = 0.025215954, step = 2388200 (2.265 sec) +2022-05-01 22:16:31,376 - tensorflow - INFO - global_step/sec: 88.071 +2022-05-01 22:16:31,377 - tensorflow - INFO - loss = 0.017948493, step = 2388400 (2.271 sec) +2022-05-01 22:16:33,645 - tensorflow - INFO - global_step/sec: 88.1614 +2022-05-01 22:16:33,645 - tensorflow - INFO - loss = 0.008095983, step = 2388600 (2.269 sec) +2022-05-01 22:16:35,909 - tensorflow - INFO - global_step/sec: 88.3501 +2022-05-01 22:16:35,909 - tensorflow - INFO - loss = 0.030127618, step = 2388800 (2.264 sec) +2022-05-01 22:16:38,171 - tensorflow - INFO - global_step/sec: 88.4056 +2022-05-01 22:16:38,171 - tensorflow - INFO - loss = 0.12454489, step = 2389000 (2.262 sec) +2022-05-01 22:16:40,432 - tensorflow - INFO - global_step/sec: 88.4749 +2022-05-01 22:16:40,432 - tensorflow - INFO - loss = 0.013886469, step = 2389200 (2.260 sec) +2022-05-01 22:16:42,694 - tensorflow - INFO - global_step/sec: 88.405 +2022-05-01 22:16:42,694 - tensorflow - INFO - loss = 0.005526525, step = 2389400 (2.262 sec) +2022-05-01 22:16:44,958 - tensorflow - INFO - global_step/sec: 88.3333 +2022-05-01 22:16:44,958 - tensorflow - INFO - loss = 0.018395491, step = 2389600 (2.264 sec) +2022-05-01 22:16:47,219 - tensorflow - INFO - global_step/sec: 88.4527 +2022-05-01 22:16:47,220 - tensorflow - INFO - loss = 0.00997955, step = 2389800 (2.261 sec) +2022-05-01 22:16:49,470 - tensorflow - INFO - global_step/sec: 88.8441 +2022-05-01 22:16:49,471 - tensorflow - INFO - loss = 0.0037690697, step = 2390000 (2.251 sec) +2022-05-01 22:16:51,721 - tensorflow - INFO - global_step/sec: 88.863 +2022-05-01 22:16:51,721 - tensorflow - INFO - loss = 0.013914542, step = 2390200 (2.251 sec) +2022-05-01 22:16:53,980 - tensorflow - INFO - global_step/sec: 88.5495 +2022-05-01 22:16:53,980 - tensorflow - INFO - loss = 0.009430278, step = 2390400 (2.259 sec) +2022-05-01 22:16:56,239 - tensorflow - INFO - global_step/sec: 88.5315 +2022-05-01 22:16:56,239 - tensorflow - INFO - loss = 0.024905298, step = 2390600 (2.259 sec) +2022-05-01 22:16:58,494 - tensorflow - INFO - global_step/sec: 88.6647 +2022-05-01 22:16:58,495 - tensorflow - INFO - loss = 0.0072831106, step = 2390800 (2.256 sec) +2022-05-01 22:17:00,745 - tensorflow - INFO - global_step/sec: 88.8468 +2022-05-01 22:17:00,746 - tensorflow - INFO - loss = 0.03236274, step = 2391000 (2.251 sec) +2022-05-01 22:17:03,000 - tensorflow - INFO - global_step/sec: 88.7116 +2022-05-01 22:17:03,000 - tensorflow - INFO - loss = 0.015077673, step = 2391200 (2.255 sec) +2022-05-01 22:17:05,257 - tensorflow - INFO - global_step/sec: 88.5927 +2022-05-01 22:17:05,258 - tensorflow - INFO - loss = 0.0067395577, step = 2391400 (2.258 sec) +2022-05-01 22:17:07,509 - tensorflow - INFO - global_step/sec: 88.8246 +2022-05-01 22:17:07,509 - tensorflow - INFO - loss = 0.019622741, step = 2391600 (2.252 sec) +2022-05-01 22:17:09,765 - tensorflow - INFO - global_step/sec: 88.6531 +2022-05-01 22:17:09,765 - tensorflow - INFO - loss = 0.014148858, step = 2391800 (2.256 sec) +2022-05-01 22:17:12,020 - tensorflow - INFO - global_step/sec: 88.6967 +2022-05-01 22:17:12,020 - tensorflow - INFO - loss = 0.005203164, step = 2392000 (2.255 sec) +2022-05-01 22:17:14,276 - tensorflow - INFO - global_step/sec: 88.645 +2022-05-01 22:17:14,277 - tensorflow - INFO - loss = 0.040775083, step = 2392200 (2.256 sec) +2022-05-01 22:17:16,531 - tensorflow - INFO - global_step/sec: 88.705 +2022-05-01 22:17:16,531 - tensorflow - INFO - loss = 0.07990815, step = 2392400 (2.255 sec) +2022-05-01 22:17:18,782 - tensorflow - INFO - global_step/sec: 88.8364 +2022-05-01 22:17:18,782 - tensorflow - INFO - loss = 0.041910116, step = 2392600 (2.251 sec) +2022-05-01 22:17:21,034 - tensorflow - INFO - global_step/sec: 88.8098 +2022-05-01 22:17:21,034 - tensorflow - INFO - loss = 0.022378735, step = 2392800 (2.252 sec) +2022-05-01 22:17:23,292 - tensorflow - INFO - global_step/sec: 88.5926 +2022-05-01 22:17:23,292 - tensorflow - INFO - loss = 0.22880238, step = 2393000 (2.258 sec) +2022-05-01 22:17:25,543 - tensorflow - INFO - global_step/sec: 88.8418 +2022-05-01 22:17:25,543 - tensorflow - INFO - loss = 0.029437074, step = 2393200 (2.251 sec) +2022-05-01 22:17:27,794 - tensorflow - INFO - global_step/sec: 88.8368 +2022-05-01 22:17:27,795 - tensorflow - INFO - loss = 0.02802345, step = 2393400 (2.251 sec) +2022-05-01 22:17:30,049 - tensorflow - INFO - global_step/sec: 88.6802 +2022-05-01 22:17:30,050 - tensorflow - INFO - loss = 0.063665904, step = 2393600 (2.255 sec) +2022-05-01 22:17:32,307 - tensorflow - INFO - global_step/sec: 88.5758 +2022-05-01 22:17:32,308 - tensorflow - INFO - loss = 0.021801218, step = 2393800 (2.258 sec) +2022-05-01 22:17:34,568 - tensorflow - INFO - global_step/sec: 88.4643 +2022-05-01 22:17:34,569 - tensorflow - INFO - loss = 0.11958213, step = 2394000 (2.261 sec) +2022-05-01 22:17:36,821 - tensorflow - INFO - global_step/sec: 88.7855 +2022-05-01 22:17:36,821 - tensorflow - INFO - loss = 0.028965376, step = 2394200 (2.253 sec) +2022-05-01 22:17:39,071 - tensorflow - INFO - global_step/sec: 88.8764 +2022-05-01 22:17:39,072 - tensorflow - INFO - loss = 0.0116002485, step = 2394400 (2.250 sec) +2022-05-01 22:17:41,326 - tensorflow - INFO - global_step/sec: 88.6799 +2022-05-01 22:17:41,327 - tensorflow - INFO - loss = 0.01459889, step = 2394600 (2.255 sec) +2022-05-01 22:17:43,582 - tensorflow - INFO - global_step/sec: 88.6758 +2022-05-01 22:17:43,582 - tensorflow - INFO - loss = 0.077938415, step = 2394800 (2.255 sec) +2022-05-01 22:17:45,836 - tensorflow - INFO - global_step/sec: 88.7167 +2022-05-01 22:17:45,837 - tensorflow - INFO - loss = 0.032044075, step = 2395000 (2.254 sec) +2022-05-01 22:17:48,097 - tensorflow - INFO - global_step/sec: 88.4702 +2022-05-01 22:17:48,097 - tensorflow - INFO - loss = 0.03634771, step = 2395200 (2.261 sec) +2022-05-01 22:17:50,352 - tensorflow - INFO - global_step/sec: 88.6998 +2022-05-01 22:17:50,352 - tensorflow - INFO - loss = 0.07824456, step = 2395400 (2.255 sec) +2022-05-01 22:17:52,603 - tensorflow - INFO - global_step/sec: 88.8301 +2022-05-01 22:17:52,603 - tensorflow - INFO - loss = 0.0139762, step = 2395600 (2.251 sec) +2022-05-01 22:17:54,855 - tensorflow - INFO - global_step/sec: 88.8065 +2022-05-01 22:17:54,856 - tensorflow - INFO - loss = 0.009006003, step = 2395800 (2.252 sec) +2022-05-01 22:17:57,107 - tensorflow - INFO - global_step/sec: 88.804 +2022-05-01 22:17:57,108 - tensorflow - INFO - loss = 0.019991372, step = 2396000 (2.252 sec) +2022-05-01 22:17:59,358 - tensorflow - INFO - global_step/sec: 88.8513 +2022-05-01 22:17:59,359 - tensorflow - INFO - loss = 0.5126882, step = 2396200 (2.251 sec) +2022-05-01 22:18:01,617 - tensorflow - INFO - global_step/sec: 88.5474 +2022-05-01 22:18:01,617 - tensorflow - INFO - loss = 0.01509488, step = 2396400 (2.259 sec) +2022-05-01 22:18:03,870 - tensorflow - INFO - global_step/sec: 88.7642 +2022-05-01 22:18:03,871 - tensorflow - INFO - loss = 0.01103553, step = 2396600 (2.253 sec) +2022-05-01 22:18:06,124 - tensorflow - INFO - global_step/sec: 88.7297 +2022-05-01 22:18:06,125 - tensorflow - INFO - loss = 0.01015729, step = 2396800 (2.254 sec) +2022-05-01 22:18:08,379 - tensorflow - INFO - global_step/sec: 88.7154 +2022-05-01 22:18:08,379 - tensorflow - INFO - loss = 0.01095845, step = 2397000 (2.254 sec) +2022-05-01 22:18:10,628 - tensorflow - INFO - global_step/sec: 88.9017 +2022-05-01 22:18:10,629 - tensorflow - INFO - loss = 0.019706031, step = 2397200 (2.250 sec) +2022-05-01 22:18:12,888 - tensorflow - INFO - global_step/sec: 88.5183 +2022-05-01 22:18:12,888 - tensorflow - INFO - loss = 0.01793155, step = 2397400 (2.259 sec) +2022-05-01 22:18:15,137 - tensorflow - INFO - global_step/sec: 88.9022 +2022-05-01 22:18:15,138 - tensorflow - INFO - loss = 0.0090178745, step = 2397600 (2.250 sec) +2022-05-01 22:18:17,389 - tensorflow - INFO - global_step/sec: 88.8423 +2022-05-01 22:18:17,389 - tensorflow - INFO - loss = 0.004080846, step = 2397800 (2.251 sec) +2022-05-01 22:18:19,643 - tensorflow - INFO - global_step/sec: 88.7274 +2022-05-01 22:18:19,643 - tensorflow - INFO - loss = 0.013300663, step = 2398000 (2.254 sec) +2022-05-01 22:18:21,900 - tensorflow - INFO - global_step/sec: 88.5801 +2022-05-01 22:18:21,901 - tensorflow - INFO - loss = 0.029440576, step = 2398200 (2.258 sec) +2022-05-01 22:18:24,157 - tensorflow - INFO - global_step/sec: 88.6256 +2022-05-01 22:18:24,158 - tensorflow - INFO - loss = 0.021608679, step = 2398400 (2.257 sec) +2022-05-01 22:18:26,413 - tensorflow - INFO - global_step/sec: 88.6579 +2022-05-01 22:18:26,413 - tensorflow - INFO - loss = 0.013700175, step = 2398600 (2.256 sec) +2022-05-01 22:18:28,669 - tensorflow - INFO - global_step/sec: 88.662 +2022-05-01 22:18:28,669 - tensorflow - INFO - loss = 0.018216874, step = 2398800 (2.256 sec) +2022-05-01 22:18:30,923 - tensorflow - INFO - global_step/sec: 88.7059 +2022-05-01 22:18:30,924 - tensorflow - INFO - loss = 0.087347396, step = 2399000 (2.255 sec) +2022-05-01 22:18:33,175 - tensorflow - INFO - global_step/sec: 88.8152 +2022-05-01 22:18:33,176 - tensorflow - INFO - loss = 0.010069039, step = 2399200 (2.252 sec) +2022-05-01 22:18:35,430 - tensorflow - INFO - global_step/sec: 88.7169 +2022-05-01 22:18:35,430 - tensorflow - INFO - loss = 0.0043498473, step = 2399400 (2.254 sec) +2022-05-01 22:18:37,685 - tensorflow - INFO - global_step/sec: 88.6671 +2022-05-01 22:18:37,686 - tensorflow - INFO - loss = 0.045280192, step = 2399600 (2.256 sec) +2022-05-01 22:18:39,940 - tensorflow - INFO - global_step/sec: 88.7167 +2022-05-01 22:18:39,940 - tensorflow - INFO - loss = 0.009058259, step = 2399800 (2.254 sec) +2022-05-01 22:18:42,178 - tensorflow - INFO - Saving checkpoints for 2400000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. +2022-05-01 22:18:42,193 - tensorflow - INFO - model.ckpt-2400000 is not in all_model_checkpoint_paths. Manually adding it. +2022-05-01 22:18:42,743 - tensorflow - INFO - Calling model_fn. +2022-05-01 22:18:42,869 - tensorflow - INFO - Done calling model_fn. +2022-05-01 22:18:42,884 - tensorflow - INFO - Starting evaluation at 2022-05-01T22:18:42Z +2022-05-01 22:18:42,947 - tensorflow - INFO - Graph was finalized. +2022-05-01 22:18:42,951 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-2400000 +2022-05-01 22:18:43,230 - tensorflow - INFO - Running local_init_op. +2022-05-01 22:18:43,338 - tensorflow - INFO - Done running local_init_op. +2022-05-01 22:18:53,570 - tensorflow - INFO - Evaluation [3990/39900] +2022-05-01 22:19:03,090 - tensorflow - INFO - Evaluation [7980/39900] +2022-05-01 22:19:12,586 - tensorflow - INFO - Evaluation [11970/39900] +2022-05-01 22:19:22,093 - tensorflow - INFO - Evaluation [15960/39900] +2022-05-01 22:19:31,631 - tensorflow - INFO - Evaluation [19950/39900] +2022-05-01 22:19:41,171 - tensorflow - INFO - Evaluation [23940/39900] +2022-05-01 22:19:50,691 - tensorflow - INFO - Evaluation [27930/39900] +2022-05-01 22:20:00,276 - tensorflow - INFO - Evaluation [31920/39900] +2022-05-01 22:20:09,832 - tensorflow - INFO - Evaluation [35910/39900] +2022-05-01 22:20:19,401 - tensorflow - INFO - Evaluation [39900/39900] +2022-05-01 22:20:19,539 - tensorflow - INFO - Finished evaluation at 2022-05-01-22:20:19 +2022-05-01 22:20:19,539 - tensorflow - INFO - Saving dict for global step 2400000: global_step = 2400000, loss = 0.0163621, mean_degree_err = 3.2771058 +2022-05-01 22:20:19,540 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 2400000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-2400000 +2022-05-01 22:20:19,553 - tensorflow - INFO - global_step/sec: 2.00776 +2022-05-01 22:20:19,554 - tensorflow - INFO - loss = 0.017489435, step = 2400000 (99.614 sec) +2022-05-01 22:20:21,826 - tensorflow - INFO - global_step/sec: 87.9881 +2022-05-01 22:20:21,827 - tensorflow - INFO - loss = 0.07216262, step = 2400200 (2.273 sec) +2022-05-01 22:20:24,081 - tensorflow - INFO - global_step/sec: 88.6938 +2022-05-01 22:20:24,082 - tensorflow - INFO - loss = 0.092333764, step = 2400400 (2.255 sec) +2022-05-01 22:20:26,335 - tensorflow - INFO - global_step/sec: 88.7409 +2022-05-01 22:20:26,336 - tensorflow - INFO - loss = 0.0081663225, step = 2400600 (2.254 sec) +2022-05-01 22:20:28,588 - tensorflow - INFO - global_step/sec: 88.7691 +2022-05-01 22:20:28,589 - tensorflow - INFO - loss = 0.008921932, step = 2400800 (2.253 sec) +2022-05-01 22:20:30,851 - tensorflow - INFO - global_step/sec: 88.3872 +2022-05-01 22:20:30,851 - tensorflow - INFO - loss = 0.020071022, step = 2401000 (2.263 sec) +2022-05-01 22:20:33,106 - tensorflow - INFO - global_step/sec: 88.6718 +2022-05-01 22:20:33,107 - tensorflow - INFO - loss = 0.029509716, step = 2401200 (2.256 sec) +2022-05-01 22:20:35,366 - tensorflow - INFO - global_step/sec: 88.5227 +2022-05-01 22:20:35,366 - tensorflow - 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INFO - loss = 0.015435829, step = 2484200 (2.256 sec) +2022-05-01 22:36:11,455 - tensorflow - INFO - global_step/sec: 88.8429 +2022-05-01 22:36:11,455 - tensorflow - INFO - loss = 0.13900219, step = 2484400 (2.251 sec) +2022-05-01 22:36:13,708 - tensorflow - INFO - global_step/sec: 88.7728 +2022-05-01 22:36:13,708 - tensorflow - INFO - loss = 0.022148643, step = 2484600 (2.253 sec) +2022-05-01 22:36:15,962 - tensorflow - INFO - global_step/sec: 88.7212 +2022-05-01 22:36:15,962 - tensorflow - INFO - loss = 0.009017587, step = 2484800 (2.254 sec) +2022-05-01 22:36:18,218 - tensorflow - INFO - global_step/sec: 88.6473 +2022-05-01 22:36:18,219 - tensorflow - INFO - loss = 0.030413501, step = 2485000 (2.256 sec) +2022-05-01 22:36:20,469 - tensorflow - INFO - global_step/sec: 88.8619 +2022-05-01 22:36:20,469 - tensorflow - INFO - loss = 0.008298157, step = 2485200 (2.251 sec) +2022-05-01 22:36:22,724 - tensorflow - INFO - global_step/sec: 88.6869 +2022-05-01 22:36:22,724 - tensorflow - INFO - loss = 0.0050102044, step = 2485400 (2.255 sec) +2022-05-01 22:36:24,979 - tensorflow - INFO - global_step/sec: 88.6926 +2022-05-01 22:36:24,979 - tensorflow - INFO - loss = 0.011594127, step = 2485600 (2.255 sec) +2022-05-01 22:36:27,234 - tensorflow - INFO - global_step/sec: 88.6906 +2022-05-01 22:36:27,234 - tensorflow - INFO - loss = 0.006304825, step = 2485800 (2.255 sec) +2022-05-01 22:36:29,490 - tensorflow - INFO - global_step/sec: 88.6356 +2022-05-01 22:36:29,491 - tensorflow - INFO - loss = 2.7082615, step = 2486000 (2.256 sec) +2022-05-01 22:36:31,756 - tensorflow - INFO - global_step/sec: 88.2866 +2022-05-01 22:36:31,756 - tensorflow - INFO - loss = 0.015781693, step = 2486200 (2.265 sec) +2022-05-01 22:36:34,013 - tensorflow - INFO - global_step/sec: 88.6173 +2022-05-01 22:36:34,013 - tensorflow - INFO - loss = 0.2971293, step = 2486400 (2.257 sec) +2022-05-01 22:36:36,267 - tensorflow - INFO - global_step/sec: 88.7221 +2022-05-01 22:36:36,267 - tensorflow - INFO - loss = 0.004865778, step = 2486600 (2.254 sec) +2022-05-01 22:36:38,517 - tensorflow - INFO - global_step/sec: 88.8855 +2022-05-01 22:36:38,517 - tensorflow - INFO - loss = 0.008420302, step = 2486800 (2.250 sec) +2022-05-01 22:36:40,768 - tensorflow - INFO - global_step/sec: 88.8477 +2022-05-01 22:36:40,768 - tensorflow - INFO - loss = 0.07152127, step = 2487000 (2.251 sec) +2022-05-01 22:36:43,024 - tensorflow - INFO - global_step/sec: 88.6567 +2022-05-01 22:36:43,024 - tensorflow - INFO - loss = 0.009115946, step = 2487200 (2.256 sec) +2022-05-01 22:36:45,277 - tensorflow - INFO - global_step/sec: 88.7715 +2022-05-01 22:36:45,277 - tensorflow - INFO - loss = 0.005687272, step = 2487400 (2.253 sec) +2022-05-01 22:36:47,526 - tensorflow - INFO - global_step/sec: 88.9087 +2022-05-01 22:36:47,527 - tensorflow - INFO - loss = 0.19188553, step = 2487600 (2.249 sec) +2022-05-01 22:36:49,780 - tensorflow - INFO - global_step/sec: 88.7629 +2022-05-01 22:36:49,780 - tensorflow - INFO - loss = 0.023123372, step = 2487800 (2.253 sec) +2022-05-01 22:36:52,035 - tensorflow - INFO - global_step/sec: 88.6651 +2022-05-01 22:36:52,036 - tensorflow - INFO - loss = 1.4362056, step = 2488000 (2.256 sec) +2022-05-01 22:36:54,287 - tensorflow - INFO - global_step/sec: 88.8069 +2022-05-01 22:36:54,288 - tensorflow - INFO - loss = 0.49593955, step = 2488200 (2.252 sec) +2022-05-01 22:36:56,547 - tensorflow - INFO - global_step/sec: 88.5042 +2022-05-01 22:36:56,547 - tensorflow - INFO - loss = 0.0058627683, step = 2488400 (2.260 sec) +2022-05-01 22:36:58,797 - tensorflow - INFO - global_step/sec: 88.8873 +2022-05-01 22:36:58,798 - tensorflow - INFO - loss = 0.04569395, step = 2488600 (2.250 sec) +2022-05-01 22:37:01,050 - tensorflow - INFO - global_step/sec: 88.7574 +2022-05-01 22:37:01,051 - tensorflow - INFO - loss = 0.1335107, step = 2488800 (2.253 sec) +2022-05-01 22:37:03,305 - tensorflow - INFO - global_step/sec: 88.6996 +2022-05-01 22:37:03,306 - tensorflow - INFO - loss = 0.01614601, step = 2489000 (2.255 sec) +2022-05-01 22:37:05,558 - tensorflow - INFO - global_step/sec: 88.7925 +2022-05-01 22:37:05,558 - tensorflow - INFO - loss = 0.007931199, step = 2489200 (2.252 sec) +2022-05-01 22:37:07,816 - tensorflow - INFO - global_step/sec: 88.5643 +2022-05-01 22:37:07,816 - tensorflow - INFO - loss = 0.05090235, step = 2489400 (2.258 sec) +2022-05-01 22:37:10,076 - tensorflow - INFO - global_step/sec: 88.504 +2022-05-01 22:37:10,076 - tensorflow - INFO - loss = 0.017152905, step = 2489600 (2.260 sec) +2022-05-01 22:37:12,325 - tensorflow - INFO - global_step/sec: 88.9359 +2022-05-01 22:37:12,325 - tensorflow - INFO - loss = 0.010166051, step = 2489800 (2.249 sec) +2022-05-01 22:37:14,576 - tensorflow - INFO - global_step/sec: 88.8497 +2022-05-01 22:37:14,576 - tensorflow - INFO - loss = 0.008025802, step = 2490000 (2.251 sec) +2022-05-01 22:37:16,828 - tensorflow - INFO - global_step/sec: 88.7933 +2022-05-01 22:37:16,828 - tensorflow - INFO - loss = 0.02635665, step = 2490200 (2.252 sec) +2022-05-01 22:37:19,082 - tensorflow - INFO - global_step/sec: 88.7142 +2022-05-01 22:37:19,083 - tensorflow - INFO - loss = 0.05959672, step = 2490400 (2.254 sec) +2022-05-01 22:37:21,338 - tensorflow - INFO - global_step/sec: 88.6671 +2022-05-01 22:37:21,338 - tensorflow - INFO - loss = 0.007345924, step = 2490600 (2.256 sec) +2022-05-01 22:37:23,601 - tensorflow - INFO - global_step/sec: 88.3764 +2022-05-01 22:37:23,601 - tensorflow - INFO - loss = 0.6229799, step = 2490800 (2.263 sec) +2022-05-01 22:37:25,862 - tensorflow - INFO - global_step/sec: 88.4762 +2022-05-01 22:37:25,862 - tensorflow - INFO - loss = 0.023782894, step = 2491000 (2.260 sec) +2022-05-01 22:37:28,124 - tensorflow - INFO - global_step/sec: 88.3935 +2022-05-01 22:37:28,125 - tensorflow - INFO - loss = 0.017684136, step = 2491200 (2.263 sec) +2022-05-01 22:37:30,389 - tensorflow - INFO - global_step/sec: 88.3029 +2022-05-01 22:37:30,390 - tensorflow - INFO - loss = 0.029672848, step = 2491400 (2.265 sec) +2022-05-01 22:37:32,652 - tensorflow - INFO - global_step/sec: 88.3824 +2022-05-01 22:37:32,652 - tensorflow - INFO - loss = 0.018330775, step = 2491600 (2.263 sec) +2022-05-01 22:37:34,917 - tensorflow - INFO - global_step/sec: 88.3055 +2022-05-01 22:37:34,917 - tensorflow - INFO - loss = 0.19323613, step = 2491800 (2.265 sec) +2022-05-01 22:37:37,180 - tensorflow - INFO - global_step/sec: 88.3689 +2022-05-01 22:37:37,181 - tensorflow - INFO - loss = 0.0035518394, step = 2492000 (2.263 sec) +2022-05-01 22:37:39,442 - tensorflow - INFO - global_step/sec: 88.4067 +2022-05-01 22:37:39,443 - tensorflow - INFO - loss = 0.0065744384, step = 2492200 (2.262 sec) +2022-05-01 22:37:41,703 - tensorflow - INFO - global_step/sec: 88.453 +2022-05-01 22:37:41,704 - tensorflow - INFO - loss = 0.007689911, step = 2492400 (2.261 sec) +2022-05-01 22:37:43,967 - tensorflow - INFO - global_step/sec: 88.3656 +2022-05-01 22:37:43,967 - tensorflow - INFO - loss = 0.03819196, step = 2492600 (2.263 sec) +2022-05-01 22:37:46,227 - tensorflow - INFO - global_step/sec: 88.476 +2022-05-01 22:37:46,228 - tensorflow - INFO - loss = 0.047451183, step = 2492800 (2.261 sec) +2022-05-01 22:37:48,487 - tensorflow - INFO - global_step/sec: 88.5072 +2022-05-01 22:37:48,487 - tensorflow - INFO - loss = 0.014459378, step = 2493000 (2.260 sec) +2022-05-01 22:37:50,747 - tensorflow - INFO - global_step/sec: 88.5062 +2022-05-01 22:37:50,747 - tensorflow - INFO - loss = 0.20836158, step = 2493200 (2.260 sec) +2022-05-01 22:37:53,006 - tensorflow - INFO - global_step/sec: 88.5224 +2022-05-01 22:37:53,006 - tensorflow - INFO - loss = 0.03848868, step = 2493400 (2.259 sec) +2022-05-01 22:37:55,263 - tensorflow - INFO - global_step/sec: 88.6129 +2022-05-01 22:37:55,263 - tensorflow - INFO - loss = 0.17222637, step = 2493600 (2.257 sec) +2022-05-01 22:37:57,517 - tensorflow - INFO - global_step/sec: 88.7318 +2022-05-01 22:37:57,517 - tensorflow - INFO - loss = 0.012568856, step = 2493800 (2.254 sec) +2022-05-01 22:37:59,768 - tensorflow - INFO - global_step/sec: 88.8338 +2022-05-01 22:37:59,769 - tensorflow - INFO - loss = 0.036384873, step = 2494000 (2.251 sec) +2022-05-01 22:38:02,022 - tensorflow - INFO - global_step/sec: 88.7564 +2022-05-01 22:38:02,022 - tensorflow - INFO - loss = 0.010013607, step = 2494200 (2.253 sec) +2022-05-01 22:38:04,274 - tensorflow - INFO - global_step/sec: 88.7988 +2022-05-01 22:38:04,274 - tensorflow - INFO - loss = 0.011140664, step = 2494400 (2.252 sec) +2022-05-01 22:38:06,524 - tensorflow - INFO - global_step/sec: 88.8942 +2022-05-01 22:38:06,524 - tensorflow - INFO - loss = 0.00332475, step = 2494600 (2.250 sec) +2022-05-01 22:38:08,779 - tensorflow - INFO - global_step/sec: 88.6708 +2022-05-01 22:38:08,780 - tensorflow - INFO - loss = 0.03149625, step = 2494800 (2.256 sec) +2022-05-01 22:38:11,030 - tensorflow - INFO - global_step/sec: 88.8648 +2022-05-01 22:38:11,030 - tensorflow - INFO - loss = 0.008571399, step = 2495000 (2.251 sec) +2022-05-01 22:38:13,281 - tensorflow - INFO - global_step/sec: 88.8418 +2022-05-01 22:38:13,282 - tensorflow - INFO - loss = 0.024274005, step = 2495200 (2.251 sec) +2022-05-01 22:38:15,539 - tensorflow - INFO - global_step/sec: 88.5933 +2022-05-01 22:38:15,539 - tensorflow - INFO - loss = 0.026418442, step = 2495400 (2.257 sec) +2022-05-01 22:38:17,792 - tensorflow - INFO - global_step/sec: 88.7513 +2022-05-01 22:38:17,793 - tensorflow - INFO - loss = 0.019441668, step = 2495600 (2.253 sec) +2022-05-01 22:38:20,042 - tensorflow - INFO - global_step/sec: 88.8905 +2022-05-01 22:38:20,043 - tensorflow - INFO - loss = 0.010732288, step = 2495800 (2.250 sec) +2022-05-01 22:38:22,287 - tensorflow - INFO - global_step/sec: 89.1048 +2022-05-01 22:38:22,287 - tensorflow - INFO - loss = 0.03142559, step = 2496000 (2.245 sec) +2022-05-01 22:38:24,533 - tensorflow - INFO - global_step/sec: 89.0417 +2022-05-01 22:38:24,533 - tensorflow - INFO - loss = 0.059488736, step = 2496200 (2.246 sec) +2022-05-01 22:38:26,792 - tensorflow - INFO - global_step/sec: 88.5427 +2022-05-01 22:38:26,792 - tensorflow - INFO - loss = 0.014048614, step = 2496400 (2.259 sec) +2022-05-01 22:38:29,042 - tensorflow - INFO - global_step/sec: 88.8708 +2022-05-01 22:38:29,043 - tensorflow - INFO - loss = 0.047158502, step = 2496600 (2.250 sec) +2022-05-01 22:38:31,297 - tensorflow - INFO - global_step/sec: 88.7053 +2022-05-01 22:38:31,297 - tensorflow - INFO - loss = 0.019878263, step = 2496800 (2.255 sec) +2022-05-01 22:38:33,548 - tensorflow - INFO - global_step/sec: 88.8446 +2022-05-01 22:38:33,548 - tensorflow - INFO - loss = 0.0071397563, step = 2497000 (2.251 sec) +2022-05-01 22:38:35,800 - tensorflow - INFO - global_step/sec: 88.7965 +2022-05-01 22:38:35,801 - tensorflow - INFO - loss = 0.7433055, step = 2497200 (2.252 sec) +2022-05-01 22:38:38,053 - tensorflow - INFO - global_step/sec: 88.7709 +2022-05-01 22:38:38,054 - tensorflow - INFO - loss = 0.0055767056, step = 2497400 (2.253 sec) +2022-05-01 22:38:40,309 - tensorflow - INFO - global_step/sec: 88.661 +2022-05-01 22:38:40,309 - tensorflow - INFO - loss = 0.012999417, step = 2497600 (2.256 sec) +2022-05-01 22:38:42,559 - tensorflow - INFO - global_step/sec: 88.8825 +2022-05-01 22:38:42,560 - tensorflow - INFO - loss = 0.009196865, step = 2497800 (2.250 sec) +2022-05-01 22:38:44,817 - tensorflow - INFO - global_step/sec: 88.5973 +2022-05-01 22:38:44,817 - tensorflow - INFO - loss = 0.019697893, step = 2498000 (2.257 sec) +2022-05-01 22:38:47,067 - tensorflow - INFO - global_step/sec: 88.8669 +2022-05-01 22:38:47,068 - tensorflow - INFO - loss = 0.0067554098, step = 2498200 (2.251 sec) +2022-05-01 22:38:49,321 - tensorflow - INFO - global_step/sec: 88.7454 +2022-05-01 22:38:49,321 - tensorflow - INFO - loss = 0.012799524, step = 2498400 (2.254 sec) +2022-05-01 22:38:51,579 - tensorflow - INFO - global_step/sec: 88.5476 +2022-05-01 22:38:51,580 - tensorflow - INFO - loss = 0.04408863, step = 2498600 (2.259 sec) +2022-05-01 22:38:53,828 - tensorflow - INFO - global_step/sec: 88.95 +2022-05-01 22:38:53,828 - tensorflow - INFO - loss = 0.056328095, step = 2498800 (2.248 sec) +2022-05-01 22:38:56,084 - tensorflow - INFO - global_step/sec: 88.6324 +2022-05-01 22:38:56,085 - tensorflow - INFO - loss = 0.011546372, step = 2499000 (2.256 sec) +2022-05-01 22:38:58,335 - tensorflow - INFO - global_step/sec: 88.8491 +2022-05-01 22:38:58,336 - tensorflow - INFO - loss = 0.0033892635, step = 2499200 (2.251 sec) +2022-05-01 22:39:00,593 - tensorflow - INFO - global_step/sec: 88.6082 +2022-05-01 22:39:00,593 - tensorflow - INFO - loss = 0.004750331, step = 2499400 (2.257 sec) +2022-05-01 22:39:02,853 - tensorflow - INFO - global_step/sec: 88.4876 +2022-05-01 22:39:02,853 - tensorflow - INFO - loss = 0.007829336, step = 2499600 (2.260 sec) +2022-05-01 22:39:05,113 - tensorflow - INFO - global_step/sec: 88.4993 +2022-05-01 22:39:05,113 - tensorflow - INFO - loss = 0.024423394, step = 2499800 (2.260 sec) +2022-05-01 22:39:07,351 - tensorflow - INFO - Saving checkpoints for 2500000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. +2022-05-01 22:39:07,369 - tensorflow - INFO - model.ckpt-2500000 is not in all_model_checkpoint_paths. Manually adding it. +2022-05-01 22:39:07,900 - tensorflow - INFO - Calling model_fn. +2022-05-01 22:39:08,027 - tensorflow - INFO - Done calling model_fn. +2022-05-01 22:39:08,042 - tensorflow - INFO - Starting evaluation at 2022-05-01T22:39:08Z +2022-05-01 22:39:08,106 - tensorflow - INFO - Graph was finalized. +2022-05-01 22:39:08,109 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-2500000 +2022-05-01 22:39:08,391 - tensorflow - INFO - Running local_init_op. +2022-05-01 22:39:08,499 - tensorflow - INFO - Done running local_init_op. +2022-05-01 22:39:18,635 - tensorflow - INFO - Evaluation [3990/39900] +2022-05-01 22:39:28,069 - tensorflow - INFO - Evaluation [7980/39900] +2022-05-01 22:39:37,498 - tensorflow - INFO - Evaluation [11970/39900] +2022-05-01 22:39:46,922 - tensorflow - INFO - Evaluation [15960/39900] +2022-05-01 22:39:56,366 - tensorflow - INFO - Evaluation [19950/39900] +2022-05-01 22:40:05,832 - tensorflow - INFO - Evaluation [23940/39900] +2022-05-01 22:40:15,280 - tensorflow - INFO - Evaluation [27930/39900] +2022-05-01 22:40:24,719 - tensorflow - INFO - Evaluation [31920/39900] +2022-05-01 22:40:34,151 - tensorflow - INFO - Evaluation [35910/39900] +2022-05-01 22:40:43,604 - tensorflow - INFO - Evaluation [39900/39900] +2022-05-01 22:40:43,741 - tensorflow - INFO - Finished evaluation at 2022-05-01-22:40:43 +2022-05-01 22:40:43,741 - tensorflow - INFO - Saving dict for global step 2500000: global_step = 2500000, loss = 0.01607213, mean_degree_err = 3.0870223 +2022-05-01 22:40:43,742 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 2500000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-2500000 +2022-05-01 22:40:43,755 - tensorflow - INFO - global_step/sec: 2.02752 +2022-05-01 22:40:43,756 - tensorflow - INFO - loss = 0.028217498, step = 2500000 (98.643 sec) +2022-05-01 22:40:46,023 - tensorflow - INFO - global_step/sec: 88.1845 +2022-05-01 22:40:46,024 - 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tensorflow - INFO - loss = 0.0019687468, step = 2597800 (2.260 sec) +2022-05-01 22:59:08,973 - tensorflow - INFO - global_step/sec: 88.772 +2022-05-01 22:59:08,973 - tensorflow - INFO - loss = 0.009858871, step = 2598000 (2.253 sec) +2022-05-01 22:59:11,226 - tensorflow - INFO - global_step/sec: 88.751 +2022-05-01 22:59:11,227 - tensorflow - INFO - loss = 0.03571446, step = 2598200 (2.253 sec) +2022-05-01 22:59:13,477 - tensorflow - INFO - global_step/sec: 88.8674 +2022-05-01 22:59:13,477 - tensorflow - INFO - loss = 0.004305632, step = 2598400 (2.251 sec) +2022-05-01 22:59:15,728 - tensorflow - INFO - global_step/sec: 88.8317 +2022-05-01 22:59:15,729 - tensorflow - INFO - loss = 0.011081892, step = 2598600 (2.251 sec) +2022-05-01 22:59:17,983 - tensorflow - INFO - global_step/sec: 88.6949 +2022-05-01 22:59:17,984 - tensorflow - INFO - loss = 0.029049512, step = 2598800 (2.255 sec) +2022-05-01 22:59:20,237 - tensorflow - INFO - global_step/sec: 88.7534 +2022-05-01 22:59:20,237 - tensorflow - INFO - loss = 0.045791313, step = 2599000 (2.253 sec) +2022-05-01 22:59:22,487 - tensorflow - INFO - global_step/sec: 88.881 +2022-05-01 22:59:22,487 - tensorflow - INFO - loss = 0.016003875, step = 2599200 (2.250 sec) +2022-05-01 22:59:24,740 - tensorflow - INFO - global_step/sec: 88.7705 +2022-05-01 22:59:24,740 - tensorflow - INFO - loss = 0.007047058, step = 2599400 (2.253 sec) +2022-05-01 22:59:26,994 - tensorflow - INFO - global_step/sec: 88.7231 +2022-05-01 22:59:26,994 - tensorflow - INFO - loss = 0.1277457, step = 2599600 (2.254 sec) +2022-05-01 22:59:29,242 - tensorflow - INFO - global_step/sec: 88.9479 +2022-05-01 22:59:29,243 - tensorflow - INFO - loss = 0.031332817, step = 2599800 (2.249 sec) +2022-05-01 22:59:31,492 - tensorflow - INFO - Saving checkpoints for 2600000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. +2022-05-01 22:59:31,508 - tensorflow - INFO - model.ckpt-2600000 is not in all_model_checkpoint_paths. Manually adding it. +2022-05-01 22:59:32,027 - tensorflow - INFO - Calling model_fn. +2022-05-01 22:59:32,158 - tensorflow - INFO - Done calling model_fn. +2022-05-01 22:59:32,173 - tensorflow - INFO - Starting evaluation at 2022-05-01T22:59:32Z +2022-05-01 22:59:32,238 - tensorflow - INFO - Graph was finalized. +2022-05-01 22:59:32,241 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-2600000 +2022-05-01 22:59:32,519 - tensorflow - INFO - Running local_init_op. +2022-05-01 22:59:32,625 - tensorflow - INFO - Done running local_init_op. +2022-05-01 22:59:42,867 - tensorflow - INFO - Evaluation [3990/39900] +2022-05-01 22:59:52,420 - tensorflow - INFO - Evaluation [7980/39900] +2022-05-01 23:00:01,983 - tensorflow - INFO - Evaluation [11970/39900] +2022-05-01 23:00:11,571 - tensorflow - INFO - Evaluation [15960/39900] +2022-05-01 23:00:21,147 - tensorflow - INFO - Evaluation [19950/39900] +2022-05-01 23:00:30,752 - tensorflow - INFO - Evaluation [23940/39900] +2022-05-01 23:00:40,323 - tensorflow - INFO - Evaluation [27930/39900] +2022-05-01 23:00:49,929 - tensorflow - INFO - Evaluation [31920/39900] +2022-05-01 23:00:59,510 - tensorflow - INFO - Evaluation [35910/39900] +2022-05-01 23:01:09,073 - tensorflow - INFO - Evaluation [39900/39900] +2022-05-01 23:01:09,208 - tensorflow - INFO - Finished evaluation at 2022-05-01-23:01:09 +2022-05-01 23:01:09,208 - tensorflow - INFO - Saving dict for global step 2600000: global_step = 2600000, loss = 0.020267775, mean_degree_err = 3.9795845 +2022-05-01 23:01:09,209 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 2600000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-2600000 +2022-05-01 23:01:09,209 - tensorflow - DEBUG - Calling exporter with the `is_the_final_export=True`. +2022-05-01 23:01:09,230 - tensorflow - INFO - Loss for final step: 0.01898054. \ No newline at end of file diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/main_point_cloud_gpu.py b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/main_point_cloud_gpu.py new file mode 100644 index 000000000..a3cbfe37d --- /dev/null +++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/main_point_cloud_gpu.py @@ -0,0 +1,431 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ + +# coding=utf-8 +# Copyright 2021 The Google Research Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Training and evaluation for the point cloud alignment experiment.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import utils_gpu +import time +from absl import flags +import numpy as np +import tensorflow as tf +import logging +FLAGS = flags.FLAGS +log = logging.getLogger('tensorflow') +log.setLevel(logging.DEBUG) + +# General flags. +flags.DEFINE_string('method', 'svd', + 'Specifies the method to use for predicting rotations. ' + 'Choices are "svd", "svd-inf", or "gs".') +flags.DEFINE_string('checkpoint_dir', '', + 'Locations for checkpoints, summaries, etc.') +flags.DEFINE_integer('train_steps', 2600000, 'Number of training iterations.') +flags.DEFINE_integer('save_checkpoints_steps', 100000, + 'How often to save checkpoints') +flags.DEFINE_integer('log_step_count', 200, 'How often to log the step count') +flags.DEFINE_integer('save_summaries_steps', 25000, + 'How often to save summaries.') +flags.DEFINE_float('learning_rate', 1e-5, 'Learning rate') +flags.DEFINE_boolean('lr_decay', False, 'Decay the learning rate if True.') +flags.DEFINE_integer('lr_decay_steps', 35000, + 'Learning rate decays steps.') +flags.DEFINE_float('lr_decay_rate', 0.95, + 'Learning rate decays rate.') +flags.DEFINE_boolean('predict_all_test', False, + 'If true, runs an eval job on latest checkpoint and ' + 'prints the error for each input.') +flags.DEFINE_integer('eval_examples', 39900, 'Number of test examples.') +flags.DEFINE_boolean('print_variable_names', False, + 'Print model variable names.') + +# Flags only used in the point cloud alignment experiment. +flags.DEFINE_integer('num_train_augmentations', 10, + 'Number of random rotations for augmenting each input ' + 'point cloud.') +flags.DEFINE_string('pt_cloud_train_files', '', + 'Expression matching all training point files, e.g. ' + '/path/to/files/pc_plane/points/*.pts') +flags.DEFINE_string('pt_cloud_test_files', '', + 'Expression matching all modified test point files, e.g. ' + '/path/to/files/pc_plane/points_test/*.pts') +flags.DEFINE_boolean('random_rotation_axang', True, + 'If true, samples random rotations using the method ' + 'from the original benchmark code. Otherwise samples ' + 'by Haar measure.') + + +def pt_features(batch_pts): + """Input shape: [B, N, 3], output shape: [B, 1024].""" + with tf.variable_scope('ptenc', reuse=tf.AUTO_REUSE): + f1 = tf.layers.conv1d(inputs=batch_pts, filters=64, kernel_size=1) + f1 = tf.nn.leaky_relu(f1) + f2 = tf.layers.conv1d(inputs=f1, filters=128, kernel_size=1) + f2 = tf.nn.leaky_relu(f2) + f3 = tf.layers.conv1d(inputs=f2, filters=1024, kernel_size=1) + + f = tf.reduce_max(f3, axis=1, keep_dims=False) + return f + + +def regress_from_features(batch_features, out_dim): + """Regress to a rotation representation from point cloud encodings. + + In Zhou et al, CVPR19, the paper describes this regression network as an MLP + mapping 2048->512->512->out_dim, but the associated code implements it with + one less layer: 2048->512->out_dim. We mimic the code. + + Args: + batch_features: [batch_size, in_dim]. + out_dim: desired output dimensionality. + + Returns: + A [batch_size, out_dim] tensor. + """ + f1 = tf.layers.dense(batch_features, 512) + f1 = tf.nn.leaky_relu(f1) + f2 = tf.layers.dense(f1, out_dim) + return f2 + + +def net_point_cloud(points1, points2, mode): + """Predict a relative rotation given two point clouds. + + Args: + points1: [batch_size, N, 3] float tensor. + points2: [batch_size, N, 3] float tensor. + mode: tf.estimator.ModeKeys. + + Returns: + [batch_size, 3, 3] matrices. + """ + f1 = pt_features(points1) + f2 = pt_features(points2) + f = tf.concat([f1, f2], axis=-1) + + if FLAGS.method == 'svd': + p = regress_from_features(f, 9) + return utils_gpu.symmetric_orthogonalization(p) + + if FLAGS.method == 'svd-inf': + p = regress_from_features(f, 9) + if mode == tf.estimator.ModeKeys.TRAIN: + return tf.reshape(p, (-1, 3, 3)) + else: + return utils_gpu.symmetric_orthogonalization(p) + + if FLAGS.method == 'gs': + p = regress_from_features(f, 6) + return utils_gpu.gs_orthogonalization(p) + + +def model_fn(features, labels, mode, params): + """The model_fn used to construct the tf.Estimator.""" + del labels, params # Unused. + if mode == tf.estimator.ModeKeys.TRAIN: + # Training data has point cloud of size [1, N, 3] and random rotations + # of size [1, FLAGS.num_train_augmentations, 3, 3] + rot = features['rot'][0] + num_rot = FLAGS.num_train_augmentations + batch_pts1 = tf.tile(features['data'], [num_rot, 1, 1]) + #batch_pts1 = tf.reshape(batch_pts1,(num_rot,-1,3)) + # In this experiment it does not matter if we pre or post-multiply the + # rotation as long as we are consistent between training and eval. + batch_pts2 = tf.matmul(batch_pts1, rot) # post-multiplying! + else: + # Test data has point cloud of size [1, N, 3] and a single random + # rotation of size [1, 3, 3] + batch_pts1 = features['data'] + rot = features['rot'] + batch_pts2 = tf.matmul(batch_pts1, rot) + rot = tf.reshape(rot, (-1, 3, 3)) + + # Predict the rotation. + r = net_point_cloud(batch_pts1, batch_pts2, mode) + + # Compute the loss. + loss = tf.nn.l2_loss(rot - r) + + # Compute the relative angle in radians. + theta = utils_gpu.relative_angle(r, rot) + + # Mean angle error over the batch. + mean_theta = tf.reduce_mean(theta) + mean_theta_deg = mean_theta * 180.0 / np.pi + + # Train, eval, or predict depending on mode. + if mode == tf.estimator.ModeKeys.TRAIN: + tf.summary.scalar('train/loss', loss) + tf.summary.scalar('train/theta', mean_theta_deg) + global_step = tf.train.get_or_create_global_step() + + if FLAGS.lr_decay: + learning_rate = tf.train.exponential_decay( + FLAGS.learning_rate, + global_step, + FLAGS.lr_decay_steps, + FLAGS.lr_decay_rate) + else: + learning_rate = FLAGS.learning_rate + + tf.summary.scalar('train/learning_rate', learning_rate) + optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate) + update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) + with tf.control_dependencies(update_ops): + train_op = optimizer.minimize(loss, global_step=global_step) + return tf.estimator.EstimatorSpec( + mode=mode, + loss=loss, + train_op=train_op) + + if mode == tf.estimator.ModeKeys.EVAL: + if FLAGS.predict_all_test: + print_error_op = tf.print('error:', mean_theta_deg) + with tf.control_dependencies([print_error_op]): + eval_metric_ops = { + 'mean_degree_err': tf.metrics.mean(mean_theta_deg), + } + else: + eval_metric_ops = { + 'mean_degree_err': tf.metrics.mean(mean_theta_deg), + } + + return tf.estimator.EstimatorSpec( + mode=mode, + loss=loss, + eval_metric_ops=eval_metric_ops) + + if mode == tf.estimator.ModeKeys.PREDICT: + pred = {'error': mean_theta_deg} + return tf.estimator.EstimatorSpec( + mode=mode, + predictions=pred) + + +def train_input_fn(): + """Generate training data iterator from the .pts files.""" + def _file_to_matrix(pts_path): + """Read Nx3 point cloud from a .pts file.""" + file_buffer = tf.read_file(pts_path) + lines = tf.string_split([file_buffer], delimiter='\n') + lines1 = tf.string_split(lines.values, delimiter='\r') + values = tf.stack(tf.decode_csv(lines1.values, + record_defaults=[[0.0], [0.0], [0.0]], + field_delim=' ')) + values = tf.transpose(values)# 3xN --> Nx3 + values = values[:(tf.shape(values)[0]// 2), :] + diff_num = 1500-tf.shape(values)[0] + repeat_pts = tf.tile(tf.reshape(values[1,:],(1,-1)),[diff_num,1]) + values = tf.concat([values,repeat_pts],axis=0) + # The experiment code in + # github.com/papagina/RotationContinuity/.../shapenet/code/train_pointnet.py + # only used the first half of the points in each file. + return values + + + + def _random_rotation(pts): + """Attach N random rotations to a point cloud.""" + if FLAGS.random_rotation_axang: + rotations = utils_gpu.random_rotation_benchmark(FLAGS.num_train_augmentations) + else: + rotations = utils_gpu.random_rotation(FLAGS.num_train_augmentations) + return pts, rotations + + pts_paths = tf.gfile.Glob(FLAGS.pt_cloud_train_files) + dataset = tf.data.Dataset.from_tensor_slices(pts_paths) + dataset = dataset.map(_file_to_matrix) + dataset = dataset.cache() # Comment out if memory cannot hold all the data. + dataset = dataset.shuffle(buffer_size=50, reshuffle_each_iteration=True) + dataset = dataset.repeat() + dataset = dataset.map(_random_rotation) + dataset = dataset.batch(1) + iterator = tf.data.make_one_shot_iterator(dataset) + batch_data, batch_rot = iterator.get_next() + features_dict = {'data': batch_data, 'rot': batch_rot} + batch_size = tf.shape(batch_data)[0] + batch_labels_dummy = tf.zeros(shape=(batch_size, 1)) + return (features_dict, batch_labels_dummy) + + + + +def eval_input_fn(): + """Generate test data from *modified* .pts files. + + See README and comments below for details on how the data is modified. + + Returns: + A tuple of features and associated labels. + """ + def _file_to_matrix(pts_path): + """Read Nx3 point cloud and 3x3 rotation matrix from a .pts file. + + The test data is a modified version of the original files. For each .pts + file we have (1) added a 3x3 rotation matrix for testing, and (2) removed + the second half of the point cloud since it is not used at all. + + Args: + pts_path: path to a .pts file. + + Returns: + A Nx3 point cloud. + A 3x3 rotation matrix. + """ + file_buffer = tf.read_file(pts_path) + lines = tf.string_split([file_buffer], delimiter='\n') + lines1 = tf.string_split(lines.values, delimiter='\r') + values = tf.stack(tf.decode_csv(lines1.values, + record_defaults=[[0.0], [0.0], [0.0]], field_delim=' ')) + values = tf.transpose(values) # 3xN --> Nx3. + diff_num = 1414-tf.shape(values)[0] + repeat_pts = tf.tile(tf.reshape(values[4,:],(1,-1)),[diff_num,1]) + + values = tf.concat([values,repeat_pts],axis=0) + # First three rows are the rotation matrix, remaining rows the point cloud. + rot = values[:3, :] + return values[4:, :], rot + + pts_paths = tf.gfile.Glob(FLAGS.pt_cloud_test_files) + dataset = tf.data.Dataset.from_tensor_slices(pts_paths) + dataset = dataset.map(_file_to_matrix) + dataset = dataset.batch(1) + iterator = tf.data.make_one_shot_iterator(dataset) + batch_data, batch_rot = iterator.get_next() + features_dict = {'data': batch_data, 'rot': batch_rot} + batch_size = tf.shape(batch_data)[0] + batch_labels_dummy = tf.zeros(shape=(batch_size, 1)) + return (features_dict, batch_labels_dummy) + + +def print_variable_names(): + """Print variable names in a model.""" + params = {'dummy': 0} + estimator = tf.estimator.Estimator( + model_fn=model_fn, + model_dir=FLAGS.checkpoint_dir, + params=params) + names = estimator.get_variable_names() + for name in names: + print(name) + + +def predict_all_test(): + """Print error statistics for the test dataset.""" + params = {'dummy': 0} + estimator = tf.estimator.Estimator( + model_fn=model_fn, + model_dir=FLAGS.checkpoint_dir, + params=params) + evals = estimator.predict(input_fn=eval_input_fn, yield_single_examples=False) + + # Print error statistics. + all_errors = [x['error'] for x in evals] + errors = np.array(all_errors) + print('Evaluated %d examples'%np.size(errors)) + print('Mean error: %f degrees', np.mean(errors)) + print('Median error: %f degrees', np.median(errors)) + print('Std: %f degrees', np.std(errors)) + sorted_errors = np.sort(errors) + n = np.size(sorted_errors) + print('\nPercentiles:') + for perc in range(1, 101): + index = np.int32(np.float32(n * perc) / 100.0) - 1 + print('%3d%%: %f'%(perc, sorted_errors[index])) + + +def train_and_eval(): + """Train and evaluate a model.""" + # create formatter and add it to the handlers + formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') + + # create file handler which logs even debug messages + fh = logging.FileHandler('loss.log') + fh.setLevel(logging.DEBUG) + fh.setFormatter(formatter) + log.addHandler(fh) + + save_summary_steps = FLAGS.save_summaries_steps + save_checkpoints_steps = FLAGS.save_checkpoints_steps + log_step_count = FLAGS.log_step_count + + config = tf.estimator.RunConfig( + save_summary_steps=save_summary_steps, + save_checkpoints_steps=save_checkpoints_steps, + log_step_count_steps=log_step_count, + keep_checkpoint_max=None) + + params = {'dummy': 0} + estimator = tf.estimator.Estimator( + model_fn=model_fn, + model_dir=FLAGS.checkpoint_dir, + config=config, + params=params) + + train_spec = tf.estimator.TrainSpec( + input_fn=train_input_fn, + max_steps=FLAGS.train_steps) + + eval_spec = tf.estimator.EvalSpec(input_fn=eval_input_fn, + start_delay_secs=60, + steps=FLAGS.eval_examples, + throttle_secs=60) + + tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec) + + +def main(argv=None): # pylint: disable=unused-argument + if FLAGS.print_variable_names: + print_variable_names() + return + + if FLAGS.predict_all_test: + predict_all_test() + else: + train_and_eval() + + +if __name__ == '__main__': + tf.app.run() diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/requirements.txt b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/requirements.txt new file mode 100644 index 000000000..de8ee8edb --- /dev/null +++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/requirements.txt @@ -0,0 +1,69 @@ +absl-py==0.11.0 + +astunparse==1.6.3 + +cachetools==4.1.1 + +certifi==2020.6.20 + +chardet==3.0.4 + +gast==0.3.3 + +google-auth==1.23.0 + +google-auth-oauthlib==0.4.2 + +google-pasta==0.2.0 + +grpcio==1.33.2 + +h5py==2.10.0 + +idna==2.10 + +importlib-metadata==2.0.0 + +Keras-Preprocessing==1.1.2 + +Markdown==3.3.3 + +numpy==1.18.5 + +oauthlib==3.1.0 + +opt-einsum==3.3.0 + +protobuf==3.13.0 + +pyasn1==0.4.8 + +pyasn1-modules==0.2.8 + +requests==2.24.0 + +requests-oauthlib==1.3.0 + +rsa==4.6 + +scipy==1.5.3 + +six==1.15.0 + +tensorboard==2.3.0 + +tensorboard-plugin-wit==1.7.0 + +tensorflow==2.3.1 + +tensorflow-estimator==2.3.0 + +termcolor==1.1.0 + +urllib3==1.25.11 + +Werkzeug==1.0.1 + +wrapt==1.12.1 + +zipp==3.4.0 diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/utils_gpu.py b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/utils_gpu.py new file mode 100644 index 000000000..a08b4fa97 --- /dev/null +++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/utils_gpu.py @@ -0,0 +1,144 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ + +# coding=utf-8 +# Copyright 2021 The Google Research Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Utility functions.""" +import numpy as np +from scipy.stats import special_ortho_group +import tensorflow as tf + + +def relative_angle(r1, r2): + """Relative angle (radians) between 3D rotation matrices.""" + rel_rot = tf.matmul(tf.transpose(r1, perm=[0, 2, 1]), r2) + trace = rel_rot[:, 0, 0] + rel_rot[:, 1, 1] + rel_rot[:, 2, 2] + cos_theta = (trace - 1.0) / 2.0 + cos_theta = tf.minimum(cos_theta, tf.ones_like(cos_theta)) + cos_theta = tf.maximum(cos_theta, (-1.0) * tf.ones_like(cos_theta)) + theta = tf.acos(cos_theta) + return theta + + +def random_rotation_benchmark_np(n): + """Sample a random 3D rotation by method used in Zhou et al, CVPR19. + + This numpy function is a copy of the PyTorch function + get_sampled_rotation_matrices_by_axisAngle() in the code made available + for Zhou et al, CVPR19, at https://github.com/papagina/RotationContinuity/. + + Args: + n: the number of rotation matrices to return. + + Returns: + [n, 3, 3] np array. + """ + theta = np.random.uniform(-1, 1, n) * np.pi + sin = np.sin(theta) + axis = np.random.randn(n, 3) + axis = axis / np.maximum(np.linalg.norm(axis, axis=-1, keepdims=True), 1e-7) + qw = np.cos(theta) + qx = axis[:, 0] * sin + qy = axis[:, 1] * sin + qz = axis[:, 2] * sin + + xx = qx*qx + yy = qy*qy + zz = qz*qz + xy = qx*qy + xz = qx*qz + yz = qy*qz + xw = qx*qw + yw = qy*qw + zw = qz*qw + + row0 = np.stack((1-2*yy-2*zz, 2*xy-2*zw, 2*xz+2*yw), axis=-1) + row1 = np.stack((2*xy+2*zw, 1-2*xx-2*zz, 2*yz-2*xw), axis=-1) + row2 = np.stack((2*xz-2*yw, 2*yz+2*xw, 1-2*xx-2*yy), axis=-1) + matrix = np.stack((row0, row1, row2), axis=1) + + return matrix + + +def random_rotation_benchmark(n): + """A TF wrapper for random_rotation_benchmark_np().""" + mat = tf.py_func( + func=lambda t: np.float32(random_rotation_benchmark_np(t)), + inp=[n], + Tout=tf.float32, + stateful=True) + return tf.reshape(mat, (n, 3, 3)) + + +def random_rotation(n): + """Sample rotations from a uniform distribution on SO(3).""" + mat = tf.py_func( + func=lambda t: np.float32(special_ortho_group.rvs(3, size=t)), + inp=[n], + Tout=tf.float32, + stateful=True) + return tf.reshape(mat, (n, 3, 3)) + + +def symmetric_orthogonalization(x): + """Maps 9D input vectors onto SO(3) via symmetric orthogonalization.""" + # Innner dimensions of the input should be 3x3 matrices. + m = tf.reshape(x, (-1, 3, 3)) + _, u, v = tf.svd(m) + det = tf.linalg.det(tf.matmul(u, v, transpose_b=True)) + r = tf.matmul( + tf.concat([u[:, :, :-1], u[:, :, -1:] * tf.reshape(det, [-1, 1, 1])], 2), + v, transpose_b=True) + return r + + +def gs_orthogonalization(p6): + """Gram-Schmidt orthogonalization from 6D input.""" + # Input should be [batch_size, 6] + x = p6[:, 0:3] + y = p6[:, 3:6] + xn = tf.math.l2_normalize(x, axis=-1) + z = tf.linalg.cross(xn, y) + zn = tf.math.l2_normalize(z, axis=-1) + y = tf.linalg.cross(zn, xn) + r = tf.stack([xn, y, zn], -1) + return r -- Gitee From edbd733e2f39657687ed13b8ae606e704681de7d Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Sat, 7 May 2022 14:08:39 +0000 Subject: [PATCH 25/88] =?UTF-8?q?GPU=E7=9B=B8=E5=85=B3=E4=BB=A3=E7=A0=81?= =?UTF-8?q?=E7=BB=93=E6=9E=9C=E6=9B=B4=E6=96=B0?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...\346\224\271\347\211\210\346\234\254).txt" | 106 + ...5\220\214NPU\347\211\210\346\234\254).txt" | 105 + .../GPU/gen_pt_test_data_gpu.py | 99 + .../SVD_ID2019_for_Tensorflow/GPU/log_gpu.log | 26651 ++++++++++++++++ .../GPU/main_point_cloud_gpu.py | 431 + .../GPU/requirements.txt | 69 + .../GPU/utils_gpu.py | 144 + 7 files changed, 27605 insertions(+) create mode 100644 "TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/GPU\347\262\276\345\272\246(github\345\216\237\344\273\243\347\240\201\346\234\252\346\233\264\346\224\271\347\211\210\346\234\254).txt" create mode 100644 "TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/GPU\347\262\276\345\272\246(\344\273\243\347\240\201\351\200\273\350\276\221\345\220\214NPU\347\211\210\346\234\254).txt" create mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/gen_pt_test_data_gpu.py create mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/log_gpu.log create mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/main_point_cloud_gpu.py create mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/requirements.txt create mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/utils_gpu.py diff --git "a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/GPU\347\262\276\345\272\246(github\345\216\237\344\273\243\347\240\201\346\234\252\346\233\264\346\224\271\347\211\210\346\234\254).txt" "b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/GPU\347\262\276\345\272\246(github\345\216\237\344\273\243\347\240\201\346\234\252\346\233\264\346\224\271\347\211\210\346\234\254).txt" new file mode 100644 index 000000000..e8a33b3ef --- /dev/null +++ "b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/GPU\347\262\276\345\272\246(github\345\216\237\344\273\243\347\240\201\346\234\252\346\233\264\346\224\271\347\211\210\346\234\254).txt" @@ -0,0 +1,106 @@ +Evaluated 39900 examples +Mean error: %f degrees 2.582546 +Median error: %f degrees 1.6805115 +Std: %f degrees 6.938891 + +Percentiles: + 1%: 0.300014 + 2%: 0.380522 + 3%: 0.437902 + 4%: 0.487788 + 5%: 0.526747 + 6%: 0.566482 + 7%: 0.602634 + 8%: 0.632420 + 9%: 0.662343 + 10%: 0.689838 + 11%: 0.714364 + 12%: 0.740193 + 13%: 0.766683 + 14%: 0.791300 + 15%: 0.816133 + 16%: 0.840466 + 17%: 0.863887 + 18%: 0.886470 + 19%: 0.909567 + 20%: 0.933351 + 21%: 0.955725 + 22%: 0.978388 + 23%: 1.002101 + 24%: 1.026220 + 25%: 1.049225 + 26%: 1.074472 + 27%: 1.098249 + 28%: 1.120823 + 29%: 1.143979 + 30%: 1.165668 + 31%: 1.189761 + 32%: 1.215631 + 33%: 1.238436 + 34%: 1.261760 + 35%: 1.286031 + 36%: 1.308956 + 37%: 1.333835 + 38%: 1.358547 + 39%: 1.383949 + 40%: 1.407643 + 41%: 1.433814 + 42%: 1.458442 + 43%: 1.485167 + 44%: 1.513619 + 45%: 1.540785 + 46%: 1.567729 + 47%: 1.595813 + 48%: 1.623171 + 49%: 1.651379 + 50%: 1.680511 + 51%: 1.709835 + 52%: 1.740463 + 53%: 1.769457 + 54%: 1.803091 + 55%: 1.837601 + 56%: 1.872417 + 57%: 1.909058 + 58%: 1.944507 + 59%: 1.979321 + 60%: 2.016059 + 61%: 2.052808 + 62%: 2.091812 + 63%: 2.131297 + 64%: 2.175648 + 65%: 2.219467 + 66%: 2.265031 + 67%: 2.307493 + 68%: 2.354846 + 69%: 2.401918 + 70%: 2.450324 + 71%: 2.507802 + 72%: 2.558491 + 73%: 2.610895 + 74%: 2.670931 + 75%: 2.737095 + 76%: 2.795334 + 77%: 2.862317 + 78%: 2.931846 + 79%: 3.004461 + 80%: 3.083880 + 81%: 3.159387 + 82%: 3.250103 + 83%: 3.346382 + 84%: 3.453207 + 85%: 3.562164 + 86%: 3.686474 + 87%: 3.808481 + 88%: 3.965510 + 89%: 4.142220 + 90%: 4.348930 + 91%: 4.576029 + 92%: 4.841852 + 93%: 5.135875 + 94%: 5.504097 + 95%: 5.996492 + 96%: 6.660559 + 97%: 7.634066 + 98%: 9.250069 + 99%: 12.710493 +100%: 178.697479 \ No newline at end of file diff --git "a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/GPU\347\262\276\345\272\246(\344\273\243\347\240\201\351\200\273\350\276\221\345\220\214NPU\347\211\210\346\234\254).txt" "b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/GPU\347\262\276\345\272\246(\344\273\243\347\240\201\351\200\273\350\276\221\345\220\214NPU\347\211\210\346\234\254).txt" new file mode 100644 index 000000000..15a4f409e --- /dev/null +++ "b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/GPU\347\262\276\345\272\246(\344\273\243\347\240\201\351\200\273\350\276\221\345\220\214NPU\347\211\210\346\234\254).txt" @@ -0,0 +1,105 @@ +Mean error: %f degrees 3.9795678 +Median error: %f degrees 2.5962727 +Std: %f degrees 9.361983 + +Percentiles: + 1%: 0.615485 + 2%: 0.765661 + 3%: 0.859573 + 4%: 0.933770 + 5%: 0.991698 + 6%: 1.044177 + 7%: 1.096108 + 8%: 1.139179 + 9%: 1.178358 + 10%: 1.218525 + 11%: 1.255853 + 12%: 1.294827 + 13%: 1.331633 + 14%: 1.368879 + 15%: 1.406948 + 16%: 1.441844 + 17%: 1.474588 + 18%: 1.508179 + 19%: 1.543069 + 20%: 1.576939 + 21%: 1.608395 + 22%: 1.644729 + 23%: 1.676781 + 24%: 1.711550 + 25%: 1.743160 + 26%: 1.777402 + 27%: 1.811321 + 28%: 1.847797 + 29%: 1.880135 + 30%: 1.913359 + 31%: 1.947926 + 32%: 1.980112 + 33%: 2.014214 + 34%: 2.047557 + 35%: 2.079143 + 36%: 2.111832 + 37%: 2.144206 + 38%: 2.178525 + 39%: 2.212224 + 40%: 2.245766 + 41%: 2.279673 + 42%: 2.314099 + 43%: 2.347187 + 44%: 2.381295 + 45%: 2.418161 + 46%: 2.452399 + 47%: 2.488370 + 48%: 2.523829 + 49%: 2.562771 + 50%: 2.596235 + 51%: 2.636707 + 52%: 2.675837 + 53%: 2.713537 + 54%: 2.753353 + 55%: 2.791760 + 56%: 2.830061 + 57%: 2.869558 + 58%: 2.909864 + 59%: 2.951013 + 60%: 2.995325 + 61%: 3.037444 + 62%: 3.085022 + 63%: 3.137624 + 64%: 3.186842 + 65%: 3.234585 + 66%: 3.286463 + 67%: 3.345505 + 68%: 3.400300 + 69%: 3.454171 + 70%: 3.511901 + 71%: 3.568425 + 72%: 3.631408 + 73%: 3.698511 + 74%: 3.770657 + 75%: 3.847002 + 76%: 3.918766 + 77%: 4.001153 + 78%: 4.092133 + 79%: 4.175701 + 80%: 4.280524 + 81%: 4.383471 + 82%: 4.505587 + 83%: 4.643044 + 84%: 4.795996 + 85%: 4.951040 + 86%: 5.116305 + 87%: 5.308140 + 88%: 5.553438 + 89%: 5.796063 + 90%: 6.108622 + 91%: 6.445337 + 92%: 6.875063 + 93%: 7.384479 + 94%: 7.999909 + 95%: 8.852993 + 96%: 10.022167 + 97%: 12.029458 + 98%: 15.299019 + 99%: 22.932156 +100%: 179.803162 \ No newline at end of file diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/gen_pt_test_data_gpu.py b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/gen_pt_test_data_gpu.py new file mode 100644 index 000000000..96b2770d7 --- /dev/null +++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/gen_pt_test_data_gpu.py @@ -0,0 +1,99 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ + +# coding=utf-8 +# Copyright 2021 The Google Research Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Generate modified point cloud test data with fixed random rotations.""" +import glob +import os +import pathlib + +import utils_gpu +from absl import app +from absl import flags +import numpy as np +from scipy.stats import special_ortho_group + +FLAGS = flags.FLAGS +flags.DEFINE_string('input_test_files', '/cache/data_modelarts/points_test/*.pts', + 'Regular expression for the original input test files.') +flags.DEFINE_string('output_directory', '/cache/data_modelarts/test_modified', + 'Output directory where new test files will be stored.') +flags.DEFINE_integer('num_rotations_per_file', 100, + 'Number of random rotation augmentations per test point ' + 'cloud.') +flags.DEFINE_boolean('random_rotation_axang', True, + 'If true, samples random rotations using the method ' + 'from the original benchmark code. Otherwise samples ' + 'by Haar measure.') + + +def gen_test_data(): + """Generate the new (modified) test data.""" + # Create output directory. + os.makedirs(FLAGS.output_directory, exist_ok=True) + + # Get all test point cloud files in the original dataset. + input_test_files = glob.glob(FLAGS.input_test_files) + + for in_file in input_test_files: + out_file_prefix = pathlib.Path(in_file).stem + pts = np.loadtxt(in_file) # N x 3 + num_pts_to_keep = pts.shape[0] // 2 + pts = pts[:num_pts_to_keep, :] # N//2 x 3. + + for k in range(FLAGS.num_rotations_per_file): + if FLAGS.random_rotation_axang: + r = utils_gpu.random_rotation_benchmark_np(1) + r = r[0] + else: + r = special_ortho_group.rvs(3) + joined = np.float32(np.concatenate((r, pts), axis=0)) # (N//2+3) x 3. + out_file = os.path.join( + FLAGS.output_directory, '%s_r%03d.pts'%(out_file_prefix, k)) + np.savetxt(out_file, joined) + + +def main(unused_argv): + gen_test_data() + +if __name__ == '__main__': + app.run(main) diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/log_gpu.log b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/log_gpu.log new file mode 100644 index 000000000..60f26f4b1 --- /dev/null +++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/log_gpu.log @@ -0,0 +1,26651 @@ +2022-05-01 14:07:22,858 - tensorflow - INFO - Using config: {'_model_dir': '/root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51', '_tf_random_seed': None, '_save_summary_steps': 25000, '_save_checkpoints_steps': 100000, '_save_checkpoints_secs': None, '_session_config': allow_soft_placement: true +graph_options { + rewrite_options { + meta_optimizer_iterations: ONE + } +} +, '_keep_checkpoint_max': None, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 200, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_service': None, '_cluster_spec': , '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1} +2022-05-01 14:07:22,859 - tensorflow - INFO - Not using Distribute Coordinator. +2022-05-01 14:07:22,859 - tensorflow - INFO - Running training and evaluation locally (non-distributed). +2022-05-01 14:07:22,860 - tensorflow - INFO - Start train and evaluate loop. The evaluate will happen after every checkpoint. Checkpoint frequency is determined based on RunConfig arguments: save_checkpoints_steps 100000 or save_checkpoints_secs None. +2022-05-01 14:07:22,871 - tensorflow - WARNING - From /root/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow_core/python/training/training_util.py:236: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version. +Instructions for updating: +Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts. +2022-05-01 14:07:22,878 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:249: The name tf.gfile.Glob is deprecated. Please use tf.io.gfile.glob instead. + +2022-05-01 14:07:24,110 - tensorflow - WARNING - From /root/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow_core/python/autograph/converters/directives.py:119: The name tf.read_file is deprecated. Please use tf.io.read_file instead. + +2022-05-01 14:07:24,110 - tensorflow - WARNING - From /root/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow_core/python/autograph/converters/directives.py:119: The name tf.decode_csv is deprecated. Please use tf.io.decode_csv instead. + +2022-05-01 14:07:24,230 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:224: calling string_split (from tensorflow.python.ops.ragged.ragged_string_ops) with delimiter is deprecated and will be removed in a future version. +Instructions for updating: +delimiter is deprecated, please use sep instead. +2022-05-01 14:07:24,412 - tensorflow - WARNING - From /root/special_orthogonalization2/utils_gpu.py:75: py_func (from tensorflow.python.ops.script_ops) is deprecated and will be removed in a future version. +Instructions for updating: +tf.py_func is deprecated in TF V2. Instead, there are two + options available in V2. + - tf.py_function takes a python function which manipulates tf eager + tensors instead of numpy arrays. It's easy to convert a tf eager tensor to + an ndarray (just call tensor.numpy()) but having access to eager tensors + means `tf.py_function`s can use accelerators such as GPUs as well as + being differentiable using a gradient tape. + - tf.numpy_function maintains the semantics of the deprecated tf.py_func + (it is not differentiable, and manipulates numpy arrays). It drops the + stateful argument making all functions stateful. + +2022-05-01 14:07:24,420 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:257: The name tf.data.make_one_shot_iterator is deprecated. Please use tf.compat.v1.data.make_one_shot_iterator instead. + +2022-05-01 14:07:24,443 - tensorflow - INFO - Calling model_fn. +2022-05-01 14:07:24,448 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:74: The name tf.variable_scope is deprecated. Please use tf.compat.v1.variable_scope instead. + +2022-05-01 14:07:24,448 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:74: The name tf.AUTO_REUSE is deprecated. Please use tf.compat.v1.AUTO_REUSE instead. + +2022-05-01 14:07:24,448 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:75: conv1d (from tensorflow.python.layers.convolutional) is deprecated and will be removed in a future version. +Instructions for updating: +Use `tf.keras.layers.Conv1D` instead. +2022-05-01 14:07:24,449 - tensorflow - WARNING - From /root/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow_core/python/layers/convolutional.py:218: Layer.apply (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version. +Instructions for updating: +Please use `layer.__call__` method instead. +2022-05-01 14:07:24,498 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:81: calling reduce_max_v1 (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version. +Instructions for updating: +keep_dims is deprecated, use keepdims instead +2022-05-01 14:07:24,521 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:99: dense (from tensorflow.python.layers.core) is deprecated and will be removed in a future version. +Instructions for updating: +Use keras.layers.Dense instead. +2022-05-01 14:07:24,545 - tensorflow - WARNING - From /root/special_orthogonalization2/utils_gpu.py:97: The name tf.svd is deprecated. Please use tf.linalg.svd instead. + +2022-05-01 14:07:24,570 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:172: The name tf.summary.scalar is deprecated. Please use tf.compat.v1.summary.scalar instead. + +2022-05-01 14:07:24,572 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:174: The name tf.train.get_or_create_global_step is deprecated. Please use tf.compat.v1.train.get_or_create_global_step instead. + +2022-05-01 14:07:24,573 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:186: The name tf.train.AdamOptimizer is deprecated. Please use tf.compat.v1.train.AdamOptimizer instead. + +2022-05-01 14:07:24,573 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:187: The name tf.get_collection is deprecated. Please use tf.compat.v1.get_collection instead. + +2022-05-01 14:07:24,574 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:187: The name tf.GraphKeys is deprecated. Please use tf.compat.v1.GraphKeys instead. + +2022-05-01 14:07:24,856 - tensorflow - INFO - Done calling model_fn. +2022-05-01 14:07:24,857 - tensorflow - INFO - Create CheckpointSaverHook. +2022-05-01 14:07:25,039 - tensorflow - WARNING - From /root/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow_core/python/ops/array_ops.py:1475: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version. +Instructions for updating: +Use tf.where in 2.0, which has the same broadcast rule as np.where +2022-05-01 14:07:25,108 - tensorflow - INFO - Graph was finalized. +2022-05-01 14:07:30,102 - tensorflow - INFO - Running local_init_op. +2022-05-01 14:07:30,119 - tensorflow - INFO - Done running local_init_op. +2022-05-01 14:07:30,573 - tensorflow - INFO - Saving checkpoints for 0 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. +2022-05-01 14:07:30,648 - tensorflow - INFO - model.ckpt-0 is not in all_model_checkpoint_paths. Manually adding it. +2022-05-01 14:07:35,533 - tensorflow - INFO - loss = 32.038086, step = 0 +2022-05-01 14:07:38,208 - tensorflow - INFO - global_step/sec: 74.7414 +2022-05-01 14:07:38,208 - tensorflow - INFO - loss = 13.331531, step = 200 (2.676 sec) +2022-05-01 14:07:40,484 - tensorflow - INFO - global_step/sec: 87.8576 +2022-05-01 14:07:40,485 - tensorflow - INFO - loss = 17.656946, step = 400 (2.276 sec) +2022-05-01 14:07:42,758 - tensorflow - INFO - global_step/sec: 87.9518 +2022-05-01 14:07:42,759 - tensorflow - INFO - loss = 13.776253, step = 600 (2.274 sec) +2022-05-01 14:07:45,033 - tensorflow - INFO - global_step/sec: 87.9402 +2022-05-01 14:07:45,033 - tensorflow - INFO - loss = 16.600023, step = 800 (2.274 sec) +2022-05-01 14:07:47,313 - tensorflow - INFO - global_step/sec: 87.7138 +2022-05-01 14:07:47,313 - tensorflow - INFO - loss = 11.457915, step = 1000 (2.280 sec) +2022-05-01 14:07:49,589 - tensorflow - INFO - global_step/sec: 87.8775 +2022-05-01 14:07:49,589 - tensorflow - INFO - loss = 18.556915, step = 1200 (2.276 sec) +2022-05-01 14:07:51,866 - tensorflow - INFO - global_step/sec: 87.8345 +2022-05-01 14:07:51,866 - tensorflow - INFO - loss = 17.53027, step = 1400 (2.277 sec) +2022-05-01 14:07:54,133 - tensorflow - INFO - global_step/sec: 88.2186 +2022-05-01 14:07:54,133 - tensorflow - INFO - loss = 15.867823, step = 1600 (2.267 sec) +2022-05-01 14:07:56,414 - tensorflow - INFO - global_step/sec: 87.6843 +2022-05-01 14:07:56,414 - tensorflow - INFO - loss = 14.14325, step = 1800 (2.281 sec) +2022-05-01 14:07:58,688 - tensorflow - INFO - global_step/sec: 87.9504 +2022-05-01 14:07:58,688 - tensorflow - INFO - loss = 14.731254, step = 2000 (2.274 sec) +2022-05-01 14:08:00,969 - tensorflow - INFO - global_step/sec: 87.6853 +2022-05-01 14:08:00,969 - tensorflow - INFO - loss = 7.6048627, step = 2200 (2.281 sec) +2022-05-01 14:08:03,225 - tensorflow - INFO - global_step/sec: 88.6196 +2022-05-01 14:08:03,226 - tensorflow - INFO - loss = 5.806036, step = 2400 (2.257 sec) +2022-05-01 14:08:05,477 - tensorflow - INFO - global_step/sec: 88.8202 +2022-05-01 14:08:05,478 - tensorflow - INFO - loss = 24.230932, step = 2600 (2.252 sec) +2022-05-01 14:08:07,731 - tensorflow - INFO - global_step/sec: 88.7332 +2022-05-01 14:08:07,732 - tensorflow - INFO - loss = 3.6557422, step = 2800 (2.254 sec) +2022-05-01 14:08:09,980 - tensorflow - INFO - global_step/sec: 88.9168 +2022-05-01 14:08:09,981 - tensorflow - INFO - loss = 22.55999, step = 3000 (2.249 sec) +2022-05-01 14:08:12,234 - tensorflow - INFO - global_step/sec: 88.7507 +2022-05-01 14:08:12,234 - tensorflow - INFO - loss = 5.92297, step = 3200 (2.254 sec) +2022-05-01 14:08:14,483 - tensorflow - INFO - global_step/sec: 88.9076 +2022-05-01 14:08:14,484 - tensorflow - INFO - loss = 9.746002, step = 3400 (2.250 sec) +2022-05-01 14:08:16,739 - tensorflow - INFO - global_step/sec: 88.6754 +2022-05-01 14:08:16,739 - tensorflow - INFO - loss = 6.337427, step = 3600 (2.255 sec) +2022-05-01 14:08:18,995 - tensorflow - INFO - global_step/sec: 88.6477 +2022-05-01 14:08:18,995 - tensorflow - INFO - loss = 13.799805, step = 3800 (2.256 sec) +2022-05-01 14:08:21,252 - tensorflow - INFO - global_step/sec: 88.6198 +2022-05-01 14:08:21,252 - tensorflow - INFO - loss = 23.801136, step = 4000 (2.257 sec) +2022-05-01 14:08:23,510 - tensorflow - INFO - global_step/sec: 88.5703 +2022-05-01 14:08:23,510 - tensorflow - INFO - loss = 22.836792, step = 4200 (2.258 sec) +2022-05-01 14:08:25,774 - tensorflow - INFO - global_step/sec: 88.3396 +2022-05-01 14:08:25,774 - tensorflow - INFO - loss = 4.7546825, step = 4400 (2.264 sec) +2022-05-01 14:08:28,036 - tensorflow - INFO - global_step/sec: 88.413 +2022-05-01 14:08:28,036 - tensorflow - INFO - loss = 4.444308, step = 4600 (2.262 sec) +2022-05-01 14:08:30,300 - tensorflow - INFO - global_step/sec: 88.3341 +2022-05-01 14:08:30,301 - tensorflow - INFO - loss = 22.58392, step = 4800 (2.264 sec) +2022-05-01 14:08:32,565 - tensorflow - INFO - global_step/sec: 88.3069 +2022-05-01 14:08:32,565 - tensorflow - INFO - loss = 2.2934127, step = 5000 (2.265 sec) +2022-05-01 14:08:34,824 - tensorflow - INFO - global_step/sec: 88.5425 +2022-05-01 14:08:34,824 - tensorflow - INFO - loss = 9.195248, step = 5200 (2.259 sec) +2022-05-01 14:08:37,085 - tensorflow - INFO - global_step/sec: 88.4473 +2022-05-01 14:08:37,085 - tensorflow - INFO - loss = 20.765274, step = 5400 (2.261 sec) +2022-05-01 14:08:39,343 - tensorflow - INFO - global_step/sec: 88.5905 +2022-05-01 14:08:39,343 - tensorflow - INFO - loss = 6.617299, step = 5600 (2.258 sec) +2022-05-01 14:08:41,606 - tensorflow - INFO - global_step/sec: 88.3606 +2022-05-01 14:08:41,606 - tensorflow - INFO - loss = 5.0666003, step = 5800 (2.263 sec) +2022-05-01 14:08:43,870 - tensorflow - INFO - global_step/sec: 88.335 +2022-05-01 14:08:43,871 - tensorflow - INFO - loss = 0.7100632, step = 6000 (2.264 sec) +2022-05-01 14:08:46,121 - tensorflow - INFO - global_step/sec: 88.8393 +2022-05-01 14:08:46,122 - tensorflow - INFO - loss = 5.172297, step = 6200 (2.251 sec) +2022-05-01 14:08:48,372 - tensorflow - INFO - global_step/sec: 88.8673 +2022-05-01 14:08:48,372 - tensorflow - INFO - loss = 2.6311967, step = 6400 (2.251 sec) +2022-05-01 14:08:50,626 - tensorflow - INFO - global_step/sec: 88.7158 +2022-05-01 14:08:50,627 - tensorflow - INFO - loss = 2.473042, step = 6600 (2.254 sec) +2022-05-01 14:08:52,889 - tensorflow - INFO - global_step/sec: 88.3812 +2022-05-01 14:08:52,890 - tensorflow - INFO - loss = 1.4561142, step = 6800 (2.263 sec) +2022-05-01 14:08:55,156 - tensorflow - INFO - global_step/sec: 88.243 +2022-05-01 14:08:55,156 - tensorflow - INFO - loss = 9.346189, step = 7000 (2.266 sec) +2022-05-01 14:08:57,421 - tensorflow - INFO - global_step/sec: 88.2959 +2022-05-01 14:08:57,421 - tensorflow - INFO - loss = 9.677809, step = 7200 (2.265 sec) +2022-05-01 14:08:59,683 - tensorflow - INFO - global_step/sec: 88.3994 +2022-05-01 14:08:59,684 - tensorflow - INFO - loss = 1.9972081, step = 7400 (2.262 sec) +2022-05-01 14:09:01,938 - tensorflow - INFO - global_step/sec: 88.7117 +2022-05-01 14:09:01,938 - tensorflow - INFO - loss = 1.6406038, step = 7600 (2.255 sec) +2022-05-01 14:09:04,212 - tensorflow - INFO - global_step/sec: 87.9525 +2022-05-01 14:09:04,212 - tensorflow - INFO - loss = 0.55838716, step = 7800 (2.274 sec) +2022-05-01 14:09:06,478 - tensorflow - INFO - global_step/sec: 88.266 +2022-05-01 14:09:06,478 - tensorflow - INFO - loss = 1.4037358, step = 8000 (2.266 sec) +2022-05-01 14:09:08,750 - tensorflow - INFO - global_step/sec: 88.0226 +2022-05-01 14:09:08,750 - tensorflow - INFO - loss = 2.4998298, step = 8200 (2.272 sec) +2022-05-01 14:09:11,032 - tensorflow - INFO - global_step/sec: 87.6369 +2022-05-01 14:09:11,032 - tensorflow - INFO - loss = 9.273857, step = 8400 (2.282 sec) +2022-05-01 14:09:13,318 - tensorflow - INFO - global_step/sec: 87.4763 +2022-05-01 14:09:13,319 - tensorflow - INFO - loss = 3.2947733, step = 8600 (2.286 sec) +2022-05-01 14:09:15,595 - tensorflow - INFO - global_step/sec: 87.8609 +2022-05-01 14:09:15,595 - tensorflow - INFO - loss = 8.8844185, step = 8800 (2.276 sec) +2022-05-01 14:09:17,887 - tensorflow - INFO - global_step/sec: 87.2339 +2022-05-01 14:09:17,888 - tensorflow - INFO - loss = 1.5026308, step = 9000 (2.293 sec) +2022-05-01 14:09:20,169 - tensorflow - INFO - global_step/sec: 87.6604 +2022-05-01 14:09:20,169 - tensorflow - INFO - loss = 1.2300944, step = 9200 (2.282 sec) +2022-05-01 14:09:22,442 - tensorflow - INFO - global_step/sec: 87.9892 +2022-05-01 14:09:22,442 - tensorflow - INFO - loss = 0.62815464, step = 9400 (2.273 sec) +2022-05-01 14:09:24,718 - tensorflow - INFO - global_step/sec: 87.8782 +2022-05-01 14:09:24,718 - tensorflow - INFO - loss = 0.56881064, step = 9600 (2.276 sec) +2022-05-01 14:09:27,028 - tensorflow - INFO - global_step/sec: 86.5549 +2022-05-01 14:09:27,029 - tensorflow - INFO - loss = 3.8879156, step = 9800 (2.311 sec) +2022-05-01 14:09:29,330 - tensorflow - INFO - global_step/sec: 86.894 +2022-05-01 14:09:29,330 - tensorflow - INFO - loss = 0.5907526, step = 10000 (2.302 sec) +2022-05-01 14:09:31,612 - tensorflow - INFO - global_step/sec: 87.6382 +2022-05-01 14:09:31,613 - tensorflow - INFO - loss = 1.1593596, step = 10200 (2.282 sec) +2022-05-01 14:09:33,912 - tensorflow - INFO - global_step/sec: 86.9791 +2022-05-01 14:09:33,912 - tensorflow - INFO - loss = 0.42828047, step = 10400 (2.299 sec) +2022-05-01 14:09:36,210 - tensorflow - INFO - global_step/sec: 86.9989 +2022-05-01 14:09:36,211 - tensorflow - INFO - loss = 0.5336071, step = 10600 (2.299 sec) +2022-05-01 14:09:38,509 - tensorflow - INFO - global_step/sec: 86.9931 +2022-05-01 14:09:38,510 - tensorflow - INFO - loss = 1.3948362, step = 10800 (2.299 sec) +2022-05-01 14:09:40,808 - tensorflow - INFO - global_step/sec: 87.0139 +2022-05-01 14:09:40,808 - tensorflow - INFO - loss = 0.6679225, step = 11000 (2.299 sec) +2022-05-01 14:09:43,115 - tensorflow - INFO - global_step/sec: 86.7037 +2022-05-01 14:09:43,115 - tensorflow - INFO - loss = 0.78241736, step = 11200 (2.307 sec) +2022-05-01 14:09:45,399 - tensorflow - INFO - global_step/sec: 87.5425 +2022-05-01 14:09:45,400 - tensorflow - INFO - loss = 5.9322524, step = 11400 (2.285 sec) +2022-05-01 14:09:47,688 - tensorflow - INFO - global_step/sec: 87.3706 +2022-05-01 14:09:47,689 - tensorflow - INFO - loss = 0.52881104, step = 11600 (2.289 sec) +2022-05-01 14:09:49,978 - tensorflow - INFO - global_step/sec: 87.3595 +2022-05-01 14:09:49,978 - tensorflow - INFO - loss = 0.56047755, step = 11800 (2.289 sec) +2022-05-01 14:09:52,269 - tensorflow - INFO - global_step/sec: 87.2898 +2022-05-01 14:09:52,269 - tensorflow - INFO - loss = 0.28127378, step = 12000 (2.291 sec) +2022-05-01 14:09:54,561 - tensorflow - INFO - global_step/sec: 87.2696 +2022-05-01 14:09:54,561 - tensorflow - INFO - loss = 25.888077, step = 12200 (2.292 sec) +2022-05-01 14:09:56,851 - tensorflow - INFO - global_step/sec: 87.3247 +2022-05-01 14:09:56,851 - tensorflow - INFO - loss = 9.627051, step = 12400 (2.290 sec) +2022-05-01 14:09:59,137 - tensorflow - INFO - global_step/sec: 87.4895 +2022-05-01 14:09:59,137 - tensorflow - INFO - loss = 1.9770696, step = 12600 (2.286 sec) +2022-05-01 14:10:01,428 - tensorflow - INFO - global_step/sec: 87.2868 +2022-05-01 14:10:01,429 - tensorflow - INFO - loss = 11.038982, step = 12800 (2.291 sec) +2022-05-01 14:10:03,714 - tensorflow - INFO - global_step/sec: 87.4859 +2022-05-01 14:10:03,715 - tensorflow - INFO - loss = 1.3177816, step = 13000 (2.286 sec) +2022-05-01 14:10:06,003 - tensorflow - INFO - global_step/sec: 87.3913 +2022-05-01 14:10:06,003 - tensorflow - INFO - loss = 12.677236, step = 13200 (2.289 sec) +2022-05-01 14:10:08,301 - tensorflow - INFO - global_step/sec: 87.0449 +2022-05-01 14:10:08,301 - tensorflow - INFO - loss = 2.7487645, step = 13400 (2.298 sec) +2022-05-01 14:10:10,583 - tensorflow - INFO - global_step/sec: 87.6369 +2022-05-01 14:10:10,583 - tensorflow - INFO - loss = 0.9024451, step = 13600 (2.282 sec) +2022-05-01 14:10:12,867 - tensorflow - INFO - global_step/sec: 87.5497 +2022-05-01 14:10:12,868 - tensorflow - INFO - loss = 2.074489, step = 13800 (2.284 sec) +2022-05-01 14:10:15,159 - tensorflow - INFO - global_step/sec: 87.256 +2022-05-01 14:10:15,160 - tensorflow - INFO - loss = 1.01836, step = 14000 (2.292 sec) +2022-05-01 14:10:17,448 - tensorflow - INFO - global_step/sec: 87.3997 +2022-05-01 14:10:17,448 - tensorflow - INFO - loss = 0.4934891, step = 14200 (2.288 sec) +2022-05-01 14:10:19,735 - tensorflow - INFO - global_step/sec: 87.4333 +2022-05-01 14:10:19,735 - tensorflow - INFO - loss = 1.0520558, step = 14400 (2.287 sec) +2022-05-01 14:10:22,022 - tensorflow - INFO - global_step/sec: 87.4513 +2022-05-01 14:10:22,022 - tensorflow - INFO - loss = 2.0643926, step = 14600 (2.287 sec) +2022-05-01 14:10:24,301 - tensorflow - INFO - global_step/sec: 87.7653 +2022-05-01 14:10:24,301 - tensorflow - INFO - loss = 1.3036213, step = 14800 (2.279 sec) +2022-05-01 14:10:26,586 - tensorflow - INFO - global_step/sec: 87.5088 +2022-05-01 14:10:26,587 - tensorflow - INFO - loss = 3.2743459, step = 15000 (2.285 sec) +2022-05-01 14:10:28,874 - tensorflow - INFO - global_step/sec: 87.4281 +2022-05-01 14:10:28,874 - tensorflow - INFO - loss = 5.0951295, step = 15200 (2.288 sec) +2022-05-01 14:10:31,172 - tensorflow - INFO - global_step/sec: 87.0174 +2022-05-01 14:10:31,173 - tensorflow - INFO - loss = 16.01076, step = 15400 (2.298 sec) +2022-05-01 14:10:33,467 - tensorflow - INFO - global_step/sec: 87.1391 +2022-05-01 14:10:33,468 - tensorflow - INFO - loss = 0.71901804, step = 15600 (2.295 sec) +2022-05-01 14:10:35,757 - tensorflow - INFO - global_step/sec: 87.368 +2022-05-01 14:10:35,757 - tensorflow - INFO - loss = 0.5623523, step = 15800 (2.289 sec) +2022-05-01 14:10:38,044 - tensorflow - INFO - global_step/sec: 87.4192 +2022-05-01 14:10:38,045 - tensorflow - INFO - loss = 6.976879, step = 16000 (2.288 sec) +2022-05-01 14:10:40,329 - tensorflow - INFO - global_step/sec: 87.5449 +2022-05-01 14:10:40,329 - tensorflow - INFO - loss = 0.54529786, step = 16200 (2.285 sec) +2022-05-01 14:10:42,620 - tensorflow - INFO - global_step/sec: 87.3064 +2022-05-01 14:10:42,620 - tensorflow - INFO - loss = 16.176308, step = 16400 (2.291 sec) +2022-05-01 14:10:44,919 - tensorflow - INFO - global_step/sec: 87.0021 +2022-05-01 14:10:44,919 - tensorflow - INFO - loss = 0.96182084, step = 16600 (2.299 sec) +2022-05-01 14:10:47,207 - tensorflow - INFO - global_step/sec: 87.4072 +2022-05-01 14:10:47,207 - tensorflow - INFO - loss = 0.30624458, step = 16800 (2.288 sec) +2022-05-01 14:10:49,499 - tensorflow - INFO - global_step/sec: 87.2616 +2022-05-01 14:10:49,499 - tensorflow - INFO - loss = 6.4469113, step = 17000 (2.292 sec) +2022-05-01 14:10:51,790 - tensorflow - INFO - global_step/sec: 87.283 +2022-05-01 14:10:51,791 - tensorflow - INFO - loss = 18.890911, step = 17200 (2.291 sec) +2022-05-01 14:10:54,078 - tensorflow - INFO - global_step/sec: 87.4266 +2022-05-01 14:10:54,078 - tensorflow - INFO - loss = 0.55347186, step = 17400 (2.288 sec) +2022-05-01 14:10:56,371 - tensorflow - INFO - global_step/sec: 87.2254 +2022-05-01 14:10:56,371 - tensorflow - INFO - loss = 0.9430723, step = 17600 (2.293 sec) +2022-05-01 14:10:58,657 - tensorflow - INFO - global_step/sec: 87.4742 +2022-05-01 14:10:58,657 - tensorflow - INFO - loss = 0.43198982, step = 17800 (2.286 sec) +2022-05-01 14:11:00,930 - tensorflow - INFO - global_step/sec: 88.0087 +2022-05-01 14:11:00,930 - tensorflow - INFO - loss = 12.870156, step = 18000 (2.273 sec) +2022-05-01 14:11:03,196 - 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tensorflow - INFO - loss = 1.8678666, step = 85600 (2.292 sec) +2022-05-01 14:23:56,851 - tensorflow - INFO - global_step/sec: 87.2976 +2022-05-01 14:23:56,851 - tensorflow - INFO - loss = 0.19676991, step = 85800 (2.291 sec) +2022-05-01 14:23:59,138 - tensorflow - INFO - global_step/sec: 87.4451 +2022-05-01 14:23:59,138 - tensorflow - INFO - loss = 8.195176, step = 86000 (2.287 sec) +2022-05-01 14:24:01,425 - tensorflow - INFO - global_step/sec: 87.4531 +2022-05-01 14:24:01,425 - tensorflow - INFO - loss = 0.08395229, step = 86200 (2.287 sec) +2022-05-01 14:24:03,717 - tensorflow - INFO - global_step/sec: 87.2589 +2022-05-01 14:24:03,717 - tensorflow - INFO - loss = 5.7395077, step = 86400 (2.292 sec) +2022-05-01 14:24:06,002 - tensorflow - INFO - global_step/sec: 87.505 +2022-05-01 14:24:06,003 - tensorflow - INFO - loss = 0.17323343, step = 86600 (2.286 sec) +2022-05-01 14:24:08,299 - tensorflow - INFO - global_step/sec: 87.0962 +2022-05-01 14:24:08,299 - tensorflow - INFO - loss = 0.32396364, step = 86800 (2.296 sec) +2022-05-01 14:24:10,590 - tensorflow - INFO - global_step/sec: 87.3045 +2022-05-01 14:24:10,590 - tensorflow - INFO - loss = 22.962328, step = 87000 (2.291 sec) +2022-05-01 14:24:12,884 - tensorflow - INFO - global_step/sec: 87.163 +2022-05-01 14:24:12,885 - tensorflow - INFO - loss = 0.19104755, step = 87200 (2.294 sec) +2022-05-01 14:24:15,173 - tensorflow - INFO - global_step/sec: 87.3884 +2022-05-01 14:24:15,173 - tensorflow - INFO - loss = 0.05848871, step = 87400 (2.289 sec) +2022-05-01 14:24:17,461 - tensorflow - INFO - global_step/sec: 87.4179 +2022-05-01 14:24:17,461 - tensorflow - INFO - loss = 0.19390681, step = 87600 (2.288 sec) +2022-05-01 14:24:19,758 - tensorflow - INFO - global_step/sec: 87.0593 +2022-05-01 14:24:19,758 - tensorflow - INFO - loss = 0.18075919, step = 87800 (2.297 sec) +2022-05-01 14:24:22,055 - tensorflow - INFO - global_step/sec: 87.0804 +2022-05-01 14:24:22,055 - tensorflow - INFO - loss = 0.29285538, step = 88000 (2.297 sec) +2022-05-01 14:24:24,338 - tensorflow - INFO - global_step/sec: 87.5764 +2022-05-01 14:24:24,339 - tensorflow - INFO - loss = 0.1336847, step = 88200 (2.284 sec) +2022-05-01 14:24:26,637 - tensorflow - INFO - global_step/sec: 86.9889 +2022-05-01 14:24:26,638 - tensorflow - INFO - loss = 20.40362, step = 88400 (2.299 sec) +2022-05-01 14:24:28,918 - tensorflow - INFO - global_step/sec: 87.7009 +2022-05-01 14:24:28,918 - tensorflow - INFO - loss = 0.1908592, step = 88600 (2.280 sec) +2022-05-01 14:24:31,213 - tensorflow - INFO - global_step/sec: 87.1393 +2022-05-01 14:24:31,214 - tensorflow - INFO - loss = 0.40158635, step = 88800 (2.295 sec) +2022-05-01 14:24:33,511 - tensorflow - INFO - global_step/sec: 87.0228 +2022-05-01 14:24:33,512 - tensorflow - INFO - loss = 0.23553915, step = 89000 (2.298 sec) +2022-05-01 14:24:35,801 - tensorflow - INFO - global_step/sec: 87.3497 +2022-05-01 14:24:35,801 - tensorflow - INFO - loss = 1.1698062, step = 89200 (2.290 sec) +2022-05-01 14:24:38,086 - tensorflow - INFO - global_step/sec: 87.5318 +2022-05-01 14:24:38,086 - tensorflow - INFO - loss = 0.11512068, step = 89400 (2.285 sec) +2022-05-01 14:24:40,375 - tensorflow - INFO - global_step/sec: 87.3655 +2022-05-01 14:24:40,376 - tensorflow - INFO - loss = 0.17605992, step = 89600 (2.289 sec) +2022-05-01 14:24:42,661 - tensorflow - INFO - global_step/sec: 87.5102 +2022-05-01 14:24:42,661 - tensorflow - INFO - loss = 0.22128661, step = 89800 (2.285 sec) +2022-05-01 14:24:44,953 - tensorflow - INFO - global_step/sec: 87.2428 +2022-05-01 14:24:44,953 - tensorflow - INFO - loss = 0.75772536, step = 90000 (2.292 sec) +2022-05-01 14:24:47,238 - tensorflow - INFO - global_step/sec: 87.5261 +2022-05-01 14:24:47,239 - tensorflow - INFO - loss = 1.981938, step = 90200 (2.285 sec) +2022-05-01 14:24:49,523 - tensorflow - INFO - global_step/sec: 87.539 +2022-05-01 14:24:49,523 - tensorflow - INFO - loss = 3.6954958, step = 90400 (2.285 sec) +2022-05-01 14:24:51,808 - tensorflow - INFO - global_step/sec: 87.5312 +2022-05-01 14:24:51,808 - tensorflow - INFO - loss = 23.398697, step = 90600 (2.285 sec) +2022-05-01 14:24:54,089 - tensorflow - INFO - global_step/sec: 87.6596 +2022-05-01 14:24:54,090 - tensorflow - INFO - loss = 0.37941447, step = 90800 (2.282 sec) +2022-05-01 14:24:56,366 - tensorflow - INFO - global_step/sec: 87.8273 +2022-05-01 14:24:56,367 - tensorflow - INFO - loss = 0.13895091, step = 91000 (2.277 sec) +2022-05-01 14:24:58,624 - tensorflow - INFO - global_step/sec: 88.588 +2022-05-01 14:24:58,624 - tensorflow - INFO - loss = 13.276718, step = 91200 (2.258 sec) +2022-05-01 14:25:00,904 - tensorflow - INFO - global_step/sec: 87.7149 +2022-05-01 14:25:00,905 - tensorflow - INFO - loss = 0.19925176, step = 91400 (2.280 sec) +2022-05-01 14:25:03,189 - tensorflow - INFO - global_step/sec: 87.5326 +2022-05-01 14:25:03,189 - tensorflow - INFO - loss = 0.14478576, step = 91600 (2.285 sec) +2022-05-01 14:25:05,479 - tensorflow - INFO - global_step/sec: 87.3428 +2022-05-01 14:25:05,479 - tensorflow - INFO - loss = 0.113933235, step = 91800 (2.290 sec) +2022-05-01 14:25:07,769 - tensorflow - INFO - global_step/sec: 87.3376 +2022-05-01 14:25:07,769 - tensorflow - INFO - loss = 0.7221588, step = 92000 (2.290 sec) +2022-05-01 14:25:10,059 - tensorflow - INFO - global_step/sec: 87.3229 +2022-05-01 14:25:10,060 - tensorflow - INFO - loss = 2.7999012, step = 92200 (2.290 sec) +2022-05-01 14:25:12,352 - tensorflow - INFO - global_step/sec: 87.2151 +2022-05-01 14:25:12,353 - tensorflow - INFO - loss = 0.29915583, step = 92400 (2.293 sec) +2022-05-01 14:25:14,645 - tensorflow - INFO - global_step/sec: 87.2291 +2022-05-01 14:25:14,646 - tensorflow - INFO - loss = 0.057438828, step = 92600 (2.293 sec) +2022-05-01 14:25:16,938 - tensorflow - INFO - global_step/sec: 87.2267 +2022-05-01 14:25:16,939 - tensorflow - INFO - loss = 0.13389596, step = 92800 (2.293 sec) +2022-05-01 14:25:19,229 - tensorflow - INFO - global_step/sec: 87.2822 +2022-05-01 14:25:19,230 - tensorflow - INFO - loss = 0.3385266, step = 93000 (2.291 sec) +2022-05-01 14:25:21,517 - tensorflow - INFO - global_step/sec: 87.4451 +2022-05-01 14:25:21,517 - tensorflow - INFO - loss = 0.16020179, step = 93200 (2.287 sec) +2022-05-01 14:25:23,806 - tensorflow - INFO - global_step/sec: 87.3654 +2022-05-01 14:25:23,806 - tensorflow - INFO - loss = 0.1617248, step = 93400 (2.289 sec) +2022-05-01 14:25:26,097 - tensorflow - INFO - global_step/sec: 87.3069 +2022-05-01 14:25:26,097 - tensorflow - INFO - loss = 0.18052283, step = 93600 (2.291 sec) +2022-05-01 14:25:28,392 - tensorflow - INFO - global_step/sec: 87.1383 +2022-05-01 14:25:28,392 - tensorflow - INFO - loss = 0.2854377, step = 93800 (2.295 sec) +2022-05-01 14:25:30,689 - tensorflow - INFO - global_step/sec: 87.0448 +2022-05-01 14:25:30,690 - tensorflow - INFO - loss = 0.65452284, step = 94000 (2.298 sec) +2022-05-01 14:25:32,974 - tensorflow - INFO - global_step/sec: 87.5423 +2022-05-01 14:25:32,975 - tensorflow - INFO - loss = 0.48012242, step = 94200 (2.285 sec) +2022-05-01 14:25:35,265 - tensorflow - INFO - global_step/sec: 87.2856 +2022-05-01 14:25:35,266 - tensorflow - INFO - loss = 0.17184481, step = 94400 (2.291 sec) +2022-05-01 14:25:37,547 - tensorflow - INFO - global_step/sec: 87.6663 +2022-05-01 14:25:37,547 - tensorflow - INFO - loss = 0.31042734, step = 94600 (2.281 sec) +2022-05-01 14:25:39,832 - tensorflow - INFO - global_step/sec: 87.5264 +2022-05-01 14:25:39,832 - tensorflow - INFO - loss = 0.15828839, step = 94800 (2.285 sec) +2022-05-01 14:25:42,126 - tensorflow - INFO - global_step/sec: 87.182 +2022-05-01 14:25:42,126 - tensorflow - INFO - loss = 0.35003784, step = 95000 (2.294 sec) +2022-05-01 14:25:44,421 - tensorflow - INFO - global_step/sec: 87.1344 +2022-05-01 14:25:44,422 - tensorflow - INFO - loss = 0.47904184, step = 95200 (2.295 sec) +2022-05-01 14:25:46,720 - tensorflow - INFO - global_step/sec: 86.983 +2022-05-01 14:25:46,721 - tensorflow - INFO - loss = 0.21531895, step = 95400 (2.299 sec) +2022-05-01 14:25:49,022 - tensorflow - INFO - global_step/sec: 86.885 +2022-05-01 14:25:49,023 - tensorflow - INFO - loss = 0.15111923, step = 95600 (2.302 sec) +2022-05-01 14:25:51,318 - tensorflow - INFO - global_step/sec: 87.116 +2022-05-01 14:25:51,319 - tensorflow - INFO - loss = 0.9417725, step = 95800 (2.296 sec) +2022-05-01 14:25:53,611 - tensorflow - INFO - global_step/sec: 87.2297 +2022-05-01 14:25:53,611 - tensorflow - INFO - loss = 0.08343916, step = 96000 (2.293 sec) +2022-05-01 14:25:55,906 - tensorflow - INFO - global_step/sec: 87.1625 +2022-05-01 14:25:55,906 - tensorflow - INFO - loss = 0.11967882, step = 96200 (2.295 sec) +2022-05-01 14:25:58,207 - tensorflow - INFO - global_step/sec: 86.9043 +2022-05-01 14:25:58,207 - tensorflow - INFO - loss = 0.2613772, step = 96400 (2.301 sec) +2022-05-01 14:26:00,505 - tensorflow - INFO - global_step/sec: 87.0403 +2022-05-01 14:26:00,505 - tensorflow - INFO - loss = 0.3371205, step = 96600 (2.298 sec) +2022-05-01 14:26:02,802 - tensorflow - INFO - global_step/sec: 87.0751 +2022-05-01 14:26:02,802 - tensorflow - INFO - loss = 0.28187898, step = 96800 (2.297 sec) +2022-05-01 14:26:05,100 - tensorflow - INFO - global_step/sec: 87.0297 +2022-05-01 14:26:05,100 - tensorflow - INFO - loss = 0.42920962, step = 97000 (2.298 sec) +2022-05-01 14:26:07,396 - tensorflow - INFO - global_step/sec: 87.1099 +2022-05-01 14:26:07,396 - tensorflow - INFO - loss = 0.056796335, step = 97200 (2.296 sec) +2022-05-01 14:26:09,684 - tensorflow - INFO - global_step/sec: 87.3879 +2022-05-01 14:26:09,685 - tensorflow - INFO - loss = 0.09578763, step = 97400 (2.289 sec) +2022-05-01 14:26:11,974 - tensorflow - INFO - global_step/sec: 87.3522 +2022-05-01 14:26:11,974 - tensorflow - INFO - loss = 2.4198983, step = 97600 (2.290 sec) +2022-05-01 14:26:14,263 - tensorflow - INFO - global_step/sec: 87.3526 +2022-05-01 14:26:14,264 - tensorflow - INFO - loss = 0.13744435, step = 97800 (2.290 sec) +2022-05-01 14:26:16,549 - tensorflow - INFO - global_step/sec: 87.4993 +2022-05-01 14:26:16,550 - tensorflow - INFO - loss = 0.20825619, step = 98000 (2.286 sec) +2022-05-01 14:26:18,837 - tensorflow - INFO - global_step/sec: 87.4067 +2022-05-01 14:26:18,838 - tensorflow - INFO - loss = 0.19770706, step = 98200 (2.288 sec) +2022-05-01 14:26:21,124 - tensorflow - INFO - global_step/sec: 87.4774 +2022-05-01 14:26:21,124 - tensorflow - INFO - loss = 0.31616545, step = 98400 (2.286 sec) +2022-05-01 14:26:23,411 - tensorflow - INFO - global_step/sec: 87.4225 +2022-05-01 14:26:23,412 - tensorflow - INFO - loss = 24.242685, step = 98600 (2.288 sec) +2022-05-01 14:26:25,696 - tensorflow - INFO - global_step/sec: 87.5488 +2022-05-01 14:26:25,696 - tensorflow - INFO - loss = 0.5029043, step = 98800 (2.284 sec) +2022-05-01 14:26:27,967 - tensorflow - INFO - global_step/sec: 88.0532 +2022-05-01 14:26:27,968 - tensorflow - INFO - loss = 0.74712604, step = 99000 (2.271 sec) +2022-05-01 14:26:30,226 - tensorflow - INFO - global_step/sec: 88.5269 +2022-05-01 14:26:30,227 - tensorflow - INFO - loss = 0.21298502, step = 99200 (2.259 sec) +2022-05-01 14:26:32,511 - tensorflow - INFO - global_step/sec: 87.5423 +2022-05-01 14:26:32,511 - tensorflow - INFO - loss = 0.21085733, step = 99400 (2.285 sec) +2022-05-01 14:26:34,811 - tensorflow - INFO - global_step/sec: 86.9667 +2022-05-01 14:26:34,811 - tensorflow - INFO - loss = 0.32836705, step = 99600 (2.300 sec) +2022-05-01 14:26:37,093 - tensorflow - INFO - global_step/sec: 87.6294 +2022-05-01 14:26:37,093 - tensorflow - INFO - loss = 0.24103443, step = 99800 (2.282 sec) +2022-05-01 14:26:39,372 - tensorflow - INFO - Saving checkpoints for 100000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. +2022-05-01 14:26:39,390 - tensorflow - INFO - model.ckpt-100000 is not in all_model_checkpoint_paths. Manually adding it. +2022-05-01 14:26:40,405 - tensorflow - INFO - Calling model_fn. +2022-05-01 14:26:40,530 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:204: The name tf.metrics.mean is deprecated. Please use tf.compat.v1.metrics.mean instead. + +2022-05-01 14:26:40,541 - tensorflow - INFO - Done calling model_fn. +2022-05-01 14:26:40,556 - tensorflow - INFO - Starting evaluation at 2022-05-01T14:26:40Z +2022-05-01 14:26:40,620 - tensorflow - INFO - Graph was finalized. +2022-05-01 14:26:40,623 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-100000 +2022-05-01 14:26:40,936 - tensorflow - INFO - Running local_init_op. +2022-05-01 14:26:41,050 - tensorflow - INFO - Done running local_init_op. +2022-05-01 14:26:56,055 - tensorflow - INFO - Evaluation [3990/39900] +2022-05-01 14:27:10,591 - tensorflow - INFO - Evaluation [7980/39900] +2022-05-01 14:27:24,889 - tensorflow - INFO - Evaluation [11970/39900] +2022-05-01 14:27:39,136 - tensorflow - INFO - Evaluation [15960/39900] +2022-05-01 14:27:53,364 - tensorflow - INFO - Evaluation [19950/39900] +2022-05-01 14:28:07,812 - tensorflow - INFO - Evaluation [23940/39900] +2022-05-01 14:28:22,113 - tensorflow - INFO - Evaluation [27930/39900] +2022-05-01 14:28:36,293 - tensorflow - INFO - Evaluation [31920/39900] +2022-05-01 14:28:50,471 - tensorflow - INFO - Evaluation [35910/39900] +2022-05-01 14:29:04,661 - tensorflow - INFO - Evaluation [39900/39900] +2022-05-01 14:29:04,800 - tensorflow - INFO - Finished evaluation at 2022-05-01-14:29:04 +2022-05-01 14:29:04,800 - tensorflow - INFO - Saving dict for global step 100000: global_step = 100000, loss = 0.18664514, mean_degree_err = 15.5360365 +2022-05-01 14:29:04,973 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 100000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-100000 +2022-05-01 14:29:04,987 - tensorflow - INFO - global_step/sec: 1.35232 +2022-05-01 14:29:04,988 - tensorflow - INFO - loss = 0.09665474, step = 100000 (147.894 sec) +2022-05-01 14:29:07,296 - tensorflow - INFO - global_step/sec: 86.6181 +2022-05-01 14:29:07,296 - tensorflow - INFO - loss = 0.11935344, step = 100200 (2.309 sec) +2022-05-01 14:29:09,600 - tensorflow - INFO - global_step/sec: 86.7917 +2022-05-01 14:29:09,601 - tensorflow - INFO - loss = 0.23293598, step = 100400 (2.304 sec) +2022-05-01 14:29:11,905 - tensorflow - INFO - global_step/sec: 86.7663 +2022-05-01 14:29:11,906 - tensorflow - INFO - loss = 13.853834, step = 100600 (2.305 sec) +2022-05-01 14:29:14,200 - tensorflow - INFO - global_step/sec: 87.1497 +2022-05-01 14:29:14,200 - tensorflow - INFO - loss = 0.28267854, step = 100800 (2.295 sec) +2022-05-01 14:29:16,493 - tensorflow - INFO - global_step/sec: 87.2407 +2022-05-01 14:29:16,493 - tensorflow - INFO - loss = 0.13818274, step = 101000 (2.292 sec) +2022-05-01 14:29:18,789 - tensorflow - INFO - global_step/sec: 87.1046 +2022-05-01 14:29:18,789 - tensorflow - INFO - loss = 0.191855, step = 101200 (2.296 sec) +2022-05-01 14:29:21,092 - tensorflow - INFO - global_step/sec: 86.8156 +2022-05-01 14:29:21,093 - tensorflow - INFO - loss = 0.09758256, step = 101400 (2.304 sec) +2022-05-01 14:29:23,394 - tensorflow - INFO - global_step/sec: 86.8777 +2022-05-01 14:29:23,395 - tensorflow - INFO - loss = 0.23463662, step = 101600 (2.302 sec) +2022-05-01 14:29:25,696 - tensorflow - INFO - global_step/sec: 86.9051 +2022-05-01 14:29:25,696 - tensorflow - INFO - loss = 0.09981724, step = 101800 (2.301 sec) +2022-05-01 14:29:27,995 - tensorflow - INFO - global_step/sec: 86.9773 +2022-05-01 14:29:27,996 - tensorflow - INFO - loss = 0.3753961, step = 102000 (2.299 sec) +2022-05-01 14:29:30,294 - tensorflow - INFO - global_step/sec: 87.017 +2022-05-01 14:29:30,294 - tensorflow - INFO - loss = 0.16868381, step = 102200 (2.298 sec) +2022-05-01 14:29:32,592 - tensorflow - INFO - global_step/sec: 87.0017 +2022-05-01 14:29:32,593 - tensorflow - INFO - loss = 8.778584, step = 102400 (2.299 sec) +2022-05-01 14:29:34,886 - tensorflow - INFO - global_step/sec: 87.2183 +2022-05-01 14:29:34,886 - tensorflow - INFO - loss = 0.090705186, step = 102600 (2.293 sec) +2022-05-01 14:29:37,177 - tensorflow - INFO - global_step/sec: 87.2853 +2022-05-01 14:29:37,177 - tensorflow - INFO - loss = 0.0820912, step = 102800 (2.291 sec) +2022-05-01 14:29:39,464 - tensorflow - INFO - global_step/sec: 87.4505 +2022-05-01 14:29:39,464 - tensorflow - INFO - loss = 0.6370168, step = 103000 (2.287 sec) +2022-05-01 14:29:41,745 - tensorflow - INFO - global_step/sec: 87.6902 +2022-05-01 14:29:41,745 - tensorflow - INFO - loss = 0.043927055, step = 103200 (2.281 sec) +2022-05-01 14:29:44,025 - tensorflow - INFO - global_step/sec: 87.704 +2022-05-01 14:29:44,026 - tensorflow - INFO - loss = 0.7123804, step = 103400 (2.280 sec) +2022-05-01 14:29:46,318 - tensorflow - INFO - global_step/sec: 87.2407 +2022-05-01 14:29:46,318 - tensorflow - INFO - loss = 0.06848099, step = 103600 (2.292 sec) +2022-05-01 14:29:48,611 - tensorflow - INFO - global_step/sec: 87.2065 +2022-05-01 14:29:48,611 - tensorflow - INFO - loss = 0.16801745, step = 103800 (2.293 sec) +2022-05-01 14:29:50,907 - tensorflow - INFO - global_step/sec: 87.0944 +2022-05-01 14:29:50,908 - tensorflow - INFO - loss = 0.18619172, step = 104000 (2.296 sec) +2022-05-01 14:29:53,209 - tensorflow - INFO - global_step/sec: 86.8939 +2022-05-01 14:29:53,209 - tensorflow - INFO - loss = 4.6792035, step = 104200 (2.302 sec) +2022-05-01 14:29:55,497 - tensorflow - INFO - global_step/sec: 87.3975 +2022-05-01 14:29:55,498 - tensorflow - INFO - loss = 5.7233157, step = 104400 (2.288 sec) +2022-05-01 14:29:57,797 - tensorflow - INFO - global_step/sec: 86.9713 +2022-05-01 14:29:57,797 - tensorflow - INFO - loss = 0.21849597, step = 104600 (2.300 sec) +2022-05-01 14:30:00,091 - tensorflow - INFO - global_step/sec: 87.165 +2022-05-01 14:30:00,092 - tensorflow - INFO - loss = 4.5911975, step = 104800 (2.294 sec) +2022-05-01 14:30:02,394 - tensorflow - INFO - global_step/sec: 86.8542 +2022-05-01 14:30:02,395 - tensorflow - INFO - loss = 10.481457, step = 105000 (2.303 sec) +2022-05-01 14:30:04,688 - tensorflow - INFO - global_step/sec: 87.1941 +2022-05-01 14:30:04,688 - tensorflow - INFO - loss = 0.26965386, step = 105200 (2.294 sec) +2022-05-01 14:30:07,005 - tensorflow - INFO - global_step/sec: 86.3277 +2022-05-01 14:30:07,005 - tensorflow - INFO - loss = 0.17812374, step = 105400 (2.317 sec) +2022-05-01 14:30:09,328 - tensorflow - INFO - global_step/sec: 86.0889 +2022-05-01 14:30:09,328 - tensorflow - INFO - loss = 0.38415286, step = 105600 (2.323 sec) +2022-05-01 14:30:11,632 - tensorflow - INFO - global_step/sec: 86.8154 +2022-05-01 14:30:11,632 - tensorflow - INFO - loss = 0.18962869, step = 105800 (2.304 sec) +2022-05-01 14:30:13,928 - tensorflow - INFO - global_step/sec: 87.0754 +2022-05-01 14:30:13,929 - tensorflow - INFO - loss = 0.104067475, step = 106000 (2.297 sec) +2022-05-01 14:30:16,219 - tensorflow - INFO - global_step/sec: 87.3113 +2022-05-01 14:30:16,220 - tensorflow - INFO - loss = 0.09176713, step = 106200 (2.291 sec) +2022-05-01 14:30:18,518 - tensorflow - INFO - global_step/sec: 86.9935 +2022-05-01 14:30:18,519 - tensorflow - INFO - loss = 0.08783853, step = 106400 (2.299 sec) +2022-05-01 14:30:20,805 - tensorflow - INFO - global_step/sec: 87.4484 +2022-05-01 14:30:20,806 - tensorflow - INFO - loss = 0.055572137, step = 106600 (2.287 sec) +2022-05-01 14:30:23,090 - tensorflow - INFO - global_step/sec: 87.549 +2022-05-01 14:30:23,090 - tensorflow - INFO - loss = 0.18945137, step = 106800 (2.284 sec) +2022-05-01 14:30:25,371 - tensorflow - INFO - global_step/sec: 87.6611 +2022-05-01 14:30:25,372 - tensorflow - INFO - loss = 0.1006712, step = 107000 (2.281 sec) +2022-05-01 14:30:27,661 - tensorflow - INFO - global_step/sec: 87.343 +2022-05-01 14:30:27,661 - tensorflow - INFO - loss = 0.41802037, step = 107200 (2.290 sec) +2022-05-01 14:30:29,968 - tensorflow - INFO - global_step/sec: 86.6983 +2022-05-01 14:30:29,968 - tensorflow - INFO - loss = 0.14229545, step = 107400 (2.307 sec) +2022-05-01 14:30:32,264 - tensorflow - INFO - global_step/sec: 87.1013 +2022-05-01 14:30:32,264 - tensorflow - INFO - loss = 0.79536784, step = 107600 (2.296 sec) +2022-05-01 14:30:34,556 - tensorflow - INFO - global_step/sec: 87.2567 +2022-05-01 14:30:34,557 - tensorflow - INFO - loss = 0.09977777, step = 107800 (2.292 sec) +2022-05-01 14:30:36,850 - tensorflow - INFO - global_step/sec: 87.1778 +2022-05-01 14:30:36,851 - tensorflow - INFO - loss = 0.26099685, step = 108000 (2.294 sec) +2022-05-01 14:30:39,144 - tensorflow - INFO - global_step/sec: 87.1819 +2022-05-01 14:30:39,145 - tensorflow - INFO - loss = 0.30809918, step = 108200 (2.294 sec) +2022-05-01 14:30:41,440 - tensorflow - INFO - global_step/sec: 87.1208 +2022-05-01 14:30:41,440 - tensorflow - INFO - loss = 0.22293222, step = 108400 (2.296 sec) +2022-05-01 14:30:43,730 - tensorflow - INFO - global_step/sec: 87.3257 +2022-05-01 14:30:43,731 - tensorflow - INFO - loss = 0.04886747, step = 108600 (2.290 sec) +2022-05-01 14:30:46,017 - tensorflow - INFO - global_step/sec: 87.4719 +2022-05-01 14:30:46,017 - tensorflow - INFO - loss = 0.23293927, step = 108800 (2.286 sec) +2022-05-01 14:30:48,304 - tensorflow - INFO - global_step/sec: 87.4261 +2022-05-01 14:30:48,305 - tensorflow - INFO - loss = 0.3128304, step = 109000 (2.288 sec) +2022-05-01 14:30:50,599 - tensorflow - INFO - global_step/sec: 87.162 +2022-05-01 14:30:50,599 - tensorflow - INFO - loss = 0.034860868, step = 109200 (2.295 sec) +2022-05-01 14:30:52,890 - tensorflow - INFO - global_step/sec: 87.2961 +2022-05-01 14:30:52,890 - tensorflow - INFO - loss = 0.2239242, step = 109400 (2.291 sec) +2022-05-01 14:30:55,186 - tensorflow - INFO - global_step/sec: 87.1116 +2022-05-01 14:30:55,186 - tensorflow - INFO - loss = 0.06228984, step = 109600 (2.296 sec) +2022-05-01 14:30:57,474 - tensorflow - INFO - global_step/sec: 87.4197 +2022-05-01 14:30:57,474 - tensorflow - INFO - loss = 0.19710249, step = 109800 (2.288 sec) +2022-05-01 14:30:59,766 - tensorflow - INFO - global_step/sec: 87.2504 +2022-05-01 14:30:59,766 - tensorflow - INFO - loss = 0.1518274, step = 110000 (2.292 sec) +2022-05-01 14:31:02,062 - tensorflow - INFO - global_step/sec: 87.0991 +2022-05-01 14:31:02,063 - tensorflow - INFO - loss = 0.13968316, step = 110200 (2.296 sec) +2022-05-01 14:31:04,365 - tensorflow - INFO - global_step/sec: 86.8329 +2022-05-01 14:31:04,366 - tensorflow - INFO - loss = 0.40964472, step = 110400 (2.303 sec) +2022-05-01 14:31:06,664 - tensorflow - INFO - global_step/sec: 87.0225 +2022-05-01 14:31:06,664 - tensorflow - INFO - loss = 1.5274282, step = 110600 (2.298 sec) +2022-05-01 14:31:08,955 - tensorflow - INFO - global_step/sec: 87.2741 +2022-05-01 14:31:08,956 - tensorflow - INFO - loss = 0.053806704, step = 110800 (2.292 sec) +2022-05-01 14:31:11,241 - tensorflow - INFO - global_step/sec: 87.4924 +2022-05-01 14:31:11,242 - tensorflow - INFO - loss = 0.24567077, step = 111000 (2.286 sec) +2022-05-01 14:31:13,523 - tensorflow - INFO - global_step/sec: 87.6389 +2022-05-01 14:31:13,524 - tensorflow - INFO - loss = 0.09340665, step = 111200 (2.282 sec) +2022-05-01 14:31:15,793 - tensorflow - INFO - global_step/sec: 88.1058 +2022-05-01 14:31:15,794 - tensorflow - INFO - loss = 0.12997474, step = 111400 (2.270 sec) +2022-05-01 14:31:18,048 - tensorflow - INFO - global_step/sec: 88.6936 +2022-05-01 14:31:18,049 - tensorflow - INFO - loss = 16.015934, step = 111600 (2.255 sec) +2022-05-01 14:31:20,339 - tensorflow - INFO - global_step/sec: 87.3116 +2022-05-01 14:31:20,339 - tensorflow - INFO - loss = 0.08188627, step = 111800 (2.291 sec) +2022-05-01 14:31:22,632 - tensorflow - INFO - global_step/sec: 87.2175 +2022-05-01 14:31:22,632 - tensorflow - INFO - loss = 0.093653485, step = 112000 (2.293 sec) +2022-05-01 14:31:24,933 - tensorflow - INFO - global_step/sec: 86.9288 +2022-05-01 14:31:24,933 - tensorflow - INFO - loss = 0.12005502, step = 112200 (2.301 sec) +2022-05-01 14:31:27,231 - tensorflow - INFO - global_step/sec: 87.0358 +2022-05-01 14:31:27,231 - tensorflow - INFO - loss = 0.051553614, step = 112400 (2.298 sec) +2022-05-01 14:31:29,530 - tensorflow - INFO - global_step/sec: 86.9869 +2022-05-01 14:31:29,530 - tensorflow - INFO - loss = 0.05601956, step = 112600 (2.299 sec) +2022-05-01 14:31:31,825 - tensorflow - INFO - global_step/sec: 87.1502 +2022-05-01 14:31:31,825 - tensorflow - INFO - loss = 0.21400702, step = 112800 (2.295 sec) +2022-05-01 14:31:34,125 - tensorflow - INFO - global_step/sec: 86.956 +2022-05-01 14:31:34,125 - tensorflow - INFO - loss = 0.80321956, step = 113000 (2.300 sec) +2022-05-01 14:31:36,417 - tensorflow - INFO - global_step/sec: 87.2408 +2022-05-01 14:31:36,418 - tensorflow - INFO - loss = 0.042050008, step = 113200 (2.292 sec) +2022-05-01 14:31:38,712 - tensorflow - INFO - global_step/sec: 87.1417 +2022-05-01 14:31:38,713 - tensorflow - INFO - loss = 0.083693594, step = 113400 (2.295 sec) +2022-05-01 14:31:41,001 - tensorflow - INFO - global_step/sec: 87.3917 +2022-05-01 14:31:41,001 - tensorflow - INFO - loss = 0.20052454, step = 113600 (2.289 sec) +2022-05-01 14:31:43,290 - tensorflow - INFO - global_step/sec: 87.3738 +2022-05-01 14:31:43,290 - tensorflow - INFO - loss = 0.17425779, step = 113800 (2.289 sec) +2022-05-01 14:31:45,585 - tensorflow - INFO - global_step/sec: 87.1585 +2022-05-01 14:31:45,585 - tensorflow - INFO - loss = 3.2166889, step = 114000 (2.295 sec) +2022-05-01 14:31:47,877 - tensorflow - INFO - global_step/sec: 87.233 +2022-05-01 14:31:47,878 - tensorflow - INFO - loss = 0.1635446, step = 114200 (2.293 sec) +2022-05-01 14:31:50,161 - tensorflow - INFO - global_step/sec: 87.5935 +2022-05-01 14:31:50,161 - tensorflow - INFO - loss = 0.5026628, step = 114400 (2.283 sec) +2022-05-01 14:31:52,442 - tensorflow - INFO - global_step/sec: 87.6832 +2022-05-01 14:31:52,442 - tensorflow - INFO - loss = 0.22293578, step = 114600 (2.281 sec) +2022-05-01 14:31:54,729 - tensorflow - INFO - global_step/sec: 87.4384 +2022-05-01 14:31:54,729 - tensorflow - INFO - loss = 0.13767406, step = 114800 (2.287 sec) +2022-05-01 14:31:57,027 - tensorflow - INFO - global_step/sec: 87.0453 +2022-05-01 14:31:57,027 - tensorflow - INFO - loss = 0.0825976, step = 115000 (2.298 sec) +2022-05-01 14:31:59,325 - tensorflow - INFO - global_step/sec: 86.9989 +2022-05-01 14:31:59,326 - tensorflow - INFO - loss = 0.50420576, step = 115200 (2.299 sec) +2022-05-01 14:32:01,606 - tensorflow - INFO - global_step/sec: 87.701 +2022-05-01 14:32:01,606 - tensorflow - INFO - loss = 0.1623515, step = 115400 (2.281 sec) +2022-05-01 14:32:03,895 - tensorflow - INFO - global_step/sec: 87.3523 +2022-05-01 14:32:03,896 - tensorflow - INFO - loss = 0.05452565, step = 115600 (2.290 sec) +2022-05-01 14:32:06,181 - tensorflow - INFO - global_step/sec: 87.512 +2022-05-01 14:32:06,181 - tensorflow - INFO - loss = 0.109019935, step = 115800 (2.285 sec) +2022-05-01 14:32:08,474 - tensorflow - INFO - global_step/sec: 87.1993 +2022-05-01 14:32:08,475 - tensorflow - INFO - loss = 0.2902754, step = 116000 (2.294 sec) +2022-05-01 14:32:10,763 - tensorflow - INFO - global_step/sec: 87.4093 +2022-05-01 14:32:10,763 - tensorflow - INFO - loss = 0.19051288, step = 116200 (2.288 sec) +2022-05-01 14:32:13,051 - tensorflow - INFO - global_step/sec: 87.4118 +2022-05-01 14:32:13,051 - tensorflow - INFO - loss = 0.09656765, step = 116400 (2.288 sec) +2022-05-01 14:32:15,346 - tensorflow - INFO - global_step/sec: 87.1497 +2022-05-01 14:32:15,346 - tensorflow - INFO - loss = 0.615744, step = 116600 (2.295 sec) +2022-05-01 14:32:17,632 - tensorflow - INFO - global_step/sec: 87.4631 +2022-05-01 14:32:17,633 - tensorflow - INFO - loss = 0.22290188, step = 116800 (2.287 sec) +2022-05-01 14:32:19,921 - tensorflow - INFO - global_step/sec: 87.3943 +2022-05-01 14:32:19,921 - tensorflow - INFO - loss = 0.41463685, step = 117000 (2.288 sec) +2022-05-01 14:32:22,203 - tensorflow - INFO - global_step/sec: 87.615 +2022-05-01 14:32:22,204 - tensorflow - INFO - loss = 0.44903523, step = 117200 (2.283 sec) +2022-05-01 14:32:24,496 - tensorflow - INFO - global_step/sec: 87.2181 +2022-05-01 14:32:24,497 - tensorflow - INFO - loss = 0.12465903, step = 117400 (2.293 sec) +2022-05-01 14:32:26,786 - tensorflow - INFO - global_step/sec: 87.3363 +2022-05-01 14:32:26,787 - tensorflow - INFO - loss = 0.07329932, step = 117600 (2.290 sec) +2022-05-01 14:32:29,081 - tensorflow - INFO - global_step/sec: 87.1561 +2022-05-01 14:32:29,082 - tensorflow - INFO - loss = 0.27690414, step = 117800 (2.295 sec) +2022-05-01 14:32:31,377 - tensorflow - INFO - global_step/sec: 87.0971 +2022-05-01 14:32:31,378 - tensorflow - INFO - loss = 0.119977236, step = 118000 (2.296 sec) +2022-05-01 14:32:33,669 - tensorflow - INFO - global_step/sec: 87.288 +2022-05-01 14:32:33,669 - tensorflow - INFO - loss = 0.16365752, step = 118200 (2.291 sec) +2022-05-01 14:32:35,962 - tensorflow - INFO - global_step/sec: 87.2152 +2022-05-01 14:32:35,962 - tensorflow - INFO - loss = 0.052812696, step = 118400 (2.293 sec) +2022-05-01 14:32:38,245 - tensorflow - INFO - global_step/sec: 87.601 +2022-05-01 14:32:38,245 - tensorflow - INFO - loss = 0.27458555, step = 118600 (2.283 sec) +2022-05-01 14:32:40,540 - tensorflow - INFO - global_step/sec: 87.153 +2022-05-01 14:32:40,540 - tensorflow - INFO - loss = 0.25292706, step = 118800 (2.295 sec) +2022-05-01 14:32:42,840 - tensorflow - INFO - global_step/sec: 86.9475 +2022-05-01 14:32:42,840 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 87.7259 +2022-05-01 14:45:09,895 - tensorflow - INFO - loss = 0.027574524, step = 184200 (2.280 sec) +2022-05-01 14:45:12,164 - tensorflow - INFO - global_step/sec: 88.1344 +2022-05-01 14:45:12,164 - tensorflow - INFO - loss = 0.14222334, step = 184400 (2.269 sec) +2022-05-01 14:45:14,420 - tensorflow - INFO - global_step/sec: 88.6393 +2022-05-01 14:45:14,420 - tensorflow - INFO - loss = 0.17393786, step = 184600 (2.256 sec) +2022-05-01 14:45:16,675 - tensorflow - INFO - global_step/sec: 88.6945 +2022-05-01 14:45:16,675 - tensorflow - INFO - loss = 0.06557072, step = 184800 (2.255 sec) +2022-05-01 14:45:18,953 - tensorflow - INFO - global_step/sec: 87.8073 +2022-05-01 14:45:18,953 - tensorflow - INFO - loss = 0.028205689, step = 185000 (2.278 sec) +2022-05-01 14:45:21,244 - tensorflow - INFO - global_step/sec: 87.2987 +2022-05-01 14:45:21,244 - tensorflow - INFO - loss = 0.041646503, step = 185200 (2.291 sec) +2022-05-01 14:45:23,530 - tensorflow - INFO - global_step/sec: 87.4761 +2022-05-01 14:45:23,530 - tensorflow - INFO - loss = 0.093024604, step = 185400 (2.286 sec) +2022-05-01 14:45:25,818 - tensorflow - INFO - global_step/sec: 87.4176 +2022-05-01 14:45:25,818 - tensorflow - INFO - loss = 0.11461769, step = 185600 (2.288 sec) +2022-05-01 14:45:28,110 - tensorflow - INFO - global_step/sec: 87.2538 +2022-05-01 14:45:28,110 - tensorflow - INFO - loss = 2.0448453, step = 185800 (2.292 sec) +2022-05-01 14:45:30,403 - tensorflow - INFO - global_step/sec: 87.2398 +2022-05-01 14:45:30,403 - tensorflow - INFO - loss = 0.15032089, step = 186000 (2.292 sec) +2022-05-01 14:45:32,694 - tensorflow - INFO - global_step/sec: 87.2979 +2022-05-01 14:45:32,694 - tensorflow - INFO - loss = 0.17604691, step = 186200 (2.291 sec) +2022-05-01 14:45:34,986 - tensorflow - INFO - global_step/sec: 87.2264 +2022-05-01 14:45:34,987 - tensorflow - INFO - loss = 0.044206016, step = 186400 (2.293 sec) +2022-05-01 14:45:37,277 - tensorflow - INFO - global_step/sec: 87.3331 +2022-05-01 14:45:37,277 - tensorflow - INFO - loss = 0.17563851, step = 186600 (2.290 sec) +2022-05-01 14:45:39,564 - tensorflow - INFO - global_step/sec: 87.4231 +2022-05-01 14:45:39,565 - tensorflow - INFO - loss = 0.14848632, step = 186800 (2.288 sec) +2022-05-01 14:45:41,852 - tensorflow - INFO - global_step/sec: 87.4146 +2022-05-01 14:45:41,853 - tensorflow - INFO - loss = 0.09012383, step = 187000 (2.288 sec) +2022-05-01 14:45:44,140 - tensorflow - INFO - global_step/sec: 87.4321 +2022-05-01 14:45:44,140 - tensorflow - INFO - loss = 0.18280177, step = 187200 (2.287 sec) +2022-05-01 14:45:46,423 - tensorflow - INFO - global_step/sec: 87.592 +2022-05-01 14:45:46,423 - tensorflow - INFO - loss = 0.048017383, step = 187400 (2.283 sec) +2022-05-01 14:45:48,722 - tensorflow - INFO - global_step/sec: 86.9997 +2022-05-01 14:45:48,722 - tensorflow - INFO - loss = 11.872378, step = 187600 (2.299 sec) +2022-05-01 14:45:51,011 - tensorflow - INFO - global_step/sec: 87.3555 +2022-05-01 14:45:51,012 - tensorflow - INFO - loss = 0.07027066, step = 187800 (2.289 sec) +2022-05-01 14:45:53,298 - tensorflow - INFO - global_step/sec: 87.4623 +2022-05-01 14:45:53,298 - tensorflow - INFO - loss = 0.16031665, step = 188000 (2.287 sec) +2022-05-01 14:45:55,589 - tensorflow - INFO - global_step/sec: 87.3037 +2022-05-01 14:45:55,589 - tensorflow - INFO - loss = 0.14436464, step = 188200 (2.291 sec) +2022-05-01 14:45:57,879 - tensorflow - INFO - global_step/sec: 87.3348 +2022-05-01 14:45:57,879 - tensorflow - INFO - loss = 0.34885636, step = 188400 (2.290 sec) +2022-05-01 14:46:00,171 - tensorflow - INFO - global_step/sec: 87.2641 +2022-05-01 14:46:00,171 - tensorflow - INFO - loss = 0.040362917, step = 188600 (2.292 sec) +2022-05-01 14:46:02,463 - tensorflow - INFO - global_step/sec: 87.2572 +2022-05-01 14:46:02,463 - tensorflow - INFO - loss = 0.04270378, step = 188800 (2.292 sec) +2022-05-01 14:46:04,750 - tensorflow - INFO - global_step/sec: 87.4552 +2022-05-01 14:46:04,750 - tensorflow - INFO - loss = 0.0711705, step = 189000 (2.287 sec) +2022-05-01 14:46:07,040 - tensorflow - INFO - global_step/sec: 87.3171 +2022-05-01 14:46:07,041 - tensorflow - INFO - loss = 0.1641151, step = 189200 (2.291 sec) +2022-05-01 14:46:09,329 - tensorflow - INFO - global_step/sec: 87.3957 +2022-05-01 14:46:09,329 - tensorflow - INFO - loss = 0.18184356, step = 189400 (2.288 sec) +2022-05-01 14:46:11,615 - tensorflow - INFO - global_step/sec: 87.4962 +2022-05-01 14:46:11,615 - tensorflow - INFO - loss = 0.17193224, step = 189600 (2.286 sec) +2022-05-01 14:46:13,914 - tensorflow - INFO - global_step/sec: 86.995 +2022-05-01 14:46:13,914 - tensorflow - INFO - loss = 0.19972838, step = 189800 (2.299 sec) +2022-05-01 14:46:16,204 - tensorflow - INFO - global_step/sec: 87.3129 +2022-05-01 14:46:16,205 - tensorflow - INFO - loss = 0.8434968, step = 190000 (2.291 sec) +2022-05-01 14:46:18,488 - tensorflow - INFO - global_step/sec: 87.5691 +2022-05-01 14:46:18,488 - tensorflow - INFO - loss = 0.1671878, step = 190200 (2.284 sec) +2022-05-01 14:46:20,777 - tensorflow - INFO - global_step/sec: 87.3945 +2022-05-01 14:46:20,777 - tensorflow - INFO - loss = 0.060731374, step = 190400 (2.288 sec) +2022-05-01 14:46:23,070 - tensorflow - INFO - global_step/sec: 87.2185 +2022-05-01 14:46:23,070 - tensorflow - INFO - loss = 0.08488855, step = 190600 (2.293 sec) +2022-05-01 14:46:25,367 - tensorflow - INFO - global_step/sec: 87.0444 +2022-05-01 14:46:25,368 - tensorflow - INFO - loss = 0.05223095, step = 190800 (2.298 sec) +2022-05-01 14:46:27,663 - tensorflow - INFO - global_step/sec: 87.1166 +2022-05-01 14:46:27,664 - tensorflow - INFO - loss = 0.067917466, step = 191000 (2.296 sec) +2022-05-01 14:46:29,958 - tensorflow - INFO - global_step/sec: 87.1681 +2022-05-01 14:46:29,958 - tensorflow - INFO - loss = 0.27582443, step = 191200 (2.294 sec) +2022-05-01 14:46:32,256 - tensorflow - INFO - global_step/sec: 87.0233 +2022-05-01 14:46:32,256 - tensorflow - INFO - loss = 0.42234778, step = 191400 (2.298 sec) +2022-05-01 14:46:34,547 - tensorflow - INFO - global_step/sec: 87.27 +2022-05-01 14:46:34,548 - tensorflow - INFO - loss = 0.10319117, step = 191600 (2.292 sec) +2022-05-01 14:46:36,838 - tensorflow - INFO - global_step/sec: 87.2976 +2022-05-01 14:46:36,839 - tensorflow - INFO - loss = 0.09261024, step = 191800 (2.291 sec) +2022-05-01 14:46:39,131 - tensorflow - INFO - global_step/sec: 87.2527 +2022-05-01 14:46:39,131 - tensorflow - INFO - loss = 0.040254287, step = 192000 (2.292 sec) +2022-05-01 14:46:41,416 - tensorflow - INFO - global_step/sec: 87.4974 +2022-05-01 14:46:41,417 - tensorflow - INFO - loss = 0.35942328, step = 192200 (2.286 sec) +2022-05-01 14:46:43,704 - tensorflow - INFO - global_step/sec: 87.4408 +2022-05-01 14:46:43,704 - tensorflow - INFO - loss = 0.08916014, step = 192400 (2.287 sec) +2022-05-01 14:46:45,988 - tensorflow - INFO - global_step/sec: 87.5359 +2022-05-01 14:46:45,989 - tensorflow - INFO - loss = 0.13586244, step = 192600 (2.285 sec) +2022-05-01 14:46:48,267 - tensorflow - INFO - global_step/sec: 87.7855 +2022-05-01 14:46:48,267 - tensorflow - INFO - loss = 0.09050533, step = 192800 (2.278 sec) +2022-05-01 14:46:50,529 - tensorflow - INFO - global_step/sec: 88.391 +2022-05-01 14:46:50,530 - tensorflow - INFO - loss = 0.13040312, step = 193000 (2.263 sec) +2022-05-01 14:46:52,829 - tensorflow - INFO - global_step/sec: 86.9863 +2022-05-01 14:46:52,829 - tensorflow - INFO - loss = 0.094426826, step = 193200 (2.299 sec) +2022-05-01 14:46:55,124 - tensorflow - INFO - global_step/sec: 87.1413 +2022-05-01 14:46:55,124 - tensorflow - INFO - loss = 0.102052435, step = 193400 (2.295 sec) +2022-05-01 14:46:57,425 - tensorflow - INFO - global_step/sec: 86.903 +2022-05-01 14:46:57,426 - tensorflow - INFO - loss = 0.05099783, step = 193600 (2.301 sec) +2022-05-01 14:46:59,725 - tensorflow - INFO - global_step/sec: 86.9525 +2022-05-01 14:46:59,726 - tensorflow - INFO - loss = 0.1822123, step = 193800 (2.300 sec) +2022-05-01 14:47:02,026 - tensorflow - INFO - global_step/sec: 86.9489 +2022-05-01 14:47:02,026 - tensorflow - INFO - loss = 0.059502125, step = 194000 (2.300 sec) +2022-05-01 14:47:04,322 - tensorflow - INFO - global_step/sec: 87.0813 +2022-05-01 14:47:04,323 - tensorflow - INFO - loss = 0.12372404, step = 194200 (2.297 sec) +2022-05-01 14:47:06,624 - tensorflow - INFO - global_step/sec: 86.9004 +2022-05-01 14:47:06,624 - tensorflow - INFO - loss = 4.9969735, step = 194400 (2.302 sec) +2022-05-01 14:47:08,917 - tensorflow - INFO - global_step/sec: 87.2197 +2022-05-01 14:47:08,917 - tensorflow - INFO - loss = 0.46680868, step = 194600 (2.293 sec) +2022-05-01 14:47:11,216 - tensorflow - INFO - global_step/sec: 86.9929 +2022-05-01 14:47:11,216 - tensorflow - INFO - loss = 0.21966107, step = 194800 (2.299 sec) +2022-05-01 14:47:13,512 - tensorflow - INFO - global_step/sec: 87.0903 +2022-05-01 14:47:13,513 - tensorflow - INFO - loss = 1.4392605, step = 195000 (2.296 sec) +2022-05-01 14:47:15,796 - tensorflow - INFO - global_step/sec: 87.5799 +2022-05-01 14:47:15,796 - tensorflow - INFO - loss = 2.9109988, step = 195200 (2.284 sec) +2022-05-01 14:47:18,088 - tensorflow - INFO - global_step/sec: 87.2639 +2022-05-01 14:47:18,088 - tensorflow - INFO - loss = 0.06750238, step = 195400 (2.292 sec) +2022-05-01 14:47:20,377 - tensorflow - INFO - global_step/sec: 87.3571 +2022-05-01 14:47:20,378 - tensorflow - INFO - loss = 0.13745224, step = 195600 (2.289 sec) +2022-05-01 14:47:22,658 - tensorflow - INFO - global_step/sec: 87.6826 +2022-05-01 14:47:22,659 - tensorflow - INFO - loss = 0.22367324, step = 195800 (2.281 sec) +2022-05-01 14:47:24,952 - tensorflow - INFO - global_step/sec: 87.1776 +2022-05-01 14:47:24,953 - tensorflow - INFO - loss = 0.29652923, step = 196000 (2.294 sec) +2022-05-01 14:47:27,238 - tensorflow - INFO - global_step/sec: 87.5058 +2022-05-01 14:47:27,238 - tensorflow - INFO - loss = 0.0928004, step = 196200 (2.286 sec) +2022-05-01 14:47:29,522 - tensorflow - INFO - global_step/sec: 87.5486 +2022-05-01 14:47:29,523 - tensorflow - INFO - loss = 7.4939165, step = 196400 (2.284 sec) +2022-05-01 14:47:31,823 - tensorflow - INFO - global_step/sec: 86.9363 +2022-05-01 14:47:31,823 - tensorflow - INFO - loss = 0.436599, step = 196600 (2.301 sec) +2022-05-01 14:47:34,107 - tensorflow - INFO - global_step/sec: 87.5778 +2022-05-01 14:47:34,107 - tensorflow - INFO - loss = 0.024931233, step = 196800 (2.284 sec) +2022-05-01 14:47:36,397 - tensorflow - INFO - global_step/sec: 87.3185 +2022-05-01 14:47:36,398 - tensorflow - INFO - loss = 0.07194897, step = 197000 (2.291 sec) +2022-05-01 14:47:38,684 - tensorflow - INFO - global_step/sec: 87.4662 +2022-05-01 14:47:38,684 - tensorflow - INFO - loss = 23.8871, step = 197200 (2.287 sec) +2022-05-01 14:47:40,978 - tensorflow - INFO - global_step/sec: 87.1584 +2022-05-01 14:47:40,979 - tensorflow - INFO - loss = 0.10765213, step = 197400 (2.295 sec) +2022-05-01 14:47:43,279 - tensorflow - INFO - global_step/sec: 86.9478 +2022-05-01 14:47:43,279 - tensorflow - INFO - loss = 0.07624713, step = 197600 (2.300 sec) +2022-05-01 14:47:45,570 - tensorflow - INFO - global_step/sec: 87.2936 +2022-05-01 14:47:45,570 - tensorflow - INFO - loss = 9.676068, step = 197800 (2.291 sec) +2022-05-01 14:47:47,857 - tensorflow - INFO - global_step/sec: 87.4508 +2022-05-01 14:47:47,857 - tensorflow - INFO - loss = 0.14174747, step = 198000 (2.287 sec) +2022-05-01 14:47:50,144 - tensorflow - INFO - global_step/sec: 87.4219 +2022-05-01 14:47:50,145 - tensorflow - INFO - loss = 0.090489924, step = 198200 (2.288 sec) +2022-05-01 14:47:52,427 - tensorflow - INFO - global_step/sec: 87.6189 +2022-05-01 14:47:52,427 - tensorflow - INFO - loss = 0.074647754, step = 198400 (2.283 sec) +2022-05-01 14:47:54,723 - tensorflow - INFO - global_step/sec: 87.1252 +2022-05-01 14:47:54,723 - tensorflow - INFO - loss = 0.0646258, step = 198600 (2.296 sec) +2022-05-01 14:47:57,018 - tensorflow - INFO - global_step/sec: 87.1412 +2022-05-01 14:47:57,018 - tensorflow - INFO - loss = 0.46771583, step = 198800 (2.295 sec) +2022-05-01 14:47:59,300 - tensorflow - INFO - global_step/sec: 87.6377 +2022-05-01 14:47:59,300 - tensorflow - INFO - loss = 0.3469265, step = 199000 (2.282 sec) +2022-05-01 14:48:01,586 - tensorflow - INFO - global_step/sec: 87.4839 +2022-05-01 14:48:01,586 - tensorflow - INFO - loss = 1.0086424, step = 199200 (2.286 sec) +2022-05-01 14:48:03,872 - tensorflow - INFO - global_step/sec: 87.4938 +2022-05-01 14:48:03,872 - tensorflow - INFO - loss = 1.1862116, step = 199400 (2.286 sec) +2022-05-01 14:48:06,165 - tensorflow - INFO - global_step/sec: 87.2047 +2022-05-01 14:48:06,166 - tensorflow - INFO - loss = 0.15060207, step = 199600 (2.293 sec) +2022-05-01 14:48:08,463 - tensorflow - INFO - global_step/sec: 87.0599 +2022-05-01 14:48:08,463 - tensorflow - INFO - loss = 0.055810157, step = 199800 (2.297 sec) +2022-05-01 14:48:10,743 - tensorflow - INFO - Saving checkpoints for 200000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. +2022-05-01 14:48:10,758 - tensorflow - INFO - model.ckpt-200000 is not in all_model_checkpoint_paths. Manually adding it. +2022-05-01 14:48:11,312 - tensorflow - INFO - Calling model_fn. +2022-05-01 14:48:11,443 - tensorflow - INFO - Done calling model_fn. +2022-05-01 14:48:11,458 - tensorflow - INFO - Starting evaluation at 2022-05-01T14:48:11Z +2022-05-01 14:48:11,523 - tensorflow - INFO - Graph was finalized. +2022-05-01 14:48:11,526 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-200000 +2022-05-01 14:48:11,811 - tensorflow - INFO - Running local_init_op. +2022-05-01 14:48:11,921 - tensorflow - INFO - Done running local_init_op. +2022-05-01 14:48:22,202 - tensorflow - INFO - Evaluation [3990/39900] +2022-05-01 14:48:31,752 - tensorflow - INFO - Evaluation [7980/39900] +2022-05-01 14:48:41,342 - tensorflow - INFO - Evaluation [11970/39900] +2022-05-01 14:48:50,922 - tensorflow - INFO - Evaluation [15960/39900] +2022-05-01 14:49:00,471 - tensorflow - INFO - Evaluation [19950/39900] +2022-05-01 14:49:10,047 - tensorflow - INFO - Evaluation [23940/39900] +2022-05-01 14:49:19,631 - tensorflow - INFO - Evaluation [27930/39900] +2022-05-01 14:49:29,222 - tensorflow - INFO - Evaluation [31920/39900] +2022-05-01 14:49:38,795 - tensorflow - INFO - Evaluation [35910/39900] +2022-05-01 14:49:48,375 - tensorflow - INFO - Evaluation [39900/39900] +2022-05-01 14:49:48,512 - tensorflow - INFO - Finished evaluation at 2022-05-01-14:49:48 +2022-05-01 14:49:48,512 - tensorflow - INFO - Saving dict for global step 200000: global_step = 200000, loss = 0.100737154, mean_degree_err = 10.012244 +2022-05-01 14:49:48,513 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 200000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-200000 +2022-05-01 14:49:48,526 - tensorflow - INFO - global_step/sec: 1.99873 +2022-05-01 14:49:48,527 - tensorflow - INFO - loss = 0.12662719, step = 200000 (100.064 sec) +2022-05-01 14:49:50,832 - tensorflow - INFO - global_step/sec: 86.7276 +2022-05-01 14:49:50,833 - tensorflow - INFO - loss = 0.8001151, step = 200200 (2.306 sec) +2022-05-01 14:49:53,113 - tensorflow - INFO - global_step/sec: 87.6802 +2022-05-01 14:49:53,114 - tensorflow - INFO - loss = 0.045994636, step = 200400 (2.281 sec) +2022-05-01 14:49:55,390 - tensorflow - INFO - global_step/sec: 87.8451 +2022-05-01 14:49:55,391 - tensorflow - INFO - loss = 4.106056, step = 200600 (2.277 sec) +2022-05-01 14:49:57,661 - tensorflow - INFO - global_step/sec: 88.0574 +2022-05-01 14:49:57,662 - tensorflow - INFO - loss = 0.4528215, step = 200800 (2.271 sec) +2022-05-01 14:49:59,923 - tensorflow - INFO - global_step/sec: 88.4209 +2022-05-01 14:49:59,924 - tensorflow - INFO - loss = 0.07186222, step = 201000 (2.262 sec) +2022-05-01 14:50:02,210 - tensorflow - INFO - global_step/sec: 87.4507 +2022-05-01 14:50:02,211 - tensorflow - INFO - loss = 0.20265803, step = 201200 (2.287 sec) +2022-05-01 14:50:04,500 - tensorflow - INFO - global_step/sec: 87.3595 +2022-05-01 14:50:04,500 - tensorflow - INFO - loss = 0.03260638, step = 201400 (2.289 sec) +2022-05-01 14:50:06,787 - tensorflow - INFO - global_step/sec: 87.4457 +2022-05-01 14:50:06,787 - tensorflow - INFO - loss = 0.11148474, step = 201600 (2.287 sec) +2022-05-01 14:50:09,072 - tensorflow - INFO - global_step/sec: 87.5177 +2022-05-01 14:50:09,072 - tensorflow - INFO - loss = 1.6456022, step = 201800 (2.285 sec) +2022-05-01 14:50:11,361 - tensorflow - INFO - global_step/sec: 87.3907 +2022-05-01 14:50:11,361 - tensorflow - INFO - loss = 0.17919509, step = 202000 (2.289 sec) +2022-05-01 14:50:13,661 - tensorflow - INFO - global_step/sec: 86.9405 +2022-05-01 14:50:13,662 - tensorflow - INFO - loss = 0.09502393, step = 202200 (2.300 sec) +2022-05-01 14:50:15,963 - tensorflow - INFO - global_step/sec: 86.8866 +2022-05-01 14:50:15,963 - tensorflow - INFO - loss = 0.05594068, step = 202400 (2.302 sec) +2022-05-01 14:50:18,261 - tensorflow - INFO - global_step/sec: 87.0316 +2022-05-01 14:50:18,261 - tensorflow - INFO - loss = 0.09115332, step = 202600 (2.298 sec) +2022-05-01 14:50:20,561 - tensorflow - INFO - global_step/sec: 86.9407 +2022-05-01 14:50:20,562 - tensorflow - INFO - loss = 10.594648, step = 202800 (2.300 sec) +2022-05-01 14:50:22,854 - tensorflow - INFO - global_step/sec: 87.2173 +2022-05-01 14:50:22,855 - tensorflow - INFO - loss = 0.09624572, step = 203000 (2.293 sec) +2022-05-01 14:50:25,156 - tensorflow - INFO - global_step/sec: 86.8796 +2022-05-01 14:50:25,157 - tensorflow - INFO - loss = 0.03879582, step = 203200 (2.302 sec) +2022-05-01 14:50:27,458 - tensorflow - INFO - global_step/sec: 86.9109 +2022-05-01 14:50:27,458 - tensorflow - INFO - loss = 1.6161735, step = 203400 (2.301 sec) +2022-05-01 14:50:29,753 - tensorflow - INFO - global_step/sec: 87.1401 +2022-05-01 14:50:29,753 - tensorflow - INFO - loss = 0.12163468, step = 203600 (2.295 sec) +2022-05-01 14:50:32,061 - tensorflow - INFO - global_step/sec: 86.646 +2022-05-01 14:50:32,062 - tensorflow - INFO - loss = 11.411995, step = 203800 (2.308 sec) +2022-05-01 14:50:34,362 - tensorflow - INFO - global_step/sec: 86.9162 +2022-05-01 14:50:34,363 - tensorflow - INFO - loss = 0.72658515, step = 204000 (2.301 sec) +2022-05-01 14:50:36,659 - tensorflow - INFO - global_step/sec: 87.0901 +2022-05-01 14:50:36,659 - tensorflow - INFO - loss = 0.3619779, step = 204200 (2.296 sec) +2022-05-01 14:50:38,961 - tensorflow - INFO - global_step/sec: 86.8832 +2022-05-01 14:50:38,961 - tensorflow - INFO - loss = 5.773318, step = 204400 (2.302 sec) +2022-05-01 14:50:41,262 - tensorflow - INFO - global_step/sec: 86.9034 +2022-05-01 14:50:41,262 - tensorflow - INFO - loss = 0.10278251, step = 204600 (2.301 sec) +2022-05-01 14:50:43,554 - tensorflow - INFO - global_step/sec: 87.2608 +2022-05-01 14:50:43,554 - tensorflow - INFO - loss = 0.21095583, step = 204800 (2.292 sec) +2022-05-01 14:50:45,840 - tensorflow - INFO - global_step/sec: 87.4786 +2022-05-01 14:50:45,841 - tensorflow - INFO - loss = 0.046599954, step = 205000 (2.286 sec) +2022-05-01 14:50:48,127 - tensorflow - INFO - global_step/sec: 87.466 +2022-05-01 14:50:48,127 - tensorflow - INFO - loss = 0.34111094, step = 205200 (2.287 sec) +2022-05-01 14:50:50,406 - tensorflow - INFO - global_step/sec: 87.7639 +2022-05-01 14:50:50,406 - tensorflow - INFO - loss = 0.14029244, step = 205400 (2.279 sec) +2022-05-01 14:50:52,706 - tensorflow - INFO - global_step/sec: 86.946 +2022-05-01 14:50:52,706 - tensorflow - INFO - loss = 0.115294844, step = 205600 (2.300 sec) +2022-05-01 14:50:54,996 - tensorflow - INFO - global_step/sec: 87.3392 +2022-05-01 14:50:54,996 - tensorflow - INFO - loss = 1.6688011, step = 205800 (2.290 sec) +2022-05-01 14:50:57,284 - tensorflow - INFO - global_step/sec: 87.426 +2022-05-01 14:50:57,284 - tensorflow - INFO - loss = 1.0651115, step = 206000 (2.288 sec) +2022-05-01 14:50:59,574 - tensorflow - INFO - global_step/sec: 87.3021 +2022-05-01 14:50:59,575 - tensorflow - INFO - loss = 0.05911135, step = 206200 (2.291 sec) +2022-05-01 14:51:01,863 - tensorflow - INFO - global_step/sec: 87.3795 +2022-05-01 14:51:01,864 - tensorflow - INFO - loss = 3.0845604, step = 206400 (2.289 sec) +2022-05-01 14:51:04,155 - tensorflow - INFO - global_step/sec: 87.2606 +2022-05-01 14:51:04,156 - tensorflow - INFO - loss = 1.0244889, step = 206600 (2.292 sec) +2022-05-01 14:51:06,450 - tensorflow - INFO - global_step/sec: 87.1581 +2022-05-01 14:51:06,450 - tensorflow - INFO - loss = 0.06411409, step = 206800 (2.295 sec) +2022-05-01 14:51:08,742 - tensorflow - INFO - global_step/sec: 87.2733 +2022-05-01 14:51:08,742 - tensorflow - INFO - loss = 0.093899466, step = 207000 (2.292 sec) +2022-05-01 14:51:11,032 - tensorflow - INFO - global_step/sec: 87.3387 +2022-05-01 14:51:11,032 - tensorflow - INFO - loss = 0.057339266, step = 207200 (2.290 sec) +2022-05-01 14:51:13,323 - tensorflow - INFO - global_step/sec: 87.2977 +2022-05-01 14:51:13,323 - tensorflow - INFO - loss = 0.079025224, step = 207400 (2.291 sec) +2022-05-01 14:51:15,614 - tensorflow - INFO - global_step/sec: 87.2702 +2022-05-01 14:51:15,615 - tensorflow - INFO - loss = 0.08560538, step = 207600 (2.292 sec) +2022-05-01 14:51:17,904 - tensorflow - INFO - global_step/sec: 87.3517 +2022-05-01 14:51:17,904 - tensorflow - INFO - loss = 0.61651057, step = 207800 (2.290 sec) +2022-05-01 14:51:20,189 - tensorflow - INFO - global_step/sec: 87.5134 +2022-05-01 14:51:20,190 - tensorflow - INFO - loss = 0.050484687, step = 208000 (2.285 sec) +2022-05-01 14:51:22,480 - tensorflow - INFO - global_step/sec: 87.3092 +2022-05-01 14:51:22,480 - tensorflow - INFO - loss = 0.12937605, step = 208200 (2.291 sec) +2022-05-01 14:51:24,772 - tensorflow - INFO - global_step/sec: 87.273 +2022-05-01 14:51:24,772 - tensorflow - INFO - loss = 0.10451312, step = 208400 (2.292 sec) +2022-05-01 14:51:27,042 - tensorflow - INFO - global_step/sec: 88.1056 +2022-05-01 14:51:27,042 - tensorflow - INFO - loss = 0.25230247, step = 208600 (2.270 sec) +2022-05-01 14:51:29,322 - tensorflow - INFO - global_step/sec: 87.6994 +2022-05-01 14:51:29,323 - tensorflow - INFO - loss = 0.11170903, step = 208800 (2.281 sec) +2022-05-01 14:51:31,588 - tensorflow - INFO - global_step/sec: 88.2585 +2022-05-01 14:51:31,589 - tensorflow - INFO - loss = 0.29632747, step = 209000 (2.266 sec) +2022-05-01 14:51:33,873 - tensorflow - INFO - global_step/sec: 87.5372 +2022-05-01 14:51:33,873 - tensorflow - INFO - loss = 0.06806228, step = 209200 (2.285 sec) +2022-05-01 14:51:36,165 - tensorflow - INFO - global_step/sec: 87.2485 +2022-05-01 14:51:36,166 - tensorflow - INFO - loss = 0.14363146, step = 209400 (2.292 sec) +2022-05-01 14:51:38,456 - tensorflow - INFO - global_step/sec: 87.2922 +2022-05-01 14:51:38,457 - tensorflow - INFO - loss = 0.1182349, step = 209600 (2.291 sec) +2022-05-01 14:51:40,745 - tensorflow - INFO - global_step/sec: 87.4044 +2022-05-01 14:51:40,745 - tensorflow - INFO - loss = 0.19589159, step = 209800 (2.288 sec) +2022-05-01 14:51:43,044 - tensorflow - INFO - global_step/sec: 86.9773 +2022-05-01 14:51:43,045 - tensorflow - INFO - loss = 0.19596815, step = 210000 (2.299 sec) +2022-05-01 14:51:45,334 - tensorflow - INFO - global_step/sec: 87.3312 +2022-05-01 14:51:45,335 - tensorflow - INFO - loss = 0.20116207, step = 210200 (2.290 sec) +2022-05-01 14:51:47,629 - tensorflow - INFO - global_step/sec: 87.1483 +2022-05-01 14:51:47,630 - tensorflow - INFO - loss = 0.029008688, step = 210400 (2.295 sec) +2022-05-01 14:51:49,921 - tensorflow - INFO - global_step/sec: 87.2641 +2022-05-01 14:51:49,922 - tensorflow - INFO - loss = 0.072042614, step = 210600 (2.292 sec) +2022-05-01 14:51:52,215 - tensorflow - INFO - global_step/sec: 87.1826 +2022-05-01 14:51:52,216 - tensorflow - INFO - loss = 0.05465828, step = 210800 (2.294 sec) +2022-05-01 14:51:54,507 - tensorflow - INFO - global_step/sec: 87.246 +2022-05-01 14:51:54,508 - tensorflow - INFO - loss = 0.060184408, step = 211000 (2.292 sec) +2022-05-01 14:51:56,803 - tensorflow - INFO - global_step/sec: 87.1235 +2022-05-01 14:51:56,803 - tensorflow - INFO - loss = 0.041051302, step = 211200 (2.296 sec) +2022-05-01 14:51:59,088 - tensorflow - INFO - global_step/sec: 87.5112 +2022-05-01 14:51:59,089 - tensorflow - INFO - loss = 0.13065943, step = 211400 (2.285 sec) +2022-05-01 14:52:01,382 - tensorflow - INFO - global_step/sec: 87.1999 +2022-05-01 14:52:01,382 - tensorflow - INFO - loss = 0.13711402, step = 211600 (2.294 sec) +2022-05-01 14:52:03,671 - tensorflow - INFO - global_step/sec: 87.3831 +2022-05-01 14:52:03,671 - tensorflow - INFO - loss = 2.0083685, step = 211800 (2.289 sec) +2022-05-01 14:52:05,955 - tensorflow - INFO - global_step/sec: 87.5641 +2022-05-01 14:52:05,955 - tensorflow - INFO - loss = 0.4175942, step = 212000 (2.284 sec) +2022-05-01 14:52:08,249 - tensorflow - INFO - global_step/sec: 87.1732 +2022-05-01 14:52:08,250 - tensorflow - INFO - loss = 1.8437014, step = 212200 (2.294 sec) +2022-05-01 14:52:10,542 - tensorflow - INFO - global_step/sec: 87.228 +2022-05-01 14:52:10,542 - tensorflow - INFO - loss = 0.0708262, step = 212400 (2.293 sec) +2022-05-01 14:52:12,831 - tensorflow - INFO - global_step/sec: 87.3639 +2022-05-01 14:52:12,832 - tensorflow - INFO - loss = 0.08293774, step = 212600 (2.289 sec) +2022-05-01 14:52:15,121 - tensorflow - INFO - global_step/sec: 87.3419 +2022-05-01 14:52:15,122 - tensorflow - INFO - loss = 0.06429095, step = 212800 (2.290 sec) +2022-05-01 14:52:17,408 - tensorflow - INFO - global_step/sec: 87.4582 +2022-05-01 14:52:17,408 - tensorflow - INFO - loss = 0.6658572, step = 213000 (2.287 sec) +2022-05-01 14:52:19,708 - tensorflow - INFO - global_step/sec: 86.9439 +2022-05-01 14:52:19,709 - tensorflow - INFO - loss = 0.72445893, step = 213200 (2.300 sec) +2022-05-01 14:52:21,994 - tensorflow - INFO - global_step/sec: 87.4842 +2022-05-01 14:52:21,995 - tensorflow - INFO - loss = 0.18320945, step = 213400 (2.286 sec) +2022-05-01 14:52:24,282 - tensorflow - INFO - global_step/sec: 87.4326 +2022-05-01 14:52:24,282 - tensorflow - INFO - loss = 0.86827636, step = 213600 (2.288 sec) +2022-05-01 14:52:26,571 - tensorflow - INFO - global_step/sec: 87.3856 +2022-05-01 14:52:26,571 - tensorflow - INFO - loss = 2.887658, step = 213800 (2.289 sec) +2022-05-01 14:52:28,861 - tensorflow - INFO - global_step/sec: 87.315 +2022-05-01 14:52:28,862 - tensorflow - INFO - loss = 0.083644465, step = 214000 (2.291 sec) +2022-05-01 14:52:31,156 - tensorflow - INFO - global_step/sec: 87.1385 +2022-05-01 14:52:31,157 - tensorflow - INFO - loss = 0.6637585, step = 214200 (2.295 sec) +2022-05-01 14:52:33,456 - tensorflow - INFO - global_step/sec: 86.9644 +2022-05-01 14:52:33,457 - tensorflow - INFO - loss = 0.18146542, step = 214400 (2.300 sec) +2022-05-01 14:52:35,736 - tensorflow - INFO - global_step/sec: 87.7042 +2022-05-01 14:52:35,737 - tensorflow - INFO - loss = 0.06060461, step = 214600 (2.280 sec) +2022-05-01 14:52:38,021 - tensorflow - INFO - global_step/sec: 87.5462 +2022-05-01 14:52:38,021 - tensorflow - INFO - loss = 0.093060724, step = 214800 (2.285 sec) +2022-05-01 14:52:40,307 - tensorflow - INFO - global_step/sec: 87.4917 +2022-05-01 14:52:40,307 - tensorflow - INFO - loss = 0.056459446, step = 215000 (2.286 sec) +2022-05-01 14:52:42,593 - tensorflow - INFO - global_step/sec: 87.4837 +2022-05-01 14:52:42,594 - tensorflow - INFO - loss = 0.09824064, step = 215200 (2.286 sec) +2022-05-01 14:52:44,888 - tensorflow - INFO - global_step/sec: 87.1658 +2022-05-01 14:52:44,888 - tensorflow - INFO - loss = 0.13120337, step = 215400 (2.294 sec) +2022-05-01 14:52:47,178 - tensorflow - INFO - global_step/sec: 87.3078 +2022-05-01 14:52:47,179 - tensorflow - INFO - loss = 0.29566246, step = 215600 (2.291 sec) +2022-05-01 14:52:49,466 - tensorflow - INFO - global_step/sec: 87.4319 +2022-05-01 14:52:49,466 - tensorflow - INFO - loss = 0.13072793, step = 215800 (2.287 sec) +2022-05-01 14:52:51,746 - tensorflow - INFO - global_step/sec: 87.7028 +2022-05-01 14:52:51,747 - tensorflow - INFO - loss = 0.028415624, step = 216000 (2.280 sec) +2022-05-01 14:52:54,041 - tensorflow - INFO - global_step/sec: 87.1664 +2022-05-01 14:52:54,041 - tensorflow - INFO - loss = 2.1365101, step = 216200 (2.294 sec) +2022-05-01 14:52:56,327 - tensorflow - INFO - global_step/sec: 87.4888 +2022-05-01 14:52:56,327 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 87.0987 +2022-05-01 14:53:12,290 - tensorflow - INFO - loss = 0.06976569, step = 217800 (2.296 sec) +2022-05-01 14:53:14,585 - tensorflow - INFO - global_step/sec: 87.129 +2022-05-01 14:53:14,585 - tensorflow - INFO - loss = 0.27322647, step = 218000 (2.295 sec) +2022-05-01 14:53:16,878 - tensorflow - INFO - global_step/sec: 87.195 +2022-05-01 14:53:16,879 - tensorflow - INFO - loss = 0.056805912, step = 218200 (2.294 sec) +2022-05-01 14:53:19,170 - tensorflow - INFO - global_step/sec: 87.2698 +2022-05-01 14:53:19,170 - tensorflow - INFO - loss = 0.06335604, step = 218400 (2.292 sec) +2022-05-01 14:53:21,456 - tensorflow - INFO - global_step/sec: 87.4844 +2022-05-01 14:53:21,457 - tensorflow - INFO - loss = 0.21221682, step = 218600 (2.286 sec) +2022-05-01 14:53:23,747 - tensorflow - INFO - global_step/sec: 87.2995 +2022-05-01 14:53:23,747 - tensorflow - INFO - loss = 0.34483534, step = 218800 (2.291 sec) +2022-05-01 14:53:26,036 - tensorflow - INFO - global_step/sec: 87.3754 +2022-05-01 14:53:26,036 - tensorflow - INFO - loss = 0.053619225, step = 219000 (2.289 sec) +2022-05-01 14:53:28,328 - tensorflow - INFO - global_step/sec: 87.2743 +2022-05-01 14:53:28,328 - tensorflow - INFO - loss = 0.21524435, step = 219200 (2.292 sec) +2022-05-01 14:53:30,618 - tensorflow - INFO - global_step/sec: 87.3074 +2022-05-01 14:53:30,619 - tensorflow - INFO - loss = 0.058280133, step = 219400 (2.291 sec) +2022-05-01 14:53:32,911 - tensorflow - INFO - global_step/sec: 87.2433 +2022-05-01 14:53:32,911 - tensorflow - INFO - loss = 0.14699472, step = 219600 (2.292 sec) +2022-05-01 14:53:35,213 - tensorflow - INFO - global_step/sec: 86.8731 +2022-05-01 14:53:35,213 - tensorflow - INFO - loss = 0.1067339, step = 219800 (2.302 sec) +2022-05-01 14:53:37,502 - tensorflow - INFO - global_step/sec: 87.384 +2022-05-01 14:53:37,502 - tensorflow - INFO - loss = 0.089757755, step = 220000 (2.289 sec) +2022-05-01 14:53:39,791 - tensorflow - INFO - global_step/sec: 87.3762 +2022-05-01 14:53:39,791 - 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tensorflow - INFO - loss = 0.10438813, step = 285200 (2.295 sec) +2022-05-01 15:06:07,047 - tensorflow - INFO - global_step/sec: 86.9643 +2022-05-01 15:06:07,047 - tensorflow - INFO - loss = 0.077885404, step = 285400 (2.300 sec) +2022-05-01 15:06:09,339 - tensorflow - INFO - global_step/sec: 87.2501 +2022-05-01 15:06:09,340 - tensorflow - INFO - loss = 1.0299636, step = 285600 (2.292 sec) +2022-05-01 15:06:11,628 - tensorflow - INFO - global_step/sec: 87.3806 +2022-05-01 15:06:11,629 - tensorflow - INFO - loss = 0.14138225, step = 285800 (2.289 sec) +2022-05-01 15:06:13,916 - tensorflow - INFO - global_step/sec: 87.4141 +2022-05-01 15:06:13,917 - tensorflow - INFO - loss = 0.20049463, step = 286000 (2.288 sec) +2022-05-01 15:06:16,208 - tensorflow - INFO - global_step/sec: 87.2772 +2022-05-01 15:06:16,208 - tensorflow - INFO - loss = 0.47803906, step = 286200 (2.292 sec) +2022-05-01 15:06:18,506 - tensorflow - INFO - global_step/sec: 87.0097 +2022-05-01 15:06:18,507 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 86.7513 +2022-05-01 15:06:34,661 - tensorflow - INFO - loss = 0.038943138, step = 287800 (2.305 sec) +2022-05-01 15:06:36,951 - tensorflow - INFO - global_step/sec: 87.3428 +2022-05-01 15:06:36,951 - tensorflow - INFO - loss = 0.724728, step = 288000 (2.290 sec) +2022-05-01 15:06:39,236 - tensorflow - INFO - global_step/sec: 87.5281 +2022-05-01 15:06:39,236 - tensorflow - INFO - loss = 0.036998108, step = 288200 (2.285 sec) +2022-05-01 15:06:41,532 - tensorflow - INFO - global_step/sec: 87.1033 +2022-05-01 15:06:41,532 - tensorflow - INFO - loss = 0.04534033, step = 288400 (2.296 sec) +2022-05-01 15:06:43,829 - tensorflow - INFO - global_step/sec: 87.0777 +2022-05-01 15:06:43,829 - tensorflow - INFO - loss = 0.03104715, step = 288600 (2.297 sec) +2022-05-01 15:06:46,118 - tensorflow - INFO - global_step/sec: 87.3417 +2022-05-01 15:06:46,119 - tensorflow - INFO - loss = 0.08332451, step = 288800 (2.290 sec) +2022-05-01 15:06:48,409 - tensorflow - INFO - global_step/sec: 87.3342 +2022-05-01 15:06:48,409 - tensorflow - INFO - loss = 0.06010709, step = 289000 (2.290 sec) +2022-05-01 15:06:50,705 - tensorflow - INFO - global_step/sec: 87.0776 +2022-05-01 15:06:50,706 - tensorflow - INFO - loss = 0.113809615, step = 289200 (2.297 sec) +2022-05-01 15:06:52,976 - tensorflow - INFO - global_step/sec: 88.0683 +2022-05-01 15:06:52,977 - tensorflow - INFO - loss = 0.054298207, step = 289400 (2.271 sec) +2022-05-01 15:06:55,252 - tensorflow - INFO - global_step/sec: 87.8883 +2022-05-01 15:06:55,252 - tensorflow - INFO - loss = 0.103841156, step = 289600 (2.276 sec) +2022-05-01 15:06:57,526 - tensorflow - INFO - global_step/sec: 87.9469 +2022-05-01 15:06:57,526 - tensorflow - INFO - loss = 0.033746388, step = 289800 (2.274 sec) +2022-05-01 15:06:59,815 - tensorflow - INFO - global_step/sec: 87.3567 +2022-05-01 15:06:59,816 - tensorflow - INFO - loss = 0.06811048, step = 290000 (2.289 sec) +2022-05-01 15:07:02,111 - tensorflow - INFO - global_step/sec: 87.1256 +2022-05-01 15:07:02,111 - tensorflow - INFO - loss = 1.4815083, step = 290200 (2.296 sec) +2022-05-01 15:07:04,405 - tensorflow - INFO - global_step/sec: 87.1876 +2022-05-01 15:07:04,405 - tensorflow - INFO - loss = 0.09884867, step = 290400 (2.294 sec) +2022-05-01 15:07:06,695 - tensorflow - INFO - global_step/sec: 87.3445 +2022-05-01 15:07:06,695 - tensorflow - INFO - loss = 0.120853096, step = 290600 (2.290 sec) +2022-05-01 15:07:08,989 - tensorflow - INFO - global_step/sec: 87.1564 +2022-05-01 15:07:08,990 - tensorflow - INFO - loss = 0.10058601, step = 290800 (2.295 sec) +2022-05-01 15:07:11,282 - tensorflow - INFO - global_step/sec: 87.2368 +2022-05-01 15:07:11,282 - tensorflow - INFO - loss = 0.019281892, step = 291000 (2.293 sec) +2022-05-01 15:07:13,572 - tensorflow - INFO - global_step/sec: 87.3316 +2022-05-01 15:07:13,573 - tensorflow - INFO - loss = 0.099681005, step = 291200 (2.290 sec) +2022-05-01 15:07:15,865 - tensorflow - INFO - global_step/sec: 87.2468 +2022-05-01 15:07:15,865 - tensorflow - INFO - loss = 0.29632992, step = 291400 (2.292 sec) +2022-05-01 15:07:18,156 - tensorflow - INFO - global_step/sec: 87.2636 +2022-05-01 15:07:18,157 - tensorflow - INFO - loss = 0.13116612, step = 291600 (2.292 sec) +2022-05-01 15:07:20,446 - tensorflow - INFO - global_step/sec: 87.3528 +2022-05-01 15:07:20,446 - tensorflow - INFO - loss = 0.04381878, step = 291800 (2.290 sec) +2022-05-01 15:07:22,734 - tensorflow - INFO - global_step/sec: 87.4134 +2022-05-01 15:07:22,734 - tensorflow - INFO - loss = 0.7160268, step = 292000 (2.288 sec) +2022-05-01 15:07:25,026 - tensorflow - INFO - global_step/sec: 87.2645 +2022-05-01 15:07:25,026 - tensorflow - INFO - loss = 0.20757207, step = 292200 (2.292 sec) +2022-05-01 15:07:27,320 - tensorflow - INFO - global_step/sec: 87.1772 +2022-05-01 15:07:27,320 - tensorflow - INFO - loss = 0.04452872, step = 292400 (2.294 sec) +2022-05-01 15:07:29,606 - tensorflow - INFO - global_step/sec: 87.4936 +2022-05-01 15:07:29,606 - tensorflow - INFO - loss = 0.09249899, step = 292600 (2.286 sec) +2022-05-01 15:07:31,906 - tensorflow - INFO - global_step/sec: 86.9628 +2022-05-01 15:07:31,906 - tensorflow - INFO - loss = 0.43281564, step = 292800 (2.300 sec) +2022-05-01 15:07:34,199 - tensorflow - INFO - global_step/sec: 87.2053 +2022-05-01 15:07:34,200 - tensorflow - INFO - loss = 0.1831511, step = 293000 (2.293 sec) +2022-05-01 15:07:36,491 - tensorflow - INFO - global_step/sec: 87.2675 +2022-05-01 15:07:36,491 - tensorflow - INFO - loss = 0.04634072, step = 293200 (2.292 sec) +2022-05-01 15:07:38,798 - tensorflow - INFO - global_step/sec: 86.7001 +2022-05-01 15:07:38,798 - tensorflow - INFO - loss = 0.04415472, step = 293400 (2.307 sec) +2022-05-01 15:07:41,097 - tensorflow - INFO - global_step/sec: 86.9977 +2022-05-01 15:07:41,097 - tensorflow - INFO - loss = 0.16743091, step = 293600 (2.299 sec) +2022-05-01 15:07:43,401 - tensorflow - INFO - global_step/sec: 86.7761 +2022-05-01 15:07:43,402 - tensorflow - INFO - loss = 0.45817947, step = 293800 (2.305 sec) +2022-05-01 15:07:45,696 - tensorflow - INFO - 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tensorflow - INFO - loss = 1.1744771, step = 295200 (2.302 sec) +2022-05-01 15:08:01,806 - tensorflow - INFO - global_step/sec: 86.8037 +2022-05-01 15:08:01,807 - tensorflow - INFO - loss = 0.07121699, step = 295400 (2.304 sec) +2022-05-01 15:08:04,117 - tensorflow - INFO - global_step/sec: 86.5692 +2022-05-01 15:08:04,117 - tensorflow - INFO - loss = 0.027087681, step = 295600 (2.310 sec) +2022-05-01 15:08:06,425 - tensorflow - INFO - global_step/sec: 86.6403 +2022-05-01 15:08:06,426 - tensorflow - INFO - loss = 0.6950201, step = 295800 (2.308 sec) +2022-05-01 15:08:08,710 - tensorflow - INFO - global_step/sec: 87.5187 +2022-05-01 15:08:08,711 - tensorflow - INFO - loss = 0.18592377, step = 296000 (2.285 sec) +2022-05-01 15:08:11,004 - tensorflow - INFO - global_step/sec: 87.2033 +2022-05-01 15:08:11,004 - tensorflow - INFO - loss = 0.05259351, step = 296200 (2.294 sec) +2022-05-01 15:08:13,291 - tensorflow - INFO - global_step/sec: 87.4529 +2022-05-01 15:08:13,291 - tensorflow - INFO - loss = 0.026855329, step = 296400 (2.287 sec) +2022-05-01 15:08:15,593 - tensorflow - INFO - global_step/sec: 86.8636 +2022-05-01 15:08:15,594 - tensorflow - INFO - loss = 0.038337093, step = 296600 (2.302 sec) +2022-05-01 15:08:17,881 - tensorflow - INFO - global_step/sec: 87.4079 +2022-05-01 15:08:17,882 - tensorflow - INFO - loss = 16.317427, step = 296800 (2.288 sec) +2022-05-01 15:08:20,174 - tensorflow - INFO - global_step/sec: 87.2457 +2022-05-01 15:08:20,174 - tensorflow - INFO - loss = 0.073704466, step = 297000 (2.292 sec) +2022-05-01 15:08:22,468 - tensorflow - INFO - global_step/sec: 87.1762 +2022-05-01 15:08:22,468 - tensorflow - INFO - loss = 0.03904153, step = 297200 (2.294 sec) +2022-05-01 15:08:24,754 - tensorflow - INFO - global_step/sec: 87.4825 +2022-05-01 15:08:24,754 - tensorflow - INFO - loss = 0.027742313, step = 297400 (2.286 sec) +2022-05-01 15:08:27,032 - tensorflow - INFO - global_step/sec: 87.7926 +2022-05-01 15:08:27,033 - tensorflow - INFO - loss = 0.20203239, step = 297600 (2.278 sec) +2022-05-01 15:08:29,305 - tensorflow - INFO - global_step/sec: 88.0037 +2022-05-01 15:08:29,305 - tensorflow - INFO - loss = 2.553576, step = 297800 (2.273 sec) +2022-05-01 15:08:31,566 - tensorflow - INFO - global_step/sec: 88.4544 +2022-05-01 15:08:31,566 - tensorflow - INFO - loss = 0.0866318, step = 298000 (2.261 sec) +2022-05-01 15:08:33,855 - tensorflow - INFO - global_step/sec: 87.3581 +2022-05-01 15:08:33,856 - tensorflow - INFO - loss = 0.067000255, step = 298200 (2.289 sec) +2022-05-01 15:08:36,143 - tensorflow - INFO - global_step/sec: 87.4357 +2022-05-01 15:08:36,143 - tensorflow - INFO - loss = 0.102808684, step = 298400 (2.287 sec) +2022-05-01 15:08:38,430 - tensorflow - INFO - global_step/sec: 87.4537 +2022-05-01 15:08:38,430 - tensorflow - INFO - loss = 0.031817265, step = 298600 (2.287 sec) +2022-05-01 15:08:40,730 - tensorflow - INFO - global_step/sec: 86.9441 +2022-05-01 15:08:40,730 - tensorflow - INFO - loss = 0.14091843, step = 298800 (2.300 sec) +2022-05-01 15:08:43,027 - tensorflow - INFO - global_step/sec: 87.0573 +2022-05-01 15:08:43,028 - tensorflow - INFO - loss = 0.042238776, step = 299000 (2.297 sec) +2022-05-01 15:08:45,310 - tensorflow - INFO - global_step/sec: 87.6187 +2022-05-01 15:08:45,310 - tensorflow - INFO - loss = 0.03812917, step = 299200 (2.283 sec) +2022-05-01 15:08:47,597 - tensorflow - INFO - global_step/sec: 87.4589 +2022-05-01 15:08:47,597 - tensorflow - INFO - loss = 0.11857946, step = 299400 (2.287 sec) +2022-05-01 15:08:49,888 - tensorflow - INFO - global_step/sec: 87.2812 +2022-05-01 15:08:49,889 - tensorflow - INFO - loss = 0.037918597, step = 299600 (2.291 sec) +2022-05-01 15:08:52,184 - tensorflow - INFO - global_step/sec: 87.0992 +2022-05-01 15:08:52,185 - tensorflow - INFO - loss = 0.12851797, step = 299800 (2.296 sec) +2022-05-01 15:08:54,471 - tensorflow - INFO - Saving checkpoints for 300000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. +2022-05-01 15:08:54,488 - tensorflow - INFO - model.ckpt-300000 is not in all_model_checkpoint_paths. Manually adding it. +2022-05-01 15:08:55,028 - tensorflow - INFO - Calling model_fn. +2022-05-01 15:08:55,159 - tensorflow - INFO - Done calling model_fn. +2022-05-01 15:08:55,174 - tensorflow - INFO - Starting evaluation at 2022-05-01T15:08:55Z +2022-05-01 15:08:55,239 - tensorflow - INFO - Graph was finalized. +2022-05-01 15:08:55,242 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-300000 +2022-05-01 15:08:55,530 - tensorflow - INFO - Running local_init_op. +2022-05-01 15:08:55,640 - tensorflow - INFO - Done running local_init_op. +2022-05-01 15:09:05,911 - tensorflow - INFO - Evaluation [3990/39900] +2022-05-01 15:09:15,477 - tensorflow - INFO - Evaluation [7980/39900] +2022-05-01 15:09:25,029 - tensorflow - INFO - Evaluation [11970/39900] +2022-05-01 15:09:34,594 - tensorflow - INFO - Evaluation [15960/39900] +2022-05-01 15:09:44,157 - tensorflow - INFO - Evaluation [19950/39900] +2022-05-01 15:09:53,715 - tensorflow - INFO - Evaluation [23940/39900] +2022-05-01 15:10:03,243 - tensorflow - INFO - Evaluation [27930/39900] +2022-05-01 15:10:12,772 - tensorflow - INFO - Evaluation [31920/39900] +2022-05-01 15:10:22,332 - tensorflow - INFO - Evaluation [35910/39900] +2022-05-01 15:10:31,903 - tensorflow - INFO - Evaluation [39900/39900] +2022-05-01 15:10:32,041 - tensorflow - INFO - Finished evaluation at 2022-05-01-15:10:32 +2022-05-01 15:10:32,042 - tensorflow - INFO - Saving dict for global step 300000: global_step = 300000, loss = 0.05468036, mean_degree_err = 7.492244 +2022-05-01 15:10:32,042 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 300000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-300000 +2022-05-01 15:10:32,056 - tensorflow - INFO - global_step/sec: 2.00257 +2022-05-01 15:10:32,057 - tensorflow - INFO - loss = 0.40683693, step = 300000 (99.872 sec) +2022-05-01 15:10:34,365 - tensorflow - INFO - global_step/sec: 86.6192 +2022-05-01 15:10:34,365 - 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tensorflow - INFO - loss = 0.05305391, step = 385200 (2.286 sec) +2022-05-01 15:26:51,210 - tensorflow - INFO - global_step/sec: 87.3046 +2022-05-01 15:26:51,210 - tensorflow - INFO - loss = 0.15249148, step = 385400 (2.291 sec) +2022-05-01 15:26:53,491 - tensorflow - INFO - global_step/sec: 87.6665 +2022-05-01 15:26:53,492 - tensorflow - INFO - loss = 0.10122527, step = 385600 (2.281 sec) +2022-05-01 15:26:55,777 - tensorflow - INFO - global_step/sec: 87.4855 +2022-05-01 15:26:55,778 - tensorflow - INFO - loss = 0.041996934, step = 385800 (2.286 sec) +2022-05-01 15:26:58,054 - tensorflow - INFO - global_step/sec: 87.8417 +2022-05-01 15:26:58,054 - tensorflow - INFO - loss = 0.086239435, step = 386000 (2.277 sec) +2022-05-01 15:27:00,322 - tensorflow - INFO - global_step/sec: 88.1912 +2022-05-01 15:27:00,322 - tensorflow - INFO - loss = 0.13029584, step = 386200 (2.268 sec) +2022-05-01 15:27:02,588 - tensorflow - INFO - global_step/sec: 88.2611 +2022-05-01 15:27:02,588 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 87.1199 +2022-05-01 15:27:18,559 - tensorflow - INFO - loss = 0.16306858, step = 387800 (2.296 sec) +2022-05-01 15:27:20,850 - tensorflow - INFO - global_step/sec: 87.2961 +2022-05-01 15:27:20,850 - tensorflow - INFO - loss = 0.12461989, step = 388000 (2.291 sec) +2022-05-01 15:27:23,139 - tensorflow - INFO - global_step/sec: 87.3612 +2022-05-01 15:27:23,140 - tensorflow - INFO - loss = 0.033755578, step = 388200 (2.289 sec) +2022-05-01 15:27:25,421 - tensorflow - INFO - global_step/sec: 87.6285 +2022-05-01 15:27:25,422 - tensorflow - INFO - loss = 0.4511457, step = 388400 (2.282 sec) +2022-05-01 15:27:27,713 - tensorflow - INFO - global_step/sec: 87.274 +2022-05-01 15:27:27,714 - tensorflow - INFO - loss = 0.1348494, step = 388600 (2.292 sec) +2022-05-01 15:27:29,998 - tensorflow - INFO - global_step/sec: 87.5477 +2022-05-01 15:27:29,998 - tensorflow - INFO - loss = 0.06808847, step = 388800 (2.284 sec) +2022-05-01 15:27:32,297 - tensorflow - INFO - global_step/sec: 86.9584 +2022-05-01 15:27:32,298 - tensorflow - INFO - loss = 0.11560288, step = 389000 (2.300 sec) +2022-05-01 15:27:34,591 - tensorflow - INFO - global_step/sec: 87.1913 +2022-05-01 15:27:34,592 - tensorflow - INFO - loss = 0.9794904, step = 389200 (2.294 sec) +2022-05-01 15:27:36,880 - tensorflow - INFO - global_step/sec: 87.3863 +2022-05-01 15:27:36,880 - tensorflow - INFO - loss = 0.26934433, step = 389400 (2.289 sec) +2022-05-01 15:27:39,166 - tensorflow - INFO - global_step/sec: 87.4754 +2022-05-01 15:27:39,167 - tensorflow - INFO - loss = 0.08934729, step = 389600 (2.286 sec) +2022-05-01 15:27:41,458 - tensorflow - INFO - global_step/sec: 87.2697 +2022-05-01 15:27:41,458 - tensorflow - INFO - loss = 0.021905141, step = 389800 (2.292 sec) +2022-05-01 15:27:43,758 - tensorflow - INFO - global_step/sec: 86.9738 +2022-05-01 15:27:43,758 - tensorflow - INFO - loss = 0.049776305, step = 390000 (2.300 sec) +2022-05-01 15:27:46,051 - tensorflow - INFO - global_step/sec: 87.1939 +2022-05-01 15:27:46,052 - tensorflow - INFO - loss = 0.040118024, step = 390200 (2.294 sec) +2022-05-01 15:27:48,335 - tensorflow - INFO - global_step/sec: 87.5897 +2022-05-01 15:27:48,335 - tensorflow - INFO - loss = 0.02597103, step = 390400 (2.283 sec) +2022-05-01 15:27:50,624 - tensorflow - INFO - global_step/sec: 87.3757 +2022-05-01 15:27:50,624 - tensorflow - INFO - loss = 0.042590633, step = 390600 (2.289 sec) +2022-05-01 15:27:52,920 - tensorflow - INFO - global_step/sec: 87.0951 +2022-05-01 15:27:52,920 - tensorflow - INFO - loss = 0.039586984, step = 390800 (2.296 sec) +2022-05-01 15:27:55,212 - tensorflow - INFO - global_step/sec: 87.2792 +2022-05-01 15:27:55,212 - tensorflow - INFO - loss = 0.04140979, step = 391000 (2.291 sec) +2022-05-01 15:27:57,504 - tensorflow - INFO - global_step/sec: 87.2236 +2022-05-01 15:27:57,505 - tensorflow - INFO - loss = 0.029703354, step = 391200 (2.293 sec) +2022-05-01 15:27:59,793 - tensorflow - INFO - global_step/sec: 87.4091 +2022-05-01 15:27:59,793 - tensorflow - INFO - loss = 0.08359525, step = 391400 (2.288 sec) +2022-05-01 15:28:02,081 - tensorflow - INFO - global_step/sec: 87.4016 +2022-05-01 15:28:02,081 - tensorflow - INFO - loss = 0.08424415, step = 391600 (2.288 sec) +2022-05-01 15:28:04,377 - tensorflow - INFO - global_step/sec: 87.0941 +2022-05-01 15:28:04,378 - tensorflow - INFO - loss = 0.02261557, step = 391800 (2.296 sec) +2022-05-01 15:28:06,668 - tensorflow - INFO - global_step/sec: 87.3149 +2022-05-01 15:28:06,668 - tensorflow - INFO - loss = 0.37749457, step = 392000 (2.291 sec) +2022-05-01 15:28:08,967 - tensorflow - INFO - global_step/sec: 86.9919 +2022-05-01 15:28:08,967 - tensorflow - INFO - loss = 0.049463645, step = 392200 (2.299 sec) +2022-05-01 15:28:11,260 - tensorflow - INFO - global_step/sec: 87.2103 +2022-05-01 15:28:11,261 - tensorflow - INFO - loss = 0.1873938, step = 392400 (2.293 sec) +2022-05-01 15:28:13,550 - tensorflow - INFO - global_step/sec: 87.3377 +2022-05-01 15:28:13,551 - tensorflow - INFO - loss = 0.02534483, step = 392600 (2.290 sec) +2022-05-01 15:28:15,844 - tensorflow - INFO - global_step/sec: 87.1993 +2022-05-01 15:28:15,844 - tensorflow - INFO - loss = 0.1180383, step = 392800 (2.294 sec) +2022-05-01 15:28:18,132 - tensorflow - INFO - global_step/sec: 87.4058 +2022-05-01 15:28:18,132 - tensorflow - INFO - loss = 0.020066151, step = 393000 (2.288 sec) +2022-05-01 15:28:20,414 - tensorflow - INFO - global_step/sec: 87.6439 +2022-05-01 15:28:20,414 - tensorflow - INFO - loss = 0.04308885, step = 393200 (2.282 sec) +2022-05-01 15:28:22,713 - tensorflow - INFO - global_step/sec: 86.9714 +2022-05-01 15:28:22,714 - tensorflow - INFO - loss = 0.08658816, step = 393400 (2.300 sec) +2022-05-01 15:28:25,006 - tensorflow - INFO - global_step/sec: 87.2448 +2022-05-01 15:28:25,006 - tensorflow - INFO - loss = 0.07704064, step = 393600 (2.292 sec) +2022-05-01 15:28:27,300 - tensorflow - INFO - global_step/sec: 87.1794 +2022-05-01 15:28:27,300 - tensorflow - INFO - loss = 0.09365944, step = 393800 (2.294 sec) +2022-05-01 15:28:29,581 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.1679577, step = 395200 (2.302 sec) +2022-05-01 15:28:45,593 - tensorflow - INFO - global_step/sec: 86.8084 +2022-05-01 15:28:45,593 - tensorflow - INFO - loss = 0.072121605, step = 395400 (2.304 sec) +2022-05-01 15:28:47,892 - tensorflow - INFO - global_step/sec: 86.9844 +2022-05-01 15:28:47,893 - tensorflow - INFO - loss = 0.27504706, step = 395600 (2.299 sec) +2022-05-01 15:28:50,194 - tensorflow - INFO - global_step/sec: 86.8971 +2022-05-01 15:28:50,194 - tensorflow - INFO - loss = 0.27844322, step = 395800 (2.302 sec) +2022-05-01 15:28:52,501 - tensorflow - INFO - global_step/sec: 86.693 +2022-05-01 15:28:52,501 - tensorflow - INFO - loss = 1.3566101, step = 396000 (2.307 sec) +2022-05-01 15:28:54,801 - tensorflow - INFO - global_step/sec: 86.9296 +2022-05-01 15:28:54,802 - tensorflow - INFO - loss = 0.06718867, step = 396200 (2.301 sec) +2022-05-01 15:28:57,104 - tensorflow - INFO - global_step/sec: 86.8481 +2022-05-01 15:28:57,105 - tensorflow - INFO - loss = 0.0668054, step = 396400 (2.303 sec) +2022-05-01 15:28:59,400 - tensorflow - INFO - global_step/sec: 87.113 +2022-05-01 15:28:59,401 - tensorflow - INFO - loss = 0.12444751, step = 396600 (2.296 sec) +2022-05-01 15:29:01,701 - tensorflow - INFO - global_step/sec: 86.9377 +2022-05-01 15:29:01,701 - tensorflow - INFO - loss = 0.03913559, step = 396800 (2.301 sec) +2022-05-01 15:29:04,007 - tensorflow - INFO - global_step/sec: 86.7135 +2022-05-01 15:29:04,008 - tensorflow - INFO - loss = 0.05213821, step = 397000 (2.306 sec) +2022-05-01 15:29:06,306 - tensorflow - INFO - global_step/sec: 87.0015 +2022-05-01 15:29:06,306 - tensorflow - INFO - loss = 0.05093122, step = 397200 (2.299 sec) +2022-05-01 15:29:08,597 - tensorflow - INFO - global_step/sec: 87.2929 +2022-05-01 15:29:08,598 - tensorflow - INFO - loss = 0.023556273, step = 397400 (2.291 sec) +2022-05-01 15:29:10,882 - tensorflow - INFO - global_step/sec: 87.5211 +2022-05-01 15:29:10,883 - tensorflow - INFO - loss = 0.04940243, step = 397600 (2.285 sec) +2022-05-01 15:29:13,174 - tensorflow - INFO - global_step/sec: 87.2662 +2022-05-01 15:29:13,175 - tensorflow - INFO - loss = 0.44391492, step = 397800 (2.292 sec) +2022-05-01 15:29:15,469 - tensorflow - INFO - global_step/sec: 87.1639 +2022-05-01 15:29:15,469 - tensorflow - INFO - loss = 0.05440904, step = 398000 (2.295 sec) +2022-05-01 15:29:17,755 - tensorflow - INFO - global_step/sec: 87.4721 +2022-05-01 15:29:17,756 - tensorflow - INFO - loss = 0.021781359, step = 398200 (2.286 sec) +2022-05-01 15:29:20,050 - tensorflow - INFO - global_step/sec: 87.1435 +2022-05-01 15:29:20,051 - tensorflow - INFO - loss = 0.1761256, step = 398400 (2.295 sec) +2022-05-01 15:29:22,337 - tensorflow - INFO - global_step/sec: 87.459 +2022-05-01 15:29:22,337 - tensorflow - INFO - loss = 0.031269975, step = 398600 (2.287 sec) +2022-05-01 15:29:24,636 - tensorflow - INFO - global_step/sec: 86.9899 +2022-05-01 15:29:24,636 - tensorflow - INFO - loss = 0.029436901, step = 398800 (2.299 sec) +2022-05-01 15:29:26,921 - tensorflow - INFO - global_step/sec: 87.5231 +2022-05-01 15:29:26,922 - tensorflow - INFO - loss = 0.036546864, step = 399000 (2.285 sec) +2022-05-01 15:29:29,216 - tensorflow - INFO - global_step/sec: 87.1351 +2022-05-01 15:29:29,217 - tensorflow - INFO - loss = 0.073827825, step = 399200 (2.295 sec) +2022-05-01 15:29:31,514 - tensorflow - INFO - global_step/sec: 87.0353 +2022-05-01 15:29:31,515 - tensorflow - INFO - loss = 0.04035163, step = 399400 (2.298 sec) +2022-05-01 15:29:33,802 - tensorflow - INFO - global_step/sec: 87.4241 +2022-05-01 15:29:33,803 - tensorflow - INFO - loss = 0.01856226, step = 399600 (2.288 sec) +2022-05-01 15:29:36,094 - tensorflow - INFO - global_step/sec: 87.2769 +2022-05-01 15:29:36,094 - tensorflow - INFO - loss = 0.58974075, step = 399800 (2.292 sec) +2022-05-01 15:29:38,371 - tensorflow - INFO - Saving checkpoints for 400000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. +2022-05-01 15:29:38,389 - tensorflow - INFO - model.ckpt-400000 is not in all_model_checkpoint_paths. Manually adding it. +2022-05-01 15:29:38,933 - tensorflow - INFO - Calling model_fn. +2022-05-01 15:29:39,066 - tensorflow - INFO - Done calling model_fn. +2022-05-01 15:29:39,082 - tensorflow - INFO - Starting evaluation at 2022-05-01T15:29:39Z +2022-05-01 15:29:39,147 - tensorflow - INFO - Graph was finalized. +2022-05-01 15:29:39,149 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-400000 +2022-05-01 15:29:39,438 - tensorflow - INFO - Running local_init_op. +2022-05-01 15:29:39,547 - tensorflow - INFO - Done running local_init_op. +2022-05-01 15:29:49,774 - tensorflow - INFO - Evaluation [3990/39900] +2022-05-01 15:29:59,312 - tensorflow - INFO - Evaluation [7980/39900] +2022-05-01 15:30:08,770 - tensorflow - INFO - Evaluation [11970/39900] +2022-05-01 15:30:18,321 - tensorflow - INFO - Evaluation [15960/39900] +2022-05-01 15:30:27,839 - tensorflow - INFO - Evaluation [19950/39900] +2022-05-01 15:30:37,374 - tensorflow - INFO - Evaluation [23940/39900] +2022-05-01 15:30:46,920 - tensorflow - INFO - Evaluation [27930/39900] +2022-05-01 15:30:56,464 - tensorflow - INFO - Evaluation [31920/39900] +2022-05-01 15:31:06,005 - tensorflow - INFO - Evaluation [35910/39900] +2022-05-01 15:31:15,544 - tensorflow - INFO - Evaluation [39900/39900] +2022-05-01 15:31:15,682 - tensorflow - INFO - Finished evaluation at 2022-05-01-15:31:15 +2022-05-01 15:31:15,682 - tensorflow - INFO - Saving dict for global step 400000: global_step = 400000, loss = 0.04516113, mean_degree_err = 6.516394 +2022-05-01 15:31:15,682 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 400000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-400000 +2022-05-01 15:31:15,696 - tensorflow - INFO - global_step/sec: 2.00798 +2022-05-01 15:31:15,697 - tensorflow - INFO - loss = 0.02225634, step = 400000 (99.603 sec) +2022-05-01 15:31:18,011 - tensorflow - INFO - global_step/sec: 86.4114 +2022-05-01 15:31:18,011 - tensorflow - INFO - loss = 0.31792247, step = 400200 (2.314 sec) +2022-05-01 15:31:20,306 - tensorflow - INFO - global_step/sec: 87.1549 +2022-05-01 15:31:20,306 - tensorflow - INFO - loss = 0.10387549, step = 400400 (2.295 sec) +2022-05-01 15:31:22,607 - tensorflow - INFO - global_step/sec: 86.9092 +2022-05-01 15:31:22,607 - tensorflow - INFO - loss = 0.12722068, step = 400600 (2.301 sec) +2022-05-01 15:31:24,903 - tensorflow - INFO - global_step/sec: 87.0888 +2022-05-01 15:31:24,904 - tensorflow - INFO - loss = 0.22603582, step = 400800 (2.297 sec) +2022-05-01 15:31:27,198 - tensorflow - INFO - global_step/sec: 87.1598 +2022-05-01 15:31:27,199 - tensorflow - INFO - loss = 0.15252268, step = 401000 (2.295 sec) +2022-05-01 15:31:29,491 - tensorflow - INFO - global_step/sec: 87.2261 +2022-05-01 15:31:29,491 - tensorflow - INFO - loss = 0.24389297, step = 401200 (2.293 sec) +2022-05-01 15:31:31,795 - tensorflow - INFO - global_step/sec: 86.8182 +2022-05-01 15:31:31,795 - tensorflow - INFO - loss = 0.015286315, step = 401400 (2.304 sec) +2022-05-01 15:31:34,096 - tensorflow - INFO - global_step/sec: 86.9031 +2022-05-01 15:31:34,096 - tensorflow - INFO - loss = 0.09249173, step = 401600 (2.301 sec) +2022-05-01 15:31:36,388 - tensorflow - INFO - global_step/sec: 87.2577 +2022-05-01 15:31:36,389 - tensorflow - INFO - loss = 0.097399205, step = 401800 (2.292 sec) +2022-05-01 15:31:38,645 - tensorflow - INFO - global_step/sec: 88.6012 +2022-05-01 15:31:38,646 - tensorflow - INFO - loss = 0.030494688, step = 402000 (2.257 sec) +2022-05-01 15:31:40,923 - tensorflow - INFO - global_step/sec: 87.8078 +2022-05-01 15:31:40,924 - tensorflow - INFO - loss = 0.111601226, step = 402200 (2.278 sec) +2022-05-01 15:31:43,212 - tensorflow - INFO - global_step/sec: 87.3939 +2022-05-01 15:31:43,212 - tensorflow - INFO - loss = 0.028065145, step = 402400 (2.288 sec) +2022-05-01 15:31:45,507 - tensorflow - INFO - global_step/sec: 87.1462 +2022-05-01 15:31:45,507 - tensorflow - INFO - loss = 0.024193406, step = 402600 (2.295 sec) +2022-05-01 15:31:47,798 - tensorflow - INFO - global_step/sec: 87.2884 +2022-05-01 15:31:47,798 - tensorflow - INFO - loss = 20.300358, step = 402800 (2.291 sec) +2022-05-01 15:31:50,098 - tensorflow - INFO - global_step/sec: 86.9538 +2022-05-01 15:31:50,098 - tensorflow - INFO - loss = 17.936117, step = 403000 (2.300 sec) +2022-05-01 15:31:52,388 - tensorflow - INFO - global_step/sec: 87.3362 +2022-05-01 15:31:52,388 - tensorflow - INFO - loss = 0.04836905, step = 403200 (2.290 sec) +2022-05-01 15:31:54,683 - tensorflow - INFO - global_step/sec: 87.148 +2022-05-01 15:31:54,683 - tensorflow - INFO - loss = 11.243692, step = 403400 (2.295 sec) +2022-05-01 15:31:56,975 - tensorflow - INFO - global_step/sec: 87.2586 +2022-05-01 15:31:56,975 - tensorflow - INFO - loss = 0.024591817, step = 403600 (2.292 sec) +2022-05-01 15:31:59,270 - tensorflow - INFO - global_step/sec: 87.1365 +2022-05-01 15:31:59,271 - tensorflow - INFO - loss = 0.10885215, step = 403800 (2.295 sec) +2022-05-01 15:32:01,569 - tensorflow - INFO - global_step/sec: 87.0194 +2022-05-01 15:32:01,569 - tensorflow - INFO - loss = 0.09319634, step = 404000 (2.298 sec) +2022-05-01 15:32:03,855 - tensorflow - INFO - global_step/sec: 87.4566 +2022-05-01 15:32:03,856 - tensorflow - INFO - loss = 0.053963237, step = 404200 (2.287 sec) +2022-05-01 15:32:06,151 - tensorflow - INFO - global_step/sec: 87.1172 +2022-05-01 15:32:06,152 - tensorflow - INFO - loss = 0.053012453, step = 404400 (2.296 sec) +2022-05-01 15:32:08,451 - tensorflow - INFO - global_step/sec: 86.9517 +2022-05-01 15:32:08,452 - tensorflow - INFO - loss = 0.12956715, step = 404600 (2.300 sec) +2022-05-01 15:32:10,755 - tensorflow - INFO - global_step/sec: 86.8279 +2022-05-01 15:32:10,755 - tensorflow - INFO - loss = 0.09968604, step = 404800 (2.303 sec) +2022-05-01 15:32:13,065 - tensorflow - INFO - global_step/sec: 86.551 +2022-05-01 15:32:13,066 - tensorflow - INFO - loss = 0.026279986, step = 405000 (2.311 sec) +2022-05-01 15:32:15,371 - tensorflow - INFO - global_step/sec: 86.7337 +2022-05-01 15:32:15,372 - tensorflow - INFO - loss = 0.09798093, step = 405200 (2.306 sec) +2022-05-01 15:32:17,675 - tensorflow - INFO - global_step/sec: 86.8323 +2022-05-01 15:32:17,675 - tensorflow - INFO - loss = 0.044201896, step = 405400 (2.303 sec) +2022-05-01 15:32:19,974 - tensorflow - INFO - global_step/sec: 86.981 +2022-05-01 15:32:19,974 - tensorflow - INFO - loss = 0.05312757, step = 405600 (2.299 sec) +2022-05-01 15:32:22,279 - tensorflow - INFO - global_step/sec: 86.7527 +2022-05-01 15:32:22,280 - tensorflow - INFO - loss = 0.10622845, step = 405800 (2.305 sec) +2022-05-01 15:32:24,581 - tensorflow - INFO - global_step/sec: 86.9053 +2022-05-01 15:32:24,581 - tensorflow - INFO - loss = 0.10170749, step = 406000 (2.301 sec) +2022-05-01 15:32:26,884 - tensorflow - INFO - global_step/sec: 86.8522 +2022-05-01 15:32:26,884 - tensorflow - INFO - loss = 0.048127078, step = 406200 (2.303 sec) +2022-05-01 15:32:29,187 - tensorflow - INFO - global_step/sec: 86.8426 +2022-05-01 15:32:29,187 - tensorflow - INFO - loss = 0.052297786, step = 406400 (2.303 sec) +2022-05-01 15:32:31,494 - tensorflow - INFO - global_step/sec: 86.6579 +2022-05-01 15:32:31,495 - tensorflow - INFO - loss = 0.08015016, step = 406600 (2.308 sec) +2022-05-01 15:32:33,796 - tensorflow - INFO - global_step/sec: 86.898 +2022-05-01 15:32:33,796 - tensorflow - INFO - loss = 0.16766617, step = 406800 (2.301 sec) +2022-05-01 15:32:36,098 - tensorflow - INFO - global_step/sec: 86.898 +2022-05-01 15:32:36,098 - tensorflow - INFO - loss = 0.8134452, step = 407000 (2.302 sec) +2022-05-01 15:32:38,395 - tensorflow - INFO - global_step/sec: 87.0599 +2022-05-01 15:32:38,395 - tensorflow - INFO - loss = 0.03777675, step = 407200 (2.297 sec) +2022-05-01 15:32:40,695 - tensorflow - INFO - global_step/sec: 86.9366 +2022-05-01 15:32:40,696 - tensorflow - INFO - loss = 0.13193761, step = 407400 (2.300 sec) +2022-05-01 15:32:42,981 - tensorflow - INFO - global_step/sec: 87.5104 +2022-05-01 15:32:42,981 - tensorflow - INFO - loss = 0.11008027, step = 407600 (2.285 sec) +2022-05-01 15:32:45,272 - tensorflow - INFO - global_step/sec: 87.3061 +2022-05-01 15:32:45,272 - tensorflow - INFO - loss = 0.0595809, step = 407800 (2.291 sec) +2022-05-01 15:32:47,562 - tensorflow - INFO - global_step/sec: 87.3225 +2022-05-01 15:32:47,562 - tensorflow - INFO - loss = 0.018416446, step = 408000 (2.290 sec) +2022-05-01 15:32:49,854 - tensorflow - INFO - global_step/sec: 87.245 +2022-05-01 15:32:49,855 - tensorflow - INFO - loss = 0.22051051, step = 408200 (2.292 sec) +2022-05-01 15:32:52,157 - tensorflow - INFO - global_step/sec: 86.8691 +2022-05-01 15:32:52,157 - tensorflow - INFO - loss = 0.5370528, step = 408400 (2.302 sec) +2022-05-01 15:32:54,452 - tensorflow - INFO - global_step/sec: 87.1151 +2022-05-01 15:32:54,453 - tensorflow - INFO - loss = 0.031772103, step = 408600 (2.296 sec) +2022-05-01 15:32:56,746 - tensorflow - INFO - global_step/sec: 87.21 +2022-05-01 15:32:56,746 - tensorflow - INFO - loss = 0.2617071, step = 408800 (2.293 sec) +2022-05-01 15:32:59,045 - tensorflow - INFO - global_step/sec: 86.9771 +2022-05-01 15:32:59,046 - tensorflow - INFO - loss = 0.093198895, step = 409000 (2.300 sec) +2022-05-01 15:33:01,340 - tensorflow - INFO - global_step/sec: 87.1726 +2022-05-01 15:33:01,340 - tensorflow - INFO - loss = 0.016394991, step = 409200 (2.294 sec) +2022-05-01 15:33:03,635 - tensorflow - INFO - global_step/sec: 87.1468 +2022-05-01 15:33:03,635 - tensorflow - INFO - loss = 0.071981266, step = 409400 (2.295 sec) +2022-05-01 15:33:05,920 - tensorflow - INFO - global_step/sec: 87.4897 +2022-05-01 15:33:05,921 - tensorflow - INFO - loss = 0.045541182, step = 409600 (2.286 sec) +2022-05-01 15:33:08,189 - tensorflow - INFO - global_step/sec: 88.1814 +2022-05-01 15:33:08,189 - tensorflow - INFO - loss = 0.08847006, step = 409800 (2.268 sec) +2022-05-01 15:33:10,443 - tensorflow - INFO - global_step/sec: 88.6944 +2022-05-01 15:33:10,444 - tensorflow - INFO - loss = 0.06049387, step = 410000 (2.255 sec) +2022-05-01 15:33:12,720 - tensorflow - INFO - global_step/sec: 87.8406 +2022-05-01 15:33:12,721 - tensorflow - INFO - loss = 0.022273019, step = 410200 (2.277 sec) +2022-05-01 15:33:15,012 - tensorflow - INFO - global_step/sec: 87.2749 +2022-05-01 15:33:15,012 - tensorflow - INFO - loss = 0.025294263, step = 410400 (2.292 sec) +2022-05-01 15:33:17,304 - tensorflow - INFO - global_step/sec: 87.2735 +2022-05-01 15:33:17,304 - tensorflow - INFO - loss = 0.020777583, step = 410600 (2.292 sec) +2022-05-01 15:33:19,597 - tensorflow - INFO - global_step/sec: 87.2056 +2022-05-01 15:33:19,597 - tensorflow - INFO - loss = 0.04804317, step = 410800 (2.293 sec) +2022-05-01 15:33:21,886 - tensorflow - INFO - global_step/sec: 87.3783 +2022-05-01 15:33:21,886 - tensorflow - INFO - loss = 0.020127455, step = 411000 (2.289 sec) +2022-05-01 15:33:24,174 - tensorflow - INFO - global_step/sec: 87.397 +2022-05-01 15:33:24,175 - tensorflow - INFO - loss = 0.11351947, step = 411200 (2.288 sec) +2022-05-01 15:33:26,461 - tensorflow - INFO - global_step/sec: 87.456 +2022-05-01 15:33:26,462 - tensorflow - INFO - loss = 0.06593898, step = 411400 (2.287 sec) +2022-05-01 15:33:28,748 - tensorflow - INFO - global_step/sec: 87.4636 +2022-05-01 15:33:28,748 - tensorflow - INFO - loss = 0.073699504, step = 411600 (2.287 sec) +2022-05-01 15:33:31,048 - tensorflow - INFO - global_step/sec: 86.9668 +2022-05-01 15:33:31,048 - tensorflow - INFO - loss = 0.1304144, step = 411800 (2.300 sec) +2022-05-01 15:33:33,340 - tensorflow - INFO - global_step/sec: 87.2568 +2022-05-01 15:33:33,340 - tensorflow - INFO - loss = 0.03475838, step = 412000 (2.292 sec) +2022-05-01 15:33:35,623 - tensorflow - INFO - global_step/sec: 87.608 +2022-05-01 15:33:35,623 - tensorflow - INFO - loss = 0.025602646, step = 412200 (2.283 sec) +2022-05-01 15:33:37,907 - tensorflow - INFO - global_step/sec: 87.5365 +2022-05-01 15:33:37,908 - tensorflow - INFO - loss = 0.4589123, step = 412400 (2.285 sec) +2022-05-01 15:33:40,196 - tensorflow - INFO - global_step/sec: 87.3716 +2022-05-01 15:33:40,197 - tensorflow - INFO - loss = 0.060308713, step = 412600 (2.289 sec) +2022-05-01 15:33:42,495 - 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global_step/sec: 87.0414 +2022-05-01 15:33:56,275 - tensorflow - INFO - loss = 0.18939602, step = 414000 (2.298 sec) +2022-05-01 15:33:58,569 - tensorflow - INFO - global_step/sec: 87.1782 +2022-05-01 15:33:58,570 - tensorflow - INFO - loss = 0.094642766, step = 414200 (2.294 sec) +2022-05-01 15:34:00,848 - tensorflow - INFO - global_step/sec: 87.7448 +2022-05-01 15:34:00,849 - tensorflow - INFO - loss = 0.036533773, step = 414400 (2.279 sec) +2022-05-01 15:34:03,138 - tensorflow - INFO - global_step/sec: 87.3375 +2022-05-01 15:34:03,139 - tensorflow - INFO - loss = 1.6116326, step = 414600 (2.290 sec) +2022-05-01 15:34:05,426 - tensorflow - INFO - global_step/sec: 87.4417 +2022-05-01 15:34:05,426 - tensorflow - INFO - loss = 0.047151618, step = 414800 (2.287 sec) +2022-05-01 15:34:07,732 - tensorflow - INFO - global_step/sec: 86.7379 +2022-05-01 15:34:07,732 - tensorflow - INFO - loss = 0.034820914, step = 415000 (2.306 sec) +2022-05-01 15:34:10,029 - tensorflow - INFO - global_step/sec: 87.0342 +2022-05-01 15:34:10,030 - tensorflow - INFO - loss = 0.25218165, step = 415200 (2.298 sec) +2022-05-01 15:34:12,326 - tensorflow - INFO - global_step/sec: 87.069 +2022-05-01 15:34:12,327 - tensorflow - INFO - loss = 0.05424107, step = 415400 (2.297 sec) +2022-05-01 15:34:14,618 - tensorflow - INFO - global_step/sec: 87.292 +2022-05-01 15:34:14,618 - tensorflow - INFO - loss = 0.1665936, step = 415600 (2.291 sec) +2022-05-01 15:34:16,904 - tensorflow - INFO - global_step/sec: 87.4635 +2022-05-01 15:34:16,905 - tensorflow - INFO - loss = 0.15828733, step = 415800 (2.287 sec) +2022-05-01 15:34:19,201 - tensorflow - INFO - global_step/sec: 87.0896 +2022-05-01 15:34:19,201 - tensorflow - INFO - loss = 0.1803932, step = 416000 (2.297 sec) +2022-05-01 15:34:21,495 - tensorflow - INFO - global_step/sec: 87.1763 +2022-05-01 15:34:21,495 - tensorflow - INFO - loss = 0.054921888, step = 416200 (2.294 sec) +2022-05-01 15:34:23,799 - tensorflow - INFO - global_step/sec: 86.8128 +2022-05-01 15:34:23,799 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 87.8584 +2022-05-01 15:34:39,843 - tensorflow - INFO - loss = 0.05170039, step = 417800 (2.276 sec) +2022-05-01 15:34:42,106 - tensorflow - INFO - global_step/sec: 88.3724 +2022-05-01 15:34:42,106 - tensorflow - INFO - loss = 0.03168484, step = 418000 (2.263 sec) +2022-05-01 15:34:44,371 - tensorflow - INFO - global_step/sec: 88.2907 +2022-05-01 15:34:44,371 - tensorflow - INFO - loss = 8.134713, step = 418200 (2.265 sec) +2022-05-01 15:34:46,660 - tensorflow - INFO - global_step/sec: 87.3752 +2022-05-01 15:34:46,660 - tensorflow - INFO - loss = 0.024514042, step = 418400 (2.289 sec) +2022-05-01 15:34:48,951 - tensorflow - INFO - global_step/sec: 87.2795 +2022-05-01 15:34:48,952 - tensorflow - INFO - loss = 0.05076325, step = 418600 (2.291 sec) +2022-05-01 15:34:51,239 - tensorflow - INFO - global_step/sec: 87.4347 +2022-05-01 15:34:51,239 - tensorflow - INFO - loss = 0.098169036, step = 418800 (2.287 sec) +2022-05-01 15:34:53,531 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.06376162, step = 485200 (2.298 sec) +2022-05-01 15:47:34,761 - tensorflow - INFO - global_step/sec: 87.1136 +2022-05-01 15:47:34,762 - tensorflow - INFO - loss = 0.0819434, step = 485400 (2.296 sec) +2022-05-01 15:47:37,049 - tensorflow - INFO - global_step/sec: 87.423 +2022-05-01 15:47:37,050 - tensorflow - INFO - loss = 0.021668667, step = 485600 (2.288 sec) +2022-05-01 15:47:39,341 - tensorflow - INFO - global_step/sec: 87.2717 +2022-05-01 15:47:39,341 - tensorflow - INFO - loss = 0.053483568, step = 485800 (2.292 sec) +2022-05-01 15:47:41,633 - tensorflow - INFO - global_step/sec: 87.2724 +2022-05-01 15:47:41,633 - tensorflow - INFO - loss = 0.04222194, step = 486000 (2.292 sec) +2022-05-01 15:47:43,928 - tensorflow - INFO - global_step/sec: 87.1425 +2022-05-01 15:47:43,928 - tensorflow - INFO - loss = 0.043749157, step = 486200 (2.295 sec) +2022-05-01 15:47:46,223 - tensorflow - INFO - global_step/sec: 87.1329 +2022-05-01 15:47:46,223 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 87.2922 +2022-05-01 15:48:02,276 - tensorflow - INFO - loss = 0.066290066, step = 487800 (2.291 sec) +2022-05-01 15:48:04,572 - tensorflow - INFO - global_step/sec: 87.0961 +2022-05-01 15:48:04,573 - tensorflow - INFO - loss = 14.458428, step = 488000 (2.296 sec) +2022-05-01 15:48:06,865 - tensorflow - INFO - global_step/sec: 87.2209 +2022-05-01 15:48:06,866 - tensorflow - INFO - loss = 0.04509601, step = 488200 (2.293 sec) +2022-05-01 15:48:09,163 - tensorflow - INFO - global_step/sec: 87.0534 +2022-05-01 15:48:09,163 - tensorflow - INFO - loss = 0.11672298, step = 488400 (2.297 sec) +2022-05-01 15:48:11,454 - tensorflow - INFO - global_step/sec: 87.2862 +2022-05-01 15:48:11,454 - tensorflow - INFO - loss = 0.051707402, step = 488600 (2.291 sec) +2022-05-01 15:48:13,742 - tensorflow - INFO - global_step/sec: 87.4171 +2022-05-01 15:48:13,742 - tensorflow - INFO - loss = 0.061385017, step = 488800 (2.288 sec) +2022-05-01 15:48:16,037 - tensorflow - INFO - global_step/sec: 87.1478 +2022-05-01 15:48:16,037 - tensorflow - INFO - loss = 0.042890646, step = 489000 (2.295 sec) +2022-05-01 15:48:18,326 - tensorflow - INFO - global_step/sec: 87.3552 +2022-05-01 15:48:18,327 - tensorflow - INFO - loss = 1.4140427, step = 489200 (2.289 sec) +2022-05-01 15:48:20,624 - tensorflow - INFO - global_step/sec: 87.0491 +2022-05-01 15:48:20,624 - tensorflow - INFO - loss = 0.0454567, step = 489400 (2.298 sec) +2022-05-01 15:48:22,920 - tensorflow - INFO - global_step/sec: 87.1186 +2022-05-01 15:48:22,920 - tensorflow - INFO - loss = 0.23944998, step = 489600 (2.296 sec) +2022-05-01 15:48:25,209 - tensorflow - INFO - global_step/sec: 87.347 +2022-05-01 15:48:25,210 - tensorflow - INFO - loss = 0.048230134, step = 489800 (2.290 sec) +2022-05-01 15:48:27,503 - tensorflow - INFO - global_step/sec: 87.2112 +2022-05-01 15:48:27,503 - tensorflow - INFO - loss = 0.17226861, step = 490000 (2.293 sec) +2022-05-01 15:48:29,793 - tensorflow - INFO - global_step/sec: 87.3317 +2022-05-01 15:48:29,793 - tensorflow - INFO - loss = 0.063290715, step = 490200 (2.290 sec) +2022-05-01 15:48:32,082 - tensorflow - INFO - global_step/sec: 87.3637 +2022-05-01 15:48:32,082 - tensorflow - INFO - loss = 0.15427542, step = 490400 (2.289 sec) +2022-05-01 15:48:34,377 - tensorflow - INFO - global_step/sec: 87.1478 +2022-05-01 15:48:34,377 - tensorflow - INFO - loss = 0.0866156, step = 490600 (2.295 sec) +2022-05-01 15:48:36,653 - tensorflow - INFO - global_step/sec: 87.8803 +2022-05-01 15:48:36,653 - tensorflow - INFO - loss = 0.11607466, step = 490800 (2.276 sec) +2022-05-01 15:48:38,915 - tensorflow - INFO - global_step/sec: 88.393 +2022-05-01 15:48:38,916 - tensorflow - INFO - loss = 0.030506007, step = 491000 (2.263 sec) +2022-05-01 15:48:41,176 - tensorflow - INFO - global_step/sec: 88.4874 +2022-05-01 15:48:41,176 - tensorflow - INFO - loss = 0.16931124, step = 491200 (2.260 sec) +2022-05-01 15:48:43,436 - tensorflow - INFO - global_step/sec: 88.4809 +2022-05-01 15:48:43,436 - tensorflow - INFO - loss = 0.07888447, step = 491400 (2.260 sec) +2022-05-01 15:48:45,734 - tensorflow - INFO - global_step/sec: 87.0183 +2022-05-01 15:48:45,735 - tensorflow - INFO - loss = 0.013879217, step = 491600 (2.298 sec) +2022-05-01 15:48:48,028 - tensorflow - INFO - global_step/sec: 87.1904 +2022-05-01 15:48:48,029 - tensorflow - INFO - loss = 0.19325846, step = 491800 (2.294 sec) +2022-05-01 15:48:50,324 - tensorflow - INFO - global_step/sec: 87.1239 +2022-05-01 15:48:50,324 - tensorflow - INFO - loss = 0.05001726, step = 492000 (2.296 sec) +2022-05-01 15:48:52,622 - tensorflow - INFO - global_step/sec: 87.034 +2022-05-01 15:48:52,622 - tensorflow - INFO - loss = 0.08362567, step = 492200 (2.298 sec) +2022-05-01 15:48:54,918 - tensorflow - INFO - global_step/sec: 87.1066 +2022-05-01 15:48:54,918 - tensorflow - INFO - loss = 0.045164224, step = 492400 (2.296 sec) +2022-05-01 15:48:57,213 - tensorflow - INFO - global_step/sec: 87.1404 +2022-05-01 15:48:57,213 - tensorflow - INFO - loss = 0.12752767, step = 492600 (2.295 sec) +2022-05-01 15:48:59,510 - tensorflow - INFO - global_step/sec: 87.0822 +2022-05-01 15:48:59,510 - tensorflow - INFO - loss = 0.060145747, step = 492800 (2.297 sec) +2022-05-01 15:49:01,809 - tensorflow - INFO - global_step/sec: 86.9762 +2022-05-01 15:49:01,810 - tensorflow - INFO - loss = 0.503282, step = 493000 (2.299 sec) +2022-05-01 15:49:04,097 - tensorflow - INFO - global_step/sec: 87.4125 +2022-05-01 15:49:04,098 - tensorflow - INFO - loss = 0.08907508, step = 493200 (2.288 sec) +2022-05-01 15:49:06,400 - tensorflow - INFO - global_step/sec: 86.839 +2022-05-01 15:49:06,401 - tensorflow - INFO - loss = 0.036446743, step = 493400 (2.303 sec) +2022-05-01 15:49:08,695 - tensorflow - INFO - global_step/sec: 87.1736 +2022-05-01 15:49:08,695 - tensorflow - INFO - loss = 3.1655476, step = 493600 (2.294 sec) +2022-05-01 15:49:10,991 - tensorflow - INFO - global_step/sec: 87.0857 +2022-05-01 15:49:10,992 - tensorflow - INFO - loss = 0.02401543, step = 493800 (2.297 sec) +2022-05-01 15:49:13,281 - tensorflow - INFO - global_step/sec: 87.3311 +2022-05-01 15:49:13,282 - tensorflow - INFO - loss = 0.02471529, step = 494000 (2.290 sec) +2022-05-01 15:49:15,573 - tensorflow - INFO - global_step/sec: 87.2857 +2022-05-01 15:49:15,573 - tensorflow - INFO - loss = 0.048128817, step = 494200 (2.291 sec) +2022-05-01 15:49:17,856 - tensorflow - INFO - global_step/sec: 87.5903 +2022-05-01 15:49:17,856 - tensorflow - INFO - loss = 0.3277765, step = 494400 (2.283 sec) +2022-05-01 15:49:20,153 - tensorflow - INFO - global_step/sec: 87.0611 +2022-05-01 15:49:20,154 - tensorflow - INFO - loss = 0.015861625, step = 494600 (2.297 sec) +2022-05-01 15:49:22,448 - tensorflow - INFO - global_step/sec: 87.1354 +2022-05-01 15:49:22,449 - tensorflow - INFO - loss = 0.028060673, step = 494800 (2.295 sec) +2022-05-01 15:49:24,744 - tensorflow - INFO - global_step/sec: 87.1415 +2022-05-01 15:49:24,744 - tensorflow - INFO - loss = 0.03264568, step = 495000 (2.295 sec) +2022-05-01 15:49:27,031 - tensorflow - INFO - global_step/sec: 87.4192 +2022-05-01 15:49:27,032 - tensorflow - INFO - loss = 0.45592192, step = 495200 (2.288 sec) +2022-05-01 15:49:29,313 - tensorflow - INFO - global_step/sec: 87.6582 +2022-05-01 15:49:29,313 - tensorflow - INFO - loss = 0.074402526, step = 495400 (2.282 sec) +2022-05-01 15:49:31,604 - tensorflow - INFO - global_step/sec: 87.2896 +2022-05-01 15:49:31,605 - tensorflow - INFO - loss = 0.34785342, step = 495600 (2.291 sec) +2022-05-01 15:49:33,894 - tensorflow - INFO - global_step/sec: 87.3332 +2022-05-01 15:49:33,895 - tensorflow - INFO - loss = 0.014895788, step = 495800 (2.290 sec) +2022-05-01 15:49:36,192 - tensorflow - INFO - global_step/sec: 87.0249 +2022-05-01 15:49:36,193 - tensorflow - INFO - loss = 0.039041396, step = 496000 (2.298 sec) +2022-05-01 15:49:38,480 - tensorflow - INFO - global_step/sec: 87.4465 +2022-05-01 15:49:38,480 - tensorflow - INFO - loss = 0.023041882, step = 496200 (2.287 sec) +2022-05-01 15:49:40,778 - tensorflow - INFO - global_step/sec: 87.01 +2022-05-01 15:49:40,779 - tensorflow - INFO - loss = 7.3349943, step = 496400 (2.299 sec) +2022-05-01 15:49:43,075 - tensorflow - INFO - global_step/sec: 87.0711 +2022-05-01 15:49:43,076 - tensorflow - INFO - loss = 0.690408, step = 496600 (2.297 sec) +2022-05-01 15:49:45,364 - tensorflow - INFO - global_step/sec: 87.3821 +2022-05-01 15:49:45,364 - tensorflow - INFO - loss = 0.042593334, step = 496800 (2.289 sec) +2022-05-01 15:49:47,666 - tensorflow - INFO - global_step/sec: 86.8956 +2022-05-01 15:49:47,666 - tensorflow - INFO - loss = 0.0668559, step = 497000 (2.302 sec) +2022-05-01 15:49:49,973 - tensorflow - INFO - global_step/sec: 86.6792 +2022-05-01 15:49:49,973 - tensorflow - INFO - loss = 0.011198775, step = 497200 (2.307 sec) +2022-05-01 15:49:52,277 - tensorflow - INFO - global_step/sec: 86.8116 +2022-05-01 15:49:52,277 - tensorflow - INFO - loss = 0.06476675, step = 497400 (2.304 sec) +2022-05-01 15:49:54,581 - tensorflow - INFO - global_step/sec: 86.8063 +2022-05-01 15:49:54,581 - tensorflow - INFO - loss = 0.043987837, step = 497600 (2.304 sec) +2022-05-01 15:49:56,880 - tensorflow - INFO - global_step/sec: 86.9838 +2022-05-01 15:49:56,880 - tensorflow - INFO - loss = 0.04835829, step = 497800 (2.299 sec) +2022-05-01 15:49:59,180 - tensorflow - INFO - global_step/sec: 86.9417 +2022-05-01 15:49:59,181 - tensorflow - INFO - loss = 0.044502974, step = 498000 (2.300 sec) +2022-05-01 15:50:01,478 - tensorflow - INFO - global_step/sec: 87.0622 +2022-05-01 15:50:01,478 - tensorflow - INFO - loss = 0.060919967, step = 498200 (2.297 sec) +2022-05-01 15:50:03,783 - tensorflow - INFO - global_step/sec: 86.7672 +2022-05-01 15:50:03,783 - tensorflow - INFO - loss = 0.12766704, step = 498400 (2.305 sec) +2022-05-01 15:50:06,076 - tensorflow - INFO - global_step/sec: 87.2122 +2022-05-01 15:50:06,076 - tensorflow - INFO - loss = 0.026480768, step = 498600 (2.293 sec) +2022-05-01 15:50:08,366 - tensorflow - INFO - global_step/sec: 87.3191 +2022-05-01 15:50:08,367 - tensorflow - INFO - loss = 1.5395248, step = 498800 (2.290 sec) +2022-05-01 15:50:10,651 - tensorflow - INFO - global_step/sec: 87.5458 +2022-05-01 15:50:10,651 - tensorflow - INFO - loss = 0.05769307, step = 499000 (2.285 sec) +2022-05-01 15:50:12,914 - tensorflow - INFO - global_step/sec: 88.3691 +2022-05-01 15:50:12,915 - tensorflow - INFO - loss = 0.06950209, step = 499200 (2.263 sec) +2022-05-01 15:50:15,180 - tensorflow - INFO - global_step/sec: 88.2645 +2022-05-01 15:50:15,180 - tensorflow - INFO - loss = 0.41808796, step = 499400 (2.266 sec) +2022-05-01 15:50:17,438 - tensorflow - INFO - global_step/sec: 88.5739 +2022-05-01 15:50:17,438 - tensorflow - INFO - loss = 0.16664134, step = 499600 (2.258 sec) +2022-05-01 15:50:19,734 - tensorflow - INFO - global_step/sec: 87.1192 +2022-05-01 15:50:19,734 - tensorflow - INFO - loss = 20.43417, step = 499800 (2.296 sec) +2022-05-01 15:50:22,016 - tensorflow - INFO - Saving checkpoints for 500000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. +2022-05-01 15:50:22,032 - tensorflow - INFO - model.ckpt-500000 is not in all_model_checkpoint_paths. Manually adding it. +2022-05-01 15:50:22,559 - tensorflow - INFO - Calling model_fn. +2022-05-01 15:50:22,692 - tensorflow - INFO - Done calling model_fn. +2022-05-01 15:50:22,707 - tensorflow - INFO - Starting evaluation at 2022-05-01T15:50:22Z +2022-05-01 15:50:22,772 - tensorflow - INFO - Graph was finalized. +2022-05-01 15:50:22,775 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-500000 +2022-05-01 15:50:23,062 - tensorflow - INFO - Running local_init_op. +2022-05-01 15:50:23,174 - tensorflow - INFO - Done running local_init_op. +2022-05-01 15:50:33,530 - tensorflow - INFO - Evaluation [3990/39900] +2022-05-01 15:50:43,155 - tensorflow - INFO - Evaluation [7980/39900] +2022-05-01 15:50:52,766 - tensorflow - INFO - Evaluation [11970/39900] +2022-05-01 15:51:02,371 - tensorflow - INFO - Evaluation [15960/39900] +2022-05-01 15:51:11,960 - tensorflow - INFO - Evaluation [19950/39900] +2022-05-01 15:51:21,578 - tensorflow - INFO - Evaluation [23940/39900] +2022-05-01 15:51:31,204 - tensorflow - INFO - Evaluation [27930/39900] +2022-05-01 15:51:40,848 - tensorflow - INFO - Evaluation [31920/39900] +2022-05-01 15:51:50,412 - tensorflow - INFO - Evaluation [35910/39900] +2022-05-01 15:52:00,044 - tensorflow - INFO - Evaluation [39900/39900] +2022-05-01 15:52:00,185 - tensorflow - INFO - Finished evaluation at 2022-05-01-15:52:00 +2022-05-01 15:52:00,185 - tensorflow - INFO - Saving dict for global step 500000: global_step = 500000, loss = 0.046809282, mean_degree_err = 6.3430543 +2022-05-01 15:52:00,186 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 500000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-500000 +2022-05-01 15:52:00,200 - tensorflow - INFO - global_step/sec: 1.99072 +2022-05-01 15:52:00,201 - tensorflow - INFO - loss = 0.063635156, step = 500000 (100.467 sec) +2022-05-01 15:52:02,510 - tensorflow - INFO - global_step/sec: 86.5758 +2022-05-01 15:52:02,510 - tensorflow - INFO - loss = 0.07470157, step = 500200 (2.310 sec) +2022-05-01 15:52:04,794 - tensorflow - INFO - global_step/sec: 87.5492 +2022-05-01 15:52:04,795 - tensorflow - INFO - loss = 0.31317902, step = 500400 (2.284 sec) +2022-05-01 15:52:07,086 - tensorflow - INFO - global_step/sec: 87.2645 +2022-05-01 15:52:07,087 - tensorflow - INFO - loss = 0.18935311, step = 500600 (2.292 sec) +2022-05-01 15:52:09,376 - tensorflow - INFO - global_step/sec: 87.3425 +2022-05-01 15:52:09,376 - tensorflow - INFO - loss = 0.020323459, step = 500800 (2.290 sec) +2022-05-01 15:52:11,673 - tensorflow - INFO - global_step/sec: 87.067 +2022-05-01 15:52:11,673 - tensorflow - INFO - loss = 0.024707984, step = 501000 (2.297 sec) +2022-05-01 15:52:13,966 - tensorflow - INFO - global_step/sec: 87.224 +2022-05-01 15:52:13,966 - tensorflow - INFO - loss = 0.02308048, step = 501200 (2.293 sec) +2022-05-01 15:52:16,256 - tensorflow - INFO - global_step/sec: 87.3316 +2022-05-01 15:52:16,257 - tensorflow - INFO - loss = 0.084123634, step = 501400 (2.290 sec) +2022-05-01 15:52:18,545 - tensorflow - INFO - global_step/sec: 87.3829 +2022-05-01 15:52:18,545 - tensorflow - INFO - loss = 0.9689673, step = 501600 (2.289 sec) +2022-05-01 15:52:20,830 - tensorflow - INFO - global_step/sec: 87.508 +2022-05-01 15:52:20,831 - tensorflow - INFO - loss = 0.04496465, step = 501800 (2.285 sec) +2022-05-01 15:52:23,122 - tensorflow - INFO - global_step/sec: 87.2854 +2022-05-01 15:52:23,122 - tensorflow - INFO - loss = 0.026626464, step = 502000 (2.291 sec) +2022-05-01 15:52:25,412 - tensorflow - INFO - global_step/sec: 87.3072 +2022-05-01 15:52:25,413 - tensorflow - INFO - loss = 0.086509205, step = 502200 (2.291 sec) +2022-05-01 15:52:27,707 - tensorflow - INFO - global_step/sec: 87.1582 +2022-05-01 15:52:27,708 - tensorflow - INFO - loss = 0.06746422, step = 502400 (2.295 sec) +2022-05-01 15:52:29,998 - tensorflow - INFO - global_step/sec: 87.3076 +2022-05-01 15:52:29,998 - tensorflow - INFO - loss = 0.0978373, step = 502600 (2.291 sec) +2022-05-01 15:52:32,295 - tensorflow - INFO - global_step/sec: 87.078 +2022-05-01 15:52:32,295 - tensorflow - INFO - loss = 0.104280345, step = 502800 (2.297 sec) +2022-05-01 15:52:34,591 - tensorflow - INFO - global_step/sec: 87.1093 +2022-05-01 15:52:34,591 - tensorflow - INFO - loss = 0.091107875, step = 503000 (2.296 sec) +2022-05-01 15:52:36,888 - tensorflow - INFO - global_step/sec: 87.0597 +2022-05-01 15:52:36,888 - tensorflow - INFO - loss = 0.033102345, step = 503200 (2.297 sec) +2022-05-01 15:52:39,175 - tensorflow - INFO - global_step/sec: 87.4474 +2022-05-01 15:52:39,175 - tensorflow - INFO - loss = 0.09425828, step = 503400 (2.287 sec) +2022-05-01 15:52:41,466 - tensorflow - INFO - global_step/sec: 87.2883 +2022-05-01 15:52:41,467 - tensorflow - INFO - loss = 0.061163433, step = 503600 (2.291 sec) +2022-05-01 15:52:43,758 - tensorflow - INFO - global_step/sec: 87.265 +2022-05-01 15:52:43,759 - tensorflow - INFO - loss = 0.13534966, step = 503800 (2.292 sec) +2022-05-01 15:52:46,054 - tensorflow - INFO - global_step/sec: 87.108 +2022-05-01 15:52:46,055 - tensorflow - INFO - loss = 0.06398092, step = 504000 (2.296 sec) +2022-05-01 15:52:48,353 - tensorflow - INFO - global_step/sec: 86.9983 +2022-05-01 15:52:48,354 - tensorflow - INFO - loss = 0.014031668, step = 504200 (2.299 sec) +2022-05-01 15:52:50,640 - tensorflow - INFO - global_step/sec: 87.4405 +2022-05-01 15:52:50,641 - tensorflow - INFO - loss = 0.049638722, step = 504400 (2.287 sec) +2022-05-01 15:52:52,947 - tensorflow - INFO - global_step/sec: 86.7174 +2022-05-01 15:52:52,947 - tensorflow - INFO - loss = 0.03826969, step = 504600 (2.306 sec) +2022-05-01 15:52:55,241 - tensorflow - INFO - global_step/sec: 87.1834 +2022-05-01 15:52:55,241 - tensorflow - INFO - loss = 0.08917885, step = 504800 (2.294 sec) +2022-05-01 15:52:57,543 - tensorflow - INFO - global_step/sec: 86.8621 +2022-05-01 15:52:57,544 - tensorflow - INFO - loss = 0.1652728, step = 505000 (2.303 sec) +2022-05-01 15:52:59,836 - tensorflow - INFO - global_step/sec: 87.2339 +2022-05-01 15:52:59,836 - tensorflow - INFO - loss = 0.17992705, step = 505200 (2.293 sec) +2022-05-01 15:53:02,139 - tensorflow - INFO - global_step/sec: 86.8256 +2022-05-01 15:53:02,140 - tensorflow - INFO - loss = 0.037294824, step = 505400 (2.303 sec) +2022-05-01 15:53:04,432 - tensorflow - INFO - global_step/sec: 87.2285 +2022-05-01 15:53:04,433 - tensorflow - INFO - loss = 0.15038131, step = 505600 (2.293 sec) +2022-05-01 15:53:06,724 - tensorflow - INFO - global_step/sec: 87.28 +2022-05-01 15:53:06,724 - tensorflow - INFO - loss = 0.065310985, step = 505800 (2.292 sec) +2022-05-01 15:53:09,012 - tensorflow - INFO - global_step/sec: 87.396 +2022-05-01 15:53:09,013 - tensorflow - INFO - loss = 0.034203827, step = 506000 (2.288 sec) +2022-05-01 15:53:11,312 - tensorflow - INFO - global_step/sec: 86.96 +2022-05-01 15:53:11,312 - tensorflow - INFO - loss = 0.16935429, step = 506200 (2.300 sec) +2022-05-01 15:53:13,611 - tensorflow - INFO - global_step/sec: 86.985 +2022-05-01 15:53:13,612 - tensorflow - INFO - loss = 0.035027437, step = 506400 (2.299 sec) +2022-05-01 15:53:15,902 - tensorflow - INFO - global_step/sec: 87.3028 +2022-05-01 15:53:15,903 - tensorflow - INFO - loss = 0.057028934, step = 506600 (2.291 sec) +2022-05-01 15:53:18,182 - tensorflow - INFO - global_step/sec: 87.7217 +2022-05-01 15:53:18,183 - tensorflow - INFO - loss = 0.0799098, step = 506800 (2.280 sec) +2022-05-01 15:53:20,471 - tensorflow - INFO - global_step/sec: 87.379 +2022-05-01 15:53:20,471 - tensorflow - INFO - loss = 0.03132668, step = 507000 (2.289 sec) +2022-05-01 15:53:22,772 - tensorflow - INFO - global_step/sec: 86.9129 +2022-05-01 15:53:22,773 - tensorflow - INFO - loss = 0.049004238, step = 507200 (2.301 sec) +2022-05-01 15:53:25,074 - tensorflow - INFO - global_step/sec: 86.9042 +2022-05-01 15:53:25,074 - tensorflow - INFO - loss = 0.05456598, step = 507400 (2.301 sec) +2022-05-01 15:53:27,378 - tensorflow - INFO - global_step/sec: 86.7786 +2022-05-01 15:53:27,379 - tensorflow - INFO - loss = 0.03548259, step = 507600 (2.305 sec) +2022-05-01 15:53:29,677 - tensorflow - INFO - global_step/sec: 86.9937 +2022-05-01 15:53:29,678 - tensorflow - INFO - loss = 0.22562142, step = 507800 (2.299 sec) +2022-05-01 15:53:31,982 - tensorflow - INFO - global_step/sec: 86.7697 +2022-05-01 15:53:31,983 - tensorflow - INFO - loss = 0.2586946, step = 508000 (2.305 sec) +2022-05-01 15:53:34,283 - tensorflow - INFO - global_step/sec: 86.9118 +2022-05-01 15:53:34,284 - tensorflow - INFO - loss = 0.07775393, step = 508200 (2.301 sec) +2022-05-01 15:53:36,583 - tensorflow - INFO - global_step/sec: 86.9646 +2022-05-01 15:53:36,584 - tensorflow - INFO - loss = 0.079266146, step = 508400 (2.300 sec) +2022-05-01 15:53:38,878 - tensorflow - INFO - global_step/sec: 87.1614 +2022-05-01 15:53:38,878 - tensorflow - INFO - loss = 0.06857913, step = 508600 (2.295 sec) +2022-05-01 15:53:41,169 - tensorflow - INFO - global_step/sec: 87.274 +2022-05-01 15:53:41,170 - tensorflow - INFO - loss = 0.028996538, step = 508800 (2.292 sec) +2022-05-01 15:53:43,466 - tensorflow - INFO - global_step/sec: 87.09 +2022-05-01 15:53:43,466 - tensorflow - INFO - loss = 0.03027647, step = 509000 (2.296 sec) +2022-05-01 15:53:45,746 - tensorflow - INFO - global_step/sec: 87.7088 +2022-05-01 15:53:45,746 - tensorflow - INFO - loss = 0.042125713, step = 509200 (2.280 sec) +2022-05-01 15:53:48,034 - tensorflow - INFO - global_step/sec: 87.4106 +2022-05-01 15:53:48,035 - tensorflow - INFO - loss = 0.019495798, step = 509400 (2.288 sec) +2022-05-01 15:53:50,322 - tensorflow - INFO - global_step/sec: 87.4146 +2022-05-01 15:53:50,323 - tensorflow - INFO - loss = 0.070728526, step = 509600 (2.288 sec) +2022-05-01 15:53:52,614 - tensorflow - INFO - global_step/sec: 87.2603 +2022-05-01 15:53:52,615 - tensorflow - INFO - loss = 0.40481895, step = 509800 (2.292 sec) +2022-05-01 15:53:54,910 - tensorflow - INFO - global_step/sec: 87.1076 +2022-05-01 15:53:54,911 - tensorflow - INFO - loss = 0.2444518, step = 510000 (2.296 sec) +2022-05-01 15:53:57,201 - tensorflow - INFO - global_step/sec: 87.3191 +2022-05-01 15:53:57,201 - tensorflow - INFO - loss = 0.16828924, step = 510200 (2.290 sec) +2022-05-01 15:53:59,487 - tensorflow - INFO - global_step/sec: 87.4854 +2022-05-01 15:53:59,487 - tensorflow - INFO - loss = 0.051806744, step = 510400 (2.286 sec) +2022-05-01 15:54:01,770 - tensorflow - INFO - global_step/sec: 87.5859 +2022-05-01 15:54:01,771 - tensorflow - INFO - loss = 0.06130576, step = 510600 (2.284 sec) +2022-05-01 15:54:04,060 - tensorflow - INFO - global_step/sec: 87.3608 +2022-05-01 15:54:04,060 - tensorflow - INFO - loss = 0.086674064, step = 510800 (2.289 sec) +2022-05-01 15:54:06,347 - tensorflow - INFO - global_step/sec: 87.4472 +2022-05-01 15:54:06,347 - tensorflow - INFO - loss = 0.15934047, step = 511000 (2.287 sec) +2022-05-01 15:54:08,632 - tensorflow - INFO - global_step/sec: 87.5184 +2022-05-01 15:54:08,632 - tensorflow - INFO - loss = 0.03121565, step = 511200 (2.285 sec) +2022-05-01 15:54:10,926 - tensorflow - INFO - global_step/sec: 87.1944 +2022-05-01 15:54:10,926 - tensorflow - INFO - loss = 0.103663206, step = 511400 (2.294 sec) +2022-05-01 15:54:13,211 - tensorflow - INFO - global_step/sec: 87.4968 +2022-05-01 15:54:13,212 - tensorflow - INFO - loss = 0.099881515, step = 511600 (2.286 sec) +2022-05-01 15:54:15,497 - tensorflow - INFO - global_step/sec: 87.5185 +2022-05-01 15:54:15,497 - tensorflow - INFO - loss = 0.038970098, step = 511800 (2.285 sec) +2022-05-01 15:54:17,785 - tensorflow - INFO - global_step/sec: 87.4104 +2022-05-01 15:54:17,785 - tensorflow - INFO - loss = 0.029831307, step = 512000 (2.288 sec) +2022-05-01 15:54:20,073 - tensorflow - INFO - global_step/sec: 87.4173 +2022-05-01 15:54:20,073 - tensorflow - INFO - loss = 0.15872079, step = 512200 (2.288 sec) +2022-05-01 15:54:22,358 - tensorflow - INFO - global_step/sec: 87.4893 +2022-05-01 15:54:22,359 - tensorflow - INFO - loss = 0.13896562, step = 512400 (2.286 sec) +2022-05-01 15:54:24,641 - tensorflow - INFO - global_step/sec: 87.633 +2022-05-01 15:54:24,641 - tensorflow - INFO - loss = 0.08064212, step = 512600 (2.282 sec) +2022-05-01 15:54:26,925 - 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tensorflow - INFO - loss = 0.26253784, step = 515200 (2.292 sec) +2022-05-01 15:54:56,602 - tensorflow - INFO - global_step/sec: 87.1934 +2022-05-01 15:54:56,602 - tensorflow - INFO - loss = 0.035267, step = 515400 (2.294 sec) +2022-05-01 15:54:58,896 - tensorflow - INFO - global_step/sec: 87.1762 +2022-05-01 15:54:58,897 - tensorflow - INFO - loss = 0.024436895, step = 515600 (2.294 sec) +2022-05-01 15:55:01,188 - tensorflow - INFO - global_step/sec: 87.2803 +2022-05-01 15:55:01,188 - tensorflow - INFO - loss = 0.08493805, step = 515800 (2.291 sec) +2022-05-01 15:55:03,485 - tensorflow - INFO - global_step/sec: 87.069 +2022-05-01 15:55:03,485 - tensorflow - INFO - loss = 0.022904579, step = 516000 (2.297 sec) +2022-05-01 15:55:05,775 - tensorflow - INFO - global_step/sec: 87.342 +2022-05-01 15:55:05,775 - tensorflow - INFO - loss = 0.06921731, step = 516200 (2.290 sec) +2022-05-01 15:55:08,071 - tensorflow - INFO - global_step/sec: 87.0845 +2022-05-01 15:55:08,072 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 87.0902 +2022-05-01 15:55:24,115 - tensorflow - INFO - loss = 0.06584891, step = 517800 (2.296 sec) +2022-05-01 15:55:26,406 - tensorflow - INFO - global_step/sec: 87.2613 +2022-05-01 15:55:26,407 - tensorflow - INFO - loss = 0.06383498, step = 518000 (2.292 sec) +2022-05-01 15:55:28,691 - tensorflow - INFO - global_step/sec: 87.5229 +2022-05-01 15:55:28,692 - tensorflow - INFO - loss = 0.29162747, step = 518200 (2.285 sec) +2022-05-01 15:55:30,992 - tensorflow - INFO - global_step/sec: 86.9451 +2022-05-01 15:55:30,992 - tensorflow - INFO - loss = 0.03455841, step = 518400 (2.300 sec) +2022-05-01 15:55:33,281 - tensorflow - INFO - global_step/sec: 87.3585 +2022-05-01 15:55:33,282 - tensorflow - INFO - loss = 0.053478647, step = 518600 (2.289 sec) +2022-05-01 15:55:35,582 - tensorflow - INFO - global_step/sec: 86.9209 +2022-05-01 15:55:35,583 - tensorflow - INFO - loss = 0.11700239, step = 518800 (2.301 sec) +2022-05-01 15:55:37,877 - tensorflow - INFO - 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INFO - global_step/sec: 87.2438 +2022-05-01 16:08:02,684 - tensorflow - INFO - loss = 12.821983, step = 584000 (2.293 sec) +2022-05-01 16:08:04,973 - tensorflow - INFO - global_step/sec: 87.3472 +2022-05-01 16:08:04,973 - tensorflow - INFO - loss = 0.018572694, step = 584200 (2.290 sec) +2022-05-01 16:08:07,267 - tensorflow - INFO - global_step/sec: 87.196 +2022-05-01 16:08:07,267 - tensorflow - INFO - loss = 0.2426543, step = 584400 (2.294 sec) +2022-05-01 16:08:09,552 - tensorflow - INFO - global_step/sec: 87.5324 +2022-05-01 16:08:09,552 - tensorflow - INFO - loss = 0.012297162, step = 584600 (2.285 sec) +2022-05-01 16:08:11,842 - tensorflow - INFO - global_step/sec: 87.3172 +2022-05-01 16:08:11,843 - tensorflow - INFO - loss = 0.02709882, step = 584800 (2.291 sec) +2022-05-01 16:08:14,135 - tensorflow - INFO - global_step/sec: 87.2379 +2022-05-01 16:08:14,135 - tensorflow - INFO - loss = 0.026977034, step = 585000 (2.292 sec) +2022-05-01 16:08:16,428 - tensorflow - INFO - global_step/sec: 87.2062 +2022-05-01 16:08:16,428 - tensorflow - INFO - loss = 0.06308341, step = 585200 (2.293 sec) +2022-05-01 16:08:18,716 - tensorflow - INFO - global_step/sec: 87.4137 +2022-05-01 16:08:18,716 - tensorflow - INFO - loss = 0.036703456, step = 585400 (2.288 sec) +2022-05-01 16:08:21,007 - tensorflow - INFO - global_step/sec: 87.3122 +2022-05-01 16:08:21,007 - tensorflow - INFO - loss = 0.017322699, step = 585600 (2.291 sec) +2022-05-01 16:08:23,302 - tensorflow - INFO - global_step/sec: 87.1363 +2022-05-01 16:08:23,302 - tensorflow - INFO - loss = 0.20249094, step = 585800 (2.295 sec) +2022-05-01 16:08:25,597 - tensorflow - INFO - global_step/sec: 87.123 +2022-05-01 16:08:25,598 - tensorflow - INFO - loss = 0.0323515, step = 586000 (2.296 sec) +2022-05-01 16:08:27,885 - tensorflow - INFO - global_step/sec: 87.4358 +2022-05-01 16:08:27,885 - tensorflow - INFO - loss = 0.044530425, step = 586200 (2.287 sec) +2022-05-01 16:08:30,182 - tensorflow - INFO - global_step/sec: 87.0825 +2022-05-01 16:08:30,182 - tensorflow - INFO - loss = 1.0501606, step = 586400 (2.297 sec) +2022-05-01 16:08:32,463 - tensorflow - INFO - global_step/sec: 87.6476 +2022-05-01 16:08:32,464 - tensorflow - INFO - loss = 0.03570166, step = 586600 (2.282 sec) +2022-05-01 16:08:34,734 - tensorflow - INFO - global_step/sec: 88.0702 +2022-05-01 16:08:34,735 - tensorflow - INFO - loss = 0.024391422, step = 586800 (2.271 sec) +2022-05-01 16:08:37,011 - tensorflow - INFO - global_step/sec: 87.8485 +2022-05-01 16:08:37,011 - tensorflow - INFO - loss = 0.020901958, step = 587000 (2.277 sec) +2022-05-01 16:08:39,281 - tensorflow - INFO - global_step/sec: 88.0869 +2022-05-01 16:08:39,282 - tensorflow - INFO - loss = 11.28486, step = 587200 (2.270 sec) +2022-05-01 16:08:41,552 - tensorflow - INFO - global_step/sec: 88.0718 +2022-05-01 16:08:41,553 - tensorflow - INFO - loss = 0.07651273, step = 587400 (2.271 sec) +2022-05-01 16:08:43,817 - tensorflow - INFO - global_step/sec: 88.3173 +2022-05-01 16:08:43,817 - tensorflow - INFO - loss = 0.048306994, step = 587600 (2.265 sec) +2022-05-01 16:08:46,088 - tensorflow - INFO - global_step/sec: 88.0657 +2022-05-01 16:08:46,088 - tensorflow - INFO - loss = 0.042515934, step = 587800 (2.271 sec) +2022-05-01 16:08:48,366 - tensorflow - INFO - global_step/sec: 87.7988 +2022-05-01 16:08:48,366 - tensorflow - INFO - loss = 0.31607643, step = 588000 (2.278 sec) +2022-05-01 16:08:50,632 - tensorflow - INFO - global_step/sec: 88.2473 +2022-05-01 16:08:50,633 - tensorflow - INFO - loss = 0.051129453, step = 588200 (2.266 sec) +2022-05-01 16:08:52,891 - tensorflow - INFO - global_step/sec: 88.5609 +2022-05-01 16:08:52,891 - tensorflow - INFO - loss = 0.060575508, step = 588400 (2.258 sec) +2022-05-01 16:08:55,143 - tensorflow - INFO - global_step/sec: 88.7751 +2022-05-01 16:08:55,144 - tensorflow - INFO - loss = 0.06876105, step = 588600 (2.253 sec) +2022-05-01 16:08:57,400 - tensorflow - INFO - global_step/sec: 88.6417 +2022-05-01 16:08:57,400 - tensorflow - INFO - loss = 0.01773398, step = 588800 (2.256 sec) +2022-05-01 16:08:59,655 - tensorflow - INFO - global_step/sec: 88.6811 +2022-05-01 16:08:59,655 - tensorflow - INFO - loss = 0.30537874, step = 589000 (2.255 sec) +2022-05-01 16:09:01,915 - tensorflow - INFO - global_step/sec: 88.4917 +2022-05-01 16:09:01,915 - tensorflow - INFO - loss = 0.19603918, step = 589200 (2.260 sec) +2022-05-01 16:09:04,165 - tensorflow - INFO - global_step/sec: 88.899 +2022-05-01 16:09:04,165 - tensorflow - INFO - loss = 0.103383504, step = 589400 (2.250 sec) +2022-05-01 16:09:06,418 - tensorflow - INFO - global_step/sec: 88.7487 +2022-05-01 16:09:06,419 - tensorflow - INFO - loss = 0.0767152, step = 589600 (2.254 sec) +2022-05-01 16:09:08,671 - tensorflow - INFO - global_step/sec: 88.7892 +2022-05-01 16:09:08,671 - tensorflow - INFO - loss = 12.67634, step = 589800 (2.253 sec) +2022-05-01 16:09:10,925 - tensorflow - INFO - global_step/sec: 88.7123 +2022-05-01 16:09:10,926 - tensorflow - INFO - loss = 0.1362737, step = 590000 (2.254 sec) +2022-05-01 16:09:13,183 - tensorflow - INFO - global_step/sec: 88.586 +2022-05-01 16:09:13,183 - tensorflow - INFO - loss = 0.059808686, step = 590200 (2.258 sec) +2022-05-01 16:09:15,441 - tensorflow - INFO - global_step/sec: 88.5846 +2022-05-01 16:09:15,441 - tensorflow - INFO - loss = 0.029569477, step = 590400 (2.258 sec) +2022-05-01 16:09:17,690 - tensorflow - INFO - global_step/sec: 88.9035 +2022-05-01 16:09:17,691 - tensorflow - INFO - loss = 0.04373379, step = 590600 (2.250 sec) +2022-05-01 16:09:19,940 - tensorflow - INFO - global_step/sec: 88.9028 +2022-05-01 16:09:19,940 - tensorflow - INFO - loss = 0.04323511, step = 590800 (2.250 sec) +2022-05-01 16:09:22,192 - tensorflow - INFO - global_step/sec: 88.8029 +2022-05-01 16:09:22,193 - tensorflow - INFO - loss = 0.039181337, step = 591000 (2.252 sec) +2022-05-01 16:09:24,451 - tensorflow - INFO - global_step/sec: 88.5384 +2022-05-01 16:09:24,452 - tensorflow - INFO - loss = 0.016992427, step = 591200 (2.259 sec) +2022-05-01 16:09:26,700 - tensorflow - INFO - global_step/sec: 88.9199 +2022-05-01 16:09:26,701 - tensorflow - INFO - loss = 0.012185383, step = 591400 (2.249 sec) +2022-05-01 16:09:28,952 - tensorflow - INFO - global_step/sec: 88.8107 +2022-05-01 16:09:28,953 - tensorflow - INFO - loss = 0.009116696, step = 591600 (2.252 sec) +2022-05-01 16:09:31,214 - tensorflow - INFO - global_step/sec: 88.4188 +2022-05-01 16:09:31,215 - tensorflow - INFO - loss = 0.017143156, step = 591800 (2.262 sec) +2022-05-01 16:09:33,469 - tensorflow - INFO - global_step/sec: 88.7175 +2022-05-01 16:09:33,469 - tensorflow - INFO - loss = 0.014566356, step = 592000 (2.254 sec) +2022-05-01 16:09:35,720 - tensorflow - INFO - global_step/sec: 88.8272 +2022-05-01 16:09:35,721 - tensorflow - INFO - loss = 0.13501108, step = 592200 (2.252 sec) +2022-05-01 16:09:37,975 - tensorflow - INFO - global_step/sec: 88.7008 +2022-05-01 16:09:37,975 - tensorflow - INFO - loss = 0.026087768, step = 592400 (2.255 sec) +2022-05-01 16:09:40,226 - tensorflow - INFO - global_step/sec: 88.8584 +2022-05-01 16:09:40,226 - tensorflow - INFO - loss = 0.03718528, step = 592600 (2.251 sec) +2022-05-01 16:09:42,472 - tensorflow - INFO - global_step/sec: 89.0448 +2022-05-01 16:09:42,472 - tensorflow - INFO - loss = 0.07650729, step = 592800 (2.246 sec) +2022-05-01 16:09:44,722 - tensorflow - INFO - global_step/sec: 88.8853 +2022-05-01 16:09:44,722 - tensorflow - INFO - loss = 3.1181715, step = 593000 (2.250 sec) +2022-05-01 16:09:46,975 - tensorflow - INFO - global_step/sec: 88.7733 +2022-05-01 16:09:46,975 - tensorflow - INFO - loss = 0.051136553, step = 593200 (2.253 sec) +2022-05-01 16:09:49,235 - tensorflow - INFO - global_step/sec: 88.4955 +2022-05-01 16:09:49,235 - tensorflow - INFO - loss = 0.32656357, step = 593400 (2.260 sec) +2022-05-01 16:09:51,488 - tensorflow - INFO - global_step/sec: 88.7762 +2022-05-01 16:09:51,488 - tensorflow - INFO - loss = 0.051417626, step = 593600 (2.253 sec) +2022-05-01 16:09:53,744 - tensorflow - INFO - global_step/sec: 88.6572 +2022-05-01 16:09:53,744 - tensorflow - INFO - loss = 0.0070130154, step = 593800 (2.256 sec) +2022-05-01 16:09:55,998 - tensorflow - INFO - global_step/sec: 88.7031 +2022-05-01 16:09:55,999 - tensorflow - INFO - loss = 0.012833834, step = 594000 (2.255 sec) +2022-05-01 16:09:58,251 - tensorflow - INFO - global_step/sec: 88.7779 +2022-05-01 16:09:58,252 - tensorflow - INFO - loss = 0.014024916, step = 594200 (2.253 sec) +2022-05-01 16:10:00,502 - tensorflow - INFO - global_step/sec: 88.8557 +2022-05-01 16:10:00,502 - tensorflow - INFO - loss = 0.07648599, step = 594400 (2.251 sec) +2022-05-01 16:10:02,763 - tensorflow - INFO - global_step/sec: 88.4516 +2022-05-01 16:10:02,764 - tensorflow - INFO - loss = 0.08582879, step = 594600 (2.261 sec) +2022-05-01 16:10:05,018 - tensorflow - INFO - global_step/sec: 88.6866 +2022-05-01 16:10:05,019 - tensorflow - INFO - loss = 0.04442747, step = 594800 (2.255 sec) +2022-05-01 16:10:07,268 - tensorflow - INFO - global_step/sec: 88.9089 +2022-05-01 16:10:07,268 - tensorflow - INFO - loss = 0.6102391, step = 595000 (2.249 sec) +2022-05-01 16:10:09,519 - tensorflow - INFO - global_step/sec: 88.8406 +2022-05-01 16:10:09,519 - tensorflow - INFO - loss = 0.00982367, step = 595200 (2.251 sec) +2022-05-01 16:10:11,772 - tensorflow - INFO - global_step/sec: 88.7721 +2022-05-01 16:10:11,772 - tensorflow - INFO - loss = 0.03102067, step = 595400 (2.253 sec) +2022-05-01 16:10:14,023 - tensorflow - INFO - global_step/sec: 88.8381 +2022-05-01 16:10:14,024 - tensorflow - INFO - loss = 0.020223914, step = 595600 (2.251 sec) +2022-05-01 16:10:16,277 - tensorflow - INFO - global_step/sec: 88.7384 +2022-05-01 16:10:16,277 - tensorflow - INFO - loss = 0.021211697, step = 595800 (2.254 sec) +2022-05-01 16:10:18,532 - tensorflow - INFO - global_step/sec: 88.6797 +2022-05-01 16:10:18,533 - tensorflow - INFO - loss = 0.3812176, step = 596000 (2.255 sec) +2022-05-01 16:10:20,787 - tensorflow - INFO - global_step/sec: 88.6834 +2022-05-01 16:10:20,788 - tensorflow - INFO - loss = 0.28526512, step = 596200 (2.255 sec) +2022-05-01 16:10:23,039 - tensorflow - INFO - global_step/sec: 88.8428 +2022-05-01 16:10:23,039 - tensorflow - INFO - loss = 0.016735926, step = 596400 (2.251 sec) +2022-05-01 16:10:25,288 - tensorflow - INFO - global_step/sec: 88.9144 +2022-05-01 16:10:25,288 - tensorflow - INFO - loss = 0.082976624, step = 596600 (2.249 sec) +2022-05-01 16:10:27,547 - tensorflow - INFO - global_step/sec: 88.5296 +2022-05-01 16:10:27,548 - tensorflow - INFO - loss = 0.045340955, step = 596800 (2.259 sec) +2022-05-01 16:10:29,798 - tensorflow - INFO - global_step/sec: 88.8455 +2022-05-01 16:10:29,799 - tensorflow - INFO - loss = 0.05019103, step = 597000 (2.251 sec) +2022-05-01 16:10:32,056 - tensorflow - INFO - global_step/sec: 88.5854 +2022-05-01 16:10:32,056 - tensorflow - INFO - loss = 0.2732757, step = 597200 (2.258 sec) +2022-05-01 16:10:34,310 - tensorflow - INFO - global_step/sec: 88.7338 +2022-05-01 16:10:34,310 - tensorflow - INFO - loss = 0.044302374, step = 597400 (2.254 sec) +2022-05-01 16:10:36,559 - tensorflow - INFO - global_step/sec: 88.9157 +2022-05-01 16:10:36,560 - tensorflow - INFO - loss = 0.033330336, step = 597600 (2.249 sec) +2022-05-01 16:10:38,815 - tensorflow - INFO - global_step/sec: 88.6414 +2022-05-01 16:10:38,816 - tensorflow - INFO - loss = 0.071330816, step = 597800 (2.256 sec) +2022-05-01 16:10:41,071 - tensorflow - INFO - global_step/sec: 88.6738 +2022-05-01 16:10:41,071 - tensorflow - INFO - loss = 0.03155005, step = 598000 (2.255 sec) +2022-05-01 16:10:43,331 - tensorflow - INFO - global_step/sec: 88.4949 +2022-05-01 16:10:43,331 - tensorflow - INFO - loss = 0.036724314, step = 598200 (2.260 sec) +2022-05-01 16:10:45,590 - tensorflow - INFO - global_step/sec: 88.5148 +2022-05-01 16:10:45,591 - tensorflow - INFO - loss = 0.058887675, step = 598400 (2.260 sec) +2022-05-01 16:10:47,850 - tensorflow - INFO - global_step/sec: 88.5321 +2022-05-01 16:10:47,850 - tensorflow - INFO - loss = 0.04405027, step = 598600 (2.259 sec) +2022-05-01 16:10:50,113 - tensorflow - INFO - global_step/sec: 88.3546 +2022-05-01 16:10:50,114 - tensorflow - INFO - loss = 0.026605247, step = 598800 (2.264 sec) +2022-05-01 16:10:52,375 - tensorflow - INFO - global_step/sec: 88.4125 +2022-05-01 16:10:52,376 - tensorflow - INFO - loss = 0.09087759, step = 599000 (2.262 sec) +2022-05-01 16:10:54,638 - tensorflow - INFO - global_step/sec: 88.3708 +2022-05-01 16:10:54,639 - tensorflow - INFO - loss = 0.050264105, step = 599200 (2.263 sec) +2022-05-01 16:10:56,895 - tensorflow - INFO - global_step/sec: 88.6399 +2022-05-01 16:10:56,895 - tensorflow - INFO - loss = 0.5043671, step = 599400 (2.256 sec) +2022-05-01 16:10:59,157 - tensorflow - INFO - global_step/sec: 88.41 +2022-05-01 16:10:59,157 - tensorflow - INFO - loss = 0.057621866, step = 599600 (2.262 sec) +2022-05-01 16:11:01,418 - tensorflow - INFO - global_step/sec: 88.4709 +2022-05-01 16:11:01,418 - tensorflow - INFO - loss = 0.04510246, step = 599800 (2.261 sec) +2022-05-01 16:11:03,669 - tensorflow - INFO - Saving checkpoints for 600000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. +2022-05-01 16:11:03,687 - tensorflow - INFO - model.ckpt-600000 is not in all_model_checkpoint_paths. Manually adding it. +2022-05-01 16:11:04,248 - tensorflow - INFO - Calling model_fn. +2022-05-01 16:11:04,379 - tensorflow - INFO - Done calling model_fn. +2022-05-01 16:11:04,394 - tensorflow - INFO - Starting evaluation at 2022-05-01T16:11:04Z +2022-05-01 16:11:04,458 - tensorflow - INFO - Graph was finalized. +2022-05-01 16:11:04,461 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-600000 +2022-05-01 16:11:04,742 - tensorflow - INFO - Running local_init_op. +2022-05-01 16:11:04,850 - tensorflow - INFO - Done running local_init_op. +2022-05-01 16:11:14,952 - tensorflow - INFO - Evaluation [3990/39900] +2022-05-01 16:11:24,367 - tensorflow - INFO - Evaluation [7980/39900] +2022-05-01 16:11:33,791 - tensorflow - INFO - Evaluation [11970/39900] +2022-05-01 16:11:43,194 - tensorflow - INFO - Evaluation [15960/39900] +2022-05-01 16:11:52,560 - tensorflow - INFO - Evaluation [19950/39900] +2022-05-01 16:12:01,964 - tensorflow - INFO - Evaluation [23940/39900] +2022-05-01 16:12:11,336 - tensorflow - INFO - Evaluation [27930/39900] +2022-05-01 16:12:20,741 - tensorflow - INFO - Evaluation [31920/39900] +2022-05-01 16:12:30,115 - tensorflow - INFO - Evaluation [35910/39900] +2022-05-01 16:12:39,506 - tensorflow - INFO - Evaluation [39900/39900] +2022-05-01 16:12:39,644 - tensorflow - INFO - Finished evaluation at 2022-05-01-16:12:39 +2022-05-01 16:12:39,644 - tensorflow - INFO - Saving dict for global step 600000: global_step = 600000, loss = 0.05211654, mean_degree_err = 7.7307415 +2022-05-01 16:12:39,645 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 600000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-600000 +2022-05-01 16:12:39,658 - tensorflow - INFO - global_step/sec: 2.03582 +2022-05-01 16:12:39,659 - tensorflow - INFO - loss = 1.0458663, step = 600000 (98.241 sec) +2022-05-01 16:12:41,927 - tensorflow - INFO - global_step/sec: 88.1445 +2022-05-01 16:12:41,928 - tensorflow - INFO - loss = 0.025115207, step = 600200 (2.269 sec) +2022-05-01 16:12:44,180 - tensorflow - INFO - global_step/sec: 88.7807 +2022-05-01 16:12:44,180 - tensorflow - INFO - loss = 0.103602424, step = 600400 (2.253 sec) +2022-05-01 16:12:46,441 - tensorflow - INFO - global_step/sec: 88.4632 +2022-05-01 16:12:46,441 - tensorflow - INFO - loss = 0.026233017, step = 600600 (2.261 sec) +2022-05-01 16:12:48,692 - tensorflow - INFO - global_step/sec: 88.8496 +2022-05-01 16:12:48,692 - tensorflow - INFO - loss = 0.9358847, step = 600800 (2.251 sec) +2022-05-01 16:12:50,948 - tensorflow - INFO - global_step/sec: 88.6401 +2022-05-01 16:12:50,949 - tensorflow - INFO - loss = 0.03394095, step = 601000 (2.256 sec) +2022-05-01 16:12:53,204 - tensorflow - INFO - global_step/sec: 88.6696 +2022-05-01 16:12:53,204 - tensorflow - INFO - loss = 0.020482913, step = 601200 (2.256 sec) +2022-05-01 16:12:55,460 - tensorflow - INFO - global_step/sec: 88.6249 +2022-05-01 16:12:55,461 - tensorflow - INFO - loss = 1.1575468, step = 601400 (2.257 sec) +2022-05-01 16:12:57,713 - tensorflow - INFO - global_step/sec: 88.7857 +2022-05-01 16:12:57,714 - tensorflow - INFO - loss = 0.05647299, step = 601600 (2.253 sec) +2022-05-01 16:12:59,964 - tensorflow - INFO - global_step/sec: 88.8566 +2022-05-01 16:12:59,964 - tensorflow - INFO - loss = 0.7979704, step = 601800 (2.251 sec) +2022-05-01 16:13:02,221 - tensorflow - INFO - global_step/sec: 88.6268 +2022-05-01 16:13:02,221 - tensorflow - INFO - loss = 0.042243555, step = 602000 (2.257 sec) +2022-05-01 16:13:04,476 - tensorflow - INFO - global_step/sec: 88.6789 +2022-05-01 16:13:04,476 - tensorflow - INFO - loss = 0.059275866, step = 602200 (2.255 sec) +2022-05-01 16:13:06,732 - tensorflow - INFO - global_step/sec: 88.6556 +2022-05-01 16:13:06,732 - tensorflow - INFO - loss = 0.6342204, step = 602400 (2.256 sec) +2022-05-01 16:13:08,989 - tensorflow - INFO - global_step/sec: 88.624 +2022-05-01 16:13:08,989 - tensorflow - INFO - loss = 0.03493088, step = 602600 (2.257 sec) +2022-05-01 16:13:11,246 - tensorflow - INFO - global_step/sec: 88.6143 +2022-05-01 16:13:11,246 - tensorflow - INFO - loss = 0.36002302, step = 602800 (2.257 sec) +2022-05-01 16:13:13,497 - tensorflow - INFO - global_step/sec: 88.818 +2022-05-01 16:13:13,498 - tensorflow - INFO - loss = 0.040594425, step = 603000 (2.252 sec) +2022-05-01 16:13:15,754 - tensorflow - INFO - global_step/sec: 88.6284 +2022-05-01 16:13:15,754 - tensorflow - INFO - loss = 0.10270108, step = 603200 (2.257 sec) +2022-05-01 16:13:18,007 - tensorflow - INFO - global_step/sec: 88.7484 +2022-05-01 16:13:18,008 - tensorflow - INFO - loss = 0.016271198, step = 603400 (2.254 sec) +2022-05-01 16:13:20,256 - tensorflow - INFO - global_step/sec: 88.9565 +2022-05-01 16:13:20,256 - tensorflow - INFO - loss = 0.071318746, step = 603600 (2.248 sec) +2022-05-01 16:13:22,507 - tensorflow - INFO - global_step/sec: 88.8344 +2022-05-01 16:13:22,508 - tensorflow - INFO - loss = 0.018505175, step = 603800 (2.251 sec) +2022-05-01 16:13:24,760 - tensorflow - INFO - global_step/sec: 88.7743 +2022-05-01 16:13:24,760 - tensorflow - INFO - loss = 0.015764019, step = 604000 (2.253 sec) +2022-05-01 16:13:27,018 - tensorflow - INFO - global_step/sec: 88.5653 +2022-05-01 16:13:27,019 - tensorflow - INFO - loss = 0.38297236, step = 604200 (2.258 sec) +2022-05-01 16:13:29,280 - tensorflow - INFO - global_step/sec: 88.4164 +2022-05-01 16:13:29,281 - tensorflow - INFO - loss = 0.022897664, step = 604400 (2.262 sec) +2022-05-01 16:13:31,540 - tensorflow - INFO - global_step/sec: 88.5055 +2022-05-01 16:13:31,541 - tensorflow - INFO - loss = 0.036261506, step = 604600 (2.260 sec) +2022-05-01 16:13:33,789 - tensorflow - INFO - global_step/sec: 88.9331 +2022-05-01 16:13:33,789 - tensorflow - INFO - loss = 0.026952295, step = 604800 (2.249 sec) +2022-05-01 16:13:36,043 - tensorflow - INFO - global_step/sec: 88.7405 +2022-05-01 16:13:36,043 - tensorflow - INFO - loss = 0.017694829, step = 605000 (2.254 sec) +2022-05-01 16:13:38,296 - tensorflow - INFO - global_step/sec: 88.7579 +2022-05-01 16:13:38,296 - tensorflow - INFO - loss = 0.017842809, step = 605200 (2.253 sec) +2022-05-01 16:13:40,550 - tensorflow - INFO - global_step/sec: 88.7298 +2022-05-01 16:13:40,550 - tensorflow - INFO - loss = 0.013539692, step = 605400 (2.254 sec) +2022-05-01 16:13:42,802 - tensorflow - INFO - global_step/sec: 88.7996 +2022-05-01 16:13:42,803 - tensorflow - INFO - loss = 1.015567, step = 605600 (2.252 sec) +2022-05-01 16:13:45,055 - tensorflow - INFO - global_step/sec: 88.78 +2022-05-01 16:13:45,055 - tensorflow - INFO - loss = 0.039732587, step = 605800 (2.253 sec) +2022-05-01 16:13:47,308 - tensorflow - INFO - global_step/sec: 88.7719 +2022-05-01 16:13:47,308 - tensorflow - INFO - loss = 0.052872486, step = 606000 (2.253 sec) +2022-05-01 16:13:49,564 - tensorflow - INFO - global_step/sec: 88.6697 +2022-05-01 16:13:49,564 - tensorflow - INFO - loss = 0.01597492, step = 606200 (2.256 sec) +2022-05-01 16:13:51,816 - tensorflow - INFO - global_step/sec: 88.781 +2022-05-01 16:13:51,817 - tensorflow - INFO - loss = 0.11118606, step = 606400 (2.253 sec) +2022-05-01 16:13:54,069 - tensorflow - INFO - global_step/sec: 88.7908 +2022-05-01 16:13:54,069 - tensorflow - INFO - loss = 8.458463, step = 606600 (2.253 sec) +2022-05-01 16:13:56,324 - tensorflow - INFO - global_step/sec: 88.6696 +2022-05-01 16:13:56,325 - tensorflow - INFO - loss = 0.6168818, step = 606800 (2.256 sec) +2022-05-01 16:13:58,577 - tensorflow - INFO - global_step/sec: 88.7971 +2022-05-01 16:13:58,577 - tensorflow - INFO - loss = 0.016155917, step = 607000 (2.252 sec) +2022-05-01 16:14:00,830 - tensorflow - INFO - global_step/sec: 88.7463 +2022-05-01 16:14:00,831 - tensorflow - INFO - loss = 0.025292601, step = 607200 (2.254 sec) +2022-05-01 16:14:03,089 - tensorflow - INFO - global_step/sec: 88.5512 +2022-05-01 16:14:03,089 - tensorflow - INFO - loss = 2.134423, step = 607400 (2.259 sec) +2022-05-01 16:14:05,340 - tensorflow - INFO - global_step/sec: 88.8408 +2022-05-01 16:14:05,341 - tensorflow - INFO - loss = 0.20809147, step = 607600 (2.251 sec) +2022-05-01 16:14:07,601 - tensorflow - INFO - global_step/sec: 88.47 +2022-05-01 16:14:07,601 - tensorflow - INFO - loss = 0.05131338, step = 607800 (2.261 sec) +2022-05-01 16:14:09,855 - tensorflow - INFO - global_step/sec: 88.7402 +2022-05-01 16:14:09,855 - tensorflow - INFO - loss = 0.039553158, step = 608000 (2.254 sec) +2022-05-01 16:14:12,113 - tensorflow - INFO - global_step/sec: 88.5686 +2022-05-01 16:14:12,113 - tensorflow - INFO - loss = 0.67503667, step = 608200 (2.258 sec) +2022-05-01 16:14:14,372 - tensorflow - INFO - global_step/sec: 88.5175 +2022-05-01 16:14:14,373 - tensorflow - INFO - loss = 1.8657655, step = 608400 (2.259 sec) +2022-05-01 16:14:16,635 - tensorflow - INFO - global_step/sec: 88.3683 +2022-05-01 16:14:16,636 - tensorflow - INFO - loss = 0.102950454, step = 608600 (2.263 sec) +2022-05-01 16:14:18,902 - tensorflow - INFO - global_step/sec: 88.255 +2022-05-01 16:14:18,902 - tensorflow - INFO - loss = 0.03498585, step = 608800 (2.266 sec) +2022-05-01 16:14:21,170 - tensorflow - INFO - global_step/sec: 88.1685 +2022-05-01 16:14:21,170 - tensorflow - INFO - loss = 2.6146653, step = 609000 (2.268 sec) +2022-05-01 16:14:23,433 - tensorflow - INFO - global_step/sec: 88.3812 +2022-05-01 16:14:23,433 - tensorflow - INFO - loss = 0.05584207, step = 609200 (2.263 sec) +2022-05-01 16:14:25,698 - tensorflow - INFO - global_step/sec: 88.2988 +2022-05-01 16:14:25,698 - tensorflow - INFO - loss = 0.061443545, step = 609400 (2.265 sec) +2022-05-01 16:14:27,960 - tensorflow - INFO - global_step/sec: 88.4167 +2022-05-01 16:14:27,960 - tensorflow - INFO - loss = 0.24088061, step = 609600 (2.262 sec) +2022-05-01 16:14:30,229 - tensorflow - INFO - global_step/sec: 88.1576 +2022-05-01 16:14:30,229 - tensorflow - INFO - loss = 0.03882327, step = 609800 (2.269 sec) +2022-05-01 16:14:32,502 - tensorflow - INFO - global_step/sec: 87.9705 +2022-05-01 16:14:32,502 - tensorflow - INFO - loss = 0.0358029, step = 610000 (2.273 sec) +2022-05-01 16:14:34,767 - tensorflow - INFO - global_step/sec: 88.2974 +2022-05-01 16:14:34,768 - tensorflow - INFO - loss = 0.02986932, step = 610200 (2.265 sec) +2022-05-01 16:14:37,032 - tensorflow - INFO - global_step/sec: 88.2992 +2022-05-01 16:14:37,033 - tensorflow - INFO - loss = 0.04424672, step = 610400 (2.265 sec) +2022-05-01 16:14:39,288 - tensorflow - INFO - global_step/sec: 88.6686 +2022-05-01 16:14:39,288 - tensorflow - INFO - loss = 0.0313861, step = 610600 (2.256 sec) +2022-05-01 16:14:41,541 - tensorflow - INFO - global_step/sec: 88.7708 +2022-05-01 16:14:41,541 - tensorflow - INFO - loss = 0.025802461, step = 610800 (2.253 sec) +2022-05-01 16:14:43,795 - tensorflow - INFO - global_step/sec: 88.7102 +2022-05-01 16:14:43,796 - tensorflow - INFO - loss = 0.050362512, step = 611000 (2.254 sec) +2022-05-01 16:14:46,052 - tensorflow - INFO - global_step/sec: 88.6139 +2022-05-01 16:14:46,053 - tensorflow - INFO - loss = 0.036653407, step = 611200 (2.257 sec) +2022-05-01 16:14:48,307 - tensorflow - INFO - global_step/sec: 88.7076 +2022-05-01 16:14:48,307 - tensorflow - INFO - loss = 0.0365748, step = 611400 (2.255 sec) +2022-05-01 16:14:50,558 - tensorflow - INFO - global_step/sec: 88.8249 +2022-05-01 16:14:50,559 - tensorflow - INFO - loss = 1.1359053, step = 611600 (2.252 sec) +2022-05-01 16:14:52,816 - tensorflow - INFO - global_step/sec: 88.6074 +2022-05-01 16:14:52,816 - tensorflow - INFO - loss = 0.1671949, step = 611800 (2.257 sec) +2022-05-01 16:14:55,071 - tensorflow - INFO - global_step/sec: 88.6709 +2022-05-01 16:14:55,072 - tensorflow - INFO - loss = 0.06732585, step = 612000 (2.256 sec) +2022-05-01 16:14:57,326 - tensorflow - INFO - global_step/sec: 88.6823 +2022-05-01 16:14:57,327 - tensorflow - INFO - loss = 0.030628437, step = 612200 (2.255 sec) +2022-05-01 16:14:59,578 - tensorflow - INFO - global_step/sec: 88.8077 +2022-05-01 16:14:59,579 - tensorflow - INFO - loss = 0.12056379, step = 612400 (2.252 sec) +2022-05-01 16:15:01,830 - tensorflow - INFO - global_step/sec: 88.8466 +2022-05-01 16:15:01,830 - tensorflow - INFO - loss = 0.044604473, step = 612600 (2.251 sec) +2022-05-01 16:15:04,081 - tensorflow - INFO - global_step/sec: 88.8407 +2022-05-01 16:15:04,081 - tensorflow - INFO - loss = 0.28650892, step = 612800 (2.251 sec) +2022-05-01 16:15:06,336 - tensorflow - INFO - global_step/sec: 88.6904 +2022-05-01 16:15:06,336 - tensorflow - INFO - loss = 0.014561885, step = 613000 (2.255 sec) +2022-05-01 16:15:08,594 - tensorflow - INFO - global_step/sec: 88.5642 +2022-05-01 16:15:08,594 - tensorflow - INFO - loss = 0.0681196, step = 613200 (2.258 sec) +2022-05-01 16:15:10,850 - tensorflow - INFO - global_step/sec: 88.656 +2022-05-01 16:15:10,850 - tensorflow - INFO - loss = 0.11148818, step = 613400 (2.256 sec) +2022-05-01 16:15:13,105 - tensorflow - INFO - global_step/sec: 88.7006 +2022-05-01 16:15:13,105 - tensorflow - INFO - loss = 0.078977704, step = 613600 (2.255 sec) +2022-05-01 16:15:15,359 - tensorflow - INFO - global_step/sec: 88.7006 +2022-05-01 16:15:15,360 - tensorflow - INFO - loss = 0.0747398, step = 613800 (2.255 sec) +2022-05-01 16:15:17,615 - tensorflow - INFO - global_step/sec: 88.6759 +2022-05-01 16:15:17,615 - tensorflow - INFO - loss = 0.032532763, step = 614000 (2.255 sec) +2022-05-01 16:15:19,866 - tensorflow - INFO - global_step/sec: 88.8421 +2022-05-01 16:15:19,866 - tensorflow - INFO - loss = 0.022528261, step = 614200 (2.251 sec) +2022-05-01 16:15:22,120 - tensorflow - INFO - global_step/sec: 88.7334 +2022-05-01 16:15:22,120 - tensorflow - INFO - loss = 0.032586053, step = 614400 (2.254 sec) +2022-05-01 16:15:24,375 - tensorflow - INFO - global_step/sec: 88.688 +2022-05-01 16:15:24,376 - tensorflow - INFO - loss = 0.006227595, step = 614600 (2.255 sec) +2022-05-01 16:15:26,626 - tensorflow - INFO - global_step/sec: 88.8601 +2022-05-01 16:15:26,626 - tensorflow - INFO - loss = 0.0127908895, step = 614800 (2.251 sec) +2022-05-01 16:15:28,881 - tensorflow - INFO - global_step/sec: 88.6947 +2022-05-01 16:15:28,881 - tensorflow - INFO - loss = 0.018400937, step = 615000 (2.255 sec) +2022-05-01 16:15:31,139 - tensorflow - INFO - global_step/sec: 88.5648 +2022-05-01 16:15:31,139 - tensorflow - INFO - loss = 0.015544417, step = 615200 (2.258 sec) +2022-05-01 16:15:33,391 - tensorflow - INFO - global_step/sec: 88.8067 +2022-05-01 16:15:33,391 - tensorflow - INFO - loss = 0.03676469, step = 615400 (2.252 sec) +2022-05-01 16:15:35,650 - tensorflow - INFO - global_step/sec: 88.5496 +2022-05-01 16:15:35,650 - tensorflow - INFO - loss = 0.024532415, step = 615600 (2.259 sec) +2022-05-01 16:15:37,904 - tensorflow - INFO - global_step/sec: 88.722 +2022-05-01 16:15:37,904 - tensorflow - INFO - loss = 0.03382394, step = 615800 (2.254 sec) +2022-05-01 16:15:40,158 - tensorflow - INFO - global_step/sec: 88.7179 +2022-05-01 16:15:40,159 - tensorflow - INFO - loss = 0.3950181, step = 616000 (2.254 sec) +2022-05-01 16:15:42,403 - tensorflow - INFO - global_step/sec: 89.0807 +2022-05-01 16:15:42,404 - tensorflow - INFO - loss = 0.038583353, step = 616200 (2.245 sec) +2022-05-01 16:15:44,659 - tensorflow - INFO - global_step/sec: 88.672 +2022-05-01 16:15:44,659 - tensorflow - INFO - loss = 0.033826016, step = 616400 (2.255 sec) +2022-05-01 16:15:46,917 - tensorflow - INFO - global_step/sec: 88.5798 +2022-05-01 16:15:46,917 - tensorflow - INFO - loss = 0.018127516, step = 616600 (2.258 sec) +2022-05-01 16:15:49,162 - tensorflow - INFO - global_step/sec: 89.089 +2022-05-01 16:15:49,162 - tensorflow - INFO - loss = 0.01999943, step = 616800 (2.245 sec) +2022-05-01 16:15:51,414 - tensorflow - INFO - global_step/sec: 88.8002 +2022-05-01 16:15:51,414 - tensorflow - INFO - loss = 0.037188075, step = 617000 (2.252 sec) +2022-05-01 16:15:53,670 - tensorflow - INFO - global_step/sec: 88.6327 +2022-05-01 16:15:53,671 - tensorflow - INFO - loss = 0.038181555, step = 617200 (2.257 sec) +2022-05-01 16:15:55,928 - tensorflow - INFO - global_step/sec: 88.586 +2022-05-01 16:15:55,929 - tensorflow - INFO - loss = 0.17563345, step = 617400 (2.258 sec) +2022-05-01 16:15:58,177 - tensorflow - INFO - global_step/sec: 88.954 +2022-05-01 16:15:58,177 - tensorflow - INFO - loss = 0.014711383, step = 617600 (2.248 sec) +2022-05-01 16:16:00,432 - tensorflow - INFO - global_step/sec: 88.6819 +2022-05-01 16:16:00,432 - tensorflow - INFO - loss = 0.02903166, step = 617800 (2.255 sec) +2022-05-01 16:16:02,685 - tensorflow - INFO - global_step/sec: 88.7609 +2022-05-01 16:16:02,685 - tensorflow - INFO - loss = 0.29104948, step = 618000 (2.253 sec) +2022-05-01 16:16:04,940 - tensorflow - INFO - global_step/sec: 88.6852 +2022-05-01 16:16:04,941 - tensorflow - INFO - loss = 0.926473, step = 618200 (2.255 sec) +2022-05-01 16:16:07,193 - tensorflow - INFO - global_step/sec: 88.7836 +2022-05-01 16:16:07,193 - tensorflow - INFO - loss = 0.06550895, step = 618400 (2.253 sec) +2022-05-01 16:16:09,449 - tensorflow - INFO - global_step/sec: 88.6348 +2022-05-01 16:16:09,450 - tensorflow - INFO - loss = 0.029829977, step = 618600 (2.256 sec) +2022-05-01 16:16:11,706 - tensorflow - INFO - global_step/sec: 88.6411 +2022-05-01 16:16:11,706 - tensorflow - INFO - loss = 0.045433816, step = 618800 (2.256 sec) +2022-05-01 16:16:13,959 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.5948 +2022-05-01 16:28:13,734 - tensorflow - INFO - loss = 0.1416041, step = 682800 (2.258 sec) +2022-05-01 16:28:16,002 - tensorflow - INFO - global_step/sec: 88.1773 +2022-05-01 16:28:16,002 - tensorflow - INFO - loss = 0.3864634, step = 683000 (2.268 sec) +2022-05-01 16:28:18,263 - tensorflow - INFO - global_step/sec: 88.4224 +2022-05-01 16:28:18,264 - tensorflow - INFO - loss = 0.041742954, step = 683200 (2.262 sec) +2022-05-01 16:28:20,529 - tensorflow - INFO - global_step/sec: 88.2897 +2022-05-01 16:28:20,529 - tensorflow - INFO - loss = 2.4299667, step = 683400 (2.265 sec) +2022-05-01 16:28:22,790 - tensorflow - INFO - global_step/sec: 88.4459 +2022-05-01 16:28:22,790 - tensorflow - INFO - loss = 0.02620551, step = 683600 (2.261 sec) +2022-05-01 16:28:25,051 - tensorflow - INFO - global_step/sec: 88.453 +2022-05-01 16:28:25,051 - tensorflow - INFO - loss = 0.028443713, step = 683800 (2.261 sec) +2022-05-01 16:28:27,321 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.03185318, step = 685200 (2.265 sec) +2022-05-01 16:28:43,177 - tensorflow - INFO - global_step/sec: 88.3747 +2022-05-01 16:28:43,178 - tensorflow - INFO - loss = 0.26845255, step = 685400 (2.263 sec) +2022-05-01 16:28:45,436 - tensorflow - INFO - global_step/sec: 88.5482 +2022-05-01 16:28:45,436 - tensorflow - INFO - loss = 0.025080893, step = 685600 (2.259 sec) +2022-05-01 16:28:47,697 - tensorflow - INFO - global_step/sec: 88.4514 +2022-05-01 16:28:47,697 - tensorflow - INFO - loss = 0.07365398, step = 685800 (2.261 sec) +2022-05-01 16:28:49,949 - tensorflow - INFO - global_step/sec: 88.8204 +2022-05-01 16:28:49,949 - tensorflow - INFO - loss = 0.0758942, step = 686000 (2.252 sec) +2022-05-01 16:28:52,208 - tensorflow - INFO - global_step/sec: 88.5143 +2022-05-01 16:28:52,209 - tensorflow - INFO - loss = 0.034178488, step = 686200 (2.260 sec) +2022-05-01 16:28:54,475 - tensorflow - INFO - global_step/sec: 88.2336 +2022-05-01 16:28:54,475 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.1105 +2022-05-01 16:29:10,426 - tensorflow - INFO - loss = 0.0153994765, step = 687800 (2.270 sec) +2022-05-01 16:29:12,701 - tensorflow - INFO - global_step/sec: 87.9208 +2022-05-01 16:29:12,701 - tensorflow - INFO - loss = 0.008310544, step = 688000 (2.275 sec) +2022-05-01 16:29:14,968 - tensorflow - INFO - global_step/sec: 88.201 +2022-05-01 16:29:14,969 - tensorflow - INFO - loss = 0.026683938, step = 688200 (2.268 sec) +2022-05-01 16:29:17,223 - tensorflow - INFO - global_step/sec: 88.6993 +2022-05-01 16:29:17,224 - tensorflow - INFO - loss = 0.00728536, step = 688400 (2.255 sec) +2022-05-01 16:29:19,482 - tensorflow - INFO - global_step/sec: 88.5408 +2022-05-01 16:29:19,482 - tensorflow - INFO - loss = 0.032275274, step = 688600 (2.259 sec) +2022-05-01 16:29:21,741 - tensorflow - INFO - global_step/sec: 88.5333 +2022-05-01 16:29:21,741 - tensorflow - INFO - loss = 0.51012313, step = 688800 (2.259 sec) +2022-05-01 16:29:23,990 - tensorflow - INFO - global_step/sec: 88.919 +2022-05-01 16:29:23,991 - tensorflow - INFO - loss = 0.014863342, step = 689000 (2.249 sec) +2022-05-01 16:29:26,246 - tensorflow - INFO - global_step/sec: 88.6489 +2022-05-01 16:29:26,247 - tensorflow - INFO - loss = 0.023834182, step = 689200 (2.256 sec) +2022-05-01 16:29:28,499 - tensorflow - INFO - global_step/sec: 88.7774 +2022-05-01 16:29:28,500 - tensorflow - INFO - loss = 0.03596233, step = 689400 (2.253 sec) +2022-05-01 16:29:30,760 - tensorflow - INFO - global_step/sec: 88.4604 +2022-05-01 16:29:30,761 - tensorflow - INFO - loss = 0.02390868, step = 689600 (2.261 sec) +2022-05-01 16:29:33,035 - tensorflow - INFO - global_step/sec: 87.9258 +2022-05-01 16:29:33,035 - tensorflow - INFO - loss = 0.021484703, step = 689800 (2.275 sec) +2022-05-01 16:29:35,300 - tensorflow - INFO - global_step/sec: 88.2833 +2022-05-01 16:29:35,301 - tensorflow - INFO - loss = 0.3240009, step = 690000 (2.265 sec) +2022-05-01 16:29:37,566 - tensorflow - INFO - global_step/sec: 88.264 +2022-05-01 16:29:37,567 - tensorflow - INFO - loss = 0.36914372, step = 690200 (2.266 sec) +2022-05-01 16:29:39,870 - tensorflow - INFO - global_step/sec: 86.8138 +2022-05-01 16:29:39,870 - tensorflow - INFO - loss = 0.34035963, step = 690400 (2.304 sec) +2022-05-01 16:29:42,161 - tensorflow - INFO - global_step/sec: 87.2855 +2022-05-01 16:29:42,162 - tensorflow - INFO - loss = 0.051499836, step = 690600 (2.291 sec) +2022-05-01 16:29:44,441 - tensorflow - INFO - global_step/sec: 87.7261 +2022-05-01 16:29:44,442 - tensorflow - INFO - loss = 0.063221216, step = 690800 (2.280 sec) +2022-05-01 16:29:46,714 - tensorflow - INFO - global_step/sec: 88.0058 +2022-05-01 16:29:46,714 - tensorflow - INFO - loss = 0.064412564, step = 691000 (2.273 sec) +2022-05-01 16:29:48,986 - tensorflow - INFO - global_step/sec: 88.0249 +2022-05-01 16:29:48,986 - tensorflow - INFO - loss = 0.03744691, step = 691200 (2.272 sec) +2022-05-01 16:29:51,261 - tensorflow - INFO - global_step/sec: 87.8832 +2022-05-01 16:29:51,262 - tensorflow - INFO - loss = 0.12669168, step = 691400 (2.276 sec) +2022-05-01 16:29:53,539 - tensorflow - INFO - global_step/sec: 87.8175 +2022-05-01 16:29:53,539 - tensorflow - INFO - loss = 0.40596506, step = 691600 (2.278 sec) +2022-05-01 16:29:55,806 - tensorflow - INFO - global_step/sec: 88.2063 +2022-05-01 16:29:55,807 - tensorflow - INFO - loss = 0.010353914, step = 691800 (2.267 sec) +2022-05-01 16:29:58,072 - tensorflow - INFO - global_step/sec: 88.2865 +2022-05-01 16:29:58,072 - tensorflow - INFO - loss = 0.040336475, step = 692000 (2.265 sec) +2022-05-01 16:30:00,327 - tensorflow - INFO - global_step/sec: 88.669 +2022-05-01 16:30:00,328 - tensorflow - INFO - loss = 0.020247448, step = 692200 (2.256 sec) +2022-05-01 16:30:02,586 - tensorflow - INFO - global_step/sec: 88.5495 +2022-05-01 16:30:02,586 - tensorflow - INFO - loss = 0.022701023, step = 692400 (2.259 sec) +2022-05-01 16:30:04,839 - tensorflow - INFO - global_step/sec: 88.7533 +2022-05-01 16:30:04,840 - tensorflow - INFO - loss = 0.012202943, step = 692600 (2.253 sec) +2022-05-01 16:30:07,093 - tensorflow - INFO - global_step/sec: 88.7234 +2022-05-01 16:30:07,094 - tensorflow - INFO - loss = 0.05705206, step = 692800 (2.254 sec) +2022-05-01 16:30:09,352 - tensorflow - INFO - global_step/sec: 88.5445 +2022-05-01 16:30:09,353 - tensorflow - INFO - loss = 0.063872375, step = 693000 (2.259 sec) +2022-05-01 16:30:11,612 - tensorflow - INFO - global_step/sec: 88.4898 +2022-05-01 16:30:11,613 - tensorflow - INFO - loss = 0.20911783, step = 693200 (2.260 sec) +2022-05-01 16:30:13,868 - tensorflow - INFO - global_step/sec: 88.6538 +2022-05-01 16:30:13,869 - tensorflow - INFO - loss = 0.17761394, step = 693400 (2.256 sec) +2022-05-01 16:30:16,123 - tensorflow - INFO - global_step/sec: 88.7222 +2022-05-01 16:30:16,123 - tensorflow - INFO - loss = 2.785748, step = 693600 (2.254 sec) +2022-05-01 16:30:18,375 - tensorflow - INFO - global_step/sec: 88.7818 +2022-05-01 16:30:18,376 - tensorflow - INFO - loss = 0.047160655, step = 693800 (2.253 sec) +2022-05-01 16:30:20,628 - tensorflow - INFO - global_step/sec: 88.7787 +2022-05-01 16:30:20,629 - tensorflow - INFO - loss = 0.14111653, step = 694000 (2.253 sec) +2022-05-01 16:30:22,884 - tensorflow - INFO - global_step/sec: 88.645 +2022-05-01 16:30:22,885 - tensorflow - INFO - loss = 0.061342344, step = 694200 (2.256 sec) +2022-05-01 16:30:25,140 - tensorflow - INFO - global_step/sec: 88.6744 +2022-05-01 16:30:25,140 - tensorflow - INFO - loss = 0.03867026, step = 694400 (2.255 sec) +2022-05-01 16:30:27,397 - tensorflow - INFO - global_step/sec: 88.6029 +2022-05-01 16:30:27,397 - tensorflow - INFO - loss = 0.034364447, step = 694600 (2.257 sec) +2022-05-01 16:30:29,651 - tensorflow - INFO - global_step/sec: 88.7231 +2022-05-01 16:30:29,652 - tensorflow - INFO - loss = 0.022616444, step = 694800 (2.254 sec) +2022-05-01 16:30:31,917 - tensorflow - INFO - global_step/sec: 88.2733 +2022-05-01 16:30:31,917 - tensorflow - INFO - loss = 0.078567564, step = 695000 (2.266 sec) +2022-05-01 16:30:34,172 - tensorflow - INFO - global_step/sec: 88.6967 +2022-05-01 16:30:34,172 - tensorflow - INFO - loss = 0.02870323, step = 695200 (2.255 sec) +2022-05-01 16:30:36,429 - tensorflow - INFO - global_step/sec: 88.6052 +2022-05-01 16:30:36,429 - tensorflow - INFO - loss = 0.027117027, step = 695400 (2.257 sec) +2022-05-01 16:30:38,675 - tensorflow - INFO - global_step/sec: 89.0403 +2022-05-01 16:30:38,676 - tensorflow - INFO - loss = 9.146388, step = 695600 (2.246 sec) +2022-05-01 16:30:40,926 - tensorflow - INFO - global_step/sec: 88.8497 +2022-05-01 16:30:40,927 - tensorflow - INFO - loss = 0.14333989, step = 695800 (2.251 sec) +2022-05-01 16:30:43,185 - tensorflow - INFO - global_step/sec: 88.5331 +2022-05-01 16:30:43,186 - tensorflow - INFO - loss = 0.3762799, step = 696000 (2.259 sec) +2022-05-01 16:30:45,458 - tensorflow - INFO - global_step/sec: 88.0085 +2022-05-01 16:30:45,458 - tensorflow - INFO - loss = 0.025378358, step = 696200 (2.272 sec) +2022-05-01 16:30:47,735 - tensorflow - INFO - global_step/sec: 87.8242 +2022-05-01 16:30:47,735 - tensorflow - INFO - loss = 0.028789608, step = 696400 (2.277 sec) +2022-05-01 16:30:50,009 - tensorflow - INFO - global_step/sec: 87.9507 +2022-05-01 16:30:50,009 - tensorflow - INFO - loss = 0.013650784, step = 696600 (2.274 sec) +2022-05-01 16:30:52,313 - tensorflow - INFO - global_step/sec: 86.8005 +2022-05-01 16:30:52,314 - tensorflow - INFO - loss = 0.029505478, step = 696800 (2.304 sec) +2022-05-01 16:30:54,587 - tensorflow - INFO - global_step/sec: 87.9393 +2022-05-01 16:30:54,588 - tensorflow - INFO - loss = 1.0471745, step = 697000 (2.274 sec) +2022-05-01 16:30:56,858 - tensorflow - INFO - global_step/sec: 88.0994 +2022-05-01 16:30:56,858 - tensorflow - INFO - loss = 0.037719212, step = 697200 (2.270 sec) +2022-05-01 16:30:59,134 - tensorflow - INFO - global_step/sec: 87.8514 +2022-05-01 16:30:59,135 - tensorflow - INFO - loss = 0.030568294, step = 697400 (2.277 sec) +2022-05-01 16:31:01,409 - tensorflow - INFO - global_step/sec: 87.9152 +2022-05-01 16:31:01,410 - tensorflow - INFO - loss = 0.13457468, step = 697600 (2.275 sec) +2022-05-01 16:31:03,682 - tensorflow - INFO - global_step/sec: 87.9968 +2022-05-01 16:31:03,682 - tensorflow - INFO - loss = 0.08395249, step = 697800 (2.273 sec) +2022-05-01 16:31:05,955 - tensorflow - INFO - global_step/sec: 87.9794 +2022-05-01 16:31:05,956 - tensorflow - INFO - loss = 0.06530699, step = 698000 (2.273 sec) +2022-05-01 16:31:08,212 - tensorflow - INFO - global_step/sec: 88.6067 +2022-05-01 16:31:08,213 - tensorflow - INFO - loss = 0.0882684, step = 698200 (2.257 sec) +2022-05-01 16:31:10,465 - tensorflow - INFO - global_step/sec: 88.7685 +2022-05-01 16:31:10,466 - tensorflow - INFO - loss = 0.060899876, step = 698400 (2.253 sec) +2022-05-01 16:31:12,720 - tensorflow - INFO - global_step/sec: 88.7142 +2022-05-01 16:31:12,720 - tensorflow - INFO - loss = 0.18980502, step = 698600 (2.254 sec) +2022-05-01 16:31:14,972 - tensorflow - INFO - global_step/sec: 88.7923 +2022-05-01 16:31:14,973 - tensorflow - INFO - loss = 0.28167945, step = 698800 (2.252 sec) +2022-05-01 16:31:17,229 - tensorflow - INFO - global_step/sec: 88.6269 +2022-05-01 16:31:17,229 - tensorflow - INFO - loss = 0.015430516, step = 699000 (2.257 sec) +2022-05-01 16:31:19,482 - tensorflow - INFO - global_step/sec: 88.7613 +2022-05-01 16:31:19,483 - tensorflow - INFO - loss = 0.020742904, step = 699200 (2.253 sec) +2022-05-01 16:31:21,733 - tensorflow - INFO - global_step/sec: 88.8653 +2022-05-01 16:31:21,733 - tensorflow - INFO - loss = 0.020765623, step = 699400 (2.251 sec) +2022-05-01 16:31:23,993 - tensorflow - INFO - global_step/sec: 88.4763 +2022-05-01 16:31:23,994 - tensorflow - INFO - loss = 0.018228173, step = 699600 (2.261 sec) +2022-05-01 16:31:26,250 - tensorflow - INFO - global_step/sec: 88.6307 +2022-05-01 16:31:26,250 - tensorflow - INFO - loss = 0.022793582, step = 699800 (2.257 sec) +2022-05-01 16:31:28,499 - tensorflow - INFO - Saving checkpoints for 700000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. +2022-05-01 16:31:28,517 - tensorflow - INFO - model.ckpt-700000 is not in all_model_checkpoint_paths. Manually adding it. +2022-05-01 16:31:29,045 - tensorflow - INFO - Calling model_fn. +2022-05-01 16:31:29,177 - tensorflow - INFO - Done calling model_fn. +2022-05-01 16:31:29,192 - tensorflow - INFO - Starting evaluation at 2022-05-01T16:31:29Z +2022-05-01 16:31:29,256 - tensorflow - INFO - Graph was finalized. +2022-05-01 16:31:29,259 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-700000 +2022-05-01 16:31:29,540 - tensorflow - INFO - Running local_init_op. +2022-05-01 16:31:29,646 - tensorflow - INFO - Done running local_init_op. +2022-05-01 16:31:39,785 - tensorflow - INFO - Evaluation [3990/39900] +2022-05-01 16:31:49,238 - tensorflow - INFO - Evaluation [7980/39900] +2022-05-01 16:31:58,714 - tensorflow - INFO - Evaluation [11970/39900] +2022-05-01 16:32:08,192 - tensorflow - INFO - Evaluation [15960/39900] +2022-05-01 16:32:17,622 - tensorflow - INFO - Evaluation [19950/39900] +2022-05-01 16:32:27,039 - tensorflow - INFO - Evaluation [23940/39900] +2022-05-01 16:32:36,492 - tensorflow - INFO - Evaluation [27930/39900] +2022-05-01 16:32:45,931 - tensorflow - INFO - Evaluation [31920/39900] +2022-05-01 16:32:55,356 - tensorflow - INFO - Evaluation [35910/39900] +2022-05-01 16:33:04,797 - tensorflow - INFO - Evaluation [39900/39900] +2022-05-01 16:33:04,934 - tensorflow - INFO - Finished evaluation at 2022-05-01-16:33:04 +2022-05-01 16:33:04,934 - tensorflow - INFO - Saving dict for global step 700000: global_step = 700000, loss = 0.03090773, mean_degree_err = 4.9194665 +2022-05-01 16:33:04,934 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 700000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-700000 +2022-05-01 16:33:04,948 - tensorflow - INFO - global_step/sec: 2.02638 +2022-05-01 16:33:04,949 - tensorflow - INFO - loss = 0.45383355, step = 700000 (98.699 sec) +2022-05-01 16:33:07,219 - tensorflow - INFO - global_step/sec: 88.0497 +2022-05-01 16:33:07,220 - tensorflow - INFO - loss = 0.02814351, step = 700200 (2.271 sec) +2022-05-01 16:33:09,476 - tensorflow - INFO - global_step/sec: 88.6366 +2022-05-01 16:33:09,476 - tensorflow - INFO - loss = 0.031599652, step = 700400 (2.256 sec) +2022-05-01 16:33:11,728 - tensorflow - INFO - global_step/sec: 88.8244 +2022-05-01 16:33:11,728 - tensorflow - INFO - loss = 0.06685158, step = 700600 (2.252 sec) +2022-05-01 16:33:13,984 - tensorflow - INFO - global_step/sec: 88.6234 +2022-05-01 16:33:13,985 - tensorflow - INFO - loss = 0.05536495, step = 700800 (2.257 sec) +2022-05-01 16:33:16,239 - tensorflow - INFO - global_step/sec: 88.7024 +2022-05-01 16:33:16,239 - tensorflow - INFO - loss = 0.009829298, step = 701000 (2.255 sec) +2022-05-01 16:33:18,494 - tensorflow - INFO - global_step/sec: 88.679 +2022-05-01 16:33:18,495 - tensorflow - INFO - loss = 0.02428996, step = 701200 (2.255 sec) +2022-05-01 16:33:20,752 - tensorflow - INFO - global_step/sec: 88.6027 +2022-05-01 16:33:20,752 - tensorflow - INFO - loss = 0.018015191, step = 701400 (2.257 sec) +2022-05-01 16:33:23,006 - tensorflow - INFO - global_step/sec: 88.7342 +2022-05-01 16:33:23,006 - tensorflow - INFO - loss = 0.07055589, step = 701600 (2.254 sec) +2022-05-01 16:33:25,255 - tensorflow - INFO - global_step/sec: 88.92 +2022-05-01 16:33:25,255 - tensorflow - INFO - loss = 0.6384876, step = 701800 (2.249 sec) +2022-05-01 16:33:27,511 - tensorflow - INFO - global_step/sec: 88.6497 +2022-05-01 16:33:27,511 - tensorflow - INFO - loss = 0.014400528, step = 702000 (2.256 sec) +2022-05-01 16:33:29,762 - tensorflow - INFO - global_step/sec: 88.8495 +2022-05-01 16:33:29,762 - tensorflow - INFO - loss = 0.030938236, step = 702200 (2.251 sec) +2022-05-01 16:33:32,022 - tensorflow - INFO - global_step/sec: 88.5049 +2022-05-01 16:33:32,022 - tensorflow - INFO - loss = 0.05836013, step = 702400 (2.260 sec) +2022-05-01 16:33:34,280 - tensorflow - INFO - global_step/sec: 88.5689 +2022-05-01 16:33:34,280 - tensorflow - INFO - loss = 0.014873834, step = 702600 (2.258 sec) +2022-05-01 16:33:36,535 - tensorflow - INFO - global_step/sec: 88.6665 +2022-05-01 16:33:36,536 - tensorflow - INFO - loss = 0.05012783, step = 702800 (2.256 sec) +2022-05-01 16:33:38,792 - tensorflow - INFO - global_step/sec: 88.6365 +2022-05-01 16:33:38,792 - tensorflow - INFO - loss = 0.04358377, step = 703000 (2.256 sec) +2022-05-01 16:33:41,051 - tensorflow - INFO - global_step/sec: 88.5095 +2022-05-01 16:33:41,052 - tensorflow - INFO - loss = 0.028191432, step = 703200 (2.260 sec) +2022-05-01 16:33:43,301 - tensorflow - INFO - global_step/sec: 88.914 +2022-05-01 16:33:43,301 - tensorflow - INFO - loss = 0.047327407, step = 703400 (2.249 sec) +2022-05-01 16:33:45,567 - tensorflow - INFO - global_step/sec: 88.2587 +2022-05-01 16:33:45,567 - tensorflow - INFO - loss = 0.06812384, step = 703600 (2.266 sec) +2022-05-01 16:33:47,818 - tensorflow - INFO - global_step/sec: 88.8492 +2022-05-01 16:33:47,818 - tensorflow - INFO - loss = 0.033104226, step = 703800 (2.251 sec) +2022-05-01 16:33:50,072 - tensorflow - INFO - global_step/sec: 88.7064 +2022-05-01 16:33:50,073 - tensorflow - INFO - loss = 0.06181826, step = 704000 (2.255 sec) +2022-05-01 16:33:52,331 - tensorflow - INFO - global_step/sec: 88.5635 +2022-05-01 16:33:52,331 - tensorflow - INFO - loss = 2.1382723, step = 704200 (2.258 sec) +2022-05-01 16:33:54,584 - tensorflow - INFO - global_step/sec: 88.7633 +2022-05-01 16:33:54,584 - tensorflow - INFO - loss = 0.01735505, step = 704400 (2.253 sec) +2022-05-01 16:33:56,845 - tensorflow - INFO - global_step/sec: 88.4379 +2022-05-01 16:33:56,846 - tensorflow - INFO - loss = 0.030577263, step = 704600 (2.261 sec) +2022-05-01 16:33:59,103 - tensorflow - INFO - global_step/sec: 88.5835 +2022-05-01 16:33:59,104 - tensorflow - INFO - loss = 0.053564556, step = 704800 (2.258 sec) +2022-05-01 16:34:01,356 - tensorflow - INFO - global_step/sec: 88.7922 +2022-05-01 16:34:01,356 - tensorflow - INFO - loss = 0.03156396, step = 705000 (2.252 sec) +2022-05-01 16:34:03,613 - tensorflow - INFO - global_step/sec: 88.6046 +2022-05-01 16:34:03,613 - tensorflow - INFO - loss = 0.023791179, step = 705200 (2.257 sec) +2022-05-01 16:34:05,863 - tensorflow - INFO - global_step/sec: 88.8913 +2022-05-01 16:34:05,863 - tensorflow - INFO - loss = 0.2363714, step = 705400 (2.250 sec) +2022-05-01 16:34:08,119 - tensorflow - INFO - global_step/sec: 88.6522 +2022-05-01 16:34:08,119 - tensorflow - INFO - loss = 0.22751844, step = 705600 (2.256 sec) +2022-05-01 16:34:10,378 - tensorflow - INFO - global_step/sec: 88.5083 +2022-05-01 16:34:10,379 - tensorflow - INFO - loss = 0.02583364, step = 705800 (2.260 sec) +2022-05-01 16:34:12,631 - tensorflow - INFO - global_step/sec: 88.7984 +2022-05-01 16:34:12,631 - tensorflow - INFO - loss = 0.24674636, step = 706000 (2.252 sec) +2022-05-01 16:34:14,882 - tensorflow - INFO - global_step/sec: 88.851 +2022-05-01 16:34:14,882 - tensorflow - INFO - loss = 0.05409362, step = 706200 (2.251 sec) +2022-05-01 16:34:17,134 - tensorflow - INFO - global_step/sec: 88.795 +2022-05-01 16:34:17,134 - tensorflow - INFO - loss = 0.018007115, step = 706400 (2.252 sec) +2022-05-01 16:34:19,391 - tensorflow - INFO - global_step/sec: 88.6251 +2022-05-01 16:34:19,391 - tensorflow - INFO - loss = 0.0582543, step = 706600 (2.257 sec) +2022-05-01 16:34:21,647 - tensorflow - INFO - global_step/sec: 88.6337 +2022-05-01 16:34:21,648 - tensorflow - INFO - loss = 0.027885325, step = 706800 (2.256 sec) +2022-05-01 16:34:23,910 - tensorflow - INFO - global_step/sec: 88.397 +2022-05-01 16:34:23,910 - tensorflow - INFO - loss = 0.13973656, step = 707000 (2.262 sec) +2022-05-01 16:34:26,168 - tensorflow - INFO - global_step/sec: 88.5628 +2022-05-01 16:34:26,168 - tensorflow - INFO - loss = 0.2939297, step = 707200 (2.258 sec) +2022-05-01 16:34:28,423 - tensorflow - INFO - global_step/sec: 88.7049 +2022-05-01 16:34:28,423 - tensorflow - INFO - loss = 0.033999607, step = 707400 (2.255 sec) +2022-05-01 16:34:30,681 - tensorflow - INFO - global_step/sec: 88.5625 +2022-05-01 16:34:30,681 - tensorflow - INFO - loss = 0.048705816, step = 707600 (2.258 sec) +2022-05-01 16:34:32,944 - tensorflow - INFO - global_step/sec: 88.3819 +2022-05-01 16:34:32,944 - tensorflow - INFO - loss = 0.22880709, step = 707800 (2.263 sec) +2022-05-01 16:34:35,210 - tensorflow - INFO - global_step/sec: 88.2509 +2022-05-01 16:34:35,211 - tensorflow - INFO - loss = 0.029884279, step = 708000 (2.266 sec) +2022-05-01 16:34:37,465 - tensorflow - INFO - global_step/sec: 88.7019 +2022-05-01 16:34:37,465 - tensorflow - INFO - loss = 0.043589737, step = 708200 (2.255 sec) +2022-05-01 16:34:39,720 - tensorflow - INFO - global_step/sec: 88.6972 +2022-05-01 16:34:39,720 - tensorflow - INFO - loss = 0.14944857, step = 708400 (2.255 sec) +2022-05-01 16:34:41,978 - tensorflow - INFO - global_step/sec: 88.5747 +2022-05-01 16:34:41,978 - tensorflow - INFO - loss = 0.038527794, step = 708600 (2.258 sec) +2022-05-01 16:34:44,235 - tensorflow - INFO - global_step/sec: 88.6068 +2022-05-01 16:34:44,235 - tensorflow - INFO - loss = 0.023336954, step = 708800 (2.257 sec) +2022-05-01 16:34:46,492 - tensorflow - INFO - global_step/sec: 88.5937 +2022-05-01 16:34:46,493 - tensorflow - INFO - loss = 0.1388255, step = 709000 (2.258 sec) +2022-05-01 16:34:48,754 - tensorflow - INFO - global_step/sec: 88.4207 +2022-05-01 16:34:48,755 - tensorflow - INFO - loss = 0.087768376, step = 709200 (2.262 sec) +2022-05-01 16:34:51,009 - tensorflow - INFO - global_step/sec: 88.7091 +2022-05-01 16:34:51,009 - tensorflow - INFO - loss = 0.58065426, step = 709400 (2.255 sec) +2022-05-01 16:34:53,268 - tensorflow - INFO - global_step/sec: 88.5182 +2022-05-01 16:34:53,269 - tensorflow - INFO - loss = 0.0979935, step = 709600 (2.259 sec) +2022-05-01 16:34:55,522 - tensorflow - INFO - global_step/sec: 88.7484 +2022-05-01 16:34:55,522 - tensorflow - INFO - loss = 0.064526305, step = 709800 (2.254 sec) +2022-05-01 16:34:57,782 - tensorflow - INFO - global_step/sec: 88.5071 +2022-05-01 16:34:57,782 - tensorflow - INFO - loss = 0.073237136, step = 710000 (2.260 sec) +2022-05-01 16:35:00,037 - tensorflow - INFO - global_step/sec: 88.6766 +2022-05-01 16:35:00,037 - tensorflow - INFO - loss = 0.16119538, step = 710200 (2.255 sec) +2022-05-01 16:35:02,300 - tensorflow - INFO - global_step/sec: 88.3645 +2022-05-01 16:35:02,301 - tensorflow - INFO - loss = 0.020417998, step = 710400 (2.263 sec) +2022-05-01 16:35:04,554 - tensorflow - INFO - global_step/sec: 88.7478 +2022-05-01 16:35:04,554 - tensorflow - INFO - loss = 0.12026501, step = 710600 (2.254 sec) +2022-05-01 16:35:06,808 - tensorflow - INFO - global_step/sec: 88.7098 +2022-05-01 16:35:06,809 - tensorflow - INFO - loss = 0.048558436, step = 710800 (2.255 sec) +2022-05-01 16:35:09,059 - tensorflow - INFO - global_step/sec: 88.8573 +2022-05-01 16:35:09,060 - tensorflow - INFO - loss = 0.059880316, step = 711000 (2.251 sec) +2022-05-01 16:35:11,314 - tensorflow - INFO - global_step/sec: 88.6877 +2022-05-01 16:35:11,315 - tensorflow - INFO - loss = 0.06387286, step = 711200 (2.255 sec) +2022-05-01 16:35:13,576 - tensorflow - INFO - global_step/sec: 88.4353 +2022-05-01 16:35:13,576 - tensorflow - INFO - loss = 0.047975764, step = 711400 (2.262 sec) +2022-05-01 16:35:15,839 - tensorflow - INFO - global_step/sec: 88.3751 +2022-05-01 16:35:15,839 - tensorflow - INFO - loss = 0.33253038, step = 711600 (2.263 sec) +2022-05-01 16:35:18,101 - tensorflow - INFO - global_step/sec: 88.4015 +2022-05-01 16:35:18,102 - tensorflow - INFO - loss = 0.06969159, step = 711800 (2.262 sec) +2022-05-01 16:35:20,366 - tensorflow - INFO - global_step/sec: 88.3211 +2022-05-01 16:35:20,366 - tensorflow - INFO - loss = 0.10640546, step = 712000 (2.264 sec) +2022-05-01 16:35:22,627 - tensorflow - INFO - global_step/sec: 88.4421 +2022-05-01 16:35:22,628 - tensorflow - INFO - loss = 0.0625714, step = 712200 (2.261 sec) +2022-05-01 16:35:24,893 - tensorflow - INFO - global_step/sec: 88.2579 +2022-05-01 16:35:24,894 - tensorflow - INFO - loss = 0.03881578, step = 712400 (2.266 sec) +2022-05-01 16:35:27,159 - tensorflow - INFO - global_step/sec: 88.2578 +2022-05-01 16:35:27,160 - tensorflow - INFO - loss = 0.075513996, step = 712600 (2.266 sec) +2022-05-01 16:35:29,422 - tensorflow - INFO - global_step/sec: 88.373 +2022-05-01 16:35:29,423 - tensorflow - INFO - loss = 0.02635355, step = 712800 (2.263 sec) +2022-05-01 16:35:31,691 - tensorflow - INFO - global_step/sec: 88.1749 +2022-05-01 16:35:31,691 - tensorflow - INFO - loss = 0.04076782, step = 713000 (2.268 sec) +2022-05-01 16:35:33,955 - tensorflow - INFO - global_step/sec: 88.3223 +2022-05-01 16:35:33,956 - tensorflow - INFO - loss = 0.053160693, step = 713200 (2.264 sec) +2022-05-01 16:35:36,221 - tensorflow - INFO - global_step/sec: 88.2492 +2022-05-01 16:35:36,222 - tensorflow - INFO - loss = 0.013308646, step = 713400 (2.266 sec) +2022-05-01 16:35:38,482 - tensorflow - INFO - global_step/sec: 88.4628 +2022-05-01 16:35:38,483 - tensorflow - INFO - loss = 0.050666675, step = 713600 (2.261 sec) +2022-05-01 16:35:40,745 - tensorflow - INFO - global_step/sec: 88.4056 +2022-05-01 16:35:40,745 - tensorflow - INFO - loss = 0.0086106695, step = 713800 (2.262 sec) +2022-05-01 16:35:42,998 - tensorflow - INFO - global_step/sec: 88.7668 +2022-05-01 16:35:42,998 - tensorflow - INFO - loss = 0.058115117, step = 714000 (2.253 sec) +2022-05-01 16:35:45,251 - tensorflow - INFO - global_step/sec: 88.7722 +2022-05-01 16:35:45,251 - tensorflow - INFO - loss = 0.6640632, step = 714200 (2.253 sec) +2022-05-01 16:35:47,504 - tensorflow - INFO - global_step/sec: 88.7755 +2022-05-01 16:35:47,504 - tensorflow - INFO - loss = 1.7317725, step = 714400 (2.253 sec) +2022-05-01 16:35:49,760 - tensorflow - INFO - global_step/sec: 88.6234 +2022-05-01 16:35:49,761 - tensorflow - INFO - loss = 0.03569136, step = 714600 (2.257 sec) +2022-05-01 16:35:52,017 - tensorflow - INFO - global_step/sec: 88.6171 +2022-05-01 16:35:52,018 - tensorflow - INFO - loss = 0.27631834, step = 714800 (2.257 sec) +2022-05-01 16:35:54,277 - tensorflow - INFO - global_step/sec: 88.4927 +2022-05-01 16:35:54,278 - tensorflow - INFO - loss = 0.008753429, step = 715000 (2.260 sec) +2022-05-01 16:35:56,533 - tensorflow - INFO - global_step/sec: 88.6488 +2022-05-01 16:35:56,534 - tensorflow - INFO - loss = 0.035814345, step = 715200 (2.256 sec) +2022-05-01 16:35:58,784 - tensorflow - INFO - global_step/sec: 88.8695 +2022-05-01 16:35:58,784 - tensorflow - INFO - loss = 0.0340343, step = 715400 (2.250 sec) +2022-05-01 16:36:01,036 - tensorflow - INFO - global_step/sec: 88.8126 +2022-05-01 16:36:01,036 - tensorflow - INFO - loss = 0.017041814, step = 715600 (2.252 sec) +2022-05-01 16:36:03,293 - tensorflow - INFO - global_step/sec: 88.5987 +2022-05-01 16:36:03,294 - tensorflow - INFO - loss = 0.013692578, step = 715800 (2.257 sec) +2022-05-01 16:36:05,554 - tensorflow - INFO - global_step/sec: 88.4608 +2022-05-01 16:36:05,554 - tensorflow - INFO - loss = 0.070862934, step = 716000 (2.261 sec) +2022-05-01 16:36:07,811 - tensorflow - INFO - global_step/sec: 88.629 +2022-05-01 16:36:07,811 - tensorflow - INFO - loss = 0.16747123, step = 716200 (2.257 sec) +2022-05-01 16:36:10,063 - tensorflow - INFO - global_step/sec: 88.8031 +2022-05-01 16:36:10,063 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.6257 +2022-05-01 16:36:25,848 - tensorflow - INFO - loss = 0.019268338, step = 717800 (2.257 sec) +2022-05-01 16:36:28,108 - tensorflow - INFO - global_step/sec: 88.4847 +2022-05-01 16:36:28,108 - tensorflow - INFO - loss = 0.017318504, step = 718000 (2.260 sec) +2022-05-01 16:36:30,372 - tensorflow - INFO - global_step/sec: 88.3151 +2022-05-01 16:36:30,373 - tensorflow - INFO - loss = 0.02075294, step = 718200 (2.265 sec) +2022-05-01 16:36:32,637 - tensorflow - INFO - global_step/sec: 88.3165 +2022-05-01 16:36:32,637 - tensorflow - INFO - loss = 0.013050616, step = 718400 (2.265 sec) +2022-05-01 16:36:34,895 - tensorflow - INFO - global_step/sec: 88.5666 +2022-05-01 16:36:34,895 - tensorflow - INFO - loss = 0.053279806, step = 718600 (2.258 sec) +2022-05-01 16:36:37,151 - tensorflow - INFO - global_step/sec: 88.6638 +2022-05-01 16:36:37,151 - tensorflow - INFO - loss = 0.06466445, step = 718800 (2.256 sec) +2022-05-01 16:36:39,406 - tensorflow - 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global_step/sec: 88.8894 +2022-05-01 16:51:41,903 - tensorflow - INFO - loss = 0.017083233, step = 799000 (2.250 sec) +2022-05-01 16:51:44,159 - tensorflow - INFO - global_step/sec: 88.6192 +2022-05-01 16:51:44,159 - tensorflow - INFO - loss = 0.042783007, step = 799200 (2.257 sec) +2022-05-01 16:51:46,421 - tensorflow - INFO - global_step/sec: 88.4185 +2022-05-01 16:51:46,421 - tensorflow - INFO - loss = 0.030692004, step = 799400 (2.262 sec) +2022-05-01 16:51:48,680 - tensorflow - INFO - global_step/sec: 88.5367 +2022-05-01 16:51:48,680 - tensorflow - INFO - loss = 0.041929305, step = 799600 (2.259 sec) +2022-05-01 16:51:50,932 - tensorflow - INFO - global_step/sec: 88.7945 +2022-05-01 16:51:50,933 - tensorflow - INFO - loss = 0.018519888, step = 799800 (2.252 sec) +2022-05-01 16:51:53,175 - tensorflow - INFO - Saving checkpoints for 800000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. +2022-05-01 16:51:53,191 - tensorflow - INFO - model.ckpt-800000 is not in all_model_checkpoint_paths. Manually adding it. +2022-05-01 16:51:53,709 - tensorflow - INFO - Calling model_fn. +2022-05-01 16:51:53,839 - tensorflow - INFO - Done calling model_fn. +2022-05-01 16:51:53,855 - tensorflow - INFO - Starting evaluation at 2022-05-01T16:51:53Z +2022-05-01 16:51:54,134 - tensorflow - INFO - Graph was finalized. +2022-05-01 16:51:54,137 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-800000 +2022-05-01 16:51:54,419 - tensorflow - INFO - Running local_init_op. +2022-05-01 16:51:54,527 - tensorflow - INFO - Done running local_init_op. +2022-05-01 16:52:04,691 - tensorflow - INFO - Evaluation [3990/39900] +2022-05-01 16:52:14,173 - tensorflow - INFO - Evaluation [7980/39900] +2022-05-01 16:52:23,645 - tensorflow - INFO - Evaluation [11970/39900] +2022-05-01 16:52:33,137 - tensorflow - INFO - Evaluation [15960/39900] +2022-05-01 16:52:42,626 - tensorflow - INFO - Evaluation [19950/39900] +2022-05-01 16:52:52,112 - tensorflow - INFO - Evaluation [23940/39900] +2022-05-01 16:53:01,615 - tensorflow - INFO - Evaluation [27930/39900] +2022-05-01 16:53:11,104 - tensorflow - INFO - Evaluation [31920/39900] +2022-05-01 16:53:20,576 - tensorflow - INFO - Evaluation [35910/39900] +2022-05-01 16:53:30,075 - tensorflow - INFO - Evaluation [39900/39900] +2022-05-01 16:53:30,215 - tensorflow - INFO - Finished evaluation at 2022-05-01-16:53:30 +2022-05-01 16:53:30,215 - tensorflow - INFO - Saving dict for global step 800000: global_step = 800000, loss = 0.026910517, mean_degree_err = 4.5198975 +2022-05-01 16:53:30,215 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 800000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-800000 +2022-05-01 16:53:30,229 - tensorflow - INFO - global_step/sec: 2.01416 +2022-05-01 16:53:30,230 - tensorflow - INFO - loss = 0.0899144, step = 800000 (99.297 sec) +2022-05-01 16:53:32,509 - tensorflow - INFO - global_step/sec: 87.7145 +2022-05-01 16:53:32,510 - tensorflow - INFO - loss = 0.010599776, step = 800200 (2.280 sec) +2022-05-01 16:53:34,761 - tensorflow - INFO - global_step/sec: 88.8073 +2022-05-01 16:53:34,762 - tensorflow - INFO - loss = 0.010874215, step = 800400 (2.252 sec) +2022-05-01 16:53:37,020 - tensorflow - INFO - global_step/sec: 88.5681 +2022-05-01 16:53:37,020 - tensorflow - INFO - loss = 0.041985117, step = 800600 (2.258 sec) +2022-05-01 16:53:39,271 - tensorflow - INFO - global_step/sec: 88.8123 +2022-05-01 16:53:39,272 - tensorflow - INFO - loss = 0.052029114, step = 800800 (2.252 sec) +2022-05-01 16:53:41,528 - tensorflow - INFO - global_step/sec: 88.6158 +2022-05-01 16:53:41,529 - tensorflow - INFO - loss = 0.103633836, step = 801000 (2.257 sec) +2022-05-01 16:53:43,789 - tensorflow - INFO - global_step/sec: 88.4821 +2022-05-01 16:53:43,789 - tensorflow - INFO - loss = 0.024310533, step = 801200 (2.260 sec) +2022-05-01 16:53:46,046 - tensorflow - INFO - global_step/sec: 88.6088 +2022-05-01 16:53:46,046 - tensorflow - INFO - loss = 0.07490441, step = 801400 (2.257 sec) +2022-05-01 16:53:48,299 - tensorflow - INFO - global_step/sec: 88.7689 +2022-05-01 16:53:48,299 - tensorflow - INFO - loss = 0.019317463, step = 801600 (2.253 sec) +2022-05-01 16:53:50,555 - tensorflow - INFO - global_step/sec: 88.636 +2022-05-01 16:53:50,556 - tensorflow - INFO - loss = 0.05756301, step = 801800 (2.256 sec) +2022-05-01 16:53:52,814 - tensorflow - INFO - global_step/sec: 88.5411 +2022-05-01 16:53:52,815 - tensorflow - INFO - loss = 2.24759, step = 802000 (2.259 sec) +2022-05-01 16:53:55,075 - tensorflow - INFO - global_step/sec: 88.4525 +2022-05-01 16:53:55,076 - tensorflow - INFO - loss = 0.10740603, step = 802200 (2.261 sec) +2022-05-01 16:53:57,332 - tensorflow - INFO - global_step/sec: 88.6125 +2022-05-01 16:53:57,333 - tensorflow - INFO - loss = 0.020427408, step = 802400 (2.257 sec) +2022-05-01 16:53:59,589 - tensorflow - INFO - global_step/sec: 88.6375 +2022-05-01 16:53:59,589 - tensorflow - INFO - loss = 0.019146318, step = 802600 (2.256 sec) +2022-05-01 16:54:01,845 - tensorflow - INFO - global_step/sec: 88.6373 +2022-05-01 16:54:01,845 - tensorflow - INFO - loss = 0.02906144, step = 802800 (2.256 sec) +2022-05-01 16:54:04,106 - tensorflow - INFO - global_step/sec: 88.4622 +2022-05-01 16:54:04,106 - tensorflow - INFO - loss = 0.036592133, step = 803000 (2.261 sec) +2022-05-01 16:54:06,359 - tensorflow - INFO - global_step/sec: 88.7492 +2022-05-01 16:54:06,360 - tensorflow - INFO - loss = 0.42497355, step = 803200 (2.254 sec) +2022-05-01 16:54:08,617 - tensorflow - INFO - global_step/sec: 88.5852 +2022-05-01 16:54:08,618 - tensorflow - INFO - loss = 0.1766534, step = 803400 (2.258 sec) +2022-05-01 16:54:10,872 - tensorflow - INFO - global_step/sec: 88.7036 +2022-05-01 16:54:10,872 - tensorflow - INFO - loss = 0.024735363, step = 803600 (2.255 sec) +2022-05-01 16:54:13,128 - tensorflow - INFO - global_step/sec: 88.6317 +2022-05-01 16:54:13,129 - tensorflow - INFO - loss = 0.030707162, step = 803800 (2.257 sec) +2022-05-01 16:54:15,383 - tensorflow - INFO - global_step/sec: 88.7 +2022-05-01 16:54:15,384 - tensorflow - INFO - loss = 0.016483106, step = 804000 (2.255 sec) +2022-05-01 16:54:17,638 - tensorflow - INFO - global_step/sec: 88.6855 +2022-05-01 16:54:17,639 - tensorflow - INFO - loss = 0.022009581, step = 804200 (2.255 sec) +2022-05-01 16:54:19,892 - tensorflow - INFO - global_step/sec: 88.7563 +2022-05-01 16:54:19,892 - tensorflow - INFO - loss = 0.029243818, step = 804400 (2.253 sec) +2022-05-01 16:54:22,151 - tensorflow - INFO - global_step/sec: 88.5227 +2022-05-01 16:54:22,151 - tensorflow - INFO - loss = 0.058547143, step = 804600 (2.259 sec) +2022-05-01 16:54:24,407 - tensorflow - INFO - global_step/sec: 88.6612 +2022-05-01 16:54:24,407 - tensorflow - INFO - loss = 0.031076716, step = 804800 (2.256 sec) +2022-05-01 16:54:26,662 - tensorflow - INFO - global_step/sec: 88.684 +2022-05-01 16:54:26,662 - tensorflow - INFO - loss = 0.031477463, step = 805000 (2.255 sec) +2022-05-01 16:54:28,920 - tensorflow - INFO - global_step/sec: 88.5908 +2022-05-01 16:54:28,920 - tensorflow - INFO - loss = 0.018799651, step = 805200 (2.258 sec) +2022-05-01 16:54:31,182 - tensorflow - INFO - global_step/sec: 88.3974 +2022-05-01 16:54:31,182 - tensorflow - INFO - loss = 0.028745413, step = 805400 (2.263 sec) +2022-05-01 16:54:33,443 - tensorflow - INFO - global_step/sec: 88.4767 +2022-05-01 16:54:33,443 - tensorflow - INFO - loss = 0.5449137, step = 805600 (2.261 sec) +2022-05-01 16:54:35,698 - tensorflow - INFO - global_step/sec: 88.6692 +2022-05-01 16:54:35,699 - tensorflow - INFO - loss = 0.24509883, step = 805800 (2.256 sec) +2022-05-01 16:54:37,954 - tensorflow - INFO - global_step/sec: 88.6504 +2022-05-01 16:54:37,955 - tensorflow - INFO - loss = 0.025008408, step = 806000 (2.256 sec) +2022-05-01 16:54:40,206 - tensorflow - INFO - global_step/sec: 88.8094 +2022-05-01 16:54:40,207 - tensorflow - INFO - loss = 0.03249572, step = 806200 (2.252 sec) +2022-05-01 16:54:42,461 - tensorflow - INFO - global_step/sec: 88.6961 +2022-05-01 16:54:42,462 - tensorflow - INFO - loss = 0.030577507, step = 806400 (2.255 sec) +2022-05-01 16:54:44,720 - tensorflow - INFO - global_step/sec: 88.5433 +2022-05-01 16:54:44,720 - tensorflow - INFO - loss = 0.020079143, step = 806600 (2.259 sec) +2022-05-01 16:54:46,975 - tensorflow - INFO - global_step/sec: 88.6837 +2022-05-01 16:54:46,975 - tensorflow - INFO - loss = 0.03394632, step = 806800 (2.255 sec) +2022-05-01 16:54:49,234 - tensorflow - INFO - global_step/sec: 88.5236 +2022-05-01 16:54:49,235 - tensorflow - INFO - loss = 0.7879622, step = 807000 (2.259 sec) +2022-05-01 16:54:51,489 - tensorflow - INFO - global_step/sec: 88.72 +2022-05-01 16:54:51,489 - tensorflow - INFO - loss = 0.049458347, step = 807200 (2.254 sec) +2022-05-01 16:54:53,747 - tensorflow - INFO - global_step/sec: 88.5466 +2022-05-01 16:54:53,748 - tensorflow - INFO - loss = 0.049946, step = 807400 (2.259 sec) +2022-05-01 16:54:56,006 - tensorflow - INFO - global_step/sec: 88.5631 +2022-05-01 16:54:56,006 - tensorflow - INFO - loss = 0.25674763, step = 807600 (2.258 sec) +2022-05-01 16:54:58,269 - tensorflow - INFO - global_step/sec: 88.3516 +2022-05-01 16:54:58,270 - tensorflow - INFO - loss = 0.063438654, step = 807800 (2.264 sec) +2022-05-01 16:55:00,527 - tensorflow - INFO - global_step/sec: 88.5901 +2022-05-01 16:55:00,527 - tensorflow - INFO - loss = 0.033711236, step = 808000 (2.258 sec) +2022-05-01 16:55:02,783 - tensorflow - INFO - global_step/sec: 88.628 +2022-05-01 16:55:02,784 - tensorflow - INFO - loss = 0.030713592, step = 808200 (2.257 sec) +2022-05-01 16:55:05,043 - tensorflow - INFO - global_step/sec: 88.5287 +2022-05-01 16:55:05,043 - tensorflow - INFO - loss = 0.3922224, step = 808400 (2.259 sec) +2022-05-01 16:55:07,305 - tensorflow - INFO - global_step/sec: 88.3993 +2022-05-01 16:55:07,306 - tensorflow - INFO - loss = 0.014256238, step = 808600 (2.262 sec) +2022-05-01 16:55:09,559 - tensorflow - INFO - global_step/sec: 88.7537 +2022-05-01 16:55:09,559 - tensorflow - INFO - loss = 0.048423983, step = 808800 (2.253 sec) +2022-05-01 16:55:11,810 - tensorflow - INFO - global_step/sec: 88.8171 +2022-05-01 16:55:11,811 - tensorflow - INFO - loss = 0.6235335, step = 809000 (2.252 sec) +2022-05-01 16:55:14,066 - tensorflow - INFO - global_step/sec: 88.6688 +2022-05-01 16:55:14,066 - tensorflow - INFO - loss = 0.040774103, step = 809200 (2.256 sec) +2022-05-01 16:55:16,325 - tensorflow - INFO - global_step/sec: 88.5454 +2022-05-01 16:55:16,325 - tensorflow - INFO - loss = 0.008105355, step = 809400 (2.259 sec) +2022-05-01 16:55:18,583 - tensorflow - INFO - global_step/sec: 88.5712 +2022-05-01 16:55:18,583 - tensorflow - INFO - loss = 0.21221718, step = 809600 (2.258 sec) +2022-05-01 16:55:20,838 - tensorflow - INFO - global_step/sec: 88.6648 +2022-05-01 16:55:20,839 - tensorflow - INFO - loss = 0.032146968, step = 809800 (2.256 sec) +2022-05-01 16:55:23,091 - tensorflow - INFO - global_step/sec: 88.7708 +2022-05-01 16:55:23,092 - tensorflow - INFO - loss = 0.034307744, step = 810000 (2.253 sec) +2022-05-01 16:55:25,342 - tensorflow - INFO - global_step/sec: 88.8554 +2022-05-01 16:55:25,343 - tensorflow - INFO - loss = 0.3609604, step = 810200 (2.251 sec) +2022-05-01 16:55:27,590 - tensorflow - INFO - global_step/sec: 88.9818 +2022-05-01 16:55:27,590 - tensorflow - INFO - loss = 0.09175968, step = 810400 (2.248 sec) +2022-05-01 16:55:29,840 - tensorflow - INFO - global_step/sec: 88.882 +2022-05-01 16:55:29,840 - tensorflow - INFO - loss = 0.026782908, step = 810600 (2.250 sec) +2022-05-01 16:55:32,097 - tensorflow - INFO - global_step/sec: 88.6085 +2022-05-01 16:55:32,098 - tensorflow - INFO - loss = 0.017560637, step = 810800 (2.257 sec) +2022-05-01 16:55:34,350 - tensorflow - INFO - global_step/sec: 88.7788 +2022-05-01 16:55:34,350 - tensorflow - INFO - loss = 0.03503574, step = 811000 (2.253 sec) +2022-05-01 16:55:36,609 - tensorflow - INFO - global_step/sec: 88.5262 +2022-05-01 16:55:36,610 - tensorflow - INFO - loss = 11.913107, step = 811200 (2.259 sec) +2022-05-01 16:55:38,864 - tensorflow - INFO - global_step/sec: 88.7198 +2022-05-01 16:55:38,864 - tensorflow - INFO - loss = 0.034631055, step = 811400 (2.254 sec) +2022-05-01 16:55:41,112 - tensorflow - INFO - global_step/sec: 88.9584 +2022-05-01 16:55:41,112 - tensorflow - INFO - loss = 0.019718375, step = 811600 (2.248 sec) +2022-05-01 16:55:43,364 - tensorflow - INFO - global_step/sec: 88.7959 +2022-05-01 16:55:43,365 - tensorflow - INFO - loss = 0.0121886395, step = 811800 (2.252 sec) +2022-05-01 16:55:45,619 - tensorflow - INFO - global_step/sec: 88.7142 +2022-05-01 16:55:45,619 - tensorflow - INFO - loss = 0.052457172, step = 812000 (2.254 sec) +2022-05-01 16:55:47,875 - tensorflow - INFO - global_step/sec: 88.6286 +2022-05-01 16:55:47,876 - tensorflow - INFO - loss = 0.039111532, step = 812200 (2.257 sec) +2022-05-01 16:55:50,129 - tensorflow - INFO - global_step/sec: 88.746 +2022-05-01 16:55:50,129 - tensorflow - INFO - loss = 0.1315316, step = 812400 (2.254 sec) +2022-05-01 16:55:52,388 - tensorflow - INFO - global_step/sec: 88.5362 +2022-05-01 16:55:52,388 - tensorflow - INFO - loss = 0.065420076, step = 812600 (2.259 sec) +2022-05-01 16:55:54,645 - tensorflow - INFO - global_step/sec: 88.5937 +2022-05-01 16:55:54,646 - tensorflow - INFO - loss = 0.22496642, step = 812800 (2.258 sec) +2022-05-01 16:55:56,898 - tensorflow - INFO - global_step/sec: 88.7743 +2022-05-01 16:55:56,899 - tensorflow - INFO - loss = 0.057431646, step = 813000 (2.253 sec) +2022-05-01 16:55:59,154 - tensorflow - INFO - global_step/sec: 88.672 +2022-05-01 16:55:59,154 - tensorflow - INFO - loss = 0.10027319, step = 813200 (2.255 sec) +2022-05-01 16:56:01,416 - tensorflow - INFO - global_step/sec: 88.4213 +2022-05-01 16:56:01,416 - tensorflow - INFO - loss = 0.017701805, step = 813400 (2.262 sec) +2022-05-01 16:56:03,668 - tensorflow - INFO - global_step/sec: 88.8079 +2022-05-01 16:56:03,668 - tensorflow - INFO - loss = 0.045442797, step = 813600 (2.252 sec) +2022-05-01 16:56:05,920 - tensorflow - INFO - global_step/sec: 88.7759 +2022-05-01 16:56:05,921 - tensorflow - INFO - loss = 0.059727803, step = 813800 (2.253 sec) +2022-05-01 16:56:08,179 - tensorflow - 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INFO - loss = 2.9744234, step = 896400 (2.250 sec) +2022-05-01 17:11:39,908 - tensorflow - INFO - global_step/sec: 88.6083 +2022-05-01 17:11:39,908 - tensorflow - INFO - loss = 0.064552605, step = 896600 (2.257 sec) +2022-05-01 17:11:42,161 - tensorflow - INFO - global_step/sec: 88.7661 +2022-05-01 17:11:42,161 - tensorflow - INFO - loss = 0.012820228, step = 896800 (2.253 sec) +2022-05-01 17:11:44,418 - tensorflow - INFO - global_step/sec: 88.6105 +2022-05-01 17:11:44,418 - tensorflow - INFO - loss = 0.19172603, step = 897000 (2.257 sec) +2022-05-01 17:11:46,673 - tensorflow - INFO - global_step/sec: 88.6802 +2022-05-01 17:11:46,674 - tensorflow - INFO - loss = 0.36441582, step = 897200 (2.255 sec) +2022-05-01 17:11:48,928 - tensorflow - INFO - global_step/sec: 88.6997 +2022-05-01 17:11:48,928 - tensorflow - INFO - loss = 0.22654104, step = 897400 (2.255 sec) +2022-05-01 17:11:51,184 - tensorflow - INFO - global_step/sec: 88.656 +2022-05-01 17:11:51,184 - tensorflow - INFO - loss = 0.014491557, step = 897600 (2.256 sec) +2022-05-01 17:11:53,435 - tensorflow - INFO - global_step/sec: 88.8386 +2022-05-01 17:11:53,435 - tensorflow - INFO - loss = 0.015789127, step = 897800 (2.251 sec) +2022-05-01 17:11:55,699 - tensorflow - INFO - global_step/sec: 88.3486 +2022-05-01 17:11:55,699 - tensorflow - INFO - loss = 0.028927404, step = 898000 (2.264 sec) +2022-05-01 17:11:57,953 - tensorflow - INFO - global_step/sec: 88.7079 +2022-05-01 17:11:57,954 - tensorflow - INFO - loss = 0.3525368, step = 898200 (2.255 sec) +2022-05-01 17:12:00,206 - tensorflow - INFO - global_step/sec: 88.8036 +2022-05-01 17:12:00,206 - tensorflow - INFO - loss = 0.094420716, step = 898400 (2.252 sec) +2022-05-01 17:12:02,463 - tensorflow - INFO - global_step/sec: 88.5853 +2022-05-01 17:12:02,464 - tensorflow - INFO - loss = 0.018449508, step = 898600 (2.258 sec) +2022-05-01 17:12:04,723 - tensorflow - INFO - global_step/sec: 88.5187 +2022-05-01 17:12:04,723 - tensorflow - INFO - loss = 0.027755884, step = 898800 (2.259 sec) +2022-05-01 17:12:06,977 - tensorflow - INFO - global_step/sec: 88.7219 +2022-05-01 17:12:06,977 - tensorflow - INFO - loss = 0.2651413, step = 899000 (2.254 sec) +2022-05-01 17:12:09,238 - tensorflow - INFO - global_step/sec: 88.4568 +2022-05-01 17:12:09,238 - tensorflow - INFO - loss = 0.113350786, step = 899200 (2.261 sec) +2022-05-01 17:12:11,490 - tensorflow - INFO - global_step/sec: 88.8042 +2022-05-01 17:12:11,490 - tensorflow - INFO - loss = 0.022238614, step = 899400 (2.252 sec) +2022-05-01 17:12:13,741 - tensorflow - INFO - global_step/sec: 88.8416 +2022-05-01 17:12:13,742 - tensorflow - INFO - loss = 0.06388148, step = 899600 (2.251 sec) +2022-05-01 17:12:15,993 - tensorflow - INFO - global_step/sec: 88.8187 +2022-05-01 17:12:15,993 - tensorflow - INFO - loss = 0.16148487, step = 899800 (2.252 sec) +2022-05-01 17:12:18,237 - tensorflow - INFO - Saving checkpoints for 900000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. +2022-05-01 17:12:18,255 - tensorflow - INFO - model.ckpt-900000 is not in all_model_checkpoint_paths. Manually adding it. +2022-05-01 17:12:18,789 - tensorflow - INFO - Calling model_fn. +2022-05-01 17:12:18,919 - tensorflow - INFO - Done calling model_fn. +2022-05-01 17:12:18,934 - tensorflow - INFO - Starting evaluation at 2022-05-01T17:12:18Z +2022-05-01 17:12:18,999 - tensorflow - INFO - Graph was finalized. +2022-05-01 17:12:19,002 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-900000 +2022-05-01 17:12:19,283 - tensorflow - INFO - Running local_init_op. +2022-05-01 17:12:19,392 - tensorflow - INFO - Done running local_init_op. +2022-05-01 17:12:29,492 - tensorflow - INFO - Evaluation [3990/39900] +2022-05-01 17:12:38,917 - tensorflow - INFO - Evaluation [7980/39900] +2022-05-01 17:12:48,325 - tensorflow - INFO - Evaluation [11970/39900] +2022-05-01 17:12:57,723 - tensorflow - INFO - Evaluation [15960/39900] +2022-05-01 17:13:07,119 - tensorflow - INFO - Evaluation [19950/39900] +2022-05-01 17:13:16,541 - tensorflow - INFO - Evaluation [23940/39900] +2022-05-01 17:13:25,933 - tensorflow - INFO - Evaluation [27930/39900] +2022-05-01 17:13:35,327 - tensorflow - INFO - Evaluation [31920/39900] +2022-05-01 17:13:44,745 - tensorflow - INFO - Evaluation [35910/39900] +2022-05-01 17:13:54,197 - tensorflow - INFO - Evaluation [39900/39900] +2022-05-01 17:13:54,334 - tensorflow - INFO - Finished evaluation at 2022-05-01-17:13:54 +2022-05-01 17:13:54,334 - tensorflow - INFO - Saving dict for global step 900000: global_step = 900000, loss = 0.035565972, mean_degree_err = 6.131773 +2022-05-01 17:13:54,336 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 900000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-900000 +2022-05-01 17:13:54,350 - tensorflow - INFO - global_step/sec: 2.03342 +2022-05-01 17:13:54,350 - tensorflow - INFO - loss = 0.013065057, step = 900000 (98.357 sec) +2022-05-01 17:13:56,636 - tensorflow - INFO - global_step/sec: 87.487 +2022-05-01 17:13:56,636 - tensorflow - INFO - loss = 0.012146402, step = 900200 (2.286 sec) +2022-05-01 17:13:58,898 - tensorflow - INFO - global_step/sec: 88.4111 +2022-05-01 17:13:58,898 - tensorflow - INFO - loss = 0.016029745, step = 900400 (2.262 sec) +2022-05-01 17:14:01,161 - tensorflow - INFO - global_step/sec: 88.3742 +2022-05-01 17:14:01,161 - tensorflow - INFO - loss = 0.031824853, step = 900600 (2.263 sec) +2022-05-01 17:14:03,421 - tensorflow - INFO - global_step/sec: 88.4699 +2022-05-01 17:14:03,422 - tensorflow - INFO - loss = 0.018616183, step = 900800 (2.261 sec) +2022-05-01 17:14:05,686 - tensorflow - INFO - global_step/sec: 88.3096 +2022-05-01 17:14:05,687 - tensorflow - INFO - loss = 0.027795551, step = 901000 (2.265 sec) +2022-05-01 17:14:07,942 - tensorflow - INFO - global_step/sec: 88.6682 +2022-05-01 17:14:07,942 - tensorflow - INFO - loss = 0.014406723, step = 901200 (2.256 sec) +2022-05-01 17:14:10,203 - tensorflow - INFO - global_step/sec: 88.4331 +2022-05-01 17:14:10,204 - tensorflow - INFO - loss = 0.017534908, step = 901400 (2.262 sec) +2022-05-01 17:14:12,455 - tensorflow - INFO - global_step/sec: 88.8078 +2022-05-01 17:14:12,456 - tensorflow - INFO - loss = 0.58057, step = 901600 (2.252 sec) +2022-05-01 17:14:14,714 - tensorflow - INFO - global_step/sec: 88.5464 +2022-05-01 17:14:14,715 - tensorflow - INFO - loss = 0.038886838, step = 901800 (2.259 sec) +2022-05-01 17:14:16,967 - tensorflow - INFO - global_step/sec: 88.7601 +2022-05-01 17:14:16,968 - tensorflow - INFO - loss = 0.14230952, step = 902000 (2.253 sec) +2022-05-01 17:14:19,222 - tensorflow - INFO - global_step/sec: 88.6934 +2022-05-01 17:14:19,223 - tensorflow - INFO - loss = 0.11496422, step = 902200 (2.255 sec) +2022-05-01 17:14:21,479 - tensorflow - INFO - global_step/sec: 88.6156 +2022-05-01 17:14:21,480 - tensorflow - INFO - loss = 0.027795611, step = 902400 (2.257 sec) +2022-05-01 17:14:23,735 - tensorflow - INFO - global_step/sec: 88.6611 +2022-05-01 17:14:23,736 - tensorflow - INFO - loss = 0.27380404, step = 902600 (2.256 sec) +2022-05-01 17:14:25,991 - tensorflow - INFO - global_step/sec: 88.6636 +2022-05-01 17:14:25,991 - tensorflow - INFO - loss = 0.018897396, step = 902800 (2.256 sec) +2022-05-01 17:14:28,242 - tensorflow - INFO - global_step/sec: 88.8552 +2022-05-01 17:14:28,242 - tensorflow - INFO - loss = 0.055229053, step = 903000 (2.251 sec) +2022-05-01 17:14:30,506 - tensorflow - INFO - global_step/sec: 88.3257 +2022-05-01 17:14:30,507 - tensorflow - INFO - loss = 0.03179595, step = 903200 (2.264 sec) +2022-05-01 17:14:32,758 - tensorflow - INFO - global_step/sec: 88.796 +2022-05-01 17:14:32,759 - tensorflow - INFO - loss = 0.036151156, step = 903400 (2.252 sec) +2022-05-01 17:14:35,012 - tensorflow - INFO - global_step/sec: 88.7354 +2022-05-01 17:14:35,013 - tensorflow - INFO - loss = 0.06902796, step = 903600 (2.254 sec) +2022-05-01 17:14:37,265 - tensorflow - INFO - global_step/sec: 88.7679 +2022-05-01 17:14:37,266 - tensorflow - INFO - loss = 0.04816742, step = 903800 (2.253 sec) +2022-05-01 17:14:39,520 - tensorflow - INFO - global_step/sec: 88.722 +2022-05-01 17:14:39,520 - tensorflow - INFO - loss = 0.020497266, step = 904000 (2.254 sec) +2022-05-01 17:14:41,776 - tensorflow - INFO - global_step/sec: 88.637 +2022-05-01 17:14:41,776 - tensorflow - INFO - loss = 0.57189643, step = 904200 (2.256 sec) +2022-05-01 17:14:44,029 - tensorflow - INFO - global_step/sec: 88.756 +2022-05-01 17:14:44,030 - tensorflow - INFO - loss = 0.0389892, step = 904400 (2.253 sec) +2022-05-01 17:14:46,287 - tensorflow - INFO - global_step/sec: 88.5882 +2022-05-01 17:14:46,287 - tensorflow - INFO - loss = 0.11031467, step = 904600 (2.258 sec) +2022-05-01 17:14:48,541 - tensorflow - INFO - global_step/sec: 88.7401 +2022-05-01 17:14:48,541 - tensorflow - INFO - loss = 0.07635092, step = 904800 (2.254 sec) +2022-05-01 17:14:50,792 - tensorflow - INFO - global_step/sec: 88.8347 +2022-05-01 17:14:50,793 - tensorflow - INFO - loss = 0.015127299, step = 905000 (2.251 sec) +2022-05-01 17:14:53,051 - tensorflow - INFO - global_step/sec: 88.5252 +2022-05-01 17:14:53,052 - tensorflow - INFO - loss = 0.21705827, step = 905200 (2.259 sec) +2022-05-01 17:14:55,312 - tensorflow - INFO - global_step/sec: 88.469 +2022-05-01 17:14:55,313 - tensorflow - INFO - loss = 0.3189776, step = 905400 (2.261 sec) +2022-05-01 17:14:57,565 - tensorflow - INFO - global_step/sec: 88.7576 +2022-05-01 17:14:57,566 - tensorflow - INFO - loss = 0.010876756, step = 905600 (2.253 sec) +2022-05-01 17:14:59,816 - tensorflow - INFO - global_step/sec: 88.8569 +2022-05-01 17:14:59,817 - tensorflow - INFO - loss = 0.5480637, step = 905800 (2.251 sec) +2022-05-01 17:15:02,075 - tensorflow - INFO - global_step/sec: 88.5619 +2022-05-01 17:15:02,075 - tensorflow - INFO - loss = 0.028712355, step = 906000 (2.258 sec) +2022-05-01 17:15:04,331 - tensorflow - INFO - global_step/sec: 88.6158 +2022-05-01 17:15:04,332 - tensorflow - INFO - loss = 0.03353673, step = 906200 (2.257 sec) +2022-05-01 17:15:06,589 - tensorflow - INFO - global_step/sec: 88.6025 +2022-05-01 17:15:06,589 - tensorflow - INFO - loss = 0.048440434, step = 906400 (2.257 sec) +2022-05-01 17:15:08,848 - tensorflow - INFO - global_step/sec: 88.5289 +2022-05-01 17:15:08,848 - tensorflow - INFO - loss = 0.11641862, step = 906600 (2.259 sec) +2022-05-01 17:15:11,106 - tensorflow - INFO - global_step/sec: 88.582 +2022-05-01 17:15:11,106 - tensorflow - INFO - loss = 0.04855759, step = 906800 (2.258 sec) +2022-05-01 17:15:13,361 - tensorflow - INFO - global_step/sec: 88.6868 +2022-05-01 17:15:13,361 - tensorflow - INFO - loss = 0.020095387, step = 907000 (2.255 sec) +2022-05-01 17:15:15,617 - tensorflow - INFO - global_step/sec: 88.6428 +2022-05-01 17:15:15,617 - tensorflow - INFO - loss = 0.021196015, step = 907200 (2.256 sec) +2022-05-01 17:15:17,879 - tensorflow - INFO - global_step/sec: 88.4285 +2022-05-01 17:15:17,879 - tensorflow - INFO - loss = 0.031916, step = 907400 (2.262 sec) +2022-05-01 17:15:20,145 - tensorflow - INFO - global_step/sec: 88.2537 +2022-05-01 17:15:20,145 - tensorflow - INFO - loss = 0.056354966, step = 907600 (2.266 sec) +2022-05-01 17:15:22,402 - tensorflow - INFO - global_step/sec: 88.6152 +2022-05-01 17:15:22,402 - tensorflow - INFO - loss = 0.05101075, step = 907800 (2.257 sec) +2022-05-01 17:15:24,656 - tensorflow - INFO - global_step/sec: 88.7127 +2022-05-01 17:15:24,657 - tensorflow - INFO - loss = 0.014117379, step = 908000 (2.254 sec) +2022-05-01 17:15:26,917 - tensorflow - INFO - global_step/sec: 88.4876 +2022-05-01 17:15:26,917 - tensorflow - INFO - loss = 0.026169278, step = 908200 (2.260 sec) +2022-05-01 17:15:29,171 - tensorflow - INFO - global_step/sec: 88.7315 +2022-05-01 17:15:29,171 - tensorflow - INFO - loss = 0.4392848, step = 908400 (2.254 sec) +2022-05-01 17:15:31,436 - tensorflow - INFO - global_step/sec: 88.2919 +2022-05-01 17:15:31,436 - tensorflow - INFO - loss = 0.010062726, step = 908600 (2.265 sec) +2022-05-01 17:15:33,691 - tensorflow - INFO - global_step/sec: 88.6653 +2022-05-01 17:15:33,692 - tensorflow - INFO - loss = 0.24155402, step = 908800 (2.256 sec) +2022-05-01 17:15:35,942 - tensorflow - INFO - global_step/sec: 88.8539 +2022-05-01 17:15:35,943 - tensorflow - INFO - loss = 2.5512338, step = 909000 (2.251 sec) +2022-05-01 17:15:38,199 - tensorflow - INFO - global_step/sec: 88.617 +2022-05-01 17:15:38,200 - tensorflow - INFO - loss = 0.021701274, step = 909200 (2.257 sec) +2022-05-01 17:15:40,454 - tensorflow - INFO - global_step/sec: 88.7107 +2022-05-01 17:15:40,454 - tensorflow - INFO - loss = 0.04103797, step = 909400 (2.254 sec) +2022-05-01 17:15:42,707 - tensorflow - INFO - global_step/sec: 88.7639 +2022-05-01 17:15:42,707 - tensorflow - INFO - loss = 0.011487696, step = 909600 (2.253 sec) +2022-05-01 17:15:44,972 - tensorflow - INFO - global_step/sec: 88.2904 +2022-05-01 17:15:44,973 - tensorflow - INFO - loss = 0.017504783, step = 909800 (2.265 sec) +2022-05-01 17:15:47,229 - tensorflow - INFO - global_step/sec: 88.6267 +2022-05-01 17:15:47,229 - tensorflow - INFO - loss = 0.057098113, step = 910000 (2.257 sec) +2022-05-01 17:15:49,484 - tensorflow - INFO - global_step/sec: 88.7044 +2022-05-01 17:15:49,484 - tensorflow - INFO - loss = 0.020508293, step = 910200 (2.255 sec) +2022-05-01 17:15:51,735 - tensorflow - INFO - global_step/sec: 88.8193 +2022-05-01 17:15:51,736 - tensorflow - INFO - loss = 0.008839138, step = 910400 (2.252 sec) +2022-05-01 17:15:53,990 - tensorflow - INFO - global_step/sec: 88.6945 +2022-05-01 17:15:53,991 - tensorflow - INFO - loss = 0.028127495, step = 910600 (2.255 sec) +2022-05-01 17:15:56,254 - tensorflow - INFO - global_step/sec: 88.3461 +2022-05-01 17:15:56,255 - tensorflow - INFO - loss = 0.049872562, step = 910800 (2.264 sec) +2022-05-01 17:15:58,513 - tensorflow - INFO - global_step/sec: 88.5308 +2022-05-01 17:15:58,514 - tensorflow - INFO - loss = 0.03336969, step = 911000 (2.259 sec) +2022-05-01 17:16:00,769 - tensorflow - INFO - global_step/sec: 88.6562 +2022-05-01 17:16:00,769 - tensorflow - INFO - loss = 0.028285317, step = 911200 (2.256 sec) +2022-05-01 17:16:03,030 - tensorflow - INFO - global_step/sec: 88.4384 +2022-05-01 17:16:03,031 - tensorflow - 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global_step/sec: 88.1738 +2022-05-01 17:31:36,738 - tensorflow - INFO - loss = 0.36183137, step = 994000 (2.268 sec) +2022-05-01 17:31:39,012 - tensorflow - INFO - global_step/sec: 87.9508 +2022-05-01 17:31:39,012 - tensorflow - INFO - loss = 0.048842005, step = 994200 (2.274 sec) +2022-05-01 17:31:41,284 - tensorflow - INFO - global_step/sec: 88.0298 +2022-05-01 17:31:41,284 - tensorflow - INFO - loss = 31.629017, step = 994400 (2.272 sec) +2022-05-01 17:31:43,550 - tensorflow - INFO - global_step/sec: 88.2431 +2022-05-01 17:31:43,551 - tensorflow - INFO - loss = 0.017132096, step = 994600 (2.267 sec) +2022-05-01 17:31:45,821 - tensorflow - INFO - global_step/sec: 88.0834 +2022-05-01 17:31:45,821 - tensorflow - INFO - loss = 0.04079227, step = 994800 (2.271 sec) +2022-05-01 17:31:48,087 - tensorflow - INFO - global_step/sec: 88.2552 +2022-05-01 17:31:48,087 - tensorflow - INFO - loss = 0.825424, step = 995000 (2.266 sec) +2022-05-01 17:31:50,356 - tensorflow - INFO - global_step/sec: 88.1421 +2022-05-01 17:31:50,356 - tensorflow - INFO - loss = 0.01457743, step = 995200 (2.269 sec) +2022-05-01 17:31:52,627 - tensorflow - INFO - global_step/sec: 88.0675 +2022-05-01 17:31:52,627 - tensorflow - INFO - loss = 0.08510444, step = 995400 (2.271 sec) +2022-05-01 17:31:54,894 - tensorflow - INFO - global_step/sec: 88.2252 +2022-05-01 17:31:54,894 - tensorflow - INFO - loss = 0.10075992, step = 995600 (2.267 sec) +2022-05-01 17:31:57,164 - tensorflow - INFO - global_step/sec: 88.1038 +2022-05-01 17:31:57,164 - tensorflow - INFO - loss = 0.5939552, step = 995800 (2.270 sec) +2022-05-01 17:31:59,431 - tensorflow - INFO - global_step/sec: 88.237 +2022-05-01 17:31:59,431 - tensorflow - INFO - loss = 0.023191817, step = 996000 (2.267 sec) +2022-05-01 17:32:01,698 - tensorflow - INFO - global_step/sec: 88.2096 +2022-05-01 17:32:01,698 - tensorflow - INFO - loss = 0.03370051, step = 996200 (2.267 sec) +2022-05-01 17:32:03,972 - tensorflow - INFO - global_step/sec: 87.941 +2022-05-01 17:32:03,972 - tensorflow - INFO - loss = 0.045667723, step = 996400 (2.274 sec) +2022-05-01 17:32:06,245 - tensorflow - INFO - global_step/sec: 88.0017 +2022-05-01 17:32:06,245 - tensorflow - INFO - loss = 5.847514, step = 996600 (2.273 sec) +2022-05-01 17:32:08,515 - tensorflow - INFO - global_step/sec: 88.1051 +2022-05-01 17:32:08,515 - tensorflow - INFO - loss = 0.0076303408, step = 996800 (2.270 sec) +2022-05-01 17:32:10,780 - tensorflow - INFO - global_step/sec: 88.3048 +2022-05-01 17:32:10,780 - tensorflow - INFO - loss = 0.014137331, step = 997000 (2.265 sec) +2022-05-01 17:32:13,053 - tensorflow - INFO - global_step/sec: 87.9674 +2022-05-01 17:32:13,054 - tensorflow - INFO - loss = 0.012826626, step = 997200 (2.274 sec) +2022-05-01 17:32:15,317 - tensorflow - INFO - global_step/sec: 88.3681 +2022-05-01 17:32:15,317 - tensorflow - INFO - loss = 0.027969304, step = 997400 (2.263 sec) +2022-05-01 17:32:17,589 - tensorflow - INFO - global_step/sec: 88.0105 +2022-05-01 17:32:17,589 - tensorflow - INFO - loss = 0.05405557, step = 997600 (2.272 sec) +2022-05-01 17:32:19,864 - tensorflow - INFO - global_step/sec: 87.9009 +2022-05-01 17:32:19,865 - tensorflow - INFO - loss = 0.095805936, step = 997800 (2.275 sec) +2022-05-01 17:32:22,134 - tensorflow - INFO - global_step/sec: 88.1227 +2022-05-01 17:32:22,134 - tensorflow - INFO - loss = 0.10985251, step = 998000 (2.270 sec) +2022-05-01 17:32:24,401 - tensorflow - INFO - global_step/sec: 88.229 +2022-05-01 17:32:24,401 - tensorflow - INFO - loss = 0.031816743, step = 998200 (2.267 sec) +2022-05-01 17:32:26,668 - tensorflow - INFO - global_step/sec: 88.2149 +2022-05-01 17:32:26,668 - tensorflow - INFO - loss = 0.28096992, step = 998400 (2.267 sec) +2022-05-01 17:32:28,937 - tensorflow - INFO - global_step/sec: 88.1398 +2022-05-01 17:32:28,937 - tensorflow - INFO - loss = 0.03208027, step = 998600 (2.269 sec) +2022-05-01 17:32:31,209 - tensorflow - INFO - global_step/sec: 88.0357 +2022-05-01 17:32:31,209 - tensorflow - INFO - loss = 0.11195407, step = 998800 (2.272 sec) +2022-05-01 17:32:33,473 - tensorflow - INFO - global_step/sec: 88.3346 +2022-05-01 17:32:33,473 - tensorflow - INFO - loss = 0.11846994, step = 999000 (2.264 sec) +2022-05-01 17:32:35,741 - tensorflow - INFO - global_step/sec: 88.1636 +2022-05-01 17:32:35,742 - tensorflow - INFO - loss = 0.01635918, step = 999200 (2.268 sec) +2022-05-01 17:32:38,008 - tensorflow - INFO - global_step/sec: 88.2422 +2022-05-01 17:32:38,008 - tensorflow - INFO - loss = 0.086311236, step = 999400 (2.267 sec) +2022-05-01 17:32:40,277 - tensorflow - INFO - global_step/sec: 88.1249 +2022-05-01 17:32:40,278 - tensorflow - INFO - loss = 0.028363196, step = 999600 (2.269 sec) +2022-05-01 17:32:42,543 - tensorflow - INFO - global_step/sec: 88.2577 +2022-05-01 17:32:42,544 - tensorflow - INFO - loss = 0.02159482, step = 999800 (2.266 sec) +2022-05-01 17:32:44,799 - tensorflow - INFO - Saving checkpoints for 1000000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. +2022-05-01 17:32:44,814 - tensorflow - INFO - model.ckpt-1000000 is not in all_model_checkpoint_paths. Manually adding it. +2022-05-01 17:32:45,367 - tensorflow - INFO - Calling model_fn. +2022-05-01 17:32:45,495 - tensorflow - INFO - Done calling model_fn. +2022-05-01 17:32:45,510 - tensorflow - INFO - Starting evaluation at 2022-05-01T17:32:45Z +2022-05-01 17:32:45,575 - tensorflow - INFO - Graph was finalized. +2022-05-01 17:32:45,578 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1000000 +2022-05-01 17:32:45,861 - tensorflow - INFO - Running local_init_op. +2022-05-01 17:32:45,969 - tensorflow - INFO - Done running local_init_op. +2022-05-01 17:32:56,249 - tensorflow - INFO - Evaluation [3990/39900] +2022-05-01 17:33:05,789 - tensorflow - INFO - Evaluation [7980/39900] +2022-05-01 17:33:15,364 - tensorflow - INFO - Evaluation [11970/39900] +2022-05-01 17:33:24,953 - tensorflow - INFO - Evaluation [15960/39900] +2022-05-01 17:33:34,512 - tensorflow - INFO - Evaluation [19950/39900] +2022-05-01 17:33:44,109 - tensorflow - INFO - Evaluation [23940/39900] +2022-05-01 17:33:53,662 - tensorflow - INFO - Evaluation [27930/39900] +2022-05-01 17:34:03,220 - tensorflow - INFO - Evaluation [31920/39900] +2022-05-01 17:34:12,787 - tensorflow - INFO - Evaluation [35910/39900] +2022-05-01 17:34:22,350 - tensorflow - INFO - Evaluation [39900/39900] +2022-05-01 17:34:22,488 - tensorflow - INFO - Finished evaluation at 2022-05-01-17:34:22 +2022-05-01 17:34:22,489 - tensorflow - INFO - Saving dict for global step 1000000: global_step = 1000000, loss = 0.024924062, mean_degree_err = 4.3142204 +2022-05-01 17:34:22,489 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 1000000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1000000 +2022-05-01 17:34:22,503 - tensorflow - INFO - global_step/sec: 2.00081 +2022-05-01 17:34:22,504 - tensorflow - INFO - loss = 0.026846617, step = 1000000 (99.960 sec) +2022-05-01 17:34:24,791 - tensorflow - INFO - global_step/sec: 87.4221 +2022-05-01 17:34:24,791 - tensorflow - INFO - loss = 0.062070694, step = 1000200 (2.287 sec) +2022-05-01 17:34:27,060 - tensorflow - INFO - global_step/sec: 88.1238 +2022-05-01 17:34:27,061 - tensorflow - INFO - loss = 0.008369792, step = 1000400 (2.270 sec) +2022-05-01 17:34:29,329 - tensorflow - INFO - global_step/sec: 88.1393 +2022-05-01 17:34:29,330 - tensorflow - INFO - loss = 0.012222083, step = 1000600 (2.269 sec) +2022-05-01 17:34:31,595 - tensorflow - INFO - global_step/sec: 88.2946 +2022-05-01 17:34:31,595 - tensorflow - INFO - loss = 0.17804483, step = 1000800 (2.265 sec) +2022-05-01 17:34:33,869 - tensorflow - INFO - global_step/sec: 87.9165 +2022-05-01 17:34:33,870 - tensorflow - INFO - loss = 0.070887625, step = 1001000 (2.275 sec) +2022-05-01 17:34:36,147 - tensorflow - INFO - global_step/sec: 87.8316 +2022-05-01 17:34:36,147 - tensorflow - INFO - loss = 0.099069044, step = 1001200 (2.277 sec) +2022-05-01 17:34:38,417 - tensorflow - INFO - global_step/sec: 88.087 +2022-05-01 17:34:38,417 - tensorflow - INFO - loss = 0.24533297, step = 1001400 (2.271 sec) +2022-05-01 17:34:40,688 - tensorflow - INFO - global_step/sec: 88.0499 +2022-05-01 17:34:40,689 - tensorflow - INFO - loss = 0.025633255, step = 1001600 (2.271 sec) +2022-05-01 17:34:42,965 - tensorflow - INFO - global_step/sec: 87.8484 +2022-05-01 17:34:42,966 - tensorflow - INFO - loss = 1.1007679, step = 1001800 (2.277 sec) +2022-05-01 17:34:45,238 - tensorflow - INFO - global_step/sec: 87.9866 +2022-05-01 17:34:45,239 - tensorflow - INFO - loss = 0.12545982, step = 1002000 (2.273 sec) +2022-05-01 17:34:47,510 - tensorflow - INFO - global_step/sec: 88.0195 +2022-05-01 17:34:47,511 - tensorflow - INFO - loss = 0.060006622, step = 1002200 (2.272 sec) +2022-05-01 17:34:49,789 - tensorflow - INFO - global_step/sec: 87.7842 +2022-05-01 17:34:49,789 - tensorflow - INFO - loss = 0.058975074, step = 1002400 (2.278 sec) +2022-05-01 17:34:52,063 - tensorflow - INFO - global_step/sec: 87.9484 +2022-05-01 17:34:52,063 - tensorflow - INFO - loss = 0.3131972, step = 1002600 (2.274 sec) +2022-05-01 17:34:54,336 - tensorflow - INFO - global_step/sec: 87.9905 +2022-05-01 17:34:54,336 - tensorflow - INFO - loss = 0.08788369, step = 1002800 (2.273 sec) +2022-05-01 17:34:56,611 - tensorflow - INFO - global_step/sec: 87.9106 +2022-05-01 17:34:56,611 - tensorflow - INFO - loss = 0.22353008, step = 1003000 (2.275 sec) +2022-05-01 17:34:58,886 - tensorflow - INFO - global_step/sec: 87.9241 +2022-05-01 17:34:58,886 - tensorflow - INFO - loss = 0.022893198, step = 1003200 (2.275 sec) +2022-05-01 17:35:01,154 - tensorflow - INFO - global_step/sec: 88.1526 +2022-05-01 17:35:01,155 - tensorflow - INFO - loss = 0.029260218, step = 1003400 (2.269 sec) +2022-05-01 17:35:03,426 - tensorflow - INFO - global_step/sec: 88.0377 +2022-05-01 17:35:03,426 - tensorflow - INFO - loss = 0.03587124, step = 1003600 (2.272 sec) +2022-05-01 17:35:05,693 - tensorflow - INFO - global_step/sec: 88.2093 +2022-05-01 17:35:05,694 - tensorflow - INFO - loss = 0.027963502, step = 1003800 (2.267 sec) +2022-05-01 17:35:07,958 - tensorflow - INFO - global_step/sec: 88.3015 +2022-05-01 17:35:07,959 - tensorflow - INFO - loss = 0.21320803, step = 1004000 (2.265 sec) +2022-05-01 17:35:10,224 - tensorflow - INFO - global_step/sec: 88.269 +2022-05-01 17:35:10,225 - tensorflow - INFO - loss = 0.010730086, step = 1004200 (2.266 sec) +2022-05-01 17:35:12,488 - tensorflow - INFO - global_step/sec: 88.3264 +2022-05-01 17:35:12,489 - tensorflow - INFO - loss = 0.020996207, step = 1004400 (2.264 sec) +2022-05-01 17:35:14,760 - tensorflow - INFO - global_step/sec: 88.0305 +2022-05-01 17:35:14,761 - tensorflow - INFO - loss = 0.014935322, step = 1004600 (2.272 sec) +2022-05-01 17:35:17,027 - tensorflow - INFO - global_step/sec: 88.238 +2022-05-01 17:35:17,027 - tensorflow - INFO - loss = 0.020521518, step = 1004800 (2.267 sec) +2022-05-01 17:35:19,299 - tensorflow - INFO - global_step/sec: 88.0149 +2022-05-01 17:35:19,300 - tensorflow - INFO - loss = 0.027829817, step = 1005000 (2.272 sec) +2022-05-01 17:35:21,569 - tensorflow - INFO - global_step/sec: 88.1069 +2022-05-01 17:35:21,570 - tensorflow - INFO - loss = 0.01738238, step = 1005200 (2.270 sec) +2022-05-01 17:35:23,837 - tensorflow - INFO - global_step/sec: 88.1844 +2022-05-01 17:35:23,838 - tensorflow - INFO - loss = 0.02370587, step = 1005400 (2.268 sec) +2022-05-01 17:35:26,101 - tensorflow - INFO - global_step/sec: 88.3633 +2022-05-01 17:35:26,101 - tensorflow - INFO - loss = 0.027154919, step = 1005600 (2.263 sec) +2022-05-01 17:35:28,371 - tensorflow - INFO - global_step/sec: 88.0921 +2022-05-01 17:35:28,371 - tensorflow - INFO - loss = 0.08494438, step = 1005800 (2.270 sec) +2022-05-01 17:35:30,638 - tensorflow - INFO - global_step/sec: 88.2119 +2022-05-01 17:35:30,639 - tensorflow - INFO - loss = 0.01766038, step = 1006000 (2.267 sec) +2022-05-01 17:35:32,904 - tensorflow - INFO - global_step/sec: 88.2707 +2022-05-01 17:35:32,904 - tensorflow - INFO - loss = 0.0381309, step = 1006200 (2.266 sec) +2022-05-01 17:35:35,171 - tensorflow - INFO - global_step/sec: 88.2355 +2022-05-01 17:35:35,171 - tensorflow - INFO - loss = 0.3142389, step = 1006400 (2.267 sec) +2022-05-01 17:35:37,437 - tensorflow - INFO - global_step/sec: 88.2609 +2022-05-01 17:35:37,437 - tensorflow - INFO - loss = 0.011612243, step = 1006600 (2.266 sec) +2022-05-01 17:35:39,705 - tensorflow - INFO - global_step/sec: 88.1593 +2022-05-01 17:35:39,706 - tensorflow - INFO - loss = 0.010148924, step = 1006800 (2.269 sec) +2022-05-01 17:35:41,969 - tensorflow - INFO - global_step/sec: 88.3549 +2022-05-01 17:35:41,969 - tensorflow - INFO - loss = 0.048177227, step = 1007000 (2.264 sec) +2022-05-01 17:35:44,237 - tensorflow - INFO - global_step/sec: 88.1994 +2022-05-01 17:35:44,237 - tensorflow - INFO - loss = 0.033830248, step = 1007200 (2.268 sec) +2022-05-01 17:35:46,503 - tensorflow - INFO - global_step/sec: 88.2257 +2022-05-01 17:35:46,504 - tensorflow - INFO - loss = 0.02257288, step = 1007400 (2.267 sec) +2022-05-01 17:35:48,772 - tensorflow - INFO - global_step/sec: 88.1676 +2022-05-01 17:35:48,772 - tensorflow - INFO - loss = 0.014890956, step = 1007600 (2.268 sec) +2022-05-01 17:35:51,043 - tensorflow - INFO - global_step/sec: 88.0437 +2022-05-01 17:35:51,044 - tensorflow - INFO - loss = 0.020477772, step = 1007800 (2.272 sec) +2022-05-01 17:35:53,317 - tensorflow - INFO - global_step/sec: 87.9613 +2022-05-01 17:35:53,318 - tensorflow - INFO - loss = 0.09701069, step = 1008000 (2.274 sec) +2022-05-01 17:35:55,584 - tensorflow - INFO - global_step/sec: 88.2138 +2022-05-01 17:35:55,585 - tensorflow - INFO - loss = 0.06696297, step = 1008200 (2.267 sec) +2022-05-01 17:35:57,854 - tensorflow - INFO - global_step/sec: 88.1281 +2022-05-01 17:35:57,854 - tensorflow - INFO - loss = 0.03343594, step = 1008400 (2.269 sec) +2022-05-01 17:36:00,118 - tensorflow - INFO - global_step/sec: 88.3367 +2022-05-01 17:36:00,118 - tensorflow - INFO - loss = 0.019149898, step = 1008600 (2.264 sec) +2022-05-01 17:36:02,386 - tensorflow - INFO - global_step/sec: 88.1685 +2022-05-01 17:36:02,387 - tensorflow - INFO - loss = 0.06799473, step = 1008800 (2.268 sec) +2022-05-01 17:36:04,654 - tensorflow - INFO - global_step/sec: 88.1859 +2022-05-01 17:36:04,655 - tensorflow - INFO - loss = 0.0144699365, step = 1009000 (2.268 sec) +2022-05-01 17:36:06,921 - tensorflow - INFO - global_step/sec: 88.231 +2022-05-01 17:36:06,921 - tensorflow - INFO - loss = 0.013778967, step = 1009200 (2.267 sec) +2022-05-01 17:36:09,190 - tensorflow - INFO - global_step/sec: 88.1347 +2022-05-01 17:36:09,191 - tensorflow - INFO - loss = 0.6359856, step = 1009400 (2.269 sec) +2022-05-01 17:36:11,455 - tensorflow - INFO - global_step/sec: 88.2936 +2022-05-01 17:36:11,456 - tensorflow - INFO - loss = 0.04642318, step = 1009600 (2.265 sec) +2022-05-01 17:36:13,720 - tensorflow - INFO - global_step/sec: 88.3184 +2022-05-01 17:36:13,720 - tensorflow - INFO - loss = 0.02470339, step = 1009800 (2.265 sec) +2022-05-01 17:36:15,986 - tensorflow - INFO - global_step/sec: 88.2754 +2022-05-01 17:36:15,986 - tensorflow - INFO - loss = 0.020680029, step = 1010000 (2.266 sec) +2022-05-01 17:36:18,257 - 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INFO - global_step/sec: 88.1626 +2022-05-01 17:51:48,657 - tensorflow - INFO - loss = 0.023764364, step = 1092200 (2.269 sec) +2022-05-01 17:51:50,922 - tensorflow - INFO - global_step/sec: 88.2985 +2022-05-01 17:51:50,922 - tensorflow - INFO - loss = 0.013630393, step = 1092400 (2.265 sec) +2022-05-01 17:51:53,192 - tensorflow - INFO - global_step/sec: 88.1159 +2022-05-01 17:51:53,192 - tensorflow - INFO - loss = 0.05786172, step = 1092600 (2.270 sec) +2022-05-01 17:51:55,455 - tensorflow - INFO - global_step/sec: 88.3758 +2022-05-01 17:51:55,455 - tensorflow - INFO - loss = 0.019399472, step = 1092800 (2.263 sec) +2022-05-01 17:51:57,732 - tensorflow - INFO - global_step/sec: 87.8392 +2022-05-01 17:51:57,732 - tensorflow - INFO - loss = 0.008102739, step = 1093000 (2.277 sec) +2022-05-01 17:51:59,999 - tensorflow - INFO - global_step/sec: 88.2145 +2022-05-01 17:51:59,999 - tensorflow - INFO - loss = 0.014448153, step = 1093200 (2.267 sec) +2022-05-01 17:52:02,269 - tensorflow - INFO - global_step/sec: 88.1059 +2022-05-01 17:52:02,269 - tensorflow - INFO - loss = 0.07463558, step = 1093400 (2.270 sec) +2022-05-01 17:52:04,539 - tensorflow - INFO - global_step/sec: 88.094 +2022-05-01 17:52:04,540 - tensorflow - INFO - loss = 0.029204894, step = 1093600 (2.270 sec) +2022-05-01 17:52:06,810 - tensorflow - INFO - global_step/sec: 88.0569 +2022-05-01 17:52:06,811 - tensorflow - INFO - loss = 0.095928356, step = 1093800 (2.271 sec) +2022-05-01 17:52:09,082 - tensorflow - INFO - global_step/sec: 88.0486 +2022-05-01 17:52:09,082 - tensorflow - INFO - loss = 0.008705968, step = 1094000 (2.272 sec) +2022-05-01 17:52:11,356 - tensorflow - INFO - global_step/sec: 87.9548 +2022-05-01 17:52:11,356 - tensorflow - INFO - loss = 0.02741694, step = 1094200 (2.274 sec) +2022-05-01 17:52:13,631 - tensorflow - INFO - global_step/sec: 87.8973 +2022-05-01 17:52:13,632 - tensorflow - INFO - loss = 0.026683709, step = 1094400 (2.275 sec) +2022-05-01 17:52:15,904 - tensorflow - INFO - global_step/sec: 87.9759 +2022-05-01 17:52:15,905 - tensorflow - INFO - loss = 0.039245937, step = 1094600 (2.273 sec) +2022-05-01 17:52:18,182 - tensorflow - INFO - global_step/sec: 87.8312 +2022-05-01 17:52:18,182 - tensorflow - INFO - loss = 0.03418677, step = 1094800 (2.277 sec) +2022-05-01 17:52:20,458 - tensorflow - INFO - global_step/sec: 87.8518 +2022-05-01 17:52:20,459 - tensorflow - INFO - loss = 0.018989494, step = 1095000 (2.277 sec) +2022-05-01 17:52:22,733 - tensorflow - INFO - global_step/sec: 87.904 +2022-05-01 17:52:22,734 - tensorflow - INFO - loss = 0.010458937, step = 1095200 (2.275 sec) +2022-05-01 17:52:25,010 - tensorflow - INFO - global_step/sec: 87.8573 +2022-05-01 17:52:25,010 - tensorflow - INFO - loss = 0.96352756, step = 1095400 (2.276 sec) +2022-05-01 17:52:27,283 - tensorflow - INFO - global_step/sec: 87.9611 +2022-05-01 17:52:27,284 - tensorflow - INFO - loss = 0.06753484, step = 1095600 (2.274 sec) +2022-05-01 17:52:29,557 - tensorflow - INFO - global_step/sec: 87.9567 +2022-05-01 17:52:29,558 - tensorflow - INFO - loss = 0.015580781, step = 1095800 (2.274 sec) +2022-05-01 17:52:31,831 - tensorflow - INFO - global_step/sec: 87.962 +2022-05-01 17:52:31,831 - tensorflow - INFO - loss = 0.15739396, step = 1096000 (2.274 sec) +2022-05-01 17:52:34,108 - tensorflow - INFO - global_step/sec: 87.8395 +2022-05-01 17:52:34,108 - tensorflow - INFO - loss = 0.05162877, step = 1096200 (2.277 sec) +2022-05-01 17:52:36,376 - tensorflow - INFO - global_step/sec: 88.1794 +2022-05-01 17:52:36,376 - tensorflow - INFO - loss = 0.011576347, step = 1096400 (2.268 sec) +2022-05-01 17:52:38,645 - tensorflow - INFO - global_step/sec: 88.1411 +2022-05-01 17:52:38,646 - tensorflow - INFO - loss = 0.010396266, step = 1096600 (2.269 sec) +2022-05-01 17:52:40,912 - tensorflow - INFO - global_step/sec: 88.2174 +2022-05-01 17:52:40,913 - tensorflow - INFO - loss = 0.022482887, step = 1096800 (2.267 sec) +2022-05-01 17:52:43,179 - tensorflow - INFO - global_step/sec: 88.2341 +2022-05-01 17:52:43,179 - tensorflow - INFO - loss = 0.010558477, step = 1097000 (2.267 sec) +2022-05-01 17:52:45,446 - tensorflow - INFO - global_step/sec: 88.2151 +2022-05-01 17:52:45,447 - tensorflow - INFO - loss = 0.036490947, step = 1097200 (2.267 sec) +2022-05-01 17:52:47,709 - tensorflow - INFO - global_step/sec: 88.3911 +2022-05-01 17:52:47,709 - tensorflow - INFO - loss = 0.15203127, step = 1097400 (2.263 sec) +2022-05-01 17:52:49,982 - tensorflow - INFO - global_step/sec: 87.9806 +2022-05-01 17:52:49,982 - tensorflow - INFO - loss = 0.05722553, step = 1097600 (2.273 sec) +2022-05-01 17:52:52,254 - tensorflow - INFO - global_step/sec: 88.0291 +2022-05-01 17:52:52,254 - tensorflow - INFO - loss = 0.051573113, step = 1097800 (2.272 sec) +2022-05-01 17:52:54,519 - tensorflow - INFO - global_step/sec: 88.3146 +2022-05-01 17:52:54,519 - tensorflow - INFO - loss = 0.042725034, step = 1098000 (2.265 sec) +2022-05-01 17:52:56,791 - tensorflow - INFO - global_step/sec: 88.0275 +2022-05-01 17:52:56,791 - tensorflow - INFO - loss = 0.2444915, step = 1098200 (2.272 sec) +2022-05-01 17:52:59,057 - tensorflow - INFO - global_step/sec: 88.2664 +2022-05-01 17:52:59,057 - tensorflow - INFO - loss = 0.01457686, step = 1098400 (2.266 sec) +2022-05-01 17:53:01,327 - tensorflow - INFO - global_step/sec: 88.0678 +2022-05-01 17:53:01,328 - tensorflow - INFO - loss = 0.008405423, step = 1098600 (2.271 sec) +2022-05-01 17:53:03,599 - tensorflow - INFO - global_step/sec: 88.0387 +2022-05-01 17:53:03,600 - tensorflow - INFO - loss = 0.023712724, step = 1098800 (2.272 sec) +2022-05-01 17:53:05,868 - tensorflow - INFO - global_step/sec: 88.1347 +2022-05-01 17:53:05,869 - tensorflow - INFO - loss = 0.017779306, step = 1099000 (2.269 sec) +2022-05-01 17:53:08,139 - tensorflow - INFO - global_step/sec: 88.0815 +2022-05-01 17:53:08,139 - tensorflow - INFO - loss = 0.03620185, step = 1099200 (2.271 sec) +2022-05-01 17:53:10,414 - tensorflow - INFO - global_step/sec: 87.921 +2022-05-01 17:53:10,414 - tensorflow - INFO - loss = 0.00687285, step = 1099400 (2.275 sec) +2022-05-01 17:53:12,683 - tensorflow - INFO - global_step/sec: 88.141 +2022-05-01 17:53:12,683 - tensorflow - INFO - loss = 0.015671916, step = 1099600 (2.269 sec) +2022-05-01 17:53:14,957 - tensorflow - INFO - global_step/sec: 87.9438 +2022-05-01 17:53:14,958 - tensorflow - INFO - loss = 0.021432642, step = 1099800 (2.274 sec) +2022-05-01 17:53:17,213 - tensorflow - INFO - Saving checkpoints for 1100000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. +2022-05-01 17:53:17,228 - tensorflow - INFO - model.ckpt-1100000 is not in all_model_checkpoint_paths. Manually adding it. +2022-05-01 17:53:17,753 - tensorflow - INFO - Calling model_fn. +2022-05-01 17:53:17,880 - tensorflow - INFO - Done calling model_fn. +2022-05-01 17:53:17,895 - tensorflow - INFO - Starting evaluation at 2022-05-01T17:53:17Z +2022-05-01 17:53:17,959 - tensorflow - INFO - Graph was finalized. +2022-05-01 17:53:17,962 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1100000 +2022-05-01 17:53:18,242 - tensorflow - INFO - Running local_init_op. +2022-05-01 17:53:18,350 - tensorflow - INFO - Done running local_init_op. +2022-05-01 17:53:28,495 - tensorflow - INFO - Evaluation [3990/39900] +2022-05-01 17:53:37,950 - tensorflow - INFO - Evaluation [7980/39900] +2022-05-01 17:53:47,393 - tensorflow - INFO - Evaluation [11970/39900] +2022-05-01 17:53:56,838 - tensorflow - INFO - Evaluation [15960/39900] +2022-05-01 17:54:06,279 - tensorflow - INFO - Evaluation [19950/39900] +2022-05-01 17:54:15,733 - tensorflow - INFO - Evaluation [23940/39900] +2022-05-01 17:54:25,160 - tensorflow - INFO - Evaluation [27930/39900] +2022-05-01 17:54:34,614 - tensorflow - INFO - Evaluation [31920/39900] +2022-05-01 17:54:44,055 - tensorflow - INFO - Evaluation [35910/39900] +2022-05-01 17:54:53,494 - tensorflow - INFO - Evaluation [39900/39900] +2022-05-01 17:54:53,631 - tensorflow - INFO - Finished evaluation at 2022-05-01-17:54:53 +2022-05-01 17:54:53,632 - tensorflow - INFO - Saving dict for global step 1100000: global_step = 1100000, loss = 0.025317876, mean_degree_err = 4.4118333 +2022-05-01 17:54:53,632 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 1100000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1100000 +2022-05-01 17:54:53,646 - tensorflow - INFO - global_step/sec: 2.02658 +2022-05-01 17:54:53,647 - tensorflow - INFO - loss = 5.8303533, step = 1100000 (98.689 sec) +2022-05-01 17:54:55,928 - tensorflow - INFO - global_step/sec: 87.6514 +2022-05-01 17:54:55,928 - tensorflow - INFO - loss = 0.017083686, step = 1100200 (2.281 sec) +2022-05-01 17:54:58,192 - tensorflow - INFO - global_step/sec: 88.3137 +2022-05-01 17:54:58,193 - tensorflow - INFO - loss = 0.029804125, step = 1100400 (2.265 sec) +2022-05-01 17:55:00,461 - tensorflow - INFO - global_step/sec: 88.1546 +2022-05-01 17:55:00,461 - tensorflow - INFO - loss = 0.16030154, step = 1100600 (2.269 sec) +2022-05-01 17:55:02,731 - tensorflow - INFO - global_step/sec: 88.1041 +2022-05-01 17:55:02,731 - tensorflow - INFO - loss = 0.014746798, step = 1100800 (2.270 sec) +2022-05-01 17:55:05,001 - tensorflow - INFO - global_step/sec: 88.1035 +2022-05-01 17:55:05,001 - tensorflow - INFO - loss = 0.018318102, step = 1101000 (2.270 sec) +2022-05-01 17:55:07,275 - tensorflow - INFO - global_step/sec: 87.959 +2022-05-01 17:55:07,275 - tensorflow - INFO - loss = 0.026991412, step = 1101200 (2.274 sec) +2022-05-01 17:55:09,546 - tensorflow - INFO - global_step/sec: 88.0721 +2022-05-01 17:55:09,546 - tensorflow - INFO - loss = 0.03456292, step = 1101400 (2.271 sec) +2022-05-01 17:55:11,813 - tensorflow - INFO - global_step/sec: 88.2073 +2022-05-01 17:55:11,813 - tensorflow - INFO - loss = 0.007666119, step = 1101600 (2.267 sec) +2022-05-01 17:55:14,081 - tensorflow - INFO - global_step/sec: 88.1868 +2022-05-01 17:55:14,081 - tensorflow - INFO - loss = 0.019554604, step = 1101800 (2.268 sec) +2022-05-01 17:55:16,350 - tensorflow - INFO - global_step/sec: 88.1564 +2022-05-01 17:55:16,350 - tensorflow - INFO - loss = 0.013642142, step = 1102000 (2.269 sec) +2022-05-01 17:55:18,618 - tensorflow - INFO - global_step/sec: 88.17 +2022-05-01 17:55:18,618 - tensorflow - INFO - loss = 0.01439528, step = 1102200 (2.268 sec) +2022-05-01 17:55:20,890 - tensorflow - INFO - global_step/sec: 88.0217 +2022-05-01 17:55:20,891 - tensorflow - INFO - loss = 0.015880037, step = 1102400 (2.272 sec) +2022-05-01 17:55:23,160 - tensorflow - INFO - global_step/sec: 88.1192 +2022-05-01 17:55:23,160 - tensorflow - INFO - loss = 0.048466884, step = 1102600 (2.270 sec) +2022-05-01 17:55:25,429 - tensorflow - INFO - global_step/sec: 88.1244 +2022-05-01 17:55:25,430 - tensorflow - INFO - loss = 0.049483653, step = 1102800 (2.270 sec) +2022-05-01 17:55:27,694 - tensorflow - INFO - global_step/sec: 88.3191 +2022-05-01 17:55:27,694 - tensorflow - INFO - loss = 0.3012721, step = 1103000 (2.265 sec) +2022-05-01 17:55:29,964 - tensorflow - INFO - global_step/sec: 88.0985 +2022-05-01 17:55:29,964 - tensorflow - INFO - loss = 0.019714927, step = 1103200 (2.270 sec) +2022-05-01 17:55:32,245 - tensorflow - INFO - global_step/sec: 87.6843 +2022-05-01 17:55:32,245 - tensorflow - INFO - loss = 0.022536471, step = 1103400 (2.281 sec) +2022-05-01 17:55:34,522 - tensorflow - INFO - global_step/sec: 87.8411 +2022-05-01 17:55:34,522 - tensorflow - INFO - loss = 0.04687898, step = 1103600 (2.277 sec) +2022-05-01 17:55:36,798 - tensorflow - INFO - global_step/sec: 87.8777 +2022-05-01 17:55:36,798 - tensorflow - INFO - loss = 0.058065794, step = 1103800 (2.276 sec) +2022-05-01 17:55:39,072 - tensorflow - INFO - global_step/sec: 87.9228 +2022-05-01 17:55:39,073 - tensorflow - INFO - loss = 0.45963365, step = 1104000 (2.275 sec) +2022-05-01 17:55:41,350 - tensorflow - INFO - global_step/sec: 87.8175 +2022-05-01 17:55:41,350 - tensorflow - INFO - loss = 0.6161173, step = 1104200 (2.277 sec) +2022-05-01 17:55:43,623 - tensorflow - INFO - global_step/sec: 87.9808 +2022-05-01 17:55:43,623 - tensorflow - INFO - loss = 0.025974642, step = 1104400 (2.273 sec) +2022-05-01 17:55:45,900 - tensorflow - INFO - global_step/sec: 87.8351 +2022-05-01 17:55:45,901 - tensorflow - INFO - loss = 0.04511058, step = 1104600 (2.277 sec) +2022-05-01 17:55:48,179 - tensorflow - INFO - global_step/sec: 87.7429 +2022-05-01 17:55:48,180 - tensorflow - INFO - loss = 0.14503525, step = 1104800 (2.279 sec) +2022-05-01 17:55:50,453 - tensorflow - INFO - global_step/sec: 87.965 +2022-05-01 17:55:50,454 - tensorflow - INFO - loss = 0.030037086, step = 1105000 (2.274 sec) +2022-05-01 17:55:52,733 - tensorflow - INFO - global_step/sec: 87.7381 +2022-05-01 17:55:52,733 - tensorflow - INFO - loss = 0.044755362, step = 1105200 (2.279 sec) +2022-05-01 17:55:55,006 - tensorflow - INFO - global_step/sec: 87.9747 +2022-05-01 17:55:55,006 - tensorflow - INFO - loss = 0.01589223, step = 1105400 (2.273 sec) +2022-05-01 17:55:57,288 - tensorflow - INFO - global_step/sec: 87.658 +2022-05-01 17:55:57,288 - tensorflow - INFO - loss = 1.0562906, step = 1105600 (2.282 sec) +2022-05-01 17:55:59,564 - tensorflow - INFO - global_step/sec: 87.867 +2022-05-01 17:55:59,564 - tensorflow - INFO - loss = 0.12573439, step = 1105800 (2.276 sec) +2022-05-01 17:56:01,843 - tensorflow - INFO - global_step/sec: 87.747 +2022-05-01 17:56:01,843 - tensorflow - INFO - loss = 0.018375054, step = 1106000 (2.279 sec) +2022-05-01 17:56:04,112 - tensorflow - INFO - global_step/sec: 88.1355 +2022-05-01 17:56:04,113 - tensorflow - INFO - loss = 0.018151755, step = 1106200 (2.269 sec) +2022-05-01 17:56:06,383 - tensorflow - INFO - global_step/sec: 88.0759 +2022-05-01 17:56:06,383 - tensorflow - INFO - loss = 0.021065243, step = 1106400 (2.271 sec) +2022-05-01 17:56:08,650 - tensorflow - INFO - global_step/sec: 88.2391 +2022-05-01 17:56:08,650 - tensorflow - INFO - loss = 0.3877352, step = 1106600 (2.267 sec) +2022-05-01 17:56:10,919 - tensorflow - INFO - global_step/sec: 88.138 +2022-05-01 17:56:10,919 - tensorflow - INFO - loss = 0.005250844, step = 1106800 (2.269 sec) +2022-05-01 17:56:13,192 - tensorflow - INFO - global_step/sec: 87.9927 +2022-05-01 17:56:13,192 - tensorflow - INFO - loss = 0.01313768, step = 1107000 (2.273 sec) +2022-05-01 17:56:15,463 - tensorflow - INFO - global_step/sec: 88.0475 +2022-05-01 17:56:15,464 - tensorflow - INFO - loss = 0.11005046, step = 1107200 (2.271 sec) +2022-05-01 17:56:17,733 - tensorflow - INFO - global_step/sec: 88.1181 +2022-05-01 17:56:17,733 - tensorflow - INFO - loss = 0.060840447, step = 1107400 (2.270 sec) +2022-05-01 17:56:20,001 - tensorflow - INFO - global_step/sec: 88.1591 +2022-05-01 17:56:20,002 - tensorflow - INFO - loss = 0.008830101, step = 1107600 (2.269 sec) +2022-05-01 17:56:22,270 - 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global_step/sec: 88.7467 +2022-05-01 18:11:47,566 - tensorflow - INFO - loss = 0.036995653, step = 1189600 (2.254 sec) +2022-05-01 18:11:49,825 - tensorflow - INFO - global_step/sec: 88.5181 +2022-05-01 18:11:49,825 - tensorflow - INFO - loss = 0.09084523, step = 1189800 (2.259 sec) +2022-05-01 18:11:52,078 - tensorflow - INFO - global_step/sec: 88.748 +2022-05-01 18:11:52,079 - tensorflow - INFO - loss = 0.10673227, step = 1190000 (2.254 sec) +2022-05-01 18:11:54,329 - tensorflow - INFO - global_step/sec: 88.8723 +2022-05-01 18:11:54,329 - tensorflow - INFO - loss = 0.0142885335, step = 1190200 (2.250 sec) +2022-05-01 18:11:56,583 - tensorflow - INFO - global_step/sec: 88.7303 +2022-05-01 18:11:56,583 - tensorflow - INFO - loss = 0.43347728, step = 1190400 (2.254 sec) +2022-05-01 18:11:58,840 - tensorflow - INFO - global_step/sec: 88.6206 +2022-05-01 18:11:58,840 - tensorflow - INFO - loss = 0.030480046, step = 1190600 (2.257 sec) +2022-05-01 18:12:01,093 - tensorflow - INFO - global_step/sec: 88.7518 +2022-05-01 18:12:01,094 - tensorflow - INFO - loss = 0.08956727, step = 1190800 (2.253 sec) +2022-05-01 18:12:03,353 - tensorflow - INFO - global_step/sec: 88.4922 +2022-05-01 18:12:03,354 - tensorflow - INFO - loss = 0.034298472, step = 1191000 (2.260 sec) +2022-05-01 18:12:05,608 - tensorflow - INFO - global_step/sec: 88.6941 +2022-05-01 18:12:05,609 - tensorflow - INFO - loss = 0.04235449, step = 1191200 (2.255 sec) +2022-05-01 18:12:07,865 - tensorflow - INFO - global_step/sec: 88.6266 +2022-05-01 18:12:07,865 - tensorflow - INFO - loss = 0.018376255, step = 1191400 (2.257 sec) +2022-05-01 18:12:10,118 - tensorflow - INFO - global_step/sec: 88.7677 +2022-05-01 18:12:10,118 - tensorflow - INFO - loss = 0.03255583, step = 1191600 (2.253 sec) +2022-05-01 18:12:12,377 - tensorflow - INFO - global_step/sec: 88.5139 +2022-05-01 18:12:12,378 - tensorflow - INFO - loss = 0.012742184, step = 1191800 (2.260 sec) +2022-05-01 18:12:14,641 - tensorflow - INFO - global_step/sec: 88.3719 +2022-05-01 18:12:14,641 - tensorflow - INFO - loss = 0.010812651, step = 1192000 (2.263 sec) +2022-05-01 18:12:16,898 - tensorflow - INFO - global_step/sec: 88.6071 +2022-05-01 18:12:16,898 - tensorflow - INFO - loss = 0.12380754, step = 1192200 (2.257 sec) +2022-05-01 18:12:19,146 - tensorflow - INFO - global_step/sec: 88.9405 +2022-05-01 18:12:19,147 - tensorflow - INFO - loss = 0.017856931, step = 1192400 (2.249 sec) +2022-05-01 18:12:21,399 - tensorflow - INFO - global_step/sec: 88.8056 +2022-05-01 18:12:21,399 - tensorflow - INFO - loss = 0.006084865, step = 1192600 (2.252 sec) +2022-05-01 18:12:23,656 - tensorflow - INFO - global_step/sec: 88.5832 +2022-05-01 18:12:23,657 - tensorflow - INFO - loss = 0.019839931, step = 1192800 (2.258 sec) +2022-05-01 18:12:25,910 - tensorflow - INFO - global_step/sec: 88.747 +2022-05-01 18:12:25,910 - tensorflow - INFO - loss = 0.057857707, step = 1193000 (2.254 sec) +2022-05-01 18:12:28,167 - tensorflow - INFO - global_step/sec: 88.6283 +2022-05-01 18:12:28,167 - tensorflow - INFO - loss = 0.026098432, step = 1193200 (2.257 sec) +2022-05-01 18:12:30,425 - tensorflow - INFO - global_step/sec: 88.5675 +2022-05-01 18:12:30,425 - tensorflow - INFO - loss = 0.017955255, step = 1193400 (2.258 sec) +2022-05-01 18:12:32,676 - tensorflow - INFO - global_step/sec: 88.8284 +2022-05-01 18:12:32,677 - tensorflow - INFO - loss = 14.991538, step = 1193600 (2.251 sec) +2022-05-01 18:12:34,933 - tensorflow - INFO - global_step/sec: 88.6301 +2022-05-01 18:12:34,933 - tensorflow - INFO - loss = 0.021420287, step = 1193800 (2.257 sec) +2022-05-01 18:12:37,184 - tensorflow - INFO - global_step/sec: 88.8432 +2022-05-01 18:12:37,184 - tensorflow - INFO - loss = 0.025775535, step = 1194000 (2.251 sec) +2022-05-01 18:12:39,442 - tensorflow - INFO - global_step/sec: 88.5622 +2022-05-01 18:12:39,443 - tensorflow - INFO - loss = 0.016893541, step = 1194200 (2.258 sec) +2022-05-01 18:12:41,697 - tensorflow - INFO - global_step/sec: 88.713 +2022-05-01 18:12:41,697 - tensorflow - INFO - loss = 0.033774905, step = 1194400 (2.254 sec) +2022-05-01 18:12:43,948 - tensorflow - INFO - global_step/sec: 88.8447 +2022-05-01 18:12:43,948 - tensorflow - INFO - loss = 0.009096233, step = 1194600 (2.251 sec) +2022-05-01 18:12:46,203 - tensorflow - INFO - global_step/sec: 88.6816 +2022-05-01 18:12:46,204 - tensorflow - INFO - loss = 0.061590552, step = 1194800 (2.255 sec) +2022-05-01 18:12:48,458 - tensorflow - INFO - global_step/sec: 88.6798 +2022-05-01 18:12:48,459 - tensorflow - INFO - loss = 0.56076455, step = 1195000 (2.255 sec) +2022-05-01 18:12:50,717 - tensorflow - INFO - global_step/sec: 88.5356 +2022-05-01 18:12:50,718 - tensorflow - INFO - loss = 0.047898788, step = 1195200 (2.259 sec) +2022-05-01 18:12:52,976 - tensorflow - INFO - global_step/sec: 88.5673 +2022-05-01 18:12:52,976 - tensorflow - INFO - loss = 0.037085086, step = 1195400 (2.258 sec) +2022-05-01 18:12:55,230 - tensorflow - INFO - global_step/sec: 88.716 +2022-05-01 18:12:55,230 - tensorflow - INFO - loss = 0.056205664, step = 1195600 (2.254 sec) +2022-05-01 18:12:57,491 - tensorflow - INFO - global_step/sec: 88.4358 +2022-05-01 18:12:57,492 - tensorflow - INFO - loss = 0.03376495, step = 1195800 (2.261 sec) +2022-05-01 18:12:59,749 - tensorflow - INFO - global_step/sec: 88.5909 +2022-05-01 18:12:59,749 - tensorflow - INFO - loss = 0.0069675804, step = 1196000 (2.258 sec) +2022-05-01 18:13:02,015 - tensorflow - INFO - global_step/sec: 88.2758 +2022-05-01 18:13:02,015 - tensorflow - INFO - loss = 0.09626718, step = 1196200 (2.266 sec) +2022-05-01 18:13:04,278 - tensorflow - INFO - global_step/sec: 88.384 +2022-05-01 18:13:04,278 - tensorflow - INFO - loss = 0.28406236, step = 1196400 (2.263 sec) +2022-05-01 18:13:06,539 - tensorflow - INFO - global_step/sec: 88.4227 +2022-05-01 18:13:06,540 - tensorflow - INFO - loss = 0.013516074, step = 1196600 (2.262 sec) +2022-05-01 18:13:08,799 - tensorflow - INFO - global_step/sec: 88.5036 +2022-05-01 18:13:08,800 - tensorflow - INFO - loss = 0.037272662, step = 1196800 (2.260 sec) +2022-05-01 18:13:11,060 - tensorflow - INFO - global_step/sec: 88.4786 +2022-05-01 18:13:11,060 - tensorflow - INFO - loss = 0.020124855, step = 1197000 (2.260 sec) +2022-05-01 18:13:13,325 - tensorflow - INFO - global_step/sec: 88.2711 +2022-05-01 18:13:13,326 - tensorflow - INFO - loss = 0.03263738, step = 1197200 (2.266 sec) +2022-05-01 18:13:15,591 - tensorflow - INFO - global_step/sec: 88.2575 +2022-05-01 18:13:15,592 - tensorflow - INFO - loss = 0.21277907, step = 1197400 (2.266 sec) +2022-05-01 18:13:17,858 - tensorflow - INFO - global_step/sec: 88.2431 +2022-05-01 18:13:17,858 - tensorflow - INFO - loss = 0.011050388, step = 1197600 (2.266 sec) +2022-05-01 18:13:20,124 - tensorflow - INFO - global_step/sec: 88.2652 +2022-05-01 18:13:20,124 - tensorflow - INFO - loss = 0.029462758, step = 1197800 (2.266 sec) +2022-05-01 18:13:22,385 - tensorflow - INFO - global_step/sec: 88.4637 +2022-05-01 18:13:22,385 - tensorflow - INFO - loss = 0.016916715, step = 1198000 (2.261 sec) +2022-05-01 18:13:24,649 - tensorflow - INFO - global_step/sec: 88.3252 +2022-05-01 18:13:24,649 - tensorflow - INFO - loss = 0.01425764, step = 1198200 (2.264 sec) +2022-05-01 18:13:26,914 - tensorflow - INFO - global_step/sec: 88.3187 +2022-05-01 18:13:26,914 - tensorflow - INFO - loss = 0.08497985, step = 1198400 (2.265 sec) +2022-05-01 18:13:29,178 - tensorflow - INFO - global_step/sec: 88.3126 +2022-05-01 18:13:29,179 - tensorflow - INFO - loss = 0.0450411, step = 1198600 (2.265 sec) +2022-05-01 18:13:31,448 - tensorflow - INFO - global_step/sec: 88.1132 +2022-05-01 18:13:31,448 - tensorflow - INFO - loss = 0.033562914, step = 1198800 (2.270 sec) +2022-05-01 18:13:33,706 - tensorflow - INFO - global_step/sec: 88.5748 +2022-05-01 18:13:33,706 - tensorflow - INFO - loss = 0.082803875, step = 1199000 (2.258 sec) +2022-05-01 18:13:35,964 - tensorflow - INFO - global_step/sec: 88.5552 +2022-05-01 18:13:35,965 - tensorflow - INFO - loss = 1.5781188, step = 1199200 (2.258 sec) +2022-05-01 18:13:38,215 - tensorflow - INFO - global_step/sec: 88.8648 +2022-05-01 18:13:38,215 - tensorflow - INFO - loss = 0.0135265775, step = 1199400 (2.251 sec) +2022-05-01 18:13:40,467 - tensorflow - INFO - global_step/sec: 88.8301 +2022-05-01 18:13:40,467 - tensorflow - INFO - loss = 0.009275254, step = 1199600 (2.252 sec) +2022-05-01 18:13:42,718 - tensorflow - INFO - global_step/sec: 88.8346 +2022-05-01 18:13:42,718 - tensorflow - INFO - loss = 0.029307913, step = 1199800 (2.251 sec) +2022-05-01 18:13:44,962 - tensorflow - INFO - Saving checkpoints for 1200000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. +2022-05-01 18:13:44,978 - tensorflow - INFO - model.ckpt-1200000 is not in all_model_checkpoint_paths. Manually adding it. +2022-05-01 18:13:45,518 - tensorflow - INFO - Calling model_fn. +2022-05-01 18:13:45,645 - tensorflow - INFO - Done calling model_fn. +2022-05-01 18:13:45,660 - tensorflow - INFO - Starting evaluation at 2022-05-01T18:13:45Z +2022-05-01 18:13:45,724 - tensorflow - INFO - Graph was finalized. +2022-05-01 18:13:45,727 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1200000 +2022-05-01 18:13:46,008 - tensorflow - INFO - Running local_init_op. +2022-05-01 18:13:46,116 - tensorflow - INFO - Done running local_init_op. +2022-05-01 18:13:56,323 - tensorflow - INFO - Evaluation [3990/39900] +2022-05-01 18:14:05,837 - tensorflow - INFO - Evaluation [7980/39900] +2022-05-01 18:14:15,350 - tensorflow - INFO - Evaluation [11970/39900] +2022-05-01 18:14:24,848 - tensorflow - INFO - Evaluation [15960/39900] +2022-05-01 18:14:34,349 - tensorflow - INFO - Evaluation [19950/39900] +2022-05-01 18:14:43,851 - tensorflow - INFO - Evaluation [23940/39900] +2022-05-01 18:14:53,368 - tensorflow - INFO - Evaluation [27930/39900] +2022-05-01 18:15:02,897 - tensorflow - INFO - Evaluation [31920/39900] +2022-05-01 18:15:12,412 - tensorflow - INFO - Evaluation [35910/39900] +2022-05-01 18:15:21,938 - tensorflow - INFO - Evaluation [39900/39900] +2022-05-01 18:15:22,073 - tensorflow - INFO - Finished evaluation at 2022-05-01-18:15:22 +2022-05-01 18:15:22,074 - tensorflow - INFO - Saving dict for global step 1200000: global_step = 1200000, loss = 0.049551684, mean_degree_err = 8.192199 +2022-05-01 18:15:22,074 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 1200000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1200000 +2022-05-01 18:15:22,088 - tensorflow - INFO - global_step/sec: 2.01268 +2022-05-01 18:15:22,089 - tensorflow - INFO - loss = 0.10029067, step = 1200000 (99.370 sec) +2022-05-01 18:15:24,354 - tensorflow - INFO - global_step/sec: 88.256 +2022-05-01 18:15:24,354 - tensorflow - INFO - loss = 0.03690188, step = 1200200 (2.266 sec) +2022-05-01 18:15:26,611 - tensorflow - INFO - global_step/sec: 88.6113 +2022-05-01 18:15:26,611 - tensorflow - INFO - loss = 0.033221673, step = 1200400 (2.257 sec) +2022-05-01 18:15:28,864 - tensorflow - INFO - global_step/sec: 88.7596 +2022-05-01 18:15:28,865 - tensorflow - INFO - loss = 0.021254009, step = 1200600 (2.253 sec) +2022-05-01 18:15:31,122 - tensorflow - INFO - global_step/sec: 88.5909 +2022-05-01 18:15:31,122 - tensorflow - INFO - loss = 0.03441968, step = 1200800 (2.258 sec) +2022-05-01 18:15:33,376 - tensorflow - INFO - global_step/sec: 88.7153 +2022-05-01 18:15:33,377 - tensorflow - INFO - loss = 0.030785479, step = 1201000 (2.254 sec) +2022-05-01 18:15:35,629 - tensorflow - INFO - global_step/sec: 88.7627 +2022-05-01 18:15:35,630 - tensorflow - INFO - loss = 0.008263586, step = 1201200 (2.253 sec) +2022-05-01 18:15:37,886 - tensorflow - INFO - global_step/sec: 88.6402 +2022-05-01 18:15:37,886 - tensorflow - INFO - loss = 0.08718412, step = 1201400 (2.256 sec) +2022-05-01 18:15:40,141 - tensorflow - INFO - global_step/sec: 88.6754 +2022-05-01 18:15:40,142 - tensorflow - INFO - loss = 0.06642119, step = 1201600 (2.255 sec) +2022-05-01 18:15:42,395 - tensorflow - INFO - global_step/sec: 88.7366 +2022-05-01 18:15:42,395 - tensorflow - INFO - loss = 0.05555537, step = 1201800 (2.254 sec) +2022-05-01 18:15:44,645 - tensorflow - INFO - global_step/sec: 88.9064 +2022-05-01 18:15:44,645 - tensorflow - INFO - loss = 1.1072648, step = 1202000 (2.250 sec) +2022-05-01 18:15:46,905 - tensorflow - INFO - global_step/sec: 88.478 +2022-05-01 18:15:46,905 - tensorflow - INFO - loss = 0.06818071, step = 1202200 (2.260 sec) +2022-05-01 18:15:49,162 - tensorflow - INFO - global_step/sec: 88.6274 +2022-05-01 18:15:49,162 - tensorflow - INFO - loss = 0.02435386, step = 1202400 (2.257 sec) +2022-05-01 18:15:51,425 - tensorflow - INFO - global_step/sec: 88.3848 +2022-05-01 18:15:51,425 - tensorflow - INFO - loss = 0.031496737, step = 1202600 (2.263 sec) +2022-05-01 18:15:53,680 - tensorflow - INFO - global_step/sec: 88.6802 +2022-05-01 18:15:53,680 - tensorflow - INFO - loss = 0.22101256, step = 1202800 (2.255 sec) +2022-05-01 18:15:55,937 - tensorflow - INFO - global_step/sec: 88.6014 +2022-05-01 18:15:55,938 - tensorflow - INFO - loss = 0.010230656, step = 1203000 (2.257 sec) +2022-05-01 18:15:58,194 - tensorflow - INFO - global_step/sec: 88.5991 +2022-05-01 18:15:58,195 - tensorflow - INFO - loss = 2.6677594, step = 1203200 (2.257 sec) +2022-05-01 18:16:00,449 - tensorflow - INFO - global_step/sec: 88.7291 +2022-05-01 18:16:00,449 - tensorflow - INFO - loss = 0.14728445, step = 1203400 (2.254 sec) +2022-05-01 18:16:02,714 - tensorflow - INFO - global_step/sec: 88.2869 +2022-05-01 18:16:02,714 - tensorflow - INFO - loss = 0.03147973, step = 1203600 (2.265 sec) +2022-05-01 18:16:04,972 - tensorflow - INFO - global_step/sec: 88.5771 +2022-05-01 18:16:04,972 - tensorflow - INFO - loss = 0.089283906, step = 1203800 (2.258 sec) +2022-05-01 18:16:07,227 - tensorflow - INFO - global_step/sec: 88.6778 +2022-05-01 18:16:07,228 - tensorflow - INFO - loss = 0.05661044, step = 1204000 (2.255 sec) +2022-05-01 18:16:09,482 - tensorflow - INFO - global_step/sec: 88.6908 +2022-05-01 18:16:09,483 - tensorflow - INFO - loss = 0.018297628, step = 1204200 (2.255 sec) +2022-05-01 18:16:11,740 - tensorflow - INFO - global_step/sec: 88.5849 +2022-05-01 18:16:11,740 - tensorflow - INFO - loss = 0.025900912, step = 1204400 (2.258 sec) +2022-05-01 18:16:14,000 - tensorflow - INFO - global_step/sec: 88.4942 +2022-05-01 18:16:14,000 - tensorflow - INFO - loss = 0.034806952, step = 1204600 (2.260 sec) +2022-05-01 18:16:16,267 - tensorflow - INFO - global_step/sec: 88.2319 +2022-05-01 18:16:16,267 - tensorflow - INFO - loss = 0.018693745, step = 1204800 (2.267 sec) +2022-05-01 18:16:18,518 - tensorflow - INFO - global_step/sec: 88.8386 +2022-05-01 18:16:18,518 - tensorflow - INFO - loss = 0.034952633, step = 1205000 (2.251 sec) +2022-05-01 18:16:20,778 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.024169106, step = 1287000 (2.252 sec) +2022-05-01 18:31:46,153 - tensorflow - INFO - global_step/sec: 88.6677 +2022-05-01 18:31:46,153 - tensorflow - INFO - loss = 0.009108191, step = 1287200 (2.256 sec) +2022-05-01 18:31:48,406 - tensorflow - INFO - global_step/sec: 88.76 +2022-05-01 18:31:48,406 - tensorflow - INFO - loss = 0.08247757, step = 1287400 (2.253 sec) +2022-05-01 18:31:50,658 - tensorflow - INFO - global_step/sec: 88.82 +2022-05-01 18:31:50,658 - tensorflow - INFO - loss = 0.010382421, step = 1287600 (2.252 sec) +2022-05-01 18:31:52,908 - tensorflow - INFO - global_step/sec: 88.8904 +2022-05-01 18:31:52,908 - tensorflow - INFO - loss = 0.4617443, step = 1287800 (2.250 sec) +2022-05-01 18:31:55,155 - tensorflow - INFO - global_step/sec: 88.9815 +2022-05-01 18:31:55,156 - tensorflow - INFO - loss = 0.018690221, step = 1288000 (2.248 sec) +2022-05-01 18:31:57,423 - tensorflow - INFO - global_step/sec: 88.1855 +2022-05-01 18:31:57,424 - tensorflow - INFO - loss = 0.006660033, step = 1288200 (2.268 sec) +2022-05-01 18:31:59,680 - tensorflow - INFO - global_step/sec: 88.626 +2022-05-01 18:31:59,680 - tensorflow - INFO - loss = 0.08108465, step = 1288400 (2.257 sec) +2022-05-01 18:32:01,939 - tensorflow - INFO - global_step/sec: 88.548 +2022-05-01 18:32:01,939 - tensorflow - INFO - loss = 0.14775956, step = 1288600 (2.259 sec) +2022-05-01 18:32:04,193 - tensorflow - INFO - global_step/sec: 88.7121 +2022-05-01 18:32:04,193 - tensorflow - INFO - loss = 0.024420854, step = 1288800 (2.254 sec) +2022-05-01 18:32:06,444 - tensorflow - INFO - global_step/sec: 88.8523 +2022-05-01 18:32:06,444 - tensorflow - INFO - loss = 2.4564295, step = 1289000 (2.251 sec) +2022-05-01 18:32:08,694 - tensorflow - INFO - global_step/sec: 88.8765 +2022-05-01 18:32:08,695 - tensorflow - INFO - loss = 0.037567124, step = 1289200 (2.250 sec) +2022-05-01 18:32:10,951 - tensorflow - INFO - global_step/sec: 88.6394 +2022-05-01 18:32:10,951 - tensorflow - INFO - loss = 0.020364668, step = 1289400 (2.256 sec) +2022-05-01 18:32:13,204 - tensorflow - INFO - global_step/sec: 88.744 +2022-05-01 18:32:13,205 - tensorflow - INFO - loss = 0.0064563705, step = 1289600 (2.254 sec) +2022-05-01 18:32:15,464 - tensorflow - INFO - global_step/sec: 88.4911 +2022-05-01 18:32:15,465 - tensorflow - INFO - loss = 0.022302406, step = 1289800 (2.260 sec) +2022-05-01 18:32:17,713 - tensorflow - INFO - global_step/sec: 88.9568 +2022-05-01 18:32:17,713 - tensorflow - INFO - loss = 0.03229746, step = 1290000 (2.248 sec) +2022-05-01 18:32:19,963 - tensorflow - INFO - global_step/sec: 88.8878 +2022-05-01 18:32:19,963 - tensorflow - INFO - loss = 0.012207605, step = 1290200 (2.250 sec) +2022-05-01 18:32:22,218 - tensorflow - INFO - global_step/sec: 88.6647 +2022-05-01 18:32:22,219 - tensorflow - INFO - loss = 2.3386588, step = 1290400 (2.256 sec) +2022-05-01 18:32:24,474 - tensorflow - INFO - global_step/sec: 88.6881 +2022-05-01 18:32:24,474 - tensorflow - INFO - loss = 0.012496609, step = 1290600 (2.255 sec) +2022-05-01 18:32:26,729 - tensorflow - INFO - global_step/sec: 88.6877 +2022-05-01 18:32:26,729 - tensorflow - INFO - loss = 0.07285924, step = 1290800 (2.255 sec) +2022-05-01 18:32:28,980 - tensorflow - INFO - global_step/sec: 88.8342 +2022-05-01 18:32:28,980 - tensorflow - INFO - loss = 0.29961962, step = 1291000 (2.251 sec) +2022-05-01 18:32:31,243 - tensorflow - INFO - global_step/sec: 88.3785 +2022-05-01 18:32:31,243 - tensorflow - INFO - loss = 0.15170337, step = 1291200 (2.263 sec) +2022-05-01 18:32:33,499 - tensorflow - INFO - global_step/sec: 88.6536 +2022-05-01 18:32:33,499 - tensorflow - INFO - loss = 0.038761783, step = 1291400 (2.256 sec) +2022-05-01 18:32:35,754 - tensorflow - INFO - global_step/sec: 88.6969 +2022-05-01 18:32:35,754 - tensorflow - INFO - loss = 0.09981961, step = 1291600 (2.255 sec) +2022-05-01 18:32:38,002 - tensorflow - INFO - global_step/sec: 88.9676 +2022-05-01 18:32:38,002 - tensorflow - INFO - loss = 0.2219835, step = 1291800 (2.248 sec) +2022-05-01 18:32:40,252 - tensorflow - INFO - global_step/sec: 88.8749 +2022-05-01 18:32:40,253 - tensorflow - INFO - loss = 0.005805756, step = 1292000 (2.250 sec) +2022-05-01 18:32:42,510 - tensorflow - INFO - global_step/sec: 88.5711 +2022-05-01 18:32:42,511 - tensorflow - INFO - loss = 0.029483391, step = 1292200 (2.258 sec) +2022-05-01 18:32:44,761 - tensorflow - INFO - global_step/sec: 88.8757 +2022-05-01 18:32:44,761 - tensorflow - INFO - loss = 0.014592943, step = 1292400 (2.250 sec) +2022-05-01 18:32:47,018 - tensorflow - INFO - global_step/sec: 88.5939 +2022-05-01 18:32:47,018 - tensorflow - INFO - loss = 0.014249931, step = 1292600 (2.258 sec) +2022-05-01 18:32:49,275 - tensorflow - INFO - global_step/sec: 88.6192 +2022-05-01 18:32:49,275 - tensorflow - INFO - loss = 0.017477775, step = 1292800 (2.257 sec) +2022-05-01 18:32:51,527 - tensorflow - INFO - global_step/sec: 88.8213 +2022-05-01 18:32:51,527 - tensorflow - INFO - loss = 0.011953859, step = 1293000 (2.252 sec) +2022-05-01 18:32:53,778 - tensorflow - INFO - global_step/sec: 88.8499 +2022-05-01 18:32:53,778 - tensorflow - INFO - loss = 0.011802371, step = 1293200 (2.251 sec) +2022-05-01 18:32:56,028 - tensorflow - INFO - global_step/sec: 88.8638 +2022-05-01 18:32:56,029 - tensorflow - INFO - loss = 0.015671462, step = 1293400 (2.251 sec) +2022-05-01 18:32:58,283 - tensorflow - INFO - global_step/sec: 88.709 +2022-05-01 18:32:58,283 - tensorflow - INFO - loss = 0.010309601, step = 1293600 (2.255 sec) +2022-05-01 18:33:00,540 - tensorflow - INFO - global_step/sec: 88.6174 +2022-05-01 18:33:00,540 - tensorflow - INFO - loss = 0.5059539, step = 1293800 (2.257 sec) +2022-05-01 18:33:02,795 - tensorflow - INFO - global_step/sec: 88.6678 +2022-05-01 18:33:02,796 - tensorflow - INFO - loss = 0.029848307, step = 1294000 (2.256 sec) +2022-05-01 18:33:05,054 - tensorflow - INFO - global_step/sec: 88.566 +2022-05-01 18:33:05,054 - tensorflow - INFO - loss = 0.008277052, step = 1294200 (2.258 sec) +2022-05-01 18:33:07,311 - tensorflow - INFO - global_step/sec: 88.5882 +2022-05-01 18:33:07,312 - tensorflow - INFO - loss = 0.20894733, step = 1294400 (2.258 sec) +2022-05-01 18:33:09,563 - tensorflow - INFO - global_step/sec: 88.8015 +2022-05-01 18:33:09,564 - tensorflow - INFO - loss = 0.15793276, step = 1294600 (2.252 sec) +2022-05-01 18:33:11,819 - tensorflow - INFO - global_step/sec: 88.6602 +2022-05-01 18:33:11,820 - tensorflow - INFO - loss = 0.010115347, step = 1294800 (2.256 sec) +2022-05-01 18:33:14,067 - tensorflow - INFO - global_step/sec: 88.9644 +2022-05-01 18:33:14,068 - tensorflow - INFO - loss = 0.01433547, step = 1295000 (2.248 sec) +2022-05-01 18:33:16,320 - tensorflow - INFO - global_step/sec: 88.7784 +2022-05-01 18:33:16,320 - tensorflow - INFO - loss = 0.05959379, step = 1295200 (2.253 sec) +2022-05-01 18:33:18,574 - tensorflow - INFO - global_step/sec: 88.7471 +2022-05-01 18:33:18,574 - tensorflow - INFO - loss = 0.009571571, step = 1295400 (2.254 sec) +2022-05-01 18:33:20,829 - tensorflow - INFO - global_step/sec: 88.688 +2022-05-01 18:33:20,829 - tensorflow - INFO - loss = 0.013059959, step = 1295600 (2.255 sec) +2022-05-01 18:33:23,086 - tensorflow - INFO - global_step/sec: 88.6207 +2022-05-01 18:33:23,086 - tensorflow - INFO - loss = 0.019144017, step = 1295800 (2.257 sec) +2022-05-01 18:33:25,348 - tensorflow - INFO - global_step/sec: 88.4099 +2022-05-01 18:33:25,348 - tensorflow - INFO - loss = 0.012158981, step = 1296000 (2.262 sec) +2022-05-01 18:33:27,601 - tensorflow - INFO - global_step/sec: 88.7429 +2022-05-01 18:33:27,602 - tensorflow - INFO - loss = 0.012564669, step = 1296200 (2.254 sec) +2022-05-01 18:33:29,853 - tensorflow - INFO - global_step/sec: 88.816 +2022-05-01 18:33:29,854 - tensorflow - INFO - loss = 6.0557833, step = 1296400 (2.252 sec) +2022-05-01 18:33:32,110 - tensorflow - INFO - global_step/sec: 88.6116 +2022-05-01 18:33:32,111 - tensorflow - INFO - loss = 0.022484478, step = 1296600 (2.257 sec) +2022-05-01 18:33:34,367 - tensorflow - INFO - global_step/sec: 88.6347 +2022-05-01 18:33:34,367 - tensorflow - INFO - loss = 0.05494872, step = 1296800 (2.256 sec) +2022-05-01 18:33:36,624 - tensorflow - INFO - global_step/sec: 88.5898 +2022-05-01 18:33:36,625 - tensorflow - INFO - loss = 0.01839782, step = 1297000 (2.258 sec) +2022-05-01 18:33:38,880 - tensorflow - INFO - global_step/sec: 88.654 +2022-05-01 18:33:38,881 - tensorflow - INFO - loss = 0.029880244, step = 1297200 (2.256 sec) +2022-05-01 18:33:41,131 - tensorflow - INFO - global_step/sec: 88.8782 +2022-05-01 18:33:41,131 - tensorflow - INFO - loss = 0.018286102, step = 1297400 (2.250 sec) +2022-05-01 18:33:43,382 - tensorflow - INFO - global_step/sec: 88.8519 +2022-05-01 18:33:43,382 - tensorflow - INFO - loss = 0.25781596, step = 1297600 (2.251 sec) +2022-05-01 18:33:45,635 - tensorflow - INFO - global_step/sec: 88.742 +2022-05-01 18:33:45,636 - tensorflow - INFO - loss = 0.016293323, step = 1297800 (2.254 sec) +2022-05-01 18:33:47,887 - tensorflow - INFO - global_step/sec: 88.8093 +2022-05-01 18:33:47,888 - tensorflow - INFO - loss = 0.0564257, step = 1298000 (2.252 sec) +2022-05-01 18:33:50,142 - tensorflow - INFO - global_step/sec: 88.7104 +2022-05-01 18:33:50,142 - tensorflow - INFO - loss = 0.01472992, step = 1298200 (2.255 sec) +2022-05-01 18:33:52,399 - tensorflow - INFO - global_step/sec: 88.6091 +2022-05-01 18:33:52,399 - tensorflow - INFO - loss = 0.21787146, step = 1298400 (2.257 sec) +2022-05-01 18:33:54,656 - tensorflow - INFO - global_step/sec: 88.6034 +2022-05-01 18:33:54,657 - tensorflow - INFO - loss = 0.030389164, step = 1298600 (2.257 sec) +2022-05-01 18:33:56,921 - tensorflow - INFO - global_step/sec: 88.3192 +2022-05-01 18:33:56,921 - tensorflow - INFO - loss = 0.014049642, step = 1298800 (2.264 sec) +2022-05-01 18:33:59,178 - tensorflow - INFO - global_step/sec: 88.611 +2022-05-01 18:33:59,178 - tensorflow - INFO - loss = 0.07121677, step = 1299000 (2.257 sec) +2022-05-01 18:34:01,441 - tensorflow - INFO - global_step/sec: 88.3786 +2022-05-01 18:34:01,441 - tensorflow - INFO - loss = 0.011796599, step = 1299200 (2.263 sec) +2022-05-01 18:34:03,706 - tensorflow - INFO - global_step/sec: 88.3047 +2022-05-01 18:34:03,706 - tensorflow - INFO - loss = 0.02658301, step = 1299400 (2.265 sec) +2022-05-01 18:34:05,964 - tensorflow - INFO - global_step/sec: 88.5573 +2022-05-01 18:34:05,964 - tensorflow - INFO - loss = 0.020927524, step = 1299600 (2.258 sec) +2022-05-01 18:34:08,224 - tensorflow - INFO - global_step/sec: 88.4939 +2022-05-01 18:34:08,225 - tensorflow - INFO - loss = 0.08239248, step = 1299800 (2.260 sec) +2022-05-01 18:34:10,473 - tensorflow - INFO - Saving checkpoints for 1300000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. +2022-05-01 18:34:10,488 - tensorflow - INFO - model.ckpt-1300000 is not in all_model_checkpoint_paths. Manually adding it. +2022-05-01 18:34:11,011 - tensorflow - INFO - Calling model_fn. +2022-05-01 18:34:11,139 - tensorflow - INFO - Done calling model_fn. +2022-05-01 18:34:11,154 - tensorflow - INFO - Starting evaluation at 2022-05-01T18:34:11Z +2022-05-01 18:34:11,218 - tensorflow - INFO - Graph was finalized. +2022-05-01 18:34:11,221 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1300000 +2022-05-01 18:34:11,503 - tensorflow - INFO - Running local_init_op. +2022-05-01 18:34:11,611 - tensorflow - INFO - Done running local_init_op. +2022-05-01 18:34:21,788 - tensorflow - INFO - Evaluation [3990/39900] +2022-05-01 18:34:31,243 - tensorflow - INFO - Evaluation [7980/39900] +2022-05-01 18:34:40,667 - tensorflow - INFO - Evaluation [11970/39900] +2022-05-01 18:34:50,117 - tensorflow - INFO - Evaluation [15960/39900] +2022-05-01 18:34:59,573 - tensorflow - INFO - Evaluation [19950/39900] +2022-05-01 18:35:09,037 - tensorflow - INFO - Evaluation [23940/39900] +2022-05-01 18:35:18,472 - tensorflow - INFO - Evaluation [27930/39900] +2022-05-01 18:35:27,938 - tensorflow - INFO - Evaluation [31920/39900] +2022-05-01 18:35:37,388 - tensorflow - INFO - Evaluation [35910/39900] +2022-05-01 18:35:46,832 - tensorflow - INFO - Evaluation [39900/39900] +2022-05-01 18:35:46,968 - tensorflow - INFO - Finished evaluation at 2022-05-01-18:35:46 +2022-05-01 18:35:46,969 - tensorflow - INFO - Saving dict for global step 1300000: global_step = 1300000, loss = 0.037376873, mean_degree_err = 5.5991697 +2022-05-01 18:35:46,969 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 1300000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1300000 +2022-05-01 18:35:46,983 - tensorflow - INFO - global_step/sec: 2.02513 +2022-05-01 18:35:46,984 - tensorflow - INFO - loss = 0.030853275, step = 1300000 (98.759 sec) +2022-05-01 18:35:49,254 - tensorflow - INFO - global_step/sec: 88.0857 +2022-05-01 18:35:49,254 - tensorflow - INFO - loss = 0.024246916, step = 1300200 (2.270 sec) +2022-05-01 18:35:51,503 - tensorflow - INFO - global_step/sec: 88.8944 +2022-05-01 18:35:51,504 - tensorflow - INFO - loss = 4.38457, step = 1300400 (2.250 sec) +2022-05-01 18:35:53,758 - tensorflow - INFO - global_step/sec: 88.7213 +2022-05-01 18:35:53,758 - tensorflow - INFO - loss = 0.03259698, step = 1300600 (2.254 sec) +2022-05-01 18:35:56,012 - tensorflow - INFO - global_step/sec: 88.7204 +2022-05-01 18:35:56,012 - tensorflow - INFO - loss = 0.026033219, step = 1300800 (2.254 sec) +2022-05-01 18:35:58,264 - tensorflow - INFO - global_step/sec: 88.8051 +2022-05-01 18:35:58,265 - tensorflow - INFO - loss = 0.03283344, step = 1301000 (2.252 sec) +2022-05-01 18:36:00,523 - tensorflow - INFO - global_step/sec: 88.5379 +2022-05-01 18:36:00,523 - tensorflow - INFO - loss = 0.012986472, step = 1301200 (2.259 sec) +2022-05-01 18:36:02,785 - tensorflow - INFO - global_step/sec: 88.4277 +2022-05-01 18:36:02,785 - tensorflow - INFO - loss = 0.078854054, step = 1301400 (2.262 sec) +2022-05-01 18:36:05,037 - tensorflow - INFO - global_step/sec: 88.801 +2022-05-01 18:36:05,037 - tensorflow - INFO - loss = 0.11142617, step = 1301600 (2.252 sec) +2022-05-01 18:36:07,299 - tensorflow - INFO - global_step/sec: 88.4314 +2022-05-01 18:36:07,299 - tensorflow - INFO - loss = 0.04029717, step = 1301800 (2.262 sec) +2022-05-01 18:36:09,551 - tensorflow - INFO - global_step/sec: 88.7994 +2022-05-01 18:36:09,551 - tensorflow - INFO - loss = 0.014742036, step = 1302000 (2.252 sec) +2022-05-01 18:36:11,808 - tensorflow - INFO - global_step/sec: 88.6242 +2022-05-01 18:36:11,808 - tensorflow - INFO - loss = 0.015955841, step = 1302200 (2.257 sec) +2022-05-01 18:36:14,065 - tensorflow - INFO - global_step/sec: 88.6135 +2022-05-01 18:36:14,065 - tensorflow - INFO - loss = 0.013542844, step = 1302400 (2.257 sec) +2022-05-01 18:36:16,323 - tensorflow - INFO - global_step/sec: 88.5507 +2022-05-01 18:36:16,324 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.5203 +2022-05-01 18:51:52,329 - tensorflow - INFO - loss = 0.0189787, step = 1385600 (2.259 sec) +2022-05-01 18:51:54,587 - tensorflow - INFO - global_step/sec: 88.5633 +2022-05-01 18:51:54,588 - tensorflow - INFO - loss = 0.02249827, step = 1385800 (2.258 sec) +2022-05-01 18:51:56,853 - tensorflow - INFO - global_step/sec: 88.2833 +2022-05-01 18:51:56,853 - tensorflow - INFO - loss = 0.013075041, step = 1386000 (2.265 sec) +2022-05-01 18:51:59,116 - tensorflow - INFO - global_step/sec: 88.3614 +2022-05-01 18:51:59,116 - tensorflow - INFO - loss = 0.008851925, step = 1386200 (2.263 sec) +2022-05-01 18:52:01,375 - tensorflow - INFO - global_step/sec: 88.5203 +2022-05-01 18:52:01,376 - tensorflow - INFO - loss = 0.109118104, step = 1386400 (2.259 sec) +2022-05-01 18:52:03,636 - tensorflow - INFO - global_step/sec: 88.4737 +2022-05-01 18:52:03,636 - tensorflow - INFO - loss = 0.011091237, step = 1386600 (2.261 sec) +2022-05-01 18:52:05,895 - tensorflow - INFO - global_step/sec: 88.5509 +2022-05-01 18:52:05,895 - tensorflow - INFO - loss = 0.023800887, step = 1386800 (2.259 sec) +2022-05-01 18:52:08,152 - tensorflow - INFO - global_step/sec: 88.6082 +2022-05-01 18:52:08,152 - tensorflow - INFO - loss = 0.011461074, step = 1387000 (2.257 sec) +2022-05-01 18:52:10,415 - tensorflow - INFO - global_step/sec: 88.3837 +2022-05-01 18:52:10,415 - tensorflow - INFO - loss = 0.027946133, step = 1387200 (2.263 sec) +2022-05-01 18:52:12,675 - tensorflow - INFO - global_step/sec: 88.4788 +2022-05-01 18:52:12,675 - tensorflow - INFO - loss = 0.028160535, step = 1387400 (2.260 sec) +2022-05-01 18:52:14,936 - tensorflow - INFO - global_step/sec: 88.4406 +2022-05-01 18:52:14,937 - tensorflow - INFO - loss = 0.037239272, step = 1387600 (2.261 sec) +2022-05-01 18:52:17,193 - tensorflow - INFO - global_step/sec: 88.6221 +2022-05-01 18:52:17,194 - tensorflow - INFO - loss = 0.009626794, step = 1387800 (2.257 sec) +2022-05-01 18:52:19,457 - tensorflow - INFO - global_step/sec: 88.3497 +2022-05-01 18:52:19,457 - tensorflow - INFO - loss = 0.0589523, step = 1388000 (2.264 sec) +2022-05-01 18:52:21,711 - tensorflow - INFO - global_step/sec: 88.7277 +2022-05-01 18:52:21,711 - tensorflow - INFO - loss = 0.069719814, step = 1388200 (2.254 sec) +2022-05-01 18:52:23,963 - tensorflow - INFO - global_step/sec: 88.8262 +2022-05-01 18:52:23,963 - tensorflow - INFO - loss = 0.014002705, step = 1388400 (2.252 sec) +2022-05-01 18:52:26,215 - tensorflow - INFO - global_step/sec: 88.7766 +2022-05-01 18:52:26,216 - tensorflow - INFO - loss = 0.04540629, step = 1388600 (2.253 sec) +2022-05-01 18:52:28,468 - tensorflow - INFO - global_step/sec: 88.7994 +2022-05-01 18:52:28,468 - tensorflow - INFO - loss = 0.02488325, step = 1388800 (2.252 sec) +2022-05-01 18:52:30,723 - tensorflow - INFO - global_step/sec: 88.6864 +2022-05-01 18:52:30,723 - tensorflow - INFO - loss = 0.012708927, step = 1389000 (2.255 sec) +2022-05-01 18:52:32,978 - tensorflow - INFO - global_step/sec: 88.6978 +2022-05-01 18:52:32,978 - tensorflow - INFO - loss = 0.015943076, step = 1389200 (2.255 sec) +2022-05-01 18:52:35,228 - tensorflow - INFO - global_step/sec: 88.8893 +2022-05-01 18:52:35,228 - tensorflow - INFO - loss = 0.20571089, step = 1389400 (2.250 sec) +2022-05-01 18:52:37,486 - tensorflow - INFO - global_step/sec: 88.5603 +2022-05-01 18:52:37,486 - tensorflow - INFO - loss = 0.05094683, step = 1389600 (2.258 sec) +2022-05-01 18:52:39,737 - tensorflow - INFO - global_step/sec: 88.8359 +2022-05-01 18:52:39,738 - tensorflow - INFO - loss = 0.007741022, step = 1389800 (2.251 sec) +2022-05-01 18:52:41,988 - tensorflow - INFO - global_step/sec: 88.845 +2022-05-01 18:52:41,989 - tensorflow - INFO - loss = 0.27258632, step = 1390000 (2.251 sec) +2022-05-01 18:52:44,242 - tensorflow - INFO - global_step/sec: 88.7345 +2022-05-01 18:52:44,243 - tensorflow - INFO - loss = 0.00971758, step = 1390200 (2.254 sec) +2022-05-01 18:52:46,495 - tensorflow - INFO - global_step/sec: 88.799 +2022-05-01 18:52:46,495 - tensorflow - INFO - loss = 0.020727014, step = 1390400 (2.252 sec) +2022-05-01 18:52:48,749 - tensorflow - INFO - global_step/sec: 88.7312 +2022-05-01 18:52:48,749 - tensorflow - INFO - loss = 0.03944362, step = 1390600 (2.254 sec) +2022-05-01 18:52:51,008 - tensorflow - INFO - global_step/sec: 88.5047 +2022-05-01 18:52:51,009 - tensorflow - INFO - loss = 0.041285716, step = 1390800 (2.260 sec) +2022-05-01 18:52:53,265 - tensorflow - INFO - global_step/sec: 88.6425 +2022-05-01 18:52:53,265 - tensorflow - INFO - loss = 0.010349192, step = 1391000 (2.256 sec) +2022-05-01 18:52:55,517 - tensorflow - INFO - global_step/sec: 88.8059 +2022-05-01 18:52:55,517 - tensorflow - INFO - loss = 0.012859952, step = 1391200 (2.252 sec) +2022-05-01 18:52:57,770 - tensorflow - INFO - global_step/sec: 88.7646 +2022-05-01 18:52:57,770 - tensorflow - INFO - loss = 0.017221091, step = 1391400 (2.253 sec) +2022-05-01 18:53:00,022 - tensorflow - INFO - global_step/sec: 88.8126 +2022-05-01 18:53:00,022 - tensorflow - INFO - loss = 0.016328134, step = 1391600 (2.252 sec) +2022-05-01 18:53:02,277 - tensorflow - INFO - global_step/sec: 88.6749 +2022-05-01 18:53:02,278 - tensorflow - INFO - loss = 0.051186584, step = 1391800 (2.255 sec) +2022-05-01 18:53:04,529 - tensorflow - INFO - global_step/sec: 88.8071 +2022-05-01 18:53:04,530 - tensorflow - INFO - loss = 0.07615837, step = 1392000 (2.252 sec) +2022-05-01 18:53:06,783 - tensorflow - INFO - global_step/sec: 88.7597 +2022-05-01 18:53:06,783 - tensorflow - INFO - loss = 0.33934033, step = 1392200 (2.253 sec) +2022-05-01 18:53:09,037 - tensorflow - INFO - global_step/sec: 88.734 +2022-05-01 18:53:09,037 - tensorflow - INFO - loss = 0.030979685, step = 1392400 (2.254 sec) +2022-05-01 18:53:11,287 - tensorflow - INFO - global_step/sec: 88.8638 +2022-05-01 18:53:11,288 - tensorflow - INFO - loss = 0.06840564, step = 1392600 (2.251 sec) +2022-05-01 18:53:13,540 - tensorflow - INFO - global_step/sec: 88.768 +2022-05-01 18:53:13,541 - tensorflow - INFO - loss = 0.06952453, step = 1392800 (2.253 sec) +2022-05-01 18:53:15,797 - tensorflow - INFO - global_step/sec: 88.6284 +2022-05-01 18:53:15,797 - tensorflow - INFO - loss = 0.08090894, step = 1393000 (2.257 sec) +2022-05-01 18:53:18,051 - tensorflow - INFO - global_step/sec: 88.7182 +2022-05-01 18:53:18,052 - tensorflow - INFO - loss = 0.049930155, step = 1393200 (2.254 sec) +2022-05-01 18:53:20,305 - tensorflow - INFO - global_step/sec: 88.7516 +2022-05-01 18:53:20,305 - tensorflow - INFO - loss = 0.007678329, step = 1393400 (2.253 sec) +2022-05-01 18:53:22,556 - tensorflow - INFO - global_step/sec: 88.844 +2022-05-01 18:53:22,556 - tensorflow - INFO - loss = 3.3933983, step = 1393600 (2.251 sec) +2022-05-01 18:53:24,812 - tensorflow - INFO - global_step/sec: 88.6503 +2022-05-01 18:53:24,812 - tensorflow - INFO - loss = 0.50099397, step = 1393800 (2.256 sec) +2022-05-01 18:53:27,063 - tensorflow - INFO - global_step/sec: 88.8246 +2022-05-01 18:53:27,064 - tensorflow - INFO - loss = 0.016014893, step = 1394000 (2.252 sec) +2022-05-01 18:53:29,320 - tensorflow - INFO - global_step/sec: 88.627 +2022-05-01 18:53:29,321 - tensorflow - INFO - loss = 0.6599004, step = 1394200 (2.257 sec) +2022-05-01 18:53:31,580 - tensorflow - INFO - global_step/sec: 88.5192 +2022-05-01 18:53:31,580 - tensorflow - INFO - loss = 0.04345376, step = 1394400 (2.259 sec) +2022-05-01 18:53:33,829 - tensorflow - INFO - global_step/sec: 88.9041 +2022-05-01 18:53:33,830 - tensorflow - INFO - loss = 0.44049037, step = 1394600 (2.250 sec) +2022-05-01 18:53:36,081 - tensorflow - INFO - global_step/sec: 88.7955 +2022-05-01 18:53:36,082 - tensorflow - INFO - loss = 0.012496112, step = 1394800 (2.252 sec) +2022-05-01 18:53:38,329 - tensorflow - INFO - global_step/sec: 88.9786 +2022-05-01 18:53:38,330 - tensorflow - INFO - loss = 0.03480921, step = 1395000 (2.248 sec) +2022-05-01 18:53:40,590 - tensorflow - INFO - global_step/sec: 88.4503 +2022-05-01 18:53:40,591 - tensorflow - INFO - loss = 0.010541502, step = 1395200 (2.261 sec) +2022-05-01 18:53:42,839 - tensorflow - INFO - global_step/sec: 88.9279 +2022-05-01 18:53:42,840 - tensorflow - INFO - loss = 0.020792527, step = 1395400 (2.249 sec) +2022-05-01 18:53:45,087 - tensorflow - INFO - global_step/sec: 88.9803 +2022-05-01 18:53:45,088 - tensorflow - INFO - loss = 0.26173452, step = 1395600 (2.248 sec) +2022-05-01 18:53:47,335 - tensorflow - INFO - global_step/sec: 88.962 +2022-05-01 18:53:47,336 - tensorflow - INFO - loss = 0.01936509, step = 1395800 (2.248 sec) +2022-05-01 18:53:49,590 - tensorflow - INFO - global_step/sec: 88.7004 +2022-05-01 18:53:49,590 - tensorflow - INFO - loss = 0.016364845, step = 1396000 (2.255 sec) +2022-05-01 18:53:51,847 - tensorflow - INFO - global_step/sec: 88.6169 +2022-05-01 18:53:51,847 - tensorflow - INFO - loss = 0.0135764945, step = 1396200 (2.257 sec) +2022-05-01 18:53:54,100 - tensorflow - INFO - global_step/sec: 88.7631 +2022-05-01 18:53:54,101 - tensorflow - INFO - loss = 0.019440118, step = 1396400 (2.253 sec) +2022-05-01 18:53:56,351 - tensorflow - INFO - global_step/sec: 88.8551 +2022-05-01 18:53:56,351 - tensorflow - INFO - loss = 0.024875076, step = 1396600 (2.251 sec) +2022-05-01 18:53:58,600 - tensorflow - INFO - global_step/sec: 88.939 +2022-05-01 18:53:58,600 - tensorflow - INFO - loss = 0.008331047, step = 1396800 (2.249 sec) +2022-05-01 18:54:00,856 - tensorflow - INFO - global_step/sec: 88.6235 +2022-05-01 18:54:00,857 - tensorflow - INFO - loss = 0.009838301, step = 1397000 (2.257 sec) +2022-05-01 18:54:03,110 - tensorflow - INFO - global_step/sec: 88.7341 +2022-05-01 18:54:03,111 - tensorflow - INFO - loss = 0.011992238, step = 1397200 (2.254 sec) +2022-05-01 18:54:05,369 - tensorflow - INFO - global_step/sec: 88.5512 +2022-05-01 18:54:05,369 - tensorflow - INFO - loss = 0.37778002, step = 1397400 (2.259 sec) +2022-05-01 18:54:07,626 - tensorflow - INFO - global_step/sec: 88.6144 +2022-05-01 18:54:07,626 - tensorflow - INFO - loss = 0.020134173, step = 1397600 (2.257 sec) +2022-05-01 18:54:09,882 - tensorflow - INFO - global_step/sec: 88.6686 +2022-05-01 18:54:09,882 - tensorflow - INFO - loss = 0.07450393, step = 1397800 (2.256 sec) +2022-05-01 18:54:12,133 - tensorflow - INFO - global_step/sec: 88.8287 +2022-05-01 18:54:12,133 - tensorflow - INFO - loss = 0.04988601, step = 1398000 (2.252 sec) +2022-05-01 18:54:14,384 - tensorflow - INFO - global_step/sec: 88.8409 +2022-05-01 18:54:14,385 - tensorflow - INFO - loss = 0.022269156, step = 1398200 (2.251 sec) +2022-05-01 18:54:16,641 - tensorflow - INFO - global_step/sec: 88.631 +2022-05-01 18:54:16,641 - tensorflow - INFO - loss = 0.025311477, step = 1398400 (2.257 sec) +2022-05-01 18:54:18,898 - tensorflow - INFO - global_step/sec: 88.5862 +2022-05-01 18:54:18,899 - tensorflow - INFO - loss = 0.43566298, step = 1398600 (2.258 sec) +2022-05-01 18:54:21,153 - tensorflow - INFO - global_step/sec: 88.7096 +2022-05-01 18:54:21,153 - tensorflow - INFO - loss = 0.011970665, step = 1398800 (2.255 sec) +2022-05-01 18:54:23,404 - tensorflow - INFO - global_step/sec: 88.8583 +2022-05-01 18:54:23,404 - tensorflow - INFO - loss = 0.060460646, step = 1399000 (2.251 sec) +2022-05-01 18:54:25,659 - tensorflow - INFO - global_step/sec: 88.698 +2022-05-01 18:54:25,659 - tensorflow - INFO - loss = 0.024868513, step = 1399200 (2.255 sec) +2022-05-01 18:54:27,915 - tensorflow - INFO - global_step/sec: 88.6522 +2022-05-01 18:54:27,915 - tensorflow - INFO - loss = 0.11175607, step = 1399400 (2.256 sec) +2022-05-01 18:54:30,173 - tensorflow - INFO - global_step/sec: 88.5635 +2022-05-01 18:54:30,173 - tensorflow - INFO - loss = 0.008654011, step = 1399600 (2.258 sec) +2022-05-01 18:54:32,427 - tensorflow - INFO - global_step/sec: 88.7413 +2022-05-01 18:54:32,427 - tensorflow - INFO - loss = 0.053722493, step = 1399800 (2.254 sec) +2022-05-01 18:54:34,666 - tensorflow - INFO - Saving checkpoints for 1400000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. +2022-05-01 18:54:34,682 - tensorflow - INFO - model.ckpt-1400000 is not in all_model_checkpoint_paths. Manually adding it. +2022-05-01 18:54:35,205 - tensorflow - INFO - Calling model_fn. +2022-05-01 18:54:35,331 - tensorflow - INFO - Done calling model_fn. +2022-05-01 18:54:35,346 - tensorflow - INFO - Starting evaluation at 2022-05-01T18:54:35Z +2022-05-01 18:54:35,409 - tensorflow - INFO - Graph was finalized. +2022-05-01 18:54:35,412 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1400000 +2022-05-01 18:54:35,695 - tensorflow - INFO - Running local_init_op. +2022-05-01 18:54:35,802 - tensorflow - INFO - Done running local_init_op. +2022-05-01 18:54:45,951 - tensorflow - INFO - Evaluation [3990/39900] +2022-05-01 18:54:55,477 - tensorflow - INFO - Evaluation [7980/39900] +2022-05-01 18:55:04,953 - tensorflow - INFO - Evaluation [11970/39900] +2022-05-01 18:55:14,432 - tensorflow - INFO - Evaluation [15960/39900] +2022-05-01 18:55:23,916 - tensorflow - INFO - Evaluation [19950/39900] +2022-05-01 18:55:33,443 - tensorflow - INFO - Evaluation [23940/39900] +2022-05-01 18:55:42,942 - tensorflow - INFO - Evaluation [27930/39900] +2022-05-01 18:55:52,441 - tensorflow - INFO - Evaluation [31920/39900] +2022-05-01 18:56:01,914 - tensorflow - INFO - Evaluation [35910/39900] +2022-05-01 18:56:11,413 - tensorflow - INFO - Evaluation [39900/39900] +2022-05-01 18:56:11,550 - tensorflow - INFO - Finished evaluation at 2022-05-01-18:56:11 +2022-05-01 18:56:11,550 - tensorflow - INFO - Saving dict for global step 1400000: global_step = 1400000, loss = 0.022675576, mean_degree_err = 4.711262 +2022-05-01 18:56:11,551 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 1400000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1400000 +2022-05-01 18:56:11,564 - tensorflow - INFO - global_step/sec: 2.01739 +2022-05-01 18:56:11,565 - tensorflow - INFO - loss = 1.4852409, step = 1400000 (99.138 sec) +2022-05-01 18:56:13,836 - tensorflow - INFO - global_step/sec: 88.0279 +2022-05-01 18:56:13,837 - 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INFO - global_step/sec: 88.6904 +2022-05-01 19:12:03,454 - tensorflow - INFO - loss = 0.017758131, step = 1484400 (2.255 sec) +2022-05-01 19:12:05,713 - tensorflow - INFO - global_step/sec: 88.5057 +2022-05-01 19:12:05,714 - tensorflow - INFO - loss = 0.012710876, step = 1484600 (2.260 sec) +2022-05-01 19:12:07,969 - tensorflow - INFO - global_step/sec: 88.6751 +2022-05-01 19:12:07,969 - tensorflow - INFO - loss = 0.01151262, step = 1484800 (2.255 sec) +2022-05-01 19:12:10,225 - tensorflow - INFO - global_step/sec: 88.6448 +2022-05-01 19:12:10,225 - tensorflow - INFO - loss = 0.018936135, step = 1485000 (2.256 sec) +2022-05-01 19:12:12,478 - tensorflow - INFO - global_step/sec: 88.7749 +2022-05-01 19:12:12,478 - tensorflow - INFO - loss = 0.012820639, step = 1485200 (2.253 sec) +2022-05-01 19:12:14,732 - tensorflow - INFO - global_step/sec: 88.7397 +2022-05-01 19:12:14,732 - tensorflow - INFO - loss = 0.018086525, step = 1485400 (2.254 sec) +2022-05-01 19:12:16,987 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.01304977, step = 1486800 (2.265 sec) +2022-05-01 19:12:32,773 - tensorflow - INFO - global_step/sec: 88.7179 +2022-05-01 19:12:32,774 - tensorflow - INFO - loss = 0.026753342, step = 1487000 (2.254 sec) +2022-05-01 19:12:35,024 - tensorflow - INFO - global_step/sec: 88.841 +2022-05-01 19:12:35,025 - tensorflow - INFO - loss = 0.016424507, step = 1487200 (2.251 sec) +2022-05-01 19:12:37,280 - tensorflow - INFO - global_step/sec: 88.6709 +2022-05-01 19:12:37,280 - tensorflow - INFO - loss = 0.11279587, step = 1487400 (2.256 sec) +2022-05-01 19:12:39,536 - tensorflow - INFO - global_step/sec: 88.6624 +2022-05-01 19:12:39,536 - tensorflow - INFO - loss = 0.11770095, step = 1487600 (2.256 sec) +2022-05-01 19:12:41,788 - tensorflow - INFO - global_step/sec: 88.806 +2022-05-01 19:12:41,788 - tensorflow - INFO - loss = 0.054372743, step = 1487800 (2.252 sec) +2022-05-01 19:12:44,047 - tensorflow - INFO - global_step/sec: 88.5252 +2022-05-01 19:12:44,047 - tensorflow - INFO - loss = 4.644326, step = 1488000 (2.259 sec) +2022-05-01 19:12:46,298 - tensorflow - INFO - global_step/sec: 88.8402 +2022-05-01 19:12:46,299 - tensorflow - INFO - loss = 0.033585217, step = 1488200 (2.251 sec) +2022-05-01 19:12:48,552 - tensorflow - INFO - global_step/sec: 88.757 +2022-05-01 19:12:48,552 - tensorflow - INFO - loss = 0.011081589, step = 1488400 (2.253 sec) +2022-05-01 19:12:50,807 - tensorflow - INFO - global_step/sec: 88.6627 +2022-05-01 19:12:50,808 - tensorflow - INFO - loss = 0.011284853, step = 1488600 (2.256 sec) +2022-05-01 19:12:53,062 - tensorflow - INFO - global_step/sec: 88.698 +2022-05-01 19:12:53,063 - tensorflow - INFO - loss = 0.0142420065, step = 1488800 (2.255 sec) +2022-05-01 19:12:55,319 - tensorflow - INFO - global_step/sec: 88.6163 +2022-05-01 19:12:55,320 - tensorflow - INFO - loss = 0.009824946, step = 1489000 (2.257 sec) +2022-05-01 19:12:57,577 - tensorflow - INFO - global_step/sec: 88.5776 +2022-05-01 19:12:57,577 - tensorflow - INFO - loss = 0.043002818, step = 1489200 (2.258 sec) +2022-05-01 19:12:59,827 - tensorflow - INFO - global_step/sec: 88.8722 +2022-05-01 19:12:59,828 - tensorflow - INFO - loss = 0.04212212, step = 1489400 (2.250 sec) +2022-05-01 19:13:02,081 - tensorflow - INFO - global_step/sec: 88.7289 +2022-05-01 19:13:02,082 - tensorflow - INFO - loss = 0.076076016, step = 1489600 (2.254 sec) +2022-05-01 19:13:04,337 - tensorflow - INFO - global_step/sec: 88.6653 +2022-05-01 19:13:04,338 - tensorflow - INFO - loss = 0.018436994, step = 1489800 (2.256 sec) +2022-05-01 19:13:06,593 - tensorflow - INFO - global_step/sec: 88.6461 +2022-05-01 19:13:06,594 - tensorflow - INFO - loss = 0.03337249, step = 1490000 (2.256 sec) +2022-05-01 19:13:08,851 - tensorflow - INFO - global_step/sec: 88.5903 +2022-05-01 19:13:08,851 - tensorflow - INFO - loss = 0.0954804, step = 1490200 (2.258 sec) +2022-05-01 19:13:11,115 - tensorflow - INFO - global_step/sec: 88.326 +2022-05-01 19:13:11,116 - tensorflow - INFO - loss = 0.038690947, step = 1490400 (2.264 sec) +2022-05-01 19:13:13,376 - tensorflow - INFO - global_step/sec: 88.4515 +2022-05-01 19:13:13,377 - tensorflow - INFO - loss = 0.022575928, step = 1490600 (2.261 sec) +2022-05-01 19:13:15,636 - tensorflow - INFO - global_step/sec: 88.5299 +2022-05-01 19:13:15,636 - tensorflow - INFO - loss = 0.04049495, step = 1490800 (2.259 sec) +2022-05-01 19:13:17,892 - tensorflow - INFO - global_step/sec: 88.6267 +2022-05-01 19:13:17,893 - tensorflow - INFO - loss = 0.053369366, step = 1491000 (2.257 sec) +2022-05-01 19:13:20,155 - tensorflow - INFO - global_step/sec: 88.3868 +2022-05-01 19:13:20,155 - tensorflow - INFO - loss = 0.0073466636, step = 1491200 (2.263 sec) +2022-05-01 19:13:22,417 - tensorflow - INFO - global_step/sec: 88.4281 +2022-05-01 19:13:22,417 - tensorflow - INFO - loss = 0.01530133, step = 1491400 (2.262 sec) +2022-05-01 19:13:24,679 - tensorflow - INFO - global_step/sec: 88.3877 +2022-05-01 19:13:24,680 - tensorflow - INFO - loss = 0.040279463, step = 1491600 (2.263 sec) +2022-05-01 19:13:26,939 - tensorflow - INFO - global_step/sec: 88.5227 +2022-05-01 19:13:26,939 - tensorflow - INFO - loss = 0.019282173, step = 1491800 (2.259 sec) +2022-05-01 19:13:29,198 - tensorflow - INFO - global_step/sec: 88.533 +2022-05-01 19:13:29,198 - tensorflow - INFO - loss = 0.008762198, step = 1492000 (2.259 sec) +2022-05-01 19:13:31,468 - tensorflow - INFO - global_step/sec: 88.1147 +2022-05-01 19:13:31,468 - tensorflow - INFO - loss = 0.56280273, step = 1492200 (2.270 sec) +2022-05-01 19:13:33,724 - tensorflow - INFO - global_step/sec: 88.644 +2022-05-01 19:13:33,724 - tensorflow - INFO - loss = 0.019971997, step = 1492400 (2.256 sec) +2022-05-01 19:13:35,979 - tensorflow - INFO - global_step/sec: 88.6862 +2022-05-01 19:13:35,979 - tensorflow - INFO - loss = 0.017949276, step = 1492600 (2.255 sec) +2022-05-01 19:13:38,232 - tensorflow - INFO - global_step/sec: 88.7758 +2022-05-01 19:13:38,232 - tensorflow - INFO - loss = 0.024088422, step = 1492800 (2.253 sec) +2022-05-01 19:13:40,482 - tensorflow - INFO - global_step/sec: 88.8973 +2022-05-01 19:13:40,482 - tensorflow - INFO - loss = 0.01847333, step = 1493000 (2.250 sec) +2022-05-01 19:13:42,738 - tensorflow - INFO - global_step/sec: 88.6168 +2022-05-01 19:13:42,739 - tensorflow - INFO - loss = 0.0061604944, step = 1493200 (2.257 sec) +2022-05-01 19:13:44,991 - tensorflow - INFO - global_step/sec: 88.8081 +2022-05-01 19:13:44,991 - tensorflow - INFO - loss = 0.031813048, step = 1493400 (2.252 sec) +2022-05-01 19:13:47,246 - tensorflow - INFO - global_step/sec: 88.6719 +2022-05-01 19:13:47,246 - tensorflow - INFO - loss = 0.011895972, step = 1493600 (2.255 sec) +2022-05-01 19:13:49,505 - tensorflow - INFO - global_step/sec: 88.5243 +2022-05-01 19:13:49,506 - tensorflow - INFO - loss = 0.03530208, step = 1493800 (2.259 sec) +2022-05-01 19:13:51,757 - tensorflow - INFO - global_step/sec: 88.8307 +2022-05-01 19:13:51,757 - tensorflow - INFO - loss = 0.042276334, step = 1494000 (2.251 sec) +2022-05-01 19:13:54,003 - tensorflow - INFO - global_step/sec: 89.0302 +2022-05-01 19:13:54,004 - tensorflow - INFO - loss = 0.05104211, step = 1494200 (2.246 sec) +2022-05-01 19:13:56,255 - tensorflow - INFO - global_step/sec: 88.829 +2022-05-01 19:13:56,255 - tensorflow - INFO - loss = 0.044423785, step = 1494400 (2.251 sec) +2022-05-01 19:13:58,512 - tensorflow - INFO - global_step/sec: 88.6044 +2022-05-01 19:13:58,512 - tensorflow - INFO - loss = 0.020210141, step = 1494600 (2.257 sec) +2022-05-01 19:14:00,765 - tensorflow - INFO - global_step/sec: 88.7685 +2022-05-01 19:14:00,765 - tensorflow - INFO - loss = 0.045281194, step = 1494800 (2.253 sec) +2022-05-01 19:14:03,026 - tensorflow - INFO - global_step/sec: 88.4692 +2022-05-01 19:14:03,026 - tensorflow - INFO - loss = 0.015739895, step = 1495000 (2.261 sec) +2022-05-01 19:14:05,279 - tensorflow - INFO - global_step/sec: 88.7514 +2022-05-01 19:14:05,280 - tensorflow - INFO - loss = 0.010020976, step = 1495200 (2.254 sec) +2022-05-01 19:14:07,533 - tensorflow - INFO - global_step/sec: 88.7238 +2022-05-01 19:14:07,534 - tensorflow - INFO - loss = 0.01750209, step = 1495400 (2.254 sec) +2022-05-01 19:14:09,787 - tensorflow - INFO - global_step/sec: 88.7341 +2022-05-01 19:14:09,788 - tensorflow - INFO - loss = 0.06548672, step = 1495600 (2.254 sec) +2022-05-01 19:14:12,040 - tensorflow - INFO - global_step/sec: 88.7937 +2022-05-01 19:14:12,040 - tensorflow - INFO - loss = 0.21525896, step = 1495800 (2.252 sec) +2022-05-01 19:14:14,296 - tensorflow - INFO - global_step/sec: 88.62 +2022-05-01 19:14:14,297 - tensorflow - INFO - loss = 0.014747351, step = 1496000 (2.257 sec) +2022-05-01 19:14:16,555 - tensorflow - INFO - global_step/sec: 88.5362 +2022-05-01 19:14:16,556 - tensorflow - INFO - loss = 0.18117778, step = 1496200 (2.259 sec) +2022-05-01 19:14:18,809 - tensorflow - INFO - global_step/sec: 88.7606 +2022-05-01 19:14:18,809 - tensorflow - INFO - loss = 0.032358292, step = 1496400 (2.253 sec) +2022-05-01 19:14:21,061 - tensorflow - INFO - global_step/sec: 88.7971 +2022-05-01 19:14:21,062 - tensorflow - INFO - loss = 0.012879986, step = 1496600 (2.252 sec) +2022-05-01 19:14:23,316 - tensorflow - INFO - global_step/sec: 88.6747 +2022-05-01 19:14:23,317 - tensorflow - INFO - loss = 0.014577984, step = 1496800 (2.255 sec) +2022-05-01 19:14:25,577 - tensorflow - INFO - global_step/sec: 88.4653 +2022-05-01 19:14:25,578 - tensorflow - INFO - loss = 1.6467451, step = 1497000 (2.261 sec) +2022-05-01 19:14:27,830 - tensorflow - INFO - global_step/sec: 88.7919 +2022-05-01 19:14:27,830 - tensorflow - INFO - loss = 0.04715134, step = 1497200 (2.252 sec) +2022-05-01 19:14:30,079 - tensorflow - INFO - global_step/sec: 88.8975 +2022-05-01 19:14:30,080 - tensorflow - INFO - loss = 0.03892113, step = 1497400 (2.250 sec) +2022-05-01 19:14:32,343 - tensorflow - INFO - global_step/sec: 88.377 +2022-05-01 19:14:32,343 - tensorflow - INFO - loss = 0.034063794, step = 1497600 (2.263 sec) +2022-05-01 19:14:34,602 - tensorflow - INFO - global_step/sec: 88.5231 +2022-05-01 19:14:34,602 - tensorflow - INFO - loss = 0.033480745, step = 1497800 (2.259 sec) +2022-05-01 19:14:36,849 - tensorflow - INFO - global_step/sec: 88.9951 +2022-05-01 19:14:36,850 - tensorflow - INFO - loss = 0.010095269, step = 1498000 (2.247 sec) +2022-05-01 19:14:39,107 - tensorflow - INFO - global_step/sec: 88.5728 +2022-05-01 19:14:39,108 - tensorflow - INFO - loss = 0.02026205, step = 1498200 (2.258 sec) +2022-05-01 19:14:41,358 - tensorflow - INFO - global_step/sec: 88.8423 +2022-05-01 19:14:41,359 - tensorflow - INFO - loss = 0.011833824, step = 1498400 (2.251 sec) +2022-05-01 19:14:43,616 - tensorflow - INFO - global_step/sec: 88.5942 +2022-05-01 19:14:43,616 - tensorflow - INFO - loss = 0.025549715, step = 1498600 (2.258 sec) +2022-05-01 19:14:45,866 - tensorflow - INFO - global_step/sec: 88.871 +2022-05-01 19:14:45,867 - tensorflow - INFO - loss = 0.014869514, step = 1498800 (2.250 sec) +2022-05-01 19:14:48,118 - tensorflow - INFO - global_step/sec: 88.814 +2022-05-01 19:14:48,119 - tensorflow - INFO - loss = 0.013517971, step = 1499000 (2.252 sec) +2022-05-01 19:14:50,376 - tensorflow - INFO - global_step/sec: 88.5686 +2022-05-01 19:14:50,377 - tensorflow - INFO - loss = 0.009027958, step = 1499200 (2.258 sec) +2022-05-01 19:14:52,636 - tensorflow - INFO - global_step/sec: 88.5199 +2022-05-01 19:14:52,636 - tensorflow - INFO - loss = 0.017943226, step = 1499400 (2.259 sec) +2022-05-01 19:14:54,887 - tensorflow - INFO - global_step/sec: 88.83 +2022-05-01 19:14:54,888 - tensorflow - INFO - loss = 19.609617, step = 1499600 (2.252 sec) +2022-05-01 19:14:57,142 - tensorflow - INFO - global_step/sec: 88.7111 +2022-05-01 19:14:57,142 - tensorflow - INFO - loss = 0.034245685, step = 1499800 (2.254 sec) +2022-05-01 19:14:59,381 - tensorflow - INFO - Saving checkpoints for 1500000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. +2022-05-01 19:14:59,397 - tensorflow - INFO - model.ckpt-1500000 is not in all_model_checkpoint_paths. Manually adding it. +2022-05-01 19:14:59,914 - tensorflow - INFO - Calling model_fn. +2022-05-01 19:15:00,041 - tensorflow - INFO - Done calling model_fn. +2022-05-01 19:15:00,056 - tensorflow - INFO - Starting evaluation at 2022-05-01T19:15:00Z +2022-05-01 19:15:00,120 - tensorflow - INFO - Graph was finalized. +2022-05-01 19:15:00,123 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1500000 +2022-05-01 19:15:00,404 - tensorflow - INFO - Running local_init_op. +2022-05-01 19:15:00,511 - tensorflow - INFO - Done running local_init_op. +2022-05-01 19:15:10,766 - tensorflow - INFO - Evaluation [3990/39900] +2022-05-01 19:15:20,318 - tensorflow - INFO - Evaluation [7980/39900] +2022-05-01 19:15:29,860 - tensorflow - INFO - Evaluation [11970/39900] +2022-05-01 19:15:39,412 - tensorflow - INFO - Evaluation [15960/39900] +2022-05-01 19:15:48,935 - tensorflow - INFO - Evaluation [19950/39900] +2022-05-01 19:15:58,483 - tensorflow - INFO - Evaluation [23940/39900] +2022-05-01 19:16:08,019 - tensorflow - INFO - Evaluation [27930/39900] +2022-05-01 19:16:17,585 - tensorflow - INFO - Evaluation [31920/39900] +2022-05-01 19:16:27,118 - tensorflow - INFO - Evaluation [35910/39900] +2022-05-01 19:16:36,684 - tensorflow - INFO - Evaluation [39900/39900] +2022-05-01 19:16:36,821 - tensorflow - INFO - Finished evaluation at 2022-05-01-19:16:36 +2022-05-01 19:16:36,821 - tensorflow - INFO - Saving dict for global step 1500000: global_step = 1500000, loss = 0.019688489, mean_degree_err = 3.836875 +2022-05-01 19:16:36,822 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 1500000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1500000 +2022-05-01 19:16:36,835 - tensorflow - INFO - global_step/sec: 2.00614 +2022-05-01 19:16:36,836 - tensorflow - INFO - loss = 0.016405582, step = 1500000 (99.694 sec) +2022-05-01 19:16:39,110 - tensorflow - INFO - global_step/sec: 87.9407 +2022-05-01 19:16:39,110 - tensorflow - INFO - loss = 0.03980479, step = 1500200 (2.274 sec) +2022-05-01 19:16:41,368 - tensorflow - INFO - global_step/sec: 88.5673 +2022-05-01 19:16:41,368 - tensorflow - INFO - loss = 0.03160281, step = 1500400 (2.258 sec) +2022-05-01 19:16:43,626 - tensorflow - INFO - global_step/sec: 88.5718 +2022-05-01 19:16:43,626 - tensorflow - INFO - loss = 0.010400597, step = 1500600 (2.258 sec) +2022-05-01 19:16:45,885 - tensorflow - INFO - global_step/sec: 88.548 +2022-05-01 19:16:45,885 - tensorflow - INFO - loss = 0.018450955, step = 1500800 (2.259 sec) +2022-05-01 19:16:48,145 - tensorflow - INFO - global_step/sec: 88.4955 +2022-05-01 19:16:48,145 - tensorflow - INFO - loss = 0.039949965, step = 1501000 (2.260 sec) +2022-05-01 19:16:50,403 - tensorflow - INFO - global_step/sec: 88.5761 +2022-05-01 19:16:50,403 - tensorflow - INFO - loss = 0.012375185, step = 1501200 (2.258 sec) +2022-05-01 19:16:52,665 - tensorflow - INFO - global_step/sec: 88.387 +2022-05-01 19:16:52,666 - tensorflow - INFO - loss = 0.013643623, step = 1501400 (2.263 sec) +2022-05-01 19:16:54,927 - tensorflow - INFO - global_step/sec: 88.4466 +2022-05-01 19:16:54,927 - tensorflow - INFO - loss = 0.039375756, step = 1501600 (2.261 sec) +2022-05-01 19:16:57,186 - tensorflow - INFO - global_step/sec: 88.536 +2022-05-01 19:16:57,186 - tensorflow - INFO - loss = 0.0076917754, step = 1501800 (2.259 sec) +2022-05-01 19:16:59,445 - tensorflow - INFO - global_step/sec: 88.5146 +2022-05-01 19:16:59,445 - tensorflow - INFO - loss = 0.009893475, step = 1502000 (2.260 sec) +2022-05-01 19:17:01,705 - tensorflow - INFO - global_step/sec: 88.4975 +2022-05-01 19:17:01,705 - tensorflow - INFO - loss = 0.047156, step = 1502200 (2.260 sec) +2022-05-01 19:17:03,962 - tensorflow - INFO - global_step/sec: 88.5943 +2022-05-01 19:17:03,963 - tensorflow - INFO - loss = 0.021427762, step = 1502400 (2.258 sec) +2022-05-01 19:17:06,219 - tensorflow - INFO - global_step/sec: 88.6152 +2022-05-01 19:17:06,220 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.7991 +2022-05-01 19:17:21,983 - tensorflow - INFO - loss = 0.24157685, step = 1504000 (2.252 sec) +2022-05-01 19:17:24,237 - tensorflow - INFO - global_step/sec: 88.7346 +2022-05-01 19:17:24,237 - tensorflow - INFO - loss = 0.037840072, step = 1504200 (2.254 sec) +2022-05-01 19:17:26,496 - tensorflow - INFO - global_step/sec: 88.5264 +2022-05-01 19:17:26,496 - tensorflow - INFO - loss = 0.013430537, step = 1504400 (2.259 sec) +2022-05-01 19:17:28,747 - tensorflow - INFO - global_step/sec: 88.8359 +2022-05-01 19:17:28,748 - tensorflow - INFO - loss = 0.0102399755, step = 1504600 (2.251 sec) +2022-05-01 19:17:31,003 - tensorflow - INFO - global_step/sec: 88.6629 +2022-05-01 19:17:31,003 - tensorflow - INFO - loss = 0.47035003, step = 1504800 (2.256 sec) +2022-05-01 19:17:33,258 - tensorflow - INFO - global_step/sec: 88.6973 +2022-05-01 19:17:33,258 - tensorflow - INFO - loss = 0.035697296, step = 1505000 (2.255 sec) +2022-05-01 19:17:35,510 - tensorflow - 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tensorflow - INFO - global_step/sec: 88.9736 +2022-05-01 19:19:55,254 - tensorflow - INFO - loss = 0.008203949, step = 1517600 (2.248 sec) +2022-05-01 19:19:57,514 - tensorflow - INFO - global_step/sec: 88.5006 +2022-05-01 19:19:57,514 - tensorflow - INFO - loss = 0.4390188, step = 1517800 (2.260 sec) +2022-05-01 19:19:59,772 - tensorflow - INFO - global_step/sec: 88.5811 +2022-05-01 19:19:59,772 - tensorflow - INFO - loss = 0.016573153, step = 1518000 (2.258 sec) +2022-05-01 19:20:02,026 - tensorflow - INFO - global_step/sec: 88.7131 +2022-05-01 19:20:02,026 - tensorflow - INFO - loss = 0.029199861, step = 1518200 (2.254 sec) +2022-05-01 19:20:04,284 - tensorflow - INFO - global_step/sec: 88.5768 +2022-05-01 19:20:04,284 - tensorflow - INFO - loss = 0.012956169, step = 1518400 (2.258 sec) +2022-05-01 19:20:06,534 - tensorflow - INFO - global_step/sec: 88.8654 +2022-05-01 19:20:06,535 - tensorflow - INFO - loss = 0.028454738, step = 1518600 (2.251 sec) +2022-05-01 19:20:08,792 - tensorflow - 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INFO - loss = 0.013440279, step = 1586800 (2.254 sec) +2022-05-01 19:32:58,032 - tensorflow - INFO - global_step/sec: 88.8497 +2022-05-01 19:32:58,033 - tensorflow - INFO - loss = 0.026137035, step = 1587000 (2.251 sec) +2022-05-01 19:33:00,284 - tensorflow - INFO - global_step/sec: 88.8006 +2022-05-01 19:33:00,285 - tensorflow - INFO - loss = 0.010146251, step = 1587200 (2.252 sec) +2022-05-01 19:33:02,539 - tensorflow - INFO - global_step/sec: 88.6921 +2022-05-01 19:33:02,540 - tensorflow - INFO - loss = 0.13608116, step = 1587400 (2.255 sec) +2022-05-01 19:33:04,799 - tensorflow - INFO - global_step/sec: 88.527 +2022-05-01 19:33:04,799 - tensorflow - INFO - loss = 0.06195675, step = 1587600 (2.259 sec) +2022-05-01 19:33:07,049 - tensorflow - INFO - global_step/sec: 88.8633 +2022-05-01 19:33:07,050 - tensorflow - INFO - loss = 0.006039193, step = 1587800 (2.251 sec) +2022-05-01 19:33:09,303 - tensorflow - INFO - global_step/sec: 88.7286 +2022-05-01 19:33:09,304 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.9413 +2022-05-01 19:33:25,065 - tensorflow - INFO - loss = 0.101530254, step = 1589400 (2.249 sec) +2022-05-01 19:33:27,315 - tensorflow - INFO - global_step/sec: 88.8613 +2022-05-01 19:33:27,316 - tensorflow - INFO - loss = 0.055434287, step = 1589600 (2.251 sec) +2022-05-01 19:33:29,567 - tensorflow - INFO - global_step/sec: 88.8333 +2022-05-01 19:33:29,567 - tensorflow - INFO - loss = 0.015367792, step = 1589800 (2.251 sec) +2022-05-01 19:33:31,822 - tensorflow - INFO - global_step/sec: 88.676 +2022-05-01 19:33:31,822 - tensorflow - INFO - loss = 0.07796347, step = 1590000 (2.255 sec) +2022-05-01 19:33:34,075 - tensorflow - INFO - global_step/sec: 88.7751 +2022-05-01 19:33:34,075 - tensorflow - INFO - loss = 0.024588708, step = 1590200 (2.253 sec) +2022-05-01 19:33:36,330 - tensorflow - INFO - global_step/sec: 88.6726 +2022-05-01 19:33:36,331 - tensorflow - INFO - loss = 0.009122549, step = 1590400 (2.256 sec) +2022-05-01 19:33:38,585 - tensorflow - INFO - global_step/sec: 88.7176 +2022-05-01 19:33:38,585 - tensorflow - INFO - loss = 0.01820556, step = 1590600 (2.254 sec) +2022-05-01 19:33:40,838 - tensorflow - INFO - global_step/sec: 88.7469 +2022-05-01 19:33:40,839 - tensorflow - INFO - loss = 0.13771403, step = 1590800 (2.254 sec) +2022-05-01 19:33:43,097 - tensorflow - INFO - global_step/sec: 88.5529 +2022-05-01 19:33:43,097 - tensorflow - INFO - loss = 0.01528587, step = 1591000 (2.259 sec) +2022-05-01 19:33:45,344 - tensorflow - INFO - global_step/sec: 89.003 +2022-05-01 19:33:45,344 - tensorflow - INFO - loss = 0.11659643, step = 1591200 (2.247 sec) +2022-05-01 19:33:47,595 - tensorflow - INFO - global_step/sec: 88.846 +2022-05-01 19:33:47,595 - tensorflow - INFO - loss = 0.016851345, step = 1591400 (2.251 sec) +2022-05-01 19:33:49,846 - tensorflow - INFO - global_step/sec: 88.8568 +2022-05-01 19:33:49,846 - tensorflow - INFO - loss = 0.013559494, step = 1591600 (2.251 sec) +2022-05-01 19:33:52,102 - tensorflow - INFO - global_step/sec: 88.6615 +2022-05-01 19:33:52,102 - tensorflow - INFO - loss = 0.04798165, step = 1591800 (2.256 sec) +2022-05-01 19:33:54,358 - tensorflow - INFO - global_step/sec: 88.6353 +2022-05-01 19:33:54,358 - tensorflow - INFO - loss = 0.018655952, step = 1592000 (2.256 sec) +2022-05-01 19:33:56,620 - tensorflow - INFO - global_step/sec: 88.4314 +2022-05-01 19:33:56,620 - tensorflow - INFO - loss = 0.03391049, step = 1592200 (2.262 sec) +2022-05-01 19:33:58,869 - tensorflow - INFO - global_step/sec: 88.9314 +2022-05-01 19:33:58,869 - tensorflow - INFO - loss = 0.069250464, step = 1592400 (2.249 sec) +2022-05-01 19:34:01,118 - tensorflow - INFO - global_step/sec: 88.9064 +2022-05-01 19:34:01,119 - tensorflow - INFO - loss = 0.035049617, step = 1592600 (2.250 sec) +2022-05-01 19:34:03,371 - tensorflow - INFO - global_step/sec: 88.7631 +2022-05-01 19:34:03,372 - tensorflow - INFO - loss = 0.009838936, step = 1592800 (2.253 sec) +2022-05-01 19:34:05,623 - tensorflow - INFO - global_step/sec: 88.8397 +2022-05-01 19:34:05,623 - tensorflow - 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tensorflow - INFO - global_step/sec: 88.4649 +2022-05-01 19:34:21,443 - tensorflow - INFO - loss = 0.021599734, step = 1594400 (2.261 sec) +2022-05-01 19:34:23,709 - tensorflow - INFO - global_step/sec: 88.2266 +2022-05-01 19:34:23,710 - tensorflow - INFO - loss = 0.020881085, step = 1594600 (2.267 sec) +2022-05-01 19:34:25,971 - tensorflow - INFO - global_step/sec: 88.4053 +2022-05-01 19:34:25,972 - tensorflow - INFO - loss = 0.034342658, step = 1594800 (2.262 sec) +2022-05-01 19:34:28,234 - tensorflow - INFO - global_step/sec: 88.3982 +2022-05-01 19:34:28,234 - tensorflow - INFO - loss = 0.025880713, step = 1595000 (2.262 sec) +2022-05-01 19:34:30,498 - tensorflow - INFO - global_step/sec: 88.3272 +2022-05-01 19:34:30,499 - tensorflow - INFO - loss = 0.130944, step = 1595200 (2.264 sec) +2022-05-01 19:34:32,759 - tensorflow - INFO - global_step/sec: 88.4758 +2022-05-01 19:34:32,759 - tensorflow - INFO - loss = 0.010540167, step = 1595400 (2.261 sec) +2022-05-01 19:34:35,021 - tensorflow - INFO - global_step/sec: 88.4221 +2022-05-01 19:34:35,021 - tensorflow - INFO - loss = 0.007979, step = 1595600 (2.262 sec) +2022-05-01 19:34:37,283 - tensorflow - INFO - global_step/sec: 88.4207 +2022-05-01 19:34:37,283 - tensorflow - INFO - loss = 0.018332234, step = 1595800 (2.262 sec) +2022-05-01 19:34:39,544 - tensorflow - INFO - global_step/sec: 88.4203 +2022-05-01 19:34:39,545 - tensorflow - INFO - loss = 0.029539935, step = 1596000 (2.262 sec) +2022-05-01 19:34:41,798 - tensorflow - INFO - global_step/sec: 88.7637 +2022-05-01 19:34:41,798 - tensorflow - INFO - loss = 0.010827591, step = 1596200 (2.253 sec) +2022-05-01 19:34:44,052 - tensorflow - INFO - global_step/sec: 88.709 +2022-05-01 19:34:44,053 - tensorflow - INFO - loss = 0.02367578, step = 1596400 (2.255 sec) +2022-05-01 19:34:46,302 - tensorflow - INFO - global_step/sec: 88.8811 +2022-05-01 19:34:46,303 - tensorflow - INFO - loss = 0.029965963, step = 1596600 (2.250 sec) +2022-05-01 19:34:48,561 - tensorflow - INFO - global_step/sec: 88.5344 +2022-05-01 19:34:48,562 - tensorflow - INFO - loss = 0.0040046135, step = 1596800 (2.259 sec) +2022-05-01 19:34:50,816 - tensorflow - INFO - global_step/sec: 88.7208 +2022-05-01 19:34:50,816 - tensorflow - INFO - loss = 0.013771693, step = 1597000 (2.254 sec) +2022-05-01 19:34:53,066 - tensorflow - INFO - global_step/sec: 88.8668 +2022-05-01 19:34:53,067 - tensorflow - INFO - loss = 0.0055685453, step = 1597200 (2.251 sec) +2022-05-01 19:34:55,317 - tensorflow - INFO - global_step/sec: 88.8493 +2022-05-01 19:34:55,318 - tensorflow - INFO - loss = 0.29111502, step = 1597400 (2.251 sec) +2022-05-01 19:34:57,567 - tensorflow - INFO - global_step/sec: 88.9108 +2022-05-01 19:34:57,567 - tensorflow - INFO - loss = 0.034654275, step = 1597600 (2.249 sec) +2022-05-01 19:34:59,819 - tensorflow - INFO - global_step/sec: 88.7871 +2022-05-01 19:34:59,820 - tensorflow - INFO - loss = 0.045142382, step = 1597800 (2.253 sec) +2022-05-01 19:35:02,077 - tensorflow - INFO - global_step/sec: 88.5762 +2022-05-01 19:35:02,078 - tensorflow - INFO - loss = 0.02174133, step = 1598000 (2.258 sec) +2022-05-01 19:35:04,335 - tensorflow - INFO - global_step/sec: 88.5995 +2022-05-01 19:35:04,335 - tensorflow - INFO - loss = 0.034647133, step = 1598200 (2.257 sec) +2022-05-01 19:35:06,584 - tensorflow - INFO - global_step/sec: 88.8987 +2022-05-01 19:35:06,585 - tensorflow - INFO - loss = 0.048062347, step = 1598400 (2.250 sec) +2022-05-01 19:35:08,836 - tensorflow - INFO - global_step/sec: 88.808 +2022-05-01 19:35:08,837 - tensorflow - INFO - loss = 0.06000845, step = 1598600 (2.252 sec) +2022-05-01 19:35:11,090 - tensorflow - INFO - global_step/sec: 88.738 +2022-05-01 19:35:11,091 - tensorflow - INFO - loss = 0.014896672, step = 1598800 (2.254 sec) +2022-05-01 19:35:13,348 - tensorflow - INFO - global_step/sec: 88.5689 +2022-05-01 19:35:13,349 - tensorflow - INFO - loss = 0.11341639, step = 1599000 (2.258 sec) +2022-05-01 19:35:15,607 - tensorflow - INFO - global_step/sec: 88.5451 +2022-05-01 19:35:15,607 - tensorflow - INFO - loss = 0.017968118, step = 1599200 (2.259 sec) +2022-05-01 19:35:17,859 - tensorflow - INFO - global_step/sec: 88.7948 +2022-05-01 19:35:17,860 - tensorflow - INFO - loss = 0.02949712, step = 1599400 (2.252 sec) +2022-05-01 19:35:20,112 - tensorflow - INFO - global_step/sec: 88.7764 +2022-05-01 19:35:20,113 - tensorflow - INFO - loss = 0.016649557, step = 1599600 (2.253 sec) +2022-05-01 19:35:22,367 - tensorflow - INFO - global_step/sec: 88.6975 +2022-05-01 19:35:22,368 - tensorflow - INFO - loss = 0.019204346, step = 1599800 (2.255 sec) +2022-05-01 19:35:24,612 - tensorflow - INFO - Saving checkpoints for 1600000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. +2022-05-01 19:35:24,629 - tensorflow - INFO - model.ckpt-1600000 is not in all_model_checkpoint_paths. Manually adding it. +2022-05-01 19:35:25,147 - tensorflow - INFO - Calling model_fn. +2022-05-01 19:35:25,275 - tensorflow - INFO - Done calling model_fn. +2022-05-01 19:35:25,290 - tensorflow - INFO - Starting evaluation at 2022-05-01T19:35:25Z +2022-05-01 19:35:25,354 - tensorflow - INFO - Graph was finalized. +2022-05-01 19:35:25,357 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1600000 +2022-05-01 19:35:25,639 - tensorflow - INFO - Running local_init_op. +2022-05-01 19:35:25,747 - tensorflow - INFO - Done running local_init_op. +2022-05-01 19:35:35,989 - tensorflow - INFO - Evaluation [3990/39900] +2022-05-01 19:35:45,552 - tensorflow - INFO - Evaluation [7980/39900] +2022-05-01 19:35:55,109 - tensorflow - INFO - Evaluation [11970/39900] +2022-05-01 19:36:04,674 - tensorflow - INFO - Evaluation [15960/39900] +2022-05-01 19:36:14,213 - tensorflow - INFO - Evaluation [19950/39900] +2022-05-01 19:36:23,777 - tensorflow - INFO - Evaluation [23940/39900] +2022-05-01 19:36:33,343 - tensorflow - INFO - Evaluation [27930/39900] +2022-05-01 19:36:42,896 - tensorflow - INFO - Evaluation [31920/39900] +2022-05-01 19:36:52,449 - tensorflow - INFO - Evaluation [35910/39900] +2022-05-01 19:37:02,022 - tensorflow - INFO - Evaluation [39900/39900] +2022-05-01 19:37:02,157 - tensorflow - INFO - Finished evaluation at 2022-05-01-19:37:02 +2022-05-01 19:37:02,157 - tensorflow - INFO - Saving dict for global step 1600000: global_step = 1600000, loss = 0.0288886, mean_degree_err = 5.0725417 +2022-05-01 19:37:02,157 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 1600000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1600000 +2022-05-01 19:37:02,171 - tensorflow - INFO - global_step/sec: 2.00392 +2022-05-01 19:37:02,172 - tensorflow - INFO - loss = 0.036867063, step = 1600000 (99.805 sec) +2022-05-01 19:37:04,446 - tensorflow - INFO - global_step/sec: 87.9391 +2022-05-01 19:37:04,446 - tensorflow - INFO - loss = 0.040967178, step = 1600200 (2.274 sec) +2022-05-01 19:37:06,704 - tensorflow - INFO - global_step/sec: 88.5445 +2022-05-01 19:37:06,705 - tensorflow - INFO - loss = 0.22369778, step = 1600400 (2.259 sec) +2022-05-01 19:37:08,963 - tensorflow - INFO - global_step/sec: 88.5558 +2022-05-01 19:37:08,963 - tensorflow - INFO - loss = 0.04972737, step = 1600600 (2.258 sec) +2022-05-01 19:37:11,221 - tensorflow - INFO - global_step/sec: 88.5523 +2022-05-01 19:37:11,222 - tensorflow - INFO - loss = 0.012088019, step = 1600800 (2.259 sec) +2022-05-01 19:37:13,476 - tensorflow - INFO - global_step/sec: 88.7227 +2022-05-01 19:37:13,476 - tensorflow - INFO - loss = 0.027309652, step = 1601000 (2.254 sec) +2022-05-01 19:37:15,733 - tensorflow - INFO - global_step/sec: 88.6028 +2022-05-01 19:37:15,733 - tensorflow - INFO - loss = 0.010206912, step = 1601200 (2.257 sec) +2022-05-01 19:37:17,986 - tensorflow - INFO - global_step/sec: 88.7572 +2022-05-01 19:37:17,987 - tensorflow - INFO - loss = 0.005925677, step = 1601400 (2.253 sec) +2022-05-01 19:37:20,241 - tensorflow - INFO - global_step/sec: 88.712 +2022-05-01 19:37:20,241 - tensorflow - INFO - loss = 0.01557181, step = 1601600 (2.254 sec) +2022-05-01 19:37:22,502 - tensorflow - INFO - global_step/sec: 88.4272 +2022-05-01 19:37:22,503 - tensorflow - INFO - loss = 0.012669121, step = 1601800 (2.262 sec) +2022-05-01 19:37:24,760 - tensorflow - INFO - global_step/sec: 88.6035 +2022-05-01 19:37:24,760 - tensorflow - INFO - loss = 0.010645354, step = 1602000 (2.257 sec) +2022-05-01 19:37:27,014 - tensorflow - INFO - global_step/sec: 88.7156 +2022-05-01 19:37:27,014 - tensorflow - INFO - loss = 0.017084083, step = 1602200 (2.254 sec) +2022-05-01 19:37:29,268 - tensorflow - INFO - global_step/sec: 88.7194 +2022-05-01 19:37:29,269 - tensorflow - INFO - loss = 0.025699474, step = 1602400 (2.254 sec) +2022-05-01 19:37:31,527 - tensorflow - INFO - global_step/sec: 88.5463 +2022-05-01 19:37:31,527 - tensorflow - INFO - loss = 0.022008544, step = 1602600 (2.259 sec) +2022-05-01 19:37:33,783 - tensorflow - INFO - global_step/sec: 88.6539 +2022-05-01 19:37:33,783 - tensorflow - INFO - loss = 0.010230547, step = 1602800 (2.256 sec) +2022-05-01 19:37:36,036 - tensorflow - INFO - global_step/sec: 88.7676 +2022-05-01 19:37:36,037 - tensorflow - INFO - loss = 0.029947232, step = 1603000 (2.253 sec) +2022-05-01 19:37:38,289 - tensorflow - INFO - global_step/sec: 88.7648 +2022-05-01 19:37:38,290 - tensorflow - INFO - loss = 0.007518686, step = 1603200 (2.253 sec) +2022-05-01 19:37:40,547 - tensorflow - INFO - global_step/sec: 88.585 +2022-05-01 19:37:40,547 - tensorflow - INFO - loss = 0.0097355675, step = 1603400 (2.258 sec) +2022-05-01 19:37:42,809 - tensorflow - INFO - global_step/sec: 88.4294 +2022-05-01 19:37:42,809 - tensorflow - INFO - loss = 0.074278854, step = 1603600 (2.262 sec) +2022-05-01 19:37:45,073 - tensorflow - INFO - global_step/sec: 88.3324 +2022-05-01 19:37:45,073 - tensorflow - INFO - loss = 0.017599702, step = 1603800 (2.264 sec) +2022-05-01 19:37:47,338 - tensorflow - INFO - global_step/sec: 88.3053 +2022-05-01 19:37:47,338 - tensorflow - INFO - loss = 0.06725284, step = 1604000 (2.265 sec) +2022-05-01 19:37:49,600 - tensorflow - INFO - global_step/sec: 88.4228 +2022-05-01 19:37:49,600 - tensorflow - INFO - loss = 0.06788803, step = 1604200 (2.262 sec) +2022-05-01 19:37:51,865 - tensorflow - INFO - global_step/sec: 88.2895 +2022-05-01 19:37:51,865 - tensorflow - INFO - loss = 0.024817625, step = 1604400 (2.265 sec) +2022-05-01 19:37:54,130 - tensorflow - INFO - global_step/sec: 88.3108 +2022-05-01 19:37:54,130 - tensorflow - INFO - loss = 0.011394728, step = 1604600 (2.265 sec) +2022-05-01 19:37:56,394 - tensorflow - INFO - global_step/sec: 88.3047 +2022-05-01 19:37:56,395 - tensorflow - INFO - loss = 0.07237558, step = 1604800 (2.265 sec) +2022-05-01 19:37:58,658 - tensorflow - INFO - global_step/sec: 88.3465 +2022-05-01 19:37:58,659 - tensorflow - INFO - loss = 0.08910976, step = 1605000 (2.264 sec) +2022-05-01 19:38:00,919 - tensorflow - INFO - global_step/sec: 88.4754 +2022-05-01 19:38:00,919 - tensorflow - INFO - loss = 0.10602189, step = 1605200 (2.261 sec) +2022-05-01 19:38:03,187 - tensorflow - INFO - global_step/sec: 88.1663 +2022-05-01 19:38:03,188 - tensorflow - INFO - loss = 0.35692906, step = 1605400 (2.268 sec) +2022-05-01 19:38:05,451 - tensorflow - INFO - global_step/sec: 88.3521 +2022-05-01 19:38:05,451 - tensorflow - INFO - loss = 0.025508648, step = 1605600 (2.264 sec) +2022-05-01 19:38:07,710 - tensorflow - INFO - global_step/sec: 88.531 +2022-05-01 19:38:07,710 - tensorflow - INFO - loss = 0.020784743, step = 1605800 (2.259 sec) +2022-05-01 19:38:09,971 - tensorflow - INFO - global_step/sec: 88.4562 +2022-05-01 19:38:09,971 - tensorflow - INFO - loss = 0.009046527, step = 1606000 (2.261 sec) +2022-05-01 19:38:12,230 - tensorflow - INFO - global_step/sec: 88.5493 +2022-05-01 19:38:12,230 - tensorflow - INFO - loss = 0.12052645, step = 1606200 (2.259 sec) +2022-05-01 19:38:14,487 - tensorflow - INFO - global_step/sec: 88.6104 +2022-05-01 19:38:14,487 - tensorflow - INFO - loss = 0.010093826, step = 1606400 (2.257 sec) +2022-05-01 19:38:16,748 - tensorflow - INFO - global_step/sec: 88.4417 +2022-05-01 19:38:16,748 - tensorflow - INFO - loss = 0.060307868, step = 1606600 (2.261 sec) +2022-05-01 19:38:19,003 - tensorflow - INFO - global_step/sec: 88.7118 +2022-05-01 19:38:19,003 - tensorflow - INFO - loss = 0.0069079367, step = 1606800 (2.255 sec) +2022-05-01 19:38:21,254 - tensorflow - INFO - global_step/sec: 88.8224 +2022-05-01 19:38:21,255 - tensorflow - INFO - loss = 0.040427912, step = 1607000 (2.252 sec) +2022-05-01 19:38:23,512 - tensorflow - INFO - global_step/sec: 88.6025 +2022-05-01 19:38:23,512 - tensorflow - INFO - loss = 0.04088958, step = 1607200 (2.257 sec) +2022-05-01 19:38:25,767 - tensorflow - INFO - global_step/sec: 88.6759 +2022-05-01 19:38:25,767 - tensorflow - INFO - loss = 0.4712106, step = 1607400 (2.255 sec) +2022-05-01 19:38:28,028 - tensorflow - INFO - global_step/sec: 88.4476 +2022-05-01 19:38:28,029 - tensorflow - INFO - loss = 0.11803105, step = 1607600 (2.261 sec) +2022-05-01 19:38:30,286 - tensorflow - INFO - global_step/sec: 88.5953 +2022-05-01 19:38:30,286 - tensorflow - INFO - loss = 0.0077702906, step = 1607800 (2.257 sec) +2022-05-01 19:38:32,542 - tensorflow - INFO - global_step/sec: 88.6321 +2022-05-01 19:38:32,543 - tensorflow - INFO - loss = 0.07963283, step = 1608000 (2.256 sec) +2022-05-01 19:38:34,799 - tensorflow - INFO - global_step/sec: 88.6339 +2022-05-01 19:38:34,799 - tensorflow - INFO - loss = 0.28873613, step = 1608200 (2.256 sec) +2022-05-01 19:38:37,053 - tensorflow - INFO - global_step/sec: 88.7161 +2022-05-01 19:38:37,053 - tensorflow - INFO - loss = 0.05215706, step = 1608400 (2.254 sec) +2022-05-01 19:38:39,307 - tensorflow - INFO - global_step/sec: 88.7456 +2022-05-01 19:38:39,307 - tensorflow - INFO - loss = 0.0728301, step = 1608600 (2.254 sec) +2022-05-01 19:38:41,563 - tensorflow - INFO - global_step/sec: 88.6317 +2022-05-01 19:38:41,564 - tensorflow - INFO - loss = 0.05030317, step = 1608800 (2.257 sec) +2022-05-01 19:38:43,819 - tensorflow - INFO - global_step/sec: 88.6564 +2022-05-01 19:38:43,819 - tensorflow - INFO - loss = 0.013282448, step = 1609000 (2.256 sec) +2022-05-01 19:38:46,071 - tensorflow - INFO - global_step/sec: 88.8167 +2022-05-01 19:38:46,071 - tensorflow - INFO - loss = 0.019527066, step = 1609200 (2.252 sec) +2022-05-01 19:38:48,327 - tensorflow - INFO - global_step/sec: 88.6506 +2022-05-01 19:38:48,327 - tensorflow - INFO - loss = 0.009463694, step = 1609400 (2.256 sec) +2022-05-01 19:38:50,578 - tensorflow - INFO - global_step/sec: 88.849 +2022-05-01 19:38:50,578 - tensorflow - INFO - loss = 0.014914087, step = 1609600 (2.251 sec) +2022-05-01 19:38:52,834 - tensorflow - INFO - global_step/sec: 88.6385 +2022-05-01 19:38:52,835 - tensorflow - INFO - loss = 0.03069091, step = 1609800 (2.256 sec) +2022-05-01 19:38:55,093 - tensorflow - INFO - global_step/sec: 88.5442 +2022-05-01 19:38:55,093 - tensorflow - INFO - loss = 0.015568502, step = 1610000 (2.259 sec) +2022-05-01 19:38:57,350 - tensorflow - INFO - global_step/sec: 88.6284 +2022-05-01 19:38:57,350 - tensorflow - INFO - loss = 0.026304193, step = 1610200 (2.257 sec) +2022-05-01 19:38:59,600 - tensorflow - INFO - global_step/sec: 88.8798 +2022-05-01 19:38:59,600 - tensorflow - INFO - loss = 0.01577914, step = 1610400 (2.250 sec) +2022-05-01 19:39:01,853 - tensorflow - INFO - global_step/sec: 88.7517 +2022-05-01 19:39:01,854 - tensorflow - INFO - loss = 0.010001397, step = 1610600 (2.253 sec) +2022-05-01 19:39:04,111 - tensorflow - INFO - global_step/sec: 88.6022 +2022-05-01 19:39:04,111 - tensorflow - INFO - loss = 0.16669104, step = 1610800 (2.257 sec) +2022-05-01 19:39:06,372 - tensorflow - INFO - global_step/sec: 88.4573 +2022-05-01 19:39:06,372 - tensorflow - INFO - loss = 0.086048484, step = 1611000 (2.261 sec) +2022-05-01 19:39:08,626 - tensorflow - INFO - global_step/sec: 88.7357 +2022-05-01 19:39:08,626 - tensorflow - INFO - loss = 0.015150867, step = 1611200 (2.254 sec) +2022-05-01 19:39:10,874 - tensorflow - INFO - global_step/sec: 88.9594 +2022-05-01 19:39:10,874 - tensorflow - INFO - loss = 0.029532706, step = 1611400 (2.248 sec) +2022-05-01 19:39:13,123 - tensorflow - INFO - global_step/sec: 88.9277 +2022-05-01 19:39:13,123 - tensorflow - INFO - loss = 0.0062228935, step = 1611600 (2.249 sec) +2022-05-01 19:39:15,379 - tensorflow - INFO - global_step/sec: 88.631 +2022-05-01 19:39:15,380 - tensorflow - INFO - loss = 0.02429897, step = 1611800 (2.257 sec) +2022-05-01 19:39:17,634 - tensorflow - INFO - global_step/sec: 88.7114 +2022-05-01 19:39:17,634 - tensorflow - INFO - loss = 0.012944584, step = 1612000 (2.254 sec) +2022-05-01 19:39:19,890 - tensorflow - INFO - global_step/sec: 88.6536 +2022-05-01 19:39:19,890 - tensorflow - INFO - loss = 0.12568352, step = 1612200 (2.256 sec) +2022-05-01 19:39:22,144 - tensorflow - INFO - global_step/sec: 88.7101 +2022-05-01 19:39:22,145 - tensorflow - INFO - loss = 0.5938471, step = 1612400 (2.255 sec) +2022-05-01 19:39:24,396 - tensorflow - INFO - global_step/sec: 88.8153 +2022-05-01 19:39:24,397 - tensorflow - INFO - loss = 0.034583516, step = 1612600 (2.252 sec) +2022-05-01 19:39:26,652 - tensorflow - INFO - global_step/sec: 88.6671 +2022-05-01 19:39:26,652 - tensorflow - INFO - loss = 0.022513185, step = 1612800 (2.256 sec) +2022-05-01 19:39:28,906 - tensorflow - INFO - global_step/sec: 88.7386 +2022-05-01 19:39:28,906 - tensorflow - INFO - loss = 0.017508483, step = 1613000 (2.254 sec) +2022-05-01 19:39:31,167 - tensorflow - INFO - global_step/sec: 88.4312 +2022-05-01 19:39:31,168 - tensorflow - INFO - loss = 0.004814804, step = 1613200 (2.262 sec) +2022-05-01 19:39:33,421 - tensorflow - INFO - global_step/sec: 88.7257 +2022-05-01 19:39:33,422 - tensorflow - INFO - loss = 0.02977703, step = 1613400 (2.254 sec) +2022-05-01 19:39:35,669 - tensorflow - INFO - global_step/sec: 88.9721 +2022-05-01 19:39:35,670 - tensorflow - INFO - loss = 0.026092917, step = 1613600 (2.248 sec) +2022-05-01 19:39:37,918 - tensorflow - INFO - global_step/sec: 88.9591 +2022-05-01 19:39:37,918 - tensorflow - INFO - loss = 0.022681203, step = 1613800 (2.248 sec) +2022-05-01 19:39:40,174 - tensorflow - INFO - global_step/sec: 88.6215 +2022-05-01 19:39:40,175 - tensorflow - INFO - loss = 0.031323865, step = 1614000 (2.257 sec) +2022-05-01 19:39:42,425 - tensorflow - INFO - global_step/sec: 88.8421 +2022-05-01 19:39:42,426 - tensorflow - INFO - loss = 0.010317453, step = 1614200 (2.251 sec) +2022-05-01 19:39:44,681 - tensorflow - INFO - global_step/sec: 88.6888 +2022-05-01 19:39:44,681 - tensorflow - INFO - loss = 0.053331293, step = 1614400 (2.255 sec) +2022-05-01 19:39:46,930 - tensorflow - INFO - global_step/sec: 88.8947 +2022-05-01 19:39:46,931 - tensorflow - INFO - loss = 0.0056943605, step = 1614600 (2.250 sec) +2022-05-01 19:39:49,187 - tensorflow - INFO - global_step/sec: 88.6119 +2022-05-01 19:39:49,188 - tensorflow - INFO - loss = 0.042760827, step = 1614800 (2.257 sec) +2022-05-01 19:39:51,442 - tensorflow - INFO - global_step/sec: 88.7208 +2022-05-01 19:39:51,442 - tensorflow - INFO - loss = 0.09538596, step = 1615000 (2.254 sec) +2022-05-01 19:39:53,698 - tensorflow - INFO - global_step/sec: 88.6245 +2022-05-01 19:39:53,699 - tensorflow - INFO - loss = 0.0061489423, step = 1615200 (2.257 sec) +2022-05-01 19:39:55,959 - tensorflow - INFO - global_step/sec: 88.4757 +2022-05-01 19:39:55,959 - tensorflow - INFO - loss = 0.025436368, step = 1615400 (2.261 sec) +2022-05-01 19:39:58,211 - tensorflow - INFO - global_step/sec: 88.8139 +2022-05-01 19:39:58,211 - tensorflow - INFO - loss = 0.019615395, step = 1615600 (2.252 sec) +2022-05-01 19:40:00,468 - tensorflow - INFO - global_step/sec: 88.6208 +2022-05-01 19:40:00,468 - tensorflow - INFO - loss = 0.008789796, step = 1615800 (2.257 sec) +2022-05-01 19:40:02,720 - tensorflow - INFO - global_step/sec: 88.7873 +2022-05-01 19:40:02,721 - tensorflow - INFO - loss = 0.014913684, step = 1616000 (2.253 sec) +2022-05-01 19:40:04,974 - tensorflow - INFO - global_step/sec: 88.7429 +2022-05-01 19:40:04,974 - tensorflow - INFO - loss = 0.07278446, step = 1616200 (2.254 sec) +2022-05-01 19:40:07,226 - tensorflow - INFO - global_step/sec: 88.8158 +2022-05-01 19:40:07,226 - tensorflow - INFO - loss = 0.12501572, step = 1616400 (2.252 sec) +2022-05-01 19:40:09,481 - tensorflow - INFO - global_step/sec: 88.6723 +2022-05-01 19:40:09,482 - tensorflow - INFO - loss = 0.28377822, step = 1616600 (2.256 sec) +2022-05-01 19:40:11,732 - tensorflow - INFO - global_step/sec: 88.8551 +2022-05-01 19:40:11,733 - tensorflow - INFO - loss = 0.021106012, step = 1616800 (2.251 sec) +2022-05-01 19:40:13,987 - tensorflow - INFO - global_step/sec: 88.7023 +2022-05-01 19:40:13,987 - tensorflow - INFO - loss = 8.388082, step = 1617000 (2.255 sec) +2022-05-01 19:40:16,238 - tensorflow - INFO - global_step/sec: 88.8266 +2022-05-01 19:40:16,239 - tensorflow - INFO - loss = 0.019565396, step = 1617200 (2.252 sec) +2022-05-01 19:40:18,491 - tensorflow - INFO - global_step/sec: 88.7973 +2022-05-01 19:40:18,491 - tensorflow - INFO - loss = 0.03616175, step = 1617400 (2.252 sec) +2022-05-01 19:40:20,743 - tensorflow - 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loss = 0.037976272, step = 1699200 (2.258 sec) +2022-05-01 19:55:43,270 - tensorflow - INFO - global_step/sec: 88.554 +2022-05-01 19:55:43,270 - tensorflow - INFO - loss = 0.017978225, step = 1699400 (2.258 sec) +2022-05-01 19:55:45,528 - tensorflow - INFO - global_step/sec: 88.5821 +2022-05-01 19:55:45,528 - tensorflow - INFO - loss = 0.012758279, step = 1699600 (2.258 sec) +2022-05-01 19:55:47,786 - tensorflow - INFO - global_step/sec: 88.5669 +2022-05-01 19:55:47,786 - tensorflow - INFO - loss = 0.022172417, step = 1699800 (2.258 sec) +2022-05-01 19:55:50,034 - tensorflow - INFO - Saving checkpoints for 1700000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. +2022-05-01 19:55:50,049 - tensorflow - INFO - model.ckpt-1700000 is not in all_model_checkpoint_paths. Manually adding it. +2022-05-01 19:55:50,567 - tensorflow - INFO - Calling model_fn. +2022-05-01 19:55:50,695 - tensorflow - INFO - Done calling model_fn. +2022-05-01 19:55:50,710 - tensorflow - INFO - Starting evaluation at 2022-05-01T19:55:50Z +2022-05-01 19:55:50,773 - tensorflow - INFO - Graph was finalized. +2022-05-01 19:55:50,777 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1700000 +2022-05-01 19:55:51,056 - tensorflow - INFO - Running local_init_op. +2022-05-01 19:55:51,164 - tensorflow - INFO - Done running local_init_op. +2022-05-01 19:56:01,425 - tensorflow - INFO - Evaluation [3990/39900] +2022-05-01 19:56:10,995 - tensorflow - INFO - Evaluation [7980/39900] +2022-05-01 19:56:20,542 - tensorflow - INFO - Evaluation [11970/39900] +2022-05-01 19:56:30,094 - tensorflow - INFO - Evaluation [15960/39900] +2022-05-01 19:56:39,656 - tensorflow - INFO - Evaluation [19950/39900] +2022-05-01 19:56:49,226 - tensorflow - INFO - Evaluation [23940/39900] +2022-05-01 19:56:58,785 - tensorflow - INFO - Evaluation [27930/39900] +2022-05-01 19:57:08,350 - tensorflow - INFO - Evaluation [31920/39900] +2022-05-01 19:57:17,900 - tensorflow - INFO - Evaluation [35910/39900] +2022-05-01 19:57:27,483 - tensorflow - INFO - Evaluation [39900/39900] +2022-05-01 19:57:27,620 - tensorflow - INFO - Finished evaluation at 2022-05-01-19:57:27 +2022-05-01 19:57:27,620 - tensorflow - INFO - Saving dict for global step 1700000: global_step = 1700000, loss = 0.016528208, mean_degree_err = 3.3223944 +2022-05-01 19:57:27,621 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 1700000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1700000 +2022-05-01 19:57:27,635 - tensorflow - INFO - global_step/sec: 2.00302 +2022-05-01 19:57:27,635 - tensorflow - INFO - loss = 0.018213827, step = 1700000 (99.849 sec) +2022-05-01 19:57:29,903 - tensorflow - INFO - global_step/sec: 88.1838 +2022-05-01 19:57:29,903 - tensorflow - INFO - loss = 0.021835033, step = 1700200 (2.268 sec) +2022-05-01 19:57:32,156 - tensorflow - INFO - global_step/sec: 88.7664 +2022-05-01 19:57:32,156 - tensorflow - INFO - loss = 0.041633718, step = 1700400 (2.253 sec) +2022-05-01 19:57:34,411 - tensorflow - INFO - global_step/sec: 88.6858 +2022-05-01 19:57:34,411 - tensorflow - INFO - loss = 0.10517413, step = 1700600 (2.255 sec) +2022-05-01 19:57:36,664 - tensorflow - INFO - global_step/sec: 88.7511 +2022-05-01 19:57:36,665 - tensorflow - INFO - loss = 0.037491966, step = 1700800 (2.254 sec) +2022-05-01 19:57:38,916 - tensorflow - INFO - global_step/sec: 88.8348 +2022-05-01 19:57:38,916 - tensorflow - INFO - loss = 0.031109095, step = 1701000 (2.251 sec) +2022-05-01 19:57:41,173 - tensorflow - INFO - global_step/sec: 88.623 +2022-05-01 19:57:41,173 - tensorflow - INFO - loss = 0.040808327, step = 1701200 (2.257 sec) +2022-05-01 19:57:43,428 - tensorflow - INFO - global_step/sec: 88.6723 +2022-05-01 19:57:43,428 - tensorflow - INFO - loss = 0.47118276, step = 1701400 (2.256 sec) +2022-05-01 19:57:45,677 - tensorflow - INFO - global_step/sec: 88.942 +2022-05-01 19:57:45,677 - tensorflow - INFO - loss = 0.040171977, step = 1701600 (2.249 sec) +2022-05-01 19:57:47,929 - tensorflow - INFO - global_step/sec: 88.8062 +2022-05-01 19:57:47,929 - tensorflow - INFO - loss = 0.01732021, step = 1701800 (2.252 sec) +2022-05-01 19:57:50,183 - tensorflow - INFO - global_step/sec: 88.7345 +2022-05-01 19:57:50,183 - tensorflow - INFO - loss = 0.013448552, step = 1702000 (2.254 sec) +2022-05-01 19:57:52,438 - tensorflow - INFO - global_step/sec: 88.6815 +2022-05-01 19:57:52,438 - tensorflow - INFO - loss = 0.017138971, step = 1702200 (2.255 sec) +2022-05-01 19:57:54,692 - tensorflow - INFO - global_step/sec: 88.7224 +2022-05-01 19:57:54,693 - tensorflow - INFO - loss = 0.01174594, step = 1702400 (2.254 sec) +2022-05-01 19:57:56,946 - tensorflow - INFO - global_step/sec: 88.7225 +2022-05-01 19:57:56,947 - tensorflow - INFO - loss = 0.041543134, step = 1702600 (2.254 sec) +2022-05-01 19:57:59,199 - tensorflow - INFO - global_step/sec: 88.7761 +2022-05-01 19:57:59,200 - tensorflow - INFO - loss = 0.048404578, step = 1702800 (2.253 sec) +2022-05-01 19:58:01,454 - tensorflow - INFO - global_step/sec: 88.7005 +2022-05-01 19:58:01,454 - tensorflow - INFO - loss = 0.14215983, step = 1703000 (2.255 sec) +2022-05-01 19:58:03,707 - tensorflow - INFO - global_step/sec: 88.7618 +2022-05-01 19:58:03,708 - tensorflow - INFO - loss = 0.027555201, step = 1703200 (2.253 sec) +2022-05-01 19:58:05,972 - tensorflow - INFO - global_step/sec: 88.3134 +2022-05-01 19:58:05,972 - tensorflow - INFO - loss = 0.045765974, step = 1703400 (2.265 sec) +2022-05-01 19:58:08,230 - tensorflow - INFO - global_step/sec: 88.5631 +2022-05-01 19:58:08,231 - tensorflow - INFO - loss = 0.037620254, step = 1703600 (2.258 sec) +2022-05-01 19:58:10,487 - tensorflow - INFO - global_step/sec: 88.638 +2022-05-01 19:58:10,487 - tensorflow - INFO - loss = 0.03932178, step = 1703800 (2.256 sec) +2022-05-01 19:58:12,738 - tensorflow - INFO - global_step/sec: 88.8331 +2022-05-01 19:58:12,738 - tensorflow - INFO - loss = 0.024784284, step = 1704000 (2.251 sec) +2022-05-01 19:58:14,990 - tensorflow - INFO - global_step/sec: 88.8245 +2022-05-01 19:58:14,990 - tensorflow - INFO - loss = 0.03867591, step = 1704200 (2.252 sec) +2022-05-01 19:58:17,242 - tensorflow - INFO - global_step/sec: 88.8119 +2022-05-01 19:58:17,242 - tensorflow - INFO - loss = 0.17101954, step = 1704400 (2.252 sec) +2022-05-01 19:58:19,502 - tensorflow - INFO - global_step/sec: 88.4969 +2022-05-01 19:58:19,502 - tensorflow - INFO - loss = 0.12228063, step = 1704600 (2.260 sec) +2022-05-01 19:58:21,751 - tensorflow - INFO - global_step/sec: 88.8995 +2022-05-01 19:58:21,752 - tensorflow - INFO - loss = 0.020781545, step = 1704800 (2.250 sec) +2022-05-01 19:58:24,001 - tensorflow - INFO - global_step/sec: 88.8852 +2022-05-01 19:58:24,002 - tensorflow - INFO - loss = 0.016060911, step = 1705000 (2.250 sec) +2022-05-01 19:58:26,257 - tensorflow - INFO - global_step/sec: 88.6791 +2022-05-01 19:58:26,257 - tensorflow - INFO - loss = 0.013135418, step = 1705200 (2.255 sec) +2022-05-01 19:58:28,508 - tensorflow - INFO - global_step/sec: 88.8238 +2022-05-01 19:58:28,509 - tensorflow - INFO - loss = 0.033608403, step = 1705400 (2.252 sec) +2022-05-01 19:58:30,774 - tensorflow - INFO - global_step/sec: 88.2905 +2022-05-01 19:58:30,774 - tensorflow - INFO - loss = 0.02455812, step = 1705600 (2.265 sec) +2022-05-01 19:58:33,024 - tensorflow - INFO - global_step/sec: 88.8609 +2022-05-01 19:58:33,025 - tensorflow - INFO - loss = 0.73054725, step = 1705800 (2.251 sec) +2022-05-01 19:58:35,271 - tensorflow - INFO - global_step/sec: 89.0251 +2022-05-01 19:58:35,271 - tensorflow - INFO - loss = 0.46992564, step = 1706000 (2.247 sec) +2022-05-01 19:58:37,521 - tensorflow - INFO - global_step/sec: 88.8733 +2022-05-01 19:58:37,522 - tensorflow - INFO - loss = 0.008819725, step = 1706200 (2.250 sec) +2022-05-01 19:58:39,774 - tensorflow - INFO - global_step/sec: 88.7783 +2022-05-01 19:58:39,774 - tensorflow - INFO - loss = 0.05270659, step = 1706400 (2.253 sec) +2022-05-01 19:58:42,030 - tensorflow - INFO - global_step/sec: 88.6664 +2022-05-01 19:58:42,030 - tensorflow - INFO - loss = 0.06409759, step = 1706600 (2.256 sec) +2022-05-01 19:58:44,289 - tensorflow - INFO - global_step/sec: 88.5222 +2022-05-01 19:58:44,289 - tensorflow - INFO - loss = 0.017197635, step = 1706800 (2.259 sec) +2022-05-01 19:58:46,544 - tensorflow - INFO - global_step/sec: 88.7045 +2022-05-01 19:58:46,544 - tensorflow - INFO - loss = 0.0068109254, step = 1707000 (2.255 sec) +2022-05-01 19:58:48,793 - tensorflow - INFO - global_step/sec: 88.9021 +2022-05-01 19:58:48,794 - tensorflow - INFO - loss = 0.005135089, step = 1707200 (2.250 sec) +2022-05-01 19:58:51,047 - tensorflow - INFO - global_step/sec: 88.7387 +2022-05-01 19:58:51,048 - tensorflow - INFO - loss = 0.015555227, step = 1707400 (2.254 sec) +2022-05-01 19:58:53,301 - tensorflow - INFO - global_step/sec: 88.7398 +2022-05-01 19:58:53,301 - tensorflow - INFO - loss = 0.008056469, step = 1707600 (2.254 sec) +2022-05-01 19:58:55,556 - tensorflow - INFO - global_step/sec: 88.6893 +2022-05-01 19:58:55,556 - tensorflow - INFO - loss = 11.810549, step = 1707800 (2.255 sec) +2022-05-01 19:58:57,820 - tensorflow - INFO - global_step/sec: 88.3409 +2022-05-01 19:58:57,820 - tensorflow - INFO - loss = 0.63033855, step = 1708000 (2.264 sec) +2022-05-01 19:59:00,084 - tensorflow - INFO - global_step/sec: 88.3282 +2022-05-01 19:59:00,085 - tensorflow - INFO - loss = 0.02858428, step = 1708200 (2.264 sec) +2022-05-01 19:59:02,342 - tensorflow - INFO - global_step/sec: 88.5962 +2022-05-01 19:59:02,342 - tensorflow - INFO - loss = 0.028509991, step = 1708400 (2.257 sec) +2022-05-01 19:59:04,599 - tensorflow - INFO - global_step/sec: 88.5975 +2022-05-01 19:59:04,599 - tensorflow - INFO - loss = 0.043081976, step = 1708600 (2.257 sec) +2022-05-01 19:59:06,856 - tensorflow - INFO - global_step/sec: 88.6067 +2022-05-01 19:59:06,857 - tensorflow - INFO - loss = 0.6489007, step = 1708800 (2.257 sec) +2022-05-01 19:59:09,115 - tensorflow - INFO - global_step/sec: 88.5532 +2022-05-01 19:59:09,115 - tensorflow - INFO - loss = 0.06143674, step = 1709000 (2.259 sec) +2022-05-01 19:59:11,374 - tensorflow - INFO - global_step/sec: 88.5094 +2022-05-01 19:59:11,375 - tensorflow - INFO - loss = 0.014285991, step = 1709200 (2.260 sec) +2022-05-01 19:59:13,635 - tensorflow - INFO - global_step/sec: 88.482 +2022-05-01 19:59:13,635 - tensorflow - INFO - loss = 0.02394065, step = 1709400 (2.260 sec) +2022-05-01 19:59:15,894 - tensorflow - INFO - global_step/sec: 88.521 +2022-05-01 19:59:15,895 - tensorflow - INFO - loss = 0.040852383, step = 1709600 (2.259 sec) +2022-05-01 19:59:18,155 - tensorflow - INFO - global_step/sec: 88.4788 +2022-05-01 19:59:18,155 - tensorflow - INFO - loss = 0.020867106, step = 1709800 (2.260 sec) +2022-05-01 19:59:20,416 - tensorflow - INFO - global_step/sec: 88.444 +2022-05-01 19:59:20,416 - tensorflow - INFO - loss = 0.015351091, step = 1710000 (2.261 sec) +2022-05-01 19:59:22,678 - tensorflow - INFO - global_step/sec: 88.4161 +2022-05-01 19:59:22,678 - tensorflow - INFO - loss = 0.008525239, step = 1710200 (2.262 sec) +2022-05-01 19:59:24,939 - tensorflow - INFO - global_step/sec: 88.4628 +2022-05-01 19:59:24,939 - tensorflow - INFO - loss = 0.041885566, step = 1710400 (2.261 sec) +2022-05-01 19:59:27,199 - tensorflow - INFO - global_step/sec: 88.4711 +2022-05-01 19:59:27,200 - tensorflow - INFO - loss = 0.018942935, step = 1710600 (2.261 sec) +2022-05-01 19:59:29,452 - tensorflow - INFO - global_step/sec: 88.8002 +2022-05-01 19:59:29,452 - tensorflow - INFO - loss = 0.008305543, step = 1710800 (2.252 sec) +2022-05-01 19:59:31,714 - tensorflow - INFO - global_step/sec: 88.3904 +2022-05-01 19:59:31,715 - tensorflow - INFO - loss = 0.02622455, step = 1711000 (2.263 sec) +2022-05-01 19:59:33,966 - tensorflow - INFO - global_step/sec: 88.8314 +2022-05-01 19:59:33,966 - tensorflow - INFO - loss = 0.03469228, step = 1711200 (2.251 sec) +2022-05-01 19:59:36,219 - tensorflow - INFO - global_step/sec: 88.7587 +2022-05-01 19:59:36,219 - tensorflow - INFO - loss = 0.012422364, step = 1711400 (2.253 sec) +2022-05-01 19:59:38,473 - tensorflow - INFO - global_step/sec: 88.7485 +2022-05-01 19:59:38,473 - tensorflow - INFO - loss = 0.012812477, step = 1711600 (2.254 sec) +2022-05-01 19:59:40,725 - tensorflow - INFO - global_step/sec: 88.7892 +2022-05-01 19:59:40,726 - tensorflow - INFO - loss = 0.028063836, step = 1711800 (2.253 sec) +2022-05-01 19:59:42,975 - tensorflow - INFO - global_step/sec: 88.9101 +2022-05-01 19:59:42,975 - tensorflow - INFO - loss = 0.010674957, step = 1712000 (2.249 sec) +2022-05-01 19:59:45,226 - tensorflow - INFO - global_step/sec: 88.832 +2022-05-01 19:59:45,226 - tensorflow - INFO - loss = 0.020355092, step = 1712200 (2.251 sec) +2022-05-01 19:59:47,477 - tensorflow - INFO - global_step/sec: 88.8343 +2022-05-01 19:59:47,478 - tensorflow - INFO - loss = 0.029279368, step = 1712400 (2.251 sec) +2022-05-01 19:59:49,735 - tensorflow - INFO - global_step/sec: 88.6006 +2022-05-01 19:59:49,735 - tensorflow - INFO - loss = 0.006299399, step = 1712600 (2.257 sec) +2022-05-01 19:59:51,983 - tensorflow - INFO - global_step/sec: 88.9729 +2022-05-01 19:59:51,983 - tensorflow - INFO - loss = 0.0369786, step = 1712800 (2.248 sec) +2022-05-01 19:59:54,232 - tensorflow - INFO - global_step/sec: 88.9232 +2022-05-01 19:59:54,232 - tensorflow - INFO - loss = 0.052752845, step = 1713000 (2.249 sec) +2022-05-01 19:59:56,482 - tensorflow - INFO - global_step/sec: 88.8903 +2022-05-01 19:59:56,482 - tensorflow - INFO - loss = 0.022305928, step = 1713200 (2.250 sec) +2022-05-01 19:59:58,729 - tensorflow - INFO - global_step/sec: 88.9813 +2022-05-01 19:59:58,730 - tensorflow - INFO - loss = 0.102901265, step = 1713400 (2.248 sec) +2022-05-01 20:00:00,985 - tensorflow - INFO - global_step/sec: 88.6524 +2022-05-01 20:00:00,986 - tensorflow - INFO - loss = 0.012466687, step = 1713600 (2.256 sec) +2022-05-01 20:00:03,245 - tensorflow - INFO - global_step/sec: 88.4958 +2022-05-01 20:00:03,246 - tensorflow - INFO - loss = 0.041081473, step = 1713800 (2.260 sec) +2022-05-01 20:00:05,498 - tensorflow - INFO - global_step/sec: 88.8039 +2022-05-01 20:00:05,498 - tensorflow - INFO - loss = 0.021235213, step = 1714000 (2.252 sec) +2022-05-01 20:00:07,749 - tensorflow - INFO - global_step/sec: 88.8408 +2022-05-01 20:00:07,749 - tensorflow - INFO - loss = 0.10852687, step = 1714200 (2.251 sec) +2022-05-01 20:00:10,004 - tensorflow - INFO - global_step/sec: 88.693 +2022-05-01 20:00:10,004 - tensorflow - INFO - loss = 0.14167748, step = 1714400 (2.255 sec) +2022-05-01 20:00:12,255 - tensorflow - INFO - global_step/sec: 88.8499 +2022-05-01 20:00:12,255 - tensorflow - INFO - loss = 0.012458287, step = 1714600 (2.251 sec) +2022-05-01 20:00:14,507 - tensorflow - INFO - global_step/sec: 88.7877 +2022-05-01 20:00:14,508 - tensorflow - INFO - loss = 0.15571158, step = 1714800 (2.253 sec) +2022-05-01 20:00:16,765 - tensorflow - INFO - global_step/sec: 88.5666 +2022-05-01 20:00:16,766 - tensorflow - INFO - loss = 0.019827556, step = 1715000 (2.258 sec) +2022-05-01 20:00:19,014 - tensorflow - INFO - global_step/sec: 88.9583 +2022-05-01 20:00:19,014 - tensorflow - INFO - loss = 3.7223036, step = 1715200 (2.248 sec) +2022-05-01 20:00:21,263 - tensorflow - INFO - global_step/sec: 88.9078 +2022-05-01 20:00:21,264 - tensorflow - INFO - loss = 0.014220778, step = 1715400 (2.249 sec) +2022-05-01 20:00:23,512 - tensorflow - INFO - global_step/sec: 88.9291 +2022-05-01 20:00:23,513 - tensorflow - INFO - loss = 0.034245614, step = 1715600 (2.249 sec) +2022-05-01 20:00:25,766 - tensorflow - INFO - global_step/sec: 88.7206 +2022-05-01 20:00:25,767 - tensorflow - INFO - loss = 0.025689885, step = 1715800 (2.254 sec) +2022-05-01 20:00:28,026 - tensorflow - INFO - global_step/sec: 88.531 +2022-05-01 20:00:28,026 - tensorflow - INFO - loss = 0.012042551, step = 1716000 (2.259 sec) +2022-05-01 20:00:30,281 - tensorflow - INFO - global_step/sec: 88.6762 +2022-05-01 20:00:30,281 - tensorflow - INFO - loss = 0.011883922, step = 1716200 (2.255 sec) +2022-05-01 20:00:32,541 - tensorflow - 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tensorflow - INFO - global_step/sec: 88.4817 +2022-05-01 20:15:39,024 - tensorflow - INFO - loss = 0.010002321, step = 1796800 (2.260 sec) +2022-05-01 20:15:41,274 - tensorflow - INFO - global_step/sec: 88.8512 +2022-05-01 20:15:41,275 - tensorflow - INFO - loss = 0.004910208, step = 1797000 (2.251 sec) +2022-05-01 20:15:43,528 - tensorflow - INFO - global_step/sec: 88.745 +2022-05-01 20:15:43,529 - tensorflow - INFO - loss = 0.96549255, step = 1797200 (2.254 sec) +2022-05-01 20:15:45,778 - tensorflow - INFO - global_step/sec: 88.9052 +2022-05-01 20:15:45,778 - tensorflow - INFO - loss = 0.012416754, step = 1797400 (2.250 sec) +2022-05-01 20:15:48,032 - tensorflow - INFO - global_step/sec: 88.7304 +2022-05-01 20:15:48,032 - tensorflow - INFO - loss = 0.25109956, step = 1797600 (2.254 sec) +2022-05-01 20:15:50,285 - tensorflow - INFO - global_step/sec: 88.7773 +2022-05-01 20:15:50,285 - tensorflow - INFO - loss = 0.017483495, step = 1797800 (2.253 sec) +2022-05-01 20:15:52,541 - tensorflow - INFO - global_step/sec: 88.6378 +2022-05-01 20:15:52,541 - tensorflow - INFO - loss = 0.028559688, step = 1798000 (2.256 sec) +2022-05-01 20:15:54,800 - tensorflow - INFO - global_step/sec: 88.5397 +2022-05-01 20:15:54,800 - tensorflow - INFO - loss = 1.5846872, step = 1798200 (2.259 sec) +2022-05-01 20:15:57,053 - tensorflow - INFO - global_step/sec: 88.7447 +2022-05-01 20:15:57,054 - tensorflow - INFO - loss = 0.008915515, step = 1798400 (2.254 sec) +2022-05-01 20:15:59,308 - tensorflow - INFO - global_step/sec: 88.7048 +2022-05-01 20:15:59,308 - tensorflow - INFO - loss = 0.01911306, step = 1798600 (2.255 sec) +2022-05-01 20:16:01,564 - tensorflow - INFO - global_step/sec: 88.6598 +2022-05-01 20:16:01,564 - tensorflow - INFO - loss = 0.0072809146, step = 1798800 (2.256 sec) +2022-05-01 20:16:03,820 - tensorflow - INFO - global_step/sec: 88.6346 +2022-05-01 20:16:03,821 - tensorflow - INFO - loss = 0.044288322, step = 1799000 (2.256 sec) +2022-05-01 20:16:06,074 - tensorflow - INFO - global_step/sec: 88.7646 +2022-05-01 20:16:06,074 - tensorflow - INFO - loss = 0.23373622, step = 1799200 (2.253 sec) +2022-05-01 20:16:08,332 - tensorflow - INFO - global_step/sec: 88.5687 +2022-05-01 20:16:08,332 - tensorflow - INFO - loss = 0.05972874, step = 1799400 (2.258 sec) +2022-05-01 20:16:10,588 - tensorflow - INFO - global_step/sec: 88.6542 +2022-05-01 20:16:10,588 - tensorflow - INFO - loss = 0.061447047, step = 1799600 (2.256 sec) +2022-05-01 20:16:12,838 - tensorflow - INFO - global_step/sec: 88.8724 +2022-05-01 20:16:12,838 - tensorflow - INFO - loss = 0.01870015, step = 1799800 (2.250 sec) +2022-05-01 20:16:15,078 - tensorflow - INFO - Saving checkpoints for 1800000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. +2022-05-01 20:16:15,094 - tensorflow - INFO - model.ckpt-1800000 is not in all_model_checkpoint_paths. Manually adding it. +2022-05-01 20:16:15,614 - tensorflow - INFO - Calling model_fn. +2022-05-01 20:16:15,741 - tensorflow - INFO - Done calling model_fn. +2022-05-01 20:16:15,756 - tensorflow - INFO - Starting evaluation at 2022-05-01T20:16:15Z +2022-05-01 20:16:15,820 - tensorflow - INFO - Graph was finalized. +2022-05-01 20:16:15,823 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1800000 +2022-05-01 20:16:16,107 - tensorflow - INFO - Running local_init_op. +2022-05-01 20:16:16,216 - tensorflow - INFO - Done running local_init_op. +2022-05-01 20:16:26,353 - tensorflow - INFO - Evaluation [3990/39900] +2022-05-01 20:16:35,826 - tensorflow - INFO - Evaluation [7980/39900] +2022-05-01 20:16:45,282 - tensorflow - INFO - Evaluation [11970/39900] +2022-05-01 20:16:54,752 - tensorflow - INFO - Evaluation [15960/39900] +2022-05-01 20:17:04,229 - tensorflow - INFO - Evaluation [19950/39900] +2022-05-01 20:17:13,709 - tensorflow - INFO - Evaluation [23940/39900] +2022-05-01 20:17:23,164 - tensorflow - INFO - Evaluation [27930/39900] +2022-05-01 20:17:32,617 - tensorflow - INFO - Evaluation [31920/39900] +2022-05-01 20:17:42,064 - tensorflow - INFO - Evaluation [35910/39900] +2022-05-01 20:17:51,527 - tensorflow - INFO - Evaluation [39900/39900] +2022-05-01 20:17:51,666 - tensorflow - INFO - Finished evaluation at 2022-05-01-20:17:51 +2022-05-01 20:17:51,666 - tensorflow - INFO - Saving dict for global step 1800000: global_step = 1800000, loss = 0.016763782, mean_degree_err = 3.5563521 +2022-05-01 20:17:51,667 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 1800000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1800000 +2022-05-01 20:17:51,681 - tensorflow - INFO - global_step/sec: 2.02342 +2022-05-01 20:17:51,682 - tensorflow - INFO - loss = 0.027914403, step = 1800000 (98.843 sec) +2022-05-01 20:17:53,950 - tensorflow - INFO - global_step/sec: 88.1532 +2022-05-01 20:17:53,950 - tensorflow - INFO - loss = 0.021869771, step = 1800200 (2.268 sec) +2022-05-01 20:17:56,207 - tensorflow - INFO - global_step/sec: 88.5779 +2022-05-01 20:17:56,208 - tensorflow - INFO - loss = 0.092984244, step = 1800400 (2.258 sec) +2022-05-01 20:17:58,460 - tensorflow - INFO - global_step/sec: 88.8035 +2022-05-01 20:17:58,460 - tensorflow - INFO - loss = 0.013734039, step = 1800600 (2.252 sec) +2022-05-01 20:18:00,715 - tensorflow - INFO - global_step/sec: 88.671 +2022-05-01 20:18:00,716 - tensorflow - INFO - loss = 0.024841797, step = 1800800 (2.256 sec) +2022-05-01 20:18:02,971 - tensorflow - INFO - global_step/sec: 88.6527 +2022-05-01 20:18:02,972 - tensorflow - INFO - loss = 0.022523532, step = 1801000 (2.256 sec) +2022-05-01 20:18:05,226 - tensorflow - INFO - global_step/sec: 88.6917 +2022-05-01 20:18:05,227 - tensorflow - INFO - loss = 0.019885017, step = 1801200 (2.255 sec) +2022-05-01 20:18:07,485 - tensorflow - INFO - global_step/sec: 88.5244 +2022-05-01 20:18:07,486 - tensorflow - INFO - loss = 0.035318907, step = 1801400 (2.259 sec) +2022-05-01 20:18:09,741 - tensorflow - INFO - global_step/sec: 88.6836 +2022-05-01 20:18:09,741 - tensorflow - INFO - loss = 0.010549978, step = 1801600 (2.255 sec) +2022-05-01 20:18:12,000 - tensorflow - INFO - global_step/sec: 88.5235 +2022-05-01 20:18:12,000 - tensorflow - INFO - loss = 0.30921438, step = 1801800 (2.259 sec) +2022-05-01 20:18:14,257 - tensorflow - INFO - global_step/sec: 88.6201 +2022-05-01 20:18:14,257 - tensorflow - INFO - loss = 0.060837887, step = 1802000 (2.257 sec) +2022-05-01 20:18:16,514 - tensorflow - INFO - global_step/sec: 88.6091 +2022-05-01 20:18:16,514 - tensorflow - INFO - loss = 0.073248446, step = 1802200 (2.257 sec) +2022-05-01 20:18:18,769 - tensorflow - INFO - global_step/sec: 88.7003 +2022-05-01 20:18:18,769 - tensorflow - INFO - loss = 0.020106304, step = 1802400 (2.255 sec) +2022-05-01 20:18:21,026 - tensorflow - INFO - global_step/sec: 88.609 +2022-05-01 20:18:21,026 - tensorflow - INFO - loss = 0.34301686, step = 1802600 (2.257 sec) +2022-05-01 20:18:23,277 - tensorflow - INFO - global_step/sec: 88.851 +2022-05-01 20:18:23,277 - tensorflow - INFO - loss = 0.03422773, step = 1802800 (2.251 sec) +2022-05-01 20:18:25,536 - tensorflow - INFO - global_step/sec: 88.5365 +2022-05-01 20:18:25,536 - tensorflow - INFO - loss = 0.010858849, step = 1803000 (2.259 sec) +2022-05-01 20:18:27,794 - tensorflow - INFO - global_step/sec: 88.5571 +2022-05-01 20:18:27,794 - tensorflow - INFO - loss = 0.28296545, step = 1803200 (2.258 sec) +2022-05-01 20:18:30,056 - tensorflow - INFO - global_step/sec: 88.4263 +2022-05-01 20:18:30,056 - tensorflow - INFO - loss = 0.04658611, step = 1803400 (2.262 sec) +2022-05-01 20:18:32,319 - tensorflow - INFO - global_step/sec: 88.3909 +2022-05-01 20:18:32,319 - tensorflow - INFO - loss = 0.048911583, step = 1803600 (2.263 sec) +2022-05-01 20:18:34,577 - tensorflow - INFO - global_step/sec: 88.5663 +2022-05-01 20:18:34,577 - tensorflow - INFO - loss = 0.01163725, step = 1803800 (2.258 sec) +2022-05-01 20:18:36,830 - tensorflow - INFO - global_step/sec: 88.7513 +2022-05-01 20:18:36,831 - tensorflow - INFO - loss = 9.297604, step = 1804000 (2.254 sec) +2022-05-01 20:18:39,083 - tensorflow - INFO - global_step/sec: 88.7746 +2022-05-01 20:18:39,084 - tensorflow - INFO - loss = 0.0068070027, step = 1804200 (2.253 sec) +2022-05-01 20:18:41,337 - tensorflow - INFO - global_step/sec: 88.7427 +2022-05-01 20:18:41,337 - tensorflow - INFO - loss = 0.015604509, step = 1804400 (2.254 sec) +2022-05-01 20:18:43,593 - tensorflow - INFO - global_step/sec: 88.6336 +2022-05-01 20:18:43,594 - tensorflow - INFO - loss = 0.0144014135, step = 1804600 (2.256 sec) +2022-05-01 20:18:45,845 - tensorflow - INFO - global_step/sec: 88.8138 +2022-05-01 20:18:45,846 - tensorflow - INFO - loss = 0.02853755, step = 1804800 (2.252 sec) +2022-05-01 20:18:48,096 - tensorflow - INFO - global_step/sec: 88.8374 +2022-05-01 20:18:48,097 - tensorflow - INFO - loss = 0.008257154, step = 1805000 (2.251 sec) +2022-05-01 20:18:50,349 - tensorflow - INFO - global_step/sec: 88.7729 +2022-05-01 20:18:50,350 - tensorflow - INFO - loss = 0.02656951, step = 1805200 (2.253 sec) +2022-05-01 20:18:52,603 - tensorflow - INFO - global_step/sec: 88.755 +2022-05-01 20:18:52,603 - tensorflow - INFO - loss = 0.01831774, step = 1805400 (2.253 sec) +2022-05-01 20:18:54,856 - tensorflow - INFO - global_step/sec: 88.7477 +2022-05-01 20:18:54,857 - tensorflow - INFO - loss = 0.023801386, step = 1805600 (2.254 sec) +2022-05-01 20:18:57,118 - tensorflow - INFO - global_step/sec: 88.4459 +2022-05-01 20:18:57,118 - tensorflow - INFO - loss = 0.65763414, step = 1805800 (2.261 sec) +2022-05-01 20:18:59,372 - tensorflow - INFO - global_step/sec: 88.7027 +2022-05-01 20:18:59,373 - tensorflow - INFO - loss = 0.101669826, step = 1806000 (2.255 sec) +2022-05-01 20:19:01,624 - tensorflow - INFO - global_step/sec: 88.82 +2022-05-01 20:19:01,625 - tensorflow - INFO - loss = 0.012000534, step = 1806200 (2.252 sec) +2022-05-01 20:19:03,880 - tensorflow - INFO - global_step/sec: 88.6715 +2022-05-01 20:19:03,880 - tensorflow - INFO - loss = 0.026554633, step = 1806400 (2.255 sec) +2022-05-01 20:19:06,133 - tensorflow - INFO - global_step/sec: 88.7724 +2022-05-01 20:19:06,133 - tensorflow - INFO - loss = 0.013799883, step = 1806600 (2.253 sec) +2022-05-01 20:19:08,392 - tensorflow - INFO - global_step/sec: 88.5263 +2022-05-01 20:19:08,392 - tensorflow - INFO - loss = 0.30768314, step = 1806800 (2.259 sec) +2022-05-01 20:19:10,646 - tensorflow - INFO - global_step/sec: 88.7053 +2022-05-01 20:19:10,647 - tensorflow - INFO - loss = 0.025185607, step = 1807000 (2.255 sec) +2022-05-01 20:19:12,902 - tensorflow - INFO - global_step/sec: 88.6882 +2022-05-01 20:19:12,902 - tensorflow - INFO - loss = 0.008936843, step = 1807200 (2.255 sec) +2022-05-01 20:19:15,156 - tensorflow - INFO - global_step/sec: 88.7325 +2022-05-01 20:19:15,156 - tensorflow - INFO - loss = 0.036230564, step = 1807400 (2.254 sec) +2022-05-01 20:19:17,411 - tensorflow - INFO - global_step/sec: 88.6543 +2022-05-01 20:19:17,412 - tensorflow - INFO - loss = 0.011483357, step = 1807600 (2.256 sec) +2022-05-01 20:19:19,671 - tensorflow - INFO - global_step/sec: 88.5007 +2022-05-01 20:19:19,672 - tensorflow - INFO - loss = 0.024457522, step = 1807800 (2.260 sec) +2022-05-01 20:19:21,933 - tensorflow - INFO - global_step/sec: 88.4353 +2022-05-01 20:19:21,933 - tensorflow - INFO - loss = 0.00525497, step = 1808000 (2.262 sec) +2022-05-01 20:19:24,185 - tensorflow - INFO - global_step/sec: 88.8016 +2022-05-01 20:19:24,186 - tensorflow - INFO - loss = 0.012021458, step = 1808200 (2.252 sec) +2022-05-01 20:19:26,438 - tensorflow - INFO - global_step/sec: 88.7816 +2022-05-01 20:19:26,438 - tensorflow - INFO - loss = 0.025556244, step = 1808400 (2.253 sec) +2022-05-01 20:19:28,695 - tensorflow - INFO - global_step/sec: 88.6239 +2022-05-01 20:19:28,695 - tensorflow - INFO - loss = 0.05274012, step = 1808600 (2.257 sec) +2022-05-01 20:19:30,950 - tensorflow - INFO - global_step/sec: 88.6846 +2022-05-01 20:19:30,950 - tensorflow - INFO - loss = 0.10206439, step = 1808800 (2.255 sec) +2022-05-01 20:19:33,206 - tensorflow - INFO - global_step/sec: 88.6274 +2022-05-01 20:19:33,207 - tensorflow - INFO - loss = 0.061892245, step = 1809000 (2.257 sec) +2022-05-01 20:19:35,468 - tensorflow - INFO - global_step/sec: 88.4147 +2022-05-01 20:19:35,469 - tensorflow - INFO - loss = 0.010666244, step = 1809200 (2.262 sec) +2022-05-01 20:19:37,716 - tensorflow - INFO - global_step/sec: 88.9914 +2022-05-01 20:19:37,716 - tensorflow - INFO - loss = 0.059726246, step = 1809400 (2.247 sec) +2022-05-01 20:19:39,964 - tensorflow - INFO - global_step/sec: 88.9669 +2022-05-01 20:19:39,964 - tensorflow - INFO - loss = 0.015109569, step = 1809600 (2.248 sec) +2022-05-01 20:19:42,218 - tensorflow - INFO - global_step/sec: 88.7319 +2022-05-01 20:19:42,218 - tensorflow - INFO - loss = 0.010054525, step = 1809800 (2.254 sec) +2022-05-01 20:19:44,470 - tensorflow - INFO - global_step/sec: 88.7908 +2022-05-01 20:19:44,471 - tensorflow - INFO - loss = 0.018031895, step = 1810000 (2.252 sec) +2022-05-01 20:19:46,722 - tensorflow - INFO - global_step/sec: 88.8368 +2022-05-01 20:19:46,722 - tensorflow - INFO - loss = 0.016368955, step = 1810200 (2.251 sec) +2022-05-01 20:19:48,981 - tensorflow - INFO - global_step/sec: 88.5389 +2022-05-01 20:19:48,981 - tensorflow - INFO - loss = 0.007853529, step = 1810400 (2.259 sec) +2022-05-01 20:19:51,232 - tensorflow - INFO - global_step/sec: 88.8182 +2022-05-01 20:19:51,233 - tensorflow - INFO - loss = 0.0068646725, step = 1810600 (2.252 sec) +2022-05-01 20:19:53,485 - tensorflow - INFO - global_step/sec: 88.767 +2022-05-01 20:19:53,486 - tensorflow - INFO - loss = 0.21444012, step = 1810800 (2.253 sec) +2022-05-01 20:19:55,739 - tensorflow - INFO - global_step/sec: 88.7615 +2022-05-01 20:19:55,739 - tensorflow - INFO - loss = 0.049558554, step = 1811000 (2.253 sec) +2022-05-01 20:19:57,990 - tensorflow - INFO - global_step/sec: 88.8386 +2022-05-01 20:19:57,990 - tensorflow - INFO - loss = 0.0171601, step = 1811200 (2.251 sec) +2022-05-01 20:20:00,245 - tensorflow - INFO - global_step/sec: 88.685 +2022-05-01 20:20:00,246 - tensorflow - INFO - loss = 0.01656272, step = 1811400 (2.255 sec) +2022-05-01 20:20:02,500 - tensorflow - INFO - global_step/sec: 88.694 +2022-05-01 20:20:02,500 - tensorflow - INFO - loss = 0.006385114, step = 1811600 (2.255 sec) +2022-05-01 20:20:04,751 - tensorflow - INFO - global_step/sec: 88.8453 +2022-05-01 20:20:04,752 - tensorflow - INFO - loss = 0.009432913, step = 1811800 (2.251 sec) +2022-05-01 20:20:07,009 - tensorflow - INFO - global_step/sec: 88.5886 +2022-05-01 20:20:07,009 - tensorflow - INFO - loss = 0.04209045, step = 1812000 (2.258 sec) +2022-05-01 20:20:09,258 - tensorflow - INFO - global_step/sec: 88.915 +2022-05-01 20:20:09,259 - tensorflow - INFO - loss = 0.009847219, step = 1812200 (2.249 sec) +2022-05-01 20:20:11,510 - tensorflow - INFO - global_step/sec: 88.8152 +2022-05-01 20:20:11,510 - tensorflow - INFO - loss = 0.051155195, step = 1812400 (2.252 sec) +2022-05-01 20:20:13,773 - tensorflow - INFO - global_step/sec: 88.3861 +2022-05-01 20:20:13,773 - tensorflow - INFO - loss = 0.010624302, step = 1812600 (2.263 sec) +2022-05-01 20:20:16,034 - tensorflow - INFO - global_step/sec: 88.4405 +2022-05-01 20:20:16,035 - tensorflow - INFO - loss = 0.007253981, step = 1812800 (2.261 sec) +2022-05-01 20:20:18,291 - tensorflow - INFO - global_step/sec: 88.6121 +2022-05-01 20:20:18,292 - tensorflow - INFO - loss = 0.006397931, step = 1813000 (2.257 sec) +2022-05-01 20:20:20,552 - tensorflow - INFO - global_step/sec: 88.4662 +2022-05-01 20:20:20,552 - tensorflow - INFO - loss = 0.038472924, step = 1813200 (2.261 sec) +2022-05-01 20:20:22,814 - tensorflow - INFO - global_step/sec: 88.4175 +2022-05-01 20:20:22,814 - tensorflow - INFO - loss = 0.025957687, step = 1813400 (2.262 sec) +2022-05-01 20:20:25,078 - tensorflow - INFO - global_step/sec: 88.3259 +2022-05-01 20:20:25,079 - tensorflow - INFO - loss = 0.01257656, step = 1813600 (2.264 sec) +2022-05-01 20:20:27,343 - tensorflow - INFO - global_step/sec: 88.2963 +2022-05-01 20:20:27,344 - tensorflow - 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loss = 0.035829056, step = 1896600 (2.260 sec) +2022-05-01 20:36:03,624 - tensorflow - INFO - global_step/sec: 88.7635 +2022-05-01 20:36:03,625 - tensorflow - INFO - loss = 0.01608836, step = 1896800 (2.253 sec) +2022-05-01 20:36:05,882 - tensorflow - INFO - global_step/sec: 88.5936 +2022-05-01 20:36:05,882 - tensorflow - INFO - loss = 0.025285728, step = 1897000 (2.258 sec) +2022-05-01 20:36:08,132 - tensorflow - INFO - global_step/sec: 88.8569 +2022-05-01 20:36:08,133 - tensorflow - INFO - loss = 0.00848588, step = 1897200 (2.251 sec) +2022-05-01 20:36:10,387 - tensorflow - INFO - global_step/sec: 88.7181 +2022-05-01 20:36:10,387 - tensorflow - INFO - loss = 0.013154034, step = 1897400 (2.254 sec) +2022-05-01 20:36:12,640 - tensorflow - INFO - global_step/sec: 88.7697 +2022-05-01 20:36:12,640 - tensorflow - INFO - loss = 0.0072760545, step = 1897600 (2.253 sec) +2022-05-01 20:36:14,900 - tensorflow - INFO - global_step/sec: 88.4853 +2022-05-01 20:36:14,901 - tensorflow - INFO - loss = 0.00759117, step = 1897800 (2.260 sec) +2022-05-01 20:36:17,156 - tensorflow - INFO - global_step/sec: 88.667 +2022-05-01 20:36:17,156 - tensorflow - INFO - loss = 0.05357273, step = 1898000 (2.256 sec) +2022-05-01 20:36:19,405 - tensorflow - INFO - global_step/sec: 88.9256 +2022-05-01 20:36:19,405 - tensorflow - INFO - loss = 0.013738202, step = 1898200 (2.249 sec) +2022-05-01 20:36:21,661 - tensorflow - INFO - global_step/sec: 88.6619 +2022-05-01 20:36:21,661 - tensorflow - INFO - loss = 0.6070651, step = 1898400 (2.256 sec) +2022-05-01 20:36:23,918 - tensorflow - INFO - global_step/sec: 88.5968 +2022-05-01 20:36:23,918 - tensorflow - INFO - loss = 0.014128826, step = 1898600 (2.257 sec) +2022-05-01 20:36:26,177 - tensorflow - INFO - global_step/sec: 88.521 +2022-05-01 20:36:26,178 - tensorflow - INFO - loss = 0.0088235345, step = 1898800 (2.259 sec) +2022-05-01 20:36:28,436 - tensorflow - INFO - global_step/sec: 88.5464 +2022-05-01 20:36:28,436 - tensorflow - INFO - loss = 0.13586804, step = 1899000 (2.259 sec) +2022-05-01 20:36:30,696 - tensorflow - INFO - global_step/sec: 88.5006 +2022-05-01 20:36:30,696 - tensorflow - INFO - loss = 0.010024523, step = 1899200 (2.260 sec) +2022-05-01 20:36:32,957 - tensorflow - INFO - global_step/sec: 88.4388 +2022-05-01 20:36:32,958 - tensorflow - INFO - loss = 0.011715906, step = 1899400 (2.261 sec) +2022-05-01 20:36:35,209 - tensorflow - INFO - global_step/sec: 88.8165 +2022-05-01 20:36:35,210 - tensorflow - INFO - loss = 0.030111665, step = 1899600 (2.252 sec) +2022-05-01 20:36:37,466 - tensorflow - INFO - global_step/sec: 88.6293 +2022-05-01 20:36:37,466 - tensorflow - INFO - loss = 0.03254773, step = 1899800 (2.257 sec) +2022-05-01 20:36:39,716 - tensorflow - INFO - Saving checkpoints for 1900000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. +2022-05-01 20:36:39,731 - tensorflow - INFO - model.ckpt-1900000 is not in all_model_checkpoint_paths. Manually adding it. +2022-05-01 20:36:40,255 - tensorflow - INFO - Calling model_fn. +2022-05-01 20:36:40,383 - tensorflow - INFO - Done calling model_fn. +2022-05-01 20:36:40,398 - tensorflow - INFO - Starting evaluation at 2022-05-01T20:36:40Z +2022-05-01 20:36:40,461 - tensorflow - INFO - Graph was finalized. +2022-05-01 20:36:40,465 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1900000 +2022-05-01 20:36:40,746 - tensorflow - INFO - Running local_init_op. +2022-05-01 20:36:40,853 - tensorflow - INFO - Done running local_init_op. +2022-05-01 20:36:50,996 - tensorflow - INFO - Evaluation [3990/39900] +2022-05-01 20:37:00,456 - tensorflow - INFO - Evaluation [7980/39900] +2022-05-01 20:37:09,895 - tensorflow - INFO - Evaluation [11970/39900] +2022-05-01 20:37:19,351 - tensorflow - INFO - Evaluation [15960/39900] +2022-05-01 20:37:28,792 - tensorflow - INFO - Evaluation [19950/39900] +2022-05-01 20:37:38,253 - tensorflow - INFO - Evaluation [23940/39900] +2022-05-01 20:37:47,688 - tensorflow - INFO - Evaluation [27930/39900] +2022-05-01 20:37:57,152 - tensorflow - INFO - Evaluation [31920/39900] +2022-05-01 20:38:06,602 - tensorflow - INFO - Evaluation [35910/39900] +2022-05-01 20:38:16,082 - tensorflow - INFO - Evaluation [39900/39900] +2022-05-01 20:38:16,222 - tensorflow - INFO - Finished evaluation at 2022-05-01-20:38:16 +2022-05-01 20:38:16,222 - tensorflow - INFO - Saving dict for global step 1900000: global_step = 1900000, loss = 0.021781396, mean_degree_err = 3.919044 +2022-05-01 20:38:16,223 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 1900000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1900000 +2022-05-01 20:38:16,236 - tensorflow - INFO - global_step/sec: 2.0249 +2022-05-01 20:38:16,237 - tensorflow - INFO - loss = 0.010318757, step = 1900000 (98.771 sec) +2022-05-01 20:38:18,518 - tensorflow - INFO - global_step/sec: 87.665 +2022-05-01 20:38:18,518 - tensorflow - INFO - loss = 0.05919086, step = 1900200 (2.281 sec) +2022-05-01 20:38:20,775 - tensorflow - INFO - global_step/sec: 88.6049 +2022-05-01 20:38:20,775 - tensorflow - INFO - loss = 0.0043990593, step = 1900400 (2.257 sec) +2022-05-01 20:38:23,037 - tensorflow - INFO - global_step/sec: 88.411 +2022-05-01 20:38:23,038 - tensorflow - INFO - loss = 0.014942108, step = 1900600 (2.262 sec) +2022-05-01 20:38:25,292 - tensorflow - INFO - global_step/sec: 88.6773 +2022-05-01 20:38:25,293 - tensorflow - INFO - loss = 0.018904645, step = 1900800 (2.255 sec) +2022-05-01 20:38:27,548 - tensorflow - INFO - global_step/sec: 88.656 +2022-05-01 20:38:27,549 - tensorflow - INFO - loss = 0.014355497, step = 1901000 (2.256 sec) +2022-05-01 20:38:29,801 - tensorflow - INFO - global_step/sec: 88.7702 +2022-05-01 20:38:29,802 - tensorflow - INFO - loss = 0.022026083, step = 1901200 (2.253 sec) +2022-05-01 20:38:32,061 - tensorflow - INFO - global_step/sec: 88.511 +2022-05-01 20:38:32,061 - tensorflow - INFO - loss = 0.026987147, step = 1901400 (2.260 sec) +2022-05-01 20:38:34,317 - tensorflow - INFO - global_step/sec: 88.64 +2022-05-01 20:38:34,318 - tensorflow - INFO - loss = 0.016082386, step = 1901600 (2.256 sec) +2022-05-01 20:38:36,573 - tensorflow - INFO - global_step/sec: 88.6813 +2022-05-01 20:38:36,573 - tensorflow - INFO - loss = 0.021116573, step = 1901800 (2.255 sec) +2022-05-01 20:38:38,827 - tensorflow - INFO - global_step/sec: 88.7107 +2022-05-01 20:38:38,828 - tensorflow - INFO - loss = 0.17873903, step = 1902000 (2.255 sec) +2022-05-01 20:38:41,079 - tensorflow - INFO - global_step/sec: 88.8149 +2022-05-01 20:38:41,079 - tensorflow - INFO - loss = 0.010280648, step = 1902200 (2.252 sec) +2022-05-01 20:38:43,339 - tensorflow - INFO - global_step/sec: 88.4946 +2022-05-01 20:38:43,339 - tensorflow - INFO - loss = 0.007372737, step = 1902400 (2.260 sec) +2022-05-01 20:38:45,596 - tensorflow - INFO - global_step/sec: 88.6306 +2022-05-01 20:38:45,596 - tensorflow - INFO - loss = 0.069900736, step = 1902600 (2.257 sec) +2022-05-01 20:38:47,853 - tensorflow - INFO - global_step/sec: 88.5961 +2022-05-01 20:38:47,853 - tensorflow - INFO - loss = 0.025056195, step = 1902800 (2.257 sec) +2022-05-01 20:38:50,112 - tensorflow - INFO - global_step/sec: 88.5546 +2022-05-01 20:38:50,112 - tensorflow - INFO - loss = 0.09052631, step = 1903000 (2.258 sec) +2022-05-01 20:38:52,368 - tensorflow - INFO - global_step/sec: 88.614 +2022-05-01 20:38:52,369 - tensorflow - INFO - loss = 0.006703171, step = 1903200 (2.257 sec) +2022-05-01 20:38:54,621 - tensorflow - INFO - global_step/sec: 88.7818 +2022-05-01 20:38:54,622 - tensorflow - INFO - loss = 1.4233687, step = 1903400 (2.253 sec) +2022-05-01 20:38:56,877 - tensorflow - INFO - global_step/sec: 88.6577 +2022-05-01 20:38:56,877 - tensorflow - INFO - loss = 0.008456467, step = 1903600 (2.256 sec) +2022-05-01 20:38:59,128 - tensorflow - INFO - global_step/sec: 88.8706 +2022-05-01 20:38:59,128 - tensorflow - INFO - loss = 0.0235586, step = 1903800 (2.250 sec) +2022-05-01 20:39:01,390 - tensorflow - INFO - global_step/sec: 88.3861 +2022-05-01 20:39:01,391 - tensorflow - INFO - loss = 0.028131448, step = 1904000 (2.263 sec) +2022-05-01 20:39:03,646 - tensorflow - INFO - global_step/sec: 88.6654 +2022-05-01 20:39:03,646 - tensorflow - INFO - loss = 0.3278071, step = 1904200 (2.256 sec) +2022-05-01 20:39:05,902 - tensorflow - INFO - global_step/sec: 88.6647 +2022-05-01 20:39:05,902 - tensorflow - INFO - loss = 0.061649084, step = 1904400 (2.256 sec) +2022-05-01 20:39:08,156 - tensorflow - INFO - global_step/sec: 88.7094 +2022-05-01 20:39:08,157 - tensorflow - INFO - loss = 0.011733083, step = 1904600 (2.255 sec) +2022-05-01 20:39:10,409 - tensorflow - INFO - global_step/sec: 88.7938 +2022-05-01 20:39:10,409 - tensorflow - INFO - loss = 0.09543198, step = 1904800 (2.252 sec) +2022-05-01 20:39:12,669 - tensorflow - INFO - global_step/sec: 88.4762 +2022-05-01 20:39:12,670 - tensorflow - INFO - loss = 0.011331211, step = 1905000 (2.261 sec) +2022-05-01 20:39:14,932 - tensorflow - INFO - global_step/sec: 88.3948 +2022-05-01 20:39:14,932 - tensorflow - INFO - loss = 0.14373742, step = 1905200 (2.262 sec) +2022-05-01 20:39:17,182 - tensorflow - INFO - global_step/sec: 88.8763 +2022-05-01 20:39:17,182 - tensorflow - INFO - loss = 0.059360716, step = 1905400 (2.250 sec) +2022-05-01 20:39:19,435 - tensorflow - INFO - global_step/sec: 88.7831 +2022-05-01 20:39:19,435 - tensorflow - INFO - loss = 0.010062706, step = 1905600 (2.253 sec) +2022-05-01 20:39:21,692 - tensorflow - INFO - global_step/sec: 88.6162 +2022-05-01 20:39:21,692 - tensorflow - INFO - loss = 0.032864153, step = 1905800 (2.257 sec) +2022-05-01 20:39:23,948 - tensorflow - INFO - global_step/sec: 88.6232 +2022-05-01 20:39:23,949 - tensorflow - INFO - loss = 0.003971881, step = 1906000 (2.257 sec) +2022-05-01 20:39:26,206 - tensorflow - INFO - global_step/sec: 88.6007 +2022-05-01 20:39:26,206 - tensorflow - INFO - loss = 0.04123966, step = 1906200 (2.257 sec) +2022-05-01 20:39:28,463 - tensorflow - INFO - global_step/sec: 88.5905 +2022-05-01 20:39:28,464 - tensorflow - INFO - loss = 0.014023264, step = 1906400 (2.258 sec) +2022-05-01 20:39:30,727 - tensorflow - INFO - global_step/sec: 88.3625 +2022-05-01 20:39:30,727 - tensorflow - INFO - loss = 0.019056926, step = 1906600 (2.263 sec) +2022-05-01 20:39:32,987 - tensorflow - INFO - global_step/sec: 88.4871 +2022-05-01 20:39:32,987 - tensorflow - INFO - loss = 0.03874336, step = 1906800 (2.260 sec) +2022-05-01 20:39:35,239 - tensorflow - INFO - global_step/sec: 88.7957 +2022-05-01 20:39:35,240 - tensorflow - INFO - loss = 0.011484627, step = 1907000 (2.252 sec) +2022-05-01 20:39:37,493 - tensorflow - INFO - global_step/sec: 88.7349 +2022-05-01 20:39:37,494 - tensorflow - INFO - loss = 0.40345308, step = 1907200 (2.254 sec) +2022-05-01 20:39:39,751 - tensorflow - INFO - global_step/sec: 88.5682 +2022-05-01 20:39:39,752 - tensorflow - INFO - loss = 0.2545933, step = 1907400 (2.258 sec) +2022-05-01 20:39:42,002 - tensorflow - INFO - global_step/sec: 88.8538 +2022-05-01 20:39:42,003 - tensorflow - INFO - loss = 0.030244552, step = 1907600 (2.251 sec) +2022-05-01 20:39:44,256 - tensorflow - INFO - global_step/sec: 88.752 +2022-05-01 20:39:44,256 - tensorflow - INFO - loss = 0.07354349, step = 1907800 (2.253 sec) +2022-05-01 20:39:46,511 - tensorflow - INFO - global_step/sec: 88.6774 +2022-05-01 20:39:46,511 - tensorflow - INFO - loss = 0.19981001, step = 1908000 (2.255 sec) +2022-05-01 20:39:48,768 - tensorflow - INFO - global_step/sec: 88.6124 +2022-05-01 20:39:48,768 - tensorflow - INFO - loss = 0.01842366, step = 1908200 (2.257 sec) +2022-05-01 20:39:51,025 - tensorflow - INFO - global_step/sec: 88.61 +2022-05-01 20:39:51,026 - tensorflow - INFO - loss = 0.029061416, step = 1908400 (2.257 sec) +2022-05-01 20:39:53,274 - tensorflow - INFO - global_step/sec: 88.9391 +2022-05-01 20:39:53,274 - tensorflow - INFO - loss = 0.49992847, step = 1908600 (2.249 sec) +2022-05-01 20:39:55,530 - tensorflow - INFO - global_step/sec: 88.6647 +2022-05-01 20:39:55,530 - tensorflow - INFO - loss = 0.0052447515, step = 1908800 (2.256 sec) +2022-05-01 20:39:57,782 - tensorflow - INFO - global_step/sec: 88.7969 +2022-05-01 20:39:57,782 - tensorflow - INFO - loss = 0.05780475, step = 1909000 (2.252 sec) +2022-05-01 20:40:00,037 - tensorflow - INFO - global_step/sec: 88.701 +2022-05-01 20:40:00,037 - tensorflow - INFO - loss = 0.014669808, step = 1909200 (2.255 sec) +2022-05-01 20:40:02,288 - tensorflow - INFO - global_step/sec: 88.83 +2022-05-01 20:40:02,289 - tensorflow - INFO - loss = 0.021751998, step = 1909400 (2.251 sec) +2022-05-01 20:40:04,547 - tensorflow - INFO - global_step/sec: 88.5593 +2022-05-01 20:40:04,547 - tensorflow - INFO - loss = 0.09459678, step = 1909600 (2.258 sec) +2022-05-01 20:40:06,803 - tensorflow - INFO - global_step/sec: 88.6219 +2022-05-01 20:40:06,804 - tensorflow - INFO - loss = 0.09258586, step = 1909800 (2.257 sec) +2022-05-01 20:40:09,053 - tensorflow - INFO - global_step/sec: 88.8807 +2022-05-01 20:40:09,054 - tensorflow - INFO - loss = 0.039172817, step = 1910000 (2.250 sec) +2022-05-01 20:40:11,304 - tensorflow - INFO - global_step/sec: 88.879 +2022-05-01 20:40:11,304 - tensorflow - INFO - loss = 0.0074850977, step = 1910200 (2.250 sec) +2022-05-01 20:40:13,556 - tensorflow - INFO - global_step/sec: 88.7955 +2022-05-01 20:40:13,557 - tensorflow - INFO - loss = 0.048138943, step = 1910400 (2.252 sec) +2022-05-01 20:40:15,812 - tensorflow - INFO - global_step/sec: 88.6453 +2022-05-01 20:40:15,813 - tensorflow - INFO - loss = 0.025071843, step = 1910600 (2.256 sec) +2022-05-01 20:40:18,067 - tensorflow - INFO - global_step/sec: 88.6936 +2022-05-01 20:40:18,068 - tensorflow - INFO - loss = 0.021888126, step = 1910800 (2.255 sec) +2022-05-01 20:40:20,321 - tensorflow - INFO - global_step/sec: 88.7523 +2022-05-01 20:40:20,321 - tensorflow - INFO - loss = 0.022394136, step = 1911000 (2.253 sec) +2022-05-01 20:40:22,574 - tensorflow - INFO - global_step/sec: 88.7484 +2022-05-01 20:40:22,575 - tensorflow - INFO - loss = 0.011828977, step = 1911200 (2.254 sec) +2022-05-01 20:40:24,823 - tensorflow - INFO - global_step/sec: 88.9248 +2022-05-01 20:40:24,824 - tensorflow - INFO - loss = 0.02373368, step = 1911400 (2.249 sec) +2022-05-01 20:40:27,075 - tensorflow - INFO - global_step/sec: 88.8402 +2022-05-01 20:40:27,075 - tensorflow - INFO - loss = 0.035408095, step = 1911600 (2.251 sec) +2022-05-01 20:40:29,330 - tensorflow - INFO - global_step/sec: 88.6673 +2022-05-01 20:40:29,331 - tensorflow - INFO - loss = 0.016631411, step = 1911800 (2.256 sec) +2022-05-01 20:40:31,587 - tensorflow - INFO - global_step/sec: 88.64 +2022-05-01 20:40:31,587 - tensorflow - INFO - loss = 0.0070046624, step = 1912000 (2.256 sec) +2022-05-01 20:40:33,841 - tensorflow - INFO - global_step/sec: 88.7216 +2022-05-01 20:40:33,841 - tensorflow - INFO - loss = 0.06245377, step = 1912200 (2.254 sec) +2022-05-01 20:40:36,094 - tensorflow - INFO - global_step/sec: 88.7758 +2022-05-01 20:40:36,094 - tensorflow - INFO - loss = 0.013396024, step = 1912400 (2.253 sec) +2022-05-01 20:40:38,353 - tensorflow - INFO - 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INFO - loss = 0.0357652, step = 1999000 (2.257 sec) +2022-05-01 20:56:54,994 - tensorflow - INFO - global_step/sec: 88.7291 +2022-05-01 20:56:54,995 - tensorflow - INFO - loss = 0.07935603, step = 1999200 (2.254 sec) +2022-05-01 20:56:57,256 - tensorflow - INFO - global_step/sec: 88.4201 +2022-05-01 20:56:57,257 - tensorflow - INFO - loss = 0.012287976, step = 1999400 (2.262 sec) +2022-05-01 20:56:59,509 - tensorflow - INFO - global_step/sec: 88.7909 +2022-05-01 20:56:59,509 - tensorflow - INFO - loss = 0.0061325766, step = 1999600 (2.252 sec) +2022-05-01 20:57:01,759 - tensorflow - INFO - global_step/sec: 88.8596 +2022-05-01 20:57:01,760 - tensorflow - INFO - loss = 0.0115417605, step = 1999800 (2.251 sec) +2022-05-01 20:57:04,005 - tensorflow - INFO - Saving checkpoints for 2000000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. +2022-05-01 20:57:04,023 - tensorflow - INFO - model.ckpt-2000000 is not in all_model_checkpoint_paths. Manually adding it. +2022-05-01 20:57:04,551 - tensorflow - INFO - Calling model_fn. +2022-05-01 20:57:04,678 - tensorflow - INFO - Done calling model_fn. +2022-05-01 20:57:04,693 - tensorflow - INFO - Starting evaluation at 2022-05-01T20:57:04Z +2022-05-01 20:57:05,038 - tensorflow - INFO - Graph was finalized. +2022-05-01 20:57:05,041 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-2000000 +2022-05-01 20:57:05,323 - tensorflow - INFO - Running local_init_op. +2022-05-01 20:57:05,432 - tensorflow - INFO - Done running local_init_op. +2022-05-01 20:57:15,574 - tensorflow - INFO - Evaluation [3990/39900] +2022-05-01 20:57:25,021 - tensorflow - INFO - Evaluation [7980/39900] +2022-05-01 20:57:34,462 - tensorflow - INFO - Evaluation [11970/39900] +2022-05-01 20:57:43,894 - tensorflow - INFO - Evaluation [15960/39900] +2022-05-01 20:57:53,335 - tensorflow - INFO - Evaluation [19950/39900] +2022-05-01 20:58:02,783 - tensorflow - INFO - Evaluation [23940/39900] +2022-05-01 20:58:12,227 - tensorflow - INFO - Evaluation [27930/39900] +2022-05-01 20:58:21,686 - tensorflow - INFO - Evaluation [31920/39900] +2022-05-01 20:58:31,130 - tensorflow - INFO - Evaluation [35910/39900] +2022-05-01 20:58:40,568 - tensorflow - INFO - Evaluation [39900/39900] +2022-05-01 20:58:40,705 - tensorflow - INFO - Finished evaluation at 2022-05-01-20:58:40 +2022-05-01 20:58:40,706 - tensorflow - INFO - Saving dict for global step 2000000: global_step = 2000000, loss = 0.015859876, mean_degree_err = 3.1788845 +2022-05-01 20:58:40,706 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 2000000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-2000000 +2022-05-01 20:58:40,720 - tensorflow - INFO - global_step/sec: 2.02101 +2022-05-01 20:58:40,721 - tensorflow - INFO - loss = 0.01467032, step = 2000000 (98.961 sec) +2022-05-01 20:58:42,981 - tensorflow - INFO - global_step/sec: 88.4545 +2022-05-01 20:58:42,982 - tensorflow - INFO - loss = 0.013274206, step = 2000200 (2.261 sec) +2022-05-01 20:58:45,239 - tensorflow - INFO - global_step/sec: 88.558 +2022-05-01 20:58:45,240 - tensorflow - INFO - loss = 0.026724704, step = 2000400 (2.258 sec) +2022-05-01 20:58:47,492 - tensorflow - INFO - global_step/sec: 88.7754 +2022-05-01 20:58:47,493 - tensorflow - INFO - loss = 0.007345394, step = 2000600 (2.253 sec) +2022-05-01 20:58:49,751 - tensorflow - INFO - global_step/sec: 88.5343 +2022-05-01 20:58:49,752 - tensorflow - INFO - loss = 0.008276798, step = 2000800 (2.259 sec) +2022-05-01 20:58:52,005 - tensorflow - INFO - global_step/sec: 88.7607 +2022-05-01 20:58:52,005 - tensorflow - INFO - loss = 0.037647825, step = 2001000 (2.253 sec) +2022-05-01 20:58:54,264 - tensorflow - INFO - global_step/sec: 88.5243 +2022-05-01 20:58:54,264 - tensorflow - INFO - loss = 0.014356632, step = 2001200 (2.259 sec) +2022-05-01 20:58:56,527 - tensorflow - INFO - global_step/sec: 88.3761 +2022-05-01 20:58:56,527 - tensorflow - INFO - loss = 0.018045077, step = 2001400 (2.263 sec) +2022-05-01 20:58:58,788 - tensorflow - INFO - global_step/sec: 88.4435 +2022-05-01 20:58:58,789 - tensorflow - INFO - loss = 0.25831372, step = 2001600 (2.261 sec) +2022-05-01 20:59:01,052 - tensorflow - INFO - global_step/sec: 88.3513 +2022-05-01 20:59:01,052 - tensorflow - INFO - loss = 0.0193472, step = 2001800 (2.264 sec) +2022-05-01 20:59:03,311 - tensorflow - INFO - global_step/sec: 88.5515 +2022-05-01 20:59:03,311 - tensorflow - INFO - loss = 0.039867893, step = 2002000 (2.259 sec) +2022-05-01 20:59:05,575 - tensorflow - INFO - global_step/sec: 88.3249 +2022-05-01 20:59:05,575 - tensorflow - INFO - loss = 0.021221787, step = 2002200 (2.264 sec) +2022-05-01 20:59:07,836 - tensorflow - INFO - global_step/sec: 88.4694 +2022-05-01 20:59:07,836 - tensorflow - INFO - loss = 0.04830868, step = 2002400 (2.261 sec) +2022-05-01 20:59:10,096 - tensorflow - INFO - global_step/sec: 88.4924 +2022-05-01 20:59:10,096 - tensorflow - INFO - loss = 1.0361093, step = 2002600 (2.260 sec) +2022-05-01 20:59:12,354 - tensorflow - INFO - global_step/sec: 88.5539 +2022-05-01 20:59:12,355 - tensorflow - INFO - loss = 0.018668309, step = 2002800 (2.259 sec) +2022-05-01 20:59:14,616 - tensorflow - INFO - global_step/sec: 88.4391 +2022-05-01 20:59:14,616 - tensorflow - INFO - loss = 0.014582523, step = 2003000 (2.261 sec) +2022-05-01 20:59:16,875 - tensorflow - INFO - global_step/sec: 88.5287 +2022-05-01 20:59:16,875 - tensorflow - INFO - loss = 0.34214815, step = 2003200 (2.259 sec) +2022-05-01 20:59:19,141 - tensorflow - INFO - global_step/sec: 88.2529 +2022-05-01 20:59:19,141 - tensorflow - INFO - loss = 0.027201526, step = 2003400 (2.266 sec) +2022-05-01 20:59:21,399 - tensorflow - INFO - global_step/sec: 88.5729 +2022-05-01 20:59:21,399 - tensorflow - INFO - loss = 0.017191805, step = 2003600 (2.258 sec) +2022-05-01 20:59:23,659 - tensorflow - INFO - global_step/sec: 88.5133 +2022-05-01 20:59:23,659 - tensorflow - INFO - loss = 0.040490415, step = 2003800 (2.260 sec) +2022-05-01 20:59:25,911 - tensorflow - INFO - global_step/sec: 88.8008 +2022-05-01 20:59:25,911 - tensorflow - INFO - loss = 0.019516265, step = 2004000 (2.252 sec) +2022-05-01 20:59:28,164 - tensorflow - INFO - global_step/sec: 88.7503 +2022-05-01 20:59:28,165 - tensorflow - INFO - loss = 0.025298692, step = 2004200 (2.254 sec) +2022-05-01 20:59:30,427 - tensorflow - INFO - global_step/sec: 88.3838 +2022-05-01 20:59:30,428 - tensorflow - INFO - loss = 0.02187489, step = 2004400 (2.263 sec) +2022-05-01 20:59:32,683 - tensorflow - INFO - global_step/sec: 88.6602 +2022-05-01 20:59:32,683 - tensorflow - INFO - loss = 0.008246376, step = 2004600 (2.256 sec) +2022-05-01 20:59:34,936 - tensorflow - INFO - global_step/sec: 88.7644 +2022-05-01 20:59:34,936 - tensorflow - INFO - loss = 0.00793772, step = 2004800 (2.253 sec) +2022-05-01 20:59:37,186 - tensorflow - INFO - global_step/sec: 88.9049 +2022-05-01 20:59:37,186 - tensorflow - INFO - loss = 0.009742752, step = 2005000 (2.250 sec) +2022-05-01 20:59:39,439 - tensorflow - INFO - global_step/sec: 88.7513 +2022-05-01 20:59:39,440 - tensorflow - INFO - loss = 0.019376889, step = 2005200 (2.253 sec) +2022-05-01 20:59:41,694 - tensorflow - INFO - global_step/sec: 88.6935 +2022-05-01 20:59:41,695 - tensorflow - INFO - loss = 0.0048106383, step = 2005400 (2.255 sec) +2022-05-01 20:59:43,958 - tensorflow - INFO - global_step/sec: 88.3361 +2022-05-01 20:59:43,959 - tensorflow - INFO - loss = 0.13213453, step = 2005600 (2.264 sec) +2022-05-01 20:59:46,211 - tensorflow - INFO - global_step/sec: 88.7763 +2022-05-01 20:59:46,211 - tensorflow - INFO - loss = 0.050713982, step = 2005800 (2.253 sec) +2022-05-01 20:59:48,461 - tensorflow - INFO - global_step/sec: 88.8766 +2022-05-01 20:59:48,462 - tensorflow - INFO - loss = 0.027069667, step = 2006000 (2.250 sec) +2022-05-01 20:59:50,712 - tensorflow - INFO - global_step/sec: 88.8684 +2022-05-01 20:59:50,712 - tensorflow - INFO - loss = 0.2192508, step = 2006200 (2.251 sec) +2022-05-01 20:59:52,968 - tensorflow - INFO - global_step/sec: 88.629 +2022-05-01 20:59:52,969 - tensorflow - INFO - loss = 0.02215605, step = 2006400 (2.257 sec) +2022-05-01 20:59:55,224 - tensorflow - INFO - global_step/sec: 88.688 +2022-05-01 20:59:55,224 - tensorflow - INFO - loss = 0.015829494, step = 2006600 (2.255 sec) +2022-05-01 20:59:57,483 - tensorflow - INFO - global_step/sec: 88.5101 +2022-05-01 20:59:57,484 - tensorflow - INFO - loss = 0.020551346, step = 2006800 (2.260 sec) +2022-05-01 20:59:59,737 - tensorflow - INFO - global_step/sec: 88.7463 +2022-05-01 20:59:59,737 - tensorflow - INFO - loss = 2.006907, step = 2007000 (2.254 sec) +2022-05-01 21:00:01,992 - tensorflow - INFO - global_step/sec: 88.6747 +2022-05-01 21:00:01,993 - tensorflow - INFO - loss = 0.009550948, step = 2007200 (2.255 sec) +2022-05-01 21:00:04,245 - tensorflow - INFO - global_step/sec: 88.7846 +2022-05-01 21:00:04,245 - tensorflow - INFO - loss = 0.03207432, step = 2007400 (2.253 sec) +2022-05-01 21:00:06,496 - tensorflow - INFO - global_step/sec: 88.8523 +2022-05-01 21:00:06,496 - tensorflow - INFO - loss = 0.008671386, step = 2007600 (2.251 sec) +2022-05-01 21:00:08,752 - tensorflow - INFO - global_step/sec: 88.6274 +2022-05-01 21:00:08,753 - tensorflow - INFO - loss = 0.081116244, step = 2007800 (2.257 sec) +2022-05-01 21:00:11,003 - tensorflow - INFO - global_step/sec: 88.8742 +2022-05-01 21:00:11,003 - tensorflow - INFO - loss = 0.024560489, step = 2008000 (2.250 sec) +2022-05-01 21:00:13,260 - tensorflow - INFO - global_step/sec: 88.625 +2022-05-01 21:00:13,260 - tensorflow - INFO - loss = 0.054812565, step = 2008200 (2.257 sec) +2022-05-01 21:00:15,514 - tensorflow - INFO - global_step/sec: 88.7045 +2022-05-01 21:00:15,515 - tensorflow - INFO - loss = 0.021981124, step = 2008400 (2.255 sec) +2022-05-01 21:00:17,765 - tensorflow - INFO - global_step/sec: 88.8768 +2022-05-01 21:00:17,765 - tensorflow - INFO - loss = 0.024330994, step = 2008600 (2.250 sec) +2022-05-01 21:00:20,021 - tensorflow - INFO - global_step/sec: 88.6422 +2022-05-01 21:00:20,021 - tensorflow - INFO - loss = 0.121265806, step = 2008800 (2.256 sec) +2022-05-01 21:00:22,279 - tensorflow - INFO - global_step/sec: 88.5661 +2022-05-01 21:00:22,279 - tensorflow - INFO - loss = 0.013724765, step = 2009000 (2.258 sec) +2022-05-01 21:00:24,529 - tensorflow - INFO - global_step/sec: 88.8898 +2022-05-01 21:00:24,529 - tensorflow - INFO - loss = 0.07820247, step = 2009200 (2.250 sec) +2022-05-01 21:00:26,781 - tensorflow - INFO - global_step/sec: 88.8091 +2022-05-01 21:00:26,781 - tensorflow - INFO - loss = 0.069547355, step = 2009400 (2.252 sec) +2022-05-01 21:00:29,039 - tensorflow - INFO - global_step/sec: 88.5597 +2022-05-01 21:00:29,040 - tensorflow - INFO - loss = 0.032171834, step = 2009600 (2.258 sec) +2022-05-01 21:00:31,300 - tensorflow - INFO - global_step/sec: 88.4701 +2022-05-01 21:00:31,300 - tensorflow - INFO - loss = 0.074208364, step = 2009800 (2.261 sec) +2022-05-01 21:00:33,551 - tensorflow - INFO - global_step/sec: 88.8576 +2022-05-01 21:00:33,551 - tensorflow - INFO - loss = 0.014253901, step = 2010000 (2.251 sec) +2022-05-01 21:00:35,801 - tensorflow - INFO - global_step/sec: 88.8754 +2022-05-01 21:00:35,802 - tensorflow - INFO - loss = 0.107017614, step = 2010200 (2.250 sec) +2022-05-01 21:00:38,052 - tensorflow - INFO - global_step/sec: 88.8646 +2022-05-01 21:00:38,052 - tensorflow - INFO - loss = 0.02333238, step = 2010400 (2.251 sec) +2022-05-01 21:00:40,304 - tensorflow - INFO - global_step/sec: 88.8074 +2022-05-01 21:00:40,304 - tensorflow - INFO - loss = 0.013060355, step = 2010600 (2.252 sec) +2022-05-01 21:00:42,550 - tensorflow - INFO - global_step/sec: 89.0439 +2022-05-01 21:00:42,550 - tensorflow - INFO - loss = 0.05624281, step = 2010800 (2.246 sec) +2022-05-01 21:00:44,804 - tensorflow - INFO - global_step/sec: 88.7119 +2022-05-01 21:00:44,805 - tensorflow - INFO - loss = 0.09383938, step = 2011000 (2.254 sec) +2022-05-01 21:00:47,063 - tensorflow - INFO - global_step/sec: 88.5444 +2022-05-01 21:00:47,064 - tensorflow - INFO - loss = 0.026484538, step = 2011200 (2.259 sec) +2022-05-01 21:00:49,314 - tensorflow - INFO - global_step/sec: 88.8738 +2022-05-01 21:00:49,314 - tensorflow - INFO - loss = 0.010374699, step = 2011400 (2.250 sec) +2022-05-01 21:00:51,568 - tensorflow - INFO - global_step/sec: 88.7215 +2022-05-01 21:00:51,568 - tensorflow - INFO - loss = 0.11623212, step = 2011600 (2.254 sec) +2022-05-01 21:00:53,815 - tensorflow - INFO - global_step/sec: 89.0112 +2022-05-01 21:00:53,815 - tensorflow - INFO - loss = 0.0196031, step = 2011800 (2.247 sec) +2022-05-01 21:00:56,068 - tensorflow - INFO - global_step/sec: 88.7529 +2022-05-01 21:00:56,069 - tensorflow - INFO - loss = 0.024542011, step = 2012000 (2.253 sec) +2022-05-01 21:00:58,320 - tensorflow - INFO - global_step/sec: 88.8249 +2022-05-01 21:00:58,320 - tensorflow - INFO - loss = 0.008934006, step = 2012200 (2.252 sec) +2022-05-01 21:01:00,578 - tensorflow - INFO - global_step/sec: 88.5485 +2022-05-01 21:01:00,579 - tensorflow - INFO - loss = 0.014190808, step = 2012400 (2.259 sec) +2022-05-01 21:01:02,830 - tensorflow - INFO - global_step/sec: 88.8435 +2022-05-01 21:01:02,830 - tensorflow - INFO - loss = 0.011069354, step = 2012600 (2.251 sec) +2022-05-01 21:01:05,082 - tensorflow - INFO - global_step/sec: 88.7971 +2022-05-01 21:01:05,082 - tensorflow - INFO - loss = 0.0054627806, step = 2012800 (2.252 sec) +2022-05-01 21:01:07,332 - tensorflow - INFO - global_step/sec: 88.8756 +2022-05-01 21:01:07,333 - tensorflow - INFO - loss = 0.03997647, step = 2013000 (2.250 sec) +2022-05-01 21:01:09,580 - tensorflow - INFO - global_step/sec: 88.9896 +2022-05-01 21:01:09,580 - tensorflow - INFO - loss = 0.07273039, step = 2013200 (2.247 sec) +2022-05-01 21:01:11,836 - tensorflow - INFO - global_step/sec: 88.6371 +2022-05-01 21:01:11,836 - tensorflow - INFO - loss = 0.07716078, step = 2013400 (2.256 sec) +2022-05-01 21:01:14,090 - tensorflow - INFO - global_step/sec: 88.7507 +2022-05-01 21:01:14,090 - tensorflow - INFO - loss = 0.00919323, step = 2013600 (2.253 sec) +2022-05-01 21:01:16,345 - tensorflow - INFO - global_step/sec: 88.6708 +2022-05-01 21:01:16,345 - tensorflow - INFO - loss = 0.006531185, step = 2013800 (2.256 sec) +2022-05-01 21:01:18,593 - tensorflow - INFO - global_step/sec: 88.9757 +2022-05-01 21:01:18,593 - tensorflow - INFO - loss = 0.009378989, step = 2014000 (2.248 sec) +2022-05-01 21:01:20,847 - tensorflow - INFO - global_step/sec: 88.7404 +2022-05-01 21:01:20,847 - tensorflow - INFO - loss = 0.010182643, step = 2014200 (2.254 sec) +2022-05-01 21:01:23,103 - tensorflow - INFO - global_step/sec: 88.6379 +2022-05-01 21:01:23,103 - tensorflow - INFO - loss = 0.03052396, step = 2014400 (2.256 sec) +2022-05-01 21:01:25,362 - tensorflow - INFO - global_step/sec: 88.5363 +2022-05-01 21:01:25,362 - tensorflow - INFO - loss = 0.021367367, step = 2014600 (2.259 sec) +2022-05-01 21:01:27,618 - tensorflow - INFO - global_step/sec: 88.632 +2022-05-01 21:01:27,619 - tensorflow - INFO - loss = 0.03744563, step = 2014800 (2.256 sec) +2022-05-01 21:01:29,875 - tensorflow - INFO - global_step/sec: 88.6486 +2022-05-01 21:01:29,875 - tensorflow - 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INFO - loss = 0.037878785, step = 2096800 (2.258 sec) +2022-05-01 21:16:54,059 - tensorflow - INFO - global_step/sec: 88.5216 +2022-05-01 21:16:54,059 - tensorflow - INFO - loss = 0.12967177, step = 2097000 (2.259 sec) +2022-05-01 21:16:56,321 - tensorflow - INFO - global_step/sec: 88.4241 +2022-05-01 21:16:56,321 - tensorflow - INFO - loss = 0.017660087, step = 2097200 (2.262 sec) +2022-05-01 21:16:58,580 - tensorflow - INFO - global_step/sec: 88.5298 +2022-05-01 21:16:58,580 - tensorflow - INFO - loss = 0.010364544, step = 2097400 (2.259 sec) +2022-05-01 21:17:00,841 - tensorflow - INFO - global_step/sec: 88.4503 +2022-05-01 21:17:00,842 - tensorflow - INFO - loss = 0.01831447, step = 2097600 (2.261 sec) +2022-05-01 21:17:03,098 - tensorflow - INFO - global_step/sec: 88.6275 +2022-05-01 21:17:03,098 - tensorflow - INFO - loss = 0.04949817, step = 2097800 (2.257 sec) +2022-05-01 21:17:05,350 - tensorflow - INFO - global_step/sec: 88.8195 +2022-05-01 21:17:05,350 - tensorflow - INFO - loss = 0.04070931, step = 2098000 (2.252 sec) +2022-05-01 21:17:07,604 - tensorflow - INFO - global_step/sec: 88.7045 +2022-05-01 21:17:07,605 - tensorflow - INFO - loss = 0.035523713, step = 2098200 (2.255 sec) +2022-05-01 21:17:09,859 - tensorflow - INFO - global_step/sec: 88.6824 +2022-05-01 21:17:09,860 - tensorflow - INFO - loss = 0.021236856, step = 2098400 (2.255 sec) +2022-05-01 21:17:12,119 - tensorflow - INFO - global_step/sec: 88.5007 +2022-05-01 21:17:12,120 - tensorflow - INFO - loss = 0.016438108, step = 2098600 (2.260 sec) +2022-05-01 21:17:14,369 - tensorflow - INFO - global_step/sec: 88.8847 +2022-05-01 21:17:14,370 - tensorflow - INFO - loss = 0.00890751, step = 2098800 (2.250 sec) +2022-05-01 21:17:16,623 - tensorflow - INFO - global_step/sec: 88.7402 +2022-05-01 21:17:16,624 - tensorflow - INFO - loss = 0.005814164, step = 2099000 (2.254 sec) +2022-05-01 21:17:18,879 - tensorflow - INFO - global_step/sec: 88.6581 +2022-05-01 21:17:18,879 - tensorflow - INFO - loss = 0.009963096, step = 2099200 (2.256 sec) +2022-05-01 21:17:21,129 - tensorflow - INFO - global_step/sec: 88.8753 +2022-05-01 21:17:21,130 - tensorflow - INFO - loss = 0.008472566, step = 2099400 (2.250 sec) +2022-05-01 21:17:23,383 - tensorflow - INFO - global_step/sec: 88.7272 +2022-05-01 21:17:23,384 - tensorflow - INFO - loss = 0.008451465, step = 2099600 (2.254 sec) +2022-05-01 21:17:25,635 - tensorflow - INFO - global_step/sec: 88.8399 +2022-05-01 21:17:25,635 - tensorflow - INFO - loss = 0.028972732, step = 2099800 (2.251 sec) +2022-05-01 21:17:27,870 - tensorflow - INFO - Saving checkpoints for 2100000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. +2022-05-01 21:17:27,885 - tensorflow - INFO - model.ckpt-2100000 is not in all_model_checkpoint_paths. Manually adding it. +2022-05-01 21:17:28,408 - tensorflow - INFO - Calling model_fn. +2022-05-01 21:17:28,535 - tensorflow - INFO - Done calling model_fn. +2022-05-01 21:17:28,550 - tensorflow - INFO - Starting evaluation at 2022-05-01T21:17:28Z +2022-05-01 21:17:28,618 - tensorflow - INFO - Graph was finalized. +2022-05-01 21:17:28,621 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-2100000 +2022-05-01 21:17:28,903 - tensorflow - INFO - Running local_init_op. +2022-05-01 21:17:29,010 - tensorflow - INFO - Done running local_init_op. +2022-05-01 21:17:39,236 - tensorflow - INFO - Evaluation [3990/39900] +2022-05-01 21:17:48,745 - tensorflow - INFO - Evaluation [7980/39900] +2022-05-01 21:17:58,242 - tensorflow - INFO - Evaluation [11970/39900] +2022-05-01 21:18:07,766 - tensorflow - INFO - Evaluation [15960/39900] +2022-05-01 21:18:17,302 - tensorflow - INFO - Evaluation [19950/39900] +2022-05-01 21:18:26,848 - tensorflow - INFO - Evaluation [23940/39900] +2022-05-01 21:18:36,360 - tensorflow - INFO - Evaluation [27930/39900] +2022-05-01 21:18:45,842 - tensorflow - INFO - Evaluation [31920/39900] +2022-05-01 21:18:55,344 - tensorflow - INFO - Evaluation [35910/39900] +2022-05-01 21:19:04,884 - tensorflow - INFO - Evaluation [39900/39900] +2022-05-01 21:19:05,022 - tensorflow - INFO - Finished evaluation at 2022-05-01-21:19:05 +2022-05-01 21:19:05,022 - tensorflow - INFO - Saving dict for global step 2100000: global_step = 2100000, loss = 0.016458333, mean_degree_err = 3.3069553 +2022-05-01 21:19:05,022 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 2100000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-2100000 +2022-05-01 21:19:05,036 - tensorflow - INFO - global_step/sec: 2.01204 +2022-05-01 21:19:05,037 - tensorflow - INFO - loss = 0.05436354, step = 2100000 (99.402 sec) +2022-05-01 21:19:07,310 - tensorflow - INFO - global_step/sec: 87.9633 +2022-05-01 21:19:07,310 - tensorflow - INFO - loss = 0.038885124, step = 2100200 (2.273 sec) +2022-05-01 21:19:09,564 - tensorflow - INFO - global_step/sec: 88.7438 +2022-05-01 21:19:09,564 - tensorflow - INFO - loss = 0.0110758245, step = 2100400 (2.254 sec) +2022-05-01 21:19:11,816 - tensorflow - INFO - global_step/sec: 88.8034 +2022-05-01 21:19:11,816 - tensorflow - INFO - loss = 0.02910995, step = 2100600 (2.252 sec) +2022-05-01 21:19:14,067 - tensorflow - INFO - global_step/sec: 88.8318 +2022-05-01 21:19:14,068 - tensorflow - INFO - loss = 0.019992223, step = 2100800 (2.251 sec) +2022-05-01 21:19:16,325 - tensorflow - INFO - global_step/sec: 88.5787 +2022-05-01 21:19:16,325 - tensorflow - INFO - loss = 0.010165205, step = 2101000 (2.258 sec) +2022-05-01 21:19:18,576 - tensorflow - INFO - global_step/sec: 88.8618 +2022-05-01 21:19:18,576 - tensorflow - INFO - loss = 0.013112518, step = 2101200 (2.251 sec) +2022-05-01 21:19:20,828 - tensorflow - INFO - global_step/sec: 88.8129 +2022-05-01 21:19:20,828 - tensorflow - INFO - loss = 0.01900612, step = 2101400 (2.252 sec) +2022-05-01 21:19:23,079 - tensorflow - INFO - global_step/sec: 88.8353 +2022-05-01 21:19:23,079 - tensorflow - INFO - loss = 0.05495621, step = 2101600 (2.251 sec) +2022-05-01 21:19:25,329 - tensorflow - INFO - global_step/sec: 88.9022 +2022-05-01 21:19:25,329 - tensorflow - INFO - loss = 0.05240432, step = 2101800 (2.250 sec) +2022-05-01 21:19:27,584 - tensorflow - INFO - global_step/sec: 88.69 +2022-05-01 21:19:27,584 - tensorflow - INFO - loss = 0.0105040055, step = 2102000 (2.255 sec) +2022-05-01 21:19:29,834 - tensorflow - INFO - global_step/sec: 88.86 +2022-05-01 21:19:29,835 - tensorflow - INFO - loss = 0.070999, step = 2102200 (2.251 sec) +2022-05-01 21:19:32,095 - tensorflow - INFO - global_step/sec: 88.4786 +2022-05-01 21:19:32,095 - tensorflow - INFO - loss = 0.016450152, step = 2102400 (2.260 sec) +2022-05-01 21:19:34,353 - tensorflow - INFO - global_step/sec: 88.5747 +2022-05-01 21:19:34,353 - tensorflow - INFO - loss = 0.009405328, step = 2102600 (2.258 sec) +2022-05-01 21:19:36,604 - tensorflow - INFO - global_step/sec: 88.8532 +2022-05-01 21:19:36,604 - tensorflow - INFO - loss = 0.09677004, step = 2102800 (2.251 sec) +2022-05-01 21:19:38,856 - tensorflow - INFO - global_step/sec: 88.8127 +2022-05-01 21:19:38,856 - tensorflow - INFO - loss = 0.010431, step = 2103000 (2.252 sec) +2022-05-01 21:19:41,114 - tensorflow - INFO - global_step/sec: 88.5512 +2022-05-01 21:19:41,115 - tensorflow - INFO - loss = 0.009252382, step = 2103200 (2.259 sec) +2022-05-01 21:19:43,362 - tensorflow - INFO - global_step/sec: 88.9678 +2022-05-01 21:19:43,363 - tensorflow - INFO - loss = 0.010826778, step = 2103400 (2.248 sec) +2022-05-01 21:19:45,612 - tensorflow - INFO - global_step/sec: 88.8826 +2022-05-01 21:19:45,613 - tensorflow - INFO - loss = 0.042679265, step = 2103600 (2.250 sec) +2022-05-01 21:19:47,860 - tensorflow - INFO - global_step/sec: 88.9817 +2022-05-01 21:19:47,860 - tensorflow - INFO - loss = 0.00809348, step = 2103800 (2.248 sec) +2022-05-01 21:19:50,115 - tensorflow - INFO - global_step/sec: 88.6784 +2022-05-01 21:19:50,116 - tensorflow - INFO - loss = 0.0068497146, step = 2104000 (2.255 sec) +2022-05-01 21:19:52,372 - tensorflow - INFO - global_step/sec: 88.617 +2022-05-01 21:19:52,373 - tensorflow - INFO - loss = 0.010443541, step = 2104200 (2.257 sec) +2022-05-01 21:19:54,626 - tensorflow - INFO - global_step/sec: 88.7574 +2022-05-01 21:19:54,626 - tensorflow - INFO - loss = 0.02146333, step = 2104400 (2.253 sec) +2022-05-01 21:19:56,883 - tensorflow - INFO - global_step/sec: 88.6164 +2022-05-01 21:19:56,883 - tensorflow - INFO - loss = 0.006963673, step = 2104600 (2.257 sec) +2022-05-01 21:19:59,136 - tensorflow - INFO - global_step/sec: 88.7392 +2022-05-01 21:19:59,137 - tensorflow - INFO - loss = 0.05600545, step = 2104800 (2.254 sec) +2022-05-01 21:20:01,392 - tensorflow - INFO - global_step/sec: 88.6644 +2022-05-01 21:20:01,392 - tensorflow - INFO - loss = 0.07515754, step = 2105000 (2.256 sec) +2022-05-01 21:20:03,651 - tensorflow - INFO - global_step/sec: 88.5435 +2022-05-01 21:20:03,651 - tensorflow - INFO - loss = 0.010558774, step = 2105200 (2.259 sec) +2022-05-01 21:20:05,915 - tensorflow - INFO - global_step/sec: 88.3282 +2022-05-01 21:20:05,916 - tensorflow - INFO - loss = 0.014063058, step = 2105400 (2.264 sec) +2022-05-01 21:20:08,178 - tensorflow - INFO - global_step/sec: 88.3788 +2022-05-01 21:20:08,179 - tensorflow - INFO - loss = 0.037848994, step = 2105600 (2.263 sec) +2022-05-01 21:20:10,439 - tensorflow - INFO - global_step/sec: 88.4571 +2022-05-01 21:20:10,439 - tensorflow - INFO - loss = 0.009343641, step = 2105800 (2.261 sec) +2022-05-01 21:20:12,700 - tensorflow - INFO - global_step/sec: 88.4639 +2022-05-01 21:20:12,700 - tensorflow - INFO - loss = 0.020438509, step = 2106000 (2.261 sec) +2022-05-01 21:20:14,959 - tensorflow - INFO - global_step/sec: 88.5247 +2022-05-01 21:20:14,960 - tensorflow - INFO - loss = 0.0067908484, step = 2106200 (2.259 sec) +2022-05-01 21:20:17,222 - tensorflow - INFO - global_step/sec: 88.3955 +2022-05-01 21:20:17,222 - tensorflow - INFO - loss = 0.10189227, step = 2106400 (2.263 sec) +2022-05-01 21:20:19,477 - tensorflow - INFO - global_step/sec: 88.6961 +2022-05-01 21:20:19,477 - tensorflow - INFO - loss = 0.029760987, step = 2106600 (2.255 sec) +2022-05-01 21:20:21,742 - tensorflow - INFO - global_step/sec: 88.2739 +2022-05-01 21:20:21,743 - tensorflow - INFO - loss = 0.034703635, step = 2106800 (2.266 sec) +2022-05-01 21:20:24,005 - tensorflow - INFO - global_step/sec: 88.3958 +2022-05-01 21:20:24,005 - tensorflow - INFO - loss = 0.017966729, step = 2107000 (2.263 sec) +2022-05-01 21:20:26,265 - tensorflow - INFO - global_step/sec: 88.4732 +2022-05-01 21:20:26,266 - tensorflow - INFO - loss = 0.019259527, step = 2107200 (2.261 sec) +2022-05-01 21:20:28,528 - tensorflow - INFO - global_step/sec: 88.4109 +2022-05-01 21:20:28,528 - tensorflow - INFO - loss = 0.010687232, step = 2107400 (2.262 sec) +2022-05-01 21:20:30,790 - tensorflow - INFO - global_step/sec: 88.4049 +2022-05-01 21:20:30,790 - tensorflow - INFO - loss = 0.03140063, step = 2107600 (2.262 sec) +2022-05-01 21:20:33,050 - tensorflow - INFO - global_step/sec: 88.4724 +2022-05-01 21:20:33,051 - tensorflow - INFO - loss = 0.0077216662, step = 2107800 (2.261 sec) +2022-05-01 21:20:35,303 - tensorflow - INFO - global_step/sec: 88.7959 +2022-05-01 21:20:35,303 - tensorflow - INFO - loss = 0.058895633, step = 2108000 (2.252 sec) +2022-05-01 21:20:37,555 - tensorflow - INFO - global_step/sec: 88.79 +2022-05-01 21:20:37,556 - tensorflow - INFO - loss = 0.2672078, step = 2108200 (2.252 sec) +2022-05-01 21:20:39,809 - tensorflow - INFO - global_step/sec: 88.7541 +2022-05-01 21:20:39,809 - tensorflow - INFO - loss = 0.0117831575, step = 2108400 (2.253 sec) +2022-05-01 21:20:42,062 - tensorflow - INFO - global_step/sec: 88.7505 +2022-05-01 21:20:42,063 - tensorflow - INFO - loss = 0.028337851, step = 2108600 (2.254 sec) +2022-05-01 21:20:44,316 - tensorflow - INFO - global_step/sec: 88.7299 +2022-05-01 21:20:44,317 - tensorflow - INFO - loss = 0.067769766, step = 2108800 (2.254 sec) +2022-05-01 21:20:46,570 - tensorflow - INFO - global_step/sec: 88.7534 +2022-05-01 21:20:46,570 - tensorflow - INFO - loss = 0.16715415, step = 2109000 (2.253 sec) +2022-05-01 21:20:48,816 - tensorflow - INFO - global_step/sec: 89.0504 +2022-05-01 21:20:48,816 - tensorflow - INFO - loss = 0.2685939, step = 2109200 (2.246 sec) +2022-05-01 21:20:51,065 - tensorflow - INFO - global_step/sec: 88.9068 +2022-05-01 21:20:51,066 - tensorflow - INFO - loss = 0.50303036, step = 2109400 (2.250 sec) +2022-05-01 21:20:53,315 - tensorflow - INFO - global_step/sec: 88.8834 +2022-05-01 21:20:53,316 - tensorflow - INFO - loss = 0.015402426, step = 2109600 (2.250 sec) +2022-05-01 21:20:55,571 - tensorflow - INFO - global_step/sec: 88.6581 +2022-05-01 21:20:55,572 - tensorflow - INFO - loss = 0.019315388, step = 2109800 (2.256 sec) +2022-05-01 21:20:57,832 - tensorflow - INFO - global_step/sec: 88.4478 +2022-05-01 21:20:57,833 - tensorflow - INFO - loss = 0.02295776, step = 2110000 (2.261 sec) +2022-05-01 21:21:00,090 - tensorflow - INFO - global_step/sec: 88.5985 +2022-05-01 21:21:00,090 - tensorflow - INFO - loss = 0.006488925, step = 2110200 (2.257 sec) +2022-05-01 21:21:02,341 - tensorflow - INFO - global_step/sec: 88.8574 +2022-05-01 21:21:02,341 - tensorflow - INFO - loss = 0.021128904, step = 2110400 (2.251 sec) +2022-05-01 21:21:04,590 - tensorflow - INFO - global_step/sec: 88.9032 +2022-05-01 21:21:04,591 - tensorflow - INFO - loss = 2.7757092, step = 2110600 (2.250 sec) +2022-05-01 21:21:06,843 - tensorflow - INFO - global_step/sec: 88.7744 +2022-05-01 21:21:06,844 - tensorflow - INFO - loss = 0.04770029, step = 2110800 (2.253 sec) +2022-05-01 21:21:09,096 - tensorflow - INFO - global_step/sec: 88.784 +2022-05-01 21:21:09,096 - tensorflow - INFO - loss = 0.26345018, step = 2111000 (2.253 sec) +2022-05-01 21:21:11,358 - tensorflow - INFO - global_step/sec: 88.4278 +2022-05-01 21:21:11,358 - tensorflow - INFO - loss = 0.006933829, step = 2111200 (2.262 sec) +2022-05-01 21:21:13,614 - tensorflow - INFO - global_step/sec: 88.6263 +2022-05-01 21:21:13,615 - tensorflow - INFO - loss = 0.03251392, step = 2111400 (2.257 sec) +2022-05-01 21:21:15,865 - tensorflow - INFO - global_step/sec: 88.8368 +2022-05-01 21:21:15,866 - tensorflow - INFO - loss = 0.0056138346, step = 2111600 (2.251 sec) +2022-05-01 21:21:18,114 - tensorflow - INFO - global_step/sec: 88.9652 +2022-05-01 21:21:18,114 - tensorflow - INFO - loss = 0.047357827, step = 2111800 (2.248 sec) +2022-05-01 21:21:20,365 - tensorflow - INFO - global_step/sec: 88.8384 +2022-05-01 21:21:20,365 - tensorflow - INFO - loss = 0.018748086, step = 2112000 (2.251 sec) +2022-05-01 21:21:22,621 - tensorflow - INFO - global_step/sec: 88.6269 +2022-05-01 21:21:22,622 - tensorflow - INFO - loss = 0.04558868, step = 2112200 (2.257 sec) +2022-05-01 21:21:24,885 - tensorflow - INFO - global_step/sec: 88.3769 +2022-05-01 21:21:24,885 - tensorflow - INFO - loss = 0.010202223, step = 2112400 (2.263 sec) +2022-05-01 21:21:27,139 - tensorflow - INFO - 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INFO - global_step/sec: 88.8493 +2022-05-01 21:36:47,245 - tensorflow - INFO - loss = 0.016532872, step = 2194200 (2.251 sec) +2022-05-01 21:36:49,500 - tensorflow - INFO - global_step/sec: 88.6702 +2022-05-01 21:36:49,500 - tensorflow - INFO - loss = 0.005099818, step = 2194400 (2.256 sec) +2022-05-01 21:36:51,756 - tensorflow - INFO - global_step/sec: 88.6475 +2022-05-01 21:36:51,756 - tensorflow - INFO - loss = 0.014568393, step = 2194600 (2.256 sec) +2022-05-01 21:36:54,003 - tensorflow - INFO - global_step/sec: 89.0025 +2022-05-01 21:36:54,003 - tensorflow - INFO - loss = 0.00928764, step = 2194800 (2.247 sec) +2022-05-01 21:36:56,258 - tensorflow - INFO - global_step/sec: 88.6935 +2022-05-01 21:36:56,258 - tensorflow - INFO - loss = 0.0219753, step = 2195000 (2.255 sec) +2022-05-01 21:36:58,511 - tensorflow - INFO - global_step/sec: 88.779 +2022-05-01 21:36:58,511 - tensorflow - INFO - loss = 1.0612338, step = 2195200 (2.253 sec) +2022-05-01 21:37:00,761 - tensorflow - INFO - global_step/sec: 88.8767 +2022-05-01 21:37:00,761 - tensorflow - INFO - loss = 0.31271794, step = 2195400 (2.250 sec) +2022-05-01 21:37:03,013 - tensorflow - INFO - global_step/sec: 88.8259 +2022-05-01 21:37:03,013 - tensorflow - INFO - loss = 0.07092461, step = 2195600 (2.252 sec) +2022-05-01 21:37:05,266 - tensorflow - INFO - global_step/sec: 88.766 +2022-05-01 21:37:05,266 - tensorflow - INFO - loss = 0.02657806, step = 2195800 (2.253 sec) +2022-05-01 21:37:07,522 - tensorflow - INFO - global_step/sec: 88.624 +2022-05-01 21:37:07,523 - tensorflow - INFO - loss = 0.012601734, step = 2196000 (2.257 sec) +2022-05-01 21:37:09,780 - tensorflow - INFO - global_step/sec: 88.5875 +2022-05-01 21:37:09,780 - tensorflow - INFO - loss = 0.010346203, step = 2196200 (2.258 sec) +2022-05-01 21:37:12,029 - tensorflow - INFO - global_step/sec: 88.94 +2022-05-01 21:37:12,029 - tensorflow - INFO - loss = 0.0631614, step = 2196400 (2.249 sec) +2022-05-01 21:37:14,278 - tensorflow - INFO - global_step/sec: 88.9297 +2022-05-01 21:37:14,278 - tensorflow - INFO - loss = 0.02143442, step = 2196600 (2.249 sec) +2022-05-01 21:37:16,528 - tensorflow - INFO - global_step/sec: 88.8974 +2022-05-01 21:37:16,528 - tensorflow - INFO - loss = 0.013591796, step = 2196800 (2.250 sec) +2022-05-01 21:37:18,783 - tensorflow - INFO - global_step/sec: 88.6885 +2022-05-01 21:37:18,783 - tensorflow - INFO - loss = 0.009670453, step = 2197000 (2.255 sec) +2022-05-01 21:37:21,040 - tensorflow - INFO - global_step/sec: 88.618 +2022-05-01 21:37:21,040 - tensorflow - INFO - loss = 0.012457259, step = 2197200 (2.257 sec) +2022-05-01 21:37:23,288 - tensorflow - INFO - global_step/sec: 88.938 +2022-05-01 21:37:23,289 - tensorflow - INFO - loss = 0.026262462, step = 2197400 (2.249 sec) +2022-05-01 21:37:25,540 - tensorflow - INFO - global_step/sec: 88.8215 +2022-05-01 21:37:25,540 - tensorflow - INFO - loss = 0.0077347336, step = 2197600 (2.252 sec) +2022-05-01 21:37:27,791 - tensorflow - INFO - global_step/sec: 88.8591 +2022-05-01 21:37:27,791 - tensorflow - INFO - loss = 0.01425202, step = 2197800 (2.251 sec) +2022-05-01 21:37:30,051 - tensorflow - INFO - global_step/sec: 88.4816 +2022-05-01 21:37:30,052 - tensorflow - INFO - loss = 0.07255714, step = 2198000 (2.260 sec) +2022-05-01 21:37:32,311 - tensorflow - INFO - global_step/sec: 88.4938 +2022-05-01 21:37:32,312 - tensorflow - INFO - loss = 0.20931415, step = 2198200 (2.260 sec) +2022-05-01 21:37:34,571 - tensorflow - INFO - global_step/sec: 88.4917 +2022-05-01 21:37:34,572 - tensorflow - INFO - loss = 0.037890192, step = 2198400 (2.260 sec) +2022-05-01 21:37:36,839 - tensorflow - INFO - global_step/sec: 88.1986 +2022-05-01 21:37:36,839 - tensorflow - INFO - loss = 0.037103213, step = 2198600 (2.268 sec) +2022-05-01 21:37:39,100 - tensorflow - INFO - global_step/sec: 88.4457 +2022-05-01 21:37:39,101 - tensorflow - INFO - loss = 0.021963807, step = 2198800 (2.261 sec) +2022-05-01 21:37:41,359 - tensorflow - INFO - global_step/sec: 88.5571 +2022-05-01 21:37:41,359 - tensorflow - INFO - loss = 0.017868139, step = 2199000 (2.258 sec) +2022-05-01 21:37:43,620 - tensorflow - INFO - global_step/sec: 88.4404 +2022-05-01 21:37:43,620 - tensorflow - INFO - loss = 0.08600532, step = 2199200 (2.261 sec) +2022-05-01 21:37:45,884 - tensorflow - INFO - global_step/sec: 88.3505 +2022-05-01 21:37:45,884 - tensorflow - INFO - loss = 0.014971986, step = 2199400 (2.264 sec) +2022-05-01 21:37:48,149 - tensorflow - INFO - global_step/sec: 88.2993 +2022-05-01 21:37:48,149 - tensorflow - INFO - loss = 0.009885784, step = 2199600 (2.265 sec) +2022-05-01 21:37:50,409 - tensorflow - INFO - global_step/sec: 88.4647 +2022-05-01 21:37:50,410 - tensorflow - INFO - loss = 0.16105652, step = 2199800 (2.261 sec) +2022-05-01 21:37:52,664 - tensorflow - INFO - Saving checkpoints for 2200000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. +2022-05-01 21:37:52,680 - tensorflow - INFO - model.ckpt-2200000 is not in all_model_checkpoint_paths. Manually adding it. +2022-05-01 21:37:53,206 - tensorflow - INFO - Calling model_fn. +2022-05-01 21:37:53,334 - tensorflow - INFO - Done calling model_fn. +2022-05-01 21:37:53,349 - tensorflow - INFO - Starting evaluation at 2022-05-01T21:37:53Z +2022-05-01 21:37:53,417 - tensorflow - INFO - Graph was finalized. +2022-05-01 21:37:53,421 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-2200000 +2022-05-01 21:37:53,703 - tensorflow - INFO - Running local_init_op. +2022-05-01 21:37:53,812 - tensorflow - INFO - Done running local_init_op. +2022-05-01 21:38:04,130 - tensorflow - INFO - Evaluation [3990/39900] +2022-05-01 21:38:13,663 - tensorflow - INFO - Evaluation [7980/39900] +2022-05-01 21:38:23,220 - tensorflow - INFO - Evaluation [11970/39900] +2022-05-01 21:38:32,772 - tensorflow - INFO - Evaluation [15960/39900] +2022-05-01 21:38:42,320 - tensorflow - INFO - Evaluation [19950/39900] +2022-05-01 21:38:51,892 - tensorflow - INFO - Evaluation [23940/39900] +2022-05-01 21:39:01,423 - tensorflow - INFO - Evaluation [27930/39900] +2022-05-01 21:39:10,981 - tensorflow - INFO - Evaluation [31920/39900] +2022-05-01 21:39:20,545 - tensorflow - INFO - Evaluation [35910/39900] +2022-05-01 21:39:30,119 - tensorflow - INFO - Evaluation [39900/39900] +2022-05-01 21:39:30,258 - tensorflow - INFO - Finished evaluation at 2022-05-01-21:39:30 +2022-05-01 21:39:30,259 - tensorflow - INFO - Saving dict for global step 2200000: global_step = 2200000, loss = 0.015515498, mean_degree_err = 3.1324177 +2022-05-01 21:39:30,259 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 2200000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-2200000 +2022-05-01 21:39:30,273 - tensorflow - INFO - global_step/sec: 2.00273 +2022-05-01 21:39:30,274 - tensorflow - INFO - loss = 0.016591474, step = 2200000 (99.864 sec) +2022-05-01 21:39:32,548 - tensorflow - INFO - global_step/sec: 87.916 +2022-05-01 21:39:32,548 - tensorflow - INFO - loss = 0.04369278, step = 2200200 (2.274 sec) +2022-05-01 21:39:34,804 - tensorflow - INFO - global_step/sec: 88.6638 +2022-05-01 21:39:34,804 - tensorflow - INFO - loss = 0.035334364, step = 2200400 (2.256 sec) +2022-05-01 21:39:37,057 - tensorflow - INFO - global_step/sec: 88.7553 +2022-05-01 21:39:37,057 - tensorflow - INFO - loss = 0.0069875494, step = 2200600 (2.253 sec) +2022-05-01 21:39:39,309 - tensorflow - INFO - global_step/sec: 88.8105 +2022-05-01 21:39:39,309 - tensorflow - INFO - loss = 0.041061778, step = 2200800 (2.252 sec) +2022-05-01 21:39:41,567 - tensorflow - INFO - global_step/sec: 88.579 +2022-05-01 21:39:41,567 - tensorflow - INFO - loss = 0.011567708, step = 2201000 (2.258 sec) +2022-05-01 21:39:43,822 - tensorflow - INFO - global_step/sec: 88.6912 +2022-05-01 21:39:43,822 - tensorflow - INFO - loss = 0.014715085, step = 2201200 (2.255 sec) +2022-05-01 21:39:46,073 - tensorflow - INFO - global_step/sec: 88.8445 +2022-05-01 21:39:46,073 - tensorflow - INFO - loss = 0.009093689, step = 2201400 (2.251 sec) +2022-05-01 21:39:48,322 - tensorflow - INFO - global_step/sec: 88.9168 +2022-05-01 21:39:48,323 - tensorflow - INFO - loss = 1.0612983, step = 2201600 (2.249 sec) +2022-05-01 21:39:50,575 - tensorflow - INFO - global_step/sec: 88.7781 +2022-05-01 21:39:50,575 - tensorflow - INFO - loss = 0.009138137, step = 2201800 (2.253 sec) +2022-05-01 21:39:52,827 - tensorflow - INFO - global_step/sec: 88.8019 +2022-05-01 21:39:52,828 - tensorflow - INFO - loss = 0.010925783, step = 2202000 (2.252 sec) +2022-05-01 21:39:55,085 - tensorflow - INFO - global_step/sec: 88.5793 +2022-05-01 21:39:55,086 - tensorflow - INFO - loss = 0.014196128, step = 2202200 (2.258 sec) +2022-05-01 21:39:57,344 - tensorflow - INFO - global_step/sec: 88.5409 +2022-05-01 21:39:57,344 - tensorflow - INFO - loss = 0.1022256, step = 2202400 (2.259 sec) +2022-05-01 21:39:59,595 - tensorflow - INFO - global_step/sec: 88.8293 +2022-05-01 21:39:59,596 - tensorflow - INFO - loss = 0.12285152, step = 2202600 (2.252 sec) +2022-05-01 21:40:01,849 - tensorflow - INFO - global_step/sec: 88.7609 +2022-05-01 21:40:01,849 - tensorflow - INFO - loss = 0.00926359, step = 2202800 (2.253 sec) +2022-05-01 21:40:04,101 - tensorflow - INFO - global_step/sec: 88.7822 +2022-05-01 21:40:04,102 - tensorflow - INFO - loss = 0.15393083, step = 2203000 (2.253 sec) +2022-05-01 21:40:06,353 - tensorflow - INFO - global_step/sec: 88.8288 +2022-05-01 21:40:06,353 - tensorflow - INFO - loss = 0.01870555, step = 2203200 (2.252 sec) +2022-05-01 21:40:08,613 - tensorflow - INFO - global_step/sec: 88.5019 +2022-05-01 21:40:08,613 - tensorflow - INFO - loss = 0.020982817, step = 2203400 (2.260 sec) +2022-05-01 21:40:10,864 - tensorflow - INFO - global_step/sec: 88.8411 +2022-05-01 21:40:10,864 - tensorflow - INFO - loss = 0.03838037, step = 2203600 (2.251 sec) +2022-05-01 21:40:13,117 - tensorflow - INFO - global_step/sec: 88.769 +2022-05-01 21:40:13,117 - tensorflow - INFO - loss = 0.019582264, step = 2203800 (2.253 sec) +2022-05-01 21:40:15,369 - tensorflow - INFO - global_step/sec: 88.8261 +2022-05-01 21:40:15,369 - tensorflow - INFO - loss = 0.0038917055, step = 2204000 (2.252 sec) +2022-05-01 21:40:17,623 - tensorflow - INFO - global_step/sec: 88.7092 +2022-05-01 21:40:17,624 - tensorflow - INFO - loss = 0.012383769, step = 2204200 (2.255 sec) +2022-05-01 21:40:19,879 - tensorflow - INFO - global_step/sec: 88.6498 +2022-05-01 21:40:19,880 - tensorflow - INFO - loss = 0.006668681, step = 2204400 (2.256 sec) +2022-05-01 21:40:22,129 - tensorflow - INFO - global_step/sec: 88.9148 +2022-05-01 21:40:22,129 - tensorflow - INFO - loss = 0.008942205, step = 2204600 (2.249 sec) +2022-05-01 21:40:24,380 - tensorflow - INFO - global_step/sec: 88.8245 +2022-05-01 21:40:24,381 - tensorflow - INFO - loss = 0.10828018, step = 2204800 (2.252 sec) +2022-05-01 21:40:26,635 - tensorflow - INFO - global_step/sec: 88.7117 +2022-05-01 21:40:26,635 - tensorflow - INFO - loss = 0.026557386, step = 2205000 (2.255 sec) +2022-05-01 21:40:28,884 - tensorflow - INFO - global_step/sec: 88.9343 +2022-05-01 21:40:28,884 - tensorflow - INFO - loss = 0.009144509, step = 2205200 (2.249 sec) +2022-05-01 21:40:31,141 - tensorflow - INFO - global_step/sec: 88.6109 +2022-05-01 21:40:31,141 - tensorflow - INFO - loss = 0.018995715, step = 2205400 (2.257 sec) +2022-05-01 21:40:33,394 - tensorflow - INFO - global_step/sec: 88.7683 +2022-05-01 21:40:33,394 - tensorflow - INFO - loss = 0.02026128, step = 2205600 (2.253 sec) +2022-05-01 21:40:35,649 - tensorflow - INFO - global_step/sec: 88.6789 +2022-05-01 21:40:35,649 - tensorflow - INFO - loss = 0.052717946, step = 2205800 (2.255 sec) +2022-05-01 21:40:37,899 - tensorflow - INFO - global_step/sec: 88.9058 +2022-05-01 21:40:37,899 - tensorflow - INFO - loss = 0.11664444, step = 2206000 (2.250 sec) +2022-05-01 21:40:40,151 - tensorflow - INFO - global_step/sec: 88.7921 +2022-05-01 21:40:40,151 - tensorflow - INFO - loss = 0.0046029845, step = 2206200 (2.252 sec) +2022-05-01 21:40:42,401 - tensorflow - INFO - global_step/sec: 88.8739 +2022-05-01 21:40:42,402 - tensorflow - INFO - loss = 0.04805688, step = 2206400 (2.250 sec) +2022-05-01 21:40:44,653 - tensorflow - INFO - global_step/sec: 88.8337 +2022-05-01 21:40:44,653 - tensorflow - INFO - loss = 0.006403135, step = 2206600 (2.251 sec) +2022-05-01 21:40:46,909 - tensorflow - INFO - global_step/sec: 88.6538 +2022-05-01 21:40:46,909 - tensorflow - INFO - loss = 0.11942297, step = 2206800 (2.256 sec) +2022-05-01 21:40:49,163 - tensorflow - INFO - global_step/sec: 88.7151 +2022-05-01 21:40:49,164 - tensorflow - INFO - loss = 18.514679, step = 2207000 (2.254 sec) +2022-05-01 21:40:51,414 - tensorflow - INFO - global_step/sec: 88.8687 +2022-05-01 21:40:51,414 - tensorflow - INFO - loss = 0.026035413, step = 2207200 (2.251 sec) +2022-05-01 21:40:53,661 - tensorflow - INFO - global_step/sec: 88.9766 +2022-05-01 21:40:53,662 - tensorflow - INFO - loss = 0.9963044, step = 2207400 (2.248 sec) +2022-05-01 21:40:55,918 - tensorflow - INFO - global_step/sec: 88.6148 +2022-05-01 21:40:55,919 - tensorflow - INFO - loss = 0.063365005, step = 2207600 (2.257 sec) +2022-05-01 21:40:58,179 - tensorflow - INFO - global_step/sec: 88.4572 +2022-05-01 21:40:58,180 - tensorflow - INFO - loss = 0.17814486, step = 2207800 (2.261 sec) +2022-05-01 21:41:00,433 - tensorflow - INFO - global_step/sec: 88.7593 +2022-05-01 21:41:00,433 - tensorflow - INFO - loss = 0.010942705, step = 2208000 (2.253 sec) +2022-05-01 21:41:02,690 - tensorflow - INFO - global_step/sec: 88.5825 +2022-05-01 21:41:02,691 - tensorflow - INFO - loss = 0.014380009, step = 2208200 (2.258 sec) +2022-05-01 21:41:04,950 - tensorflow - INFO - global_step/sec: 88.5144 +2022-05-01 21:41:04,950 - tensorflow - INFO - loss = 0.0478812, step = 2208400 (2.259 sec) +2022-05-01 21:41:07,209 - tensorflow - INFO - global_step/sec: 88.5299 +2022-05-01 21:41:07,210 - tensorflow - INFO - loss = 0.07858298, step = 2208600 (2.259 sec) +2022-05-01 21:41:09,468 - tensorflow - INFO - global_step/sec: 88.5227 +2022-05-01 21:41:09,469 - tensorflow - INFO - loss = 0.021009544, step = 2208800 (2.259 sec) +2022-05-01 21:41:11,730 - tensorflow - INFO - global_step/sec: 88.417 +2022-05-01 21:41:11,731 - tensorflow - INFO - loss = 0.020054996, step = 2209000 (2.262 sec) +2022-05-01 21:41:13,986 - tensorflow - INFO - global_step/sec: 88.6608 +2022-05-01 21:41:13,987 - tensorflow - INFO - loss = 0.008389406, step = 2209200 (2.256 sec) +2022-05-01 21:41:16,247 - tensorflow - INFO - global_step/sec: 88.4481 +2022-05-01 21:41:16,248 - tensorflow - INFO - loss = 0.013282923, step = 2209400 (2.261 sec) +2022-05-01 21:41:18,505 - tensorflow - INFO - global_step/sec: 88.5847 +2022-05-01 21:41:18,506 - tensorflow - INFO - loss = 0.010178466, step = 2209600 (2.258 sec) +2022-05-01 21:41:20,769 - tensorflow - INFO - global_step/sec: 88.3552 +2022-05-01 21:41:20,769 - tensorflow - INFO - loss = 0.013363117, step = 2209800 (2.264 sec) +2022-05-01 21:41:23,029 - tensorflow - INFO - global_step/sec: 88.4893 +2022-05-01 21:41:23,029 - tensorflow - INFO - loss = 0.035251763, step = 2210000 (2.260 sec) +2022-05-01 21:41:25,289 - tensorflow - 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global_step/sec: 88.5879 +2022-05-01 21:56:58,929 - tensorflow - INFO - loss = 0.0038347745, step = 2293000 (2.258 sec) +2022-05-01 21:57:01,179 - tensorflow - INFO - global_step/sec: 88.8582 +2022-05-01 21:57:01,180 - tensorflow - INFO - loss = 0.014080549, step = 2293200 (2.251 sec) +2022-05-01 21:57:03,439 - tensorflow - INFO - global_step/sec: 88.5001 +2022-05-01 21:57:03,440 - tensorflow - INFO - loss = 0.0055178367, step = 2293400 (2.260 sec) +2022-05-01 21:57:05,691 - tensorflow - INFO - global_step/sec: 88.8284 +2022-05-01 21:57:05,691 - tensorflow - INFO - loss = 0.031844318, step = 2293600 (2.252 sec) +2022-05-01 21:57:07,944 - tensorflow - INFO - global_step/sec: 88.7622 +2022-05-01 21:57:07,945 - tensorflow - INFO - loss = 1.3870256, step = 2293800 (2.253 sec) +2022-05-01 21:57:10,202 - tensorflow - INFO - global_step/sec: 88.5595 +2022-05-01 21:57:10,203 - tensorflow - INFO - loss = 0.010294217, step = 2294000 (2.258 sec) +2022-05-01 21:57:12,453 - tensorflow - INFO - global_step/sec: 88.8624 +2022-05-01 21:57:12,454 - tensorflow - INFO - loss = 0.014056441, step = 2294200 (2.251 sec) +2022-05-01 21:57:14,700 - tensorflow - INFO - global_step/sec: 89.0314 +2022-05-01 21:57:14,700 - tensorflow - INFO - loss = 0.028425688, step = 2294400 (2.246 sec) +2022-05-01 21:57:16,956 - tensorflow - INFO - global_step/sec: 88.6322 +2022-05-01 21:57:16,956 - tensorflow - INFO - loss = 0.14513147, step = 2294600 (2.257 sec) +2022-05-01 21:57:19,212 - tensorflow - INFO - global_step/sec: 88.6421 +2022-05-01 21:57:19,213 - tensorflow - INFO - loss = 0.018518772, step = 2294800 (2.256 sec) +2022-05-01 21:57:21,462 - tensorflow - INFO - global_step/sec: 88.8915 +2022-05-01 21:57:21,463 - tensorflow - INFO - loss = 0.0112281665, step = 2295000 (2.250 sec) +2022-05-01 21:57:23,724 - tensorflow - INFO - global_step/sec: 88.4416 +2022-05-01 21:57:23,724 - tensorflow - INFO - loss = 0.033915814, step = 2295200 (2.261 sec) +2022-05-01 21:57:25,974 - tensorflow - INFO - global_step/sec: 88.8701 +2022-05-01 21:57:25,975 - tensorflow - INFO - loss = 0.045164473, step = 2295400 (2.250 sec) +2022-05-01 21:57:28,221 - tensorflow - INFO - global_step/sec: 88.998 +2022-05-01 21:57:28,222 - tensorflow - INFO - loss = 0.015295318, step = 2295600 (2.247 sec) +2022-05-01 21:57:30,477 - tensorflow - INFO - global_step/sec: 88.6528 +2022-05-01 21:57:30,478 - tensorflow - INFO - loss = 0.055552155, step = 2295800 (2.256 sec) +2022-05-01 21:57:32,728 - tensorflow - INFO - global_step/sec: 88.8457 +2022-05-01 21:57:32,729 - tensorflow - INFO - loss = 0.007291389, step = 2296000 (2.251 sec) +2022-05-01 21:57:34,982 - tensorflow - INFO - global_step/sec: 88.7465 +2022-05-01 21:57:34,983 - tensorflow - INFO - loss = 0.009181721, step = 2296200 (2.254 sec) +2022-05-01 21:57:37,241 - tensorflow - INFO - global_step/sec: 88.5505 +2022-05-01 21:57:37,241 - tensorflow - INFO - loss = 0.015211635, step = 2296400 (2.259 sec) +2022-05-01 21:57:39,493 - tensorflow - INFO - global_step/sec: 88.8017 +2022-05-01 21:57:39,493 - tensorflow - INFO - loss = 0.07218133, step = 2296600 (2.252 sec) +2022-05-01 21:57:41,741 - tensorflow - INFO - global_step/sec: 88.98 +2022-05-01 21:57:41,741 - tensorflow - INFO - loss = 0.2888913, step = 2296800 (2.248 sec) +2022-05-01 21:57:43,991 - tensorflow - INFO - global_step/sec: 88.8873 +2022-05-01 21:57:43,991 - tensorflow - INFO - loss = 0.011046013, step = 2297000 (2.250 sec) +2022-05-01 21:57:46,238 - tensorflow - INFO - global_step/sec: 88.9803 +2022-05-01 21:57:46,239 - tensorflow - INFO - loss = 0.14288104, step = 2297200 (2.248 sec) +2022-05-01 21:57:48,495 - tensorflow - INFO - global_step/sec: 88.6299 +2022-05-01 21:57:48,495 - tensorflow - INFO - loss = 0.010061863, step = 2297400 (2.257 sec) +2022-05-01 21:57:50,747 - tensorflow - INFO - global_step/sec: 88.7933 +2022-05-01 21:57:50,748 - tensorflow - INFO - loss = 0.011489116, step = 2297600 (2.252 sec) +2022-05-01 21:57:52,996 - tensorflow - INFO - global_step/sec: 88.9238 +2022-05-01 21:57:52,997 - tensorflow - INFO - loss = 0.004584601, step = 2297800 (2.249 sec) +2022-05-01 21:57:55,247 - tensorflow - INFO - global_step/sec: 88.8742 +2022-05-01 21:57:55,247 - tensorflow - INFO - loss = 0.010511144, step = 2298000 (2.250 sec) +2022-05-01 21:57:57,500 - tensorflow - INFO - global_step/sec: 88.7639 +2022-05-01 21:57:57,500 - tensorflow - INFO - loss = 0.0056808935, step = 2298200 (2.253 sec) +2022-05-01 21:57:59,753 - tensorflow - INFO - global_step/sec: 88.7811 +2022-05-01 21:57:59,753 - tensorflow - INFO - loss = 0.01763389, step = 2298400 (2.253 sec) +2022-05-01 21:58:02,004 - tensorflow - INFO - global_step/sec: 88.8373 +2022-05-01 21:58:02,004 - tensorflow - INFO - loss = 0.11997159, step = 2298600 (2.251 sec) +2022-05-01 21:58:04,258 - tensorflow - INFO - global_step/sec: 88.7125 +2022-05-01 21:58:04,259 - tensorflow - INFO - loss = 0.06352675, step = 2298800 (2.254 sec) +2022-05-01 21:58:06,511 - tensorflow - INFO - global_step/sec: 88.7991 +2022-05-01 21:58:06,511 - tensorflow - INFO - loss = 0.18617159, step = 2299000 (2.252 sec) +2022-05-01 21:58:08,764 - tensorflow - INFO - global_step/sec: 88.7447 +2022-05-01 21:58:08,765 - tensorflow - INFO - loss = 0.010075515, step = 2299200 (2.254 sec) +2022-05-01 21:58:11,020 - tensorflow - INFO - global_step/sec: 88.6623 +2022-05-01 21:58:11,021 - tensorflow - INFO - loss = 0.07672944, step = 2299400 (2.256 sec) +2022-05-01 21:58:13,274 - tensorflow - INFO - global_step/sec: 88.7209 +2022-05-01 21:58:13,275 - tensorflow - INFO - loss = 0.012490427, step = 2299600 (2.254 sec) +2022-05-01 21:58:15,521 - tensorflow - INFO - global_step/sec: 89.0263 +2022-05-01 21:58:15,521 - tensorflow - INFO - loss = 0.084134504, step = 2299800 (2.246 sec) +2022-05-01 21:58:17,758 - tensorflow - INFO - Saving checkpoints for 2300000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. +2022-05-01 21:58:17,774 - tensorflow - INFO - model.ckpt-2300000 is not in all_model_checkpoint_paths. Manually adding it. +2022-05-01 21:58:18,309 - tensorflow - INFO - Calling model_fn. +2022-05-01 21:58:18,436 - tensorflow - INFO - Done calling model_fn. +2022-05-01 21:58:18,451 - tensorflow - INFO - Starting evaluation at 2022-05-01T21:58:18Z +2022-05-01 21:58:18,514 - tensorflow - INFO - Graph was finalized. +2022-05-01 21:58:18,517 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-2300000 +2022-05-01 21:58:18,797 - tensorflow - INFO - Running local_init_op. +2022-05-01 21:58:18,905 - tensorflow - INFO - Done running local_init_op. +2022-05-01 21:58:29,046 - tensorflow - INFO - Evaluation [3990/39900] +2022-05-01 21:58:38,480 - tensorflow - INFO - Evaluation [7980/39900] +2022-05-01 21:58:47,929 - tensorflow - INFO - Evaluation [11970/39900] +2022-05-01 21:58:57,409 - tensorflow - INFO - Evaluation [15960/39900] +2022-05-01 21:59:06,885 - tensorflow - INFO - Evaluation [19950/39900] +2022-05-01 21:59:16,344 - tensorflow - INFO - Evaluation [23940/39900] +2022-05-01 21:59:25,817 - tensorflow - INFO - Evaluation [27930/39900] +2022-05-01 21:59:35,288 - tensorflow - INFO - Evaluation [31920/39900] +2022-05-01 21:59:44,735 - tensorflow - INFO - Evaluation [35910/39900] +2022-05-01 21:59:54,202 - tensorflow - INFO - Evaluation [39900/39900] +2022-05-01 21:59:54,339 - tensorflow - INFO - Finished evaluation at 2022-05-01-21:59:54 +2022-05-01 21:59:54,339 - tensorflow - INFO - Saving dict for global step 2300000: global_step = 2300000, loss = 0.017903524, mean_degree_err = 3.449092 +2022-05-01 21:59:54,340 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 2300000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-2300000 +2022-05-01 21:59:54,353 - tensorflow - INFO - global_step/sec: 2.02363 +2022-05-01 21:59:54,354 - tensorflow - INFO - loss = 0.0091372905, step = 2300000 (98.833 sec) +2022-05-01 21:59:56,633 - tensorflow - INFO - global_step/sec: 87.7322 +2022-05-01 21:59:56,633 - tensorflow - INFO - loss = 0.024899326, step = 2300200 (2.279 sec) +2022-05-01 21:59:58,886 - tensorflow - INFO - global_step/sec: 88.7735 +2022-05-01 21:59:58,886 - tensorflow - INFO - loss = 0.016124004, step = 2300400 (2.253 sec) +2022-05-01 22:00:01,143 - tensorflow - INFO - global_step/sec: 88.6166 +2022-05-01 22:00:01,143 - tensorflow - INFO - loss = 0.01588392, step = 2300600 (2.257 sec) +2022-05-01 22:00:03,397 - tensorflow - INFO - global_step/sec: 88.7324 +2022-05-01 22:00:03,397 - tensorflow - INFO - loss = 0.010000843, step = 2300800 (2.254 sec) +2022-05-01 22:00:05,651 - tensorflow - INFO - global_step/sec: 88.7368 +2022-05-01 22:00:05,651 - tensorflow - INFO - loss = 0.059633113, step = 2301000 (2.254 sec) +2022-05-01 22:00:07,911 - tensorflow - INFO - global_step/sec: 88.4922 +2022-05-01 22:00:07,911 - tensorflow - INFO - loss = 0.0062330198, step = 2301200 (2.260 sec) +2022-05-01 22:00:10,169 - tensorflow - INFO - global_step/sec: 88.568 +2022-05-01 22:00:10,169 - tensorflow - INFO - loss = 0.23845582, step = 2301400 (2.258 sec) +2022-05-01 22:00:12,422 - tensorflow - INFO - global_step/sec: 88.7789 +2022-05-01 22:00:12,422 - tensorflow - INFO - loss = 0.011025475, step = 2301600 (2.253 sec) +2022-05-01 22:00:14,674 - tensorflow - INFO - global_step/sec: 88.8013 +2022-05-01 22:00:14,674 - tensorflow - INFO - loss = 0.0045896634, step = 2301800 (2.252 sec) +2022-05-01 22:00:16,926 - tensorflow - INFO - global_step/sec: 88.8024 +2022-05-01 22:00:16,927 - tensorflow - INFO - loss = 0.0063096797, step = 2302000 (2.252 sec) +2022-05-01 22:00:19,178 - tensorflow - INFO - global_step/sec: 88.8262 +2022-05-01 22:00:19,178 - tensorflow - INFO - loss = 0.006863452, step = 2302200 (2.252 sec) +2022-05-01 22:00:21,436 - tensorflow - INFO - global_step/sec: 88.5556 +2022-05-01 22:00:21,437 - tensorflow - INFO - loss = 0.048675433, step = 2302400 (2.259 sec) +2022-05-01 22:00:23,687 - tensorflow - INFO - global_step/sec: 88.8532 +2022-05-01 22:00:23,687 - tensorflow - INFO - loss = 0.0055150455, step = 2302600 (2.251 sec) +2022-05-01 22:00:25,941 - tensorflow - INFO - global_step/sec: 88.7483 +2022-05-01 22:00:25,941 - tensorflow - INFO - loss = 0.01271037, step = 2302800 (2.254 sec) +2022-05-01 22:00:28,193 - tensorflow - INFO - global_step/sec: 88.7814 +2022-05-01 22:00:28,194 - tensorflow - INFO - loss = 0.009561626, step = 2303000 (2.253 sec) +2022-05-01 22:00:30,452 - tensorflow - INFO - global_step/sec: 88.5499 +2022-05-01 22:00:30,452 - tensorflow - INFO - loss = 0.10002799, step = 2303200 (2.259 sec) +2022-05-01 22:00:32,708 - tensorflow - INFO - global_step/sec: 88.6695 +2022-05-01 22:00:32,708 - tensorflow - INFO - loss = 0.005737627, step = 2303400 (2.256 sec) +2022-05-01 22:00:34,966 - tensorflow - INFO - global_step/sec: 88.5713 +2022-05-01 22:00:34,966 - tensorflow - INFO - loss = 0.04569764, step = 2303600 (2.258 sec) +2022-05-01 22:00:37,215 - tensorflow - INFO - global_step/sec: 88.9007 +2022-05-01 22:00:37,216 - tensorflow - INFO - loss = 0.004944335, step = 2303800 (2.250 sec) +2022-05-01 22:00:39,464 - tensorflow - INFO - global_step/sec: 88.9471 +2022-05-01 22:00:39,464 - tensorflow - INFO - loss = 0.009186769, step = 2304000 (2.249 sec) +2022-05-01 22:00:41,715 - tensorflow - INFO - global_step/sec: 88.8285 +2022-05-01 22:00:41,716 - tensorflow - INFO - loss = 0.1479854, step = 2304200 (2.252 sec) +2022-05-01 22:00:43,969 - tensorflow - INFO - global_step/sec: 88.7419 +2022-05-01 22:00:43,969 - tensorflow - INFO - loss = 0.022605823, step = 2304400 (2.254 sec) +2022-05-01 22:00:46,227 - tensorflow - INFO - global_step/sec: 88.5717 +2022-05-01 22:00:46,228 - tensorflow - INFO - loss = 0.021883419, step = 2304600 (2.258 sec) +2022-05-01 22:00:48,478 - tensorflow - INFO - global_step/sec: 88.8408 +2022-05-01 22:00:48,479 - tensorflow - INFO - loss = 0.009133572, step = 2304800 (2.251 sec) +2022-05-01 22:00:50,734 - tensorflow - INFO - global_step/sec: 88.6823 +2022-05-01 22:00:50,734 - tensorflow - INFO - loss = 0.011011441, step = 2305000 (2.255 sec) +2022-05-01 22:00:52,985 - tensorflow - INFO - global_step/sec: 88.8334 +2022-05-01 22:00:52,985 - tensorflow - INFO - loss = 0.021263834, step = 2305200 (2.251 sec) +2022-05-01 22:00:55,239 - tensorflow - INFO - global_step/sec: 88.7191 +2022-05-01 22:00:55,240 - tensorflow - INFO - loss = 3.0099537, step = 2305400 (2.254 sec) +2022-05-01 22:00:57,496 - tensorflow - INFO - global_step/sec: 88.6135 +2022-05-01 22:00:57,497 - tensorflow - INFO - loss = 0.00849932, step = 2305600 (2.257 sec) +2022-05-01 22:00:59,751 - tensorflow - INFO - global_step/sec: 88.7239 +2022-05-01 22:00:59,751 - tensorflow - INFO - loss = 0.009330539, step = 2305800 (2.254 sec) +2022-05-01 22:01:02,003 - tensorflow - INFO - global_step/sec: 88.8097 +2022-05-01 22:01:02,003 - tensorflow - INFO - loss = 0.25501916, step = 2306000 (2.252 sec) +2022-05-01 22:01:04,259 - tensorflow - INFO - global_step/sec: 88.6387 +2022-05-01 22:01:04,259 - tensorflow - INFO - loss = 0.0076217586, step = 2306200 (2.256 sec) +2022-05-01 22:01:06,512 - tensorflow - INFO - global_step/sec: 88.7569 +2022-05-01 22:01:06,513 - tensorflow - INFO - loss = 0.2942369, step = 2306400 (2.253 sec) +2022-05-01 22:01:08,766 - tensorflow - INFO - global_step/sec: 88.7346 +2022-05-01 22:01:08,767 - tensorflow - INFO - loss = 0.007971099, step = 2306600 (2.254 sec) +2022-05-01 22:01:11,026 - tensorflow - INFO - global_step/sec: 88.4867 +2022-05-01 22:01:11,027 - tensorflow - INFO - loss = 0.0067938194, step = 2306800 (2.260 sec) +2022-05-01 22:01:13,279 - tensorflow - INFO - global_step/sec: 88.7929 +2022-05-01 22:01:13,279 - tensorflow - INFO - loss = 0.024233544, step = 2307000 (2.252 sec) +2022-05-01 22:01:15,529 - tensorflow - INFO - global_step/sec: 88.8989 +2022-05-01 22:01:15,529 - tensorflow - INFO - loss = 0.030882917, step = 2307200 (2.250 sec) +2022-05-01 22:01:17,782 - tensorflow - INFO - global_step/sec: 88.7576 +2022-05-01 22:01:17,782 - tensorflow - INFO - loss = 0.39104134, step = 2307400 (2.253 sec) +2022-05-01 22:01:20,035 - tensorflow - INFO - global_step/sec: 88.7829 +2022-05-01 22:01:20,035 - tensorflow - INFO - loss = 0.02334808, step = 2307600 (2.253 sec) +2022-05-01 22:01:22,294 - tensorflow - INFO - global_step/sec: 88.5144 +2022-05-01 22:01:22,294 - tensorflow - INFO - loss = 0.017138243, step = 2307800 (2.260 sec) +2022-05-01 22:01:24,555 - tensorflow - INFO - global_step/sec: 88.4762 +2022-05-01 22:01:24,555 - tensorflow - INFO - loss = 0.090016745, step = 2308000 (2.261 sec) +2022-05-01 22:01:26,813 - tensorflow - INFO - global_step/sec: 88.5436 +2022-05-01 22:01:26,814 - tensorflow - INFO - loss = 0.030420732, step = 2308200 (2.259 sec) +2022-05-01 22:01:29,070 - tensorflow - INFO - global_step/sec: 88.6409 +2022-05-01 22:01:29,070 - tensorflow - INFO - loss = 0.09480525, step = 2308400 (2.256 sec) +2022-05-01 22:01:31,332 - tensorflow - INFO - global_step/sec: 88.3843 +2022-05-01 22:01:31,333 - tensorflow - INFO - loss = 0.009265394, step = 2308600 (2.263 sec) +2022-05-01 22:01:33,594 - tensorflow - INFO - global_step/sec: 88.446 +2022-05-01 22:01:33,594 - tensorflow - 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tensorflow - INFO - global_step/sec: 88.5315 +2022-05-01 22:16:56,239 - tensorflow - INFO - loss = 0.024905298, step = 2390600 (2.259 sec) +2022-05-01 22:16:58,494 - tensorflow - INFO - global_step/sec: 88.6647 +2022-05-01 22:16:58,495 - tensorflow - INFO - loss = 0.0072831106, step = 2390800 (2.256 sec) +2022-05-01 22:17:00,745 - tensorflow - INFO - global_step/sec: 88.8468 +2022-05-01 22:17:00,746 - tensorflow - INFO - loss = 0.03236274, step = 2391000 (2.251 sec) +2022-05-01 22:17:03,000 - tensorflow - INFO - global_step/sec: 88.7116 +2022-05-01 22:17:03,000 - tensorflow - INFO - loss = 0.015077673, step = 2391200 (2.255 sec) +2022-05-01 22:17:05,257 - tensorflow - INFO - global_step/sec: 88.5927 +2022-05-01 22:17:05,258 - tensorflow - INFO - loss = 0.0067395577, step = 2391400 (2.258 sec) +2022-05-01 22:17:07,509 - tensorflow - INFO - global_step/sec: 88.8246 +2022-05-01 22:17:07,509 - tensorflow - INFO - loss = 0.019622741, step = 2391600 (2.252 sec) +2022-05-01 22:17:09,765 - tensorflow - INFO - global_step/sec: 88.6531 +2022-05-01 22:17:09,765 - tensorflow - INFO - loss = 0.014148858, step = 2391800 (2.256 sec) +2022-05-01 22:17:12,020 - tensorflow - INFO - global_step/sec: 88.6967 +2022-05-01 22:17:12,020 - tensorflow - INFO - loss = 0.005203164, step = 2392000 (2.255 sec) +2022-05-01 22:17:14,276 - tensorflow - INFO - global_step/sec: 88.645 +2022-05-01 22:17:14,277 - tensorflow - INFO - loss = 0.040775083, step = 2392200 (2.256 sec) +2022-05-01 22:17:16,531 - tensorflow - INFO - global_step/sec: 88.705 +2022-05-01 22:17:16,531 - tensorflow - INFO - loss = 0.07990815, step = 2392400 (2.255 sec) +2022-05-01 22:17:18,782 - tensorflow - INFO - global_step/sec: 88.8364 +2022-05-01 22:17:18,782 - tensorflow - INFO - loss = 0.041910116, step = 2392600 (2.251 sec) +2022-05-01 22:17:21,034 - tensorflow - INFO - global_step/sec: 88.8098 +2022-05-01 22:17:21,034 - tensorflow - INFO - loss = 0.022378735, step = 2392800 (2.252 sec) +2022-05-01 22:17:23,292 - tensorflow - INFO - global_step/sec: 88.5926 +2022-05-01 22:17:23,292 - tensorflow - INFO - loss = 0.22880238, step = 2393000 (2.258 sec) +2022-05-01 22:17:25,543 - tensorflow - INFO - global_step/sec: 88.8418 +2022-05-01 22:17:25,543 - tensorflow - INFO - loss = 0.029437074, step = 2393200 (2.251 sec) +2022-05-01 22:17:27,794 - tensorflow - INFO - global_step/sec: 88.8368 +2022-05-01 22:17:27,795 - tensorflow - INFO - loss = 0.02802345, step = 2393400 (2.251 sec) +2022-05-01 22:17:30,049 - tensorflow - INFO - global_step/sec: 88.6802 +2022-05-01 22:17:30,050 - tensorflow - INFO - loss = 0.063665904, step = 2393600 (2.255 sec) +2022-05-01 22:17:32,307 - tensorflow - INFO - global_step/sec: 88.5758 +2022-05-01 22:17:32,308 - tensorflow - INFO - loss = 0.021801218, step = 2393800 (2.258 sec) +2022-05-01 22:17:34,568 - tensorflow - INFO - global_step/sec: 88.4643 +2022-05-01 22:17:34,569 - tensorflow - INFO - loss = 0.11958213, step = 2394000 (2.261 sec) +2022-05-01 22:17:36,821 - tensorflow - INFO - global_step/sec: 88.7855 +2022-05-01 22:17:36,821 - tensorflow - INFO - loss = 0.028965376, step = 2394200 (2.253 sec) +2022-05-01 22:17:39,071 - tensorflow - INFO - global_step/sec: 88.8764 +2022-05-01 22:17:39,072 - tensorflow - INFO - loss = 0.0116002485, step = 2394400 (2.250 sec) +2022-05-01 22:17:41,326 - tensorflow - INFO - global_step/sec: 88.6799 +2022-05-01 22:17:41,327 - tensorflow - INFO - loss = 0.01459889, step = 2394600 (2.255 sec) +2022-05-01 22:17:43,582 - tensorflow - INFO - global_step/sec: 88.6758 +2022-05-01 22:17:43,582 - tensorflow - INFO - loss = 0.077938415, step = 2394800 (2.255 sec) +2022-05-01 22:17:45,836 - tensorflow - INFO - global_step/sec: 88.7167 +2022-05-01 22:17:45,837 - tensorflow - INFO - loss = 0.032044075, step = 2395000 (2.254 sec) +2022-05-01 22:17:48,097 - tensorflow - INFO - global_step/sec: 88.4702 +2022-05-01 22:17:48,097 - tensorflow - INFO - loss = 0.03634771, step = 2395200 (2.261 sec) +2022-05-01 22:17:50,352 - tensorflow - INFO - global_step/sec: 88.6998 +2022-05-01 22:17:50,352 - tensorflow - INFO - loss = 0.07824456, step = 2395400 (2.255 sec) +2022-05-01 22:17:52,603 - tensorflow - INFO - global_step/sec: 88.8301 +2022-05-01 22:17:52,603 - tensorflow - INFO - loss = 0.0139762, step = 2395600 (2.251 sec) +2022-05-01 22:17:54,855 - tensorflow - INFO - global_step/sec: 88.8065 +2022-05-01 22:17:54,856 - tensorflow - INFO - loss = 0.009006003, step = 2395800 (2.252 sec) +2022-05-01 22:17:57,107 - tensorflow - INFO - global_step/sec: 88.804 +2022-05-01 22:17:57,108 - tensorflow - INFO - loss = 0.019991372, step = 2396000 (2.252 sec) +2022-05-01 22:17:59,358 - tensorflow - INFO - global_step/sec: 88.8513 +2022-05-01 22:17:59,359 - tensorflow - INFO - loss = 0.5126882, step = 2396200 (2.251 sec) +2022-05-01 22:18:01,617 - tensorflow - INFO - global_step/sec: 88.5474 +2022-05-01 22:18:01,617 - tensorflow - INFO - loss = 0.01509488, step = 2396400 (2.259 sec) +2022-05-01 22:18:03,870 - tensorflow - INFO - global_step/sec: 88.7642 +2022-05-01 22:18:03,871 - tensorflow - INFO - loss = 0.01103553, step = 2396600 (2.253 sec) +2022-05-01 22:18:06,124 - tensorflow - INFO - global_step/sec: 88.7297 +2022-05-01 22:18:06,125 - tensorflow - INFO - loss = 0.01015729, step = 2396800 (2.254 sec) +2022-05-01 22:18:08,379 - tensorflow - INFO - global_step/sec: 88.7154 +2022-05-01 22:18:08,379 - tensorflow - INFO - loss = 0.01095845, step = 2397000 (2.254 sec) +2022-05-01 22:18:10,628 - tensorflow - INFO - global_step/sec: 88.9017 +2022-05-01 22:18:10,629 - tensorflow - INFO - loss = 0.019706031, step = 2397200 (2.250 sec) +2022-05-01 22:18:12,888 - tensorflow - INFO - global_step/sec: 88.5183 +2022-05-01 22:18:12,888 - tensorflow - INFO - loss = 0.01793155, step = 2397400 (2.259 sec) +2022-05-01 22:18:15,137 - tensorflow - INFO - global_step/sec: 88.9022 +2022-05-01 22:18:15,138 - tensorflow - INFO - loss = 0.0090178745, step = 2397600 (2.250 sec) +2022-05-01 22:18:17,389 - tensorflow - INFO - global_step/sec: 88.8423 +2022-05-01 22:18:17,389 - tensorflow - INFO - loss = 0.004080846, step = 2397800 (2.251 sec) +2022-05-01 22:18:19,643 - tensorflow - INFO - global_step/sec: 88.7274 +2022-05-01 22:18:19,643 - tensorflow - INFO - loss = 0.013300663, step = 2398000 (2.254 sec) +2022-05-01 22:18:21,900 - tensorflow - INFO - global_step/sec: 88.5801 +2022-05-01 22:18:21,901 - tensorflow - INFO - loss = 0.029440576, step = 2398200 (2.258 sec) +2022-05-01 22:18:24,157 - tensorflow - INFO - global_step/sec: 88.6256 +2022-05-01 22:18:24,158 - tensorflow - INFO - loss = 0.021608679, step = 2398400 (2.257 sec) +2022-05-01 22:18:26,413 - tensorflow - INFO - global_step/sec: 88.6579 +2022-05-01 22:18:26,413 - tensorflow - INFO - loss = 0.013700175, step = 2398600 (2.256 sec) +2022-05-01 22:18:28,669 - tensorflow - INFO - global_step/sec: 88.662 +2022-05-01 22:18:28,669 - tensorflow - INFO - loss = 0.018216874, step = 2398800 (2.256 sec) +2022-05-01 22:18:30,923 - tensorflow - INFO - global_step/sec: 88.7059 +2022-05-01 22:18:30,924 - tensorflow - INFO - loss = 0.087347396, step = 2399000 (2.255 sec) +2022-05-01 22:18:33,175 - tensorflow - INFO - global_step/sec: 88.8152 +2022-05-01 22:18:33,176 - tensorflow - INFO - loss = 0.010069039, step = 2399200 (2.252 sec) +2022-05-01 22:18:35,430 - tensorflow - INFO - global_step/sec: 88.7169 +2022-05-01 22:18:35,430 - tensorflow - INFO - loss = 0.0043498473, step = 2399400 (2.254 sec) +2022-05-01 22:18:37,685 - tensorflow - INFO - global_step/sec: 88.6671 +2022-05-01 22:18:37,686 - tensorflow - INFO - loss = 0.045280192, step = 2399600 (2.256 sec) +2022-05-01 22:18:39,940 - tensorflow - INFO - global_step/sec: 88.7167 +2022-05-01 22:18:39,940 - tensorflow - INFO - loss = 0.009058259, step = 2399800 (2.254 sec) +2022-05-01 22:18:42,178 - tensorflow - INFO - Saving checkpoints for 2400000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. +2022-05-01 22:18:42,193 - tensorflow - INFO - model.ckpt-2400000 is not in all_model_checkpoint_paths. Manually adding it. +2022-05-01 22:18:42,743 - tensorflow - INFO - Calling model_fn. +2022-05-01 22:18:42,869 - tensorflow - INFO - Done calling model_fn. +2022-05-01 22:18:42,884 - tensorflow - INFO - Starting evaluation at 2022-05-01T22:18:42Z +2022-05-01 22:18:42,947 - tensorflow - INFO - Graph was finalized. +2022-05-01 22:18:42,951 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-2400000 +2022-05-01 22:18:43,230 - tensorflow - INFO - Running local_init_op. +2022-05-01 22:18:43,338 - tensorflow - INFO - Done running local_init_op. +2022-05-01 22:18:53,570 - tensorflow - INFO - Evaluation [3990/39900] +2022-05-01 22:19:03,090 - tensorflow - INFO - Evaluation [7980/39900] +2022-05-01 22:19:12,586 - tensorflow - INFO - Evaluation [11970/39900] +2022-05-01 22:19:22,093 - tensorflow - INFO - Evaluation [15960/39900] +2022-05-01 22:19:31,631 - tensorflow - INFO - Evaluation [19950/39900] +2022-05-01 22:19:41,171 - tensorflow - INFO - Evaluation [23940/39900] +2022-05-01 22:19:50,691 - tensorflow - INFO - Evaluation [27930/39900] +2022-05-01 22:20:00,276 - tensorflow - INFO - Evaluation [31920/39900] +2022-05-01 22:20:09,832 - tensorflow - INFO - Evaluation [35910/39900] +2022-05-01 22:20:19,401 - tensorflow - INFO - Evaluation [39900/39900] +2022-05-01 22:20:19,539 - tensorflow - INFO - Finished evaluation at 2022-05-01-22:20:19 +2022-05-01 22:20:19,539 - tensorflow - INFO - Saving dict for global step 2400000: global_step = 2400000, loss = 0.0163621, mean_degree_err = 3.2771058 +2022-05-01 22:20:19,540 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 2400000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-2400000 +2022-05-01 22:20:19,553 - tensorflow - INFO - global_step/sec: 2.00776 +2022-05-01 22:20:19,554 - tensorflow - INFO - loss = 0.017489435, step = 2400000 (99.614 sec) +2022-05-01 22:20:21,826 - tensorflow - INFO - global_step/sec: 87.9881 +2022-05-01 22:20:21,827 - tensorflow - INFO - loss = 0.07216262, step = 2400200 (2.273 sec) +2022-05-01 22:20:24,081 - tensorflow - INFO - global_step/sec: 88.6938 +2022-05-01 22:20:24,082 - tensorflow - INFO - loss = 0.092333764, step = 2400400 (2.255 sec) +2022-05-01 22:20:26,335 - tensorflow - INFO - global_step/sec: 88.7409 +2022-05-01 22:20:26,336 - tensorflow - INFO - loss = 0.0081663225, step = 2400600 (2.254 sec) +2022-05-01 22:20:28,588 - tensorflow - INFO - global_step/sec: 88.7691 +2022-05-01 22:20:28,589 - tensorflow - INFO - loss = 0.008921932, step = 2400800 (2.253 sec) +2022-05-01 22:20:30,851 - tensorflow - INFO - global_step/sec: 88.3872 +2022-05-01 22:20:30,851 - tensorflow - INFO - loss = 0.020071022, step = 2401000 (2.263 sec) +2022-05-01 22:20:33,106 - tensorflow - INFO - global_step/sec: 88.6718 +2022-05-01 22:20:33,107 - tensorflow - INFO - loss = 0.029509716, step = 2401200 (2.256 sec) +2022-05-01 22:20:35,366 - tensorflow - INFO - global_step/sec: 88.5227 +2022-05-01 22:20:35,366 - tensorflow - INFO - loss = 0.021414602, step = 2401400 (2.259 sec) +2022-05-01 22:20:37,615 - tensorflow - INFO - global_step/sec: 88.9051 +2022-05-01 22:20:37,616 - tensorflow - INFO - loss = 0.0034311865, step = 2401600 (2.250 sec) +2022-05-01 22:20:39,872 - tensorflow - INFO - global_step/sec: 88.6275 +2022-05-01 22:20:39,872 - tensorflow - INFO - loss = 0.045313366, step = 2401800 (2.257 sec) +2022-05-01 22:20:42,122 - tensorflow - INFO - global_step/sec: 88.9013 +2022-05-01 22:20:42,122 - tensorflow - INFO - loss = 0.018083166, step = 2402000 (2.250 sec) +2022-05-01 22:20:44,372 - tensorflow - INFO - global_step/sec: 88.8594 +2022-05-01 22:20:44,373 - tensorflow - INFO - loss = 0.011318088, step = 2402200 (2.251 sec) +2022-05-01 22:20:46,626 - tensorflow - INFO - global_step/sec: 88.7272 +2022-05-01 22:20:46,627 - tensorflow - INFO - loss = 0.011177439, step = 2402400 (2.254 sec) +2022-05-01 22:20:48,879 - tensorflow - INFO - global_step/sec: 88.7988 +2022-05-01 22:20:48,879 - tensorflow - INFO - loss = 0.036629625, step = 2402600 (2.252 sec) +2022-05-01 22:20:51,136 - tensorflow - INFO - global_step/sec: 88.5901 +2022-05-01 22:20:51,137 - tensorflow - INFO - loss = 0.09368414, step = 2402800 (2.258 sec) +2022-05-01 22:20:53,387 - tensorflow - INFO - global_step/sec: 88.8506 +2022-05-01 22:20:53,388 - tensorflow - INFO - loss = 0.07625322, step = 2403000 (2.251 sec) +2022-05-01 22:20:55,643 - tensorflow - INFO - global_step/sec: 88.6679 +2022-05-01 22:20:55,643 - tensorflow - INFO - loss = 0.0075386334, step = 2403200 (2.256 sec) +2022-05-01 22:20:57,896 - tensorflow - INFO - global_step/sec: 88.7743 +2022-05-01 22:20:57,896 - tensorflow - INFO - loss = 0.01009267, step = 2403400 (2.253 sec) +2022-05-01 22:21:00,153 - tensorflow - INFO - global_step/sec: 88.6053 +2022-05-01 22:21:00,153 - tensorflow - INFO - loss = 0.021714091, step = 2403600 (2.257 sec) +2022-05-01 22:21:02,406 - tensorflow - INFO - global_step/sec: 88.7812 +2022-05-01 22:21:02,406 - tensorflow - INFO - loss = 0.03484129, step = 2403800 (2.253 sec) +2022-05-01 22:21:04,653 - tensorflow - INFO - global_step/sec: 88.9997 +2022-05-01 22:21:04,653 - tensorflow - INFO - loss = 0.046384007, step = 2404000 (2.247 sec) +2022-05-01 22:21:06,901 - tensorflow - INFO - global_step/sec: 88.9751 +2022-05-01 22:21:06,901 - tensorflow - INFO - loss = 0.017480731, step = 2404200 (2.248 sec) +2022-05-01 22:21:09,153 - tensorflow - INFO - global_step/sec: 88.8041 +2022-05-01 22:21:09,153 - tensorflow - INFO - loss = 0.02646036, step = 2404400 (2.252 sec) +2022-05-01 22:21:11,411 - tensorflow - INFO - global_step/sec: 88.5687 +2022-05-01 22:21:11,411 - tensorflow - INFO - loss = 0.01452543, step = 2404600 (2.258 sec) +2022-05-01 22:21:13,662 - tensorflow - INFO - global_step/sec: 88.8672 +2022-05-01 22:21:13,662 - tensorflow - INFO - loss = 0.00674339, step = 2404800 (2.251 sec) +2022-05-01 22:21:15,916 - tensorflow - INFO - global_step/sec: 88.7083 +2022-05-01 22:21:15,917 - tensorflow - INFO - loss = 0.013400199, step = 2405000 (2.255 sec) +2022-05-01 22:21:18,168 - tensorflow - INFO - global_step/sec: 88.8052 +2022-05-01 22:21:18,169 - tensorflow - INFO - loss = 0.010974922, step = 2405200 (2.252 sec) +2022-05-01 22:21:20,419 - tensorflow - INFO - global_step/sec: 88.8626 +2022-05-01 22:21:20,419 - tensorflow - INFO - loss = 0.013504675, step = 2405400 (2.251 sec) +2022-05-01 22:21:22,675 - tensorflow - INFO - global_step/sec: 88.6573 +2022-05-01 22:21:22,675 - tensorflow - INFO - loss = 0.07476987, step = 2405600 (2.256 sec) +2022-05-01 22:21:24,939 - tensorflow - INFO - global_step/sec: 88.3407 +2022-05-01 22:21:24,939 - tensorflow - INFO - loss = 0.07896981, step = 2405800 (2.264 sec) +2022-05-01 22:21:27,199 - tensorflow - INFO - global_step/sec: 88.4758 +2022-05-01 22:21:27,200 - tensorflow - INFO - loss = 0.022436867, step = 2406000 (2.260 sec) +2022-05-01 22:21:29,450 - tensorflow - INFO - global_step/sec: 88.8492 +2022-05-01 22:21:29,451 - tensorflow - INFO - loss = 0.012631004, step = 2406200 (2.251 sec) +2022-05-01 22:21:31,712 - tensorflow - INFO - 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INFO - global_step/sec: 88.6651 +2022-05-01 22:36:52,036 - tensorflow - INFO - loss = 1.4362056, step = 2488000 (2.256 sec) +2022-05-01 22:36:54,287 - tensorflow - INFO - global_step/sec: 88.8069 +2022-05-01 22:36:54,288 - tensorflow - INFO - loss = 0.49593955, step = 2488200 (2.252 sec) +2022-05-01 22:36:56,547 - tensorflow - INFO - global_step/sec: 88.5042 +2022-05-01 22:36:56,547 - tensorflow - INFO - loss = 0.0058627683, step = 2488400 (2.260 sec) +2022-05-01 22:36:58,797 - tensorflow - INFO - global_step/sec: 88.8873 +2022-05-01 22:36:58,798 - tensorflow - INFO - loss = 0.04569395, step = 2488600 (2.250 sec) +2022-05-01 22:37:01,050 - tensorflow - INFO - global_step/sec: 88.7574 +2022-05-01 22:37:01,051 - tensorflow - INFO - loss = 0.1335107, step = 2488800 (2.253 sec) +2022-05-01 22:37:03,305 - tensorflow - INFO - global_step/sec: 88.6996 +2022-05-01 22:37:03,306 - tensorflow - INFO - loss = 0.01614601, step = 2489000 (2.255 sec) +2022-05-01 22:37:05,558 - tensorflow - INFO - global_step/sec: 88.7925 +2022-05-01 22:37:05,558 - tensorflow - INFO - loss = 0.007931199, step = 2489200 (2.252 sec) +2022-05-01 22:37:07,816 - tensorflow - INFO - global_step/sec: 88.5643 +2022-05-01 22:37:07,816 - tensorflow - INFO - loss = 0.05090235, step = 2489400 (2.258 sec) +2022-05-01 22:37:10,076 - tensorflow - INFO - global_step/sec: 88.504 +2022-05-01 22:37:10,076 - tensorflow - INFO - loss = 0.017152905, step = 2489600 (2.260 sec) +2022-05-01 22:37:12,325 - tensorflow - INFO - global_step/sec: 88.9359 +2022-05-01 22:37:12,325 - tensorflow - INFO - loss = 0.010166051, step = 2489800 (2.249 sec) +2022-05-01 22:37:14,576 - tensorflow - INFO - global_step/sec: 88.8497 +2022-05-01 22:37:14,576 - tensorflow - INFO - loss = 0.008025802, step = 2490000 (2.251 sec) +2022-05-01 22:37:16,828 - tensorflow - INFO - global_step/sec: 88.7933 +2022-05-01 22:37:16,828 - tensorflow - INFO - loss = 0.02635665, step = 2490200 (2.252 sec) +2022-05-01 22:37:19,082 - tensorflow - INFO - global_step/sec: 88.7142 +2022-05-01 22:37:19,083 - tensorflow - INFO - loss = 0.05959672, step = 2490400 (2.254 sec) +2022-05-01 22:37:21,338 - tensorflow - INFO - global_step/sec: 88.6671 +2022-05-01 22:37:21,338 - tensorflow - INFO - loss = 0.007345924, step = 2490600 (2.256 sec) +2022-05-01 22:37:23,601 - tensorflow - INFO - global_step/sec: 88.3764 +2022-05-01 22:37:23,601 - tensorflow - INFO - loss = 0.6229799, step = 2490800 (2.263 sec) +2022-05-01 22:37:25,862 - tensorflow - INFO - global_step/sec: 88.4762 +2022-05-01 22:37:25,862 - tensorflow - INFO - loss = 0.023782894, step = 2491000 (2.260 sec) +2022-05-01 22:37:28,124 - tensorflow - INFO - global_step/sec: 88.3935 +2022-05-01 22:37:28,125 - tensorflow - INFO - loss = 0.017684136, step = 2491200 (2.263 sec) +2022-05-01 22:37:30,389 - tensorflow - INFO - global_step/sec: 88.3029 +2022-05-01 22:37:30,390 - tensorflow - INFO - loss = 0.029672848, step = 2491400 (2.265 sec) +2022-05-01 22:37:32,652 - tensorflow - INFO - global_step/sec: 88.3824 +2022-05-01 22:37:32,652 - tensorflow - INFO - loss = 0.018330775, step = 2491600 (2.263 sec) +2022-05-01 22:37:34,917 - tensorflow - INFO - global_step/sec: 88.3055 +2022-05-01 22:37:34,917 - tensorflow - INFO - loss = 0.19323613, step = 2491800 (2.265 sec) +2022-05-01 22:37:37,180 - tensorflow - INFO - global_step/sec: 88.3689 +2022-05-01 22:37:37,181 - tensorflow - INFO - loss = 0.0035518394, step = 2492000 (2.263 sec) +2022-05-01 22:37:39,442 - tensorflow - INFO - global_step/sec: 88.4067 +2022-05-01 22:37:39,443 - tensorflow - INFO - loss = 0.0065744384, step = 2492200 (2.262 sec) +2022-05-01 22:37:41,703 - tensorflow - INFO - global_step/sec: 88.453 +2022-05-01 22:37:41,704 - tensorflow - INFO - loss = 0.007689911, step = 2492400 (2.261 sec) +2022-05-01 22:37:43,967 - tensorflow - INFO - global_step/sec: 88.3656 +2022-05-01 22:37:43,967 - tensorflow - INFO - loss = 0.03819196, step = 2492600 (2.263 sec) +2022-05-01 22:37:46,227 - tensorflow - INFO - global_step/sec: 88.476 +2022-05-01 22:37:46,228 - tensorflow - INFO - loss = 0.047451183, step = 2492800 (2.261 sec) +2022-05-01 22:37:48,487 - tensorflow - INFO - global_step/sec: 88.5072 +2022-05-01 22:37:48,487 - tensorflow - INFO - loss = 0.014459378, step = 2493000 (2.260 sec) +2022-05-01 22:37:50,747 - tensorflow - INFO - global_step/sec: 88.5062 +2022-05-01 22:37:50,747 - tensorflow - INFO - loss = 0.20836158, step = 2493200 (2.260 sec) +2022-05-01 22:37:53,006 - tensorflow - INFO - global_step/sec: 88.5224 +2022-05-01 22:37:53,006 - tensorflow - INFO - loss = 0.03848868, step = 2493400 (2.259 sec) +2022-05-01 22:37:55,263 - tensorflow - INFO - global_step/sec: 88.6129 +2022-05-01 22:37:55,263 - tensorflow - INFO - loss = 0.17222637, step = 2493600 (2.257 sec) +2022-05-01 22:37:57,517 - tensorflow - INFO - global_step/sec: 88.7318 +2022-05-01 22:37:57,517 - tensorflow - INFO - loss = 0.012568856, step = 2493800 (2.254 sec) +2022-05-01 22:37:59,768 - tensorflow - INFO - global_step/sec: 88.8338 +2022-05-01 22:37:59,769 - tensorflow - INFO - loss = 0.036384873, step = 2494000 (2.251 sec) +2022-05-01 22:38:02,022 - tensorflow - INFO - global_step/sec: 88.7564 +2022-05-01 22:38:02,022 - tensorflow - INFO - loss = 0.010013607, step = 2494200 (2.253 sec) +2022-05-01 22:38:04,274 - tensorflow - INFO - global_step/sec: 88.7988 +2022-05-01 22:38:04,274 - tensorflow - INFO - loss = 0.011140664, step = 2494400 (2.252 sec) +2022-05-01 22:38:06,524 - tensorflow - INFO - global_step/sec: 88.8942 +2022-05-01 22:38:06,524 - tensorflow - INFO - loss = 0.00332475, step = 2494600 (2.250 sec) +2022-05-01 22:38:08,779 - tensorflow - INFO - global_step/sec: 88.6708 +2022-05-01 22:38:08,780 - tensorflow - INFO - loss = 0.03149625, step = 2494800 (2.256 sec) +2022-05-01 22:38:11,030 - tensorflow - INFO - global_step/sec: 88.8648 +2022-05-01 22:38:11,030 - tensorflow - INFO - loss = 0.008571399, step = 2495000 (2.251 sec) +2022-05-01 22:38:13,281 - tensorflow - INFO - global_step/sec: 88.8418 +2022-05-01 22:38:13,282 - tensorflow - INFO - loss = 0.024274005, step = 2495200 (2.251 sec) +2022-05-01 22:38:15,539 - tensorflow - INFO - global_step/sec: 88.5933 +2022-05-01 22:38:15,539 - tensorflow - INFO - loss = 0.026418442, step = 2495400 (2.257 sec) +2022-05-01 22:38:17,792 - tensorflow - INFO - global_step/sec: 88.7513 +2022-05-01 22:38:17,793 - tensorflow - INFO - loss = 0.019441668, step = 2495600 (2.253 sec) +2022-05-01 22:38:20,042 - tensorflow - INFO - global_step/sec: 88.8905 +2022-05-01 22:38:20,043 - tensorflow - INFO - loss = 0.010732288, step = 2495800 (2.250 sec) +2022-05-01 22:38:22,287 - tensorflow - INFO - global_step/sec: 89.1048 +2022-05-01 22:38:22,287 - tensorflow - INFO - loss = 0.03142559, step = 2496000 (2.245 sec) +2022-05-01 22:38:24,533 - tensorflow - INFO - global_step/sec: 89.0417 +2022-05-01 22:38:24,533 - tensorflow - INFO - loss = 0.059488736, step = 2496200 (2.246 sec) +2022-05-01 22:38:26,792 - tensorflow - INFO - global_step/sec: 88.5427 +2022-05-01 22:38:26,792 - tensorflow - INFO - loss = 0.014048614, step = 2496400 (2.259 sec) +2022-05-01 22:38:29,042 - tensorflow - INFO - global_step/sec: 88.8708 +2022-05-01 22:38:29,043 - tensorflow - INFO - loss = 0.047158502, step = 2496600 (2.250 sec) +2022-05-01 22:38:31,297 - tensorflow - INFO - global_step/sec: 88.7053 +2022-05-01 22:38:31,297 - tensorflow - INFO - loss = 0.019878263, step = 2496800 (2.255 sec) +2022-05-01 22:38:33,548 - tensorflow - INFO - global_step/sec: 88.8446 +2022-05-01 22:38:33,548 - tensorflow - INFO - loss = 0.0071397563, step = 2497000 (2.251 sec) +2022-05-01 22:38:35,800 - tensorflow - INFO - global_step/sec: 88.7965 +2022-05-01 22:38:35,801 - tensorflow - INFO - loss = 0.7433055, step = 2497200 (2.252 sec) +2022-05-01 22:38:38,053 - tensorflow - INFO - global_step/sec: 88.7709 +2022-05-01 22:38:38,054 - tensorflow - INFO - loss = 0.0055767056, step = 2497400 (2.253 sec) +2022-05-01 22:38:40,309 - tensorflow - INFO - global_step/sec: 88.661 +2022-05-01 22:38:40,309 - tensorflow - INFO - loss = 0.012999417, step = 2497600 (2.256 sec) +2022-05-01 22:38:42,559 - tensorflow - INFO - global_step/sec: 88.8825 +2022-05-01 22:38:42,560 - tensorflow - INFO - loss = 0.009196865, step = 2497800 (2.250 sec) +2022-05-01 22:38:44,817 - tensorflow - INFO - global_step/sec: 88.5973 +2022-05-01 22:38:44,817 - tensorflow - INFO - loss = 0.019697893, step = 2498000 (2.257 sec) +2022-05-01 22:38:47,067 - tensorflow - INFO - global_step/sec: 88.8669 +2022-05-01 22:38:47,068 - tensorflow - INFO - loss = 0.0067554098, step = 2498200 (2.251 sec) +2022-05-01 22:38:49,321 - tensorflow - INFO - global_step/sec: 88.7454 +2022-05-01 22:38:49,321 - tensorflow - INFO - loss = 0.012799524, step = 2498400 (2.254 sec) +2022-05-01 22:38:51,579 - tensorflow - INFO - global_step/sec: 88.5476 +2022-05-01 22:38:51,580 - tensorflow - INFO - loss = 0.04408863, step = 2498600 (2.259 sec) +2022-05-01 22:38:53,828 - tensorflow - INFO - global_step/sec: 88.95 +2022-05-01 22:38:53,828 - tensorflow - INFO - loss = 0.056328095, step = 2498800 (2.248 sec) +2022-05-01 22:38:56,084 - tensorflow - INFO - global_step/sec: 88.6324 +2022-05-01 22:38:56,085 - tensorflow - INFO - loss = 0.011546372, step = 2499000 (2.256 sec) +2022-05-01 22:38:58,335 - tensorflow - INFO - global_step/sec: 88.8491 +2022-05-01 22:38:58,336 - tensorflow - INFO - loss = 0.0033892635, step = 2499200 (2.251 sec) +2022-05-01 22:39:00,593 - tensorflow - INFO - global_step/sec: 88.6082 +2022-05-01 22:39:00,593 - tensorflow - INFO - loss = 0.004750331, step = 2499400 (2.257 sec) +2022-05-01 22:39:02,853 - tensorflow - INFO - global_step/sec: 88.4876 +2022-05-01 22:39:02,853 - tensorflow - INFO - loss = 0.007829336, step = 2499600 (2.260 sec) +2022-05-01 22:39:05,113 - tensorflow - INFO - global_step/sec: 88.4993 +2022-05-01 22:39:05,113 - tensorflow - INFO - loss = 0.024423394, step = 2499800 (2.260 sec) +2022-05-01 22:39:07,351 - tensorflow - INFO - Saving checkpoints for 2500000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. +2022-05-01 22:39:07,369 - tensorflow - INFO - model.ckpt-2500000 is not in all_model_checkpoint_paths. Manually adding it. +2022-05-01 22:39:07,900 - tensorflow - INFO - Calling model_fn. +2022-05-01 22:39:08,027 - tensorflow - INFO - Done calling model_fn. +2022-05-01 22:39:08,042 - tensorflow - INFO - Starting evaluation at 2022-05-01T22:39:08Z +2022-05-01 22:39:08,106 - tensorflow - INFO - Graph was finalized. +2022-05-01 22:39:08,109 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-2500000 +2022-05-01 22:39:08,391 - tensorflow - INFO - Running local_init_op. +2022-05-01 22:39:08,499 - tensorflow - INFO - Done running local_init_op. +2022-05-01 22:39:18,635 - tensorflow - INFO - Evaluation [3990/39900] +2022-05-01 22:39:28,069 - tensorflow - INFO - Evaluation [7980/39900] +2022-05-01 22:39:37,498 - tensorflow - INFO - Evaluation [11970/39900] +2022-05-01 22:39:46,922 - tensorflow - INFO - Evaluation [15960/39900] +2022-05-01 22:39:56,366 - tensorflow - INFO - Evaluation [19950/39900] +2022-05-01 22:40:05,832 - tensorflow - INFO - Evaluation [23940/39900] +2022-05-01 22:40:15,280 - tensorflow - INFO - Evaluation [27930/39900] +2022-05-01 22:40:24,719 - tensorflow - INFO - Evaluation [31920/39900] +2022-05-01 22:40:34,151 - tensorflow - INFO - Evaluation [35910/39900] +2022-05-01 22:40:43,604 - tensorflow - INFO - Evaluation [39900/39900] +2022-05-01 22:40:43,741 - tensorflow - INFO - Finished evaluation at 2022-05-01-22:40:43 +2022-05-01 22:40:43,741 - tensorflow - INFO - Saving dict for global step 2500000: global_step = 2500000, loss = 0.01607213, mean_degree_err = 3.0870223 +2022-05-01 22:40:43,742 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 2500000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-2500000 +2022-05-01 22:40:43,755 - tensorflow - INFO - global_step/sec: 2.02752 +2022-05-01 22:40:43,756 - tensorflow - INFO - loss = 0.028217498, step = 2500000 (98.643 sec) +2022-05-01 22:40:46,023 - tensorflow - INFO - global_step/sec: 88.1845 +2022-05-01 22:40:46,024 - tensorflow - INFO - loss = 0.01755622, step = 2500200 (2.268 sec) +2022-05-01 22:40:48,279 - tensorflow - INFO - global_step/sec: 88.675 +2022-05-01 22:40:48,279 - tensorflow - INFO - loss = 0.015188168, step = 2500400 (2.255 sec) +2022-05-01 22:40:50,536 - tensorflow - INFO - global_step/sec: 88.597 +2022-05-01 22:40:50,537 - tensorflow - INFO - loss = 0.011648623, step = 2500600 (2.257 sec) +2022-05-01 22:40:52,787 - tensorflow - INFO - global_step/sec: 88.8475 +2022-05-01 22:40:52,788 - tensorflow - INFO - loss = 0.08985994, step = 2500800 (2.251 sec) +2022-05-01 22:40:55,051 - tensorflow - INFO - global_step/sec: 88.3396 +2022-05-01 22:40:55,052 - tensorflow - INFO - loss = 0.0031446307, step = 2501000 (2.264 sec) +2022-05-01 22:40:57,317 - tensorflow - INFO - global_step/sec: 88.2696 +2022-05-01 22:40:57,317 - tensorflow - INFO - loss = 0.007888079, step = 2501200 (2.266 sec) +2022-05-01 22:40:59,584 - tensorflow - INFO - global_step/sec: 88.2311 +2022-05-01 22:40:59,584 - tensorflow - INFO - loss = 0.0051378547, step = 2501400 (2.267 sec) +2022-05-01 22:41:01,844 - tensorflow - INFO - global_step/sec: 88.5079 +2022-05-01 22:41:01,844 - tensorflow - INFO - loss = 0.013122154, step = 2501600 (2.260 sec) +2022-05-01 22:41:04,105 - tensorflow - INFO - global_step/sec: 88.4465 +2022-05-01 22:41:04,105 - tensorflow - INFO - loss = 0.01367711, step = 2501800 (2.261 sec) +2022-05-01 22:41:06,365 - tensorflow - INFO - global_step/sec: 88.4751 +2022-05-01 22:41:06,366 - tensorflow - INFO - loss = 0.014673911, step = 2502000 (2.261 sec) +2022-05-01 22:41:08,624 - tensorflow - INFO - global_step/sec: 88.5279 +2022-05-01 22:41:08,625 - tensorflow - INFO - loss = 0.010844378, step = 2502200 (2.259 sec) +2022-05-01 22:41:10,879 - tensorflow - INFO - global_step/sec: 88.6954 +2022-05-01 22:41:10,880 - tensorflow - INFO - loss = 0.011447811, step = 2502400 (2.255 sec) +2022-05-01 22:41:13,145 - tensorflow - INFO - global_step/sec: 88.2901 +2022-05-01 22:41:13,145 - tensorflow - INFO - loss = 0.043297, step = 2502600 (2.265 sec) +2022-05-01 22:41:15,407 - tensorflow - INFO - global_step/sec: 88.4005 +2022-05-01 22:41:15,407 - tensorflow - INFO - loss = 0.027249252, step = 2502800 (2.262 sec) +2022-05-01 22:41:17,667 - tensorflow - INFO - global_step/sec: 88.4985 +2022-05-01 22:41:17,667 - tensorflow - INFO - loss = 0.01280329, step = 2503000 (2.260 sec) +2022-05-01 22:41:19,928 - tensorflow - INFO - global_step/sec: 88.4401 +2022-05-01 22:41:19,929 - tensorflow - INFO - loss = 0.01926428, step = 2503200 (2.261 sec) +2022-05-01 22:41:22,187 - tensorflow - INFO - global_step/sec: 88.5565 +2022-05-01 22:41:22,187 - tensorflow - INFO - loss = 0.07693437, step = 2503400 (2.258 sec) +2022-05-01 22:41:24,446 - tensorflow - INFO - global_step/sec: 88.5255 +2022-05-01 22:41:24,447 - tensorflow - INFO - loss = 0.89175195, step = 2503600 (2.259 sec) +2022-05-01 22:41:26,705 - tensorflow - INFO - global_step/sec: 88.5356 +2022-05-01 22:41:26,705 - tensorflow - INFO - loss = 0.10346782, step = 2503800 (2.259 sec) +2022-05-01 22:41:28,960 - 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INFO - loss = 0.06566036, step = 2585400 (2.255 sec) +2022-05-01 22:56:49,111 - tensorflow - INFO - global_step/sec: 88.9266 +2022-05-01 22:56:49,112 - tensorflow - INFO - loss = 0.014203774, step = 2585600 (2.249 sec) +2022-05-01 22:56:51,365 - tensorflow - INFO - global_step/sec: 88.7547 +2022-05-01 22:56:51,365 - tensorflow - INFO - loss = 0.013998233, step = 2585800 (2.253 sec) +2022-05-01 22:56:53,618 - tensorflow - INFO - global_step/sec: 88.7695 +2022-05-01 22:56:53,618 - tensorflow - INFO - loss = 0.012911035, step = 2586000 (2.253 sec) +2022-05-01 22:56:55,873 - tensorflow - INFO - global_step/sec: 88.6755 +2022-05-01 22:56:55,874 - tensorflow - INFO - loss = 0.013721411, step = 2586200 (2.255 sec) +2022-05-01 22:56:58,133 - tensorflow - INFO - global_step/sec: 88.523 +2022-05-01 22:56:58,133 - tensorflow - INFO - loss = 0.0067030694, step = 2586400 (2.259 sec) +2022-05-01 22:57:00,384 - tensorflow - INFO - global_step/sec: 88.8318 +2022-05-01 22:57:00,384 - tensorflow - INFO - loss = 0.009887196, step = 2586600 (2.251 sec) +2022-05-01 22:57:02,643 - tensorflow - INFO - global_step/sec: 88.547 +2022-05-01 22:57:02,643 - tensorflow - INFO - loss = 0.31959456, step = 2586800 (2.259 sec) +2022-05-01 22:57:04,896 - tensorflow - INFO - global_step/sec: 88.7475 +2022-05-01 22:57:04,897 - tensorflow - INFO - loss = 0.018577524, step = 2587000 (2.254 sec) +2022-05-01 22:57:07,148 - tensorflow - INFO - global_step/sec: 88.8043 +2022-05-01 22:57:07,149 - tensorflow - INFO - loss = 0.0077270763, step = 2587200 (2.252 sec) +2022-05-01 22:57:09,406 - tensorflow - INFO - global_step/sec: 88.5889 +2022-05-01 22:57:09,406 - tensorflow - INFO - loss = 0.057520434, step = 2587400 (2.258 sec) +2022-05-01 22:57:11,659 - tensorflow - INFO - global_step/sec: 88.7908 +2022-05-01 22:57:11,659 - tensorflow - INFO - loss = 0.15892008, step = 2587600 (2.253 sec) +2022-05-01 22:57:13,909 - tensorflow - INFO - global_step/sec: 88.8624 +2022-05-01 22:57:13,910 - tensorflow - INFO - loss = 0.03677657, step = 2587800 (2.251 sec) +2022-05-01 22:57:16,159 - tensorflow - INFO - global_step/sec: 88.9004 +2022-05-01 22:57:16,159 - tensorflow - INFO - loss = 0.011227585, step = 2588000 (2.250 sec) +2022-05-01 22:57:18,409 - tensorflow - INFO - global_step/sec: 88.8677 +2022-05-01 22:57:18,410 - tensorflow - INFO - loss = 0.050311897, step = 2588200 (2.251 sec) +2022-05-01 22:57:20,664 - tensorflow - INFO - global_step/sec: 88.6989 +2022-05-01 22:57:20,665 - tensorflow - INFO - loss = 0.0051274067, step = 2588400 (2.255 sec) +2022-05-01 22:57:22,915 - tensorflow - INFO - global_step/sec: 88.8735 +2022-05-01 22:57:22,915 - tensorflow - INFO - loss = 0.031046556, step = 2588600 (2.250 sec) +2022-05-01 22:57:25,170 - tensorflow - INFO - global_step/sec: 88.6787 +2022-05-01 22:57:25,170 - tensorflow - INFO - loss = 0.008272649, step = 2588800 (2.255 sec) +2022-05-01 22:57:27,428 - tensorflow - INFO - global_step/sec: 88.5865 +2022-05-01 22:57:27,428 - tensorflow - INFO - loss = 0.0075667934, step = 2589000 (2.258 sec) +2022-05-01 22:57:29,683 - tensorflow - INFO - global_step/sec: 88.6647 +2022-05-01 22:57:29,684 - tensorflow - INFO - loss = 0.03137114, step = 2589200 (2.256 sec) +2022-05-01 22:57:31,937 - tensorflow - INFO - global_step/sec: 88.749 +2022-05-01 22:57:31,937 - tensorflow - INFO - loss = 0.0040960596, step = 2589400 (2.254 sec) +2022-05-01 22:57:34,189 - tensorflow - INFO - global_step/sec: 88.7869 +2022-05-01 22:57:34,190 - tensorflow - INFO - loss = 0.01772147, step = 2589600 (2.253 sec) +2022-05-01 22:57:36,445 - tensorflow - INFO - global_step/sec: 88.6831 +2022-05-01 22:57:36,445 - tensorflow - INFO - loss = 0.050396413, step = 2589800 (2.255 sec) +2022-05-01 22:57:38,696 - tensorflow - INFO - global_step/sec: 88.8194 +2022-05-01 22:57:38,697 - tensorflow - INFO - loss = 0.01502031, step = 2590000 (2.252 sec) +2022-05-01 22:57:40,950 - tensorflow - INFO - global_step/sec: 88.768 +2022-05-01 22:57:40,950 - tensorflow - INFO - loss = 0.009751061, step = 2590200 (2.253 sec) +2022-05-01 22:57:43,205 - tensorflow - INFO - global_step/sec: 88.6663 +2022-05-01 22:57:43,206 - tensorflow - INFO - loss = 0.021950861, step = 2590400 (2.256 sec) +2022-05-01 22:57:45,458 - tensorflow - INFO - global_step/sec: 88.7737 +2022-05-01 22:57:45,458 - tensorflow - INFO - loss = 0.022604462, step = 2590600 (2.253 sec) +2022-05-01 22:57:47,712 - tensorflow - INFO - global_step/sec: 88.7264 +2022-05-01 22:57:47,713 - tensorflow - INFO - loss = 0.0073060235, step = 2590800 (2.254 sec) +2022-05-01 22:57:49,968 - tensorflow - INFO - global_step/sec: 88.671 +2022-05-01 22:57:49,968 - tensorflow - INFO - loss = 0.013161661, step = 2591000 (2.256 sec) +2022-05-01 22:57:52,221 - tensorflow - INFO - global_step/sec: 88.76 +2022-05-01 22:57:52,221 - tensorflow - INFO - loss = 0.038956575, step = 2591200 (2.253 sec) +2022-05-01 22:57:54,471 - tensorflow - INFO - global_step/sec: 88.8912 +2022-05-01 22:57:54,471 - tensorflow - INFO - loss = 0.0062237633, step = 2591400 (2.250 sec) +2022-05-01 22:57:56,727 - tensorflow - INFO - global_step/sec: 88.6646 +2022-05-01 22:57:56,727 - tensorflow - INFO - loss = 0.013718772, step = 2591600 (2.256 sec) +2022-05-01 22:57:58,979 - tensorflow - INFO - global_step/sec: 88.7919 +2022-05-01 22:57:58,980 - tensorflow - INFO - loss = 0.7088275, step = 2591800 (2.252 sec) +2022-05-01 22:58:01,239 - tensorflow - INFO - global_step/sec: 88.5091 +2022-05-01 22:58:01,239 - tensorflow - INFO - loss = 0.006887424, step = 2592000 (2.260 sec) +2022-05-01 22:58:03,492 - tensorflow - INFO - global_step/sec: 88.7505 +2022-05-01 22:58:03,493 - tensorflow - INFO - loss = 0.11528784, step = 2592200 (2.254 sec) +2022-05-01 22:58:05,743 - tensorflow - INFO - global_step/sec: 88.8667 +2022-05-01 22:58:05,743 - tensorflow - INFO - loss = 0.009931682, step = 2592400 (2.250 sec) +2022-05-01 22:58:07,996 - tensorflow - INFO - global_step/sec: 88.7633 +2022-05-01 22:58:07,996 - tensorflow - INFO - loss = 0.013525553, step = 2592600 (2.253 sec) +2022-05-01 22:58:10,249 - tensorflow - INFO - global_step/sec: 88.7874 +2022-05-01 22:58:10,249 - tensorflow - INFO - loss = 0.007436597, step = 2592800 (2.253 sec) +2022-05-01 22:58:12,503 - tensorflow - INFO - global_step/sec: 88.7123 +2022-05-01 22:58:12,503 - tensorflow - INFO - loss = 0.0054980563, step = 2593000 (2.254 sec) +2022-05-01 22:58:14,757 - tensorflow - INFO - global_step/sec: 88.7201 +2022-05-01 22:58:14,758 - tensorflow - INFO - loss = 0.0050919726, step = 2593200 (2.254 sec) +2022-05-01 22:58:17,015 - tensorflow - INFO - global_step/sec: 88.6058 +2022-05-01 22:58:17,015 - tensorflow - INFO - loss = 0.0148495585, step = 2593400 (2.257 sec) +2022-05-01 22:58:19,265 - tensorflow - INFO - global_step/sec: 88.8656 +2022-05-01 22:58:19,266 - tensorflow - INFO - loss = 0.10807957, step = 2593600 (2.251 sec) +2022-05-01 22:58:21,516 - tensorflow - INFO - global_step/sec: 88.8721 +2022-05-01 22:58:21,516 - tensorflow - INFO - loss = 0.03904371, step = 2593800 (2.250 sec) +2022-05-01 22:58:23,771 - tensorflow - INFO - global_step/sec: 88.6903 +2022-05-01 22:58:23,771 - tensorflow - INFO - loss = 0.010118168, step = 2594000 (2.255 sec) +2022-05-01 22:58:26,028 - tensorflow - INFO - global_step/sec: 88.6155 +2022-05-01 22:58:26,028 - tensorflow - INFO - loss = 0.026852738, step = 2594200 (2.257 sec) +2022-05-01 22:58:28,280 - tensorflow - INFO - global_step/sec: 88.7723 +2022-05-01 22:58:28,281 - tensorflow - INFO - loss = 0.01683753, step = 2594400 (2.253 sec) +2022-05-01 22:58:30,540 - tensorflow - INFO - global_step/sec: 88.5013 +2022-05-01 22:58:30,541 - tensorflow - INFO - loss = 0.033845864, step = 2594600 (2.260 sec) +2022-05-01 22:58:32,804 - tensorflow - INFO - global_step/sec: 88.3342 +2022-05-01 22:58:32,805 - tensorflow - INFO - loss = 0.12980647, step = 2594800 (2.264 sec) +2022-05-01 22:58:35,062 - tensorflow - INFO - global_step/sec: 88.5774 +2022-05-01 22:58:35,063 - tensorflow - INFO - loss = 0.008408929, step = 2595000 (2.258 sec) +2022-05-01 22:58:37,324 - tensorflow - INFO - global_step/sec: 88.417 +2022-05-01 22:58:37,325 - tensorflow - INFO - loss = 0.018310972, step = 2595200 (2.262 sec) +2022-05-01 22:58:39,589 - tensorflow - INFO - global_step/sec: 88.3019 +2022-05-01 22:58:39,590 - tensorflow - INFO - loss = 0.013260505, step = 2595400 (2.265 sec) +2022-05-01 22:58:41,845 - tensorflow - INFO - global_step/sec: 88.6845 +2022-05-01 22:58:41,845 - tensorflow - INFO - loss = 0.019862723, step = 2595600 (2.255 sec) +2022-05-01 22:58:44,108 - tensorflow - INFO - global_step/sec: 88.3498 +2022-05-01 22:58:44,109 - tensorflow - INFO - loss = 0.0025866749, step = 2595800 (2.264 sec) +2022-05-01 22:58:46,372 - tensorflow - INFO - global_step/sec: 88.3395 +2022-05-01 22:58:46,373 - tensorflow - INFO - loss = 0.013068748, step = 2596000 (2.264 sec) +2022-05-01 22:58:48,631 - tensorflow - INFO - global_step/sec: 88.5417 +2022-05-01 22:58:48,631 - tensorflow - INFO - loss = 0.018583922, step = 2596200 (2.259 sec) +2022-05-01 22:58:50,891 - tensorflow - INFO - global_step/sec: 88.4857 +2022-05-01 22:58:50,892 - tensorflow - INFO - loss = 0.019659905, step = 2596400 (2.260 sec) +2022-05-01 22:58:53,154 - tensorflow - INFO - global_step/sec: 88.3723 +2022-05-01 22:58:53,155 - tensorflow - INFO - loss = 0.06279365, step = 2596600 (2.263 sec) +2022-05-01 22:58:55,418 - tensorflow - INFO - global_step/sec: 88.3445 +2022-05-01 22:58:55,419 - tensorflow - INFO - loss = 0.017705481, step = 2596800 (2.264 sec) +2022-05-01 22:58:57,682 - tensorflow - INFO - global_step/sec: 88.3529 +2022-05-01 22:58:57,682 - tensorflow - INFO - loss = 0.01881864, step = 2597000 (2.264 sec) +2022-05-01 22:58:59,948 - tensorflow - INFO - global_step/sec: 88.2484 +2022-05-01 22:58:59,949 - tensorflow - INFO - loss = 0.0077836798, step = 2597200 (2.266 sec) +2022-05-01 22:59:02,204 - tensorflow - INFO - global_step/sec: 88.6699 +2022-05-01 22:59:02,204 - tensorflow - INFO - loss = 0.16888902, step = 2597400 (2.255 sec) +2022-05-01 22:59:04,459 - tensorflow - INFO - global_step/sec: 88.6693 +2022-05-01 22:59:04,460 - tensorflow - INFO - loss = 0.023880437, step = 2597600 (2.256 sec) +2022-05-01 22:59:06,720 - tensorflow - INFO - global_step/sec: 88.4835 +2022-05-01 22:59:06,720 - tensorflow - INFO - loss = 0.0019687468, step = 2597800 (2.260 sec) +2022-05-01 22:59:08,973 - tensorflow - INFO - global_step/sec: 88.772 +2022-05-01 22:59:08,973 - tensorflow - INFO - loss = 0.009858871, step = 2598000 (2.253 sec) +2022-05-01 22:59:11,226 - tensorflow - INFO - global_step/sec: 88.751 +2022-05-01 22:59:11,227 - tensorflow - INFO - loss = 0.03571446, step = 2598200 (2.253 sec) +2022-05-01 22:59:13,477 - tensorflow - INFO - global_step/sec: 88.8674 +2022-05-01 22:59:13,477 - tensorflow - INFO - loss = 0.004305632, step = 2598400 (2.251 sec) +2022-05-01 22:59:15,728 - tensorflow - INFO - global_step/sec: 88.8317 +2022-05-01 22:59:15,729 - tensorflow - INFO - loss = 0.011081892, step = 2598600 (2.251 sec) +2022-05-01 22:59:17,983 - tensorflow - INFO - global_step/sec: 88.6949 +2022-05-01 22:59:17,984 - tensorflow - INFO - loss = 0.029049512, step = 2598800 (2.255 sec) +2022-05-01 22:59:20,237 - tensorflow - INFO - global_step/sec: 88.7534 +2022-05-01 22:59:20,237 - tensorflow - INFO - loss = 0.045791313, step = 2599000 (2.253 sec) +2022-05-01 22:59:22,487 - tensorflow - INFO - global_step/sec: 88.881 +2022-05-01 22:59:22,487 - tensorflow - INFO - loss = 0.016003875, step = 2599200 (2.250 sec) +2022-05-01 22:59:24,740 - tensorflow - INFO - global_step/sec: 88.7705 +2022-05-01 22:59:24,740 - tensorflow - INFO - loss = 0.007047058, step = 2599400 (2.253 sec) +2022-05-01 22:59:26,994 - tensorflow - INFO - global_step/sec: 88.7231 +2022-05-01 22:59:26,994 - tensorflow - INFO - loss = 0.1277457, step = 2599600 (2.254 sec) +2022-05-01 22:59:29,242 - tensorflow - INFO - global_step/sec: 88.9479 +2022-05-01 22:59:29,243 - tensorflow - INFO - loss = 0.031332817, step = 2599800 (2.249 sec) +2022-05-01 22:59:31,492 - tensorflow - INFO - Saving checkpoints for 2600000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. +2022-05-01 22:59:31,508 - tensorflow - INFO - model.ckpt-2600000 is not in all_model_checkpoint_paths. Manually adding it. +2022-05-01 22:59:32,027 - tensorflow - INFO - Calling model_fn. +2022-05-01 22:59:32,158 - tensorflow - INFO - Done calling model_fn. +2022-05-01 22:59:32,173 - tensorflow - INFO - Starting evaluation at 2022-05-01T22:59:32Z +2022-05-01 22:59:32,238 - tensorflow - INFO - Graph was finalized. +2022-05-01 22:59:32,241 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-2600000 +2022-05-01 22:59:32,519 - tensorflow - INFO - Running local_init_op. +2022-05-01 22:59:32,625 - tensorflow - INFO - Done running local_init_op. +2022-05-01 22:59:42,867 - tensorflow - INFO - Evaluation [3990/39900] +2022-05-01 22:59:52,420 - tensorflow - INFO - Evaluation [7980/39900] +2022-05-01 23:00:01,983 - tensorflow - INFO - Evaluation [11970/39900] +2022-05-01 23:00:11,571 - tensorflow - INFO - Evaluation [15960/39900] +2022-05-01 23:00:21,147 - tensorflow - INFO - Evaluation [19950/39900] +2022-05-01 23:00:30,752 - tensorflow - INFO - Evaluation [23940/39900] +2022-05-01 23:00:40,323 - tensorflow - INFO - Evaluation [27930/39900] +2022-05-01 23:00:49,929 - tensorflow - INFO - Evaluation [31920/39900] +2022-05-01 23:00:59,510 - tensorflow - INFO - Evaluation [35910/39900] +2022-05-01 23:01:09,073 - tensorflow - INFO - Evaluation [39900/39900] +2022-05-01 23:01:09,208 - tensorflow - INFO - Finished evaluation at 2022-05-01-23:01:09 +2022-05-01 23:01:09,208 - tensorflow - INFO - Saving dict for global step 2600000: global_step = 2600000, loss = 0.020267775, mean_degree_err = 3.9795845 +2022-05-01 23:01:09,209 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 2600000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-2600000 +2022-05-01 23:01:09,209 - tensorflow - DEBUG - Calling exporter with the `is_the_final_export=True`. +2022-05-01 23:01:09,230 - tensorflow - INFO - Loss for final step: 0.01898054. \ No newline at end of file diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/main_point_cloud_gpu.py b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/main_point_cloud_gpu.py new file mode 100644 index 000000000..a3cbfe37d --- /dev/null +++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/main_point_cloud_gpu.py @@ -0,0 +1,431 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ + +# coding=utf-8 +# Copyright 2021 The Google Research Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Training and evaluation for the point cloud alignment experiment.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import utils_gpu +import time +from absl import flags +import numpy as np +import tensorflow as tf +import logging +FLAGS = flags.FLAGS +log = logging.getLogger('tensorflow') +log.setLevel(logging.DEBUG) + +# General flags. +flags.DEFINE_string('method', 'svd', + 'Specifies the method to use for predicting rotations. ' + 'Choices are "svd", "svd-inf", or "gs".') +flags.DEFINE_string('checkpoint_dir', '', + 'Locations for checkpoints, summaries, etc.') +flags.DEFINE_integer('train_steps', 2600000, 'Number of training iterations.') +flags.DEFINE_integer('save_checkpoints_steps', 100000, + 'How often to save checkpoints') +flags.DEFINE_integer('log_step_count', 200, 'How often to log the step count') +flags.DEFINE_integer('save_summaries_steps', 25000, + 'How often to save summaries.') +flags.DEFINE_float('learning_rate', 1e-5, 'Learning rate') +flags.DEFINE_boolean('lr_decay', False, 'Decay the learning rate if True.') +flags.DEFINE_integer('lr_decay_steps', 35000, + 'Learning rate decays steps.') +flags.DEFINE_float('lr_decay_rate', 0.95, + 'Learning rate decays rate.') +flags.DEFINE_boolean('predict_all_test', False, + 'If true, runs an eval job on latest checkpoint and ' + 'prints the error for each input.') +flags.DEFINE_integer('eval_examples', 39900, 'Number of test examples.') +flags.DEFINE_boolean('print_variable_names', False, + 'Print model variable names.') + +# Flags only used in the point cloud alignment experiment. +flags.DEFINE_integer('num_train_augmentations', 10, + 'Number of random rotations for augmenting each input ' + 'point cloud.') +flags.DEFINE_string('pt_cloud_train_files', '', + 'Expression matching all training point files, e.g. ' + '/path/to/files/pc_plane/points/*.pts') +flags.DEFINE_string('pt_cloud_test_files', '', + 'Expression matching all modified test point files, e.g. ' + '/path/to/files/pc_plane/points_test/*.pts') +flags.DEFINE_boolean('random_rotation_axang', True, + 'If true, samples random rotations using the method ' + 'from the original benchmark code. Otherwise samples ' + 'by Haar measure.') + + +def pt_features(batch_pts): + """Input shape: [B, N, 3], output shape: [B, 1024].""" + with tf.variable_scope('ptenc', reuse=tf.AUTO_REUSE): + f1 = tf.layers.conv1d(inputs=batch_pts, filters=64, kernel_size=1) + f1 = tf.nn.leaky_relu(f1) + f2 = tf.layers.conv1d(inputs=f1, filters=128, kernel_size=1) + f2 = tf.nn.leaky_relu(f2) + f3 = tf.layers.conv1d(inputs=f2, filters=1024, kernel_size=1) + + f = tf.reduce_max(f3, axis=1, keep_dims=False) + return f + + +def regress_from_features(batch_features, out_dim): + """Regress to a rotation representation from point cloud encodings. + + In Zhou et al, CVPR19, the paper describes this regression network as an MLP + mapping 2048->512->512->out_dim, but the associated code implements it with + one less layer: 2048->512->out_dim. We mimic the code. + + Args: + batch_features: [batch_size, in_dim]. + out_dim: desired output dimensionality. + + Returns: + A [batch_size, out_dim] tensor. + """ + f1 = tf.layers.dense(batch_features, 512) + f1 = tf.nn.leaky_relu(f1) + f2 = tf.layers.dense(f1, out_dim) + return f2 + + +def net_point_cloud(points1, points2, mode): + """Predict a relative rotation given two point clouds. + + Args: + points1: [batch_size, N, 3] float tensor. + points2: [batch_size, N, 3] float tensor. + mode: tf.estimator.ModeKeys. + + Returns: + [batch_size, 3, 3] matrices. + """ + f1 = pt_features(points1) + f2 = pt_features(points2) + f = tf.concat([f1, f2], axis=-1) + + if FLAGS.method == 'svd': + p = regress_from_features(f, 9) + return utils_gpu.symmetric_orthogonalization(p) + + if FLAGS.method == 'svd-inf': + p = regress_from_features(f, 9) + if mode == tf.estimator.ModeKeys.TRAIN: + return tf.reshape(p, (-1, 3, 3)) + else: + return utils_gpu.symmetric_orthogonalization(p) + + if FLAGS.method == 'gs': + p = regress_from_features(f, 6) + return utils_gpu.gs_orthogonalization(p) + + +def model_fn(features, labels, mode, params): + """The model_fn used to construct the tf.Estimator.""" + del labels, params # Unused. + if mode == tf.estimator.ModeKeys.TRAIN: + # Training data has point cloud of size [1, N, 3] and random rotations + # of size [1, FLAGS.num_train_augmentations, 3, 3] + rot = features['rot'][0] + num_rot = FLAGS.num_train_augmentations + batch_pts1 = tf.tile(features['data'], [num_rot, 1, 1]) + #batch_pts1 = tf.reshape(batch_pts1,(num_rot,-1,3)) + # In this experiment it does not matter if we pre or post-multiply the + # rotation as long as we are consistent between training and eval. + batch_pts2 = tf.matmul(batch_pts1, rot) # post-multiplying! + else: + # Test data has point cloud of size [1, N, 3] and a single random + # rotation of size [1, 3, 3] + batch_pts1 = features['data'] + rot = features['rot'] + batch_pts2 = tf.matmul(batch_pts1, rot) + rot = tf.reshape(rot, (-1, 3, 3)) + + # Predict the rotation. + r = net_point_cloud(batch_pts1, batch_pts2, mode) + + # Compute the loss. + loss = tf.nn.l2_loss(rot - r) + + # Compute the relative angle in radians. + theta = utils_gpu.relative_angle(r, rot) + + # Mean angle error over the batch. + mean_theta = tf.reduce_mean(theta) + mean_theta_deg = mean_theta * 180.0 / np.pi + + # Train, eval, or predict depending on mode. + if mode == tf.estimator.ModeKeys.TRAIN: + tf.summary.scalar('train/loss', loss) + tf.summary.scalar('train/theta', mean_theta_deg) + global_step = tf.train.get_or_create_global_step() + + if FLAGS.lr_decay: + learning_rate = tf.train.exponential_decay( + FLAGS.learning_rate, + global_step, + FLAGS.lr_decay_steps, + FLAGS.lr_decay_rate) + else: + learning_rate = FLAGS.learning_rate + + tf.summary.scalar('train/learning_rate', learning_rate) + optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate) + update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) + with tf.control_dependencies(update_ops): + train_op = optimizer.minimize(loss, global_step=global_step) + return tf.estimator.EstimatorSpec( + mode=mode, + loss=loss, + train_op=train_op) + + if mode == tf.estimator.ModeKeys.EVAL: + if FLAGS.predict_all_test: + print_error_op = tf.print('error:', mean_theta_deg) + with tf.control_dependencies([print_error_op]): + eval_metric_ops = { + 'mean_degree_err': tf.metrics.mean(mean_theta_deg), + } + else: + eval_metric_ops = { + 'mean_degree_err': tf.metrics.mean(mean_theta_deg), + } + + return tf.estimator.EstimatorSpec( + mode=mode, + loss=loss, + eval_metric_ops=eval_metric_ops) + + if mode == tf.estimator.ModeKeys.PREDICT: + pred = {'error': mean_theta_deg} + return tf.estimator.EstimatorSpec( + mode=mode, + predictions=pred) + + +def train_input_fn(): + """Generate training data iterator from the .pts files.""" + def _file_to_matrix(pts_path): + """Read Nx3 point cloud from a .pts file.""" + file_buffer = tf.read_file(pts_path) + lines = tf.string_split([file_buffer], delimiter='\n') + lines1 = tf.string_split(lines.values, delimiter='\r') + values = tf.stack(tf.decode_csv(lines1.values, + record_defaults=[[0.0], [0.0], [0.0]], + field_delim=' ')) + values = tf.transpose(values)# 3xN --> Nx3 + values = values[:(tf.shape(values)[0]// 2), :] + diff_num = 1500-tf.shape(values)[0] + repeat_pts = tf.tile(tf.reshape(values[1,:],(1,-1)),[diff_num,1]) + values = tf.concat([values,repeat_pts],axis=0) + # The experiment code in + # github.com/papagina/RotationContinuity/.../shapenet/code/train_pointnet.py + # only used the first half of the points in each file. + return values + + + + def _random_rotation(pts): + """Attach N random rotations to a point cloud.""" + if FLAGS.random_rotation_axang: + rotations = utils_gpu.random_rotation_benchmark(FLAGS.num_train_augmentations) + else: + rotations = utils_gpu.random_rotation(FLAGS.num_train_augmentations) + return pts, rotations + + pts_paths = tf.gfile.Glob(FLAGS.pt_cloud_train_files) + dataset = tf.data.Dataset.from_tensor_slices(pts_paths) + dataset = dataset.map(_file_to_matrix) + dataset = dataset.cache() # Comment out if memory cannot hold all the data. + dataset = dataset.shuffle(buffer_size=50, reshuffle_each_iteration=True) + dataset = dataset.repeat() + dataset = dataset.map(_random_rotation) + dataset = dataset.batch(1) + iterator = tf.data.make_one_shot_iterator(dataset) + batch_data, batch_rot = iterator.get_next() + features_dict = {'data': batch_data, 'rot': batch_rot} + batch_size = tf.shape(batch_data)[0] + batch_labels_dummy = tf.zeros(shape=(batch_size, 1)) + return (features_dict, batch_labels_dummy) + + + + +def eval_input_fn(): + """Generate test data from *modified* .pts files. + + See README and comments below for details on how the data is modified. + + Returns: + A tuple of features and associated labels. + """ + def _file_to_matrix(pts_path): + """Read Nx3 point cloud and 3x3 rotation matrix from a .pts file. + + The test data is a modified version of the original files. For each .pts + file we have (1) added a 3x3 rotation matrix for testing, and (2) removed + the second half of the point cloud since it is not used at all. + + Args: + pts_path: path to a .pts file. + + Returns: + A Nx3 point cloud. + A 3x3 rotation matrix. + """ + file_buffer = tf.read_file(pts_path) + lines = tf.string_split([file_buffer], delimiter='\n') + lines1 = tf.string_split(lines.values, delimiter='\r') + values = tf.stack(tf.decode_csv(lines1.values, + record_defaults=[[0.0], [0.0], [0.0]], field_delim=' ')) + values = tf.transpose(values) # 3xN --> Nx3. + diff_num = 1414-tf.shape(values)[0] + repeat_pts = tf.tile(tf.reshape(values[4,:],(1,-1)),[diff_num,1]) + + values = tf.concat([values,repeat_pts],axis=0) + # First three rows are the rotation matrix, remaining rows the point cloud. + rot = values[:3, :] + return values[4:, :], rot + + pts_paths = tf.gfile.Glob(FLAGS.pt_cloud_test_files) + dataset = tf.data.Dataset.from_tensor_slices(pts_paths) + dataset = dataset.map(_file_to_matrix) + dataset = dataset.batch(1) + iterator = tf.data.make_one_shot_iterator(dataset) + batch_data, batch_rot = iterator.get_next() + features_dict = {'data': batch_data, 'rot': batch_rot} + batch_size = tf.shape(batch_data)[0] + batch_labels_dummy = tf.zeros(shape=(batch_size, 1)) + return (features_dict, batch_labels_dummy) + + +def print_variable_names(): + """Print variable names in a model.""" + params = {'dummy': 0} + estimator = tf.estimator.Estimator( + model_fn=model_fn, + model_dir=FLAGS.checkpoint_dir, + params=params) + names = estimator.get_variable_names() + for name in names: + print(name) + + +def predict_all_test(): + """Print error statistics for the test dataset.""" + params = {'dummy': 0} + estimator = tf.estimator.Estimator( + model_fn=model_fn, + model_dir=FLAGS.checkpoint_dir, + params=params) + evals = estimator.predict(input_fn=eval_input_fn, yield_single_examples=False) + + # Print error statistics. + all_errors = [x['error'] for x in evals] + errors = np.array(all_errors) + print('Evaluated %d examples'%np.size(errors)) + print('Mean error: %f degrees', np.mean(errors)) + print('Median error: %f degrees', np.median(errors)) + print('Std: %f degrees', np.std(errors)) + sorted_errors = np.sort(errors) + n = np.size(sorted_errors) + print('\nPercentiles:') + for perc in range(1, 101): + index = np.int32(np.float32(n * perc) / 100.0) - 1 + print('%3d%%: %f'%(perc, sorted_errors[index])) + + +def train_and_eval(): + """Train and evaluate a model.""" + # create formatter and add it to the handlers + formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') + + # create file handler which logs even debug messages + fh = logging.FileHandler('loss.log') + fh.setLevel(logging.DEBUG) + fh.setFormatter(formatter) + log.addHandler(fh) + + save_summary_steps = FLAGS.save_summaries_steps + save_checkpoints_steps = FLAGS.save_checkpoints_steps + log_step_count = FLAGS.log_step_count + + config = tf.estimator.RunConfig( + save_summary_steps=save_summary_steps, + save_checkpoints_steps=save_checkpoints_steps, + log_step_count_steps=log_step_count, + keep_checkpoint_max=None) + + params = {'dummy': 0} + estimator = tf.estimator.Estimator( + model_fn=model_fn, + model_dir=FLAGS.checkpoint_dir, + config=config, + params=params) + + train_spec = tf.estimator.TrainSpec( + input_fn=train_input_fn, + max_steps=FLAGS.train_steps) + + eval_spec = tf.estimator.EvalSpec(input_fn=eval_input_fn, + start_delay_secs=60, + steps=FLAGS.eval_examples, + throttle_secs=60) + + tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec) + + +def main(argv=None): # pylint: disable=unused-argument + if FLAGS.print_variable_names: + print_variable_names() + return + + if FLAGS.predict_all_test: + predict_all_test() + else: + train_and_eval() + + +if __name__ == '__main__': + tf.app.run() diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/requirements.txt b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/requirements.txt new file mode 100644 index 000000000..de8ee8edb --- /dev/null +++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/requirements.txt @@ -0,0 +1,69 @@ +absl-py==0.11.0 + +astunparse==1.6.3 + +cachetools==4.1.1 + +certifi==2020.6.20 + +chardet==3.0.4 + +gast==0.3.3 + +google-auth==1.23.0 + +google-auth-oauthlib==0.4.2 + +google-pasta==0.2.0 + +grpcio==1.33.2 + +h5py==2.10.0 + +idna==2.10 + +importlib-metadata==2.0.0 + +Keras-Preprocessing==1.1.2 + +Markdown==3.3.3 + +numpy==1.18.5 + +oauthlib==3.1.0 + +opt-einsum==3.3.0 + +protobuf==3.13.0 + +pyasn1==0.4.8 + +pyasn1-modules==0.2.8 + +requests==2.24.0 + +requests-oauthlib==1.3.0 + +rsa==4.6 + +scipy==1.5.3 + +six==1.15.0 + +tensorboard==2.3.0 + +tensorboard-plugin-wit==1.7.0 + +tensorflow==2.3.1 + +tensorflow-estimator==2.3.0 + +termcolor==1.1.0 + +urllib3==1.25.11 + +Werkzeug==1.0.1 + +wrapt==1.12.1 + +zipp==3.4.0 diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/utils_gpu.py b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/utils_gpu.py new file mode 100644 index 000000000..a08b4fa97 --- /dev/null +++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/utils_gpu.py @@ -0,0 +1,144 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ + +# coding=utf-8 +# Copyright 2021 The Google Research Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Utility functions.""" +import numpy as np +from scipy.stats import special_ortho_group +import tensorflow as tf + + +def relative_angle(r1, r2): + """Relative angle (radians) between 3D rotation matrices.""" + rel_rot = tf.matmul(tf.transpose(r1, perm=[0, 2, 1]), r2) + trace = rel_rot[:, 0, 0] + rel_rot[:, 1, 1] + rel_rot[:, 2, 2] + cos_theta = (trace - 1.0) / 2.0 + cos_theta = tf.minimum(cos_theta, tf.ones_like(cos_theta)) + cos_theta = tf.maximum(cos_theta, (-1.0) * tf.ones_like(cos_theta)) + theta = tf.acos(cos_theta) + return theta + + +def random_rotation_benchmark_np(n): + """Sample a random 3D rotation by method used in Zhou et al, CVPR19. + + This numpy function is a copy of the PyTorch function + get_sampled_rotation_matrices_by_axisAngle() in the code made available + for Zhou et al, CVPR19, at https://github.com/papagina/RotationContinuity/. + + Args: + n: the number of rotation matrices to return. + + Returns: + [n, 3, 3] np array. + """ + theta = np.random.uniform(-1, 1, n) * np.pi + sin = np.sin(theta) + axis = np.random.randn(n, 3) + axis = axis / np.maximum(np.linalg.norm(axis, axis=-1, keepdims=True), 1e-7) + qw = np.cos(theta) + qx = axis[:, 0] * sin + qy = axis[:, 1] * sin + qz = axis[:, 2] * sin + + xx = qx*qx + yy = qy*qy + zz = qz*qz + xy = qx*qy + xz = qx*qz + yz = qy*qz + xw = qx*qw + yw = qy*qw + zw = qz*qw + + row0 = np.stack((1-2*yy-2*zz, 2*xy-2*zw, 2*xz+2*yw), axis=-1) + row1 = np.stack((2*xy+2*zw, 1-2*xx-2*zz, 2*yz-2*xw), axis=-1) + row2 = np.stack((2*xz-2*yw, 2*yz+2*xw, 1-2*xx-2*yy), axis=-1) + matrix = np.stack((row0, row1, row2), axis=1) + + return matrix + + +def random_rotation_benchmark(n): + """A TF wrapper for random_rotation_benchmark_np().""" + mat = tf.py_func( + func=lambda t: np.float32(random_rotation_benchmark_np(t)), + inp=[n], + Tout=tf.float32, + stateful=True) + return tf.reshape(mat, (n, 3, 3)) + + +def random_rotation(n): + """Sample rotations from a uniform distribution on SO(3).""" + mat = tf.py_func( + func=lambda t: np.float32(special_ortho_group.rvs(3, size=t)), + inp=[n], + Tout=tf.float32, + stateful=True) + return tf.reshape(mat, (n, 3, 3)) + + +def symmetric_orthogonalization(x): + """Maps 9D input vectors onto SO(3) via symmetric orthogonalization.""" + # Innner dimensions of the input should be 3x3 matrices. + m = tf.reshape(x, (-1, 3, 3)) + _, u, v = tf.svd(m) + det = tf.linalg.det(tf.matmul(u, v, transpose_b=True)) + r = tf.matmul( + tf.concat([u[:, :, :-1], u[:, :, -1:] * tf.reshape(det, [-1, 1, 1])], 2), + v, transpose_b=True) + return r + + +def gs_orthogonalization(p6): + """Gram-Schmidt orthogonalization from 6D input.""" + # Input should be [batch_size, 6] + x = p6[:, 0:3] + y = p6[:, 3:6] + xn = tf.math.l2_normalize(x, axis=-1) + z = tf.linalg.cross(xn, y) + zn = tf.math.l2_normalize(z, axis=-1) + y = tf.linalg.cross(zn, xn) + r = tf.stack([xn, y, zn], -1) + return r -- Gitee From 69676ea36d007a0c3b358b84f366306c8989f784 Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Sat, 7 May 2022 14:08:50 +0000 Subject: [PATCH 26/88] =?UTF-8?q?=E5=88=A0=E9=99=A4=E6=96=87=E4=BB=B6=20Te?= =?UTF-8?q?nsorFlow/contrib/cv/SVD=5FID2019=5Ffor=5FTensorflow/GPU?= =?UTF-8?q?=E7=B2=BE=E5=BA=A6(github=E5=8E=9F=E4=BB=A3=E7=A0=81=E6=9C=AA?= =?UTF-8?q?=E6=9B=B4=E6=94=B9=E7=89=88=E6=9C=AC).txt?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...\346\224\271\347\211\210\346\234\254).txt" | 106 ------------------ 1 file changed, 106 deletions(-) delete mode 100644 "TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU\347\262\276\345\272\246(github\345\216\237\344\273\243\347\240\201\346\234\252\346\233\264\346\224\271\347\211\210\346\234\254).txt" diff --git "a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU\347\262\276\345\272\246(github\345\216\237\344\273\243\347\240\201\346\234\252\346\233\264\346\224\271\347\211\210\346\234\254).txt" "b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU\347\262\276\345\272\246(github\345\216\237\344\273\243\347\240\201\346\234\252\346\233\264\346\224\271\347\211\210\346\234\254).txt" deleted file mode 100644 index e8a33b3ef..000000000 --- "a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU\347\262\276\345\272\246(github\345\216\237\344\273\243\347\240\201\346\234\252\346\233\264\346\224\271\347\211\210\346\234\254).txt" +++ /dev/null @@ -1,106 +0,0 @@ -Evaluated 39900 examples -Mean error: %f degrees 2.582546 -Median error: %f degrees 1.6805115 -Std: %f degrees 6.938891 - -Percentiles: - 1%: 0.300014 - 2%: 0.380522 - 3%: 0.437902 - 4%: 0.487788 - 5%: 0.526747 - 6%: 0.566482 - 7%: 0.602634 - 8%: 0.632420 - 9%: 0.662343 - 10%: 0.689838 - 11%: 0.714364 - 12%: 0.740193 - 13%: 0.766683 - 14%: 0.791300 - 15%: 0.816133 - 16%: 0.840466 - 17%: 0.863887 - 18%: 0.886470 - 19%: 0.909567 - 20%: 0.933351 - 21%: 0.955725 - 22%: 0.978388 - 23%: 1.002101 - 24%: 1.026220 - 25%: 1.049225 - 26%: 1.074472 - 27%: 1.098249 - 28%: 1.120823 - 29%: 1.143979 - 30%: 1.165668 - 31%: 1.189761 - 32%: 1.215631 - 33%: 1.238436 - 34%: 1.261760 - 35%: 1.286031 - 36%: 1.308956 - 37%: 1.333835 - 38%: 1.358547 - 39%: 1.383949 - 40%: 1.407643 - 41%: 1.433814 - 42%: 1.458442 - 43%: 1.485167 - 44%: 1.513619 - 45%: 1.540785 - 46%: 1.567729 - 47%: 1.595813 - 48%: 1.623171 - 49%: 1.651379 - 50%: 1.680511 - 51%: 1.709835 - 52%: 1.740463 - 53%: 1.769457 - 54%: 1.803091 - 55%: 1.837601 - 56%: 1.872417 - 57%: 1.909058 - 58%: 1.944507 - 59%: 1.979321 - 60%: 2.016059 - 61%: 2.052808 - 62%: 2.091812 - 63%: 2.131297 - 64%: 2.175648 - 65%: 2.219467 - 66%: 2.265031 - 67%: 2.307493 - 68%: 2.354846 - 69%: 2.401918 - 70%: 2.450324 - 71%: 2.507802 - 72%: 2.558491 - 73%: 2.610895 - 74%: 2.670931 - 75%: 2.737095 - 76%: 2.795334 - 77%: 2.862317 - 78%: 2.931846 - 79%: 3.004461 - 80%: 3.083880 - 81%: 3.159387 - 82%: 3.250103 - 83%: 3.346382 - 84%: 3.453207 - 85%: 3.562164 - 86%: 3.686474 - 87%: 3.808481 - 88%: 3.965510 - 89%: 4.142220 - 90%: 4.348930 - 91%: 4.576029 - 92%: 4.841852 - 93%: 5.135875 - 94%: 5.504097 - 95%: 5.996492 - 96%: 6.660559 - 97%: 7.634066 - 98%: 9.250069 - 99%: 12.710493 -100%: 178.697479 \ No newline at end of file -- Gitee From 5ea9f724d73bb1915aa79ef98ccfe62543fdaf91 Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Sat, 7 May 2022 14:09:01 +0000 Subject: [PATCH 27/88] =?UTF-8?q?=E5=88=A0=E9=99=A4=E6=96=87=E4=BB=B6=20Te?= =?UTF-8?q?nsorFlow/contrib/cv/SVD=5FID2019=5Ffor=5FTensorflow/gen=5Fpt=5F?= =?UTF-8?q?test=5Fdata=5Fgpu.py?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../gen_pt_test_data_gpu.py | 99 ------------------- 1 file changed, 99 deletions(-) delete mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/gen_pt_test_data_gpu.py diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/gen_pt_test_data_gpu.py b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/gen_pt_test_data_gpu.py deleted file mode 100644 index 96b2770d7..000000000 --- a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/gen_pt_test_data_gpu.py +++ /dev/null @@ -1,99 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ - -# coding=utf-8 -# Copyright 2021 The Google Research Authors. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -"""Generate modified point cloud test data with fixed random rotations.""" -import glob -import os -import pathlib - -import utils_gpu -from absl import app -from absl import flags -import numpy as np -from scipy.stats import special_ortho_group - -FLAGS = flags.FLAGS -flags.DEFINE_string('input_test_files', '/cache/data_modelarts/points_test/*.pts', - 'Regular expression for the original input test files.') -flags.DEFINE_string('output_directory', '/cache/data_modelarts/test_modified', - 'Output directory where new test files will be stored.') -flags.DEFINE_integer('num_rotations_per_file', 100, - 'Number of random rotation augmentations per test point ' - 'cloud.') -flags.DEFINE_boolean('random_rotation_axang', True, - 'If true, samples random rotations using the method ' - 'from the original benchmark code. Otherwise samples ' - 'by Haar measure.') - - -def gen_test_data(): - """Generate the new (modified) test data.""" - # Create output directory. - os.makedirs(FLAGS.output_directory, exist_ok=True) - - # Get all test point cloud files in the original dataset. - input_test_files = glob.glob(FLAGS.input_test_files) - - for in_file in input_test_files: - out_file_prefix = pathlib.Path(in_file).stem - pts = np.loadtxt(in_file) # N x 3 - num_pts_to_keep = pts.shape[0] // 2 - pts = pts[:num_pts_to_keep, :] # N//2 x 3. - - for k in range(FLAGS.num_rotations_per_file): - if FLAGS.random_rotation_axang: - r = utils_gpu.random_rotation_benchmark_np(1) - r = r[0] - else: - r = special_ortho_group.rvs(3) - joined = np.float32(np.concatenate((r, pts), axis=0)) # (N//2+3) x 3. - out_file = os.path.join( - FLAGS.output_directory, '%s_r%03d.pts'%(out_file_prefix, k)) - np.savetxt(out_file, joined) - - -def main(unused_argv): - gen_test_data() - -if __name__ == '__main__': - app.run(main) -- Gitee From 9e937f1c3f2e1870d7d5ec167ff77af968172ba5 Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Sat, 7 May 2022 14:09:06 +0000 Subject: [PATCH 28/88] =?UTF-8?q?=E5=88=A0=E9=99=A4=E6=96=87=E4=BB=B6=20Te?= =?UTF-8?q?nsorFlow/contrib/cv/SVD=5FID2019=5Ffor=5FTensorflow/log=5Fgpu.l?= =?UTF-8?q?og?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../cv/SVD_ID2019_for_Tensorflow/log_gpu.log | 26651 ---------------- 1 file changed, 26651 deletions(-) delete mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/log_gpu.log diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/log_gpu.log b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/log_gpu.log deleted file mode 100644 index 60f26f4b1..000000000 --- a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/log_gpu.log +++ /dev/null @@ -1,26651 +0,0 @@ -2022-05-01 14:07:22,858 - tensorflow - INFO - Using config: {'_model_dir': '/root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51', '_tf_random_seed': None, '_save_summary_steps': 25000, '_save_checkpoints_steps': 100000, '_save_checkpoints_secs': None, '_session_config': allow_soft_placement: true -graph_options { - rewrite_options { - meta_optimizer_iterations: ONE - } -} -, '_keep_checkpoint_max': None, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 200, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_service': None, '_cluster_spec': , '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1} -2022-05-01 14:07:22,859 - tensorflow - INFO - Not using Distribute Coordinator. -2022-05-01 14:07:22,859 - tensorflow - INFO - Running training and evaluation locally (non-distributed). -2022-05-01 14:07:22,860 - tensorflow - INFO - Start train and evaluate loop. The evaluate will happen after every checkpoint. Checkpoint frequency is determined based on RunConfig arguments: save_checkpoints_steps 100000 or save_checkpoints_secs None. -2022-05-01 14:07:22,871 - tensorflow - WARNING - From /root/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow_core/python/training/training_util.py:236: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version. -Instructions for updating: -Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts. -2022-05-01 14:07:22,878 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:249: The name tf.gfile.Glob is deprecated. Please use tf.io.gfile.glob instead. - -2022-05-01 14:07:24,110 - tensorflow - WARNING - From /root/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow_core/python/autograph/converters/directives.py:119: The name tf.read_file is deprecated. Please use tf.io.read_file instead. - -2022-05-01 14:07:24,110 - tensorflow - WARNING - From /root/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow_core/python/autograph/converters/directives.py:119: The name tf.decode_csv is deprecated. Please use tf.io.decode_csv instead. - -2022-05-01 14:07:24,230 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:224: calling string_split (from tensorflow.python.ops.ragged.ragged_string_ops) with delimiter is deprecated and will be removed in a future version. -Instructions for updating: -delimiter is deprecated, please use sep instead. -2022-05-01 14:07:24,412 - tensorflow - WARNING - From /root/special_orthogonalization2/utils_gpu.py:75: py_func (from tensorflow.python.ops.script_ops) is deprecated and will be removed in a future version. -Instructions for updating: -tf.py_func is deprecated in TF V2. Instead, there are two - options available in V2. - - tf.py_function takes a python function which manipulates tf eager - tensors instead of numpy arrays. It's easy to convert a tf eager tensor to - an ndarray (just call tensor.numpy()) but having access to eager tensors - means `tf.py_function`s can use accelerators such as GPUs as well as - being differentiable using a gradient tape. - - tf.numpy_function maintains the semantics of the deprecated tf.py_func - (it is not differentiable, and manipulates numpy arrays). It drops the - stateful argument making all functions stateful. - -2022-05-01 14:07:24,420 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:257: The name tf.data.make_one_shot_iterator is deprecated. Please use tf.compat.v1.data.make_one_shot_iterator instead. - -2022-05-01 14:07:24,443 - tensorflow - INFO - Calling model_fn. -2022-05-01 14:07:24,448 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:74: The name tf.variable_scope is deprecated. Please use tf.compat.v1.variable_scope instead. - -2022-05-01 14:07:24,448 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:74: The name tf.AUTO_REUSE is deprecated. Please use tf.compat.v1.AUTO_REUSE instead. - -2022-05-01 14:07:24,448 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:75: conv1d (from tensorflow.python.layers.convolutional) is deprecated and will be removed in a future version. -Instructions for updating: -Use `tf.keras.layers.Conv1D` instead. -2022-05-01 14:07:24,449 - tensorflow - WARNING - From /root/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow_core/python/layers/convolutional.py:218: Layer.apply (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version. -Instructions for updating: -Please use `layer.__call__` method instead. -2022-05-01 14:07:24,498 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:81: calling reduce_max_v1 (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version. -Instructions for updating: -keep_dims is deprecated, use keepdims instead -2022-05-01 14:07:24,521 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:99: dense (from tensorflow.python.layers.core) is deprecated and will be removed in a future version. -Instructions for updating: -Use keras.layers.Dense instead. -2022-05-01 14:07:24,545 - tensorflow - WARNING - From /root/special_orthogonalization2/utils_gpu.py:97: The name tf.svd is deprecated. Please use tf.linalg.svd instead. - -2022-05-01 14:07:24,570 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:172: The name tf.summary.scalar is deprecated. Please use tf.compat.v1.summary.scalar instead. - -2022-05-01 14:07:24,572 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:174: The name tf.train.get_or_create_global_step is deprecated. Please use tf.compat.v1.train.get_or_create_global_step instead. - -2022-05-01 14:07:24,573 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:186: The name tf.train.AdamOptimizer is deprecated. Please use tf.compat.v1.train.AdamOptimizer instead. - -2022-05-01 14:07:24,573 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:187: The name tf.get_collection is deprecated. Please use tf.compat.v1.get_collection instead. - -2022-05-01 14:07:24,574 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:187: The name tf.GraphKeys is deprecated. Please use tf.compat.v1.GraphKeys instead. - -2022-05-01 14:07:24,856 - tensorflow - INFO - Done calling model_fn. -2022-05-01 14:07:24,857 - tensorflow - INFO - Create CheckpointSaverHook. -2022-05-01 14:07:25,039 - tensorflow - WARNING - From /root/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow_core/python/ops/array_ops.py:1475: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version. -Instructions for updating: -Use tf.where in 2.0, which has the same broadcast rule as np.where -2022-05-01 14:07:25,108 - tensorflow - INFO - Graph was finalized. -2022-05-01 14:07:30,102 - tensorflow - INFO - Running local_init_op. -2022-05-01 14:07:30,119 - tensorflow - INFO - Done running local_init_op. -2022-05-01 14:07:30,573 - tensorflow - INFO - Saving checkpoints for 0 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. -2022-05-01 14:07:30,648 - tensorflow - INFO - model.ckpt-0 is not in all_model_checkpoint_paths. Manually adding it. -2022-05-01 14:07:35,533 - tensorflow - INFO - loss = 32.038086, step = 0 -2022-05-01 14:07:38,208 - tensorflow - INFO - global_step/sec: 74.7414 -2022-05-01 14:07:38,208 - tensorflow - INFO - loss = 13.331531, step = 200 (2.676 sec) -2022-05-01 14:07:40,484 - tensorflow - INFO - global_step/sec: 87.8576 -2022-05-01 14:07:40,485 - tensorflow - INFO - loss = 17.656946, step = 400 (2.276 sec) -2022-05-01 14:07:42,758 - tensorflow - INFO - global_step/sec: 87.9518 -2022-05-01 14:07:42,759 - tensorflow - INFO - loss = 13.776253, step = 600 (2.274 sec) -2022-05-01 14:07:45,033 - tensorflow - INFO - global_step/sec: 87.9402 -2022-05-01 14:07:45,033 - tensorflow - INFO - loss = 16.600023, step = 800 (2.274 sec) -2022-05-01 14:07:47,313 - tensorflow - INFO - global_step/sec: 87.7138 -2022-05-01 14:07:47,313 - tensorflow - INFO - loss = 11.457915, step = 1000 (2.280 sec) -2022-05-01 14:07:49,589 - tensorflow - INFO - global_step/sec: 87.8775 -2022-05-01 14:07:49,589 - tensorflow - INFO - loss = 18.556915, step = 1200 (2.276 sec) -2022-05-01 14:07:51,866 - tensorflow - INFO - global_step/sec: 87.8345 -2022-05-01 14:07:51,866 - tensorflow - INFO - loss = 17.53027, step = 1400 (2.277 sec) -2022-05-01 14:07:54,133 - tensorflow - INFO - global_step/sec: 88.2186 -2022-05-01 14:07:54,133 - tensorflow - INFO - loss = 15.867823, step = 1600 (2.267 sec) -2022-05-01 14:07:56,414 - tensorflow - INFO - global_step/sec: 87.6843 -2022-05-01 14:07:56,414 - tensorflow - INFO - loss = 14.14325, step = 1800 (2.281 sec) -2022-05-01 14:07:58,688 - tensorflow - INFO - global_step/sec: 87.9504 -2022-05-01 14:07:58,688 - tensorflow - INFO - loss = 14.731254, step = 2000 (2.274 sec) -2022-05-01 14:08:00,969 - tensorflow - INFO - global_step/sec: 87.6853 -2022-05-01 14:08:00,969 - tensorflow - INFO - loss = 7.6048627, step = 2200 (2.281 sec) -2022-05-01 14:08:03,225 - tensorflow - INFO - global_step/sec: 88.6196 -2022-05-01 14:08:03,226 - tensorflow - INFO - loss = 5.806036, step = 2400 (2.257 sec) -2022-05-01 14:08:05,477 - tensorflow - INFO - global_step/sec: 88.8202 -2022-05-01 14:08:05,478 - tensorflow - INFO - loss = 24.230932, step = 2600 (2.252 sec) -2022-05-01 14:08:07,731 - tensorflow - INFO - global_step/sec: 88.7332 -2022-05-01 14:08:07,732 - tensorflow - INFO - loss = 3.6557422, step = 2800 (2.254 sec) -2022-05-01 14:08:09,980 - tensorflow - INFO - global_step/sec: 88.9168 -2022-05-01 14:08:09,981 - tensorflow - INFO - loss = 22.55999, step = 3000 (2.249 sec) -2022-05-01 14:08:12,234 - tensorflow - INFO - global_step/sec: 88.7507 -2022-05-01 14:08:12,234 - tensorflow - INFO - loss = 5.92297, step = 3200 (2.254 sec) -2022-05-01 14:08:14,483 - tensorflow - INFO - global_step/sec: 88.9076 -2022-05-01 14:08:14,484 - tensorflow - INFO - loss = 9.746002, step = 3400 (2.250 sec) -2022-05-01 14:08:16,739 - tensorflow - INFO - global_step/sec: 88.6754 -2022-05-01 14:08:16,739 - tensorflow - INFO - loss = 6.337427, step = 3600 (2.255 sec) -2022-05-01 14:08:18,995 - tensorflow - INFO - global_step/sec: 88.6477 -2022-05-01 14:08:18,995 - tensorflow - INFO - loss = 13.799805, step = 3800 (2.256 sec) -2022-05-01 14:08:21,252 - tensorflow - INFO - global_step/sec: 88.6198 -2022-05-01 14:08:21,252 - tensorflow - INFO - loss = 23.801136, step = 4000 (2.257 sec) -2022-05-01 14:08:23,510 - tensorflow - INFO - global_step/sec: 88.5703 -2022-05-01 14:08:23,510 - tensorflow - INFO - loss = 22.836792, step = 4200 (2.258 sec) -2022-05-01 14:08:25,774 - tensorflow - INFO - global_step/sec: 88.3396 -2022-05-01 14:08:25,774 - tensorflow - INFO - loss = 4.7546825, step = 4400 (2.264 sec) -2022-05-01 14:08:28,036 - tensorflow - INFO - global_step/sec: 88.413 -2022-05-01 14:08:28,036 - tensorflow - INFO - loss = 4.444308, step = 4600 (2.262 sec) -2022-05-01 14:08:30,300 - tensorflow - INFO - global_step/sec: 88.3341 -2022-05-01 14:08:30,301 - tensorflow - INFO - loss = 22.58392, step = 4800 (2.264 sec) -2022-05-01 14:08:32,565 - tensorflow - INFO - global_step/sec: 88.3069 -2022-05-01 14:08:32,565 - tensorflow - INFO - loss = 2.2934127, step = 5000 (2.265 sec) -2022-05-01 14:08:34,824 - tensorflow - INFO - global_step/sec: 88.5425 -2022-05-01 14:08:34,824 - tensorflow - INFO - loss = 9.195248, step = 5200 (2.259 sec) -2022-05-01 14:08:37,085 - tensorflow - INFO - global_step/sec: 88.4473 -2022-05-01 14:08:37,085 - tensorflow - INFO - loss = 20.765274, step = 5400 (2.261 sec) -2022-05-01 14:08:39,343 - tensorflow - INFO - global_step/sec: 88.5905 -2022-05-01 14:08:39,343 - tensorflow - INFO - loss = 6.617299, step = 5600 (2.258 sec) -2022-05-01 14:08:41,606 - tensorflow - INFO - global_step/sec: 88.3606 -2022-05-01 14:08:41,606 - tensorflow - INFO - loss = 5.0666003, step = 5800 (2.263 sec) -2022-05-01 14:08:43,870 - tensorflow - INFO - global_step/sec: 88.335 -2022-05-01 14:08:43,871 - tensorflow - INFO - loss = 0.7100632, step = 6000 (2.264 sec) -2022-05-01 14:08:46,121 - tensorflow - INFO - global_step/sec: 88.8393 -2022-05-01 14:08:46,122 - tensorflow - INFO - loss = 5.172297, step = 6200 (2.251 sec) -2022-05-01 14:08:48,372 - tensorflow - INFO - global_step/sec: 88.8673 -2022-05-01 14:08:48,372 - tensorflow - INFO - loss = 2.6311967, step = 6400 (2.251 sec) -2022-05-01 14:08:50,626 - tensorflow - INFO - global_step/sec: 88.7158 -2022-05-01 14:08:50,627 - tensorflow - INFO - loss = 2.473042, step = 6600 (2.254 sec) -2022-05-01 14:08:52,889 - tensorflow - INFO - global_step/sec: 88.3812 -2022-05-01 14:08:52,890 - tensorflow - INFO - loss = 1.4561142, step = 6800 (2.263 sec) -2022-05-01 14:08:55,156 - tensorflow - INFO - global_step/sec: 88.243 -2022-05-01 14:08:55,156 - tensorflow - INFO - loss = 9.346189, step = 7000 (2.266 sec) -2022-05-01 14:08:57,421 - tensorflow - INFO - global_step/sec: 88.2959 -2022-05-01 14:08:57,421 - tensorflow - INFO - loss = 9.677809, step = 7200 (2.265 sec) -2022-05-01 14:08:59,683 - tensorflow - INFO - global_step/sec: 88.3994 -2022-05-01 14:08:59,684 - tensorflow - INFO - loss = 1.9972081, step = 7400 (2.262 sec) -2022-05-01 14:09:01,938 - tensorflow - INFO - global_step/sec: 88.7117 -2022-05-01 14:09:01,938 - tensorflow - INFO - loss = 1.6406038, step = 7600 (2.255 sec) -2022-05-01 14:09:04,212 - tensorflow - INFO - global_step/sec: 87.9525 -2022-05-01 14:09:04,212 - tensorflow - INFO - loss = 0.55838716, step = 7800 (2.274 sec) -2022-05-01 14:09:06,478 - tensorflow - INFO - global_step/sec: 88.266 -2022-05-01 14:09:06,478 - tensorflow - INFO - loss = 1.4037358, step = 8000 (2.266 sec) -2022-05-01 14:09:08,750 - tensorflow - INFO - global_step/sec: 88.0226 -2022-05-01 14:09:08,750 - tensorflow - INFO - loss = 2.4998298, step = 8200 (2.272 sec) -2022-05-01 14:09:11,032 - tensorflow - INFO - global_step/sec: 87.6369 -2022-05-01 14:09:11,032 - tensorflow - INFO - loss = 9.273857, step = 8400 (2.282 sec) -2022-05-01 14:09:13,318 - tensorflow - INFO - global_step/sec: 87.4763 -2022-05-01 14:09:13,319 - tensorflow - INFO - loss = 3.2947733, step = 8600 (2.286 sec) -2022-05-01 14:09:15,595 - tensorflow - INFO - global_step/sec: 87.8609 -2022-05-01 14:09:15,595 - tensorflow - INFO - loss = 8.8844185, step = 8800 (2.276 sec) -2022-05-01 14:09:17,887 - tensorflow - INFO - global_step/sec: 87.2339 -2022-05-01 14:09:17,888 - tensorflow - INFO - loss = 1.5026308, step = 9000 (2.293 sec) -2022-05-01 14:09:20,169 - tensorflow - INFO - global_step/sec: 87.6604 -2022-05-01 14:09:20,169 - tensorflow - INFO - loss = 1.2300944, step = 9200 (2.282 sec) -2022-05-01 14:09:22,442 - tensorflow - INFO - global_step/sec: 87.9892 -2022-05-01 14:09:22,442 - tensorflow - INFO - loss = 0.62815464, step = 9400 (2.273 sec) -2022-05-01 14:09:24,718 - tensorflow - INFO - global_step/sec: 87.8782 -2022-05-01 14:09:24,718 - tensorflow - INFO - loss = 0.56881064, step = 9600 (2.276 sec) -2022-05-01 14:09:27,028 - tensorflow - INFO - global_step/sec: 86.5549 -2022-05-01 14:09:27,029 - tensorflow - INFO - loss = 3.8879156, step = 9800 (2.311 sec) -2022-05-01 14:09:29,330 - tensorflow - INFO - global_step/sec: 86.894 -2022-05-01 14:09:29,330 - tensorflow - INFO - loss = 0.5907526, step = 10000 (2.302 sec) -2022-05-01 14:09:31,612 - tensorflow - INFO - global_step/sec: 87.6382 -2022-05-01 14:09:31,613 - tensorflow - INFO - loss = 1.1593596, step = 10200 (2.282 sec) -2022-05-01 14:09:33,912 - tensorflow - INFO - global_step/sec: 86.9791 -2022-05-01 14:09:33,912 - tensorflow - INFO - loss = 0.42828047, step = 10400 (2.299 sec) -2022-05-01 14:09:36,210 - tensorflow - INFO - global_step/sec: 86.9989 -2022-05-01 14:09:36,211 - tensorflow - INFO - loss = 0.5336071, step = 10600 (2.299 sec) -2022-05-01 14:09:38,509 - tensorflow - INFO - global_step/sec: 86.9931 -2022-05-01 14:09:38,510 - tensorflow - INFO - loss = 1.3948362, step = 10800 (2.299 sec) -2022-05-01 14:09:40,808 - tensorflow - INFO - global_step/sec: 87.0139 -2022-05-01 14:09:40,808 - tensorflow - INFO - loss = 0.6679225, step = 11000 (2.299 sec) -2022-05-01 14:09:43,115 - tensorflow - INFO - global_step/sec: 86.7037 -2022-05-01 14:09:43,115 - tensorflow - INFO - loss = 0.78241736, step = 11200 (2.307 sec) -2022-05-01 14:09:45,399 - tensorflow - INFO - global_step/sec: 87.5425 -2022-05-01 14:09:45,400 - tensorflow - INFO - loss = 5.9322524, step = 11400 (2.285 sec) -2022-05-01 14:09:47,688 - tensorflow - INFO - global_step/sec: 87.3706 -2022-05-01 14:09:47,689 - tensorflow - INFO - loss = 0.52881104, step = 11600 (2.289 sec) -2022-05-01 14:09:49,978 - tensorflow - INFO - global_step/sec: 87.3595 -2022-05-01 14:09:49,978 - tensorflow - INFO - loss = 0.56047755, step = 11800 (2.289 sec) -2022-05-01 14:09:52,269 - tensorflow - INFO - global_step/sec: 87.2898 -2022-05-01 14:09:52,269 - tensorflow - INFO - loss = 0.28127378, step = 12000 (2.291 sec) -2022-05-01 14:09:54,561 - tensorflow - INFO - global_step/sec: 87.2696 -2022-05-01 14:09:54,561 - tensorflow - INFO - loss = 25.888077, step = 12200 (2.292 sec) -2022-05-01 14:09:56,851 - tensorflow - INFO - global_step/sec: 87.3247 -2022-05-01 14:09:56,851 - tensorflow - INFO - loss = 9.627051, step = 12400 (2.290 sec) -2022-05-01 14:09:59,137 - tensorflow - INFO - global_step/sec: 87.4895 -2022-05-01 14:09:59,137 - tensorflow - INFO - loss = 1.9770696, step = 12600 (2.286 sec) -2022-05-01 14:10:01,428 - tensorflow - INFO - global_step/sec: 87.2868 -2022-05-01 14:10:01,429 - tensorflow - INFO - loss = 11.038982, step = 12800 (2.291 sec) -2022-05-01 14:10:03,714 - tensorflow - INFO - global_step/sec: 87.4859 -2022-05-01 14:10:03,715 - tensorflow - INFO - loss = 1.3177816, step = 13000 (2.286 sec) -2022-05-01 14:10:06,003 - tensorflow - INFO - global_step/sec: 87.3913 -2022-05-01 14:10:06,003 - tensorflow - INFO - loss = 12.677236, step = 13200 (2.289 sec) -2022-05-01 14:10:08,301 - tensorflow - INFO - global_step/sec: 87.0449 -2022-05-01 14:10:08,301 - tensorflow - INFO - loss = 2.7487645, step = 13400 (2.298 sec) -2022-05-01 14:10:10,583 - tensorflow - INFO - global_step/sec: 87.6369 -2022-05-01 14:10:10,583 - tensorflow - INFO - loss = 0.9024451, step = 13600 (2.282 sec) -2022-05-01 14:10:12,867 - tensorflow - INFO - global_step/sec: 87.5497 -2022-05-01 14:10:12,868 - tensorflow - INFO - loss = 2.074489, step = 13800 (2.284 sec) -2022-05-01 14:10:15,159 - tensorflow - INFO - global_step/sec: 87.256 -2022-05-01 14:10:15,160 - tensorflow - INFO - loss = 1.01836, step = 14000 (2.292 sec) -2022-05-01 14:10:17,448 - tensorflow - INFO - global_step/sec: 87.3997 -2022-05-01 14:10:17,448 - tensorflow - INFO - loss = 0.4934891, step = 14200 (2.288 sec) -2022-05-01 14:10:19,735 - tensorflow - INFO - global_step/sec: 87.4333 -2022-05-01 14:10:19,735 - tensorflow - INFO - loss = 1.0520558, step = 14400 (2.287 sec) -2022-05-01 14:10:22,022 - tensorflow - INFO - global_step/sec: 87.4513 -2022-05-01 14:10:22,022 - tensorflow - INFO - loss = 2.0643926, step = 14600 (2.287 sec) -2022-05-01 14:10:24,301 - tensorflow - INFO - global_step/sec: 87.7653 -2022-05-01 14:10:24,301 - tensorflow - INFO - loss = 1.3036213, step = 14800 (2.279 sec) -2022-05-01 14:10:26,586 - tensorflow - INFO - global_step/sec: 87.5088 -2022-05-01 14:10:26,587 - tensorflow - INFO - loss = 3.2743459, step = 15000 (2.285 sec) -2022-05-01 14:10:28,874 - tensorflow - INFO - global_step/sec: 87.4281 -2022-05-01 14:10:28,874 - tensorflow - INFO - loss = 5.0951295, step = 15200 (2.288 sec) -2022-05-01 14:10:31,172 - tensorflow - INFO - global_step/sec: 87.0174 -2022-05-01 14:10:31,173 - tensorflow - INFO - loss = 16.01076, step = 15400 (2.298 sec) -2022-05-01 14:10:33,467 - tensorflow - INFO - global_step/sec: 87.1391 -2022-05-01 14:10:33,468 - tensorflow - INFO - loss = 0.71901804, step = 15600 (2.295 sec) -2022-05-01 14:10:35,757 - tensorflow - INFO - global_step/sec: 87.368 -2022-05-01 14:10:35,757 - tensorflow - INFO - loss = 0.5623523, step = 15800 (2.289 sec) -2022-05-01 14:10:38,044 - tensorflow - INFO - global_step/sec: 87.4192 -2022-05-01 14:10:38,045 - tensorflow - INFO - loss = 6.976879, step = 16000 (2.288 sec) -2022-05-01 14:10:40,329 - tensorflow - INFO - global_step/sec: 87.5449 -2022-05-01 14:10:40,329 - tensorflow - INFO - loss = 0.54529786, step = 16200 (2.285 sec) -2022-05-01 14:10:42,620 - tensorflow - INFO - global_step/sec: 87.3064 -2022-05-01 14:10:42,620 - tensorflow - INFO - loss = 16.176308, step = 16400 (2.291 sec) -2022-05-01 14:10:44,919 - tensorflow - INFO - global_step/sec: 87.0021 -2022-05-01 14:10:44,919 - tensorflow - INFO - loss = 0.96182084, step = 16600 (2.299 sec) -2022-05-01 14:10:47,207 - tensorflow - INFO - global_step/sec: 87.4072 -2022-05-01 14:10:47,207 - tensorflow - INFO - loss = 0.30624458, step = 16800 (2.288 sec) -2022-05-01 14:10:49,499 - tensorflow - INFO - global_step/sec: 87.2616 -2022-05-01 14:10:49,499 - tensorflow - INFO - loss = 6.4469113, step = 17000 (2.292 sec) -2022-05-01 14:10:51,790 - tensorflow - INFO - global_step/sec: 87.283 -2022-05-01 14:10:51,791 - tensorflow - INFO - loss = 18.890911, step = 17200 (2.291 sec) -2022-05-01 14:10:54,078 - tensorflow - INFO - global_step/sec: 87.4266 -2022-05-01 14:10:54,078 - tensorflow - INFO - loss = 0.55347186, step = 17400 (2.288 sec) -2022-05-01 14:10:56,371 - tensorflow - INFO - global_step/sec: 87.2254 -2022-05-01 14:10:56,371 - tensorflow - INFO - loss = 0.9430723, step = 17600 (2.293 sec) -2022-05-01 14:10:58,657 - tensorflow - INFO - global_step/sec: 87.4742 -2022-05-01 14:10:58,657 - tensorflow - INFO - loss = 0.43198982, step = 17800 (2.286 sec) -2022-05-01 14:11:00,930 - tensorflow - INFO - global_step/sec: 88.0087 -2022-05-01 14:11:00,930 - tensorflow - INFO - loss = 12.870156, step = 18000 (2.273 sec) -2022-05-01 14:11:03,196 - tensorflow - INFO - global_step/sec: 88.2255 -2022-05-01 14:11:03,197 - tensorflow - INFO - loss = 27.15581, step = 18200 (2.267 sec) -2022-05-01 14:11:05,465 - tensorflow - INFO - global_step/sec: 88.1747 -2022-05-01 14:11:05,465 - tensorflow - INFO - loss = 0.55553377, step = 18400 (2.268 sec) -2022-05-01 14:11:07,756 - tensorflow - INFO - global_step/sec: 87.2868 -2022-05-01 14:11:07,756 - tensorflow - INFO - loss = 21.419441, step = 18600 (2.291 sec) -2022-05-01 14:11:10,045 - tensorflow - INFO - global_step/sec: 87.3783 -2022-05-01 14:11:10,045 - tensorflow - INFO - loss = 0.6544138, step = 18800 (2.289 sec) -2022-05-01 14:11:12,337 - tensorflow - INFO - global_step/sec: 87.27 -2022-05-01 14:11:12,337 - tensorflow - INFO - loss = 0.8737732, step = 19000 (2.292 sec) -2022-05-01 14:11:14,623 - tensorflow - INFO - global_step/sec: 87.4608 -2022-05-01 14:11:14,624 - tensorflow - INFO - loss = 0.18358433, step = 19200 (2.287 sec) -2022-05-01 14:11:16,918 - tensorflow - INFO - global_step/sec: 87.1595 -2022-05-01 14:11:16,918 - tensorflow - INFO - loss = 0.38425702, step = 19400 (2.295 sec) -2022-05-01 14:11:19,213 - tensorflow - INFO - global_step/sec: 87.1535 -2022-05-01 14:11:19,213 - tensorflow - INFO - loss = 0.3676651, step = 19600 (2.295 sec) -2022-05-01 14:11:21,504 - tensorflow - INFO - global_step/sec: 87.2938 -2022-05-01 14:11:21,504 - tensorflow - INFO - loss = 3.4475195, step = 19800 (2.291 sec) -2022-05-01 14:11:23,801 - tensorflow - INFO - global_step/sec: 87.054 -2022-05-01 14:11:23,803 - tensorflow - INFO - loss = 0.83847857, step = 20000 (2.298 sec) -2022-05-01 14:11:26,099 - tensorflow - INFO - global_step/sec: 87.0605 -2022-05-01 14:11:26,099 - tensorflow - INFO - loss = 0.7814198, step = 20200 (2.296 sec) -2022-05-01 14:11:28,389 - tensorflow - INFO - global_step/sec: 87.3206 -2022-05-01 14:11:28,390 - tensorflow - INFO - loss = 0.8575868, step = 20400 (2.291 sec) -2022-05-01 14:11:30,686 - tensorflow - INFO - global_step/sec: 87.0852 -2022-05-01 14:11:30,686 - tensorflow - INFO - loss = 1.1916295, step = 20600 (2.297 sec) -2022-05-01 14:11:32,977 - tensorflow - INFO - global_step/sec: 87.285 -2022-05-01 14:11:32,977 - tensorflow - INFO - loss = 0.56961715, step = 20800 (2.291 sec) -2022-05-01 14:11:35,276 - tensorflow - INFO - global_step/sec: 86.9831 -2022-05-01 14:11:35,277 - tensorflow - INFO - loss = 0.42915964, step = 21000 (2.299 sec) -2022-05-01 14:11:37,564 - tensorflow - INFO - global_step/sec: 87.431 -2022-05-01 14:11:37,564 - tensorflow - INFO - loss = 4.5065575, step = 21200 (2.287 sec) -2022-05-01 14:11:39,850 - tensorflow - INFO - global_step/sec: 87.4836 -2022-05-01 14:11:39,850 - tensorflow - INFO - loss = 1.0244131, step = 21400 (2.286 sec) -2022-05-01 14:11:42,141 - tensorflow - INFO - global_step/sec: 87.2897 -2022-05-01 14:11:42,142 - tensorflow - INFO - loss = 7.548943, step = 21600 (2.291 sec) -2022-05-01 14:11:44,435 - tensorflow - INFO - global_step/sec: 87.1926 -2022-05-01 14:11:44,435 - tensorflow - INFO - loss = 0.250482, step = 21800 (2.294 sec) -2022-05-01 14:11:46,738 - tensorflow - INFO - global_step/sec: 86.8491 -2022-05-01 14:11:46,738 - 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tensorflow - INFO - loss = 0.16938096, step = 70000 (2.296 sec) -2022-05-01 14:20:58,377 - tensorflow - INFO - global_step/sec: 87.2423 -2022-05-01 14:20:58,377 - tensorflow - INFO - loss = 0.10998814, step = 70200 (2.292 sec) -2022-05-01 14:21:00,656 - tensorflow - INFO - global_step/sec: 87.7521 -2022-05-01 14:21:00,656 - tensorflow - INFO - loss = 0.27979133, step = 70400 (2.279 sec) -2022-05-01 14:21:02,940 - tensorflow - INFO - global_step/sec: 87.5467 -2022-05-01 14:21:02,941 - tensorflow - INFO - loss = 0.7517905, step = 70600 (2.284 sec) -2022-05-01 14:21:05,228 - tensorflow - INFO - global_step/sec: 87.4383 -2022-05-01 14:21:05,228 - tensorflow - INFO - loss = 0.13313249, step = 70800 (2.287 sec) -2022-05-01 14:21:07,513 - tensorflow - INFO - global_step/sec: 87.4928 -2022-05-01 14:21:07,514 - tensorflow - INFO - loss = 0.23826009, step = 71000 (2.286 sec) -2022-05-01 14:21:09,803 - tensorflow - INFO - global_step/sec: 87.3701 -2022-05-01 14:21:09,803 - tensorflow - INFO - loss = 0.46423978, step = 71200 (2.289 sec) -2022-05-01 14:21:12,084 - tensorflow - INFO - global_step/sec: 87.6815 -2022-05-01 14:21:12,084 - tensorflow - INFO - loss = 0.53649414, step = 71400 (2.281 sec) -2022-05-01 14:21:14,369 - tensorflow - INFO - global_step/sec: 87.5214 -2022-05-01 14:21:14,369 - tensorflow - INFO - loss = 0.10013757, step = 71600 (2.285 sec) -2022-05-01 14:21:16,654 - tensorflow - INFO - global_step/sec: 87.498 -2022-05-01 14:21:16,655 - tensorflow - INFO - loss = 0.24275637, step = 71800 (2.286 sec) -2022-05-01 14:21:18,943 - tensorflow - INFO - global_step/sec: 87.3831 -2022-05-01 14:21:18,944 - tensorflow - INFO - loss = 0.3097723, step = 72000 (2.289 sec) -2022-05-01 14:21:21,234 - tensorflow - INFO - global_step/sec: 87.2993 -2022-05-01 14:21:21,235 - tensorflow - INFO - loss = 1.8372321, step = 72200 (2.291 sec) -2022-05-01 14:21:23,528 - tensorflow - INFO - global_step/sec: 87.1784 -2022-05-01 14:21:23,529 - tensorflow - INFO - loss = 0.06818677, step = 72400 (2.294 sec) -2022-05-01 14:21:25,817 - tensorflow - INFO - global_step/sec: 87.3689 -2022-05-01 14:21:25,818 - tensorflow - INFO - loss = 0.55630684, step = 72600 (2.289 sec) -2022-05-01 14:21:28,107 - tensorflow - INFO - global_step/sec: 87.3563 -2022-05-01 14:21:28,107 - tensorflow - INFO - loss = 0.08491814, step = 72800 (2.289 sec) -2022-05-01 14:21:30,403 - tensorflow - INFO - global_step/sec: 87.1065 -2022-05-01 14:21:30,403 - tensorflow - INFO - loss = 0.17645128, step = 73000 (2.296 sec) -2022-05-01 14:21:32,697 - tensorflow - INFO - global_step/sec: 87.2005 -2022-05-01 14:21:32,697 - tensorflow - INFO - loss = 0.15488961, step = 73200 (2.294 sec) -2022-05-01 14:21:34,992 - tensorflow - INFO - global_step/sec: 87.1258 -2022-05-01 14:21:34,993 - tensorflow - INFO - loss = 0.083493516, step = 73400 (2.296 sec) -2022-05-01 14:21:37,279 - tensorflow - INFO - global_step/sec: 87.4536 -2022-05-01 14:21:37,279 - tensorflow - INFO - loss = 1.62941, step = 73600 (2.287 sec) -2022-05-01 14:21:39,563 - tensorflow - INFO - global_step/sec: 87.5628 -2022-05-01 14:21:39,563 - tensorflow - INFO - loss = 0.85496366, step = 73800 (2.284 sec) -2022-05-01 14:21:41,845 - 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tensorflow - INFO - global_step/sec: 87.4851 -2022-05-01 14:23:11,166 - tensorflow - INFO - loss = 1.1635952, step = 81800 (2.286 sec) -2022-05-01 14:23:13,452 - tensorflow - INFO - global_step/sec: 87.4907 -2022-05-01 14:23:13,452 - tensorflow - INFO - loss = 0.024671812, step = 82000 (2.286 sec) -2022-05-01 14:23:15,744 - tensorflow - INFO - global_step/sec: 87.2501 -2022-05-01 14:23:15,744 - tensorflow - INFO - loss = 2.2958317, step = 82200 (2.292 sec) -2022-05-01 14:23:18,029 - tensorflow - INFO - global_step/sec: 87.5457 -2022-05-01 14:23:18,029 - tensorflow - INFO - loss = 11.681719, step = 82400 (2.284 sec) -2022-05-01 14:23:20,304 - tensorflow - INFO - global_step/sec: 87.8899 -2022-05-01 14:23:20,304 - tensorflow - INFO - loss = 0.07709272, step = 82600 (2.276 sec) -2022-05-01 14:23:22,561 - tensorflow - INFO - global_step/sec: 88.604 -2022-05-01 14:23:22,562 - tensorflow - INFO - loss = 0.13194595, step = 82800 (2.257 sec) -2022-05-01 14:23:24,815 - tensorflow - INFO - global_step/sec: 88.7406 -2022-05-01 14:23:24,816 - tensorflow - INFO - loss = 10.650043, step = 83000 (2.254 sec) -2022-05-01 14:23:27,084 - tensorflow - INFO - global_step/sec: 88.1501 -2022-05-01 14:23:27,084 - tensorflow - INFO - loss = 0.20955583, step = 83200 (2.269 sec) -2022-05-01 14:23:29,373 - tensorflow - INFO - global_step/sec: 87.3541 -2022-05-01 14:23:29,374 - tensorflow - INFO - loss = 0.5238167, step = 83400 (2.290 sec) -2022-05-01 14:23:31,677 - tensorflow - INFO - global_step/sec: 86.8098 -2022-05-01 14:23:31,678 - tensorflow - INFO - loss = 0.08702626, step = 83600 (2.304 sec) -2022-05-01 14:23:33,964 - tensorflow - INFO - global_step/sec: 87.4468 -2022-05-01 14:23:33,965 - tensorflow - INFO - loss = 24.471504, step = 83800 (2.287 sec) -2022-05-01 14:23:36,242 - tensorflow - INFO - global_step/sec: 87.8097 -2022-05-01 14:23:36,243 - tensorflow - INFO - loss = 0.095092624, step = 84000 (2.278 sec) -2022-05-01 14:23:38,529 - tensorflow - INFO - global_step/sec: 87.4433 -2022-05-01 14:23:38,530 - tensorflow - INFO - loss = 16.328243, step = 84200 (2.287 sec) -2022-05-01 14:23:40,822 - tensorflow - INFO - global_step/sec: 87.2364 -2022-05-01 14:23:40,822 - tensorflow - INFO - loss = 0.13105452, step = 84400 (2.293 sec) -2022-05-01 14:23:43,117 - tensorflow - INFO - global_step/sec: 87.1583 -2022-05-01 14:23:43,117 - tensorflow - INFO - loss = 3.072318, step = 84600 (2.295 sec) -2022-05-01 14:23:45,404 - tensorflow - INFO - global_step/sec: 87.419 -2022-05-01 14:23:45,405 - tensorflow - INFO - loss = 0.15890606, step = 84800 (2.288 sec) -2022-05-01 14:23:47,692 - tensorflow - INFO - global_step/sec: 87.417 -2022-05-01 14:23:47,693 - tensorflow - INFO - loss = 0.10856928, step = 85000 (2.288 sec) -2022-05-01 14:23:49,980 - tensorflow - INFO - global_step/sec: 87.4108 -2022-05-01 14:23:49,981 - tensorflow - INFO - loss = 0.07617241, step = 85200 (2.288 sec) -2022-05-01 14:23:52,268 - tensorflow - INFO - global_step/sec: 87.4251 -2022-05-01 14:23:52,268 - tensorflow - INFO - loss = 0.24240248, step = 85400 (2.288 sec) -2022-05-01 14:23:54,560 - tensorflow - INFO - global_step/sec: 87.2736 -2022-05-01 14:23:54,560 - tensorflow - INFO - loss = 1.8678666, step = 85600 (2.292 sec) -2022-05-01 14:23:56,851 - tensorflow - INFO - global_step/sec: 87.2976 -2022-05-01 14:23:56,851 - tensorflow - INFO - loss = 0.19676991, step = 85800 (2.291 sec) -2022-05-01 14:23:59,138 - tensorflow - INFO - global_step/sec: 87.4451 -2022-05-01 14:23:59,138 - tensorflow - INFO - loss = 8.195176, step = 86000 (2.287 sec) -2022-05-01 14:24:01,425 - tensorflow - INFO - global_step/sec: 87.4531 -2022-05-01 14:24:01,425 - tensorflow - INFO - loss = 0.08395229, step = 86200 (2.287 sec) -2022-05-01 14:24:03,717 - tensorflow - INFO - global_step/sec: 87.2589 -2022-05-01 14:24:03,717 - tensorflow - INFO - loss = 5.7395077, step = 86400 (2.292 sec) -2022-05-01 14:24:06,002 - tensorflow - INFO - global_step/sec: 87.505 -2022-05-01 14:24:06,003 - tensorflow - INFO - loss = 0.17323343, step = 86600 (2.286 sec) -2022-05-01 14:24:08,299 - tensorflow - INFO - global_step/sec: 87.0962 -2022-05-01 14:24:08,299 - tensorflow - INFO - loss = 0.32396364, step = 86800 (2.296 sec) -2022-05-01 14:24:10,590 - tensorflow - INFO - global_step/sec: 87.3045 -2022-05-01 14:24:10,590 - tensorflow - INFO - loss = 22.962328, step = 87000 (2.291 sec) -2022-05-01 14:24:12,884 - tensorflow - INFO - global_step/sec: 87.163 -2022-05-01 14:24:12,885 - tensorflow - INFO - loss = 0.19104755, step = 87200 (2.294 sec) -2022-05-01 14:24:15,173 - tensorflow - INFO - global_step/sec: 87.3884 -2022-05-01 14:24:15,173 - tensorflow - INFO - loss = 0.05848871, step = 87400 (2.289 sec) -2022-05-01 14:24:17,461 - tensorflow - INFO - global_step/sec: 87.4179 -2022-05-01 14:24:17,461 - tensorflow - INFO - loss = 0.19390681, step = 87600 (2.288 sec) -2022-05-01 14:24:19,758 - tensorflow - INFO - global_step/sec: 87.0593 -2022-05-01 14:24:19,758 - tensorflow - INFO - loss = 0.18075919, step = 87800 (2.297 sec) -2022-05-01 14:24:22,055 - tensorflow - INFO - global_step/sec: 87.0804 -2022-05-01 14:24:22,055 - tensorflow - INFO - loss = 0.29285538, step = 88000 (2.297 sec) -2022-05-01 14:24:24,338 - tensorflow - INFO - global_step/sec: 87.5764 -2022-05-01 14:24:24,339 - tensorflow - INFO - loss = 0.1336847, step = 88200 (2.284 sec) -2022-05-01 14:24:26,637 - tensorflow - INFO - global_step/sec: 86.9889 -2022-05-01 14:24:26,638 - tensorflow - INFO - loss = 20.40362, step = 88400 (2.299 sec) -2022-05-01 14:24:28,918 - tensorflow - INFO - global_step/sec: 87.7009 -2022-05-01 14:24:28,918 - tensorflow - INFO - loss = 0.1908592, step = 88600 (2.280 sec) -2022-05-01 14:24:31,213 - tensorflow - INFO - global_step/sec: 87.1393 -2022-05-01 14:24:31,214 - tensorflow - INFO - loss = 0.40158635, step = 88800 (2.295 sec) -2022-05-01 14:24:33,511 - tensorflow - INFO - global_step/sec: 87.0228 -2022-05-01 14:24:33,512 - tensorflow - INFO - loss = 0.23553915, step = 89000 (2.298 sec) -2022-05-01 14:24:35,801 - tensorflow - INFO - global_step/sec: 87.3497 -2022-05-01 14:24:35,801 - tensorflow - INFO - loss = 1.1698062, step = 89200 (2.290 sec) -2022-05-01 14:24:38,086 - tensorflow - INFO - global_step/sec: 87.5318 -2022-05-01 14:24:38,086 - tensorflow - INFO - loss = 0.11512068, step = 89400 (2.285 sec) -2022-05-01 14:24:40,375 - tensorflow - INFO - global_step/sec: 87.3655 -2022-05-01 14:24:40,376 - tensorflow - INFO - loss = 0.17605992, step = 89600 (2.289 sec) -2022-05-01 14:24:42,661 - tensorflow - INFO - global_step/sec: 87.5102 -2022-05-01 14:24:42,661 - tensorflow - INFO - loss = 0.22128661, step = 89800 (2.285 sec) -2022-05-01 14:24:44,953 - tensorflow - INFO - global_step/sec: 87.2428 -2022-05-01 14:24:44,953 - tensorflow - INFO - loss = 0.75772536, step = 90000 (2.292 sec) -2022-05-01 14:24:47,238 - tensorflow - INFO - global_step/sec: 87.5261 -2022-05-01 14:24:47,239 - tensorflow - INFO - loss = 1.981938, step = 90200 (2.285 sec) -2022-05-01 14:24:49,523 - tensorflow - INFO - global_step/sec: 87.539 -2022-05-01 14:24:49,523 - tensorflow - INFO - loss = 3.6954958, step = 90400 (2.285 sec) -2022-05-01 14:24:51,808 - tensorflow - INFO - global_step/sec: 87.5312 -2022-05-01 14:24:51,808 - tensorflow - INFO - loss = 23.398697, step = 90600 (2.285 sec) -2022-05-01 14:24:54,089 - tensorflow - INFO - global_step/sec: 87.6596 -2022-05-01 14:24:54,090 - tensorflow - INFO - loss = 0.37941447, step = 90800 (2.282 sec) -2022-05-01 14:24:56,366 - tensorflow - INFO - global_step/sec: 87.8273 -2022-05-01 14:24:56,367 - tensorflow - INFO - loss = 0.13895091, step = 91000 (2.277 sec) -2022-05-01 14:24:58,624 - tensorflow - INFO - global_step/sec: 88.588 -2022-05-01 14:24:58,624 - tensorflow - INFO - loss = 13.276718, step = 91200 (2.258 sec) -2022-05-01 14:25:00,904 - tensorflow - INFO - global_step/sec: 87.7149 -2022-05-01 14:25:00,905 - tensorflow - INFO - loss = 0.19925176, step = 91400 (2.280 sec) -2022-05-01 14:25:03,189 - tensorflow - INFO - global_step/sec: 87.5326 -2022-05-01 14:25:03,189 - tensorflow - INFO - loss = 0.14478576, step = 91600 (2.285 sec) -2022-05-01 14:25:05,479 - tensorflow - INFO - global_step/sec: 87.3428 -2022-05-01 14:25:05,479 - tensorflow - INFO - loss = 0.113933235, step = 91800 (2.290 sec) -2022-05-01 14:25:07,769 - tensorflow - INFO - global_step/sec: 87.3376 -2022-05-01 14:25:07,769 - tensorflow - INFO - loss = 0.7221588, step = 92000 (2.290 sec) -2022-05-01 14:25:10,059 - tensorflow - INFO - global_step/sec: 87.3229 -2022-05-01 14:25:10,060 - tensorflow - INFO - loss = 2.7999012, step = 92200 (2.290 sec) -2022-05-01 14:25:12,352 - tensorflow - INFO - global_step/sec: 87.2151 -2022-05-01 14:25:12,353 - tensorflow - INFO - loss = 0.29915583, step = 92400 (2.293 sec) -2022-05-01 14:25:14,645 - tensorflow - INFO - global_step/sec: 87.2291 -2022-05-01 14:25:14,646 - tensorflow - INFO - loss = 0.057438828, step = 92600 (2.293 sec) -2022-05-01 14:25:16,938 - tensorflow - INFO - global_step/sec: 87.2267 -2022-05-01 14:25:16,939 - tensorflow - INFO - loss = 0.13389596, step = 92800 (2.293 sec) -2022-05-01 14:25:19,229 - tensorflow - INFO - global_step/sec: 87.2822 -2022-05-01 14:25:19,230 - tensorflow - INFO - loss = 0.3385266, step = 93000 (2.291 sec) -2022-05-01 14:25:21,517 - tensorflow - INFO - global_step/sec: 87.4451 -2022-05-01 14:25:21,517 - tensorflow - INFO - loss = 0.16020179, step = 93200 (2.287 sec) -2022-05-01 14:25:23,806 - tensorflow - INFO - global_step/sec: 87.3654 -2022-05-01 14:25:23,806 - tensorflow - INFO - loss = 0.1617248, step = 93400 (2.289 sec) -2022-05-01 14:25:26,097 - tensorflow - INFO - global_step/sec: 87.3069 -2022-05-01 14:25:26,097 - tensorflow - INFO - loss = 0.18052283, step = 93600 (2.291 sec) -2022-05-01 14:25:28,392 - tensorflow - INFO - global_step/sec: 87.1383 -2022-05-01 14:25:28,392 - tensorflow - INFO - loss = 0.2854377, step = 93800 (2.295 sec) -2022-05-01 14:25:30,689 - tensorflow - INFO - global_step/sec: 87.0448 -2022-05-01 14:25:30,690 - tensorflow - INFO - loss = 0.65452284, step = 94000 (2.298 sec) -2022-05-01 14:25:32,974 - tensorflow - INFO - global_step/sec: 87.5423 -2022-05-01 14:25:32,975 - tensorflow - INFO - loss = 0.48012242, step = 94200 (2.285 sec) -2022-05-01 14:25:35,265 - tensorflow - INFO - global_step/sec: 87.2856 -2022-05-01 14:25:35,266 - tensorflow - INFO - loss = 0.17184481, step = 94400 (2.291 sec) -2022-05-01 14:25:37,547 - tensorflow - INFO - global_step/sec: 87.6663 -2022-05-01 14:25:37,547 - tensorflow - INFO - loss = 0.31042734, step = 94600 (2.281 sec) -2022-05-01 14:25:39,832 - tensorflow - INFO - global_step/sec: 87.5264 -2022-05-01 14:25:39,832 - tensorflow - INFO - loss = 0.15828839, step = 94800 (2.285 sec) -2022-05-01 14:25:42,126 - tensorflow - INFO - global_step/sec: 87.182 -2022-05-01 14:25:42,126 - tensorflow - INFO - loss = 0.35003784, step = 95000 (2.294 sec) -2022-05-01 14:25:44,421 - tensorflow - INFO - global_step/sec: 87.1344 -2022-05-01 14:25:44,422 - tensorflow - INFO - loss = 0.47904184, step = 95200 (2.295 sec) -2022-05-01 14:25:46,720 - tensorflow - INFO - global_step/sec: 86.983 -2022-05-01 14:25:46,721 - tensorflow - INFO - loss = 0.21531895, step = 95400 (2.299 sec) -2022-05-01 14:25:49,022 - tensorflow - INFO - global_step/sec: 86.885 -2022-05-01 14:25:49,023 - tensorflow - INFO - loss = 0.15111923, step = 95600 (2.302 sec) -2022-05-01 14:25:51,318 - tensorflow - INFO - global_step/sec: 87.116 -2022-05-01 14:25:51,319 - tensorflow - INFO - loss = 0.9417725, step = 95800 (2.296 sec) -2022-05-01 14:25:53,611 - tensorflow - INFO - global_step/sec: 87.2297 -2022-05-01 14:25:53,611 - tensorflow - INFO - loss = 0.08343916, step = 96000 (2.293 sec) -2022-05-01 14:25:55,906 - tensorflow - INFO - global_step/sec: 87.1625 -2022-05-01 14:25:55,906 - tensorflow - INFO - loss = 0.11967882, step = 96200 (2.295 sec) -2022-05-01 14:25:58,207 - tensorflow - INFO - global_step/sec: 86.9043 -2022-05-01 14:25:58,207 - tensorflow - INFO - loss = 0.2613772, step = 96400 (2.301 sec) -2022-05-01 14:26:00,505 - tensorflow - INFO - global_step/sec: 87.0403 -2022-05-01 14:26:00,505 - tensorflow - INFO - loss = 0.3371205, step = 96600 (2.298 sec) -2022-05-01 14:26:02,802 - tensorflow - INFO - global_step/sec: 87.0751 -2022-05-01 14:26:02,802 - tensorflow - INFO - loss = 0.28187898, step = 96800 (2.297 sec) -2022-05-01 14:26:05,100 - tensorflow - INFO - global_step/sec: 87.0297 -2022-05-01 14:26:05,100 - tensorflow - INFO - loss = 0.42920962, step = 97000 (2.298 sec) -2022-05-01 14:26:07,396 - tensorflow - INFO - global_step/sec: 87.1099 -2022-05-01 14:26:07,396 - tensorflow - INFO - loss = 0.056796335, step = 97200 (2.296 sec) -2022-05-01 14:26:09,684 - tensorflow - INFO - global_step/sec: 87.3879 -2022-05-01 14:26:09,685 - tensorflow - INFO - loss = 0.09578763, step = 97400 (2.289 sec) -2022-05-01 14:26:11,974 - tensorflow - INFO - global_step/sec: 87.3522 -2022-05-01 14:26:11,974 - tensorflow - INFO - loss = 2.4198983, step = 97600 (2.290 sec) -2022-05-01 14:26:14,263 - tensorflow - INFO - global_step/sec: 87.3526 -2022-05-01 14:26:14,264 - tensorflow - INFO - loss = 0.13744435, step = 97800 (2.290 sec) -2022-05-01 14:26:16,549 - tensorflow - INFO - global_step/sec: 87.4993 -2022-05-01 14:26:16,550 - tensorflow - INFO - loss = 0.20825619, step = 98000 (2.286 sec) -2022-05-01 14:26:18,837 - tensorflow - INFO - global_step/sec: 87.4067 -2022-05-01 14:26:18,838 - tensorflow - INFO - loss = 0.19770706, step = 98200 (2.288 sec) -2022-05-01 14:26:21,124 - tensorflow - INFO - global_step/sec: 87.4774 -2022-05-01 14:26:21,124 - tensorflow - INFO - loss = 0.31616545, step = 98400 (2.286 sec) -2022-05-01 14:26:23,411 - tensorflow - INFO - global_step/sec: 87.4225 -2022-05-01 14:26:23,412 - tensorflow - INFO - loss = 24.242685, step = 98600 (2.288 sec) -2022-05-01 14:26:25,696 - tensorflow - INFO - global_step/sec: 87.5488 -2022-05-01 14:26:25,696 - tensorflow - INFO - loss = 0.5029043, step = 98800 (2.284 sec) -2022-05-01 14:26:27,967 - tensorflow - INFO - global_step/sec: 88.0532 -2022-05-01 14:26:27,968 - tensorflow - INFO - loss = 0.74712604, step = 99000 (2.271 sec) -2022-05-01 14:26:30,226 - tensorflow - INFO - global_step/sec: 88.5269 -2022-05-01 14:26:30,227 - tensorflow - INFO - loss = 0.21298502, step = 99200 (2.259 sec) -2022-05-01 14:26:32,511 - tensorflow - INFO - global_step/sec: 87.5423 -2022-05-01 14:26:32,511 - tensorflow - INFO - loss = 0.21085733, step = 99400 (2.285 sec) -2022-05-01 14:26:34,811 - tensorflow - INFO - global_step/sec: 86.9667 -2022-05-01 14:26:34,811 - tensorflow - INFO - loss = 0.32836705, step = 99600 (2.300 sec) -2022-05-01 14:26:37,093 - tensorflow - INFO - global_step/sec: 87.6294 -2022-05-01 14:26:37,093 - tensorflow - INFO - loss = 0.24103443, step = 99800 (2.282 sec) -2022-05-01 14:26:39,372 - tensorflow - INFO - Saving checkpoints for 100000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. -2022-05-01 14:26:39,390 - tensorflow - INFO - model.ckpt-100000 is not in all_model_checkpoint_paths. Manually adding it. -2022-05-01 14:26:40,405 - tensorflow - INFO - Calling model_fn. -2022-05-01 14:26:40,530 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:204: The name tf.metrics.mean is deprecated. Please use tf.compat.v1.metrics.mean instead. - -2022-05-01 14:26:40,541 - tensorflow - INFO - Done calling model_fn. -2022-05-01 14:26:40,556 - tensorflow - INFO - Starting evaluation at 2022-05-01T14:26:40Z -2022-05-01 14:26:40,620 - tensorflow - INFO - Graph was finalized. -2022-05-01 14:26:40,623 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-100000 -2022-05-01 14:26:40,936 - tensorflow - INFO - Running local_init_op. -2022-05-01 14:26:41,050 - tensorflow - INFO - Done running local_init_op. -2022-05-01 14:26:56,055 - tensorflow - INFO - Evaluation [3990/39900] -2022-05-01 14:27:10,591 - tensorflow - INFO - Evaluation [7980/39900] -2022-05-01 14:27:24,889 - tensorflow - INFO - Evaluation [11970/39900] -2022-05-01 14:27:39,136 - tensorflow - INFO - Evaluation [15960/39900] -2022-05-01 14:27:53,364 - tensorflow - INFO - Evaluation [19950/39900] -2022-05-01 14:28:07,812 - tensorflow - INFO - Evaluation [23940/39900] -2022-05-01 14:28:22,113 - tensorflow - INFO - Evaluation [27930/39900] -2022-05-01 14:28:36,293 - tensorflow - INFO - Evaluation [31920/39900] -2022-05-01 14:28:50,471 - tensorflow - INFO - Evaluation [35910/39900] -2022-05-01 14:29:04,661 - tensorflow - INFO - Evaluation [39900/39900] -2022-05-01 14:29:04,800 - tensorflow - INFO - Finished evaluation at 2022-05-01-14:29:04 -2022-05-01 14:29:04,800 - tensorflow - INFO - Saving dict for global step 100000: global_step = 100000, loss = 0.18664514, mean_degree_err = 15.5360365 -2022-05-01 14:29:04,973 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 100000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-100000 -2022-05-01 14:29:04,987 - tensorflow - INFO - global_step/sec: 1.35232 -2022-05-01 14:29:04,988 - tensorflow - INFO - loss = 0.09665474, step = 100000 (147.894 sec) -2022-05-01 14:29:07,296 - tensorflow - INFO - global_step/sec: 86.6181 -2022-05-01 14:29:07,296 - tensorflow - INFO - loss = 0.11935344, step = 100200 (2.309 sec) -2022-05-01 14:29:09,600 - tensorflow - INFO - global_step/sec: 86.7917 -2022-05-01 14:29:09,601 - tensorflow - INFO - loss = 0.23293598, step = 100400 (2.304 sec) -2022-05-01 14:29:11,905 - tensorflow - INFO - global_step/sec: 86.7663 -2022-05-01 14:29:11,906 - tensorflow - INFO - loss = 13.853834, step = 100600 (2.305 sec) -2022-05-01 14:29:14,200 - tensorflow - INFO - global_step/sec: 87.1497 -2022-05-01 14:29:14,200 - tensorflow - INFO - loss = 0.28267854, step = 100800 (2.295 sec) -2022-05-01 14:29:16,493 - tensorflow - INFO - global_step/sec: 87.2407 -2022-05-01 14:29:16,493 - tensorflow - INFO - loss = 0.13818274, step = 101000 (2.292 sec) -2022-05-01 14:29:18,789 - tensorflow - INFO - global_step/sec: 87.1046 -2022-05-01 14:29:18,789 - tensorflow - INFO - loss = 0.191855, step = 101200 (2.296 sec) -2022-05-01 14:29:21,092 - tensorflow - INFO - global_step/sec: 86.8156 -2022-05-01 14:29:21,093 - tensorflow - INFO - loss = 0.09758256, step = 101400 (2.304 sec) -2022-05-01 14:29:23,394 - tensorflow - INFO - global_step/sec: 86.8777 -2022-05-01 14:29:23,395 - tensorflow - INFO - loss = 0.23463662, step = 101600 (2.302 sec) -2022-05-01 14:29:25,696 - tensorflow - INFO - global_step/sec: 86.9051 -2022-05-01 14:29:25,696 - tensorflow - INFO - loss = 0.09981724, step = 101800 (2.301 sec) -2022-05-01 14:29:27,995 - tensorflow - INFO - global_step/sec: 86.9773 -2022-05-01 14:29:27,996 - tensorflow - INFO - loss = 0.3753961, step = 102000 (2.299 sec) -2022-05-01 14:29:30,294 - tensorflow - INFO - global_step/sec: 87.017 -2022-05-01 14:29:30,294 - tensorflow - INFO - loss = 0.16868381, step = 102200 (2.298 sec) -2022-05-01 14:29:32,592 - tensorflow - INFO - global_step/sec: 87.0017 -2022-05-01 14:29:32,593 - tensorflow - INFO - loss = 8.778584, step = 102400 (2.299 sec) -2022-05-01 14:29:34,886 - tensorflow - INFO - global_step/sec: 87.2183 -2022-05-01 14:29:34,886 - tensorflow - INFO - loss = 0.090705186, step = 102600 (2.293 sec) -2022-05-01 14:29:37,177 - tensorflow - INFO - global_step/sec: 87.2853 -2022-05-01 14:29:37,177 - 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tensorflow - INFO - loss = 0.7926186, step = 181600 (2.290 sec) -2022-05-01 14:44:42,431 - tensorflow - INFO - global_step/sec: 87.1673 -2022-05-01 14:44:42,432 - tensorflow - INFO - loss = 0.1802915, step = 181800 (2.294 sec) -2022-05-01 14:44:44,721 - tensorflow - INFO - global_step/sec: 87.3559 -2022-05-01 14:44:44,721 - tensorflow - INFO - loss = 0.032432634, step = 182000 (2.289 sec) -2022-05-01 14:44:47,010 - tensorflow - INFO - global_step/sec: 87.3801 -2022-05-01 14:44:47,010 - tensorflow - INFO - loss = 0.06632277, step = 182200 (2.289 sec) -2022-05-01 14:44:49,299 - tensorflow - INFO - global_step/sec: 87.3673 -2022-05-01 14:44:49,299 - tensorflow - INFO - loss = 0.15085332, step = 182400 (2.289 sec) -2022-05-01 14:44:51,585 - tensorflow - INFO - global_step/sec: 87.4909 -2022-05-01 14:44:51,585 - tensorflow - INFO - loss = 0.20117849, step = 182600 (2.286 sec) -2022-05-01 14:44:53,881 - tensorflow - INFO - global_step/sec: 87.0908 -2022-05-01 14:44:53,882 - tensorflow - INFO - loss = 0.11522024, step = 182800 (2.296 sec) -2022-05-01 14:44:56,172 - tensorflow - INFO - global_step/sec: 87.324 -2022-05-01 14:44:56,172 - tensorflow - INFO - loss = 0.3229064, step = 183000 (2.290 sec) -2022-05-01 14:44:58,457 - tensorflow - INFO - global_step/sec: 87.5155 -2022-05-01 14:44:58,457 - tensorflow - INFO - loss = 0.34929866, step = 183200 (2.285 sec) -2022-05-01 14:45:00,751 - tensorflow - INFO - global_step/sec: 87.1811 -2022-05-01 14:45:00,751 - tensorflow - INFO - loss = 0.10278164, step = 183400 (2.294 sec) -2022-05-01 14:45:03,042 - tensorflow - INFO - global_step/sec: 87.2927 -2022-05-01 14:45:03,042 - tensorflow - INFO - loss = 0.105486035, step = 183600 (2.291 sec) -2022-05-01 14:45:05,327 - tensorflow - INFO - global_step/sec: 87.537 -2022-05-01 14:45:05,327 - tensorflow - INFO - loss = 16.610962, step = 183800 (2.285 sec) -2022-05-01 14:45:07,615 - tensorflow - INFO - global_step/sec: 87.4199 -2022-05-01 14:45:07,615 - tensorflow - INFO - loss = 0.04139348, step = 184000 (2.288 sec) -2022-05-01 14:45:09,894 - tensorflow - INFO - global_step/sec: 87.7259 -2022-05-01 14:45:09,895 - tensorflow - INFO - loss = 0.027574524, step = 184200 (2.280 sec) -2022-05-01 14:45:12,164 - tensorflow - INFO - global_step/sec: 88.1344 -2022-05-01 14:45:12,164 - tensorflow - INFO - loss = 0.14222334, step = 184400 (2.269 sec) -2022-05-01 14:45:14,420 - tensorflow - INFO - global_step/sec: 88.6393 -2022-05-01 14:45:14,420 - tensorflow - INFO - loss = 0.17393786, step = 184600 (2.256 sec) -2022-05-01 14:45:16,675 - tensorflow - INFO - global_step/sec: 88.6945 -2022-05-01 14:45:16,675 - tensorflow - INFO - loss = 0.06557072, step = 184800 (2.255 sec) -2022-05-01 14:45:18,953 - tensorflow - INFO - global_step/sec: 87.8073 -2022-05-01 14:45:18,953 - tensorflow - INFO - loss = 0.028205689, step = 185000 (2.278 sec) -2022-05-01 14:45:21,244 - tensorflow - INFO - global_step/sec: 87.2987 -2022-05-01 14:45:21,244 - tensorflow - INFO - loss = 0.041646503, step = 185200 (2.291 sec) -2022-05-01 14:45:23,530 - tensorflow - INFO - global_step/sec: 87.4761 -2022-05-01 14:45:23,530 - tensorflow - INFO - loss = 0.093024604, step = 185400 (2.286 sec) -2022-05-01 14:45:25,818 - tensorflow - INFO - global_step/sec: 87.4176 -2022-05-01 14:45:25,818 - tensorflow - INFO - loss = 0.11461769, step = 185600 (2.288 sec) -2022-05-01 14:45:28,110 - tensorflow - INFO - global_step/sec: 87.2538 -2022-05-01 14:45:28,110 - tensorflow - INFO - loss = 2.0448453, step = 185800 (2.292 sec) -2022-05-01 14:45:30,403 - tensorflow - INFO - global_step/sec: 87.2398 -2022-05-01 14:45:30,403 - tensorflow - INFO - loss = 0.15032089, step = 186000 (2.292 sec) -2022-05-01 14:45:32,694 - tensorflow - INFO - global_step/sec: 87.2979 -2022-05-01 14:45:32,694 - tensorflow - INFO - loss = 0.17604691, step = 186200 (2.291 sec) -2022-05-01 14:45:34,986 - tensorflow - INFO - global_step/sec: 87.2264 -2022-05-01 14:45:34,987 - tensorflow - INFO - loss = 0.044206016, step = 186400 (2.293 sec) -2022-05-01 14:45:37,277 - tensorflow - INFO - global_step/sec: 87.3331 -2022-05-01 14:45:37,277 - tensorflow - INFO - loss = 0.17563851, step = 186600 (2.290 sec) -2022-05-01 14:45:39,564 - tensorflow - INFO - global_step/sec: 87.4231 -2022-05-01 14:45:39,565 - tensorflow - INFO - loss = 0.14848632, step = 186800 (2.288 sec) -2022-05-01 14:45:41,852 - tensorflow - INFO - global_step/sec: 87.4146 -2022-05-01 14:45:41,853 - tensorflow - INFO - loss = 0.09012383, step = 187000 (2.288 sec) -2022-05-01 14:45:44,140 - tensorflow - INFO - global_step/sec: 87.4321 -2022-05-01 14:45:44,140 - tensorflow - INFO - loss = 0.18280177, step = 187200 (2.287 sec) -2022-05-01 14:45:46,423 - tensorflow - INFO - global_step/sec: 87.592 -2022-05-01 14:45:46,423 - tensorflow - INFO - loss = 0.048017383, step = 187400 (2.283 sec) -2022-05-01 14:45:48,722 - tensorflow - INFO - global_step/sec: 86.9997 -2022-05-01 14:45:48,722 - tensorflow - INFO - loss = 11.872378, step = 187600 (2.299 sec) -2022-05-01 14:45:51,011 - tensorflow - INFO - global_step/sec: 87.3555 -2022-05-01 14:45:51,012 - tensorflow - INFO - loss = 0.07027066, step = 187800 (2.289 sec) -2022-05-01 14:45:53,298 - tensorflow - INFO - global_step/sec: 87.4623 -2022-05-01 14:45:53,298 - tensorflow - INFO - loss = 0.16031665, step = 188000 (2.287 sec) -2022-05-01 14:45:55,589 - tensorflow - INFO - global_step/sec: 87.3037 -2022-05-01 14:45:55,589 - tensorflow - INFO - loss = 0.14436464, step = 188200 (2.291 sec) -2022-05-01 14:45:57,879 - tensorflow - INFO - global_step/sec: 87.3348 -2022-05-01 14:45:57,879 - tensorflow - INFO - loss = 0.34885636, step = 188400 (2.290 sec) -2022-05-01 14:46:00,171 - tensorflow - INFO - global_step/sec: 87.2641 -2022-05-01 14:46:00,171 - tensorflow - INFO - loss = 0.040362917, step = 188600 (2.292 sec) -2022-05-01 14:46:02,463 - tensorflow - INFO - global_step/sec: 87.2572 -2022-05-01 14:46:02,463 - tensorflow - INFO - loss = 0.04270378, step = 188800 (2.292 sec) -2022-05-01 14:46:04,750 - tensorflow - INFO - global_step/sec: 87.4552 -2022-05-01 14:46:04,750 - tensorflow - INFO - loss = 0.0711705, step = 189000 (2.287 sec) -2022-05-01 14:46:07,040 - tensorflow - INFO - global_step/sec: 87.3171 -2022-05-01 14:46:07,041 - tensorflow - INFO - loss = 0.1641151, step = 189200 (2.291 sec) -2022-05-01 14:46:09,329 - tensorflow - INFO - global_step/sec: 87.3957 -2022-05-01 14:46:09,329 - tensorflow - INFO - loss = 0.18184356, step = 189400 (2.288 sec) -2022-05-01 14:46:11,615 - tensorflow - INFO - global_step/sec: 87.4962 -2022-05-01 14:46:11,615 - tensorflow - INFO - loss = 0.17193224, step = 189600 (2.286 sec) -2022-05-01 14:46:13,914 - tensorflow - INFO - global_step/sec: 86.995 -2022-05-01 14:46:13,914 - tensorflow - INFO - loss = 0.19972838, step = 189800 (2.299 sec) -2022-05-01 14:46:16,204 - tensorflow - INFO - global_step/sec: 87.3129 -2022-05-01 14:46:16,205 - tensorflow - INFO - loss = 0.8434968, step = 190000 (2.291 sec) -2022-05-01 14:46:18,488 - tensorflow - INFO - global_step/sec: 87.5691 -2022-05-01 14:46:18,488 - tensorflow - INFO - loss = 0.1671878, step = 190200 (2.284 sec) -2022-05-01 14:46:20,777 - tensorflow - INFO - global_step/sec: 87.3945 -2022-05-01 14:46:20,777 - tensorflow - INFO - loss = 0.060731374, step = 190400 (2.288 sec) -2022-05-01 14:46:23,070 - tensorflow - INFO - global_step/sec: 87.2185 -2022-05-01 14:46:23,070 - tensorflow - INFO - loss = 0.08488855, step = 190600 (2.293 sec) -2022-05-01 14:46:25,367 - tensorflow - INFO - global_step/sec: 87.0444 -2022-05-01 14:46:25,368 - tensorflow - INFO - loss = 0.05223095, step = 190800 (2.298 sec) -2022-05-01 14:46:27,663 - tensorflow - INFO - global_step/sec: 87.1166 -2022-05-01 14:46:27,664 - tensorflow - INFO - loss = 0.067917466, step = 191000 (2.296 sec) -2022-05-01 14:46:29,958 - tensorflow - INFO - global_step/sec: 87.1681 -2022-05-01 14:46:29,958 - tensorflow - INFO - loss = 0.27582443, step = 191200 (2.294 sec) -2022-05-01 14:46:32,256 - tensorflow - INFO - global_step/sec: 87.0233 -2022-05-01 14:46:32,256 - tensorflow - INFO - loss = 0.42234778, step = 191400 (2.298 sec) -2022-05-01 14:46:34,547 - tensorflow - INFO - global_step/sec: 87.27 -2022-05-01 14:46:34,548 - tensorflow - INFO - loss = 0.10319117, step = 191600 (2.292 sec) -2022-05-01 14:46:36,838 - tensorflow - INFO - global_step/sec: 87.2976 -2022-05-01 14:46:36,839 - tensorflow - INFO - loss = 0.09261024, step = 191800 (2.291 sec) -2022-05-01 14:46:39,131 - tensorflow - INFO - global_step/sec: 87.2527 -2022-05-01 14:46:39,131 - tensorflow - INFO - loss = 0.040254287, step = 192000 (2.292 sec) -2022-05-01 14:46:41,416 - tensorflow - INFO - global_step/sec: 87.4974 -2022-05-01 14:46:41,417 - tensorflow - INFO - loss = 0.35942328, step = 192200 (2.286 sec) -2022-05-01 14:46:43,704 - tensorflow - INFO - global_step/sec: 87.4408 -2022-05-01 14:46:43,704 - tensorflow - INFO - loss = 0.08916014, step = 192400 (2.287 sec) -2022-05-01 14:46:45,988 - tensorflow - INFO - global_step/sec: 87.5359 -2022-05-01 14:46:45,989 - tensorflow - INFO - loss = 0.13586244, step = 192600 (2.285 sec) -2022-05-01 14:46:48,267 - tensorflow - INFO - global_step/sec: 87.7855 -2022-05-01 14:46:48,267 - tensorflow - INFO - loss = 0.09050533, step = 192800 (2.278 sec) -2022-05-01 14:46:50,529 - tensorflow - INFO - global_step/sec: 88.391 -2022-05-01 14:46:50,530 - tensorflow - INFO - loss = 0.13040312, step = 193000 (2.263 sec) -2022-05-01 14:46:52,829 - tensorflow - INFO - global_step/sec: 86.9863 -2022-05-01 14:46:52,829 - tensorflow - INFO - loss = 0.094426826, step = 193200 (2.299 sec) -2022-05-01 14:46:55,124 - tensorflow - INFO - global_step/sec: 87.1413 -2022-05-01 14:46:55,124 - tensorflow - INFO - loss = 0.102052435, step = 193400 (2.295 sec) -2022-05-01 14:46:57,425 - tensorflow - INFO - global_step/sec: 86.903 -2022-05-01 14:46:57,426 - tensorflow - INFO - loss = 0.05099783, step = 193600 (2.301 sec) -2022-05-01 14:46:59,725 - tensorflow - INFO - global_step/sec: 86.9525 -2022-05-01 14:46:59,726 - tensorflow - INFO - loss = 0.1822123, step = 193800 (2.300 sec) -2022-05-01 14:47:02,026 - tensorflow - INFO - global_step/sec: 86.9489 -2022-05-01 14:47:02,026 - tensorflow - INFO - loss = 0.059502125, step = 194000 (2.300 sec) -2022-05-01 14:47:04,322 - tensorflow - INFO - global_step/sec: 87.0813 -2022-05-01 14:47:04,323 - tensorflow - INFO - loss = 0.12372404, step = 194200 (2.297 sec) -2022-05-01 14:47:06,624 - tensorflow - INFO - global_step/sec: 86.9004 -2022-05-01 14:47:06,624 - tensorflow - INFO - loss = 4.9969735, step = 194400 (2.302 sec) -2022-05-01 14:47:08,917 - tensorflow - INFO - global_step/sec: 87.2197 -2022-05-01 14:47:08,917 - tensorflow - INFO - loss = 0.46680868, step = 194600 (2.293 sec) -2022-05-01 14:47:11,216 - tensorflow - INFO - global_step/sec: 86.9929 -2022-05-01 14:47:11,216 - tensorflow - INFO - loss = 0.21966107, step = 194800 (2.299 sec) -2022-05-01 14:47:13,512 - tensorflow - INFO - global_step/sec: 87.0903 -2022-05-01 14:47:13,513 - tensorflow - INFO - loss = 1.4392605, step = 195000 (2.296 sec) -2022-05-01 14:47:15,796 - tensorflow - INFO - global_step/sec: 87.5799 -2022-05-01 14:47:15,796 - tensorflow - INFO - loss = 2.9109988, step = 195200 (2.284 sec) -2022-05-01 14:47:18,088 - tensorflow - INFO - global_step/sec: 87.2639 -2022-05-01 14:47:18,088 - tensorflow - INFO - loss = 0.06750238, step = 195400 (2.292 sec) -2022-05-01 14:47:20,377 - tensorflow - INFO - global_step/sec: 87.3571 -2022-05-01 14:47:20,378 - tensorflow - INFO - loss = 0.13745224, step = 195600 (2.289 sec) -2022-05-01 14:47:22,658 - tensorflow - INFO - global_step/sec: 87.6826 -2022-05-01 14:47:22,659 - tensorflow - INFO - loss = 0.22367324, step = 195800 (2.281 sec) -2022-05-01 14:47:24,952 - tensorflow - INFO - global_step/sec: 87.1776 -2022-05-01 14:47:24,953 - tensorflow - INFO - loss = 0.29652923, step = 196000 (2.294 sec) -2022-05-01 14:47:27,238 - tensorflow - INFO - global_step/sec: 87.5058 -2022-05-01 14:47:27,238 - tensorflow - INFO - loss = 0.0928004, step = 196200 (2.286 sec) -2022-05-01 14:47:29,522 - tensorflow - INFO - global_step/sec: 87.5486 -2022-05-01 14:47:29,523 - tensorflow - INFO - loss = 7.4939165, step = 196400 (2.284 sec) -2022-05-01 14:47:31,823 - tensorflow - INFO - global_step/sec: 86.9363 -2022-05-01 14:47:31,823 - tensorflow - INFO - loss = 0.436599, step = 196600 (2.301 sec) -2022-05-01 14:47:34,107 - tensorflow - INFO - global_step/sec: 87.5778 -2022-05-01 14:47:34,107 - tensorflow - INFO - loss = 0.024931233, step = 196800 (2.284 sec) -2022-05-01 14:47:36,397 - tensorflow - INFO - global_step/sec: 87.3185 -2022-05-01 14:47:36,398 - tensorflow - INFO - loss = 0.07194897, step = 197000 (2.291 sec) -2022-05-01 14:47:38,684 - tensorflow - INFO - global_step/sec: 87.4662 -2022-05-01 14:47:38,684 - tensorflow - INFO - loss = 23.8871, step = 197200 (2.287 sec) -2022-05-01 14:47:40,978 - tensorflow - INFO - global_step/sec: 87.1584 -2022-05-01 14:47:40,979 - tensorflow - INFO - loss = 0.10765213, step = 197400 (2.295 sec) -2022-05-01 14:47:43,279 - tensorflow - INFO - global_step/sec: 86.9478 -2022-05-01 14:47:43,279 - tensorflow - INFO - loss = 0.07624713, step = 197600 (2.300 sec) -2022-05-01 14:47:45,570 - tensorflow - INFO - global_step/sec: 87.2936 -2022-05-01 14:47:45,570 - tensorflow - INFO - loss = 9.676068, step = 197800 (2.291 sec) -2022-05-01 14:47:47,857 - tensorflow - INFO - global_step/sec: 87.4508 -2022-05-01 14:47:47,857 - tensorflow - INFO - loss = 0.14174747, step = 198000 (2.287 sec) -2022-05-01 14:47:50,144 - tensorflow - INFO - global_step/sec: 87.4219 -2022-05-01 14:47:50,145 - tensorflow - INFO - loss = 0.090489924, step = 198200 (2.288 sec) -2022-05-01 14:47:52,427 - tensorflow - INFO - global_step/sec: 87.6189 -2022-05-01 14:47:52,427 - tensorflow - INFO - loss = 0.074647754, step = 198400 (2.283 sec) -2022-05-01 14:47:54,723 - tensorflow - INFO - global_step/sec: 87.1252 -2022-05-01 14:47:54,723 - tensorflow - INFO - loss = 0.0646258, step = 198600 (2.296 sec) -2022-05-01 14:47:57,018 - tensorflow - INFO - global_step/sec: 87.1412 -2022-05-01 14:47:57,018 - tensorflow - INFO - loss = 0.46771583, step = 198800 (2.295 sec) -2022-05-01 14:47:59,300 - tensorflow - INFO - global_step/sec: 87.6377 -2022-05-01 14:47:59,300 - tensorflow - INFO - loss = 0.3469265, step = 199000 (2.282 sec) -2022-05-01 14:48:01,586 - tensorflow - INFO - global_step/sec: 87.4839 -2022-05-01 14:48:01,586 - tensorflow - INFO - loss = 1.0086424, step = 199200 (2.286 sec) -2022-05-01 14:48:03,872 - tensorflow - INFO - global_step/sec: 87.4938 -2022-05-01 14:48:03,872 - tensorflow - INFO - loss = 1.1862116, step = 199400 (2.286 sec) -2022-05-01 14:48:06,165 - tensorflow - INFO - global_step/sec: 87.2047 -2022-05-01 14:48:06,166 - tensorflow - INFO - loss = 0.15060207, step = 199600 (2.293 sec) -2022-05-01 14:48:08,463 - tensorflow - INFO - global_step/sec: 87.0599 -2022-05-01 14:48:08,463 - tensorflow - INFO - loss = 0.055810157, step = 199800 (2.297 sec) -2022-05-01 14:48:10,743 - tensorflow - INFO - Saving checkpoints for 200000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. -2022-05-01 14:48:10,758 - tensorflow - INFO - model.ckpt-200000 is not in all_model_checkpoint_paths. Manually adding it. -2022-05-01 14:48:11,312 - tensorflow - INFO - Calling model_fn. -2022-05-01 14:48:11,443 - tensorflow - INFO - Done calling model_fn. -2022-05-01 14:48:11,458 - tensorflow - INFO - Starting evaluation at 2022-05-01T14:48:11Z -2022-05-01 14:48:11,523 - tensorflow - INFO - Graph was finalized. -2022-05-01 14:48:11,526 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-200000 -2022-05-01 14:48:11,811 - tensorflow - INFO - Running local_init_op. -2022-05-01 14:48:11,921 - tensorflow - INFO - Done running local_init_op. -2022-05-01 14:48:22,202 - tensorflow - INFO - Evaluation [3990/39900] -2022-05-01 14:48:31,752 - tensorflow - INFO - Evaluation [7980/39900] -2022-05-01 14:48:41,342 - tensorflow - INFO - Evaluation [11970/39900] -2022-05-01 14:48:50,922 - tensorflow - INFO - Evaluation [15960/39900] -2022-05-01 14:49:00,471 - tensorflow - INFO - Evaluation [19950/39900] -2022-05-01 14:49:10,047 - tensorflow - INFO - Evaluation [23940/39900] -2022-05-01 14:49:19,631 - tensorflow - INFO - Evaluation [27930/39900] -2022-05-01 14:49:29,222 - tensorflow - INFO - Evaluation [31920/39900] -2022-05-01 14:49:38,795 - tensorflow - INFO - Evaluation [35910/39900] -2022-05-01 14:49:48,375 - tensorflow - INFO - Evaluation [39900/39900] -2022-05-01 14:49:48,512 - tensorflow - INFO - Finished evaluation at 2022-05-01-14:49:48 -2022-05-01 14:49:48,512 - tensorflow - INFO - Saving dict for global step 200000: global_step = 200000, loss = 0.100737154, mean_degree_err = 10.012244 -2022-05-01 14:49:48,513 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 200000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-200000 -2022-05-01 14:49:48,526 - tensorflow - INFO - global_step/sec: 1.99873 -2022-05-01 14:49:48,527 - tensorflow - INFO - loss = 0.12662719, step = 200000 (100.064 sec) -2022-05-01 14:49:50,832 - tensorflow - INFO - global_step/sec: 86.7276 -2022-05-01 14:49:50,833 - tensorflow - INFO - loss = 0.8001151, step = 200200 (2.306 sec) -2022-05-01 14:49:53,113 - tensorflow - INFO - global_step/sec: 87.6802 -2022-05-01 14:49:53,114 - tensorflow - INFO - loss = 0.045994636, step = 200400 (2.281 sec) -2022-05-01 14:49:55,390 - tensorflow - INFO - global_step/sec: 87.8451 -2022-05-01 14:49:55,391 - tensorflow - INFO - loss = 4.106056, step = 200600 (2.277 sec) -2022-05-01 14:49:57,661 - tensorflow - INFO - global_step/sec: 88.0574 -2022-05-01 14:49:57,662 - tensorflow - INFO - loss = 0.4528215, step = 200800 (2.271 sec) -2022-05-01 14:49:59,923 - tensorflow - INFO - global_step/sec: 88.4209 -2022-05-01 14:49:59,924 - tensorflow - INFO - loss = 0.07186222, step = 201000 (2.262 sec) -2022-05-01 14:50:02,210 - tensorflow - INFO - global_step/sec: 87.4507 -2022-05-01 14:50:02,211 - tensorflow - INFO - loss = 0.20265803, step = 201200 (2.287 sec) -2022-05-01 14:50:04,500 - tensorflow - INFO - global_step/sec: 87.3595 -2022-05-01 14:50:04,500 - tensorflow - INFO - loss = 0.03260638, step = 201400 (2.289 sec) -2022-05-01 14:50:06,787 - tensorflow - INFO - global_step/sec: 87.4457 -2022-05-01 14:50:06,787 - tensorflow - INFO - loss = 0.11148474, step = 201600 (2.287 sec) -2022-05-01 14:50:09,072 - tensorflow - INFO - global_step/sec: 87.5177 -2022-05-01 14:50:09,072 - tensorflow - INFO - loss = 1.6456022, step = 201800 (2.285 sec) -2022-05-01 14:50:11,361 - tensorflow - INFO - global_step/sec: 87.3907 -2022-05-01 14:50:11,361 - tensorflow - INFO - loss = 0.17919509, step = 202000 (2.289 sec) -2022-05-01 14:50:13,661 - tensorflow - INFO - global_step/sec: 86.9405 -2022-05-01 14:50:13,662 - tensorflow - INFO - loss = 0.09502393, step = 202200 (2.300 sec) -2022-05-01 14:50:15,963 - tensorflow - INFO - global_step/sec: 86.8866 -2022-05-01 14:50:15,963 - tensorflow - INFO - loss = 0.05594068, step = 202400 (2.302 sec) -2022-05-01 14:50:18,261 - tensorflow - INFO - global_step/sec: 87.0316 -2022-05-01 14:50:18,261 - tensorflow - INFO - loss = 0.09115332, step = 202600 (2.298 sec) -2022-05-01 14:50:20,561 - tensorflow - INFO - global_step/sec: 86.9407 -2022-05-01 14:50:20,562 - tensorflow - INFO - loss = 10.594648, step = 202800 (2.300 sec) -2022-05-01 14:50:22,854 - tensorflow - INFO - global_step/sec: 87.2173 -2022-05-01 14:50:22,855 - tensorflow - INFO - loss = 0.09624572, step = 203000 (2.293 sec) -2022-05-01 14:50:25,156 - tensorflow - INFO - global_step/sec: 86.8796 -2022-05-01 14:50:25,157 - tensorflow - INFO - loss = 0.03879582, step = 203200 (2.302 sec) -2022-05-01 14:50:27,458 - tensorflow - INFO - global_step/sec: 86.9109 -2022-05-01 14:50:27,458 - tensorflow - INFO - loss = 1.6161735, step = 203400 (2.301 sec) -2022-05-01 14:50:29,753 - tensorflow - INFO - global_step/sec: 87.1401 -2022-05-01 14:50:29,753 - tensorflow - INFO - loss = 0.12163468, step = 203600 (2.295 sec) -2022-05-01 14:50:32,061 - tensorflow - INFO - global_step/sec: 86.646 -2022-05-01 14:50:32,062 - tensorflow - INFO - loss = 11.411995, step = 203800 (2.308 sec) -2022-05-01 14:50:34,362 - tensorflow - INFO - global_step/sec: 86.9162 -2022-05-01 14:50:34,363 - tensorflow - INFO - loss = 0.72658515, step = 204000 (2.301 sec) -2022-05-01 14:50:36,659 - tensorflow - INFO - global_step/sec: 87.0901 -2022-05-01 14:50:36,659 - tensorflow - INFO - loss = 0.3619779, step = 204200 (2.296 sec) -2022-05-01 14:50:38,961 - tensorflow - INFO - global_step/sec: 86.8832 -2022-05-01 14:50:38,961 - tensorflow - INFO - loss = 5.773318, step = 204400 (2.302 sec) -2022-05-01 14:50:41,262 - tensorflow - INFO - global_step/sec: 86.9034 -2022-05-01 14:50:41,262 - tensorflow - INFO - loss = 0.10278251, step = 204600 (2.301 sec) -2022-05-01 14:50:43,554 - tensorflow - INFO - global_step/sec: 87.2608 -2022-05-01 14:50:43,554 - tensorflow - INFO - loss = 0.21095583, step = 204800 (2.292 sec) -2022-05-01 14:50:45,840 - tensorflow - INFO - global_step/sec: 87.4786 -2022-05-01 14:50:45,841 - tensorflow - INFO - loss = 0.046599954, step = 205000 (2.286 sec) -2022-05-01 14:50:48,127 - tensorflow - INFO - global_step/sec: 87.466 -2022-05-01 14:50:48,127 - tensorflow - INFO - loss = 0.34111094, step = 205200 (2.287 sec) -2022-05-01 14:50:50,406 - tensorflow - INFO - global_step/sec: 87.7639 -2022-05-01 14:50:50,406 - tensorflow - INFO - loss = 0.14029244, step = 205400 (2.279 sec) -2022-05-01 14:50:52,706 - tensorflow - INFO - global_step/sec: 86.946 -2022-05-01 14:50:52,706 - tensorflow - INFO - loss = 0.115294844, step = 205600 (2.300 sec) -2022-05-01 14:50:54,996 - tensorflow - INFO - global_step/sec: 87.3392 -2022-05-01 14:50:54,996 - tensorflow - INFO - loss = 1.6688011, step = 205800 (2.290 sec) -2022-05-01 14:50:57,284 - tensorflow - INFO - global_step/sec: 87.426 -2022-05-01 14:50:57,284 - tensorflow - INFO - loss = 1.0651115, step = 206000 (2.288 sec) -2022-05-01 14:50:59,574 - tensorflow - INFO - global_step/sec: 87.3021 -2022-05-01 14:50:59,575 - tensorflow - INFO - loss = 0.05911135, step = 206200 (2.291 sec) -2022-05-01 14:51:01,863 - tensorflow - INFO - global_step/sec: 87.3795 -2022-05-01 14:51:01,864 - tensorflow - INFO - loss = 3.0845604, step = 206400 (2.289 sec) -2022-05-01 14:51:04,155 - tensorflow - INFO - global_step/sec: 87.2606 -2022-05-01 14:51:04,156 - tensorflow - INFO - loss = 1.0244889, step = 206600 (2.292 sec) -2022-05-01 14:51:06,450 - tensorflow - INFO - global_step/sec: 87.1581 -2022-05-01 14:51:06,450 - tensorflow - INFO - loss = 0.06411409, step = 206800 (2.295 sec) -2022-05-01 14:51:08,742 - tensorflow - INFO - global_step/sec: 87.2733 -2022-05-01 14:51:08,742 - tensorflow - INFO - loss = 0.093899466, step = 207000 (2.292 sec) -2022-05-01 14:51:11,032 - tensorflow - INFO - global_step/sec: 87.3387 -2022-05-01 14:51:11,032 - tensorflow - INFO - loss = 0.057339266, step = 207200 (2.290 sec) -2022-05-01 14:51:13,323 - tensorflow - INFO - global_step/sec: 87.2977 -2022-05-01 14:51:13,323 - tensorflow - INFO - loss = 0.079025224, step = 207400 (2.291 sec) -2022-05-01 14:51:15,614 - tensorflow - INFO - global_step/sec: 87.2702 -2022-05-01 14:51:15,615 - tensorflow - INFO - loss = 0.08560538, step = 207600 (2.292 sec) -2022-05-01 14:51:17,904 - tensorflow - INFO - global_step/sec: 87.3517 -2022-05-01 14:51:17,904 - tensorflow - INFO - loss = 0.61651057, step = 207800 (2.290 sec) -2022-05-01 14:51:20,189 - tensorflow - INFO - global_step/sec: 87.5134 -2022-05-01 14:51:20,190 - tensorflow - INFO - loss = 0.050484687, step = 208000 (2.285 sec) -2022-05-01 14:51:22,480 - tensorflow - INFO - global_step/sec: 87.3092 -2022-05-01 14:51:22,480 - tensorflow - INFO - loss = 0.12937605, step = 208200 (2.291 sec) -2022-05-01 14:51:24,772 - tensorflow - INFO - global_step/sec: 87.273 -2022-05-01 14:51:24,772 - tensorflow - INFO - loss = 0.10451312, step = 208400 (2.292 sec) -2022-05-01 14:51:27,042 - tensorflow - INFO - global_step/sec: 88.1056 -2022-05-01 14:51:27,042 - tensorflow - INFO - loss = 0.25230247, step = 208600 (2.270 sec) -2022-05-01 14:51:29,322 - tensorflow - INFO - global_step/sec: 87.6994 -2022-05-01 14:51:29,323 - tensorflow - INFO - loss = 0.11170903, step = 208800 (2.281 sec) -2022-05-01 14:51:31,588 - tensorflow - INFO - global_step/sec: 88.2585 -2022-05-01 14:51:31,589 - tensorflow - INFO - loss = 0.29632747, step = 209000 (2.266 sec) -2022-05-01 14:51:33,873 - tensorflow - INFO - global_step/sec: 87.5372 -2022-05-01 14:51:33,873 - tensorflow - INFO - loss = 0.06806228, step = 209200 (2.285 sec) -2022-05-01 14:51:36,165 - tensorflow - INFO - global_step/sec: 87.2485 -2022-05-01 14:51:36,166 - tensorflow - INFO - loss = 0.14363146, step = 209400 (2.292 sec) -2022-05-01 14:51:38,456 - tensorflow - INFO - global_step/sec: 87.2922 -2022-05-01 14:51:38,457 - tensorflow - INFO - loss = 0.1182349, step = 209600 (2.291 sec) -2022-05-01 14:51:40,745 - tensorflow - INFO - global_step/sec: 87.4044 -2022-05-01 14:51:40,745 - tensorflow - INFO - loss = 0.19589159, step = 209800 (2.288 sec) -2022-05-01 14:51:43,044 - tensorflow - INFO - global_step/sec: 86.9773 -2022-05-01 14:51:43,045 - tensorflow - INFO - loss = 0.19596815, step = 210000 (2.299 sec) -2022-05-01 14:51:45,334 - tensorflow - INFO - global_step/sec: 87.3312 -2022-05-01 14:51:45,335 - tensorflow - INFO - loss = 0.20116207, step = 210200 (2.290 sec) -2022-05-01 14:51:47,629 - tensorflow - INFO - global_step/sec: 87.1483 -2022-05-01 14:51:47,630 - tensorflow - INFO - loss = 0.029008688, step = 210400 (2.295 sec) -2022-05-01 14:51:49,921 - tensorflow - INFO - global_step/sec: 87.2641 -2022-05-01 14:51:49,922 - tensorflow - INFO - loss = 0.072042614, step = 210600 (2.292 sec) -2022-05-01 14:51:52,215 - tensorflow - INFO - global_step/sec: 87.1826 -2022-05-01 14:51:52,216 - tensorflow - INFO - loss = 0.05465828, step = 210800 (2.294 sec) -2022-05-01 14:51:54,507 - tensorflow - INFO - global_step/sec: 87.246 -2022-05-01 14:51:54,508 - tensorflow - INFO - loss = 0.060184408, step = 211000 (2.292 sec) -2022-05-01 14:51:56,803 - tensorflow - INFO - global_step/sec: 87.1235 -2022-05-01 14:51:56,803 - tensorflow - INFO - loss = 0.041051302, step = 211200 (2.296 sec) -2022-05-01 14:51:59,088 - tensorflow - INFO - global_step/sec: 87.5112 -2022-05-01 14:51:59,089 - tensorflow - INFO - loss = 0.13065943, step = 211400 (2.285 sec) -2022-05-01 14:52:01,382 - tensorflow - INFO - global_step/sec: 87.1999 -2022-05-01 14:52:01,382 - tensorflow - INFO - loss = 0.13711402, step = 211600 (2.294 sec) -2022-05-01 14:52:03,671 - tensorflow - INFO - global_step/sec: 87.3831 -2022-05-01 14:52:03,671 - tensorflow - INFO - loss = 2.0083685, step = 211800 (2.289 sec) -2022-05-01 14:52:05,955 - tensorflow - INFO - global_step/sec: 87.5641 -2022-05-01 14:52:05,955 - tensorflow - INFO - loss = 0.4175942, step = 212000 (2.284 sec) -2022-05-01 14:52:08,249 - tensorflow - INFO - global_step/sec: 87.1732 -2022-05-01 14:52:08,250 - tensorflow - INFO - loss = 1.8437014, step = 212200 (2.294 sec) -2022-05-01 14:52:10,542 - tensorflow - INFO - global_step/sec: 87.228 -2022-05-01 14:52:10,542 - tensorflow - INFO - loss = 0.0708262, step = 212400 (2.293 sec) -2022-05-01 14:52:12,831 - tensorflow - INFO - global_step/sec: 87.3639 -2022-05-01 14:52:12,832 - tensorflow - INFO - loss = 0.08293774, step = 212600 (2.289 sec) -2022-05-01 14:52:15,121 - tensorflow - INFO - global_step/sec: 87.3419 -2022-05-01 14:52:15,122 - tensorflow - INFO - loss = 0.06429095, step = 212800 (2.290 sec) -2022-05-01 14:52:17,408 - tensorflow - INFO - global_step/sec: 87.4582 -2022-05-01 14:52:17,408 - tensorflow - INFO - loss = 0.6658572, step = 213000 (2.287 sec) -2022-05-01 14:52:19,708 - tensorflow - INFO - global_step/sec: 86.9439 -2022-05-01 14:52:19,709 - tensorflow - INFO - loss = 0.72445893, step = 213200 (2.300 sec) -2022-05-01 14:52:21,994 - tensorflow - INFO - global_step/sec: 87.4842 -2022-05-01 14:52:21,995 - tensorflow - INFO - loss = 0.18320945, step = 213400 (2.286 sec) -2022-05-01 14:52:24,282 - tensorflow - INFO - global_step/sec: 87.4326 -2022-05-01 14:52:24,282 - tensorflow - INFO - loss = 0.86827636, step = 213600 (2.288 sec) -2022-05-01 14:52:26,571 - tensorflow - INFO - global_step/sec: 87.3856 -2022-05-01 14:52:26,571 - tensorflow - INFO - loss = 2.887658, step = 213800 (2.289 sec) -2022-05-01 14:52:28,861 - tensorflow - INFO - global_step/sec: 87.315 -2022-05-01 14:52:28,862 - tensorflow - INFO - loss = 0.083644465, step = 214000 (2.291 sec) -2022-05-01 14:52:31,156 - tensorflow - INFO - global_step/sec: 87.1385 -2022-05-01 14:52:31,157 - tensorflow - INFO - loss = 0.6637585, step = 214200 (2.295 sec) -2022-05-01 14:52:33,456 - tensorflow - INFO - global_step/sec: 86.9644 -2022-05-01 14:52:33,457 - tensorflow - INFO - loss = 0.18146542, step = 214400 (2.300 sec) -2022-05-01 14:52:35,736 - tensorflow - INFO - global_step/sec: 87.7042 -2022-05-01 14:52:35,737 - tensorflow - INFO - loss = 0.06060461, step = 214600 (2.280 sec) -2022-05-01 14:52:38,021 - tensorflow - INFO - global_step/sec: 87.5462 -2022-05-01 14:52:38,021 - tensorflow - INFO - loss = 0.093060724, step = 214800 (2.285 sec) -2022-05-01 14:52:40,307 - tensorflow - INFO - global_step/sec: 87.4917 -2022-05-01 14:52:40,307 - tensorflow - INFO - loss = 0.056459446, step = 215000 (2.286 sec) -2022-05-01 14:52:42,593 - tensorflow - INFO - global_step/sec: 87.4837 -2022-05-01 14:52:42,594 - tensorflow - INFO - loss = 0.09824064, step = 215200 (2.286 sec) -2022-05-01 14:52:44,888 - tensorflow - INFO - global_step/sec: 87.1658 -2022-05-01 14:52:44,888 - tensorflow - INFO - loss = 0.13120337, step = 215400 (2.294 sec) -2022-05-01 14:52:47,178 - tensorflow - INFO - global_step/sec: 87.3078 -2022-05-01 14:52:47,179 - tensorflow - INFO - loss = 0.29566246, step = 215600 (2.291 sec) -2022-05-01 14:52:49,466 - tensorflow - INFO - global_step/sec: 87.4319 -2022-05-01 14:52:49,466 - tensorflow - INFO - loss = 0.13072793, step = 215800 (2.287 sec) -2022-05-01 14:52:51,746 - tensorflow - INFO - global_step/sec: 87.7028 -2022-05-01 14:52:51,747 - tensorflow - INFO - loss = 0.028415624, step = 216000 (2.280 sec) -2022-05-01 14:52:54,041 - tensorflow - INFO - global_step/sec: 87.1664 -2022-05-01 14:52:54,041 - tensorflow - INFO - loss = 2.1365101, step = 216200 (2.294 sec) -2022-05-01 14:52:56,327 - tensorflow - INFO - global_step/sec: 87.4888 -2022-05-01 14:52:56,327 - tensorflow - INFO - loss = 0.3180861, step = 216400 (2.286 sec) -2022-05-01 14:52:58,600 - tensorflow - INFO - global_step/sec: 87.9768 -2022-05-01 14:52:58,600 - tensorflow - INFO - loss = 0.13694525, step = 216600 (2.273 sec) -2022-05-01 14:53:00,856 - tensorflow - INFO - global_step/sec: 88.6491 -2022-05-01 14:53:00,857 - tensorflow - INFO - loss = 0.14439583, step = 216800 (2.256 sec) -2022-05-01 14:53:03,129 - tensorflow - INFO - global_step/sec: 87.9793 -2022-05-01 14:53:03,130 - tensorflow - INFO - loss = 0.12243773, step = 217000 (2.273 sec) -2022-05-01 14:53:05,416 - tensorflow - INFO - global_step/sec: 87.4588 -2022-05-01 14:53:05,417 - tensorflow - INFO - loss = 0.8855525, step = 217200 (2.287 sec) -2022-05-01 14:53:07,701 - tensorflow - INFO - global_step/sec: 87.524 -2022-05-01 14:53:07,702 - tensorflow - INFO - loss = 0.041931666, step = 217400 (2.285 sec) -2022-05-01 14:53:09,993 - tensorflow - INFO - global_step/sec: 87.2756 -2022-05-01 14:53:09,993 - tensorflow - INFO - loss = 0.22627923, step = 217600 (2.292 sec) -2022-05-01 14:53:12,289 - tensorflow - INFO - global_step/sec: 87.0987 -2022-05-01 14:53:12,290 - tensorflow - INFO - loss = 0.06976569, step = 217800 (2.296 sec) -2022-05-01 14:53:14,585 - tensorflow - INFO - global_step/sec: 87.129 -2022-05-01 14:53:14,585 - tensorflow - INFO - loss = 0.27322647, step = 218000 (2.295 sec) -2022-05-01 14:53:16,878 - tensorflow - INFO - global_step/sec: 87.195 -2022-05-01 14:53:16,879 - tensorflow - INFO - loss = 0.056805912, step = 218200 (2.294 sec) -2022-05-01 14:53:19,170 - tensorflow - INFO - global_step/sec: 87.2698 -2022-05-01 14:53:19,170 - tensorflow - INFO - loss = 0.06335604, step = 218400 (2.292 sec) -2022-05-01 14:53:21,456 - tensorflow - INFO - global_step/sec: 87.4844 -2022-05-01 14:53:21,457 - tensorflow - INFO - loss = 0.21221682, step = 218600 (2.286 sec) -2022-05-01 14:53:23,747 - tensorflow - INFO - global_step/sec: 87.2995 -2022-05-01 14:53:23,747 - tensorflow - INFO - loss = 0.34483534, step = 218800 (2.291 sec) -2022-05-01 14:53:26,036 - tensorflow - INFO - global_step/sec: 87.3754 -2022-05-01 14:53:26,036 - tensorflow - INFO - loss = 0.053619225, step = 219000 (2.289 sec) -2022-05-01 14:53:28,328 - tensorflow - INFO - global_step/sec: 87.2743 -2022-05-01 14:53:28,328 - tensorflow - INFO - loss = 0.21524435, step = 219200 (2.292 sec) -2022-05-01 14:53:30,618 - tensorflow - INFO - global_step/sec: 87.3074 -2022-05-01 14:53:30,619 - tensorflow - INFO - loss = 0.058280133, step = 219400 (2.291 sec) -2022-05-01 14:53:32,911 - tensorflow - INFO - global_step/sec: 87.2433 -2022-05-01 14:53:32,911 - tensorflow - INFO - loss = 0.14699472, step = 219600 (2.292 sec) -2022-05-01 14:53:35,213 - tensorflow - INFO - global_step/sec: 86.8731 -2022-05-01 14:53:35,213 - tensorflow - INFO - loss = 0.1067339, step = 219800 (2.302 sec) -2022-05-01 14:53:37,502 - tensorflow - INFO - global_step/sec: 87.384 -2022-05-01 14:53:37,502 - tensorflow - INFO - loss = 0.089757755, step = 220000 (2.289 sec) -2022-05-01 14:53:39,791 - tensorflow - INFO - global_step/sec: 87.3762 -2022-05-01 14:53:39,791 - 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tensorflow - INFO - global_step/sec: 86.8695 -2022-05-01 14:58:56,201 - tensorflow - INFO - loss = 0.039689027, step = 247800 (2.302 sec) -2022-05-01 14:58:58,490 - tensorflow - INFO - global_step/sec: 87.3444 -2022-05-01 14:58:58,490 - tensorflow - INFO - loss = 0.40929103, step = 248000 (2.290 sec) -2022-05-01 14:59:00,778 - tensorflow - INFO - global_step/sec: 87.4123 -2022-05-01 14:59:00,778 - tensorflow - INFO - loss = 0.051886313, step = 248200 (2.288 sec) -2022-05-01 14:59:03,070 - tensorflow - INFO - global_step/sec: 87.2644 -2022-05-01 14:59:03,070 - tensorflow - INFO - loss = 0.08060451, step = 248400 (2.292 sec) -2022-05-01 14:59:05,365 - tensorflow - INFO - global_step/sec: 87.1437 -2022-05-01 14:59:05,365 - tensorflow - INFO - loss = 0.046783365, step = 248600 (2.295 sec) -2022-05-01 14:59:07,649 - tensorflow - INFO - global_step/sec: 87.5733 -2022-05-01 14:59:07,649 - tensorflow - INFO - loss = 0.05765033, step = 248800 (2.284 sec) -2022-05-01 14:59:09,915 - tensorflow - INFO - global_step/sec: 88.262 -2022-05-01 14:59:09,915 - tensorflow - INFO - loss = 0.14893916, step = 249000 (2.266 sec) -2022-05-01 14:59:12,175 - tensorflow - INFO - global_step/sec: 88.4652 -2022-05-01 14:59:12,176 - tensorflow - INFO - loss = 0.10018232, step = 249200 (2.261 sec) -2022-05-01 14:59:14,441 - tensorflow - INFO - global_step/sec: 88.2923 -2022-05-01 14:59:14,441 - tensorflow - INFO - loss = 0.10555412, step = 249400 (2.265 sec) -2022-05-01 14:59:16,733 - tensorflow - INFO - global_step/sec: 87.2362 -2022-05-01 14:59:16,734 - tensorflow - INFO - loss = 0.09281883, step = 249600 (2.293 sec) -2022-05-01 14:59:19,021 - tensorflow - INFO - global_step/sec: 87.4269 -2022-05-01 14:59:19,021 - tensorflow - INFO - loss = 0.05891163, step = 249800 (2.288 sec) -2022-05-01 14:59:21,321 - tensorflow - INFO - global_step/sec: 86.9676 -2022-05-01 14:59:21,322 - tensorflow - INFO - loss = 1.1568319, step = 250000 (2.300 sec) -2022-05-01 14:59:23,613 - tensorflow - INFO - global_step/sec: 87.2617 -2022-05-01 14:59:23,613 - tensorflow - INFO - loss = 0.13074644, step = 250200 (2.292 sec) -2022-05-01 14:59:25,911 - tensorflow - INFO - global_step/sec: 87.0066 -2022-05-01 14:59:25,912 - tensorflow - INFO - loss = 0.055333816, step = 250400 (2.299 sec) -2022-05-01 14:59:28,210 - tensorflow - INFO - global_step/sec: 87.0189 -2022-05-01 14:59:28,210 - tensorflow - INFO - loss = 0.08679605, step = 250600 (2.298 sec) -2022-05-01 14:59:30,510 - tensorflow - INFO - global_step/sec: 86.9563 -2022-05-01 14:59:30,510 - tensorflow - INFO - loss = 0.08136305, step = 250800 (2.300 sec) -2022-05-01 14:59:32,809 - tensorflow - INFO - global_step/sec: 86.9873 -2022-05-01 14:59:32,809 - tensorflow - INFO - loss = 0.080287755, step = 251000 (2.299 sec) -2022-05-01 14:59:35,106 - tensorflow - INFO - global_step/sec: 87.0502 -2022-05-01 14:59:35,107 - tensorflow - INFO - loss = 0.23235756, step = 251200 (2.297 sec) -2022-05-01 14:59:37,392 - tensorflow - INFO - global_step/sec: 87.507 -2022-05-01 14:59:37,392 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 87.1889 -2022-05-01 14:59:53,443 - tensorflow - INFO - loss = 0.045073427, step = 252800 (2.294 sec) -2022-05-01 14:59:55,745 - tensorflow - INFO - global_step/sec: 86.8662 -2022-05-01 14:59:55,745 - tensorflow - INFO - loss = 0.051353894, step = 253000 (2.302 sec) -2022-05-01 14:59:58,041 - tensorflow - INFO - global_step/sec: 87.0985 -2022-05-01 14:59:58,042 - tensorflow - INFO - loss = 0.18644147, step = 253200 (2.296 sec) -2022-05-01 15:00:00,330 - tensorflow - INFO - global_step/sec: 87.3774 -2022-05-01 15:00:00,330 - tensorflow - INFO - loss = 4.138243, step = 253400 (2.289 sec) -2022-05-01 15:00:02,628 - tensorflow - INFO - global_step/sec: 87.0497 -2022-05-01 15:00:02,628 - tensorflow - INFO - loss = 0.25942516, step = 253600 (2.298 sec) -2022-05-01 15:00:04,911 - tensorflow - INFO - global_step/sec: 87.5704 -2022-05-01 15:00:04,912 - tensorflow - INFO - loss = 0.10229901, step = 253800 (2.284 sec) -2022-05-01 15:00:07,208 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 86.9734 -2022-05-01 15:00:50,695 - tensorflow - INFO - loss = 18.685802, step = 257800 (2.300 sec) -2022-05-01 15:00:52,997 - tensorflow - INFO - global_step/sec: 86.8724 -2022-05-01 15:00:52,997 - tensorflow - INFO - loss = 0.12100357, step = 258000 (2.302 sec) -2022-05-01 15:00:55,296 - tensorflow - INFO - global_step/sec: 86.9925 -2022-05-01 15:00:55,296 - tensorflow - INFO - loss = 0.16185558, step = 258200 (2.299 sec) -2022-05-01 15:00:57,602 - tensorflow - INFO - global_step/sec: 86.7302 -2022-05-01 15:00:57,602 - tensorflow - INFO - loss = 0.069863684, step = 258400 (2.306 sec) -2022-05-01 15:00:59,902 - tensorflow - INFO - global_step/sec: 86.926 -2022-05-01 15:00:59,903 - tensorflow - INFO - loss = 0.36021155, step = 258600 (2.301 sec) -2022-05-01 15:01:02,205 - tensorflow - INFO - global_step/sec: 86.8712 -2022-05-01 15:01:02,205 - tensorflow - INFO - loss = 0.11978319, step = 258800 (2.302 sec) -2022-05-01 15:01:04,500 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.052727006, step = 260200 (2.286 sec) -2022-05-01 15:01:20,561 - tensorflow - INFO - global_step/sec: 86.9511 -2022-05-01 15:01:20,561 - tensorflow - INFO - loss = 0.4041173, step = 260400 (2.300 sec) -2022-05-01 15:01:22,860 - tensorflow - INFO - global_step/sec: 86.9927 -2022-05-01 15:01:22,860 - tensorflow - INFO - loss = 0.05172576, step = 260600 (2.299 sec) -2022-05-01 15:01:25,157 - tensorflow - INFO - global_step/sec: 87.0586 -2022-05-01 15:01:25,157 - tensorflow - INFO - loss = 0.0435863, step = 260800 (2.297 sec) -2022-05-01 15:01:27,449 - tensorflow - INFO - global_step/sec: 87.2463 -2022-05-01 15:01:27,450 - tensorflow - INFO - loss = 0.064769745, step = 261000 (2.292 sec) -2022-05-01 15:01:29,750 - tensorflow - INFO - global_step/sec: 86.927 -2022-05-01 15:01:29,750 - tensorflow - INFO - loss = 0.6562233, step = 261200 (2.301 sec) -2022-05-01 15:01:32,051 - tensorflow - INFO - global_step/sec: 86.9166 -2022-05-01 15:01:32,052 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 87.5329 -2022-05-01 15:01:48,087 - tensorflow - INFO - loss = 0.11152685, step = 262800 (2.285 sec) -2022-05-01 15:01:50,376 - tensorflow - INFO - global_step/sec: 87.3485 -2022-05-01 15:01:50,377 - tensorflow - INFO - loss = 2.0604374, step = 263000 (2.290 sec) -2022-05-01 15:01:52,677 - tensorflow - INFO - global_step/sec: 86.9378 -2022-05-01 15:01:52,677 - tensorflow - INFO - loss = 0.083162636, step = 263200 (2.300 sec) -2022-05-01 15:01:54,979 - tensorflow - INFO - global_step/sec: 86.8771 -2022-05-01 15:01:54,979 - tensorflow - INFO - loss = 0.079089604, step = 263400 (2.302 sec) -2022-05-01 15:01:57,277 - tensorflow - INFO - global_step/sec: 87.012 -2022-05-01 15:01:57,278 - tensorflow - INFO - loss = 0.03496746, step = 263600 (2.299 sec) -2022-05-01 15:01:59,570 - tensorflow - INFO - global_step/sec: 87.2137 -2022-05-01 15:01:59,571 - tensorflow - INFO - loss = 0.2738903, step = 263800 (2.293 sec) -2022-05-01 15:02:01,870 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 87.5161 -2022-05-01 15:03:42,609 - tensorflow - INFO - loss = 0.2851866, step = 272800 (2.285 sec) -2022-05-01 15:03:44,905 - tensorflow - INFO - global_step/sec: 87.0933 -2022-05-01 15:03:44,906 - tensorflow - INFO - loss = 0.19924763, step = 273000 (2.296 sec) -2022-05-01 15:03:47,187 - tensorflow - INFO - global_step/sec: 87.6544 -2022-05-01 15:03:47,187 - tensorflow - INFO - loss = 0.048016094, step = 273200 (2.282 sec) -2022-05-01 15:03:49,456 - tensorflow - INFO - global_step/sec: 88.1225 -2022-05-01 15:03:49,457 - tensorflow - INFO - loss = 0.10881289, step = 273400 (2.270 sec) -2022-05-01 15:03:51,733 - tensorflow - INFO - global_step/sec: 87.8351 -2022-05-01 15:03:51,734 - tensorflow - INFO - loss = 0.15864506, step = 273600 (2.277 sec) -2022-05-01 15:03:54,027 - tensorflow - INFO - global_step/sec: 87.1961 -2022-05-01 15:03:54,028 - tensorflow - INFO - loss = 0.21109626, step = 273800 (2.294 sec) -2022-05-01 15:03:56,311 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.03577576, step = 280200 (2.291 sec) -2022-05-01 15:05:09,845 - tensorflow - INFO - global_step/sec: 87.4674 -2022-05-01 15:05:09,845 - tensorflow - INFO - loss = 0.036211703, step = 280400 (2.287 sec) -2022-05-01 15:05:12,149 - tensorflow - INFO - global_step/sec: 86.8219 -2022-05-01 15:05:12,149 - tensorflow - INFO - loss = 0.10441546, step = 280600 (2.303 sec) -2022-05-01 15:05:14,439 - tensorflow - INFO - global_step/sec: 87.3036 -2022-05-01 15:05:14,440 - tensorflow - INFO - loss = 0.3937174, step = 280800 (2.291 sec) -2022-05-01 15:05:16,733 - tensorflow - INFO - global_step/sec: 87.2133 -2022-05-01 15:05:16,733 - tensorflow - INFO - loss = 0.07562833, step = 281000 (2.293 sec) -2022-05-01 15:05:19,009 - tensorflow - INFO - global_step/sec: 87.8687 -2022-05-01 15:05:19,009 - tensorflow - INFO - loss = 17.1679, step = 281200 (2.276 sec) -2022-05-01 15:05:21,268 - tensorflow - INFO - global_step/sec: 88.5244 -2022-05-01 15:05:21,269 - tensorflow - INFO - loss = 0.22217152, step = 281400 (2.259 sec) -2022-05-01 15:05:23,530 - tensorflow - INFO - global_step/sec: 88.4288 -2022-05-01 15:05:23,530 - tensorflow - INFO - loss = 0.044527538, step = 281600 (2.262 sec) -2022-05-01 15:05:25,788 - tensorflow - INFO - global_step/sec: 88.5624 -2022-05-01 15:05:25,788 - tensorflow - INFO - loss = 0.047858927, step = 281800 (2.258 sec) -2022-05-01 15:05:28,084 - tensorflow - INFO - global_step/sec: 87.0944 -2022-05-01 15:05:28,085 - tensorflow - INFO - loss = 0.273233, step = 282000 (2.296 sec) -2022-05-01 15:05:30,376 - tensorflow - INFO - global_step/sec: 87.2719 -2022-05-01 15:05:30,376 - tensorflow - INFO - loss = 0.12601215, step = 282200 (2.292 sec) -2022-05-01 15:05:32,664 - tensorflow - INFO - global_step/sec: 87.3965 -2022-05-01 15:05:32,665 - tensorflow - INFO - loss = 0.070912674, step = 282400 (2.288 sec) -2022-05-01 15:05:34,959 - tensorflow - INFO - global_step/sec: 87.1493 -2022-05-01 15:05:34,960 - tensorflow - INFO - loss = 0.06730513, step = 282600 (2.295 sec) -2022-05-01 15:05:37,245 - tensorflow - INFO - global_step/sec: 87.4996 -2022-05-01 15:05:37,246 - tensorflow - INFO - loss = 0.07735733, step = 282800 (2.286 sec) -2022-05-01 15:05:39,537 - tensorflow - INFO - global_step/sec: 87.2568 -2022-05-01 15:05:39,538 - tensorflow - INFO - loss = 0.9476394, step = 283000 (2.292 sec) -2022-05-01 15:05:41,831 - tensorflow - INFO - global_step/sec: 87.1754 -2022-05-01 15:05:41,832 - tensorflow - INFO - loss = 0.034305893, step = 283200 (2.294 sec) -2022-05-01 15:05:44,117 - tensorflow - INFO - global_step/sec: 87.5036 -2022-05-01 15:05:44,118 - tensorflow - INFO - loss = 0.06655292, step = 283400 (2.286 sec) -2022-05-01 15:05:46,409 - tensorflow - INFO - global_step/sec: 87.2471 -2022-05-01 15:05:46,410 - tensorflow - INFO - loss = 0.12519856, step = 283600 (2.292 sec) -2022-05-01 15:05:48,704 - tensorflow - INFO - global_step/sec: 87.177 -2022-05-01 15:05:48,704 - tensorflow - INFO - loss = 0.037646405, step = 283800 (2.294 sec) -2022-05-01 15:05:50,993 - tensorflow - INFO - global_step/sec: 87.3466 -2022-05-01 15:05:50,994 - tensorflow - INFO - loss = 0.31579885, step = 284000 (2.290 sec) -2022-05-01 15:05:53,291 - tensorflow - INFO - global_step/sec: 87.041 -2022-05-01 15:05:53,291 - tensorflow - INFO - loss = 0.0757684, step = 284200 (2.298 sec) -2022-05-01 15:05:55,586 - tensorflow - INFO - global_step/sec: 87.1638 -2022-05-01 15:05:55,586 - tensorflow - INFO - loss = 0.06544681, step = 284400 (2.295 sec) -2022-05-01 15:05:57,874 - tensorflow - INFO - global_step/sec: 87.3884 -2022-05-01 15:05:57,875 - tensorflow - INFO - loss = 0.17665538, step = 284600 (2.289 sec) -2022-05-01 15:06:00,160 - tensorflow - INFO - global_step/sec: 87.5141 -2022-05-01 15:06:00,160 - tensorflow - INFO - loss = 0.07511485, step = 284800 (2.285 sec) -2022-05-01 15:06:02,452 - tensorflow - INFO - global_step/sec: 87.2328 -2022-05-01 15:06:02,453 - tensorflow - INFO - loss = 0.041726876, step = 285000 (2.293 sec) -2022-05-01 15:06:04,747 - tensorflow - INFO - global_step/sec: 87.149 -2022-05-01 15:06:04,748 - tensorflow - INFO - loss = 0.10438813, step = 285200 (2.295 sec) -2022-05-01 15:06:07,047 - tensorflow - INFO - global_step/sec: 86.9643 -2022-05-01 15:06:07,047 - tensorflow - INFO - loss = 0.077885404, step = 285400 (2.300 sec) -2022-05-01 15:06:09,339 - tensorflow - INFO - global_step/sec: 87.2501 -2022-05-01 15:06:09,340 - tensorflow - INFO - loss = 1.0299636, step = 285600 (2.292 sec) -2022-05-01 15:06:11,628 - tensorflow - INFO - global_step/sec: 87.3806 -2022-05-01 15:06:11,629 - tensorflow - INFO - loss = 0.14138225, step = 285800 (2.289 sec) -2022-05-01 15:06:13,916 - tensorflow - INFO - global_step/sec: 87.4141 -2022-05-01 15:06:13,917 - tensorflow - INFO - loss = 0.20049463, step = 286000 (2.288 sec) -2022-05-01 15:06:16,208 - tensorflow - INFO - global_step/sec: 87.2772 -2022-05-01 15:06:16,208 - tensorflow - INFO - loss = 0.47803906, step = 286200 (2.292 sec) -2022-05-01 15:06:18,506 - tensorflow - INFO - global_step/sec: 87.0097 -2022-05-01 15:06:18,507 - tensorflow - INFO - loss = 0.15883633, step = 286400 (2.299 sec) -2022-05-01 15:06:20,803 - tensorflow - INFO - global_step/sec: 87.0895 -2022-05-01 15:06:20,803 - tensorflow - INFO - loss = 2.694594, step = 286600 (2.297 sec) -2022-05-01 15:06:23,105 - tensorflow - INFO - global_step/sec: 86.8571 -2022-05-01 15:06:23,106 - tensorflow - INFO - loss = 0.08450651, step = 286800 (2.303 sec) -2022-05-01 15:06:25,416 - tensorflow - INFO - global_step/sec: 86.5552 -2022-05-01 15:06:25,416 - tensorflow - INFO - loss = 2.9337602, step = 287000 (2.311 sec) -2022-05-01 15:06:27,729 - tensorflow - INFO - global_step/sec: 86.4548 -2022-05-01 15:06:27,730 - tensorflow - INFO - loss = 0.10834767, step = 287200 (2.313 sec) -2022-05-01 15:06:30,034 - tensorflow - INFO - global_step/sec: 86.7786 -2022-05-01 15:06:30,034 - tensorflow - INFO - loss = 0.06139145, step = 287400 (2.305 sec) -2022-05-01 15:06:32,355 - tensorflow - INFO - global_step/sec: 86.156 -2022-05-01 15:06:32,356 - tensorflow - INFO - loss = 0.8649575, step = 287600 (2.321 sec) -2022-05-01 15:06:34,661 - tensorflow - INFO - global_step/sec: 86.7513 -2022-05-01 15:06:34,661 - tensorflow - INFO - loss = 0.038943138, step = 287800 (2.305 sec) -2022-05-01 15:06:36,951 - tensorflow - INFO - global_step/sec: 87.3428 -2022-05-01 15:06:36,951 - tensorflow - INFO - loss = 0.724728, step = 288000 (2.290 sec) -2022-05-01 15:06:39,236 - tensorflow - INFO - global_step/sec: 87.5281 -2022-05-01 15:06:39,236 - tensorflow - INFO - loss = 0.036998108, step = 288200 (2.285 sec) -2022-05-01 15:06:41,532 - tensorflow - INFO - global_step/sec: 87.1033 -2022-05-01 15:06:41,532 - tensorflow - INFO - loss = 0.04534033, step = 288400 (2.296 sec) -2022-05-01 15:06:43,829 - tensorflow - INFO - global_step/sec: 87.0777 -2022-05-01 15:06:43,829 - tensorflow - INFO - loss = 0.03104715, step = 288600 (2.297 sec) -2022-05-01 15:06:46,118 - tensorflow - INFO - global_step/sec: 87.3417 -2022-05-01 15:06:46,119 - tensorflow - INFO - loss = 0.08332451, step = 288800 (2.290 sec) -2022-05-01 15:06:48,409 - tensorflow - INFO - global_step/sec: 87.3342 -2022-05-01 15:06:48,409 - tensorflow - INFO - loss = 0.06010709, step = 289000 (2.290 sec) -2022-05-01 15:06:50,705 - tensorflow - INFO - global_step/sec: 87.0776 -2022-05-01 15:06:50,706 - tensorflow - INFO - loss = 0.113809615, step = 289200 (2.297 sec) -2022-05-01 15:06:52,976 - tensorflow - INFO - global_step/sec: 88.0683 -2022-05-01 15:06:52,977 - tensorflow - INFO - loss = 0.054298207, step = 289400 (2.271 sec) -2022-05-01 15:06:55,252 - tensorflow - INFO - global_step/sec: 87.8883 -2022-05-01 15:06:55,252 - tensorflow - INFO - loss = 0.103841156, step = 289600 (2.276 sec) -2022-05-01 15:06:57,526 - tensorflow - INFO - global_step/sec: 87.9469 -2022-05-01 15:06:57,526 - tensorflow - INFO - loss = 0.033746388, step = 289800 (2.274 sec) -2022-05-01 15:06:59,815 - tensorflow - INFO - global_step/sec: 87.3567 -2022-05-01 15:06:59,816 - tensorflow - INFO - loss = 0.06811048, step = 290000 (2.289 sec) -2022-05-01 15:07:02,111 - tensorflow - INFO - global_step/sec: 87.1256 -2022-05-01 15:07:02,111 - tensorflow - INFO - loss = 1.4815083, step = 290200 (2.296 sec) -2022-05-01 15:07:04,405 - tensorflow - INFO - global_step/sec: 87.1876 -2022-05-01 15:07:04,405 - tensorflow - INFO - loss = 0.09884867, step = 290400 (2.294 sec) -2022-05-01 15:07:06,695 - tensorflow - INFO - global_step/sec: 87.3445 -2022-05-01 15:07:06,695 - tensorflow - INFO - loss = 0.120853096, step = 290600 (2.290 sec) -2022-05-01 15:07:08,989 - tensorflow - INFO - global_step/sec: 87.1564 -2022-05-01 15:07:08,990 - tensorflow - INFO - loss = 0.10058601, step = 290800 (2.295 sec) -2022-05-01 15:07:11,282 - tensorflow - INFO - global_step/sec: 87.2368 -2022-05-01 15:07:11,282 - tensorflow - INFO - loss = 0.019281892, step = 291000 (2.293 sec) -2022-05-01 15:07:13,572 - tensorflow - INFO - global_step/sec: 87.3316 -2022-05-01 15:07:13,573 - tensorflow - INFO - loss = 0.099681005, step = 291200 (2.290 sec) -2022-05-01 15:07:15,865 - tensorflow - INFO - global_step/sec: 87.2468 -2022-05-01 15:07:15,865 - tensorflow - INFO - loss = 0.29632992, step = 291400 (2.292 sec) -2022-05-01 15:07:18,156 - tensorflow - INFO - global_step/sec: 87.2636 -2022-05-01 15:07:18,157 - tensorflow - INFO - loss = 0.13116612, step = 291600 (2.292 sec) -2022-05-01 15:07:20,446 - tensorflow - INFO - global_step/sec: 87.3528 -2022-05-01 15:07:20,446 - tensorflow - INFO - loss = 0.04381878, step = 291800 (2.290 sec) -2022-05-01 15:07:22,734 - tensorflow - INFO - global_step/sec: 87.4134 -2022-05-01 15:07:22,734 - tensorflow - INFO - loss = 0.7160268, step = 292000 (2.288 sec) -2022-05-01 15:07:25,026 - tensorflow - INFO - global_step/sec: 87.2645 -2022-05-01 15:07:25,026 - tensorflow - INFO - loss = 0.20757207, step = 292200 (2.292 sec) -2022-05-01 15:07:27,320 - tensorflow - INFO - global_step/sec: 87.1772 -2022-05-01 15:07:27,320 - tensorflow - INFO - loss = 0.04452872, step = 292400 (2.294 sec) -2022-05-01 15:07:29,606 - tensorflow - INFO - global_step/sec: 87.4936 -2022-05-01 15:07:29,606 - tensorflow - INFO - loss = 0.09249899, step = 292600 (2.286 sec) -2022-05-01 15:07:31,906 - tensorflow - INFO - global_step/sec: 86.9628 -2022-05-01 15:07:31,906 - tensorflow - INFO - loss = 0.43281564, step = 292800 (2.300 sec) -2022-05-01 15:07:34,199 - tensorflow - INFO - global_step/sec: 87.2053 -2022-05-01 15:07:34,200 - tensorflow - INFO - loss = 0.1831511, step = 293000 (2.293 sec) -2022-05-01 15:07:36,491 - tensorflow - INFO - global_step/sec: 87.2675 -2022-05-01 15:07:36,491 - tensorflow - INFO - loss = 0.04634072, step = 293200 (2.292 sec) -2022-05-01 15:07:38,798 - tensorflow - INFO - global_step/sec: 86.7001 -2022-05-01 15:07:38,798 - tensorflow - INFO - loss = 0.04415472, step = 293400 (2.307 sec) -2022-05-01 15:07:41,097 - tensorflow - INFO - global_step/sec: 86.9977 -2022-05-01 15:07:41,097 - tensorflow - INFO - loss = 0.16743091, step = 293600 (2.299 sec) -2022-05-01 15:07:43,401 - tensorflow - INFO - global_step/sec: 86.7761 -2022-05-01 15:07:43,402 - tensorflow - INFO - loss = 0.45817947, step = 293800 (2.305 sec) -2022-05-01 15:07:45,696 - tensorflow - INFO - global_step/sec: 87.165 -2022-05-01 15:07:45,696 - tensorflow - INFO - loss = 0.07792872, step = 294000 (2.295 sec) -2022-05-01 15:07:47,998 - tensorflow - INFO - global_step/sec: 86.8976 -2022-05-01 15:07:47,998 - tensorflow - INFO - loss = 0.022944633, step = 294200 (2.302 sec) -2022-05-01 15:07:50,299 - tensorflow - INFO - global_step/sec: 86.8999 -2022-05-01 15:07:50,299 - tensorflow - INFO - loss = 0.08060293, step = 294400 (2.302 sec) -2022-05-01 15:07:52,601 - tensorflow - INFO - global_step/sec: 86.8839 -2022-05-01 15:07:52,601 - tensorflow - INFO - loss = 0.18352179, step = 294600 (2.302 sec) -2022-05-01 15:07:54,902 - tensorflow - INFO - global_step/sec: 86.9161 -2022-05-01 15:07:54,902 - tensorflow - INFO - loss = 4.4136095, step = 294800 (2.301 sec) -2022-05-01 15:07:57,200 - tensorflow - INFO - global_step/sec: 87.0263 -2022-05-01 15:07:57,201 - tensorflow - INFO - loss = 0.062210653, step = 295000 (2.298 sec) -2022-05-01 15:07:59,502 - tensorflow - INFO - global_step/sec: 86.8744 -2022-05-01 15:07:59,503 - tensorflow - INFO - loss = 1.1744771, step = 295200 (2.302 sec) -2022-05-01 15:08:01,806 - tensorflow - INFO - global_step/sec: 86.8037 -2022-05-01 15:08:01,807 - tensorflow - INFO - loss = 0.07121699, step = 295400 (2.304 sec) -2022-05-01 15:08:04,117 - tensorflow - INFO - global_step/sec: 86.5692 -2022-05-01 15:08:04,117 - tensorflow - INFO - loss = 0.027087681, step = 295600 (2.310 sec) -2022-05-01 15:08:06,425 - tensorflow - INFO - global_step/sec: 86.6403 -2022-05-01 15:08:06,426 - tensorflow - INFO - loss = 0.6950201, step = 295800 (2.308 sec) -2022-05-01 15:08:08,710 - tensorflow - INFO - global_step/sec: 87.5187 -2022-05-01 15:08:08,711 - tensorflow - INFO - loss = 0.18592377, step = 296000 (2.285 sec) -2022-05-01 15:08:11,004 - tensorflow - INFO - global_step/sec: 87.2033 -2022-05-01 15:08:11,004 - tensorflow - INFO - loss = 0.05259351, step = 296200 (2.294 sec) -2022-05-01 15:08:13,291 - tensorflow - INFO - global_step/sec: 87.4529 -2022-05-01 15:08:13,291 - tensorflow - INFO - loss = 0.026855329, step = 296400 (2.287 sec) -2022-05-01 15:08:15,593 - tensorflow - INFO - global_step/sec: 86.8636 -2022-05-01 15:08:15,594 - tensorflow - INFO - loss = 0.038337093, step = 296600 (2.302 sec) -2022-05-01 15:08:17,881 - tensorflow - INFO - global_step/sec: 87.4079 -2022-05-01 15:08:17,882 - tensorflow - INFO - loss = 16.317427, step = 296800 (2.288 sec) -2022-05-01 15:08:20,174 - tensorflow - INFO - global_step/sec: 87.2457 -2022-05-01 15:08:20,174 - tensorflow - INFO - loss = 0.073704466, step = 297000 (2.292 sec) -2022-05-01 15:08:22,468 - tensorflow - INFO - global_step/sec: 87.1762 -2022-05-01 15:08:22,468 - tensorflow - INFO - loss = 0.03904153, step = 297200 (2.294 sec) -2022-05-01 15:08:24,754 - tensorflow - INFO - global_step/sec: 87.4825 -2022-05-01 15:08:24,754 - tensorflow - INFO - loss = 0.027742313, step = 297400 (2.286 sec) -2022-05-01 15:08:27,032 - tensorflow - INFO - global_step/sec: 87.7926 -2022-05-01 15:08:27,033 - tensorflow - INFO - loss = 0.20203239, step = 297600 (2.278 sec) -2022-05-01 15:08:29,305 - tensorflow - INFO - global_step/sec: 88.0037 -2022-05-01 15:08:29,305 - tensorflow - INFO - loss = 2.553576, step = 297800 (2.273 sec) -2022-05-01 15:08:31,566 - tensorflow - INFO - global_step/sec: 88.4544 -2022-05-01 15:08:31,566 - tensorflow - INFO - loss = 0.0866318, step = 298000 (2.261 sec) -2022-05-01 15:08:33,855 - tensorflow - INFO - global_step/sec: 87.3581 -2022-05-01 15:08:33,856 - tensorflow - INFO - loss = 0.067000255, step = 298200 (2.289 sec) -2022-05-01 15:08:36,143 - tensorflow - INFO - global_step/sec: 87.4357 -2022-05-01 15:08:36,143 - tensorflow - INFO - loss = 0.102808684, step = 298400 (2.287 sec) -2022-05-01 15:08:38,430 - tensorflow - INFO - global_step/sec: 87.4537 -2022-05-01 15:08:38,430 - tensorflow - INFO - loss = 0.031817265, step = 298600 (2.287 sec) -2022-05-01 15:08:40,730 - tensorflow - INFO - global_step/sec: 86.9441 -2022-05-01 15:08:40,730 - tensorflow - INFO - loss = 0.14091843, step = 298800 (2.300 sec) -2022-05-01 15:08:43,027 - tensorflow - INFO - global_step/sec: 87.0573 -2022-05-01 15:08:43,028 - tensorflow - INFO - loss = 0.042238776, step = 299000 (2.297 sec) -2022-05-01 15:08:45,310 - tensorflow - INFO - global_step/sec: 87.6187 -2022-05-01 15:08:45,310 - tensorflow - INFO - loss = 0.03812917, step = 299200 (2.283 sec) -2022-05-01 15:08:47,597 - tensorflow - INFO - global_step/sec: 87.4589 -2022-05-01 15:08:47,597 - tensorflow - INFO - loss = 0.11857946, step = 299400 (2.287 sec) -2022-05-01 15:08:49,888 - tensorflow - INFO - global_step/sec: 87.2812 -2022-05-01 15:08:49,889 - tensorflow - INFO - loss = 0.037918597, step = 299600 (2.291 sec) -2022-05-01 15:08:52,184 - tensorflow - INFO - global_step/sec: 87.0992 -2022-05-01 15:08:52,185 - tensorflow - INFO - loss = 0.12851797, step = 299800 (2.296 sec) -2022-05-01 15:08:54,471 - tensorflow - INFO - Saving checkpoints for 300000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. -2022-05-01 15:08:54,488 - tensorflow - INFO - model.ckpt-300000 is not in all_model_checkpoint_paths. Manually adding it. -2022-05-01 15:08:55,028 - tensorflow - INFO - Calling model_fn. -2022-05-01 15:08:55,159 - tensorflow - INFO - Done calling model_fn. -2022-05-01 15:08:55,174 - tensorflow - INFO - Starting evaluation at 2022-05-01T15:08:55Z -2022-05-01 15:08:55,239 - tensorflow - INFO - Graph was finalized. -2022-05-01 15:08:55,242 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-300000 -2022-05-01 15:08:55,530 - tensorflow - INFO - Running local_init_op. -2022-05-01 15:08:55,640 - tensorflow - INFO - Done running local_init_op. -2022-05-01 15:09:05,911 - tensorflow - INFO - Evaluation [3990/39900] -2022-05-01 15:09:15,477 - tensorflow - INFO - Evaluation [7980/39900] -2022-05-01 15:09:25,029 - tensorflow - INFO - Evaluation [11970/39900] -2022-05-01 15:09:34,594 - tensorflow - INFO - Evaluation [15960/39900] -2022-05-01 15:09:44,157 - tensorflow - INFO - Evaluation [19950/39900] -2022-05-01 15:09:53,715 - tensorflow - INFO - Evaluation [23940/39900] -2022-05-01 15:10:03,243 - tensorflow - INFO - Evaluation [27930/39900] -2022-05-01 15:10:12,772 - tensorflow - INFO - Evaluation [31920/39900] -2022-05-01 15:10:22,332 - tensorflow - INFO - Evaluation [35910/39900] -2022-05-01 15:10:31,903 - tensorflow - INFO - Evaluation [39900/39900] -2022-05-01 15:10:32,041 - tensorflow - INFO - Finished evaluation at 2022-05-01-15:10:32 -2022-05-01 15:10:32,042 - tensorflow - INFO - Saving dict for global step 300000: global_step = 300000, loss = 0.05468036, mean_degree_err = 7.492244 -2022-05-01 15:10:32,042 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 300000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-300000 -2022-05-01 15:10:32,056 - tensorflow - INFO - global_step/sec: 2.00257 -2022-05-01 15:10:32,057 - tensorflow - INFO - loss = 0.40683693, step = 300000 (99.872 sec) -2022-05-01 15:10:34,365 - tensorflow - INFO - global_step/sec: 86.6192 -2022-05-01 15:10:34,365 - tensorflow - INFO - loss = 0.07048734, step = 300200 (2.308 sec) -2022-05-01 15:10:36,657 - tensorflow - INFO - global_step/sec: 87.2609 -2022-05-01 15:10:36,657 - tensorflow - INFO - loss = 0.06908274, step = 300400 (2.292 sec) -2022-05-01 15:10:38,956 - tensorflow - INFO - global_step/sec: 86.9731 -2022-05-01 15:10:38,957 - tensorflow - INFO - loss = 0.031588264, step = 300600 (2.300 sec) -2022-05-01 15:10:41,244 - tensorflow - INFO - global_step/sec: 87.4289 -2022-05-01 15:10:41,244 - tensorflow - INFO - loss = 0.059713908, step = 300800 (2.288 sec) -2022-05-01 15:10:43,538 - tensorflow - INFO - global_step/sec: 87.1703 -2022-05-01 15:10:43,539 - tensorflow - INFO - loss = 0.06908436, step = 301000 (2.294 sec) -2022-05-01 15:10:45,839 - tensorflow - INFO - global_step/sec: 86.9113 -2022-05-01 15:10:45,840 - tensorflow - INFO - loss = 0.058627427, step = 301200 (2.301 sec) -2022-05-01 15:10:48,130 - tensorflow - INFO - global_step/sec: 87.3322 -2022-05-01 15:10:48,130 - tensorflow - INFO - loss = 0.22693896, step = 301400 (2.290 sec) -2022-05-01 15:10:50,416 - tensorflow - INFO - global_step/sec: 87.459 -2022-05-01 15:10:50,417 - tensorflow - INFO - loss = 0.033071175, step = 301600 (2.287 sec) -2022-05-01 15:10:52,714 - tensorflow - INFO - global_step/sec: 87.0478 -2022-05-01 15:10:52,714 - tensorflow - INFO - loss = 0.20088597, step = 301800 (2.298 sec) -2022-05-01 15:10:55,012 - tensorflow - INFO - global_step/sec: 87.0374 -2022-05-01 15:10:55,012 - tensorflow - INFO - loss = 0.11038767, step = 302000 (2.298 sec) -2022-05-01 15:10:57,312 - tensorflow - INFO - global_step/sec: 86.9659 -2022-05-01 15:10:57,312 - tensorflow - INFO - loss = 0.024178479, step = 302200 (2.300 sec) -2022-05-01 15:10:59,607 - tensorflow - INFO - global_step/sec: 87.1475 -2022-05-01 15:10:59,607 - tensorflow - INFO - loss = 0.051052075, step = 302400 (2.295 sec) -2022-05-01 15:11:01,904 - tensorflow - INFO - global_step/sec: 87.058 -2022-05-01 15:11:01,904 - tensorflow - INFO - loss = 0.12262677, step = 302600 (2.297 sec) -2022-05-01 15:11:04,191 - 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tensorflow - INFO - global_step/sec: 87.188 -2022-05-01 15:26:21,364 - tensorflow - INFO - loss = 0.21068093, step = 382800 (2.294 sec) -2022-05-01 15:26:23,660 - tensorflow - INFO - global_step/sec: 87.0763 -2022-05-01 15:26:23,661 - tensorflow - INFO - loss = 0.024292879, step = 383000 (2.297 sec) -2022-05-01 15:26:25,958 - tensorflow - INFO - global_step/sec: 87.0598 -2022-05-01 15:26:25,958 - tensorflow - INFO - loss = 0.21530095, step = 383200 (2.297 sec) -2022-05-01 15:26:28,268 - tensorflow - INFO - global_step/sec: 86.5787 -2022-05-01 15:26:28,268 - tensorflow - INFO - loss = 0.19381899, step = 383400 (2.310 sec) -2022-05-01 15:26:30,574 - tensorflow - INFO - global_step/sec: 86.7005 -2022-05-01 15:26:30,575 - tensorflow - INFO - loss = 0.09407639, step = 383600 (2.307 sec) -2022-05-01 15:26:32,876 - tensorflow - INFO - global_step/sec: 86.9126 -2022-05-01 15:26:32,876 - tensorflow - INFO - loss = 0.21737504, step = 383800 (2.301 sec) -2022-05-01 15:26:35,180 - tensorflow - INFO - global_step/sec: 86.8002 -2022-05-01 15:26:35,180 - tensorflow - INFO - loss = 0.019173391, step = 384000 (2.304 sec) -2022-05-01 15:26:37,474 - tensorflow - INFO - global_step/sec: 87.1888 -2022-05-01 15:26:37,474 - tensorflow - INFO - loss = 0.05019468, step = 384200 (2.294 sec) -2022-05-01 15:26:39,761 - tensorflow - INFO - global_step/sec: 87.4405 -2022-05-01 15:26:39,761 - tensorflow - INFO - loss = 1.2204313, step = 384400 (2.287 sec) -2022-05-01 15:26:42,055 - tensorflow - INFO - global_step/sec: 87.1834 -2022-05-01 15:26:42,055 - tensorflow - INFO - loss = 0.026852375, step = 384600 (2.294 sec) -2022-05-01 15:26:44,346 - tensorflow - INFO - global_step/sec: 87.3054 -2022-05-01 15:26:44,346 - tensorflow - INFO - loss = 0.05077, step = 384800 (2.291 sec) -2022-05-01 15:26:46,633 - tensorflow - INFO - global_step/sec: 87.439 -2022-05-01 15:26:46,633 - tensorflow - INFO - loss = 0.3412217, step = 385000 (2.287 sec) -2022-05-01 15:26:48,919 - tensorflow - INFO - global_step/sec: 87.4908 -2022-05-01 15:26:48,919 - tensorflow - INFO - loss = 0.05305391, step = 385200 (2.286 sec) -2022-05-01 15:26:51,210 - tensorflow - INFO - global_step/sec: 87.3046 -2022-05-01 15:26:51,210 - tensorflow - INFO - loss = 0.15249148, step = 385400 (2.291 sec) -2022-05-01 15:26:53,491 - tensorflow - INFO - global_step/sec: 87.6665 -2022-05-01 15:26:53,492 - tensorflow - INFO - loss = 0.10122527, step = 385600 (2.281 sec) -2022-05-01 15:26:55,777 - tensorflow - INFO - global_step/sec: 87.4855 -2022-05-01 15:26:55,778 - tensorflow - INFO - loss = 0.041996934, step = 385800 (2.286 sec) -2022-05-01 15:26:58,054 - tensorflow - INFO - global_step/sec: 87.8417 -2022-05-01 15:26:58,054 - tensorflow - INFO - loss = 0.086239435, step = 386000 (2.277 sec) -2022-05-01 15:27:00,322 - tensorflow - INFO - global_step/sec: 88.1912 -2022-05-01 15:27:00,322 - tensorflow - INFO - loss = 0.13029584, step = 386200 (2.268 sec) -2022-05-01 15:27:02,588 - tensorflow - INFO - global_step/sec: 88.2611 -2022-05-01 15:27:02,588 - tensorflow - INFO - loss = 0.020974861, step = 386400 (2.266 sec) -2022-05-01 15:27:04,849 - tensorflow - INFO - global_step/sec: 88.4635 -2022-05-01 15:27:04,849 - tensorflow - INFO - loss = 0.20385835, step = 386600 (2.261 sec) -2022-05-01 15:27:07,115 - tensorflow - INFO - global_step/sec: 88.2618 -2022-05-01 15:27:07,115 - tensorflow - INFO - loss = 0.05887437, step = 386800 (2.266 sec) -2022-05-01 15:27:09,406 - tensorflow - INFO - global_step/sec: 87.3043 -2022-05-01 15:27:09,406 - tensorflow - INFO - loss = 0.3467544, step = 387000 (2.291 sec) -2022-05-01 15:27:11,690 - tensorflow - INFO - global_step/sec: 87.5366 -2022-05-01 15:27:11,691 - tensorflow - INFO - loss = 0.22042203, step = 387200 (2.285 sec) -2022-05-01 15:27:13,973 - tensorflow - INFO - global_step/sec: 87.622 -2022-05-01 15:27:13,973 - tensorflow - INFO - loss = 0.063067436, step = 387400 (2.283 sec) -2022-05-01 15:27:16,263 - tensorflow - INFO - global_step/sec: 87.3299 -2022-05-01 15:27:16,263 - tensorflow - INFO - loss = 0.0660839, step = 387600 (2.290 sec) -2022-05-01 15:27:18,559 - tensorflow - INFO - global_step/sec: 87.1199 -2022-05-01 15:27:18,559 - tensorflow - INFO - loss = 0.16306858, step = 387800 (2.296 sec) -2022-05-01 15:27:20,850 - tensorflow - INFO - global_step/sec: 87.2961 -2022-05-01 15:27:20,850 - tensorflow - INFO - loss = 0.12461989, step = 388000 (2.291 sec) -2022-05-01 15:27:23,139 - tensorflow - INFO - global_step/sec: 87.3612 -2022-05-01 15:27:23,140 - tensorflow - INFO - loss = 0.033755578, step = 388200 (2.289 sec) -2022-05-01 15:27:25,421 - tensorflow - INFO - global_step/sec: 87.6285 -2022-05-01 15:27:25,422 - tensorflow - INFO - loss = 0.4511457, step = 388400 (2.282 sec) -2022-05-01 15:27:27,713 - tensorflow - INFO - global_step/sec: 87.274 -2022-05-01 15:27:27,714 - tensorflow - INFO - loss = 0.1348494, step = 388600 (2.292 sec) -2022-05-01 15:27:29,998 - tensorflow - INFO - global_step/sec: 87.5477 -2022-05-01 15:27:29,998 - tensorflow - INFO - loss = 0.06808847, step = 388800 (2.284 sec) -2022-05-01 15:27:32,297 - tensorflow - INFO - global_step/sec: 86.9584 -2022-05-01 15:27:32,298 - tensorflow - INFO - loss = 0.11560288, step = 389000 (2.300 sec) -2022-05-01 15:27:34,591 - tensorflow - INFO - global_step/sec: 87.1913 -2022-05-01 15:27:34,592 - tensorflow - INFO - loss = 0.9794904, step = 389200 (2.294 sec) -2022-05-01 15:27:36,880 - tensorflow - INFO - global_step/sec: 87.3863 -2022-05-01 15:27:36,880 - tensorflow - INFO - loss = 0.26934433, step = 389400 (2.289 sec) -2022-05-01 15:27:39,166 - tensorflow - INFO - global_step/sec: 87.4754 -2022-05-01 15:27:39,167 - tensorflow - INFO - loss = 0.08934729, step = 389600 (2.286 sec) -2022-05-01 15:27:41,458 - tensorflow - INFO - global_step/sec: 87.2697 -2022-05-01 15:27:41,458 - tensorflow - INFO - loss = 0.021905141, step = 389800 (2.292 sec) -2022-05-01 15:27:43,758 - tensorflow - INFO - global_step/sec: 86.9738 -2022-05-01 15:27:43,758 - tensorflow - INFO - loss = 0.049776305, step = 390000 (2.300 sec) -2022-05-01 15:27:46,051 - tensorflow - INFO - global_step/sec: 87.1939 -2022-05-01 15:27:46,052 - tensorflow - INFO - loss = 0.040118024, step = 390200 (2.294 sec) -2022-05-01 15:27:48,335 - tensorflow - INFO - global_step/sec: 87.5897 -2022-05-01 15:27:48,335 - tensorflow - INFO - loss = 0.02597103, step = 390400 (2.283 sec) -2022-05-01 15:27:50,624 - tensorflow - INFO - global_step/sec: 87.3757 -2022-05-01 15:27:50,624 - tensorflow - INFO - loss = 0.042590633, step = 390600 (2.289 sec) -2022-05-01 15:27:52,920 - tensorflow - INFO - global_step/sec: 87.0951 -2022-05-01 15:27:52,920 - tensorflow - INFO - loss = 0.039586984, step = 390800 (2.296 sec) -2022-05-01 15:27:55,212 - tensorflow - INFO - global_step/sec: 87.2792 -2022-05-01 15:27:55,212 - tensorflow - INFO - loss = 0.04140979, step = 391000 (2.291 sec) -2022-05-01 15:27:57,504 - tensorflow - INFO - global_step/sec: 87.2236 -2022-05-01 15:27:57,505 - tensorflow - INFO - loss = 0.029703354, step = 391200 (2.293 sec) -2022-05-01 15:27:59,793 - tensorflow - INFO - global_step/sec: 87.4091 -2022-05-01 15:27:59,793 - tensorflow - INFO - loss = 0.08359525, step = 391400 (2.288 sec) -2022-05-01 15:28:02,081 - tensorflow - INFO - global_step/sec: 87.4016 -2022-05-01 15:28:02,081 - tensorflow - INFO - loss = 0.08424415, step = 391600 (2.288 sec) -2022-05-01 15:28:04,377 - tensorflow - INFO - global_step/sec: 87.0941 -2022-05-01 15:28:04,378 - tensorflow - INFO - loss = 0.02261557, step = 391800 (2.296 sec) -2022-05-01 15:28:06,668 - tensorflow - INFO - global_step/sec: 87.3149 -2022-05-01 15:28:06,668 - tensorflow - INFO - loss = 0.37749457, step = 392000 (2.291 sec) -2022-05-01 15:28:08,967 - tensorflow - INFO - global_step/sec: 86.9919 -2022-05-01 15:28:08,967 - tensorflow - INFO - loss = 0.049463645, step = 392200 (2.299 sec) -2022-05-01 15:28:11,260 - tensorflow - INFO - global_step/sec: 87.2103 -2022-05-01 15:28:11,261 - tensorflow - INFO - loss = 0.1873938, step = 392400 (2.293 sec) -2022-05-01 15:28:13,550 - tensorflow - INFO - global_step/sec: 87.3377 -2022-05-01 15:28:13,551 - tensorflow - INFO - loss = 0.02534483, step = 392600 (2.290 sec) -2022-05-01 15:28:15,844 - tensorflow - INFO - global_step/sec: 87.1993 -2022-05-01 15:28:15,844 - tensorflow - INFO - loss = 0.1180383, step = 392800 (2.294 sec) -2022-05-01 15:28:18,132 - tensorflow - INFO - global_step/sec: 87.4058 -2022-05-01 15:28:18,132 - tensorflow - INFO - loss = 0.020066151, step = 393000 (2.288 sec) -2022-05-01 15:28:20,414 - tensorflow - INFO - global_step/sec: 87.6439 -2022-05-01 15:28:20,414 - tensorflow - INFO - loss = 0.04308885, step = 393200 (2.282 sec) -2022-05-01 15:28:22,713 - tensorflow - INFO - global_step/sec: 86.9714 -2022-05-01 15:28:22,714 - tensorflow - INFO - loss = 0.08658816, step = 393400 (2.300 sec) -2022-05-01 15:28:25,006 - tensorflow - INFO - global_step/sec: 87.2448 -2022-05-01 15:28:25,006 - tensorflow - INFO - loss = 0.07704064, step = 393600 (2.292 sec) -2022-05-01 15:28:27,300 - tensorflow - INFO - global_step/sec: 87.1794 -2022-05-01 15:28:27,300 - tensorflow - INFO - loss = 0.09365944, step = 393800 (2.294 sec) -2022-05-01 15:28:29,581 - tensorflow - INFO - global_step/sec: 87.6698 -2022-05-01 15:28:29,582 - tensorflow - INFO - loss = 0.120907575, step = 394000 (2.281 sec) -2022-05-01 15:28:31,857 - tensorflow - INFO - global_step/sec: 87.9 -2022-05-01 15:28:31,857 - tensorflow - INFO - loss = 0.2553339, step = 394200 (2.275 sec) -2022-05-01 15:28:34,119 - tensorflow - INFO - global_step/sec: 88.3854 -2022-05-01 15:28:34,120 - tensorflow - INFO - loss = 0.20861246, step = 394400 (2.263 sec) -2022-05-01 15:28:36,389 - tensorflow - INFO - global_step/sec: 88.1201 -2022-05-01 15:28:36,389 - tensorflow - INFO - loss = 0.024497438, step = 394600 (2.270 sec) -2022-05-01 15:28:38,690 - tensorflow - INFO - global_step/sec: 86.9248 -2022-05-01 15:28:38,690 - tensorflow - INFO - loss = 0.2303257, step = 394800 (2.301 sec) -2022-05-01 15:28:40,987 - tensorflow - INFO - global_step/sec: 87.0582 -2022-05-01 15:28:40,988 - tensorflow - INFO - loss = 6.556267, step = 395000 (2.297 sec) -2022-05-01 15:28:43,289 - tensorflow - INFO - global_step/sec: 86.8876 -2022-05-01 15:28:43,289 - tensorflow - INFO - loss = 0.1679577, step = 395200 (2.302 sec) -2022-05-01 15:28:45,593 - tensorflow - INFO - global_step/sec: 86.8084 -2022-05-01 15:28:45,593 - tensorflow - INFO - loss = 0.072121605, step = 395400 (2.304 sec) -2022-05-01 15:28:47,892 - tensorflow - INFO - global_step/sec: 86.9844 -2022-05-01 15:28:47,893 - tensorflow - INFO - loss = 0.27504706, step = 395600 (2.299 sec) -2022-05-01 15:28:50,194 - tensorflow - INFO - global_step/sec: 86.8971 -2022-05-01 15:28:50,194 - tensorflow - INFO - loss = 0.27844322, step = 395800 (2.302 sec) -2022-05-01 15:28:52,501 - tensorflow - INFO - global_step/sec: 86.693 -2022-05-01 15:28:52,501 - tensorflow - INFO - loss = 1.3566101, step = 396000 (2.307 sec) -2022-05-01 15:28:54,801 - tensorflow - INFO - global_step/sec: 86.9296 -2022-05-01 15:28:54,802 - tensorflow - INFO - loss = 0.06718867, step = 396200 (2.301 sec) -2022-05-01 15:28:57,104 - tensorflow - INFO - global_step/sec: 86.8481 -2022-05-01 15:28:57,105 - tensorflow - INFO - loss = 0.0668054, step = 396400 (2.303 sec) -2022-05-01 15:28:59,400 - tensorflow - INFO - global_step/sec: 87.113 -2022-05-01 15:28:59,401 - tensorflow - INFO - loss = 0.12444751, step = 396600 (2.296 sec) -2022-05-01 15:29:01,701 - tensorflow - INFO - global_step/sec: 86.9377 -2022-05-01 15:29:01,701 - tensorflow - INFO - loss = 0.03913559, step = 396800 (2.301 sec) -2022-05-01 15:29:04,007 - tensorflow - INFO - global_step/sec: 86.7135 -2022-05-01 15:29:04,008 - tensorflow - INFO - loss = 0.05213821, step = 397000 (2.306 sec) -2022-05-01 15:29:06,306 - tensorflow - INFO - global_step/sec: 87.0015 -2022-05-01 15:29:06,306 - tensorflow - INFO - loss = 0.05093122, step = 397200 (2.299 sec) -2022-05-01 15:29:08,597 - tensorflow - INFO - global_step/sec: 87.2929 -2022-05-01 15:29:08,598 - tensorflow - INFO - loss = 0.023556273, step = 397400 (2.291 sec) -2022-05-01 15:29:10,882 - tensorflow - INFO - global_step/sec: 87.5211 -2022-05-01 15:29:10,883 - tensorflow - INFO - loss = 0.04940243, step = 397600 (2.285 sec) -2022-05-01 15:29:13,174 - tensorflow - INFO - global_step/sec: 87.2662 -2022-05-01 15:29:13,175 - tensorflow - INFO - loss = 0.44391492, step = 397800 (2.292 sec) -2022-05-01 15:29:15,469 - tensorflow - INFO - global_step/sec: 87.1639 -2022-05-01 15:29:15,469 - tensorflow - INFO - loss = 0.05440904, step = 398000 (2.295 sec) -2022-05-01 15:29:17,755 - tensorflow - INFO - global_step/sec: 87.4721 -2022-05-01 15:29:17,756 - tensorflow - INFO - loss = 0.021781359, step = 398200 (2.286 sec) -2022-05-01 15:29:20,050 - tensorflow - INFO - global_step/sec: 87.1435 -2022-05-01 15:29:20,051 - tensorflow - INFO - loss = 0.1761256, step = 398400 (2.295 sec) -2022-05-01 15:29:22,337 - tensorflow - INFO - global_step/sec: 87.459 -2022-05-01 15:29:22,337 - tensorflow - INFO - loss = 0.031269975, step = 398600 (2.287 sec) -2022-05-01 15:29:24,636 - tensorflow - INFO - global_step/sec: 86.9899 -2022-05-01 15:29:24,636 - tensorflow - INFO - loss = 0.029436901, step = 398800 (2.299 sec) -2022-05-01 15:29:26,921 - tensorflow - INFO - global_step/sec: 87.5231 -2022-05-01 15:29:26,922 - tensorflow - INFO - loss = 0.036546864, step = 399000 (2.285 sec) -2022-05-01 15:29:29,216 - tensorflow - INFO - global_step/sec: 87.1351 -2022-05-01 15:29:29,217 - tensorflow - INFO - loss = 0.073827825, step = 399200 (2.295 sec) -2022-05-01 15:29:31,514 - tensorflow - INFO - global_step/sec: 87.0353 -2022-05-01 15:29:31,515 - tensorflow - INFO - loss = 0.04035163, step = 399400 (2.298 sec) -2022-05-01 15:29:33,802 - tensorflow - INFO - global_step/sec: 87.4241 -2022-05-01 15:29:33,803 - tensorflow - INFO - loss = 0.01856226, step = 399600 (2.288 sec) -2022-05-01 15:29:36,094 - tensorflow - INFO - global_step/sec: 87.2769 -2022-05-01 15:29:36,094 - tensorflow - INFO - loss = 0.58974075, step = 399800 (2.292 sec) -2022-05-01 15:29:38,371 - tensorflow - INFO - Saving checkpoints for 400000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. -2022-05-01 15:29:38,389 - tensorflow - INFO - model.ckpt-400000 is not in all_model_checkpoint_paths. Manually adding it. -2022-05-01 15:29:38,933 - tensorflow - INFO - Calling model_fn. -2022-05-01 15:29:39,066 - tensorflow - INFO - Done calling model_fn. -2022-05-01 15:29:39,082 - tensorflow - INFO - Starting evaluation at 2022-05-01T15:29:39Z -2022-05-01 15:29:39,147 - tensorflow - INFO - Graph was finalized. -2022-05-01 15:29:39,149 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-400000 -2022-05-01 15:29:39,438 - tensorflow - INFO - Running local_init_op. -2022-05-01 15:29:39,547 - tensorflow - INFO - Done running local_init_op. -2022-05-01 15:29:49,774 - tensorflow - INFO - Evaluation [3990/39900] -2022-05-01 15:29:59,312 - tensorflow - INFO - Evaluation [7980/39900] -2022-05-01 15:30:08,770 - tensorflow - INFO - Evaluation [11970/39900] -2022-05-01 15:30:18,321 - tensorflow - INFO - Evaluation [15960/39900] -2022-05-01 15:30:27,839 - tensorflow - INFO - Evaluation [19950/39900] -2022-05-01 15:30:37,374 - tensorflow - INFO - Evaluation [23940/39900] -2022-05-01 15:30:46,920 - tensorflow - INFO - Evaluation [27930/39900] -2022-05-01 15:30:56,464 - tensorflow - INFO - Evaluation [31920/39900] -2022-05-01 15:31:06,005 - tensorflow - INFO - Evaluation [35910/39900] -2022-05-01 15:31:15,544 - tensorflow - INFO - Evaluation [39900/39900] -2022-05-01 15:31:15,682 - tensorflow - INFO - Finished evaluation at 2022-05-01-15:31:15 -2022-05-01 15:31:15,682 - tensorflow - INFO - Saving dict for global step 400000: global_step = 400000, loss = 0.04516113, mean_degree_err = 6.516394 -2022-05-01 15:31:15,682 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 400000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-400000 -2022-05-01 15:31:15,696 - tensorflow - INFO - global_step/sec: 2.00798 -2022-05-01 15:31:15,697 - tensorflow - INFO - loss = 0.02225634, step = 400000 (99.603 sec) -2022-05-01 15:31:18,011 - tensorflow - INFO - global_step/sec: 86.4114 -2022-05-01 15:31:18,011 - tensorflow - INFO - loss = 0.31792247, step = 400200 (2.314 sec) -2022-05-01 15:31:20,306 - tensorflow - INFO - global_step/sec: 87.1549 -2022-05-01 15:31:20,306 - tensorflow - INFO - loss = 0.10387549, step = 400400 (2.295 sec) -2022-05-01 15:31:22,607 - tensorflow - INFO - global_step/sec: 86.9092 -2022-05-01 15:31:22,607 - tensorflow - INFO - loss = 0.12722068, step = 400600 (2.301 sec) -2022-05-01 15:31:24,903 - tensorflow - INFO - global_step/sec: 87.0888 -2022-05-01 15:31:24,904 - tensorflow - INFO - loss = 0.22603582, step = 400800 (2.297 sec) -2022-05-01 15:31:27,198 - tensorflow - INFO - global_step/sec: 87.1598 -2022-05-01 15:31:27,199 - tensorflow - INFO - loss = 0.15252268, step = 401000 (2.295 sec) -2022-05-01 15:31:29,491 - tensorflow - INFO - global_step/sec: 87.2261 -2022-05-01 15:31:29,491 - tensorflow - INFO - loss = 0.24389297, step = 401200 (2.293 sec) -2022-05-01 15:31:31,795 - tensorflow - INFO - global_step/sec: 86.8182 -2022-05-01 15:31:31,795 - tensorflow - INFO - loss = 0.015286315, step = 401400 (2.304 sec) -2022-05-01 15:31:34,096 - tensorflow - INFO - global_step/sec: 86.9031 -2022-05-01 15:31:34,096 - tensorflow - INFO - loss = 0.09249173, step = 401600 (2.301 sec) -2022-05-01 15:31:36,388 - tensorflow - INFO - global_step/sec: 87.2577 -2022-05-01 15:31:36,389 - tensorflow - INFO - loss = 0.097399205, step = 401800 (2.292 sec) -2022-05-01 15:31:38,645 - tensorflow - INFO - global_step/sec: 88.6012 -2022-05-01 15:31:38,646 - tensorflow - INFO - loss = 0.030494688, step = 402000 (2.257 sec) -2022-05-01 15:31:40,923 - tensorflow - INFO - global_step/sec: 87.8078 -2022-05-01 15:31:40,924 - tensorflow - INFO - loss = 0.111601226, step = 402200 (2.278 sec) -2022-05-01 15:31:43,212 - tensorflow - INFO - global_step/sec: 87.3939 -2022-05-01 15:31:43,212 - tensorflow - INFO - loss = 0.028065145, step = 402400 (2.288 sec) -2022-05-01 15:31:45,507 - tensorflow - INFO - global_step/sec: 87.1462 -2022-05-01 15:31:45,507 - tensorflow - INFO - loss = 0.024193406, step = 402600 (2.295 sec) -2022-05-01 15:31:47,798 - tensorflow - INFO - global_step/sec: 87.2884 -2022-05-01 15:31:47,798 - tensorflow - INFO - loss = 20.300358, step = 402800 (2.291 sec) -2022-05-01 15:31:50,098 - tensorflow - INFO - global_step/sec: 86.9538 -2022-05-01 15:31:50,098 - tensorflow - INFO - loss = 17.936117, step = 403000 (2.300 sec) -2022-05-01 15:31:52,388 - tensorflow - INFO - global_step/sec: 87.3362 -2022-05-01 15:31:52,388 - tensorflow - INFO - loss = 0.04836905, step = 403200 (2.290 sec) -2022-05-01 15:31:54,683 - tensorflow - INFO - global_step/sec: 87.148 -2022-05-01 15:31:54,683 - tensorflow - INFO - loss = 11.243692, step = 403400 (2.295 sec) -2022-05-01 15:31:56,975 - tensorflow - INFO - global_step/sec: 87.2586 -2022-05-01 15:31:56,975 - tensorflow - INFO - loss = 0.024591817, step = 403600 (2.292 sec) -2022-05-01 15:31:59,270 - tensorflow - INFO - global_step/sec: 87.1365 -2022-05-01 15:31:59,271 - tensorflow - INFO - loss = 0.10885215, step = 403800 (2.295 sec) -2022-05-01 15:32:01,569 - tensorflow - INFO - global_step/sec: 87.0194 -2022-05-01 15:32:01,569 - tensorflow - INFO - loss = 0.09319634, step = 404000 (2.298 sec) -2022-05-01 15:32:03,855 - tensorflow - INFO - global_step/sec: 87.4566 -2022-05-01 15:32:03,856 - tensorflow - INFO - loss = 0.053963237, step = 404200 (2.287 sec) -2022-05-01 15:32:06,151 - tensorflow - INFO - global_step/sec: 87.1172 -2022-05-01 15:32:06,152 - tensorflow - INFO - loss = 0.053012453, step = 404400 (2.296 sec) -2022-05-01 15:32:08,451 - tensorflow - INFO - global_step/sec: 86.9517 -2022-05-01 15:32:08,452 - tensorflow - INFO - loss = 0.12956715, step = 404600 (2.300 sec) -2022-05-01 15:32:10,755 - tensorflow - INFO - global_step/sec: 86.8279 -2022-05-01 15:32:10,755 - tensorflow - INFO - loss = 0.09968604, step = 404800 (2.303 sec) -2022-05-01 15:32:13,065 - tensorflow - INFO - global_step/sec: 86.551 -2022-05-01 15:32:13,066 - tensorflow - INFO - loss = 0.026279986, step = 405000 (2.311 sec) -2022-05-01 15:32:15,371 - tensorflow - INFO - global_step/sec: 86.7337 -2022-05-01 15:32:15,372 - tensorflow - INFO - loss = 0.09798093, step = 405200 (2.306 sec) -2022-05-01 15:32:17,675 - tensorflow - INFO - global_step/sec: 86.8323 -2022-05-01 15:32:17,675 - tensorflow - INFO - loss = 0.044201896, step = 405400 (2.303 sec) -2022-05-01 15:32:19,974 - tensorflow - INFO - global_step/sec: 86.981 -2022-05-01 15:32:19,974 - tensorflow - INFO - loss = 0.05312757, step = 405600 (2.299 sec) -2022-05-01 15:32:22,279 - tensorflow - INFO - global_step/sec: 86.7527 -2022-05-01 15:32:22,280 - tensorflow - INFO - loss = 0.10622845, step = 405800 (2.305 sec) -2022-05-01 15:32:24,581 - tensorflow - INFO - global_step/sec: 86.9053 -2022-05-01 15:32:24,581 - tensorflow - INFO - loss = 0.10170749, step = 406000 (2.301 sec) -2022-05-01 15:32:26,884 - tensorflow - INFO - global_step/sec: 86.8522 -2022-05-01 15:32:26,884 - tensorflow - INFO - loss = 0.048127078, step = 406200 (2.303 sec) -2022-05-01 15:32:29,187 - tensorflow - INFO - global_step/sec: 86.8426 -2022-05-01 15:32:29,187 - tensorflow - INFO - loss = 0.052297786, step = 406400 (2.303 sec) -2022-05-01 15:32:31,494 - tensorflow - INFO - global_step/sec: 86.6579 -2022-05-01 15:32:31,495 - tensorflow - INFO - loss = 0.08015016, step = 406600 (2.308 sec) -2022-05-01 15:32:33,796 - tensorflow - INFO - global_step/sec: 86.898 -2022-05-01 15:32:33,796 - tensorflow - INFO - loss = 0.16766617, step = 406800 (2.301 sec) -2022-05-01 15:32:36,098 - tensorflow - INFO - global_step/sec: 86.898 -2022-05-01 15:32:36,098 - tensorflow - INFO - loss = 0.8134452, step = 407000 (2.302 sec) -2022-05-01 15:32:38,395 - tensorflow - INFO - global_step/sec: 87.0599 -2022-05-01 15:32:38,395 - tensorflow - INFO - loss = 0.03777675, step = 407200 (2.297 sec) -2022-05-01 15:32:40,695 - tensorflow - INFO - global_step/sec: 86.9366 -2022-05-01 15:32:40,696 - tensorflow - INFO - loss = 0.13193761, step = 407400 (2.300 sec) -2022-05-01 15:32:42,981 - tensorflow - INFO - global_step/sec: 87.5104 -2022-05-01 15:32:42,981 - tensorflow - INFO - loss = 0.11008027, step = 407600 (2.285 sec) -2022-05-01 15:32:45,272 - tensorflow - INFO - global_step/sec: 87.3061 -2022-05-01 15:32:45,272 - tensorflow - INFO - loss = 0.0595809, step = 407800 (2.291 sec) -2022-05-01 15:32:47,562 - tensorflow - INFO - global_step/sec: 87.3225 -2022-05-01 15:32:47,562 - tensorflow - INFO - loss = 0.018416446, step = 408000 (2.290 sec) -2022-05-01 15:32:49,854 - tensorflow - INFO - global_step/sec: 87.245 -2022-05-01 15:32:49,855 - tensorflow - INFO - loss = 0.22051051, step = 408200 (2.292 sec) -2022-05-01 15:32:52,157 - tensorflow - INFO - global_step/sec: 86.8691 -2022-05-01 15:32:52,157 - tensorflow - INFO - loss = 0.5370528, step = 408400 (2.302 sec) -2022-05-01 15:32:54,452 - tensorflow - INFO - global_step/sec: 87.1151 -2022-05-01 15:32:54,453 - tensorflow - INFO - loss = 0.031772103, step = 408600 (2.296 sec) -2022-05-01 15:32:56,746 - tensorflow - INFO - global_step/sec: 87.21 -2022-05-01 15:32:56,746 - tensorflow - INFO - loss = 0.2617071, step = 408800 (2.293 sec) -2022-05-01 15:32:59,045 - tensorflow - INFO - global_step/sec: 86.9771 -2022-05-01 15:32:59,046 - tensorflow - INFO - loss = 0.093198895, step = 409000 (2.300 sec) -2022-05-01 15:33:01,340 - tensorflow - INFO - global_step/sec: 87.1726 -2022-05-01 15:33:01,340 - tensorflow - INFO - loss = 0.016394991, step = 409200 (2.294 sec) -2022-05-01 15:33:03,635 - tensorflow - INFO - global_step/sec: 87.1468 -2022-05-01 15:33:03,635 - tensorflow - INFO - loss = 0.071981266, step = 409400 (2.295 sec) -2022-05-01 15:33:05,920 - tensorflow - INFO - global_step/sec: 87.4897 -2022-05-01 15:33:05,921 - tensorflow - INFO - loss = 0.045541182, step = 409600 (2.286 sec) -2022-05-01 15:33:08,189 - tensorflow - INFO - global_step/sec: 88.1814 -2022-05-01 15:33:08,189 - tensorflow - INFO - loss = 0.08847006, step = 409800 (2.268 sec) -2022-05-01 15:33:10,443 - tensorflow - INFO - global_step/sec: 88.6944 -2022-05-01 15:33:10,444 - tensorflow - INFO - loss = 0.06049387, step = 410000 (2.255 sec) -2022-05-01 15:33:12,720 - tensorflow - INFO - global_step/sec: 87.8406 -2022-05-01 15:33:12,721 - tensorflow - INFO - loss = 0.022273019, step = 410200 (2.277 sec) -2022-05-01 15:33:15,012 - tensorflow - INFO - global_step/sec: 87.2749 -2022-05-01 15:33:15,012 - tensorflow - INFO - loss = 0.025294263, step = 410400 (2.292 sec) -2022-05-01 15:33:17,304 - tensorflow - INFO - global_step/sec: 87.2735 -2022-05-01 15:33:17,304 - tensorflow - INFO - loss = 0.020777583, step = 410600 (2.292 sec) -2022-05-01 15:33:19,597 - tensorflow - INFO - global_step/sec: 87.2056 -2022-05-01 15:33:19,597 - tensorflow - INFO - loss = 0.04804317, step = 410800 (2.293 sec) -2022-05-01 15:33:21,886 - tensorflow - INFO - global_step/sec: 87.3783 -2022-05-01 15:33:21,886 - tensorflow - INFO - loss = 0.020127455, step = 411000 (2.289 sec) -2022-05-01 15:33:24,174 - tensorflow - INFO - global_step/sec: 87.397 -2022-05-01 15:33:24,175 - tensorflow - INFO - loss = 0.11351947, step = 411200 (2.288 sec) -2022-05-01 15:33:26,461 - tensorflow - INFO - global_step/sec: 87.456 -2022-05-01 15:33:26,462 - tensorflow - INFO - loss = 0.06593898, step = 411400 (2.287 sec) -2022-05-01 15:33:28,748 - tensorflow - INFO - global_step/sec: 87.4636 -2022-05-01 15:33:28,748 - tensorflow - INFO - loss = 0.073699504, step = 411600 (2.287 sec) -2022-05-01 15:33:31,048 - tensorflow - INFO - global_step/sec: 86.9668 -2022-05-01 15:33:31,048 - tensorflow - INFO - loss = 0.1304144, step = 411800 (2.300 sec) -2022-05-01 15:33:33,340 - tensorflow - INFO - global_step/sec: 87.2568 -2022-05-01 15:33:33,340 - tensorflow - INFO - loss = 0.03475838, step = 412000 (2.292 sec) -2022-05-01 15:33:35,623 - tensorflow - INFO - global_step/sec: 87.608 -2022-05-01 15:33:35,623 - tensorflow - INFO - loss = 0.025602646, step = 412200 (2.283 sec) -2022-05-01 15:33:37,907 - tensorflow - INFO - global_step/sec: 87.5365 -2022-05-01 15:33:37,908 - tensorflow - INFO - loss = 0.4589123, step = 412400 (2.285 sec) -2022-05-01 15:33:40,196 - tensorflow - INFO - global_step/sec: 87.3716 -2022-05-01 15:33:40,197 - tensorflow - INFO - loss = 0.060308713, step = 412600 (2.289 sec) -2022-05-01 15:33:42,495 - tensorflow - INFO - global_step/sec: 86.9984 -2022-05-01 15:33:42,496 - tensorflow - INFO - loss = 0.039986394, step = 412800 (2.299 sec) -2022-05-01 15:33:44,789 - tensorflow - INFO - global_step/sec: 87.2141 -2022-05-01 15:33:44,789 - tensorflow - INFO - loss = 0.036025546, step = 413000 (2.293 sec) -2022-05-01 15:33:47,078 - tensorflow - INFO - global_step/sec: 87.3417 -2022-05-01 15:33:47,079 - tensorflow - INFO - loss = 12.220699, step = 413200 (2.290 sec) -2022-05-01 15:33:49,378 - tensorflow - INFO - global_step/sec: 86.9718 -2022-05-01 15:33:49,378 - tensorflow - INFO - loss = 0.026839843, step = 413400 (2.300 sec) -2022-05-01 15:33:51,680 - tensorflow - INFO - global_step/sec: 86.8678 -2022-05-01 15:33:51,681 - tensorflow - INFO - loss = 6.3637915, step = 413600 (2.302 sec) -2022-05-01 15:33:53,977 - tensorflow - INFO - global_step/sec: 87.0721 -2022-05-01 15:33:53,978 - tensorflow - INFO - loss = 0.050607696, step = 413800 (2.297 sec) -2022-05-01 15:33:56,275 - tensorflow - INFO - global_step/sec: 87.0414 -2022-05-01 15:33:56,275 - tensorflow - INFO - loss = 0.18939602, step = 414000 (2.298 sec) -2022-05-01 15:33:58,569 - tensorflow - INFO - global_step/sec: 87.1782 -2022-05-01 15:33:58,570 - tensorflow - INFO - loss = 0.094642766, step = 414200 (2.294 sec) -2022-05-01 15:34:00,848 - tensorflow - INFO - global_step/sec: 87.7448 -2022-05-01 15:34:00,849 - tensorflow - INFO - loss = 0.036533773, step = 414400 (2.279 sec) -2022-05-01 15:34:03,138 - tensorflow - INFO - global_step/sec: 87.3375 -2022-05-01 15:34:03,139 - tensorflow - INFO - loss = 1.6116326, step = 414600 (2.290 sec) -2022-05-01 15:34:05,426 - tensorflow - INFO - global_step/sec: 87.4417 -2022-05-01 15:34:05,426 - tensorflow - INFO - loss = 0.047151618, step = 414800 (2.287 sec) -2022-05-01 15:34:07,732 - tensorflow - INFO - global_step/sec: 86.7379 -2022-05-01 15:34:07,732 - tensorflow - INFO - loss = 0.034820914, step = 415000 (2.306 sec) -2022-05-01 15:34:10,029 - tensorflow - INFO - global_step/sec: 87.0342 -2022-05-01 15:34:10,030 - tensorflow - INFO - loss = 0.25218165, step = 415200 (2.298 sec) -2022-05-01 15:34:12,326 - tensorflow - INFO - global_step/sec: 87.069 -2022-05-01 15:34:12,327 - tensorflow - INFO - loss = 0.05424107, step = 415400 (2.297 sec) -2022-05-01 15:34:14,618 - tensorflow - INFO - global_step/sec: 87.292 -2022-05-01 15:34:14,618 - tensorflow - INFO - loss = 0.1665936, step = 415600 (2.291 sec) -2022-05-01 15:34:16,904 - tensorflow - INFO - global_step/sec: 87.4635 -2022-05-01 15:34:16,905 - tensorflow - INFO - loss = 0.15828733, step = 415800 (2.287 sec) -2022-05-01 15:34:19,201 - tensorflow - INFO - global_step/sec: 87.0896 -2022-05-01 15:34:19,201 - tensorflow - INFO - loss = 0.1803932, step = 416000 (2.297 sec) -2022-05-01 15:34:21,495 - tensorflow - INFO - global_step/sec: 87.1763 -2022-05-01 15:34:21,495 - tensorflow - INFO - loss = 0.054921888, step = 416200 (2.294 sec) -2022-05-01 15:34:23,799 - tensorflow - INFO - global_step/sec: 86.8128 -2022-05-01 15:34:23,799 - tensorflow - INFO - loss = 0.048329223, step = 416400 (2.304 sec) -2022-05-01 15:34:26,096 - tensorflow - INFO - global_step/sec: 87.0795 -2022-05-01 15:34:26,096 - tensorflow - INFO - loss = 0.05945764, step = 416600 (2.297 sec) -2022-05-01 15:34:28,390 - tensorflow - INFO - global_step/sec: 87.1598 -2022-05-01 15:34:28,391 - tensorflow - INFO - loss = 0.0472399, step = 416800 (2.295 sec) -2022-05-01 15:34:30,688 - tensorflow - INFO - global_step/sec: 87.0552 -2022-05-01 15:34:30,688 - tensorflow - INFO - loss = 0.12121238, step = 417000 (2.297 sec) -2022-05-01 15:34:32,987 - tensorflow - INFO - global_step/sec: 86.9954 -2022-05-01 15:34:32,987 - tensorflow - INFO - loss = 0.03905348, step = 417200 (2.299 sec) -2022-05-01 15:34:35,280 - tensorflow - INFO - global_step/sec: 87.198 -2022-05-01 15:34:35,281 - tensorflow - INFO - loss = 0.021892853, step = 417400 (2.294 sec) -2022-05-01 15:34:37,566 - tensorflow - INFO - global_step/sec: 87.4964 -2022-05-01 15:34:37,566 - tensorflow - INFO - loss = 0.27863014, step = 417600 (2.286 sec) -2022-05-01 15:34:39,842 - tensorflow - INFO - global_step/sec: 87.8584 -2022-05-01 15:34:39,843 - tensorflow - INFO - loss = 0.05170039, step = 417800 (2.276 sec) -2022-05-01 15:34:42,106 - tensorflow - INFO - global_step/sec: 88.3724 -2022-05-01 15:34:42,106 - tensorflow - INFO - loss = 0.03168484, step = 418000 (2.263 sec) -2022-05-01 15:34:44,371 - tensorflow - INFO - global_step/sec: 88.2907 -2022-05-01 15:34:44,371 - tensorflow - INFO - loss = 8.134713, step = 418200 (2.265 sec) -2022-05-01 15:34:46,660 - tensorflow - INFO - global_step/sec: 87.3752 -2022-05-01 15:34:46,660 - tensorflow - INFO - loss = 0.024514042, step = 418400 (2.289 sec) -2022-05-01 15:34:48,951 - tensorflow - INFO - global_step/sec: 87.2795 -2022-05-01 15:34:48,952 - tensorflow - INFO - loss = 0.05076325, step = 418600 (2.291 sec) -2022-05-01 15:34:51,239 - tensorflow - INFO - global_step/sec: 87.4347 -2022-05-01 15:34:51,239 - tensorflow - INFO - loss = 0.098169036, step = 418800 (2.287 sec) -2022-05-01 15:34:53,531 - tensorflow - INFO - global_step/sec: 87.2405 -2022-05-01 15:34:53,532 - tensorflow - INFO - loss = 0.50190496, step = 419000 (2.293 sec) -2022-05-01 15:34:55,830 - tensorflow - INFO - global_step/sec: 86.9896 -2022-05-01 15:34:55,831 - tensorflow - INFO - loss = 0.028723195, step = 419200 (2.299 sec) -2022-05-01 15:34:58,126 - tensorflow - INFO - global_step/sec: 87.116 -2022-05-01 15:34:58,127 - tensorflow - INFO - loss = 0.023914592, step = 419400 (2.296 sec) -2022-05-01 15:35:00,418 - tensorflow - INFO - global_step/sec: 87.2632 -2022-05-01 15:35:00,418 - tensorflow - INFO - loss = 0.068822294, step = 419600 (2.292 sec) -2022-05-01 15:35:02,710 - tensorflow - INFO - global_step/sec: 87.2717 -2022-05-01 15:35:02,710 - tensorflow - INFO - loss = 0.029412974, step = 419800 (2.292 sec) -2022-05-01 15:35:05,003 - tensorflow - INFO - global_step/sec: 87.2044 -2022-05-01 15:35:05,004 - tensorflow - INFO - loss = 0.073240936, step = 420000 (2.293 sec) -2022-05-01 15:35:07,296 - tensorflow - INFO - global_step/sec: 87.2374 -2022-05-01 15:35:07,296 - 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tensorflow - INFO - global_step/sec: 87.5461 -2022-05-01 15:47:05,009 - tensorflow - INFO - loss = 0.45276707, step = 482800 (2.285 sec) -2022-05-01 15:47:07,290 - tensorflow - INFO - global_step/sec: 87.678 -2022-05-01 15:47:07,290 - tensorflow - INFO - loss = 1.5098001, step = 483000 (2.281 sec) -2022-05-01 15:47:09,557 - tensorflow - INFO - global_step/sec: 88.2376 -2022-05-01 15:47:09,557 - tensorflow - INFO - loss = 0.07556595, step = 483200 (2.267 sec) -2022-05-01 15:47:11,813 - tensorflow - INFO - global_step/sec: 88.6504 -2022-05-01 15:47:11,813 - tensorflow - INFO - loss = 0.038961302, step = 483400 (2.256 sec) -2022-05-01 15:47:14,105 - tensorflow - INFO - global_step/sec: 87.2409 -2022-05-01 15:47:14,106 - tensorflow - INFO - loss = 0.06715776, step = 483600 (2.293 sec) -2022-05-01 15:47:16,401 - tensorflow - INFO - global_step/sec: 87.1298 -2022-05-01 15:47:16,401 - tensorflow - INFO - loss = 0.022315247, step = 483800 (2.295 sec) -2022-05-01 15:47:18,699 - tensorflow - INFO - global_step/sec: 87.0005 -2022-05-01 15:47:18,700 - tensorflow - INFO - loss = 0.08670243, step = 484000 (2.299 sec) -2022-05-01 15:47:21,000 - tensorflow - INFO - global_step/sec: 86.9297 -2022-05-01 15:47:21,001 - tensorflow - INFO - loss = 0.048578028, step = 484200 (2.301 sec) -2022-05-01 15:47:23,292 - tensorflow - INFO - global_step/sec: 87.2653 -2022-05-01 15:47:23,292 - tensorflow - INFO - loss = 0.57092, step = 484400 (2.292 sec) -2022-05-01 15:47:25,584 - tensorflow - INFO - global_step/sec: 87.2686 -2022-05-01 15:47:25,584 - tensorflow - INFO - loss = 0.055992827, step = 484600 (2.292 sec) -2022-05-01 15:47:27,876 - tensorflow - INFO - global_step/sec: 87.2445 -2022-05-01 15:47:27,877 - tensorflow - INFO - loss = 0.04843991, step = 484800 (2.292 sec) -2022-05-01 15:47:30,168 - tensorflow - INFO - global_step/sec: 87.2737 -2022-05-01 15:47:30,168 - tensorflow - INFO - loss = 0.04905627, step = 485000 (2.292 sec) -2022-05-01 15:47:32,466 - tensorflow - INFO - global_step/sec: 87.0441 -2022-05-01 15:47:32,466 - tensorflow - INFO - loss = 0.06376162, step = 485200 (2.298 sec) -2022-05-01 15:47:34,761 - tensorflow - INFO - global_step/sec: 87.1136 -2022-05-01 15:47:34,762 - tensorflow - INFO - loss = 0.0819434, step = 485400 (2.296 sec) -2022-05-01 15:47:37,049 - tensorflow - INFO - global_step/sec: 87.423 -2022-05-01 15:47:37,050 - tensorflow - INFO - loss = 0.021668667, step = 485600 (2.288 sec) -2022-05-01 15:47:39,341 - tensorflow - INFO - global_step/sec: 87.2717 -2022-05-01 15:47:39,341 - tensorflow - INFO - loss = 0.053483568, step = 485800 (2.292 sec) -2022-05-01 15:47:41,633 - tensorflow - INFO - global_step/sec: 87.2724 -2022-05-01 15:47:41,633 - tensorflow - INFO - loss = 0.04222194, step = 486000 (2.292 sec) -2022-05-01 15:47:43,928 - tensorflow - INFO - global_step/sec: 87.1425 -2022-05-01 15:47:43,928 - tensorflow - INFO - loss = 0.043749157, step = 486200 (2.295 sec) -2022-05-01 15:47:46,223 - tensorflow - INFO - global_step/sec: 87.1329 -2022-05-01 15:47:46,223 - tensorflow - INFO - loss = 0.15084764, step = 486400 (2.295 sec) -2022-05-01 15:47:48,516 - tensorflow - INFO - global_step/sec: 87.2074 -2022-05-01 15:47:48,517 - tensorflow - INFO - loss = 0.07187637, step = 486600 (2.293 sec) -2022-05-01 15:47:50,806 - tensorflow - INFO - global_step/sec: 87.3643 -2022-05-01 15:47:50,806 - tensorflow - INFO - loss = 0.053894468, step = 486800 (2.289 sec) -2022-05-01 15:47:53,102 - tensorflow - INFO - global_step/sec: 87.0776 -2022-05-01 15:47:53,103 - tensorflow - INFO - loss = 0.1304493, step = 487000 (2.297 sec) -2022-05-01 15:47:55,398 - tensorflow - INFO - global_step/sec: 87.1353 -2022-05-01 15:47:55,398 - tensorflow - INFO - loss = 11.150118, step = 487200 (2.295 sec) -2022-05-01 15:47:57,695 - tensorflow - INFO - global_step/sec: 87.0469 -2022-05-01 15:47:57,696 - tensorflow - INFO - loss = 0.039552987, step = 487400 (2.298 sec) -2022-05-01 15:47:59,985 - tensorflow - INFO - global_step/sec: 87.3537 -2022-05-01 15:47:59,985 - tensorflow - INFO - loss = 4.6886973, step = 487600 (2.289 sec) -2022-05-01 15:48:02,276 - tensorflow - INFO - global_step/sec: 87.2922 -2022-05-01 15:48:02,276 - tensorflow - INFO - loss = 0.066290066, step = 487800 (2.291 sec) -2022-05-01 15:48:04,572 - tensorflow - INFO - global_step/sec: 87.0961 -2022-05-01 15:48:04,573 - tensorflow - INFO - loss = 14.458428, step = 488000 (2.296 sec) -2022-05-01 15:48:06,865 - tensorflow - INFO - global_step/sec: 87.2209 -2022-05-01 15:48:06,866 - tensorflow - INFO - loss = 0.04509601, step = 488200 (2.293 sec) -2022-05-01 15:48:09,163 - tensorflow - INFO - global_step/sec: 87.0534 -2022-05-01 15:48:09,163 - tensorflow - INFO - loss = 0.11672298, step = 488400 (2.297 sec) -2022-05-01 15:48:11,454 - tensorflow - INFO - global_step/sec: 87.2862 -2022-05-01 15:48:11,454 - tensorflow - INFO - loss = 0.051707402, step = 488600 (2.291 sec) -2022-05-01 15:48:13,742 - tensorflow - INFO - global_step/sec: 87.4171 -2022-05-01 15:48:13,742 - tensorflow - INFO - loss = 0.061385017, step = 488800 (2.288 sec) -2022-05-01 15:48:16,037 - tensorflow - INFO - global_step/sec: 87.1478 -2022-05-01 15:48:16,037 - tensorflow - INFO - loss = 0.042890646, step = 489000 (2.295 sec) -2022-05-01 15:48:18,326 - tensorflow - INFO - global_step/sec: 87.3552 -2022-05-01 15:48:18,327 - tensorflow - INFO - loss = 1.4140427, step = 489200 (2.289 sec) -2022-05-01 15:48:20,624 - tensorflow - INFO - global_step/sec: 87.0491 -2022-05-01 15:48:20,624 - tensorflow - INFO - loss = 0.0454567, step = 489400 (2.298 sec) -2022-05-01 15:48:22,920 - tensorflow - INFO - global_step/sec: 87.1186 -2022-05-01 15:48:22,920 - tensorflow - INFO - loss = 0.23944998, step = 489600 (2.296 sec) -2022-05-01 15:48:25,209 - tensorflow - INFO - global_step/sec: 87.347 -2022-05-01 15:48:25,210 - tensorflow - INFO - loss = 0.048230134, step = 489800 (2.290 sec) -2022-05-01 15:48:27,503 - tensorflow - INFO - global_step/sec: 87.2112 -2022-05-01 15:48:27,503 - tensorflow - INFO - loss = 0.17226861, step = 490000 (2.293 sec) -2022-05-01 15:48:29,793 - tensorflow - INFO - global_step/sec: 87.3317 -2022-05-01 15:48:29,793 - tensorflow - INFO - loss = 0.063290715, step = 490200 (2.290 sec) -2022-05-01 15:48:32,082 - tensorflow - INFO - global_step/sec: 87.3637 -2022-05-01 15:48:32,082 - tensorflow - INFO - loss = 0.15427542, step = 490400 (2.289 sec) -2022-05-01 15:48:34,377 - tensorflow - INFO - global_step/sec: 87.1478 -2022-05-01 15:48:34,377 - tensorflow - INFO - loss = 0.0866156, step = 490600 (2.295 sec) -2022-05-01 15:48:36,653 - tensorflow - INFO - global_step/sec: 87.8803 -2022-05-01 15:48:36,653 - tensorflow - INFO - loss = 0.11607466, step = 490800 (2.276 sec) -2022-05-01 15:48:38,915 - tensorflow - INFO - global_step/sec: 88.393 -2022-05-01 15:48:38,916 - tensorflow - INFO - loss = 0.030506007, step = 491000 (2.263 sec) -2022-05-01 15:48:41,176 - tensorflow - INFO - global_step/sec: 88.4874 -2022-05-01 15:48:41,176 - tensorflow - INFO - loss = 0.16931124, step = 491200 (2.260 sec) -2022-05-01 15:48:43,436 - tensorflow - INFO - global_step/sec: 88.4809 -2022-05-01 15:48:43,436 - tensorflow - INFO - loss = 0.07888447, step = 491400 (2.260 sec) -2022-05-01 15:48:45,734 - tensorflow - INFO - global_step/sec: 87.0183 -2022-05-01 15:48:45,735 - tensorflow - INFO - loss = 0.013879217, step = 491600 (2.298 sec) -2022-05-01 15:48:48,028 - tensorflow - INFO - global_step/sec: 87.1904 -2022-05-01 15:48:48,029 - tensorflow - INFO - loss = 0.19325846, step = 491800 (2.294 sec) -2022-05-01 15:48:50,324 - tensorflow - INFO - global_step/sec: 87.1239 -2022-05-01 15:48:50,324 - tensorflow - INFO - loss = 0.05001726, step = 492000 (2.296 sec) -2022-05-01 15:48:52,622 - tensorflow - INFO - global_step/sec: 87.034 -2022-05-01 15:48:52,622 - tensorflow - INFO - loss = 0.08362567, step = 492200 (2.298 sec) -2022-05-01 15:48:54,918 - tensorflow - INFO - global_step/sec: 87.1066 -2022-05-01 15:48:54,918 - tensorflow - INFO - loss = 0.045164224, step = 492400 (2.296 sec) -2022-05-01 15:48:57,213 - tensorflow - INFO - global_step/sec: 87.1404 -2022-05-01 15:48:57,213 - tensorflow - INFO - loss = 0.12752767, step = 492600 (2.295 sec) -2022-05-01 15:48:59,510 - tensorflow - INFO - global_step/sec: 87.0822 -2022-05-01 15:48:59,510 - tensorflow - INFO - loss = 0.060145747, step = 492800 (2.297 sec) -2022-05-01 15:49:01,809 - tensorflow - INFO - global_step/sec: 86.9762 -2022-05-01 15:49:01,810 - tensorflow - INFO - loss = 0.503282, step = 493000 (2.299 sec) -2022-05-01 15:49:04,097 - tensorflow - INFO - global_step/sec: 87.4125 -2022-05-01 15:49:04,098 - tensorflow - INFO - loss = 0.08907508, step = 493200 (2.288 sec) -2022-05-01 15:49:06,400 - tensorflow - INFO - global_step/sec: 86.839 -2022-05-01 15:49:06,401 - tensorflow - INFO - loss = 0.036446743, step = 493400 (2.303 sec) -2022-05-01 15:49:08,695 - tensorflow - INFO - global_step/sec: 87.1736 -2022-05-01 15:49:08,695 - tensorflow - INFO - loss = 3.1655476, step = 493600 (2.294 sec) -2022-05-01 15:49:10,991 - tensorflow - INFO - global_step/sec: 87.0857 -2022-05-01 15:49:10,992 - tensorflow - INFO - loss = 0.02401543, step = 493800 (2.297 sec) -2022-05-01 15:49:13,281 - tensorflow - INFO - global_step/sec: 87.3311 -2022-05-01 15:49:13,282 - tensorflow - INFO - loss = 0.02471529, step = 494000 (2.290 sec) -2022-05-01 15:49:15,573 - tensorflow - INFO - global_step/sec: 87.2857 -2022-05-01 15:49:15,573 - tensorflow - INFO - loss = 0.048128817, step = 494200 (2.291 sec) -2022-05-01 15:49:17,856 - tensorflow - INFO - global_step/sec: 87.5903 -2022-05-01 15:49:17,856 - tensorflow - INFO - loss = 0.3277765, step = 494400 (2.283 sec) -2022-05-01 15:49:20,153 - tensorflow - INFO - global_step/sec: 87.0611 -2022-05-01 15:49:20,154 - tensorflow - INFO - loss = 0.015861625, step = 494600 (2.297 sec) -2022-05-01 15:49:22,448 - tensorflow - INFO - global_step/sec: 87.1354 -2022-05-01 15:49:22,449 - tensorflow - INFO - loss = 0.028060673, step = 494800 (2.295 sec) -2022-05-01 15:49:24,744 - tensorflow - INFO - global_step/sec: 87.1415 -2022-05-01 15:49:24,744 - tensorflow - INFO - loss = 0.03264568, step = 495000 (2.295 sec) -2022-05-01 15:49:27,031 - tensorflow - INFO - global_step/sec: 87.4192 -2022-05-01 15:49:27,032 - tensorflow - INFO - loss = 0.45592192, step = 495200 (2.288 sec) -2022-05-01 15:49:29,313 - tensorflow - INFO - global_step/sec: 87.6582 -2022-05-01 15:49:29,313 - tensorflow - INFO - loss = 0.074402526, step = 495400 (2.282 sec) -2022-05-01 15:49:31,604 - tensorflow - INFO - global_step/sec: 87.2896 -2022-05-01 15:49:31,605 - tensorflow - INFO - loss = 0.34785342, step = 495600 (2.291 sec) -2022-05-01 15:49:33,894 - tensorflow - INFO - global_step/sec: 87.3332 -2022-05-01 15:49:33,895 - tensorflow - INFO - loss = 0.014895788, step = 495800 (2.290 sec) -2022-05-01 15:49:36,192 - tensorflow - INFO - global_step/sec: 87.0249 -2022-05-01 15:49:36,193 - tensorflow - INFO - loss = 0.039041396, step = 496000 (2.298 sec) -2022-05-01 15:49:38,480 - tensorflow - INFO - global_step/sec: 87.4465 -2022-05-01 15:49:38,480 - tensorflow - INFO - loss = 0.023041882, step = 496200 (2.287 sec) -2022-05-01 15:49:40,778 - tensorflow - INFO - global_step/sec: 87.01 -2022-05-01 15:49:40,779 - tensorflow - INFO - loss = 7.3349943, step = 496400 (2.299 sec) -2022-05-01 15:49:43,075 - tensorflow - INFO - global_step/sec: 87.0711 -2022-05-01 15:49:43,076 - tensorflow - INFO - loss = 0.690408, step = 496600 (2.297 sec) -2022-05-01 15:49:45,364 - tensorflow - INFO - global_step/sec: 87.3821 -2022-05-01 15:49:45,364 - tensorflow - INFO - loss = 0.042593334, step = 496800 (2.289 sec) -2022-05-01 15:49:47,666 - tensorflow - INFO - global_step/sec: 86.8956 -2022-05-01 15:49:47,666 - tensorflow - INFO - loss = 0.0668559, step = 497000 (2.302 sec) -2022-05-01 15:49:49,973 - tensorflow - INFO - global_step/sec: 86.6792 -2022-05-01 15:49:49,973 - tensorflow - INFO - loss = 0.011198775, step = 497200 (2.307 sec) -2022-05-01 15:49:52,277 - tensorflow - INFO - global_step/sec: 86.8116 -2022-05-01 15:49:52,277 - tensorflow - INFO - loss = 0.06476675, step = 497400 (2.304 sec) -2022-05-01 15:49:54,581 - tensorflow - INFO - global_step/sec: 86.8063 -2022-05-01 15:49:54,581 - tensorflow - INFO - loss = 0.043987837, step = 497600 (2.304 sec) -2022-05-01 15:49:56,880 - tensorflow - INFO - global_step/sec: 86.9838 -2022-05-01 15:49:56,880 - tensorflow - INFO - loss = 0.04835829, step = 497800 (2.299 sec) -2022-05-01 15:49:59,180 - tensorflow - INFO - global_step/sec: 86.9417 -2022-05-01 15:49:59,181 - tensorflow - INFO - loss = 0.044502974, step = 498000 (2.300 sec) -2022-05-01 15:50:01,478 - tensorflow - INFO - global_step/sec: 87.0622 -2022-05-01 15:50:01,478 - tensorflow - INFO - loss = 0.060919967, step = 498200 (2.297 sec) -2022-05-01 15:50:03,783 - tensorflow - INFO - global_step/sec: 86.7672 -2022-05-01 15:50:03,783 - tensorflow - INFO - loss = 0.12766704, step = 498400 (2.305 sec) -2022-05-01 15:50:06,076 - tensorflow - INFO - global_step/sec: 87.2122 -2022-05-01 15:50:06,076 - tensorflow - INFO - loss = 0.026480768, step = 498600 (2.293 sec) -2022-05-01 15:50:08,366 - tensorflow - INFO - global_step/sec: 87.3191 -2022-05-01 15:50:08,367 - tensorflow - INFO - loss = 1.5395248, step = 498800 (2.290 sec) -2022-05-01 15:50:10,651 - tensorflow - INFO - global_step/sec: 87.5458 -2022-05-01 15:50:10,651 - tensorflow - INFO - loss = 0.05769307, step = 499000 (2.285 sec) -2022-05-01 15:50:12,914 - tensorflow - INFO - global_step/sec: 88.3691 -2022-05-01 15:50:12,915 - tensorflow - INFO - loss = 0.06950209, step = 499200 (2.263 sec) -2022-05-01 15:50:15,180 - tensorflow - INFO - global_step/sec: 88.2645 -2022-05-01 15:50:15,180 - tensorflow - INFO - loss = 0.41808796, step = 499400 (2.266 sec) -2022-05-01 15:50:17,438 - tensorflow - INFO - global_step/sec: 88.5739 -2022-05-01 15:50:17,438 - tensorflow - INFO - loss = 0.16664134, step = 499600 (2.258 sec) -2022-05-01 15:50:19,734 - tensorflow - INFO - global_step/sec: 87.1192 -2022-05-01 15:50:19,734 - tensorflow - INFO - loss = 20.43417, step = 499800 (2.296 sec) -2022-05-01 15:50:22,016 - tensorflow - INFO - Saving checkpoints for 500000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. -2022-05-01 15:50:22,032 - tensorflow - INFO - model.ckpt-500000 is not in all_model_checkpoint_paths. Manually adding it. -2022-05-01 15:50:22,559 - tensorflow - INFO - Calling model_fn. -2022-05-01 15:50:22,692 - tensorflow - INFO - Done calling model_fn. -2022-05-01 15:50:22,707 - tensorflow - INFO - Starting evaluation at 2022-05-01T15:50:22Z -2022-05-01 15:50:22,772 - tensorflow - INFO - Graph was finalized. -2022-05-01 15:50:22,775 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-500000 -2022-05-01 15:50:23,062 - tensorflow - INFO - Running local_init_op. -2022-05-01 15:50:23,174 - tensorflow - INFO - Done running local_init_op. -2022-05-01 15:50:33,530 - tensorflow - INFO - Evaluation [3990/39900] -2022-05-01 15:50:43,155 - tensorflow - INFO - Evaluation [7980/39900] -2022-05-01 15:50:52,766 - tensorflow - INFO - Evaluation [11970/39900] -2022-05-01 15:51:02,371 - tensorflow - INFO - Evaluation [15960/39900] -2022-05-01 15:51:11,960 - tensorflow - INFO - Evaluation [19950/39900] -2022-05-01 15:51:21,578 - tensorflow - INFO - Evaluation [23940/39900] -2022-05-01 15:51:31,204 - tensorflow - INFO - Evaluation [27930/39900] -2022-05-01 15:51:40,848 - tensorflow - INFO - Evaluation [31920/39900] -2022-05-01 15:51:50,412 - tensorflow - INFO - Evaluation [35910/39900] -2022-05-01 15:52:00,044 - tensorflow - INFO - Evaluation [39900/39900] -2022-05-01 15:52:00,185 - tensorflow - INFO - Finished evaluation at 2022-05-01-15:52:00 -2022-05-01 15:52:00,185 - tensorflow - INFO - Saving dict for global step 500000: global_step = 500000, loss = 0.046809282, mean_degree_err = 6.3430543 -2022-05-01 15:52:00,186 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 500000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-500000 -2022-05-01 15:52:00,200 - tensorflow - INFO - global_step/sec: 1.99072 -2022-05-01 15:52:00,201 - tensorflow - INFO - loss = 0.063635156, step = 500000 (100.467 sec) -2022-05-01 15:52:02,510 - tensorflow - INFO - global_step/sec: 86.5758 -2022-05-01 15:52:02,510 - tensorflow - INFO - loss = 0.07470157, step = 500200 (2.310 sec) -2022-05-01 15:52:04,794 - tensorflow - INFO - global_step/sec: 87.5492 -2022-05-01 15:52:04,795 - tensorflow - INFO - loss = 0.31317902, step = 500400 (2.284 sec) -2022-05-01 15:52:07,086 - tensorflow - INFO - global_step/sec: 87.2645 -2022-05-01 15:52:07,087 - tensorflow - INFO - loss = 0.18935311, step = 500600 (2.292 sec) -2022-05-01 15:52:09,376 - tensorflow - INFO - global_step/sec: 87.3425 -2022-05-01 15:52:09,376 - tensorflow - INFO - loss = 0.020323459, step = 500800 (2.290 sec) -2022-05-01 15:52:11,673 - tensorflow - INFO - global_step/sec: 87.067 -2022-05-01 15:52:11,673 - tensorflow - INFO - loss = 0.024707984, step = 501000 (2.297 sec) -2022-05-01 15:52:13,966 - tensorflow - INFO - global_step/sec: 87.224 -2022-05-01 15:52:13,966 - tensorflow - INFO - loss = 0.02308048, step = 501200 (2.293 sec) -2022-05-01 15:52:16,256 - tensorflow - INFO - global_step/sec: 87.3316 -2022-05-01 15:52:16,257 - tensorflow - INFO - loss = 0.084123634, step = 501400 (2.290 sec) -2022-05-01 15:52:18,545 - tensorflow - INFO - global_step/sec: 87.3829 -2022-05-01 15:52:18,545 - tensorflow - INFO - loss = 0.9689673, step = 501600 (2.289 sec) -2022-05-01 15:52:20,830 - tensorflow - INFO - global_step/sec: 87.508 -2022-05-01 15:52:20,831 - tensorflow - INFO - loss = 0.04496465, step = 501800 (2.285 sec) -2022-05-01 15:52:23,122 - tensorflow - INFO - global_step/sec: 87.2854 -2022-05-01 15:52:23,122 - tensorflow - INFO - loss = 0.026626464, step = 502000 (2.291 sec) -2022-05-01 15:52:25,412 - tensorflow - INFO - global_step/sec: 87.3072 -2022-05-01 15:52:25,413 - tensorflow - INFO - loss = 0.086509205, step = 502200 (2.291 sec) -2022-05-01 15:52:27,707 - tensorflow - INFO - global_step/sec: 87.1582 -2022-05-01 15:52:27,708 - tensorflow - INFO - loss = 0.06746422, step = 502400 (2.295 sec) -2022-05-01 15:52:29,998 - tensorflow - INFO - global_step/sec: 87.3076 -2022-05-01 15:52:29,998 - tensorflow - INFO - loss = 0.0978373, step = 502600 (2.291 sec) -2022-05-01 15:52:32,295 - tensorflow - INFO - global_step/sec: 87.078 -2022-05-01 15:52:32,295 - tensorflow - INFO - loss = 0.104280345, step = 502800 (2.297 sec) -2022-05-01 15:52:34,591 - tensorflow - INFO - global_step/sec: 87.1093 -2022-05-01 15:52:34,591 - tensorflow - INFO - loss = 0.091107875, step = 503000 (2.296 sec) -2022-05-01 15:52:36,888 - tensorflow - INFO - global_step/sec: 87.0597 -2022-05-01 15:52:36,888 - tensorflow - INFO - loss = 0.033102345, step = 503200 (2.297 sec) -2022-05-01 15:52:39,175 - tensorflow - INFO - global_step/sec: 87.4474 -2022-05-01 15:52:39,175 - tensorflow - INFO - loss = 0.09425828, step = 503400 (2.287 sec) -2022-05-01 15:52:41,466 - tensorflow - INFO - global_step/sec: 87.2883 -2022-05-01 15:52:41,467 - tensorflow - INFO - loss = 0.061163433, step = 503600 (2.291 sec) -2022-05-01 15:52:43,758 - tensorflow - INFO - global_step/sec: 87.265 -2022-05-01 15:52:43,759 - tensorflow - INFO - loss = 0.13534966, step = 503800 (2.292 sec) -2022-05-01 15:52:46,054 - tensorflow - INFO - global_step/sec: 87.108 -2022-05-01 15:52:46,055 - tensorflow - INFO - loss = 0.06398092, step = 504000 (2.296 sec) -2022-05-01 15:52:48,353 - tensorflow - INFO - global_step/sec: 86.9983 -2022-05-01 15:52:48,354 - tensorflow - INFO - loss = 0.014031668, step = 504200 (2.299 sec) -2022-05-01 15:52:50,640 - tensorflow - INFO - global_step/sec: 87.4405 -2022-05-01 15:52:50,641 - tensorflow - INFO - loss = 0.049638722, step = 504400 (2.287 sec) -2022-05-01 15:52:52,947 - tensorflow - INFO - global_step/sec: 86.7174 -2022-05-01 15:52:52,947 - tensorflow - INFO - loss = 0.03826969, step = 504600 (2.306 sec) -2022-05-01 15:52:55,241 - tensorflow - INFO - global_step/sec: 87.1834 -2022-05-01 15:52:55,241 - tensorflow - INFO - loss = 0.08917885, step = 504800 (2.294 sec) -2022-05-01 15:52:57,543 - tensorflow - INFO - global_step/sec: 86.8621 -2022-05-01 15:52:57,544 - tensorflow - INFO - loss = 0.1652728, step = 505000 (2.303 sec) -2022-05-01 15:52:59,836 - tensorflow - INFO - global_step/sec: 87.2339 -2022-05-01 15:52:59,836 - tensorflow - INFO - loss = 0.17992705, step = 505200 (2.293 sec) -2022-05-01 15:53:02,139 - tensorflow - INFO - global_step/sec: 86.8256 -2022-05-01 15:53:02,140 - tensorflow - INFO - loss = 0.037294824, step = 505400 (2.303 sec) -2022-05-01 15:53:04,432 - tensorflow - INFO - global_step/sec: 87.2285 -2022-05-01 15:53:04,433 - tensorflow - INFO - loss = 0.15038131, step = 505600 (2.293 sec) -2022-05-01 15:53:06,724 - tensorflow - INFO - global_step/sec: 87.28 -2022-05-01 15:53:06,724 - tensorflow - INFO - loss = 0.065310985, step = 505800 (2.292 sec) -2022-05-01 15:53:09,012 - tensorflow - INFO - global_step/sec: 87.396 -2022-05-01 15:53:09,013 - tensorflow - INFO - loss = 0.034203827, step = 506000 (2.288 sec) -2022-05-01 15:53:11,312 - tensorflow - INFO - global_step/sec: 86.96 -2022-05-01 15:53:11,312 - tensorflow - INFO - loss = 0.16935429, step = 506200 (2.300 sec) -2022-05-01 15:53:13,611 - tensorflow - INFO - global_step/sec: 86.985 -2022-05-01 15:53:13,612 - tensorflow - INFO - loss = 0.035027437, step = 506400 (2.299 sec) -2022-05-01 15:53:15,902 - tensorflow - INFO - global_step/sec: 87.3028 -2022-05-01 15:53:15,903 - tensorflow - INFO - loss = 0.057028934, step = 506600 (2.291 sec) -2022-05-01 15:53:18,182 - tensorflow - INFO - global_step/sec: 87.7217 -2022-05-01 15:53:18,183 - tensorflow - INFO - loss = 0.0799098, step = 506800 (2.280 sec) -2022-05-01 15:53:20,471 - tensorflow - INFO - global_step/sec: 87.379 -2022-05-01 15:53:20,471 - tensorflow - INFO - loss = 0.03132668, step = 507000 (2.289 sec) -2022-05-01 15:53:22,772 - tensorflow - INFO - global_step/sec: 86.9129 -2022-05-01 15:53:22,773 - tensorflow - INFO - loss = 0.049004238, step = 507200 (2.301 sec) -2022-05-01 15:53:25,074 - tensorflow - INFO - global_step/sec: 86.9042 -2022-05-01 15:53:25,074 - tensorflow - INFO - loss = 0.05456598, step = 507400 (2.301 sec) -2022-05-01 15:53:27,378 - tensorflow - INFO - global_step/sec: 86.7786 -2022-05-01 15:53:27,379 - tensorflow - INFO - loss = 0.03548259, step = 507600 (2.305 sec) -2022-05-01 15:53:29,677 - tensorflow - INFO - global_step/sec: 86.9937 -2022-05-01 15:53:29,678 - tensorflow - INFO - loss = 0.22562142, step = 507800 (2.299 sec) -2022-05-01 15:53:31,982 - tensorflow - INFO - global_step/sec: 86.7697 -2022-05-01 15:53:31,983 - tensorflow - INFO - loss = 0.2586946, step = 508000 (2.305 sec) -2022-05-01 15:53:34,283 - tensorflow - INFO - global_step/sec: 86.9118 -2022-05-01 15:53:34,284 - tensorflow - INFO - loss = 0.07775393, step = 508200 (2.301 sec) -2022-05-01 15:53:36,583 - tensorflow - INFO - global_step/sec: 86.9646 -2022-05-01 15:53:36,584 - tensorflow - INFO - loss = 0.079266146, step = 508400 (2.300 sec) -2022-05-01 15:53:38,878 - tensorflow - INFO - global_step/sec: 87.1614 -2022-05-01 15:53:38,878 - tensorflow - INFO - loss = 0.06857913, step = 508600 (2.295 sec) -2022-05-01 15:53:41,169 - tensorflow - INFO - global_step/sec: 87.274 -2022-05-01 15:53:41,170 - tensorflow - INFO - loss = 0.028996538, step = 508800 (2.292 sec) -2022-05-01 15:53:43,466 - tensorflow - INFO - global_step/sec: 87.09 -2022-05-01 15:53:43,466 - tensorflow - INFO - loss = 0.03027647, step = 509000 (2.296 sec) -2022-05-01 15:53:45,746 - tensorflow - INFO - global_step/sec: 87.7088 -2022-05-01 15:53:45,746 - tensorflow - INFO - loss = 0.042125713, step = 509200 (2.280 sec) -2022-05-01 15:53:48,034 - tensorflow - INFO - global_step/sec: 87.4106 -2022-05-01 15:53:48,035 - tensorflow - INFO - loss = 0.019495798, step = 509400 (2.288 sec) -2022-05-01 15:53:50,322 - tensorflow - INFO - global_step/sec: 87.4146 -2022-05-01 15:53:50,323 - tensorflow - INFO - loss = 0.070728526, step = 509600 (2.288 sec) -2022-05-01 15:53:52,614 - tensorflow - INFO - global_step/sec: 87.2603 -2022-05-01 15:53:52,615 - tensorflow - INFO - loss = 0.40481895, step = 509800 (2.292 sec) -2022-05-01 15:53:54,910 - tensorflow - INFO - global_step/sec: 87.1076 -2022-05-01 15:53:54,911 - tensorflow - INFO - loss = 0.2444518, step = 510000 (2.296 sec) -2022-05-01 15:53:57,201 - tensorflow - INFO - global_step/sec: 87.3191 -2022-05-01 15:53:57,201 - tensorflow - INFO - loss = 0.16828924, step = 510200 (2.290 sec) -2022-05-01 15:53:59,487 - tensorflow - INFO - global_step/sec: 87.4854 -2022-05-01 15:53:59,487 - tensorflow - INFO - loss = 0.051806744, step = 510400 (2.286 sec) -2022-05-01 15:54:01,770 - tensorflow - INFO - global_step/sec: 87.5859 -2022-05-01 15:54:01,771 - tensorflow - INFO - loss = 0.06130576, step = 510600 (2.284 sec) -2022-05-01 15:54:04,060 - tensorflow - INFO - global_step/sec: 87.3608 -2022-05-01 15:54:04,060 - tensorflow - INFO - loss = 0.086674064, step = 510800 (2.289 sec) -2022-05-01 15:54:06,347 - tensorflow - INFO - global_step/sec: 87.4472 -2022-05-01 15:54:06,347 - tensorflow - INFO - loss = 0.15934047, step = 511000 (2.287 sec) -2022-05-01 15:54:08,632 - tensorflow - INFO - global_step/sec: 87.5184 -2022-05-01 15:54:08,632 - tensorflow - INFO - loss = 0.03121565, step = 511200 (2.285 sec) -2022-05-01 15:54:10,926 - tensorflow - INFO - global_step/sec: 87.1944 -2022-05-01 15:54:10,926 - tensorflow - INFO - loss = 0.103663206, step = 511400 (2.294 sec) -2022-05-01 15:54:13,211 - tensorflow - INFO - global_step/sec: 87.4968 -2022-05-01 15:54:13,212 - tensorflow - INFO - loss = 0.099881515, step = 511600 (2.286 sec) -2022-05-01 15:54:15,497 - tensorflow - INFO - global_step/sec: 87.5185 -2022-05-01 15:54:15,497 - tensorflow - INFO - loss = 0.038970098, step = 511800 (2.285 sec) -2022-05-01 15:54:17,785 - tensorflow - INFO - global_step/sec: 87.4104 -2022-05-01 15:54:17,785 - tensorflow - INFO - loss = 0.029831307, step = 512000 (2.288 sec) -2022-05-01 15:54:20,073 - tensorflow - INFO - global_step/sec: 87.4173 -2022-05-01 15:54:20,073 - tensorflow - INFO - loss = 0.15872079, step = 512200 (2.288 sec) -2022-05-01 15:54:22,358 - tensorflow - INFO - global_step/sec: 87.4893 -2022-05-01 15:54:22,359 - tensorflow - INFO - loss = 0.13896562, step = 512400 (2.286 sec) -2022-05-01 15:54:24,641 - tensorflow - INFO - global_step/sec: 87.633 -2022-05-01 15:54:24,641 - tensorflow - INFO - loss = 0.08064212, step = 512600 (2.282 sec) -2022-05-01 15:54:26,925 - tensorflow - INFO - global_step/sec: 87.5531 -2022-05-01 15:54:26,926 - tensorflow - INFO - loss = 0.045587465, step = 512800 (2.284 sec) -2022-05-01 15:54:29,211 - tensorflow - INFO - global_step/sec: 87.478 -2022-05-01 15:54:29,212 - tensorflow - INFO - loss = 0.02319471, step = 513000 (2.286 sec) -2022-05-01 15:54:31,510 - tensorflow - INFO - global_step/sec: 87.0126 -2022-05-01 15:54:31,510 - tensorflow - INFO - loss = 0.06806821, step = 513200 (2.299 sec) -2022-05-01 15:54:33,808 - tensorflow - INFO - global_step/sec: 87.0461 -2022-05-01 15:54:33,808 - tensorflow - INFO - loss = 0.0633014, step = 513400 (2.298 sec) -2022-05-01 15:54:36,093 - tensorflow - INFO - global_step/sec: 87.4934 -2022-05-01 15:54:36,094 - tensorflow - INFO - loss = 0.06634851, step = 513600 (2.286 sec) -2022-05-01 15:54:38,378 - tensorflow - INFO - global_step/sec: 87.5331 -2022-05-01 15:54:38,379 - tensorflow - INFO - loss = 0.030684706, step = 513800 (2.285 sec) -2022-05-01 15:54:40,657 - tensorflow - INFO - global_step/sec: 87.7827 -2022-05-01 15:54:40,657 - tensorflow - INFO - loss = 0.04401341, step = 514000 (2.278 sec) -2022-05-01 15:54:42,921 - tensorflow - INFO - global_step/sec: 88.306 -2022-05-01 15:54:42,922 - tensorflow - INFO - loss = 0.026993984, step = 514200 (2.265 sec) -2022-05-01 15:54:45,189 - tensorflow - INFO - global_step/sec: 88.218 -2022-05-01 15:54:45,189 - tensorflow - INFO - loss = 0.034656197, step = 514400 (2.267 sec) -2022-05-01 15:54:47,444 - tensorflow - INFO - global_step/sec: 88.6682 -2022-05-01 15:54:47,445 - tensorflow - INFO - loss = 0.020687716, step = 514600 (2.256 sec) -2022-05-01 15:54:49,721 - tensorflow - INFO - global_step/sec: 87.8501 -2022-05-01 15:54:49,721 - tensorflow - INFO - loss = 9.952393, step = 514800 (2.277 sec) -2022-05-01 15:54:52,016 - tensorflow - INFO - global_step/sec: 87.129 -2022-05-01 15:54:52,017 - tensorflow - INFO - loss = 0.080806784, step = 515000 (2.295 sec) -2022-05-01 15:54:54,308 - tensorflow - INFO - global_step/sec: 87.2597 -2022-05-01 15:54:54,309 - tensorflow - INFO - loss = 0.26253784, step = 515200 (2.292 sec) -2022-05-01 15:54:56,602 - tensorflow - INFO - global_step/sec: 87.1934 -2022-05-01 15:54:56,602 - tensorflow - INFO - loss = 0.035267, step = 515400 (2.294 sec) -2022-05-01 15:54:58,896 - tensorflow - INFO - global_step/sec: 87.1762 -2022-05-01 15:54:58,897 - tensorflow - INFO - loss = 0.024436895, step = 515600 (2.294 sec) -2022-05-01 15:55:01,188 - tensorflow - INFO - global_step/sec: 87.2803 -2022-05-01 15:55:01,188 - tensorflow - INFO - loss = 0.08493805, step = 515800 (2.291 sec) -2022-05-01 15:55:03,485 - tensorflow - INFO - global_step/sec: 87.069 -2022-05-01 15:55:03,485 - tensorflow - INFO - loss = 0.022904579, step = 516000 (2.297 sec) -2022-05-01 15:55:05,775 - tensorflow - INFO - global_step/sec: 87.342 -2022-05-01 15:55:05,775 - tensorflow - INFO - loss = 0.06921731, step = 516200 (2.290 sec) -2022-05-01 15:55:08,071 - tensorflow - INFO - global_step/sec: 87.0845 -2022-05-01 15:55:08,072 - tensorflow - INFO - loss = 0.01737911, step = 516400 (2.297 sec) -2022-05-01 15:55:10,364 - tensorflow - INFO - global_step/sec: 87.2378 -2022-05-01 15:55:10,364 - tensorflow - INFO - loss = 0.2211785, step = 516600 (2.293 sec) -2022-05-01 15:55:12,651 - tensorflow - INFO - global_step/sec: 87.4553 -2022-05-01 15:55:12,651 - tensorflow - INFO - loss = 0.041149385, step = 516800 (2.287 sec) -2022-05-01 15:55:14,943 - tensorflow - INFO - global_step/sec: 87.2573 -2022-05-01 15:55:14,943 - tensorflow - INFO - loss = 0.38384295, step = 517000 (2.292 sec) -2022-05-01 15:55:17,234 - tensorflow - INFO - global_step/sec: 87.2799 -2022-05-01 15:55:17,235 - tensorflow - INFO - loss = 0.029199786, step = 517200 (2.291 sec) -2022-05-01 15:55:19,527 - tensorflow - INFO - global_step/sec: 87.2295 -2022-05-01 15:55:19,527 - tensorflow - INFO - loss = 0.052903645, step = 517400 (2.293 sec) -2022-05-01 15:55:21,818 - tensorflow - INFO - global_step/sec: 87.2992 -2022-05-01 15:55:21,818 - tensorflow - INFO - loss = 0.052237667, step = 517600 (2.291 sec) -2022-05-01 15:55:24,114 - tensorflow - INFO - global_step/sec: 87.0902 -2022-05-01 15:55:24,115 - tensorflow - INFO - loss = 0.06584891, step = 517800 (2.296 sec) -2022-05-01 15:55:26,406 - tensorflow - INFO - global_step/sec: 87.2613 -2022-05-01 15:55:26,407 - tensorflow - INFO - loss = 0.06383498, step = 518000 (2.292 sec) -2022-05-01 15:55:28,691 - tensorflow - INFO - global_step/sec: 87.5229 -2022-05-01 15:55:28,692 - tensorflow - INFO - loss = 0.29162747, step = 518200 (2.285 sec) -2022-05-01 15:55:30,992 - tensorflow - INFO - global_step/sec: 86.9451 -2022-05-01 15:55:30,992 - tensorflow - INFO - loss = 0.03455841, step = 518400 (2.300 sec) -2022-05-01 15:55:33,281 - tensorflow - INFO - global_step/sec: 87.3585 -2022-05-01 15:55:33,282 - tensorflow - INFO - loss = 0.053478647, step = 518600 (2.289 sec) -2022-05-01 15:55:35,582 - tensorflow - INFO - global_step/sec: 86.9209 -2022-05-01 15:55:35,583 - tensorflow - INFO - loss = 0.11700239, step = 518800 (2.301 sec) -2022-05-01 15:55:37,877 - tensorflow - INFO - global_step/sec: 87.1446 -2022-05-01 15:55:37,878 - tensorflow - INFO - loss = 0.057507105, step = 519000 (2.295 sec) -2022-05-01 15:55:40,162 - tensorflow - INFO - global_step/sec: 87.5462 -2022-05-01 15:55:40,162 - tensorflow - INFO - loss = 0.10073204, step = 519200 (2.284 sec) -2022-05-01 15:55:42,452 - tensorflow - INFO - global_step/sec: 87.3273 -2022-05-01 15:55:42,452 - tensorflow - INFO - loss = 0.023482446, step = 519400 (2.290 sec) -2022-05-01 15:55:44,733 - tensorflow - INFO - global_step/sec: 87.6978 -2022-05-01 15:55:44,733 - tensorflow - INFO - loss = 0.26619273, step = 519600 (2.281 sec) -2022-05-01 15:55:47,025 - tensorflow - INFO - global_step/sec: 87.2419 -2022-05-01 15:55:47,025 - tensorflow - INFO - loss = 0.98466, step = 519800 (2.292 sec) -2022-05-01 15:55:49,318 - tensorflow - INFO - global_step/sec: 87.2102 -2022-05-01 15:55:49,319 - tensorflow - INFO - loss = 0.020632338, step = 520000 (2.293 sec) -2022-05-01 15:55:51,606 - tensorflow - INFO - global_step/sec: 87.4303 -2022-05-01 15:55:51,606 - 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tensorflow - INFO - loss = 0.027606444, step = 580200 (2.305 sec) -2022-05-01 16:07:21,337 - tensorflow - INFO - global_step/sec: 86.973 -2022-05-01 16:07:21,338 - tensorflow - INFO - loss = 0.08346665, step = 580400 (2.300 sec) -2022-05-01 16:07:23,642 - tensorflow - INFO - global_step/sec: 86.7654 -2022-05-01 16:07:23,643 - tensorflow - INFO - loss = 0.22735195, step = 580600 (2.305 sec) -2022-05-01 16:07:25,943 - tensorflow - INFO - global_step/sec: 86.9335 -2022-05-01 16:07:25,943 - tensorflow - INFO - loss = 0.056020238, step = 580800 (2.301 sec) -2022-05-01 16:07:28,249 - tensorflow - INFO - global_step/sec: 86.7091 -2022-05-01 16:07:28,250 - tensorflow - INFO - loss = 0.044762574, step = 581000 (2.307 sec) -2022-05-01 16:07:30,554 - tensorflow - INFO - global_step/sec: 86.7724 -2022-05-01 16:07:30,555 - tensorflow - INFO - loss = 0.15219203, step = 581200 (2.305 sec) -2022-05-01 16:07:32,863 - tensorflow - INFO - global_step/sec: 86.6233 -2022-05-01 16:07:32,864 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 87.4129 -2022-05-01 16:07:48,933 - tensorflow - INFO - loss = 0.03210587, step = 582800 (2.288 sec) -2022-05-01 16:07:51,219 - tensorflow - INFO - global_step/sec: 87.5012 -2022-05-01 16:07:51,219 - tensorflow - INFO - loss = 0.025355924, step = 583000 (2.286 sec) -2022-05-01 16:07:53,513 - tensorflow - INFO - global_step/sec: 87.1829 -2022-05-01 16:07:53,513 - tensorflow - INFO - loss = 0.024808213, step = 583200 (2.294 sec) -2022-05-01 16:07:55,804 - tensorflow - INFO - global_step/sec: 87.3027 -2022-05-01 16:07:55,804 - tensorflow - INFO - loss = 0.04379979, step = 583400 (2.291 sec) -2022-05-01 16:07:58,099 - tensorflow - INFO - global_step/sec: 87.1296 -2022-05-01 16:07:58,099 - tensorflow - INFO - loss = 0.026920808, step = 583600 (2.295 sec) -2022-05-01 16:08:00,391 - tensorflow - INFO - global_step/sec: 87.2648 -2022-05-01 16:08:00,391 - tensorflow - INFO - loss = 0.09960953, step = 583800 (2.292 sec) -2022-05-01 16:08:02,683 - tensorflow - INFO - global_step/sec: 87.2438 -2022-05-01 16:08:02,684 - tensorflow - INFO - loss = 12.821983, step = 584000 (2.293 sec) -2022-05-01 16:08:04,973 - tensorflow - INFO - global_step/sec: 87.3472 -2022-05-01 16:08:04,973 - tensorflow - INFO - loss = 0.018572694, step = 584200 (2.290 sec) -2022-05-01 16:08:07,267 - tensorflow - INFO - global_step/sec: 87.196 -2022-05-01 16:08:07,267 - tensorflow - INFO - loss = 0.2426543, step = 584400 (2.294 sec) -2022-05-01 16:08:09,552 - tensorflow - INFO - global_step/sec: 87.5324 -2022-05-01 16:08:09,552 - tensorflow - INFO - loss = 0.012297162, step = 584600 (2.285 sec) -2022-05-01 16:08:11,842 - tensorflow - INFO - global_step/sec: 87.3172 -2022-05-01 16:08:11,843 - tensorflow - INFO - loss = 0.02709882, step = 584800 (2.291 sec) -2022-05-01 16:08:14,135 - tensorflow - INFO - global_step/sec: 87.2379 -2022-05-01 16:08:14,135 - tensorflow - INFO - loss = 0.026977034, step = 585000 (2.292 sec) -2022-05-01 16:08:16,428 - tensorflow - INFO - global_step/sec: 87.2062 -2022-05-01 16:08:16,428 - tensorflow - INFO - loss = 0.06308341, step = 585200 (2.293 sec) -2022-05-01 16:08:18,716 - tensorflow - INFO - global_step/sec: 87.4137 -2022-05-01 16:08:18,716 - tensorflow - INFO - loss = 0.036703456, step = 585400 (2.288 sec) -2022-05-01 16:08:21,007 - tensorflow - INFO - global_step/sec: 87.3122 -2022-05-01 16:08:21,007 - tensorflow - INFO - loss = 0.017322699, step = 585600 (2.291 sec) -2022-05-01 16:08:23,302 - tensorflow - INFO - global_step/sec: 87.1363 -2022-05-01 16:08:23,302 - tensorflow - INFO - loss = 0.20249094, step = 585800 (2.295 sec) -2022-05-01 16:08:25,597 - tensorflow - INFO - global_step/sec: 87.123 -2022-05-01 16:08:25,598 - tensorflow - INFO - loss = 0.0323515, step = 586000 (2.296 sec) -2022-05-01 16:08:27,885 - tensorflow - INFO - global_step/sec: 87.4358 -2022-05-01 16:08:27,885 - tensorflow - INFO - loss = 0.044530425, step = 586200 (2.287 sec) -2022-05-01 16:08:30,182 - tensorflow - INFO - global_step/sec: 87.0825 -2022-05-01 16:08:30,182 - tensorflow - INFO - loss = 1.0501606, step = 586400 (2.297 sec) -2022-05-01 16:08:32,463 - tensorflow - INFO - global_step/sec: 87.6476 -2022-05-01 16:08:32,464 - tensorflow - INFO - loss = 0.03570166, step = 586600 (2.282 sec) -2022-05-01 16:08:34,734 - tensorflow - INFO - global_step/sec: 88.0702 -2022-05-01 16:08:34,735 - tensorflow - INFO - loss = 0.024391422, step = 586800 (2.271 sec) -2022-05-01 16:08:37,011 - tensorflow - INFO - global_step/sec: 87.8485 -2022-05-01 16:08:37,011 - tensorflow - INFO - loss = 0.020901958, step = 587000 (2.277 sec) -2022-05-01 16:08:39,281 - tensorflow - INFO - global_step/sec: 88.0869 -2022-05-01 16:08:39,282 - tensorflow - INFO - loss = 11.28486, step = 587200 (2.270 sec) -2022-05-01 16:08:41,552 - tensorflow - INFO - global_step/sec: 88.0718 -2022-05-01 16:08:41,553 - tensorflow - INFO - loss = 0.07651273, step = 587400 (2.271 sec) -2022-05-01 16:08:43,817 - tensorflow - INFO - global_step/sec: 88.3173 -2022-05-01 16:08:43,817 - tensorflow - INFO - loss = 0.048306994, step = 587600 (2.265 sec) -2022-05-01 16:08:46,088 - tensorflow - INFO - global_step/sec: 88.0657 -2022-05-01 16:08:46,088 - tensorflow - INFO - loss = 0.042515934, step = 587800 (2.271 sec) -2022-05-01 16:08:48,366 - tensorflow - INFO - global_step/sec: 87.7988 -2022-05-01 16:08:48,366 - tensorflow - INFO - loss = 0.31607643, step = 588000 (2.278 sec) -2022-05-01 16:08:50,632 - tensorflow - INFO - global_step/sec: 88.2473 -2022-05-01 16:08:50,633 - tensorflow - INFO - loss = 0.051129453, step = 588200 (2.266 sec) -2022-05-01 16:08:52,891 - tensorflow - INFO - global_step/sec: 88.5609 -2022-05-01 16:08:52,891 - tensorflow - INFO - loss = 0.060575508, step = 588400 (2.258 sec) -2022-05-01 16:08:55,143 - tensorflow - INFO - global_step/sec: 88.7751 -2022-05-01 16:08:55,144 - tensorflow - INFO - loss = 0.06876105, step = 588600 (2.253 sec) -2022-05-01 16:08:57,400 - tensorflow - INFO - global_step/sec: 88.6417 -2022-05-01 16:08:57,400 - tensorflow - INFO - loss = 0.01773398, step = 588800 (2.256 sec) -2022-05-01 16:08:59,655 - tensorflow - INFO - global_step/sec: 88.6811 -2022-05-01 16:08:59,655 - tensorflow - INFO - loss = 0.30537874, step = 589000 (2.255 sec) -2022-05-01 16:09:01,915 - tensorflow - INFO - global_step/sec: 88.4917 -2022-05-01 16:09:01,915 - tensorflow - INFO - loss = 0.19603918, step = 589200 (2.260 sec) -2022-05-01 16:09:04,165 - tensorflow - INFO - global_step/sec: 88.899 -2022-05-01 16:09:04,165 - tensorflow - INFO - loss = 0.103383504, step = 589400 (2.250 sec) -2022-05-01 16:09:06,418 - tensorflow - INFO - global_step/sec: 88.7487 -2022-05-01 16:09:06,419 - tensorflow - INFO - loss = 0.0767152, step = 589600 (2.254 sec) -2022-05-01 16:09:08,671 - tensorflow - INFO - global_step/sec: 88.7892 -2022-05-01 16:09:08,671 - tensorflow - INFO - loss = 12.67634, step = 589800 (2.253 sec) -2022-05-01 16:09:10,925 - tensorflow - INFO - global_step/sec: 88.7123 -2022-05-01 16:09:10,926 - tensorflow - INFO - loss = 0.1362737, step = 590000 (2.254 sec) -2022-05-01 16:09:13,183 - tensorflow - INFO - global_step/sec: 88.586 -2022-05-01 16:09:13,183 - tensorflow - INFO - loss = 0.059808686, step = 590200 (2.258 sec) -2022-05-01 16:09:15,441 - tensorflow - INFO - global_step/sec: 88.5846 -2022-05-01 16:09:15,441 - tensorflow - INFO - loss = 0.029569477, step = 590400 (2.258 sec) -2022-05-01 16:09:17,690 - tensorflow - INFO - global_step/sec: 88.9035 -2022-05-01 16:09:17,691 - tensorflow - INFO - loss = 0.04373379, step = 590600 (2.250 sec) -2022-05-01 16:09:19,940 - tensorflow - INFO - global_step/sec: 88.9028 -2022-05-01 16:09:19,940 - tensorflow - INFO - loss = 0.04323511, step = 590800 (2.250 sec) -2022-05-01 16:09:22,192 - tensorflow - INFO - global_step/sec: 88.8029 -2022-05-01 16:09:22,193 - tensorflow - INFO - loss = 0.039181337, step = 591000 (2.252 sec) -2022-05-01 16:09:24,451 - tensorflow - INFO - global_step/sec: 88.5384 -2022-05-01 16:09:24,452 - tensorflow - INFO - loss = 0.016992427, step = 591200 (2.259 sec) -2022-05-01 16:09:26,700 - tensorflow - INFO - global_step/sec: 88.9199 -2022-05-01 16:09:26,701 - tensorflow - INFO - loss = 0.012185383, step = 591400 (2.249 sec) -2022-05-01 16:09:28,952 - tensorflow - INFO - global_step/sec: 88.8107 -2022-05-01 16:09:28,953 - tensorflow - INFO - loss = 0.009116696, step = 591600 (2.252 sec) -2022-05-01 16:09:31,214 - tensorflow - INFO - global_step/sec: 88.4188 -2022-05-01 16:09:31,215 - tensorflow - INFO - loss = 0.017143156, step = 591800 (2.262 sec) -2022-05-01 16:09:33,469 - tensorflow - INFO - global_step/sec: 88.7175 -2022-05-01 16:09:33,469 - tensorflow - INFO - loss = 0.014566356, step = 592000 (2.254 sec) -2022-05-01 16:09:35,720 - tensorflow - INFO - global_step/sec: 88.8272 -2022-05-01 16:09:35,721 - tensorflow - INFO - loss = 0.13501108, step = 592200 (2.252 sec) -2022-05-01 16:09:37,975 - tensorflow - INFO - global_step/sec: 88.7008 -2022-05-01 16:09:37,975 - tensorflow - INFO - loss = 0.026087768, step = 592400 (2.255 sec) -2022-05-01 16:09:40,226 - tensorflow - INFO - global_step/sec: 88.8584 -2022-05-01 16:09:40,226 - tensorflow - INFO - loss = 0.03718528, step = 592600 (2.251 sec) -2022-05-01 16:09:42,472 - tensorflow - INFO - global_step/sec: 89.0448 -2022-05-01 16:09:42,472 - tensorflow - INFO - loss = 0.07650729, step = 592800 (2.246 sec) -2022-05-01 16:09:44,722 - tensorflow - INFO - global_step/sec: 88.8853 -2022-05-01 16:09:44,722 - tensorflow - INFO - loss = 3.1181715, step = 593000 (2.250 sec) -2022-05-01 16:09:46,975 - tensorflow - INFO - global_step/sec: 88.7733 -2022-05-01 16:09:46,975 - tensorflow - INFO - loss = 0.051136553, step = 593200 (2.253 sec) -2022-05-01 16:09:49,235 - tensorflow - INFO - global_step/sec: 88.4955 -2022-05-01 16:09:49,235 - tensorflow - INFO - loss = 0.32656357, step = 593400 (2.260 sec) -2022-05-01 16:09:51,488 - tensorflow - INFO - global_step/sec: 88.7762 -2022-05-01 16:09:51,488 - tensorflow - INFO - loss = 0.051417626, step = 593600 (2.253 sec) -2022-05-01 16:09:53,744 - tensorflow - INFO - global_step/sec: 88.6572 -2022-05-01 16:09:53,744 - tensorflow - INFO - loss = 0.0070130154, step = 593800 (2.256 sec) -2022-05-01 16:09:55,998 - tensorflow - INFO - global_step/sec: 88.7031 -2022-05-01 16:09:55,999 - tensorflow - INFO - loss = 0.012833834, step = 594000 (2.255 sec) -2022-05-01 16:09:58,251 - tensorflow - INFO - global_step/sec: 88.7779 -2022-05-01 16:09:58,252 - tensorflow - INFO - loss = 0.014024916, step = 594200 (2.253 sec) -2022-05-01 16:10:00,502 - tensorflow - INFO - global_step/sec: 88.8557 -2022-05-01 16:10:00,502 - tensorflow - INFO - loss = 0.07648599, step = 594400 (2.251 sec) -2022-05-01 16:10:02,763 - tensorflow - INFO - global_step/sec: 88.4516 -2022-05-01 16:10:02,764 - tensorflow - INFO - loss = 0.08582879, step = 594600 (2.261 sec) -2022-05-01 16:10:05,018 - tensorflow - INFO - global_step/sec: 88.6866 -2022-05-01 16:10:05,019 - tensorflow - INFO - loss = 0.04442747, step = 594800 (2.255 sec) -2022-05-01 16:10:07,268 - tensorflow - INFO - global_step/sec: 88.9089 -2022-05-01 16:10:07,268 - tensorflow - INFO - loss = 0.6102391, step = 595000 (2.249 sec) -2022-05-01 16:10:09,519 - tensorflow - INFO - global_step/sec: 88.8406 -2022-05-01 16:10:09,519 - tensorflow - INFO - loss = 0.00982367, step = 595200 (2.251 sec) -2022-05-01 16:10:11,772 - tensorflow - INFO - global_step/sec: 88.7721 -2022-05-01 16:10:11,772 - tensorflow - INFO - loss = 0.03102067, step = 595400 (2.253 sec) -2022-05-01 16:10:14,023 - tensorflow - INFO - global_step/sec: 88.8381 -2022-05-01 16:10:14,024 - tensorflow - INFO - loss = 0.020223914, step = 595600 (2.251 sec) -2022-05-01 16:10:16,277 - tensorflow - INFO - global_step/sec: 88.7384 -2022-05-01 16:10:16,277 - tensorflow - INFO - loss = 0.021211697, step = 595800 (2.254 sec) -2022-05-01 16:10:18,532 - tensorflow - INFO - global_step/sec: 88.6797 -2022-05-01 16:10:18,533 - tensorflow - INFO - loss = 0.3812176, step = 596000 (2.255 sec) -2022-05-01 16:10:20,787 - tensorflow - INFO - global_step/sec: 88.6834 -2022-05-01 16:10:20,788 - tensorflow - INFO - loss = 0.28526512, step = 596200 (2.255 sec) -2022-05-01 16:10:23,039 - tensorflow - INFO - global_step/sec: 88.8428 -2022-05-01 16:10:23,039 - tensorflow - INFO - loss = 0.016735926, step = 596400 (2.251 sec) -2022-05-01 16:10:25,288 - tensorflow - INFO - global_step/sec: 88.9144 -2022-05-01 16:10:25,288 - tensorflow - INFO - loss = 0.082976624, step = 596600 (2.249 sec) -2022-05-01 16:10:27,547 - tensorflow - INFO - global_step/sec: 88.5296 -2022-05-01 16:10:27,548 - tensorflow - INFO - loss = 0.045340955, step = 596800 (2.259 sec) -2022-05-01 16:10:29,798 - tensorflow - INFO - global_step/sec: 88.8455 -2022-05-01 16:10:29,799 - tensorflow - INFO - loss = 0.05019103, step = 597000 (2.251 sec) -2022-05-01 16:10:32,056 - tensorflow - INFO - global_step/sec: 88.5854 -2022-05-01 16:10:32,056 - tensorflow - INFO - loss = 0.2732757, step = 597200 (2.258 sec) -2022-05-01 16:10:34,310 - tensorflow - INFO - global_step/sec: 88.7338 -2022-05-01 16:10:34,310 - tensorflow - INFO - loss = 0.044302374, step = 597400 (2.254 sec) -2022-05-01 16:10:36,559 - tensorflow - INFO - global_step/sec: 88.9157 -2022-05-01 16:10:36,560 - tensorflow - INFO - loss = 0.033330336, step = 597600 (2.249 sec) -2022-05-01 16:10:38,815 - tensorflow - INFO - global_step/sec: 88.6414 -2022-05-01 16:10:38,816 - tensorflow - INFO - loss = 0.071330816, step = 597800 (2.256 sec) -2022-05-01 16:10:41,071 - tensorflow - INFO - global_step/sec: 88.6738 -2022-05-01 16:10:41,071 - tensorflow - INFO - loss = 0.03155005, step = 598000 (2.255 sec) -2022-05-01 16:10:43,331 - tensorflow - INFO - global_step/sec: 88.4949 -2022-05-01 16:10:43,331 - tensorflow - INFO - loss = 0.036724314, step = 598200 (2.260 sec) -2022-05-01 16:10:45,590 - tensorflow - INFO - global_step/sec: 88.5148 -2022-05-01 16:10:45,591 - tensorflow - INFO - loss = 0.058887675, step = 598400 (2.260 sec) -2022-05-01 16:10:47,850 - tensorflow - INFO - global_step/sec: 88.5321 -2022-05-01 16:10:47,850 - tensorflow - INFO - loss = 0.04405027, step = 598600 (2.259 sec) -2022-05-01 16:10:50,113 - tensorflow - INFO - global_step/sec: 88.3546 -2022-05-01 16:10:50,114 - tensorflow - INFO - loss = 0.026605247, step = 598800 (2.264 sec) -2022-05-01 16:10:52,375 - tensorflow - INFO - global_step/sec: 88.4125 -2022-05-01 16:10:52,376 - tensorflow - INFO - loss = 0.09087759, step = 599000 (2.262 sec) -2022-05-01 16:10:54,638 - tensorflow - INFO - global_step/sec: 88.3708 -2022-05-01 16:10:54,639 - tensorflow - INFO - loss = 0.050264105, step = 599200 (2.263 sec) -2022-05-01 16:10:56,895 - tensorflow - INFO - global_step/sec: 88.6399 -2022-05-01 16:10:56,895 - tensorflow - INFO - loss = 0.5043671, step = 599400 (2.256 sec) -2022-05-01 16:10:59,157 - tensorflow - INFO - global_step/sec: 88.41 -2022-05-01 16:10:59,157 - tensorflow - INFO - loss = 0.057621866, step = 599600 (2.262 sec) -2022-05-01 16:11:01,418 - tensorflow - INFO - global_step/sec: 88.4709 -2022-05-01 16:11:01,418 - tensorflow - INFO - loss = 0.04510246, step = 599800 (2.261 sec) -2022-05-01 16:11:03,669 - tensorflow - INFO - Saving checkpoints for 600000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. -2022-05-01 16:11:03,687 - tensorflow - INFO - model.ckpt-600000 is not in all_model_checkpoint_paths. Manually adding it. -2022-05-01 16:11:04,248 - tensorflow - INFO - Calling model_fn. -2022-05-01 16:11:04,379 - tensorflow - INFO - Done calling model_fn. -2022-05-01 16:11:04,394 - tensorflow - INFO - Starting evaluation at 2022-05-01T16:11:04Z -2022-05-01 16:11:04,458 - tensorflow - INFO - Graph was finalized. -2022-05-01 16:11:04,461 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-600000 -2022-05-01 16:11:04,742 - tensorflow - INFO - Running local_init_op. -2022-05-01 16:11:04,850 - tensorflow - INFO - Done running local_init_op. -2022-05-01 16:11:14,952 - tensorflow - INFO - Evaluation [3990/39900] -2022-05-01 16:11:24,367 - tensorflow - INFO - Evaluation [7980/39900] -2022-05-01 16:11:33,791 - tensorflow - INFO - Evaluation [11970/39900] -2022-05-01 16:11:43,194 - tensorflow - INFO - Evaluation [15960/39900] -2022-05-01 16:11:52,560 - tensorflow - INFO - Evaluation [19950/39900] -2022-05-01 16:12:01,964 - tensorflow - INFO - Evaluation [23940/39900] -2022-05-01 16:12:11,336 - tensorflow - INFO - Evaluation [27930/39900] -2022-05-01 16:12:20,741 - tensorflow - INFO - Evaluation [31920/39900] -2022-05-01 16:12:30,115 - tensorflow - INFO - Evaluation [35910/39900] -2022-05-01 16:12:39,506 - tensorflow - INFO - Evaluation [39900/39900] -2022-05-01 16:12:39,644 - tensorflow - INFO - Finished evaluation at 2022-05-01-16:12:39 -2022-05-01 16:12:39,644 - tensorflow - INFO - Saving dict for global step 600000: global_step = 600000, loss = 0.05211654, mean_degree_err = 7.7307415 -2022-05-01 16:12:39,645 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 600000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-600000 -2022-05-01 16:12:39,658 - tensorflow - INFO - global_step/sec: 2.03582 -2022-05-01 16:12:39,659 - tensorflow - INFO - loss = 1.0458663, step = 600000 (98.241 sec) -2022-05-01 16:12:41,927 - tensorflow - INFO - global_step/sec: 88.1445 -2022-05-01 16:12:41,928 - tensorflow - INFO - loss = 0.025115207, step = 600200 (2.269 sec) -2022-05-01 16:12:44,180 - tensorflow - INFO - global_step/sec: 88.7807 -2022-05-01 16:12:44,180 - tensorflow - INFO - loss = 0.103602424, step = 600400 (2.253 sec) -2022-05-01 16:12:46,441 - tensorflow - INFO - global_step/sec: 88.4632 -2022-05-01 16:12:46,441 - tensorflow - INFO - loss = 0.026233017, step = 600600 (2.261 sec) -2022-05-01 16:12:48,692 - tensorflow - INFO - global_step/sec: 88.8496 -2022-05-01 16:12:48,692 - tensorflow - INFO - loss = 0.9358847, step = 600800 (2.251 sec) -2022-05-01 16:12:50,948 - tensorflow - INFO - global_step/sec: 88.6401 -2022-05-01 16:12:50,949 - tensorflow - INFO - loss = 0.03394095, step = 601000 (2.256 sec) -2022-05-01 16:12:53,204 - tensorflow - INFO - global_step/sec: 88.6696 -2022-05-01 16:12:53,204 - tensorflow - INFO - loss = 0.020482913, step = 601200 (2.256 sec) -2022-05-01 16:12:55,460 - tensorflow - INFO - global_step/sec: 88.6249 -2022-05-01 16:12:55,461 - tensorflow - INFO - loss = 1.1575468, step = 601400 (2.257 sec) -2022-05-01 16:12:57,713 - tensorflow - INFO - global_step/sec: 88.7857 -2022-05-01 16:12:57,714 - tensorflow - INFO - loss = 0.05647299, step = 601600 (2.253 sec) -2022-05-01 16:12:59,964 - tensorflow - INFO - global_step/sec: 88.8566 -2022-05-01 16:12:59,964 - tensorflow - INFO - loss = 0.7979704, step = 601800 (2.251 sec) -2022-05-01 16:13:02,221 - tensorflow - INFO - global_step/sec: 88.6268 -2022-05-01 16:13:02,221 - tensorflow - INFO - loss = 0.042243555, step = 602000 (2.257 sec) -2022-05-01 16:13:04,476 - tensorflow - INFO - global_step/sec: 88.6789 -2022-05-01 16:13:04,476 - tensorflow - INFO - loss = 0.059275866, step = 602200 (2.255 sec) -2022-05-01 16:13:06,732 - tensorflow - INFO - global_step/sec: 88.6556 -2022-05-01 16:13:06,732 - tensorflow - INFO - loss = 0.6342204, step = 602400 (2.256 sec) -2022-05-01 16:13:08,989 - tensorflow - INFO - global_step/sec: 88.624 -2022-05-01 16:13:08,989 - tensorflow - INFO - loss = 0.03493088, step = 602600 (2.257 sec) -2022-05-01 16:13:11,246 - tensorflow - INFO - global_step/sec: 88.6143 -2022-05-01 16:13:11,246 - tensorflow - INFO - loss = 0.36002302, step = 602800 (2.257 sec) -2022-05-01 16:13:13,497 - tensorflow - INFO - global_step/sec: 88.818 -2022-05-01 16:13:13,498 - tensorflow - INFO - loss = 0.040594425, step = 603000 (2.252 sec) -2022-05-01 16:13:15,754 - tensorflow - INFO - global_step/sec: 88.6284 -2022-05-01 16:13:15,754 - tensorflow - INFO - loss = 0.10270108, step = 603200 (2.257 sec) -2022-05-01 16:13:18,007 - tensorflow - INFO - global_step/sec: 88.7484 -2022-05-01 16:13:18,008 - tensorflow - INFO - loss = 0.016271198, step = 603400 (2.254 sec) -2022-05-01 16:13:20,256 - tensorflow - INFO - global_step/sec: 88.9565 -2022-05-01 16:13:20,256 - tensorflow - INFO - loss = 0.071318746, step = 603600 (2.248 sec) -2022-05-01 16:13:22,507 - tensorflow - INFO - global_step/sec: 88.8344 -2022-05-01 16:13:22,508 - tensorflow - INFO - loss = 0.018505175, step = 603800 (2.251 sec) -2022-05-01 16:13:24,760 - tensorflow - INFO - global_step/sec: 88.7743 -2022-05-01 16:13:24,760 - tensorflow - INFO - loss = 0.015764019, step = 604000 (2.253 sec) -2022-05-01 16:13:27,018 - tensorflow - INFO - global_step/sec: 88.5653 -2022-05-01 16:13:27,019 - tensorflow - INFO - loss = 0.38297236, step = 604200 (2.258 sec) -2022-05-01 16:13:29,280 - tensorflow - INFO - global_step/sec: 88.4164 -2022-05-01 16:13:29,281 - tensorflow - INFO - loss = 0.022897664, step = 604400 (2.262 sec) -2022-05-01 16:13:31,540 - tensorflow - INFO - global_step/sec: 88.5055 -2022-05-01 16:13:31,541 - tensorflow - INFO - loss = 0.036261506, step = 604600 (2.260 sec) -2022-05-01 16:13:33,789 - tensorflow - INFO - global_step/sec: 88.9331 -2022-05-01 16:13:33,789 - tensorflow - INFO - loss = 0.026952295, step = 604800 (2.249 sec) -2022-05-01 16:13:36,043 - tensorflow - INFO - global_step/sec: 88.7405 -2022-05-01 16:13:36,043 - tensorflow - INFO - loss = 0.017694829, step = 605000 (2.254 sec) -2022-05-01 16:13:38,296 - tensorflow - INFO - global_step/sec: 88.7579 -2022-05-01 16:13:38,296 - tensorflow - INFO - loss = 0.017842809, step = 605200 (2.253 sec) -2022-05-01 16:13:40,550 - tensorflow - INFO - global_step/sec: 88.7298 -2022-05-01 16:13:40,550 - tensorflow - INFO - loss = 0.013539692, step = 605400 (2.254 sec) -2022-05-01 16:13:42,802 - tensorflow - INFO - global_step/sec: 88.7996 -2022-05-01 16:13:42,803 - tensorflow - INFO - loss = 1.015567, step = 605600 (2.252 sec) -2022-05-01 16:13:45,055 - tensorflow - INFO - global_step/sec: 88.78 -2022-05-01 16:13:45,055 - tensorflow - INFO - loss = 0.039732587, step = 605800 (2.253 sec) -2022-05-01 16:13:47,308 - tensorflow - INFO - global_step/sec: 88.7719 -2022-05-01 16:13:47,308 - tensorflow - INFO - loss = 0.052872486, step = 606000 (2.253 sec) -2022-05-01 16:13:49,564 - tensorflow - INFO - global_step/sec: 88.6697 -2022-05-01 16:13:49,564 - tensorflow - INFO - loss = 0.01597492, step = 606200 (2.256 sec) -2022-05-01 16:13:51,816 - tensorflow - INFO - global_step/sec: 88.781 -2022-05-01 16:13:51,817 - tensorflow - INFO - loss = 0.11118606, step = 606400 (2.253 sec) -2022-05-01 16:13:54,069 - tensorflow - INFO - global_step/sec: 88.7908 -2022-05-01 16:13:54,069 - tensorflow - INFO - loss = 8.458463, step = 606600 (2.253 sec) -2022-05-01 16:13:56,324 - tensorflow - INFO - global_step/sec: 88.6696 -2022-05-01 16:13:56,325 - tensorflow - INFO - loss = 0.6168818, step = 606800 (2.256 sec) -2022-05-01 16:13:58,577 - tensorflow - INFO - global_step/sec: 88.7971 -2022-05-01 16:13:58,577 - tensorflow - INFO - loss = 0.016155917, step = 607000 (2.252 sec) -2022-05-01 16:14:00,830 - tensorflow - INFO - global_step/sec: 88.7463 -2022-05-01 16:14:00,831 - tensorflow - INFO - loss = 0.025292601, step = 607200 (2.254 sec) -2022-05-01 16:14:03,089 - tensorflow - INFO - global_step/sec: 88.5512 -2022-05-01 16:14:03,089 - tensorflow - INFO - loss = 2.134423, step = 607400 (2.259 sec) -2022-05-01 16:14:05,340 - tensorflow - INFO - global_step/sec: 88.8408 -2022-05-01 16:14:05,341 - tensorflow - INFO - loss = 0.20809147, step = 607600 (2.251 sec) -2022-05-01 16:14:07,601 - tensorflow - INFO - global_step/sec: 88.47 -2022-05-01 16:14:07,601 - tensorflow - INFO - loss = 0.05131338, step = 607800 (2.261 sec) -2022-05-01 16:14:09,855 - tensorflow - INFO - global_step/sec: 88.7402 -2022-05-01 16:14:09,855 - tensorflow - INFO - loss = 0.039553158, step = 608000 (2.254 sec) -2022-05-01 16:14:12,113 - tensorflow - INFO - global_step/sec: 88.5686 -2022-05-01 16:14:12,113 - tensorflow - INFO - loss = 0.67503667, step = 608200 (2.258 sec) -2022-05-01 16:14:14,372 - tensorflow - INFO - global_step/sec: 88.5175 -2022-05-01 16:14:14,373 - tensorflow - INFO - loss = 1.8657655, step = 608400 (2.259 sec) -2022-05-01 16:14:16,635 - tensorflow - INFO - global_step/sec: 88.3683 -2022-05-01 16:14:16,636 - tensorflow - INFO - loss = 0.102950454, step = 608600 (2.263 sec) -2022-05-01 16:14:18,902 - tensorflow - INFO - global_step/sec: 88.255 -2022-05-01 16:14:18,902 - tensorflow - INFO - loss = 0.03498585, step = 608800 (2.266 sec) -2022-05-01 16:14:21,170 - tensorflow - INFO - global_step/sec: 88.1685 -2022-05-01 16:14:21,170 - tensorflow - INFO - loss = 2.6146653, step = 609000 (2.268 sec) -2022-05-01 16:14:23,433 - tensorflow - INFO - global_step/sec: 88.3812 -2022-05-01 16:14:23,433 - tensorflow - INFO - loss = 0.05584207, step = 609200 (2.263 sec) -2022-05-01 16:14:25,698 - tensorflow - INFO - global_step/sec: 88.2988 -2022-05-01 16:14:25,698 - tensorflow - INFO - loss = 0.061443545, step = 609400 (2.265 sec) -2022-05-01 16:14:27,960 - tensorflow - INFO - global_step/sec: 88.4167 -2022-05-01 16:14:27,960 - tensorflow - INFO - loss = 0.24088061, step = 609600 (2.262 sec) -2022-05-01 16:14:30,229 - tensorflow - INFO - global_step/sec: 88.1576 -2022-05-01 16:14:30,229 - tensorflow - INFO - loss = 0.03882327, step = 609800 (2.269 sec) -2022-05-01 16:14:32,502 - tensorflow - INFO - global_step/sec: 87.9705 -2022-05-01 16:14:32,502 - tensorflow - INFO - loss = 0.0358029, step = 610000 (2.273 sec) -2022-05-01 16:14:34,767 - tensorflow - INFO - global_step/sec: 88.2974 -2022-05-01 16:14:34,768 - tensorflow - INFO - loss = 0.02986932, step = 610200 (2.265 sec) -2022-05-01 16:14:37,032 - tensorflow - INFO - global_step/sec: 88.2992 -2022-05-01 16:14:37,033 - tensorflow - INFO - loss = 0.04424672, step = 610400 (2.265 sec) -2022-05-01 16:14:39,288 - tensorflow - INFO - global_step/sec: 88.6686 -2022-05-01 16:14:39,288 - tensorflow - INFO - loss = 0.0313861, step = 610600 (2.256 sec) -2022-05-01 16:14:41,541 - tensorflow - INFO - global_step/sec: 88.7708 -2022-05-01 16:14:41,541 - tensorflow - INFO - loss = 0.025802461, step = 610800 (2.253 sec) -2022-05-01 16:14:43,795 - tensorflow - INFO - global_step/sec: 88.7102 -2022-05-01 16:14:43,796 - tensorflow - INFO - loss = 0.050362512, step = 611000 (2.254 sec) -2022-05-01 16:14:46,052 - tensorflow - INFO - global_step/sec: 88.6139 -2022-05-01 16:14:46,053 - tensorflow - INFO - loss = 0.036653407, step = 611200 (2.257 sec) -2022-05-01 16:14:48,307 - tensorflow - INFO - global_step/sec: 88.7076 -2022-05-01 16:14:48,307 - tensorflow - INFO - loss = 0.0365748, step = 611400 (2.255 sec) -2022-05-01 16:14:50,558 - tensorflow - INFO - global_step/sec: 88.8249 -2022-05-01 16:14:50,559 - tensorflow - INFO - loss = 1.1359053, step = 611600 (2.252 sec) -2022-05-01 16:14:52,816 - tensorflow - INFO - global_step/sec: 88.6074 -2022-05-01 16:14:52,816 - tensorflow - INFO - loss = 0.1671949, step = 611800 (2.257 sec) -2022-05-01 16:14:55,071 - tensorflow - INFO - global_step/sec: 88.6709 -2022-05-01 16:14:55,072 - tensorflow - INFO - loss = 0.06732585, step = 612000 (2.256 sec) -2022-05-01 16:14:57,326 - tensorflow - INFO - global_step/sec: 88.6823 -2022-05-01 16:14:57,327 - tensorflow - INFO - loss = 0.030628437, step = 612200 (2.255 sec) -2022-05-01 16:14:59,578 - tensorflow - INFO - global_step/sec: 88.8077 -2022-05-01 16:14:59,579 - tensorflow - INFO - loss = 0.12056379, step = 612400 (2.252 sec) -2022-05-01 16:15:01,830 - tensorflow - INFO - global_step/sec: 88.8466 -2022-05-01 16:15:01,830 - tensorflow - INFO - loss = 0.044604473, step = 612600 (2.251 sec) -2022-05-01 16:15:04,081 - tensorflow - INFO - global_step/sec: 88.8407 -2022-05-01 16:15:04,081 - tensorflow - INFO - loss = 0.28650892, step = 612800 (2.251 sec) -2022-05-01 16:15:06,336 - tensorflow - INFO - global_step/sec: 88.6904 -2022-05-01 16:15:06,336 - tensorflow - INFO - loss = 0.014561885, step = 613000 (2.255 sec) -2022-05-01 16:15:08,594 - tensorflow - INFO - global_step/sec: 88.5642 -2022-05-01 16:15:08,594 - tensorflow - INFO - loss = 0.0681196, step = 613200 (2.258 sec) -2022-05-01 16:15:10,850 - tensorflow - INFO - global_step/sec: 88.656 -2022-05-01 16:15:10,850 - tensorflow - INFO - loss = 0.11148818, step = 613400 (2.256 sec) -2022-05-01 16:15:13,105 - tensorflow - INFO - global_step/sec: 88.7006 -2022-05-01 16:15:13,105 - tensorflow - INFO - loss = 0.078977704, step = 613600 (2.255 sec) -2022-05-01 16:15:15,359 - tensorflow - INFO - global_step/sec: 88.7006 -2022-05-01 16:15:15,360 - tensorflow - INFO - loss = 0.0747398, step = 613800 (2.255 sec) -2022-05-01 16:15:17,615 - tensorflow - INFO - global_step/sec: 88.6759 -2022-05-01 16:15:17,615 - tensorflow - INFO - loss = 0.032532763, step = 614000 (2.255 sec) -2022-05-01 16:15:19,866 - tensorflow - INFO - global_step/sec: 88.8421 -2022-05-01 16:15:19,866 - tensorflow - INFO - loss = 0.022528261, step = 614200 (2.251 sec) -2022-05-01 16:15:22,120 - tensorflow - INFO - global_step/sec: 88.7334 -2022-05-01 16:15:22,120 - tensorflow - INFO - loss = 0.032586053, step = 614400 (2.254 sec) -2022-05-01 16:15:24,375 - tensorflow - INFO - global_step/sec: 88.688 -2022-05-01 16:15:24,376 - tensorflow - INFO - loss = 0.006227595, step = 614600 (2.255 sec) -2022-05-01 16:15:26,626 - tensorflow - INFO - global_step/sec: 88.8601 -2022-05-01 16:15:26,626 - tensorflow - INFO - loss = 0.0127908895, step = 614800 (2.251 sec) -2022-05-01 16:15:28,881 - tensorflow - INFO - global_step/sec: 88.6947 -2022-05-01 16:15:28,881 - tensorflow - INFO - loss = 0.018400937, step = 615000 (2.255 sec) -2022-05-01 16:15:31,139 - tensorflow - INFO - global_step/sec: 88.5648 -2022-05-01 16:15:31,139 - tensorflow - INFO - loss = 0.015544417, step = 615200 (2.258 sec) -2022-05-01 16:15:33,391 - tensorflow - INFO - global_step/sec: 88.8067 -2022-05-01 16:15:33,391 - tensorflow - INFO - loss = 0.03676469, step = 615400 (2.252 sec) -2022-05-01 16:15:35,650 - tensorflow - INFO - global_step/sec: 88.5496 -2022-05-01 16:15:35,650 - tensorflow - INFO - loss = 0.024532415, step = 615600 (2.259 sec) -2022-05-01 16:15:37,904 - tensorflow - INFO - global_step/sec: 88.722 -2022-05-01 16:15:37,904 - tensorflow - INFO - loss = 0.03382394, step = 615800 (2.254 sec) -2022-05-01 16:15:40,158 - tensorflow - INFO - global_step/sec: 88.7179 -2022-05-01 16:15:40,159 - tensorflow - INFO - loss = 0.3950181, step = 616000 (2.254 sec) -2022-05-01 16:15:42,403 - tensorflow - INFO - global_step/sec: 89.0807 -2022-05-01 16:15:42,404 - tensorflow - INFO - loss = 0.038583353, step = 616200 (2.245 sec) -2022-05-01 16:15:44,659 - tensorflow - INFO - global_step/sec: 88.672 -2022-05-01 16:15:44,659 - tensorflow - INFO - loss = 0.033826016, step = 616400 (2.255 sec) -2022-05-01 16:15:46,917 - tensorflow - INFO - global_step/sec: 88.5798 -2022-05-01 16:15:46,917 - tensorflow - INFO - loss = 0.018127516, step = 616600 (2.258 sec) -2022-05-01 16:15:49,162 - tensorflow - INFO - global_step/sec: 89.089 -2022-05-01 16:15:49,162 - tensorflow - INFO - loss = 0.01999943, step = 616800 (2.245 sec) -2022-05-01 16:15:51,414 - tensorflow - INFO - global_step/sec: 88.8002 -2022-05-01 16:15:51,414 - tensorflow - INFO - loss = 0.037188075, step = 617000 (2.252 sec) -2022-05-01 16:15:53,670 - tensorflow - INFO - global_step/sec: 88.6327 -2022-05-01 16:15:53,671 - tensorflow - INFO - loss = 0.038181555, step = 617200 (2.257 sec) -2022-05-01 16:15:55,928 - tensorflow - INFO - global_step/sec: 88.586 -2022-05-01 16:15:55,929 - tensorflow - INFO - loss = 0.17563345, step = 617400 (2.258 sec) -2022-05-01 16:15:58,177 - tensorflow - INFO - global_step/sec: 88.954 -2022-05-01 16:15:58,177 - tensorflow - INFO - loss = 0.014711383, step = 617600 (2.248 sec) -2022-05-01 16:16:00,432 - tensorflow - INFO - global_step/sec: 88.6819 -2022-05-01 16:16:00,432 - tensorflow - INFO - loss = 0.02903166, step = 617800 (2.255 sec) -2022-05-01 16:16:02,685 - tensorflow - INFO - global_step/sec: 88.7609 -2022-05-01 16:16:02,685 - tensorflow - INFO - loss = 0.29104948, step = 618000 (2.253 sec) -2022-05-01 16:16:04,940 - tensorflow - INFO - global_step/sec: 88.6852 -2022-05-01 16:16:04,941 - tensorflow - INFO - loss = 0.926473, step = 618200 (2.255 sec) -2022-05-01 16:16:07,193 - tensorflow - INFO - global_step/sec: 88.7836 -2022-05-01 16:16:07,193 - tensorflow - INFO - loss = 0.06550895, step = 618400 (2.253 sec) -2022-05-01 16:16:09,449 - tensorflow - INFO - global_step/sec: 88.6348 -2022-05-01 16:16:09,450 - tensorflow - INFO - loss = 0.029829977, step = 618600 (2.256 sec) -2022-05-01 16:16:11,706 - tensorflow - INFO - global_step/sec: 88.6411 -2022-05-01 16:16:11,706 - tensorflow - INFO - loss = 0.045433816, step = 618800 (2.256 sec) -2022-05-01 16:16:13,959 - tensorflow - INFO - global_step/sec: 88.7573 -2022-05-01 16:16:13,959 - tensorflow - INFO - loss = 0.031515054, step = 619000 (2.253 sec) -2022-05-01 16:16:16,211 - tensorflow - INFO - global_step/sec: 88.8166 -2022-05-01 16:16:16,211 - tensorflow - INFO - loss = 0.040826708, step = 619200 (2.252 sec) -2022-05-01 16:16:18,464 - tensorflow - INFO - global_step/sec: 88.7635 -2022-05-01 16:16:18,464 - tensorflow - INFO - loss = 0.051567324, step = 619400 (2.253 sec) -2022-05-01 16:16:20,722 - tensorflow - INFO - global_step/sec: 88.5837 -2022-05-01 16:16:20,722 - tensorflow - INFO - loss = 0.059158325, step = 619600 (2.258 sec) -2022-05-01 16:16:22,981 - tensorflow - INFO - global_step/sec: 88.5231 -2022-05-01 16:16:22,981 - tensorflow - INFO - loss = 0.026983757, step = 619800 (2.259 sec) -2022-05-01 16:16:25,241 - tensorflow - INFO - global_step/sec: 88.4976 -2022-05-01 16:16:25,241 - tensorflow - INFO - loss = 0.023714647, step = 620000 (2.260 sec) -2022-05-01 16:16:27,498 - tensorflow - INFO - global_step/sec: 88.5979 -2022-05-01 16:16:27,499 - tensorflow - INFO - loss = 0.026506381, step = 620200 (2.257 sec) -2022-05-01 16:16:29,752 - tensorflow - INFO - global_step/sec: 88.7358 -2022-05-01 16:16:29,753 - tensorflow - INFO - loss = 0.028152559, step = 620400 (2.254 sec) -2022-05-01 16:16:32,010 - tensorflow - INFO - global_step/sec: 88.564 -2022-05-01 16:16:32,011 - tensorflow - INFO - loss = 0.03125519, step = 620600 (2.258 sec) -2022-05-01 16:16:34,270 - tensorflow - INFO - global_step/sec: 88.5308 -2022-05-01 16:16:34,270 - tensorflow - INFO - loss = 0.020148389, step = 620800 (2.259 sec) -2022-05-01 16:16:36,529 - tensorflow - INFO - global_step/sec: 88.5157 -2022-05-01 16:16:36,530 - tensorflow - INFO - loss = 0.3082172, step = 621000 (2.260 sec) -2022-05-01 16:16:38,791 - tensorflow - INFO - global_step/sec: 88.4366 -2022-05-01 16:16:38,791 - tensorflow - INFO - loss = 0.06370544, step = 621200 (2.262 sec) -2022-05-01 16:16:41,042 - tensorflow - INFO - global_step/sec: 88.8395 -2022-05-01 16:16:41,042 - tensorflow - INFO - 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INFO - global_step/sec: 88.6099 -2022-05-01 16:17:10,362 - tensorflow - INFO - loss = 1.8016558, step = 624000 (2.257 sec) -2022-05-01 16:17:12,615 - tensorflow - INFO - global_step/sec: 88.7634 -2022-05-01 16:17:12,615 - tensorflow - INFO - loss = 0.045792885, step = 624200 (2.253 sec) -2022-05-01 16:17:14,874 - tensorflow - INFO - global_step/sec: 88.5117 -2022-05-01 16:17:14,875 - tensorflow - INFO - loss = 0.10698426, step = 624400 (2.260 sec) -2022-05-01 16:17:17,129 - tensorflow - INFO - global_step/sec: 88.7203 -2022-05-01 16:17:17,129 - tensorflow - INFO - loss = 0.020351615, step = 624600 (2.254 sec) -2022-05-01 16:17:19,380 - tensorflow - INFO - global_step/sec: 88.8468 -2022-05-01 16:17:19,380 - tensorflow - INFO - loss = 0.054590203, step = 624800 (2.251 sec) -2022-05-01 16:17:21,629 - tensorflow - INFO - global_step/sec: 88.9023 -2022-05-01 16:17:21,630 - tensorflow - INFO - loss = 0.051437117, step = 625000 (2.250 sec) -2022-05-01 16:17:23,881 - tensorflow - INFO - global_step/sec: 88.8118 -2022-05-01 16:17:23,882 - 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tensorflow - INFO - global_step/sec: 88.3127 -2022-05-01 16:17:53,248 - tensorflow - INFO - loss = 0.013710188, step = 627800 (2.265 sec) -2022-05-01 16:17:55,511 - tensorflow - INFO - global_step/sec: 88.3642 -2022-05-01 16:17:55,511 - tensorflow - INFO - loss = 0.034602355, step = 628000 (2.263 sec) -2022-05-01 16:17:57,779 - tensorflow - INFO - global_step/sec: 88.1545 -2022-05-01 16:17:57,780 - tensorflow - INFO - loss = 0.03215067, step = 628200 (2.269 sec) -2022-05-01 16:18:00,041 - tensorflow - INFO - global_step/sec: 88.4334 -2022-05-01 16:18:00,041 - tensorflow - INFO - loss = 0.046506256, step = 628400 (2.262 sec) -2022-05-01 16:18:02,302 - tensorflow - INFO - global_step/sec: 88.4437 -2022-05-01 16:18:02,303 - tensorflow - INFO - loss = 0.015580835, step = 628600 (2.261 sec) -2022-05-01 16:18:04,567 - tensorflow - INFO - global_step/sec: 88.3299 -2022-05-01 16:18:04,567 - tensorflow - INFO - loss = 0.064582884, step = 628800 (2.264 sec) -2022-05-01 16:18:06,819 - tensorflow - INFO - global_step/sec: 88.7805 -2022-05-01 16:18:06,820 - tensorflow - INFO - loss = 0.026806299, step = 629000 (2.253 sec) -2022-05-01 16:18:09,083 - tensorflow - INFO - global_step/sec: 88.3463 -2022-05-01 16:18:09,084 - tensorflow - INFO - loss = 0.09207547, step = 629200 (2.264 sec) -2022-05-01 16:18:11,333 - tensorflow - INFO - global_step/sec: 88.8901 -2022-05-01 16:18:11,334 - tensorflow - INFO - loss = 0.06292535, step = 629400 (2.250 sec) -2022-05-01 16:18:13,587 - tensorflow - INFO - global_step/sec: 88.7517 -2022-05-01 16:18:13,587 - tensorflow - INFO - loss = 0.036125876, step = 629600 (2.253 sec) -2022-05-01 16:18:15,839 - tensorflow - INFO - global_step/sec: 88.8127 -2022-05-01 16:18:15,839 - tensorflow - INFO - loss = 0.013562234, step = 629800 (2.252 sec) -2022-05-01 16:18:18,095 - tensorflow - INFO - global_step/sec: 88.6485 -2022-05-01 16:18:18,095 - tensorflow - INFO - loss = 0.13024516, step = 630000 (2.256 sec) -2022-05-01 16:18:20,352 - tensorflow - INFO - global_step/sec: 88.5861 -2022-05-01 16:18:20,353 - tensorflow - INFO - loss = 0.048751697, step = 630200 (2.258 sec) -2022-05-01 16:18:22,610 - tensorflow - INFO - global_step/sec: 88.5673 -2022-05-01 16:18:22,611 - tensorflow - INFO - loss = 0.02033265, step = 630400 (2.258 sec) -2022-05-01 16:18:24,865 - tensorflow - INFO - global_step/sec: 88.692 -2022-05-01 16:18:24,866 - tensorflow - INFO - loss = 0.05609814, step = 630600 (2.255 sec) -2022-05-01 16:18:27,118 - tensorflow - INFO - global_step/sec: 88.7745 -2022-05-01 16:18:27,119 - tensorflow - INFO - loss = 0.06963114, step = 630800 (2.253 sec) -2022-05-01 16:18:29,376 - tensorflow - INFO - global_step/sec: 88.5837 -2022-05-01 16:18:29,377 - tensorflow - INFO - loss = 0.022002216, step = 631000 (2.258 sec) -2022-05-01 16:18:31,637 - tensorflow - INFO - global_step/sec: 88.4497 -2022-05-01 16:18:31,638 - tensorflow - INFO - loss = 0.06723067, step = 631200 (2.261 sec) -2022-05-01 16:18:33,896 - tensorflow - INFO - global_step/sec: 88.5521 -2022-05-01 16:18:33,896 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.6306 -2022-05-01 16:18:49,692 - tensorflow - INFO - loss = 0.022168528, step = 632800 (2.257 sec) -2022-05-01 16:18:51,950 - tensorflow - INFO - global_step/sec: 88.5786 -2022-05-01 16:18:51,950 - tensorflow - INFO - loss = 16.258793, step = 633000 (2.258 sec) -2022-05-01 16:18:54,203 - tensorflow - INFO - global_step/sec: 88.7603 -2022-05-01 16:18:54,203 - tensorflow - INFO - loss = 0.033347547, step = 633200 (2.253 sec) -2022-05-01 16:18:56,458 - tensorflow - INFO - global_step/sec: 88.6695 -2022-05-01 16:18:56,459 - tensorflow - INFO - loss = 0.02900518, step = 633400 (2.256 sec) -2022-05-01 16:18:58,718 - tensorflow - INFO - global_step/sec: 88.5048 -2022-05-01 16:18:58,719 - tensorflow - INFO - loss = 5.884887, step = 633600 (2.260 sec) -2022-05-01 16:19:00,968 - tensorflow - INFO - global_step/sec: 88.8969 -2022-05-01 16:19:00,968 - tensorflow - INFO - loss = 17.619848, step = 633800 (2.250 sec) -2022-05-01 16:19:03,220 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.7667 -2022-05-01 16:19:46,061 - tensorflow - INFO - loss = 0.12344111, step = 637800 (2.253 sec) -2022-05-01 16:19:48,313 - tensorflow - INFO - global_step/sec: 88.7675 -2022-05-01 16:19:48,314 - tensorflow - INFO - loss = 0.090517946, step = 638000 (2.253 sec) -2022-05-01 16:19:50,569 - tensorflow - INFO - global_step/sec: 88.6549 -2022-05-01 16:19:50,570 - tensorflow - INFO - loss = 0.036494423, step = 638200 (2.256 sec) -2022-05-01 16:19:52,826 - tensorflow - INFO - global_step/sec: 88.6316 -2022-05-01 16:19:52,826 - tensorflow - INFO - loss = 0.107932344, step = 638400 (2.257 sec) -2022-05-01 16:19:55,079 - tensorflow - INFO - global_step/sec: 88.7781 -2022-05-01 16:19:55,079 - tensorflow - INFO - loss = 0.027406814, step = 638600 (2.253 sec) -2022-05-01 16:19:57,332 - tensorflow - INFO - global_step/sec: 88.7408 -2022-05-01 16:19:57,333 - tensorflow - INFO - loss = 0.040648628, step = 638800 (2.254 sec) -2022-05-01 16:19:59,592 - tensorflow - INFO - global_step/sec: 88.5269 -2022-05-01 16:19:59,592 - tensorflow - INFO - loss = 0.05482356, step = 639000 (2.259 sec) -2022-05-01 16:20:01,853 - tensorflow - INFO - global_step/sec: 88.4537 -2022-05-01 16:20:01,853 - tensorflow - INFO - loss = 0.16037363, step = 639200 (2.261 sec) -2022-05-01 16:20:04,107 - tensorflow - INFO - global_step/sec: 88.7335 -2022-05-01 16:20:04,107 - tensorflow - INFO - loss = 0.015529158, step = 639400 (2.254 sec) -2022-05-01 16:20:06,359 - tensorflow - INFO - global_step/sec: 88.8011 -2022-05-01 16:20:06,359 - tensorflow - INFO - loss = 0.05927797, step = 639600 (2.252 sec) -2022-05-01 16:20:08,611 - tensorflow - INFO - global_step/sec: 88.797 -2022-05-01 16:20:08,612 - tensorflow - INFO - loss = 0.12955801, step = 639800 (2.252 sec) -2022-05-01 16:20:10,865 - tensorflow - INFO - global_step/sec: 88.7532 -2022-05-01 16:20:10,865 - tensorflow - INFO - loss = 0.026449252, step = 640000 (2.253 sec) -2022-05-01 16:20:13,123 - tensorflow - INFO - global_step/sec: 88.5512 -2022-05-01 16:20:13,124 - 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tensorflow - INFO - loss = 0.03987413, step = 645200 (2.258 sec) -2022-05-01 16:21:11,730 - tensorflow - INFO - global_step/sec: 88.4371 -2022-05-01 16:21:11,730 - tensorflow - INFO - loss = 0.05389357, step = 645400 (2.262 sec) -2022-05-01 16:21:13,989 - tensorflow - INFO - global_step/sec: 88.517 -2022-05-01 16:21:13,990 - tensorflow - INFO - loss = 0.05849466, step = 645600 (2.259 sec) -2022-05-01 16:21:16,253 - tensorflow - INFO - global_step/sec: 88.3277 -2022-05-01 16:21:16,254 - tensorflow - INFO - loss = 0.03608981, step = 645800 (2.264 sec) -2022-05-01 16:21:18,514 - tensorflow - INFO - global_step/sec: 88.4795 -2022-05-01 16:21:18,514 - tensorflow - INFO - loss = 0.039586894, step = 646000 (2.260 sec) -2022-05-01 16:21:20,778 - tensorflow - INFO - global_step/sec: 88.3225 -2022-05-01 16:21:20,779 - tensorflow - INFO - loss = 0.08338522, step = 646200 (2.264 sec) -2022-05-01 16:21:23,041 - tensorflow - INFO - global_step/sec: 88.3985 -2022-05-01 16:21:23,041 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.3025 -2022-05-01 16:21:38,895 - tensorflow - INFO - loss = 0.07743402, step = 647800 (2.265 sec) -2022-05-01 16:21:41,156 - tensorflow - INFO - global_step/sec: 88.4322 -2022-05-01 16:21:41,156 - tensorflow - INFO - loss = 0.017677356, step = 648000 (2.262 sec) -2022-05-01 16:21:43,417 - tensorflow - INFO - global_step/sec: 88.4554 -2022-05-01 16:21:43,418 - tensorflow - INFO - loss = 0.019578267, step = 648200 (2.261 sec) -2022-05-01 16:21:45,677 - tensorflow - INFO - global_step/sec: 88.509 -2022-05-01 16:21:45,677 - tensorflow - INFO - loss = 0.06198241, step = 648400 (2.260 sec) -2022-05-01 16:21:47,928 - tensorflow - INFO - global_step/sec: 88.8393 -2022-05-01 16:21:47,928 - tensorflow - INFO - loss = 0.17423952, step = 648600 (2.251 sec) -2022-05-01 16:21:50,181 - tensorflow - INFO - global_step/sec: 88.7596 -2022-05-01 16:21:50,182 - tensorflow - INFO - loss = 0.026366603, step = 648800 (2.253 sec) -2022-05-01 16:21:52,429 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.06208293, step = 650200 (2.258 sec) -2022-05-01 16:22:08,223 - tensorflow - INFO - global_step/sec: 88.7065 -2022-05-01 16:22:08,224 - tensorflow - INFO - loss = 0.08045256, step = 650400 (2.255 sec) -2022-05-01 16:22:10,479 - tensorflow - INFO - global_step/sec: 88.6741 -2022-05-01 16:22:10,479 - tensorflow - INFO - loss = 0.013809038, step = 650600 (2.256 sec) -2022-05-01 16:22:12,726 - tensorflow - INFO - global_step/sec: 89.011 -2022-05-01 16:22:12,726 - tensorflow - INFO - loss = 0.06669504, step = 650800 (2.247 sec) -2022-05-01 16:22:14,977 - tensorflow - INFO - global_step/sec: 88.8457 -2022-05-01 16:22:14,977 - tensorflow - INFO - loss = 0.050167628, step = 651000 (2.251 sec) -2022-05-01 16:22:17,232 - tensorflow - INFO - global_step/sec: 88.6608 -2022-05-01 16:22:17,233 - tensorflow - INFO - loss = 0.2818935, step = 651200 (2.256 sec) -2022-05-01 16:22:19,485 - tensorflow - INFO - global_step/sec: 88.778 -2022-05-01 16:22:19,486 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.5094 -2022-05-01 16:22:35,274 - tensorflow - INFO - loss = 0.041744143, step = 652800 (2.260 sec) -2022-05-01 16:22:37,521 - tensorflow - INFO - global_step/sec: 89.0115 -2022-05-01 16:22:37,521 - tensorflow - INFO - loss = 0.64099216, step = 653000 (2.247 sec) -2022-05-01 16:22:39,779 - tensorflow - INFO - global_step/sec: 88.577 -2022-05-01 16:22:39,779 - tensorflow - INFO - loss = 0.20521967, step = 653200 (2.258 sec) -2022-05-01 16:22:42,031 - tensorflow - INFO - global_step/sec: 88.7804 -2022-05-01 16:22:42,032 - tensorflow - INFO - loss = 0.0182476, step = 653400 (2.253 sec) -2022-05-01 16:22:44,283 - tensorflow - INFO - global_step/sec: 88.8102 -2022-05-01 16:22:44,284 - tensorflow - INFO - loss = 0.04948442, step = 653600 (2.252 sec) -2022-05-01 16:22:46,542 - tensorflow - INFO - global_step/sec: 88.5635 -2022-05-01 16:22:46,542 - tensorflow - INFO - loss = 0.070475, step = 653800 (2.258 sec) -2022-05-01 16:22:48,800 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.016314765, step = 655200 (2.254 sec) -2022-05-01 16:23:04,586 - tensorflow - INFO - global_step/sec: 88.747 -2022-05-01 16:23:04,587 - tensorflow - INFO - loss = 0.06032333, step = 655400 (2.254 sec) -2022-05-01 16:23:06,841 - tensorflow - INFO - global_step/sec: 88.695 -2022-05-01 16:23:06,842 - tensorflow - INFO - loss = 0.053679515, step = 655600 (2.255 sec) -2022-05-01 16:23:09,095 - tensorflow - INFO - global_step/sec: 88.7328 -2022-05-01 16:23:09,096 - tensorflow - INFO - loss = 0.022284854, step = 655800 (2.254 sec) -2022-05-01 16:23:11,354 - tensorflow - INFO - global_step/sec: 88.531 -2022-05-01 16:23:11,355 - tensorflow - INFO - loss = 0.032551527, step = 656000 (2.259 sec) -2022-05-01 16:23:13,605 - tensorflow - INFO - global_step/sec: 88.8541 -2022-05-01 16:23:13,606 - tensorflow - INFO - loss = 0.015460408, step = 656200 (2.251 sec) -2022-05-01 16:23:15,859 - tensorflow - INFO - global_step/sec: 88.7214 -2022-05-01 16:23:15,860 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.1183 -2022-05-01 16:23:31,654 - tensorflow - INFO - loss = 1.6347156, step = 657800 (2.270 sec) -2022-05-01 16:23:33,910 - tensorflow - INFO - global_step/sec: 88.6339 -2022-05-01 16:23:33,910 - tensorflow - INFO - loss = 0.07399118, step = 658000 (2.256 sec) -2022-05-01 16:23:36,170 - tensorflow - INFO - global_step/sec: 88.4972 -2022-05-01 16:23:36,170 - tensorflow - INFO - loss = 0.017538741, step = 658200 (2.260 sec) -2022-05-01 16:23:38,427 - tensorflow - INFO - global_step/sec: 88.6295 -2022-05-01 16:23:38,427 - tensorflow - INFO - loss = 0.114778236, step = 658400 (2.257 sec) -2022-05-01 16:23:40,679 - tensorflow - INFO - global_step/sec: 88.7745 -2022-05-01 16:23:40,680 - tensorflow - INFO - loss = 0.03924323, step = 658600 (2.253 sec) -2022-05-01 16:23:42,931 - tensorflow - INFO - global_step/sec: 88.831 -2022-05-01 16:23:42,931 - tensorflow - INFO - loss = 0.016615653, step = 658800 (2.251 sec) -2022-05-01 16:23:45,185 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.021737969, step = 660200 (2.253 sec) -2022-05-01 16:24:00,973 - tensorflow - INFO - global_step/sec: 88.537 -2022-05-01 16:24:00,974 - tensorflow - INFO - loss = 0.06891701, step = 660400 (2.259 sec) -2022-05-01 16:24:03,232 - tensorflow - INFO - global_step/sec: 88.5562 -2022-05-01 16:24:03,232 - tensorflow - INFO - loss = 0.04322044, step = 660600 (2.258 sec) -2022-05-01 16:24:05,484 - tensorflow - INFO - global_step/sec: 88.7816 -2022-05-01 16:24:05,485 - tensorflow - INFO - loss = 0.11884213, step = 660800 (2.253 sec) -2022-05-01 16:24:07,738 - tensorflow - INFO - global_step/sec: 88.7323 -2022-05-01 16:24:07,739 - tensorflow - INFO - loss = 0.058298614, step = 661000 (2.254 sec) -2022-05-01 16:24:09,994 - tensorflow - INFO - global_step/sec: 88.6667 -2022-05-01 16:24:09,994 - tensorflow - INFO - loss = 0.012113831, step = 661200 (2.256 sec) -2022-05-01 16:24:12,251 - tensorflow - INFO - global_step/sec: 88.6203 -2022-05-01 16:24:12,251 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.7383 -2022-05-01 16:24:28,039 - tensorflow - INFO - loss = 0.029402863, step = 662800 (2.254 sec) -2022-05-01 16:24:30,300 - tensorflow - INFO - global_step/sec: 88.4568 -2022-05-01 16:24:30,300 - tensorflow - INFO - loss = 0.041033566, step = 663000 (2.261 sec) -2022-05-01 16:24:32,560 - tensorflow - INFO - global_step/sec: 88.4877 -2022-05-01 16:24:32,560 - tensorflow - INFO - loss = 10.794538, step = 663200 (2.260 sec) -2022-05-01 16:24:34,813 - tensorflow - INFO - global_step/sec: 88.758 -2022-05-01 16:24:34,814 - tensorflow - INFO - loss = 0.020068303, step = 663400 (2.253 sec) -2022-05-01 16:24:37,070 - tensorflow - INFO - global_step/sec: 88.6263 -2022-05-01 16:24:37,070 - tensorflow - INFO - loss = 0.025392253, step = 663600 (2.257 sec) -2022-05-01 16:24:39,328 - tensorflow - INFO - global_step/sec: 88.5509 -2022-05-01 16:24:39,329 - tensorflow - INFO - loss = 0.027187895, step = 663800 (2.259 sec) -2022-05-01 16:24:41,582 - tensorflow - INFO - global_step/sec: 88.761 -2022-05-01 16:24:41,582 - tensorflow - INFO - loss = 0.05288632, step = 664000 (2.253 sec) -2022-05-01 16:24:43,845 - tensorflow - INFO - global_step/sec: 88.3699 -2022-05-01 16:24:43,845 - tensorflow - INFO - loss = 0.02741351, step = 664200 (2.263 sec) -2022-05-01 16:24:46,105 - tensorflow - INFO - global_step/sec: 88.4847 -2022-05-01 16:24:46,106 - tensorflow - INFO - loss = 0.020498091, step = 664400 (2.260 sec) -2022-05-01 16:24:48,366 - tensorflow - INFO - global_step/sec: 88.4642 -2022-05-01 16:24:48,366 - tensorflow - INFO - loss = 0.03579387, step = 664600 (2.261 sec) -2022-05-01 16:24:50,629 - tensorflow - INFO - global_step/sec: 88.3919 -2022-05-01 16:24:50,629 - tensorflow - INFO - loss = 0.03373597, step = 664800 (2.263 sec) -2022-05-01 16:24:52,892 - tensorflow - INFO - global_step/sec: 88.3467 -2022-05-01 16:24:52,893 - tensorflow - INFO - loss = 0.032407492, step = 665000 (2.264 sec) -2022-05-01 16:24:55,157 - tensorflow - INFO - global_step/sec: 88.3251 -2022-05-01 16:24:55,157 - tensorflow - INFO - loss = 0.12163025, step = 665200 (2.264 sec) -2022-05-01 16:24:57,419 - tensorflow - INFO - global_step/sec: 88.4285 -2022-05-01 16:24:57,419 - tensorflow - INFO - loss = 0.16891573, step = 665400 (2.262 sec) -2022-05-01 16:24:59,682 - tensorflow - INFO - global_step/sec: 88.3761 -2022-05-01 16:24:59,682 - tensorflow - INFO - loss = 0.42661345, step = 665600 (2.263 sec) -2022-05-01 16:25:01,945 - tensorflow - INFO - global_step/sec: 88.381 -2022-05-01 16:25:01,945 - tensorflow - INFO - loss = 0.030454159, step = 665800 (2.263 sec) -2022-05-01 16:25:04,208 - tensorflow - INFO - global_step/sec: 88.3589 -2022-05-01 16:25:04,208 - tensorflow - INFO - loss = 0.026694577, step = 666000 (2.263 sec) -2022-05-01 16:25:06,470 - tensorflow - INFO - global_step/sec: 88.4072 -2022-05-01 16:25:06,471 - tensorflow - INFO - loss = 0.16553432, step = 666200 (2.262 sec) -2022-05-01 16:25:08,731 - tensorflow - INFO - global_step/sec: 88.4817 -2022-05-01 16:25:08,731 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.7381 -2022-05-01 16:25:24,529 - tensorflow - INFO - loss = 0.079008505, step = 667800 (2.254 sec) -2022-05-01 16:25:26,777 - tensorflow - INFO - global_step/sec: 88.927 -2022-05-01 16:25:26,778 - tensorflow - INFO - loss = 0.025905298, step = 668000 (2.249 sec) -2022-05-01 16:25:29,034 - tensorflow - INFO - global_step/sec: 88.6283 -2022-05-01 16:25:29,034 - tensorflow - INFO - loss = 0.74142873, step = 668200 (2.257 sec) -2022-05-01 16:25:31,295 - tensorflow - INFO - global_step/sec: 88.4364 -2022-05-01 16:25:31,296 - tensorflow - INFO - loss = 0.019601302, step = 668400 (2.262 sec) -2022-05-01 16:25:33,550 - tensorflow - INFO - global_step/sec: 88.6887 -2022-05-01 16:25:33,551 - tensorflow - INFO - loss = 0.24653198, step = 668600 (2.255 sec) -2022-05-01 16:25:35,802 - tensorflow - INFO - global_step/sec: 88.8237 -2022-05-01 16:25:35,802 - tensorflow - INFO - loss = 0.030292703, step = 668800 (2.252 sec) -2022-05-01 16:25:38,055 - tensorflow - INFO - global_step/sec: 88.7672 -2022-05-01 16:25:38,056 - tensorflow - INFO - loss = 0.029847289, step = 669000 (2.253 sec) -2022-05-01 16:25:40,312 - tensorflow - INFO - global_step/sec: 88.5988 -2022-05-01 16:25:40,313 - tensorflow - INFO - loss = 0.014756154, step = 669200 (2.257 sec) -2022-05-01 16:25:42,568 - tensorflow - INFO - global_step/sec: 88.6584 -2022-05-01 16:25:42,569 - tensorflow - INFO - loss = 0.014029396, step = 669400 (2.256 sec) -2022-05-01 16:25:44,829 - tensorflow - INFO - global_step/sec: 88.4862 -2022-05-01 16:25:44,829 - tensorflow - INFO - loss = 0.24055506, step = 669600 (2.260 sec) -2022-05-01 16:25:47,081 - tensorflow - INFO - global_step/sec: 88.7959 -2022-05-01 16:25:47,081 - tensorflow - INFO - loss = 0.06759047, step = 669800 (2.252 sec) -2022-05-01 16:25:49,334 - tensorflow - INFO - global_step/sec: 88.7823 -2022-05-01 16:25:49,334 - tensorflow - INFO - loss = 0.17116967, step = 670000 (2.253 sec) -2022-05-01 16:25:51,589 - tensorflow - INFO - global_step/sec: 88.6613 -2022-05-01 16:25:51,590 - tensorflow - INFO - loss = 0.029213611, step = 670200 (2.256 sec) -2022-05-01 16:25:53,841 - tensorflow - INFO - global_step/sec: 88.8295 -2022-05-01 16:25:53,841 - tensorflow - INFO - loss = 0.19025126, step = 670400 (2.252 sec) -2022-05-01 16:25:56,108 - tensorflow - INFO - global_step/sec: 88.2132 -2022-05-01 16:25:56,109 - tensorflow - INFO - loss = 0.057157777, step = 670600 (2.267 sec) -2022-05-01 16:25:58,367 - tensorflow - INFO - global_step/sec: 88.5263 -2022-05-01 16:25:58,368 - tensorflow - INFO - loss = 0.031075079, step = 670800 (2.259 sec) -2022-05-01 16:26:00,622 - tensorflow - INFO - global_step/sec: 88.6948 -2022-05-01 16:26:00,623 - tensorflow - INFO - loss = 1.3746629, step = 671000 (2.255 sec) -2022-05-01 16:26:02,876 - tensorflow - INFO - global_step/sec: 88.7547 -2022-05-01 16:26:02,876 - tensorflow - INFO - loss = 0.06964992, step = 671200 (2.253 sec) -2022-05-01 16:26:05,132 - tensorflow - INFO - global_step/sec: 88.6228 -2022-05-01 16:26:05,133 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.4567 -2022-05-01 16:26:20,922 - tensorflow - INFO - loss = 0.5688815, step = 672800 (2.261 sec) -2022-05-01 16:26:23,183 - tensorflow - INFO - global_step/sec: 88.4171 -2022-05-01 16:26:23,184 - tensorflow - INFO - loss = 0.03952047, step = 673000 (2.262 sec) -2022-05-01 16:26:25,439 - tensorflow - INFO - global_step/sec: 88.6751 -2022-05-01 16:26:25,439 - tensorflow - INFO - loss = 0.017576523, step = 673200 (2.255 sec) -2022-05-01 16:26:27,693 - tensorflow - INFO - global_step/sec: 88.7138 -2022-05-01 16:26:27,694 - tensorflow - INFO - loss = 0.050946683, step = 673400 (2.254 sec) -2022-05-01 16:26:29,954 - tensorflow - INFO - global_step/sec: 88.4774 -2022-05-01 16:26:29,954 - tensorflow - INFO - loss = 0.05332943, step = 673600 (2.260 sec) -2022-05-01 16:26:32,220 - tensorflow - INFO - global_step/sec: 88.2588 -2022-05-01 16:26:32,220 - tensorflow - INFO - loss = 0.051412985, step = 673800 (2.266 sec) -2022-05-01 16:26:34,480 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.60917056, step = 675200 (2.259 sec) -2022-05-01 16:26:50,276 - tensorflow - INFO - global_step/sec: 88.6203 -2022-05-01 16:26:50,277 - tensorflow - INFO - loss = 0.38935432, step = 675400 (2.257 sec) -2022-05-01 16:26:52,524 - tensorflow - INFO - global_step/sec: 88.9838 -2022-05-01 16:26:52,524 - tensorflow - INFO - loss = 0.07833657, step = 675600 (2.248 sec) -2022-05-01 16:26:54,775 - tensorflow - INFO - global_step/sec: 88.8475 -2022-05-01 16:26:54,775 - tensorflow - INFO - loss = 0.050954167, step = 675800 (2.251 sec) -2022-05-01 16:26:57,036 - tensorflow - INFO - global_step/sec: 88.4687 -2022-05-01 16:26:57,036 - tensorflow - INFO - loss = 0.06862058, step = 676000 (2.261 sec) -2022-05-01 16:26:59,291 - tensorflow - INFO - global_step/sec: 88.6875 -2022-05-01 16:26:59,291 - tensorflow - INFO - loss = 0.03530959, step = 676200 (2.255 sec) -2022-05-01 16:27:01,554 - tensorflow - INFO - global_step/sec: 88.3878 -2022-05-01 16:27:01,554 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.866 -2022-05-01 16:27:17,347 - tensorflow - INFO - loss = 0.034631237, step = 677800 (2.251 sec) -2022-05-01 16:27:19,598 - tensorflow - INFO - global_step/sec: 88.8338 -2022-05-01 16:27:19,598 - tensorflow - INFO - loss = 0.019333193, step = 678000 (2.251 sec) -2022-05-01 16:27:21,854 - tensorflow - INFO - global_step/sec: 88.6438 -2022-05-01 16:27:21,854 - tensorflow - INFO - loss = 0.05202195, step = 678200 (2.256 sec) -2022-05-01 16:27:24,110 - tensorflow - INFO - global_step/sec: 88.6356 -2022-05-01 16:27:24,111 - tensorflow - INFO - loss = 0.010659963, step = 678400 (2.256 sec) -2022-05-01 16:27:26,364 - tensorflow - INFO - global_step/sec: 88.7312 -2022-05-01 16:27:26,365 - tensorflow - INFO - loss = 0.05443508, step = 678600 (2.254 sec) -2022-05-01 16:27:28,616 - tensorflow - INFO - global_step/sec: 88.8137 -2022-05-01 16:27:28,616 - tensorflow - INFO - loss = 0.041451752, step = 678800 (2.252 sec) -2022-05-01 16:27:30,876 - tensorflow - INFO - global_step/sec: 88.485 -2022-05-01 16:27:30,877 - tensorflow - INFO - loss = 0.043228835, step = 679000 (2.260 sec) -2022-05-01 16:27:33,128 - tensorflow - INFO - global_step/sec: 88.8059 -2022-05-01 16:27:33,129 - tensorflow - INFO - loss = 0.058606185, step = 679200 (2.252 sec) -2022-05-01 16:27:35,380 - tensorflow - INFO - global_step/sec: 88.823 -2022-05-01 16:27:35,381 - tensorflow - INFO - loss = 0.4918099, step = 679400 (2.252 sec) -2022-05-01 16:27:37,642 - tensorflow - INFO - global_step/sec: 88.4342 -2022-05-01 16:27:37,642 - tensorflow - INFO - loss = 0.46750993, step = 679600 (2.262 sec) -2022-05-01 16:27:39,897 - tensorflow - INFO - global_step/sec: 88.6828 -2022-05-01 16:27:39,897 - tensorflow - INFO - loss = 0.046240468, step = 679800 (2.255 sec) -2022-05-01 16:27:42,150 - tensorflow - INFO - global_step/sec: 88.7543 -2022-05-01 16:27:42,151 - tensorflow - INFO - loss = 0.22815502, step = 680000 (2.253 sec) -2022-05-01 16:27:44,405 - tensorflow - INFO - global_step/sec: 88.7155 -2022-05-01 16:27:44,405 - tensorflow - INFO - loss = 0.05310607, step = 680200 (2.254 sec) -2022-05-01 16:27:46,658 - tensorflow - INFO - global_step/sec: 88.7544 -2022-05-01 16:27:46,659 - tensorflow - INFO - loss = 0.023877185, step = 680400 (2.253 sec) -2022-05-01 16:27:48,919 - tensorflow - INFO - global_step/sec: 88.4575 -2022-05-01 16:27:48,920 - tensorflow - INFO - loss = 0.4059246, step = 680600 (2.261 sec) -2022-05-01 16:27:51,175 - tensorflow - INFO - global_step/sec: 88.6596 -2022-05-01 16:27:51,175 - tensorflow - INFO - loss = 0.027581796, step = 680800 (2.256 sec) -2022-05-01 16:27:53,428 - tensorflow - INFO - global_step/sec: 88.7847 -2022-05-01 16:27:53,428 - tensorflow - INFO - loss = 0.014304858, step = 681000 (2.253 sec) -2022-05-01 16:27:55,687 - tensorflow - INFO - global_step/sec: 88.5016 -2022-05-01 16:27:55,688 - tensorflow - INFO - loss = 0.14080632, step = 681200 (2.260 sec) -2022-05-01 16:27:57,942 - tensorflow - INFO - global_step/sec: 88.7175 -2022-05-01 16:27:57,942 - tensorflow - INFO - loss = 0.013051263, step = 681400 (2.254 sec) -2022-05-01 16:28:00,194 - tensorflow - INFO - global_step/sec: 88.7813 -2022-05-01 16:28:00,195 - tensorflow - INFO - loss = 0.023506679, step = 681600 (2.253 sec) -2022-05-01 16:28:02,457 - tensorflow - INFO - global_step/sec: 88.3871 -2022-05-01 16:28:02,458 - tensorflow - INFO - loss = 0.026280262, step = 681800 (2.263 sec) -2022-05-01 16:28:04,711 - tensorflow - INFO - global_step/sec: 88.7497 -2022-05-01 16:28:04,711 - tensorflow - INFO - loss = 0.019026008, step = 682000 (2.254 sec) -2022-05-01 16:28:06,968 - tensorflow - INFO - global_step/sec: 88.6207 -2022-05-01 16:28:06,968 - tensorflow - INFO - loss = 0.10160596, step = 682200 (2.257 sec) -2022-05-01 16:28:09,220 - tensorflow - INFO - global_step/sec: 88.7838 -2022-05-01 16:28:09,221 - tensorflow - INFO - loss = 0.48572725, step = 682400 (2.253 sec) -2022-05-01 16:28:11,476 - tensorflow - INFO - global_step/sec: 88.6661 -2022-05-01 16:28:11,476 - tensorflow - INFO - loss = 0.030404504, step = 682600 (2.256 sec) -2022-05-01 16:28:13,733 - tensorflow - INFO - global_step/sec: 88.5948 -2022-05-01 16:28:13,734 - tensorflow - INFO - loss = 0.1416041, step = 682800 (2.258 sec) -2022-05-01 16:28:16,002 - tensorflow - INFO - global_step/sec: 88.1773 -2022-05-01 16:28:16,002 - tensorflow - INFO - loss = 0.3864634, step = 683000 (2.268 sec) -2022-05-01 16:28:18,263 - tensorflow - INFO - global_step/sec: 88.4224 -2022-05-01 16:28:18,264 - tensorflow - INFO - loss = 0.041742954, step = 683200 (2.262 sec) -2022-05-01 16:28:20,529 - tensorflow - INFO - global_step/sec: 88.2897 -2022-05-01 16:28:20,529 - tensorflow - INFO - loss = 2.4299667, step = 683400 (2.265 sec) -2022-05-01 16:28:22,790 - tensorflow - INFO - global_step/sec: 88.4459 -2022-05-01 16:28:22,790 - tensorflow - INFO - loss = 0.02620551, step = 683600 (2.261 sec) -2022-05-01 16:28:25,051 - tensorflow - INFO - global_step/sec: 88.453 -2022-05-01 16:28:25,051 - tensorflow - INFO - loss = 0.028443713, step = 683800 (2.261 sec) -2022-05-01 16:28:27,321 - tensorflow - INFO - global_step/sec: 88.1258 -2022-05-01 16:28:27,321 - tensorflow - INFO - loss = 0.04064524, step = 684000 (2.269 sec) -2022-05-01 16:28:29,584 - tensorflow - INFO - global_step/sec: 88.35 -2022-05-01 16:28:29,585 - tensorflow - INFO - loss = 0.13384426, step = 684200 (2.264 sec) -2022-05-01 16:28:31,855 - tensorflow - INFO - global_step/sec: 88.0722 -2022-05-01 16:28:31,856 - tensorflow - INFO - loss = 0.22158429, step = 684400 (2.271 sec) -2022-05-01 16:28:34,117 - tensorflow - INFO - global_step/sec: 88.4196 -2022-05-01 16:28:34,117 - tensorflow - INFO - loss = 0.049566347, step = 684600 (2.262 sec) -2022-05-01 16:28:36,384 - tensorflow - INFO - global_step/sec: 88.2218 -2022-05-01 16:28:36,384 - tensorflow - INFO - loss = 0.027135855, step = 684800 (2.267 sec) -2022-05-01 16:28:38,649 - tensorflow - INFO - global_step/sec: 88.3124 -2022-05-01 16:28:38,649 - tensorflow - INFO - loss = 0.022400133, step = 685000 (2.265 sec) -2022-05-01 16:28:40,914 - tensorflow - INFO - global_step/sec: 88.2866 -2022-05-01 16:28:40,915 - tensorflow - INFO - loss = 0.03185318, step = 685200 (2.265 sec) -2022-05-01 16:28:43,177 - tensorflow - INFO - global_step/sec: 88.3747 -2022-05-01 16:28:43,178 - tensorflow - INFO - loss = 0.26845255, step = 685400 (2.263 sec) -2022-05-01 16:28:45,436 - tensorflow - INFO - global_step/sec: 88.5482 -2022-05-01 16:28:45,436 - tensorflow - INFO - loss = 0.025080893, step = 685600 (2.259 sec) -2022-05-01 16:28:47,697 - tensorflow - INFO - global_step/sec: 88.4514 -2022-05-01 16:28:47,697 - tensorflow - INFO - loss = 0.07365398, step = 685800 (2.261 sec) -2022-05-01 16:28:49,949 - tensorflow - INFO - global_step/sec: 88.8204 -2022-05-01 16:28:49,949 - tensorflow - INFO - loss = 0.0758942, step = 686000 (2.252 sec) -2022-05-01 16:28:52,208 - tensorflow - INFO - global_step/sec: 88.5143 -2022-05-01 16:28:52,209 - tensorflow - INFO - loss = 0.034178488, step = 686200 (2.260 sec) -2022-05-01 16:28:54,475 - tensorflow - INFO - global_step/sec: 88.2336 -2022-05-01 16:28:54,475 - tensorflow - INFO - loss = 0.043272763, step = 686400 (2.267 sec) -2022-05-01 16:28:56,747 - tensorflow - INFO - global_step/sec: 88.0416 -2022-05-01 16:28:56,747 - tensorflow - INFO - loss = 0.033819206, step = 686600 (2.272 sec) -2022-05-01 16:28:59,028 - tensorflow - INFO - global_step/sec: 87.6627 -2022-05-01 16:28:59,029 - tensorflow - INFO - loss = 0.045066155, step = 686800 (2.282 sec) -2022-05-01 16:29:01,329 - tensorflow - INFO - global_step/sec: 86.9087 -2022-05-01 16:29:01,330 - tensorflow - INFO - loss = 0.14871101, step = 687000 (2.301 sec) -2022-05-01 16:29:03,605 - tensorflow - INFO - global_step/sec: 87.8715 -2022-05-01 16:29:03,606 - tensorflow - INFO - loss = 0.22603975, step = 687200 (2.276 sec) -2022-05-01 16:29:05,879 - tensorflow - INFO - global_step/sec: 87.9516 -2022-05-01 16:29:05,880 - tensorflow - INFO - loss = 0.03430932, step = 687400 (2.274 sec) -2022-05-01 16:29:08,156 - tensorflow - INFO - global_step/sec: 87.8438 -2022-05-01 16:29:08,157 - tensorflow - INFO - loss = 0.0072114863, step = 687600 (2.277 sec) -2022-05-01 16:29:10,426 - tensorflow - INFO - global_step/sec: 88.1105 -2022-05-01 16:29:10,426 - tensorflow - INFO - loss = 0.0153994765, step = 687800 (2.270 sec) -2022-05-01 16:29:12,701 - tensorflow - INFO - global_step/sec: 87.9208 -2022-05-01 16:29:12,701 - tensorflow - INFO - loss = 0.008310544, step = 688000 (2.275 sec) -2022-05-01 16:29:14,968 - tensorflow - INFO - global_step/sec: 88.201 -2022-05-01 16:29:14,969 - tensorflow - INFO - loss = 0.026683938, step = 688200 (2.268 sec) -2022-05-01 16:29:17,223 - tensorflow - INFO - global_step/sec: 88.6993 -2022-05-01 16:29:17,224 - tensorflow - INFO - loss = 0.00728536, step = 688400 (2.255 sec) -2022-05-01 16:29:19,482 - tensorflow - INFO - global_step/sec: 88.5408 -2022-05-01 16:29:19,482 - tensorflow - INFO - loss = 0.032275274, step = 688600 (2.259 sec) -2022-05-01 16:29:21,741 - tensorflow - INFO - global_step/sec: 88.5333 -2022-05-01 16:29:21,741 - tensorflow - INFO - loss = 0.51012313, step = 688800 (2.259 sec) -2022-05-01 16:29:23,990 - tensorflow - INFO - global_step/sec: 88.919 -2022-05-01 16:29:23,991 - tensorflow - INFO - loss = 0.014863342, step = 689000 (2.249 sec) -2022-05-01 16:29:26,246 - tensorflow - INFO - global_step/sec: 88.6489 -2022-05-01 16:29:26,247 - tensorflow - INFO - loss = 0.023834182, step = 689200 (2.256 sec) -2022-05-01 16:29:28,499 - tensorflow - INFO - global_step/sec: 88.7774 -2022-05-01 16:29:28,500 - tensorflow - INFO - loss = 0.03596233, step = 689400 (2.253 sec) -2022-05-01 16:29:30,760 - tensorflow - INFO - global_step/sec: 88.4604 -2022-05-01 16:29:30,761 - tensorflow - INFO - loss = 0.02390868, step = 689600 (2.261 sec) -2022-05-01 16:29:33,035 - tensorflow - INFO - global_step/sec: 87.9258 -2022-05-01 16:29:33,035 - tensorflow - INFO - loss = 0.021484703, step = 689800 (2.275 sec) -2022-05-01 16:29:35,300 - tensorflow - INFO - global_step/sec: 88.2833 -2022-05-01 16:29:35,301 - tensorflow - INFO - loss = 0.3240009, step = 690000 (2.265 sec) -2022-05-01 16:29:37,566 - tensorflow - INFO - global_step/sec: 88.264 -2022-05-01 16:29:37,567 - tensorflow - INFO - loss = 0.36914372, step = 690200 (2.266 sec) -2022-05-01 16:29:39,870 - tensorflow - INFO - global_step/sec: 86.8138 -2022-05-01 16:29:39,870 - tensorflow - INFO - loss = 0.34035963, step = 690400 (2.304 sec) -2022-05-01 16:29:42,161 - tensorflow - INFO - global_step/sec: 87.2855 -2022-05-01 16:29:42,162 - tensorflow - INFO - loss = 0.051499836, step = 690600 (2.291 sec) -2022-05-01 16:29:44,441 - tensorflow - INFO - global_step/sec: 87.7261 -2022-05-01 16:29:44,442 - tensorflow - INFO - loss = 0.063221216, step = 690800 (2.280 sec) -2022-05-01 16:29:46,714 - tensorflow - INFO - global_step/sec: 88.0058 -2022-05-01 16:29:46,714 - tensorflow - INFO - loss = 0.064412564, step = 691000 (2.273 sec) -2022-05-01 16:29:48,986 - tensorflow - INFO - global_step/sec: 88.0249 -2022-05-01 16:29:48,986 - tensorflow - INFO - loss = 0.03744691, step = 691200 (2.272 sec) -2022-05-01 16:29:51,261 - tensorflow - INFO - global_step/sec: 87.8832 -2022-05-01 16:29:51,262 - tensorflow - INFO - loss = 0.12669168, step = 691400 (2.276 sec) -2022-05-01 16:29:53,539 - tensorflow - INFO - global_step/sec: 87.8175 -2022-05-01 16:29:53,539 - tensorflow - INFO - loss = 0.40596506, step = 691600 (2.278 sec) -2022-05-01 16:29:55,806 - tensorflow - INFO - global_step/sec: 88.2063 -2022-05-01 16:29:55,807 - tensorflow - INFO - loss = 0.010353914, step = 691800 (2.267 sec) -2022-05-01 16:29:58,072 - tensorflow - INFO - global_step/sec: 88.2865 -2022-05-01 16:29:58,072 - tensorflow - INFO - loss = 0.040336475, step = 692000 (2.265 sec) -2022-05-01 16:30:00,327 - tensorflow - INFO - global_step/sec: 88.669 -2022-05-01 16:30:00,328 - tensorflow - INFO - loss = 0.020247448, step = 692200 (2.256 sec) -2022-05-01 16:30:02,586 - tensorflow - INFO - global_step/sec: 88.5495 -2022-05-01 16:30:02,586 - tensorflow - INFO - loss = 0.022701023, step = 692400 (2.259 sec) -2022-05-01 16:30:04,839 - tensorflow - INFO - global_step/sec: 88.7533 -2022-05-01 16:30:04,840 - tensorflow - INFO - loss = 0.012202943, step = 692600 (2.253 sec) -2022-05-01 16:30:07,093 - tensorflow - INFO - global_step/sec: 88.7234 -2022-05-01 16:30:07,094 - tensorflow - INFO - loss = 0.05705206, step = 692800 (2.254 sec) -2022-05-01 16:30:09,352 - tensorflow - INFO - global_step/sec: 88.5445 -2022-05-01 16:30:09,353 - tensorflow - INFO - loss = 0.063872375, step = 693000 (2.259 sec) -2022-05-01 16:30:11,612 - tensorflow - INFO - global_step/sec: 88.4898 -2022-05-01 16:30:11,613 - tensorflow - INFO - loss = 0.20911783, step = 693200 (2.260 sec) -2022-05-01 16:30:13,868 - tensorflow - INFO - global_step/sec: 88.6538 -2022-05-01 16:30:13,869 - tensorflow - INFO - loss = 0.17761394, step = 693400 (2.256 sec) -2022-05-01 16:30:16,123 - tensorflow - INFO - global_step/sec: 88.7222 -2022-05-01 16:30:16,123 - tensorflow - INFO - loss = 2.785748, step = 693600 (2.254 sec) -2022-05-01 16:30:18,375 - tensorflow - INFO - global_step/sec: 88.7818 -2022-05-01 16:30:18,376 - tensorflow - INFO - loss = 0.047160655, step = 693800 (2.253 sec) -2022-05-01 16:30:20,628 - tensorflow - INFO - global_step/sec: 88.7787 -2022-05-01 16:30:20,629 - tensorflow - INFO - loss = 0.14111653, step = 694000 (2.253 sec) -2022-05-01 16:30:22,884 - tensorflow - INFO - global_step/sec: 88.645 -2022-05-01 16:30:22,885 - tensorflow - INFO - loss = 0.061342344, step = 694200 (2.256 sec) -2022-05-01 16:30:25,140 - tensorflow - INFO - global_step/sec: 88.6744 -2022-05-01 16:30:25,140 - tensorflow - INFO - loss = 0.03867026, step = 694400 (2.255 sec) -2022-05-01 16:30:27,397 - tensorflow - INFO - global_step/sec: 88.6029 -2022-05-01 16:30:27,397 - tensorflow - INFO - loss = 0.034364447, step = 694600 (2.257 sec) -2022-05-01 16:30:29,651 - tensorflow - INFO - global_step/sec: 88.7231 -2022-05-01 16:30:29,652 - tensorflow - INFO - loss = 0.022616444, step = 694800 (2.254 sec) -2022-05-01 16:30:31,917 - tensorflow - INFO - global_step/sec: 88.2733 -2022-05-01 16:30:31,917 - tensorflow - INFO - loss = 0.078567564, step = 695000 (2.266 sec) -2022-05-01 16:30:34,172 - tensorflow - INFO - global_step/sec: 88.6967 -2022-05-01 16:30:34,172 - tensorflow - INFO - loss = 0.02870323, step = 695200 (2.255 sec) -2022-05-01 16:30:36,429 - tensorflow - INFO - global_step/sec: 88.6052 -2022-05-01 16:30:36,429 - tensorflow - INFO - loss = 0.027117027, step = 695400 (2.257 sec) -2022-05-01 16:30:38,675 - tensorflow - INFO - global_step/sec: 89.0403 -2022-05-01 16:30:38,676 - tensorflow - INFO - loss = 9.146388, step = 695600 (2.246 sec) -2022-05-01 16:30:40,926 - tensorflow - INFO - global_step/sec: 88.8497 -2022-05-01 16:30:40,927 - tensorflow - INFO - loss = 0.14333989, step = 695800 (2.251 sec) -2022-05-01 16:30:43,185 - tensorflow - INFO - global_step/sec: 88.5331 -2022-05-01 16:30:43,186 - tensorflow - INFO - loss = 0.3762799, step = 696000 (2.259 sec) -2022-05-01 16:30:45,458 - tensorflow - INFO - global_step/sec: 88.0085 -2022-05-01 16:30:45,458 - tensorflow - INFO - loss = 0.025378358, step = 696200 (2.272 sec) -2022-05-01 16:30:47,735 - tensorflow - INFO - global_step/sec: 87.8242 -2022-05-01 16:30:47,735 - tensorflow - INFO - loss = 0.028789608, step = 696400 (2.277 sec) -2022-05-01 16:30:50,009 - tensorflow - INFO - global_step/sec: 87.9507 -2022-05-01 16:30:50,009 - tensorflow - INFO - loss = 0.013650784, step = 696600 (2.274 sec) -2022-05-01 16:30:52,313 - tensorflow - INFO - global_step/sec: 86.8005 -2022-05-01 16:30:52,314 - tensorflow - INFO - loss = 0.029505478, step = 696800 (2.304 sec) -2022-05-01 16:30:54,587 - tensorflow - INFO - global_step/sec: 87.9393 -2022-05-01 16:30:54,588 - tensorflow - INFO - loss = 1.0471745, step = 697000 (2.274 sec) -2022-05-01 16:30:56,858 - tensorflow - INFO - global_step/sec: 88.0994 -2022-05-01 16:30:56,858 - tensorflow - INFO - loss = 0.037719212, step = 697200 (2.270 sec) -2022-05-01 16:30:59,134 - tensorflow - INFO - global_step/sec: 87.8514 -2022-05-01 16:30:59,135 - tensorflow - INFO - loss = 0.030568294, step = 697400 (2.277 sec) -2022-05-01 16:31:01,409 - tensorflow - INFO - global_step/sec: 87.9152 -2022-05-01 16:31:01,410 - tensorflow - INFO - loss = 0.13457468, step = 697600 (2.275 sec) -2022-05-01 16:31:03,682 - tensorflow - INFO - global_step/sec: 87.9968 -2022-05-01 16:31:03,682 - tensorflow - INFO - loss = 0.08395249, step = 697800 (2.273 sec) -2022-05-01 16:31:05,955 - tensorflow - INFO - global_step/sec: 87.9794 -2022-05-01 16:31:05,956 - tensorflow - INFO - loss = 0.06530699, step = 698000 (2.273 sec) -2022-05-01 16:31:08,212 - tensorflow - INFO - global_step/sec: 88.6067 -2022-05-01 16:31:08,213 - tensorflow - INFO - loss = 0.0882684, step = 698200 (2.257 sec) -2022-05-01 16:31:10,465 - tensorflow - INFO - global_step/sec: 88.7685 -2022-05-01 16:31:10,466 - tensorflow - INFO - loss = 0.060899876, step = 698400 (2.253 sec) -2022-05-01 16:31:12,720 - tensorflow - INFO - global_step/sec: 88.7142 -2022-05-01 16:31:12,720 - tensorflow - INFO - loss = 0.18980502, step = 698600 (2.254 sec) -2022-05-01 16:31:14,972 - tensorflow - INFO - global_step/sec: 88.7923 -2022-05-01 16:31:14,973 - tensorflow - INFO - loss = 0.28167945, step = 698800 (2.252 sec) -2022-05-01 16:31:17,229 - tensorflow - INFO - global_step/sec: 88.6269 -2022-05-01 16:31:17,229 - tensorflow - INFO - loss = 0.015430516, step = 699000 (2.257 sec) -2022-05-01 16:31:19,482 - tensorflow - INFO - global_step/sec: 88.7613 -2022-05-01 16:31:19,483 - tensorflow - INFO - loss = 0.020742904, step = 699200 (2.253 sec) -2022-05-01 16:31:21,733 - tensorflow - INFO - global_step/sec: 88.8653 -2022-05-01 16:31:21,733 - tensorflow - INFO - loss = 0.020765623, step = 699400 (2.251 sec) -2022-05-01 16:31:23,993 - tensorflow - INFO - global_step/sec: 88.4763 -2022-05-01 16:31:23,994 - tensorflow - INFO - loss = 0.018228173, step = 699600 (2.261 sec) -2022-05-01 16:31:26,250 - tensorflow - INFO - global_step/sec: 88.6307 -2022-05-01 16:31:26,250 - tensorflow - INFO - loss = 0.022793582, step = 699800 (2.257 sec) -2022-05-01 16:31:28,499 - tensorflow - INFO - Saving checkpoints for 700000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. -2022-05-01 16:31:28,517 - tensorflow - INFO - model.ckpt-700000 is not in all_model_checkpoint_paths. Manually adding it. -2022-05-01 16:31:29,045 - tensorflow - INFO - Calling model_fn. -2022-05-01 16:31:29,177 - tensorflow - INFO - Done calling model_fn. -2022-05-01 16:31:29,192 - tensorflow - INFO - Starting evaluation at 2022-05-01T16:31:29Z -2022-05-01 16:31:29,256 - tensorflow - INFO - Graph was finalized. -2022-05-01 16:31:29,259 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-700000 -2022-05-01 16:31:29,540 - tensorflow - INFO - Running local_init_op. -2022-05-01 16:31:29,646 - tensorflow - INFO - Done running local_init_op. -2022-05-01 16:31:39,785 - tensorflow - INFO - Evaluation [3990/39900] -2022-05-01 16:31:49,238 - tensorflow - INFO - Evaluation [7980/39900] -2022-05-01 16:31:58,714 - tensorflow - INFO - Evaluation [11970/39900] -2022-05-01 16:32:08,192 - tensorflow - INFO - Evaluation [15960/39900] -2022-05-01 16:32:17,622 - tensorflow - INFO - Evaluation [19950/39900] -2022-05-01 16:32:27,039 - tensorflow - INFO - Evaluation [23940/39900] -2022-05-01 16:32:36,492 - tensorflow - INFO - Evaluation [27930/39900] -2022-05-01 16:32:45,931 - tensorflow - INFO - Evaluation [31920/39900] -2022-05-01 16:32:55,356 - tensorflow - INFO - Evaluation [35910/39900] -2022-05-01 16:33:04,797 - tensorflow - INFO - Evaluation [39900/39900] -2022-05-01 16:33:04,934 - tensorflow - INFO - Finished evaluation at 2022-05-01-16:33:04 -2022-05-01 16:33:04,934 - tensorflow - INFO - Saving dict for global step 700000: global_step = 700000, loss = 0.03090773, mean_degree_err = 4.9194665 -2022-05-01 16:33:04,934 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 700000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-700000 -2022-05-01 16:33:04,948 - tensorflow - INFO - global_step/sec: 2.02638 -2022-05-01 16:33:04,949 - tensorflow - INFO - loss = 0.45383355, step = 700000 (98.699 sec) -2022-05-01 16:33:07,219 - tensorflow - INFO - global_step/sec: 88.0497 -2022-05-01 16:33:07,220 - tensorflow - INFO - loss = 0.02814351, step = 700200 (2.271 sec) -2022-05-01 16:33:09,476 - tensorflow - INFO - global_step/sec: 88.6366 -2022-05-01 16:33:09,476 - tensorflow - INFO - loss = 0.031599652, step = 700400 (2.256 sec) -2022-05-01 16:33:11,728 - tensorflow - INFO - global_step/sec: 88.8244 -2022-05-01 16:33:11,728 - tensorflow - INFO - loss = 0.06685158, step = 700600 (2.252 sec) -2022-05-01 16:33:13,984 - tensorflow - INFO - global_step/sec: 88.6234 -2022-05-01 16:33:13,985 - tensorflow - INFO - loss = 0.05536495, step = 700800 (2.257 sec) -2022-05-01 16:33:16,239 - tensorflow - INFO - global_step/sec: 88.7024 -2022-05-01 16:33:16,239 - tensorflow - INFO - loss = 0.009829298, step = 701000 (2.255 sec) -2022-05-01 16:33:18,494 - tensorflow - INFO - global_step/sec: 88.679 -2022-05-01 16:33:18,495 - tensorflow - INFO - loss = 0.02428996, step = 701200 (2.255 sec) -2022-05-01 16:33:20,752 - tensorflow - INFO - global_step/sec: 88.6027 -2022-05-01 16:33:20,752 - tensorflow - INFO - loss = 0.018015191, step = 701400 (2.257 sec) -2022-05-01 16:33:23,006 - tensorflow - INFO - global_step/sec: 88.7342 -2022-05-01 16:33:23,006 - tensorflow - INFO - loss = 0.07055589, step = 701600 (2.254 sec) -2022-05-01 16:33:25,255 - tensorflow - INFO - global_step/sec: 88.92 -2022-05-01 16:33:25,255 - tensorflow - INFO - loss = 0.6384876, step = 701800 (2.249 sec) -2022-05-01 16:33:27,511 - tensorflow - INFO - global_step/sec: 88.6497 -2022-05-01 16:33:27,511 - tensorflow - INFO - loss = 0.014400528, step = 702000 (2.256 sec) -2022-05-01 16:33:29,762 - tensorflow - INFO - global_step/sec: 88.8495 -2022-05-01 16:33:29,762 - tensorflow - INFO - loss = 0.030938236, step = 702200 (2.251 sec) -2022-05-01 16:33:32,022 - tensorflow - INFO - global_step/sec: 88.5049 -2022-05-01 16:33:32,022 - tensorflow - INFO - loss = 0.05836013, step = 702400 (2.260 sec) -2022-05-01 16:33:34,280 - tensorflow - INFO - global_step/sec: 88.5689 -2022-05-01 16:33:34,280 - tensorflow - INFO - loss = 0.014873834, step = 702600 (2.258 sec) -2022-05-01 16:33:36,535 - tensorflow - INFO - global_step/sec: 88.6665 -2022-05-01 16:33:36,536 - tensorflow - INFO - loss = 0.05012783, step = 702800 (2.256 sec) -2022-05-01 16:33:38,792 - tensorflow - INFO - global_step/sec: 88.6365 -2022-05-01 16:33:38,792 - tensorflow - INFO - loss = 0.04358377, step = 703000 (2.256 sec) -2022-05-01 16:33:41,051 - tensorflow - INFO - global_step/sec: 88.5095 -2022-05-01 16:33:41,052 - tensorflow - INFO - loss = 0.028191432, step = 703200 (2.260 sec) -2022-05-01 16:33:43,301 - tensorflow - INFO - global_step/sec: 88.914 -2022-05-01 16:33:43,301 - tensorflow - INFO - loss = 0.047327407, step = 703400 (2.249 sec) -2022-05-01 16:33:45,567 - tensorflow - INFO - global_step/sec: 88.2587 -2022-05-01 16:33:45,567 - tensorflow - INFO - loss = 0.06812384, step = 703600 (2.266 sec) -2022-05-01 16:33:47,818 - tensorflow - INFO - global_step/sec: 88.8492 -2022-05-01 16:33:47,818 - tensorflow - INFO - loss = 0.033104226, step = 703800 (2.251 sec) -2022-05-01 16:33:50,072 - tensorflow - INFO - global_step/sec: 88.7064 -2022-05-01 16:33:50,073 - tensorflow - INFO - loss = 0.06181826, step = 704000 (2.255 sec) -2022-05-01 16:33:52,331 - tensorflow - INFO - global_step/sec: 88.5635 -2022-05-01 16:33:52,331 - tensorflow - INFO - loss = 2.1382723, step = 704200 (2.258 sec) -2022-05-01 16:33:54,584 - tensorflow - INFO - global_step/sec: 88.7633 -2022-05-01 16:33:54,584 - tensorflow - INFO - loss = 0.01735505, step = 704400 (2.253 sec) -2022-05-01 16:33:56,845 - tensorflow - INFO - global_step/sec: 88.4379 -2022-05-01 16:33:56,846 - tensorflow - INFO - loss = 0.030577263, step = 704600 (2.261 sec) -2022-05-01 16:33:59,103 - tensorflow - INFO - global_step/sec: 88.5835 -2022-05-01 16:33:59,104 - tensorflow - INFO - loss = 0.053564556, step = 704800 (2.258 sec) -2022-05-01 16:34:01,356 - tensorflow - INFO - global_step/sec: 88.7922 -2022-05-01 16:34:01,356 - tensorflow - INFO - loss = 0.03156396, step = 705000 (2.252 sec) -2022-05-01 16:34:03,613 - tensorflow - INFO - global_step/sec: 88.6046 -2022-05-01 16:34:03,613 - tensorflow - INFO - loss = 0.023791179, step = 705200 (2.257 sec) -2022-05-01 16:34:05,863 - tensorflow - INFO - global_step/sec: 88.8913 -2022-05-01 16:34:05,863 - tensorflow - INFO - loss = 0.2363714, step = 705400 (2.250 sec) -2022-05-01 16:34:08,119 - tensorflow - INFO - global_step/sec: 88.6522 -2022-05-01 16:34:08,119 - tensorflow - INFO - loss = 0.22751844, step = 705600 (2.256 sec) -2022-05-01 16:34:10,378 - tensorflow - INFO - global_step/sec: 88.5083 -2022-05-01 16:34:10,379 - tensorflow - INFO - loss = 0.02583364, step = 705800 (2.260 sec) -2022-05-01 16:34:12,631 - tensorflow - INFO - global_step/sec: 88.7984 -2022-05-01 16:34:12,631 - tensorflow - INFO - loss = 0.24674636, step = 706000 (2.252 sec) -2022-05-01 16:34:14,882 - tensorflow - INFO - global_step/sec: 88.851 -2022-05-01 16:34:14,882 - tensorflow - INFO - loss = 0.05409362, step = 706200 (2.251 sec) -2022-05-01 16:34:17,134 - tensorflow - INFO - global_step/sec: 88.795 -2022-05-01 16:34:17,134 - tensorflow - INFO - loss = 0.018007115, step = 706400 (2.252 sec) -2022-05-01 16:34:19,391 - tensorflow - INFO - global_step/sec: 88.6251 -2022-05-01 16:34:19,391 - tensorflow - INFO - loss = 0.0582543, step = 706600 (2.257 sec) -2022-05-01 16:34:21,647 - tensorflow - INFO - global_step/sec: 88.6337 -2022-05-01 16:34:21,648 - tensorflow - INFO - loss = 0.027885325, step = 706800 (2.256 sec) -2022-05-01 16:34:23,910 - tensorflow - INFO - global_step/sec: 88.397 -2022-05-01 16:34:23,910 - tensorflow - INFO - loss = 0.13973656, step = 707000 (2.262 sec) -2022-05-01 16:34:26,168 - tensorflow - INFO - global_step/sec: 88.5628 -2022-05-01 16:34:26,168 - tensorflow - INFO - loss = 0.2939297, step = 707200 (2.258 sec) -2022-05-01 16:34:28,423 - tensorflow - INFO - global_step/sec: 88.7049 -2022-05-01 16:34:28,423 - tensorflow - INFO - loss = 0.033999607, step = 707400 (2.255 sec) -2022-05-01 16:34:30,681 - tensorflow - INFO - global_step/sec: 88.5625 -2022-05-01 16:34:30,681 - tensorflow - INFO - loss = 0.048705816, step = 707600 (2.258 sec) -2022-05-01 16:34:32,944 - tensorflow - INFO - global_step/sec: 88.3819 -2022-05-01 16:34:32,944 - tensorflow - INFO - loss = 0.22880709, step = 707800 (2.263 sec) -2022-05-01 16:34:35,210 - tensorflow - INFO - global_step/sec: 88.2509 -2022-05-01 16:34:35,211 - tensorflow - INFO - loss = 0.029884279, step = 708000 (2.266 sec) -2022-05-01 16:34:37,465 - tensorflow - INFO - global_step/sec: 88.7019 -2022-05-01 16:34:37,465 - tensorflow - INFO - loss = 0.043589737, step = 708200 (2.255 sec) -2022-05-01 16:34:39,720 - tensorflow - INFO - global_step/sec: 88.6972 -2022-05-01 16:34:39,720 - tensorflow - INFO - loss = 0.14944857, step = 708400 (2.255 sec) -2022-05-01 16:34:41,978 - tensorflow - INFO - global_step/sec: 88.5747 -2022-05-01 16:34:41,978 - tensorflow - INFO - loss = 0.038527794, step = 708600 (2.258 sec) -2022-05-01 16:34:44,235 - tensorflow - INFO - global_step/sec: 88.6068 -2022-05-01 16:34:44,235 - tensorflow - INFO - loss = 0.023336954, step = 708800 (2.257 sec) -2022-05-01 16:34:46,492 - tensorflow - INFO - global_step/sec: 88.5937 -2022-05-01 16:34:46,493 - tensorflow - INFO - loss = 0.1388255, step = 709000 (2.258 sec) -2022-05-01 16:34:48,754 - tensorflow - INFO - global_step/sec: 88.4207 -2022-05-01 16:34:48,755 - tensorflow - INFO - loss = 0.087768376, step = 709200 (2.262 sec) -2022-05-01 16:34:51,009 - tensorflow - INFO - global_step/sec: 88.7091 -2022-05-01 16:34:51,009 - tensorflow - INFO - loss = 0.58065426, step = 709400 (2.255 sec) -2022-05-01 16:34:53,268 - tensorflow - INFO - global_step/sec: 88.5182 -2022-05-01 16:34:53,269 - tensorflow - INFO - loss = 0.0979935, step = 709600 (2.259 sec) -2022-05-01 16:34:55,522 - tensorflow - INFO - global_step/sec: 88.7484 -2022-05-01 16:34:55,522 - tensorflow - INFO - loss = 0.064526305, step = 709800 (2.254 sec) -2022-05-01 16:34:57,782 - tensorflow - INFO - global_step/sec: 88.5071 -2022-05-01 16:34:57,782 - tensorflow - INFO - loss = 0.073237136, step = 710000 (2.260 sec) -2022-05-01 16:35:00,037 - tensorflow - INFO - global_step/sec: 88.6766 -2022-05-01 16:35:00,037 - tensorflow - INFO - loss = 0.16119538, step = 710200 (2.255 sec) -2022-05-01 16:35:02,300 - tensorflow - INFO - global_step/sec: 88.3645 -2022-05-01 16:35:02,301 - tensorflow - INFO - loss = 0.020417998, step = 710400 (2.263 sec) -2022-05-01 16:35:04,554 - tensorflow - INFO - global_step/sec: 88.7478 -2022-05-01 16:35:04,554 - tensorflow - INFO - loss = 0.12026501, step = 710600 (2.254 sec) -2022-05-01 16:35:06,808 - tensorflow - INFO - global_step/sec: 88.7098 -2022-05-01 16:35:06,809 - tensorflow - INFO - loss = 0.048558436, step = 710800 (2.255 sec) -2022-05-01 16:35:09,059 - tensorflow - INFO - global_step/sec: 88.8573 -2022-05-01 16:35:09,060 - tensorflow - INFO - loss = 0.059880316, step = 711000 (2.251 sec) -2022-05-01 16:35:11,314 - tensorflow - INFO - global_step/sec: 88.6877 -2022-05-01 16:35:11,315 - tensorflow - INFO - loss = 0.06387286, step = 711200 (2.255 sec) -2022-05-01 16:35:13,576 - tensorflow - INFO - global_step/sec: 88.4353 -2022-05-01 16:35:13,576 - tensorflow - INFO - loss = 0.047975764, step = 711400 (2.262 sec) -2022-05-01 16:35:15,839 - tensorflow - INFO - global_step/sec: 88.3751 -2022-05-01 16:35:15,839 - tensorflow - INFO - loss = 0.33253038, step = 711600 (2.263 sec) -2022-05-01 16:35:18,101 - tensorflow - INFO - global_step/sec: 88.4015 -2022-05-01 16:35:18,102 - tensorflow - INFO - loss = 0.06969159, step = 711800 (2.262 sec) -2022-05-01 16:35:20,366 - tensorflow - INFO - global_step/sec: 88.3211 -2022-05-01 16:35:20,366 - tensorflow - INFO - loss = 0.10640546, step = 712000 (2.264 sec) -2022-05-01 16:35:22,627 - tensorflow - INFO - global_step/sec: 88.4421 -2022-05-01 16:35:22,628 - tensorflow - INFO - loss = 0.0625714, step = 712200 (2.261 sec) -2022-05-01 16:35:24,893 - tensorflow - INFO - global_step/sec: 88.2579 -2022-05-01 16:35:24,894 - tensorflow - INFO - loss = 0.03881578, step = 712400 (2.266 sec) -2022-05-01 16:35:27,159 - tensorflow - INFO - global_step/sec: 88.2578 -2022-05-01 16:35:27,160 - tensorflow - INFO - loss = 0.075513996, step = 712600 (2.266 sec) -2022-05-01 16:35:29,422 - tensorflow - INFO - global_step/sec: 88.373 -2022-05-01 16:35:29,423 - tensorflow - INFO - loss = 0.02635355, step = 712800 (2.263 sec) -2022-05-01 16:35:31,691 - tensorflow - INFO - global_step/sec: 88.1749 -2022-05-01 16:35:31,691 - tensorflow - INFO - loss = 0.04076782, step = 713000 (2.268 sec) -2022-05-01 16:35:33,955 - tensorflow - INFO - global_step/sec: 88.3223 -2022-05-01 16:35:33,956 - tensorflow - INFO - loss = 0.053160693, step = 713200 (2.264 sec) -2022-05-01 16:35:36,221 - tensorflow - INFO - global_step/sec: 88.2492 -2022-05-01 16:35:36,222 - tensorflow - INFO - loss = 0.013308646, step = 713400 (2.266 sec) -2022-05-01 16:35:38,482 - tensorflow - INFO - global_step/sec: 88.4628 -2022-05-01 16:35:38,483 - tensorflow - INFO - loss = 0.050666675, step = 713600 (2.261 sec) -2022-05-01 16:35:40,745 - tensorflow - INFO - global_step/sec: 88.4056 -2022-05-01 16:35:40,745 - tensorflow - INFO - loss = 0.0086106695, step = 713800 (2.262 sec) -2022-05-01 16:35:42,998 - tensorflow - INFO - global_step/sec: 88.7668 -2022-05-01 16:35:42,998 - tensorflow - INFO - loss = 0.058115117, step = 714000 (2.253 sec) -2022-05-01 16:35:45,251 - tensorflow - INFO - global_step/sec: 88.7722 -2022-05-01 16:35:45,251 - tensorflow - INFO - loss = 0.6640632, step = 714200 (2.253 sec) -2022-05-01 16:35:47,504 - tensorflow - INFO - global_step/sec: 88.7755 -2022-05-01 16:35:47,504 - tensorflow - INFO - loss = 1.7317725, step = 714400 (2.253 sec) -2022-05-01 16:35:49,760 - tensorflow - INFO - global_step/sec: 88.6234 -2022-05-01 16:35:49,761 - tensorflow - INFO - loss = 0.03569136, step = 714600 (2.257 sec) -2022-05-01 16:35:52,017 - tensorflow - INFO - global_step/sec: 88.6171 -2022-05-01 16:35:52,018 - tensorflow - INFO - loss = 0.27631834, step = 714800 (2.257 sec) -2022-05-01 16:35:54,277 - tensorflow - INFO - global_step/sec: 88.4927 -2022-05-01 16:35:54,278 - tensorflow - INFO - loss = 0.008753429, step = 715000 (2.260 sec) -2022-05-01 16:35:56,533 - tensorflow - INFO - global_step/sec: 88.6488 -2022-05-01 16:35:56,534 - tensorflow - INFO - loss = 0.035814345, step = 715200 (2.256 sec) -2022-05-01 16:35:58,784 - tensorflow - INFO - global_step/sec: 88.8695 -2022-05-01 16:35:58,784 - tensorflow - INFO - loss = 0.0340343, step = 715400 (2.250 sec) -2022-05-01 16:36:01,036 - tensorflow - INFO - global_step/sec: 88.8126 -2022-05-01 16:36:01,036 - tensorflow - INFO - loss = 0.017041814, step = 715600 (2.252 sec) -2022-05-01 16:36:03,293 - tensorflow - INFO - global_step/sec: 88.5987 -2022-05-01 16:36:03,294 - tensorflow - INFO - loss = 0.013692578, step = 715800 (2.257 sec) -2022-05-01 16:36:05,554 - tensorflow - INFO - global_step/sec: 88.4608 -2022-05-01 16:36:05,554 - tensorflow - INFO - loss = 0.070862934, step = 716000 (2.261 sec) -2022-05-01 16:36:07,811 - tensorflow - INFO - global_step/sec: 88.629 -2022-05-01 16:36:07,811 - tensorflow - INFO - loss = 0.16747123, step = 716200 (2.257 sec) -2022-05-01 16:36:10,063 - tensorflow - INFO - global_step/sec: 88.8031 -2022-05-01 16:36:10,063 - tensorflow - INFO - loss = 0.035815883, step = 716400 (2.252 sec) -2022-05-01 16:36:12,311 - tensorflow - INFO - global_step/sec: 88.9743 -2022-05-01 16:36:12,311 - tensorflow - INFO - loss = 0.033828985, step = 716600 (2.248 sec) -2022-05-01 16:36:14,566 - tensorflow - INFO - global_step/sec: 88.6695 -2022-05-01 16:36:14,567 - tensorflow - INFO - loss = 0.020342097, step = 716800 (2.256 sec) -2022-05-01 16:36:16,819 - tensorflow - INFO - global_step/sec: 88.7713 -2022-05-01 16:36:16,820 - tensorflow - INFO - loss = 0.29343623, step = 717000 (2.253 sec) -2022-05-01 16:36:19,081 - tensorflow - INFO - global_step/sec: 88.4343 -2022-05-01 16:36:19,081 - tensorflow - INFO - loss = 0.07206562, step = 717200 (2.262 sec) -2022-05-01 16:36:21,332 - tensorflow - INFO - global_step/sec: 88.8478 -2022-05-01 16:36:21,332 - tensorflow - INFO - loss = 0.13875133, step = 717400 (2.251 sec) -2022-05-01 16:36:23,591 - tensorflow - INFO - global_step/sec: 88.5388 -2022-05-01 16:36:23,591 - tensorflow - INFO - loss = 0.075220086, step = 717600 (2.259 sec) -2022-05-01 16:36:25,847 - tensorflow - INFO - global_step/sec: 88.6257 -2022-05-01 16:36:25,848 - tensorflow - INFO - loss = 0.019268338, step = 717800 (2.257 sec) -2022-05-01 16:36:28,108 - tensorflow - INFO - global_step/sec: 88.4847 -2022-05-01 16:36:28,108 - tensorflow - INFO - loss = 0.017318504, step = 718000 (2.260 sec) -2022-05-01 16:36:30,372 - tensorflow - INFO - global_step/sec: 88.3151 -2022-05-01 16:36:30,373 - tensorflow - INFO - loss = 0.02075294, step = 718200 (2.265 sec) -2022-05-01 16:36:32,637 - tensorflow - INFO - global_step/sec: 88.3165 -2022-05-01 16:36:32,637 - tensorflow - INFO - loss = 0.013050616, step = 718400 (2.265 sec) -2022-05-01 16:36:34,895 - tensorflow - INFO - global_step/sec: 88.5666 -2022-05-01 16:36:34,895 - tensorflow - INFO - loss = 0.053279806, step = 718600 (2.258 sec) -2022-05-01 16:36:37,151 - tensorflow - INFO - global_step/sec: 88.6638 -2022-05-01 16:36:37,151 - tensorflow - INFO - loss = 0.06466445, step = 718800 (2.256 sec) -2022-05-01 16:36:39,406 - tensorflow - INFO - global_step/sec: 88.6843 -2022-05-01 16:36:39,406 - tensorflow - INFO - loss = 0.06200307, step = 719000 (2.255 sec) -2022-05-01 16:36:41,657 - tensorflow - INFO - global_step/sec: 88.8374 -2022-05-01 16:36:41,658 - tensorflow - INFO - loss = 0.123050734, step = 719200 (2.251 sec) -2022-05-01 16:36:43,917 - tensorflow - INFO - global_step/sec: 88.5099 -2022-05-01 16:36:43,917 - tensorflow - INFO - loss = 0.017426074, step = 719400 (2.260 sec) -2022-05-01 16:36:46,170 - tensorflow - INFO - global_step/sec: 88.7647 -2022-05-01 16:36:46,170 - tensorflow - INFO - loss = 0.01955615, step = 719600 (2.253 sec) -2022-05-01 16:36:48,424 - tensorflow - INFO - global_step/sec: 88.7369 -2022-05-01 16:36:48,424 - tensorflow - INFO - loss = 0.020969622, step = 719800 (2.254 sec) -2022-05-01 16:36:50,677 - tensorflow - INFO - global_step/sec: 88.7524 -2022-05-01 16:36:50,678 - tensorflow - INFO - loss = 0.012453387, step = 720000 (2.253 sec) -2022-05-01 16:36:52,936 - tensorflow - INFO - global_step/sec: 88.5565 -2022-05-01 16:36:52,936 - tensorflow - INFO - loss = 1.1794183, step = 720200 (2.258 sec) -2022-05-01 16:36:55,188 - tensorflow - INFO - global_step/sec: 88.7919 -2022-05-01 16:36:55,189 - tensorflow - INFO - loss = 0.03199632, step = 720400 (2.252 sec) -2022-05-01 16:36:57,452 - tensorflow - INFO - global_step/sec: 88.3383 -2022-05-01 16:36:57,453 - tensorflow - INFO - loss = 0.015764184, step = 720600 (2.264 sec) -2022-05-01 16:36:59,708 - tensorflow - INFO - global_step/sec: 88.6761 -2022-05-01 16:36:59,708 - tensorflow - INFO - loss = 0.045435835, step = 720800 (2.255 sec) -2022-05-01 16:37:01,959 - tensorflow - INFO - global_step/sec: 88.8219 -2022-05-01 16:37:01,960 - tensorflow - INFO - loss = 0.029593222, step = 721000 (2.252 sec) -2022-05-01 16:37:04,213 - tensorflow - INFO - global_step/sec: 88.7433 -2022-05-01 16:37:04,213 - tensorflow - INFO - loss = 0.03411385, step = 721200 (2.254 sec) -2022-05-01 16:37:06,463 - tensorflow - INFO - global_step/sec: 88.8707 -2022-05-01 16:37:06,464 - tensorflow - INFO - loss = 0.07939213, step = 721400 (2.250 sec) -2022-05-01 16:37:08,723 - tensorflow - INFO - global_step/sec: 88.5307 -2022-05-01 16:37:08,723 - tensorflow - INFO - loss = 0.026088836, step = 721600 (2.259 sec) -2022-05-01 16:37:10,975 - tensorflow - INFO - global_step/sec: 88.7871 -2022-05-01 16:37:10,976 - tensorflow - INFO - loss = 0.06628123, step = 721800 (2.253 sec) -2022-05-01 16:37:13,226 - tensorflow - INFO - global_step/sec: 88.8584 -2022-05-01 16:37:13,226 - tensorflow - INFO - loss = 0.033783603, step = 722000 (2.251 sec) -2022-05-01 16:37:15,485 - tensorflow - INFO - global_step/sec: 88.5277 -2022-05-01 16:37:15,486 - tensorflow - INFO - loss = 0.008120764, step = 722200 (2.259 sec) -2022-05-01 16:37:17,739 - tensorflow - INFO - global_step/sec: 88.7371 -2022-05-01 16:37:17,739 - tensorflow - INFO - loss = 0.064204775, step = 722400 (2.254 sec) -2022-05-01 16:37:19,994 - tensorflow - INFO - global_step/sec: 88.6942 -2022-05-01 16:37:19,994 - tensorflow - INFO - loss = 0.024690893, step = 722600 (2.255 sec) -2022-05-01 16:37:22,252 - tensorflow - INFO - global_step/sec: 88.5544 -2022-05-01 16:37:22,253 - tensorflow - INFO - loss = 0.027418625, step = 722800 (2.259 sec) -2022-05-01 16:37:24,504 - tensorflow - INFO - global_step/sec: 88.8227 -2022-05-01 16:37:24,504 - tensorflow - INFO - loss = 0.020150892, step = 723000 (2.252 sec) -2022-05-01 16:37:26,756 - tensorflow - INFO - global_step/sec: 88.8016 -2022-05-01 16:37:26,757 - tensorflow - INFO - loss = 0.04431514, step = 723200 (2.252 sec) -2022-05-01 16:37:29,015 - tensorflow - INFO - global_step/sec: 88.5523 -2022-05-01 16:37:29,015 - tensorflow - INFO - loss = 0.073784016, step = 723400 (2.259 sec) -2022-05-01 16:37:31,281 - tensorflow - INFO - global_step/sec: 88.2482 -2022-05-01 16:37:31,282 - tensorflow - INFO - loss = 0.05834811, step = 723600 (2.266 sec) -2022-05-01 16:37:33,546 - tensorflow - INFO - global_step/sec: 88.3134 -2022-05-01 16:37:33,546 - tensorflow - INFO - loss = 0.018725693, step = 723800 (2.265 sec) -2022-05-01 16:37:35,798 - tensorflow - INFO - global_step/sec: 88.8227 -2022-05-01 16:37:35,798 - tensorflow - INFO - loss = 0.014359465, step = 724000 (2.252 sec) -2022-05-01 16:37:38,054 - tensorflow - INFO - global_step/sec: 88.6434 -2022-05-01 16:37:38,054 - tensorflow - INFO - loss = 0.23278178, step = 724200 (2.256 sec) -2022-05-01 16:37:40,302 - tensorflow - INFO - global_step/sec: 88.9386 -2022-05-01 16:37:40,303 - tensorflow - INFO - loss = 0.092707746, step = 724400 (2.249 sec) -2022-05-01 16:37:42,553 - tensorflow - INFO - global_step/sec: 88.8561 -2022-05-01 16:37:42,554 - tensorflow - INFO - loss = 0.01683535, step = 724600 (2.251 sec) -2022-05-01 16:37:44,808 - tensorflow - INFO - global_step/sec: 88.6938 -2022-05-01 16:37:44,809 - tensorflow - INFO - loss = 0.29092342, step = 724800 (2.255 sec) -2022-05-01 16:37:47,066 - tensorflow - INFO - global_step/sec: 88.5925 -2022-05-01 16:37:47,067 - tensorflow - INFO - loss = 16.705872, step = 725000 (2.258 sec) -2022-05-01 16:37:49,320 - tensorflow - INFO - global_step/sec: 88.7268 -2022-05-01 16:37:49,320 - tensorflow - INFO - loss = 0.10117188, step = 725200 (2.254 sec) -2022-05-01 16:37:51,576 - tensorflow - INFO - global_step/sec: 88.6574 -2022-05-01 16:37:51,576 - tensorflow - INFO - loss = 0.482465, step = 725400 (2.256 sec) -2022-05-01 16:37:53,832 - tensorflow - INFO - global_step/sec: 88.6507 -2022-05-01 16:37:53,832 - tensorflow - INFO - loss = 0.037618928, step = 725600 (2.256 sec) -2022-05-01 16:37:56,088 - tensorflow - INFO - global_step/sec: 88.6363 -2022-05-01 16:37:56,089 - tensorflow - INFO - loss = 0.04519862, step = 725800 (2.256 sec) -2022-05-01 16:37:58,346 - tensorflow - INFO - global_step/sec: 88.589 -2022-05-01 16:37:58,346 - tensorflow - INFO - loss = 0.03897709, step = 726000 (2.258 sec) -2022-05-01 16:38:00,599 - tensorflow - INFO - global_step/sec: 88.7544 -2022-05-01 16:38:00,600 - tensorflow - INFO - loss = 0.05559309, step = 726200 (2.253 sec) -2022-05-01 16:38:02,854 - tensorflow - INFO - global_step/sec: 88.6839 -2022-05-01 16:38:02,855 - tensorflow - INFO - loss = 0.16982447, step = 726400 (2.255 sec) -2022-05-01 16:38:05,108 - tensorflow - INFO - global_step/sec: 88.7426 -2022-05-01 16:38:05,109 - tensorflow - INFO - loss = 0.09494547, step = 726600 (2.254 sec) -2022-05-01 16:38:07,365 - tensorflow - INFO - global_step/sec: 88.6271 -2022-05-01 16:38:07,365 - tensorflow - INFO - loss = 0.09298262, step = 726800 (2.257 sec) -2022-05-01 16:38:09,618 - tensorflow - INFO - global_step/sec: 88.7821 -2022-05-01 16:38:09,618 - tensorflow - INFO - loss = 0.044713005, step = 727000 (2.253 sec) -2022-05-01 16:38:11,875 - tensorflow - INFO - global_step/sec: 88.6112 -2022-05-01 16:38:11,875 - tensorflow - INFO - loss = 0.027567424, step = 727200 (2.257 sec) -2022-05-01 16:38:14,128 - tensorflow - INFO - global_step/sec: 88.7501 -2022-05-01 16:38:14,129 - tensorflow - INFO - loss = 0.027084675, step = 727400 (2.254 sec) -2022-05-01 16:38:16,380 - tensorflow - INFO - global_step/sec: 88.8139 -2022-05-01 16:38:16,380 - tensorflow - INFO - loss = 0.025534812, step = 727600 (2.252 sec) -2022-05-01 16:38:18,628 - tensorflow - INFO - global_step/sec: 88.959 -2022-05-01 16:38:18,629 - tensorflow - INFO - loss = 9.381817, step = 727800 (2.248 sec) -2022-05-01 16:38:20,882 - tensorflow - INFO - global_step/sec: 88.7315 -2022-05-01 16:38:20,883 - tensorflow - INFO - loss = 0.07206005, step = 728000 (2.254 sec) -2022-05-01 16:38:23,140 - tensorflow - INFO - global_step/sec: 88.5869 -2022-05-01 16:38:23,140 - tensorflow - INFO - loss = 0.038026977, step = 728200 (2.258 sec) -2022-05-01 16:38:25,395 - tensorflow - INFO - global_step/sec: 88.6972 -2022-05-01 16:38:25,395 - tensorflow - INFO - loss = 0.057235397, step = 728400 (2.255 sec) -2022-05-01 16:38:27,647 - tensorflow - INFO - global_step/sec: 88.7843 -2022-05-01 16:38:27,648 - tensorflow - INFO - loss = 0.03770804, step = 728600 (2.253 sec) -2022-05-01 16:38:29,900 - tensorflow - INFO - global_step/sec: 88.803 -2022-05-01 16:38:29,900 - tensorflow - INFO - loss = 18.11336, step = 728800 (2.252 sec) -2022-05-01 16:38:32,163 - tensorflow - INFO - global_step/sec: 88.3691 -2022-05-01 16:38:32,163 - tensorflow - INFO - loss = 0.01627003, step = 729000 (2.263 sec) -2022-05-01 16:38:34,418 - tensorflow - INFO - global_step/sec: 88.6737 -2022-05-01 16:38:34,419 - tensorflow - INFO - loss = 0.020768674, step = 729200 (2.256 sec) -2022-05-01 16:38:36,676 - tensorflow - INFO - global_step/sec: 88.596 -2022-05-01 16:38:36,676 - tensorflow - INFO - loss = 0.013888532, step = 729400 (2.257 sec) -2022-05-01 16:38:38,930 - tensorflow - INFO - global_step/sec: 88.7166 -2022-05-01 16:38:38,930 - tensorflow - INFO - loss = 0.12845708, step = 729600 (2.254 sec) -2022-05-01 16:38:41,190 - tensorflow - INFO - global_step/sec: 88.5067 -2022-05-01 16:38:41,190 - tensorflow - INFO - loss = 0.12044362, step = 729800 (2.260 sec) -2022-05-01 16:38:43,455 - tensorflow - INFO - global_step/sec: 88.2746 -2022-05-01 16:38:43,456 - tensorflow - INFO - loss = 0.026952935, step = 730000 (2.266 sec) -2022-05-01 16:38:45,721 - tensorflow - INFO - global_step/sec: 88.2832 -2022-05-01 16:38:45,721 - tensorflow - INFO - loss = 0.022035994, step = 730200 (2.265 sec) -2022-05-01 16:38:47,986 - tensorflow - INFO - global_step/sec: 88.2769 -2022-05-01 16:38:47,987 - tensorflow - INFO - loss = 0.020752344, step = 730400 (2.266 sec) -2022-05-01 16:38:50,249 - tensorflow - INFO - global_step/sec: 88.4036 -2022-05-01 16:38:50,249 - tensorflow - INFO - loss = 0.2895414, step = 730600 (2.262 sec) -2022-05-01 16:38:52,512 - tensorflow - INFO - global_step/sec: 88.3743 -2022-05-01 16:38:52,512 - tensorflow - INFO - loss = 0.024152901, step = 730800 (2.263 sec) -2022-05-01 16:38:54,774 - tensorflow - INFO - global_step/sec: 88.4154 -2022-05-01 16:38:54,774 - tensorflow - INFO - loss = 0.09352809, step = 731000 (2.262 sec) -2022-05-01 16:38:57,039 - tensorflow - INFO - global_step/sec: 88.285 -2022-05-01 16:38:57,040 - tensorflow - INFO - loss = 0.03484805, step = 731200 (2.265 sec) -2022-05-01 16:38:59,301 - tensorflow - INFO - global_step/sec: 88.417 -2022-05-01 16:38:59,302 - tensorflow - INFO - loss = 0.07325423, step = 731400 (2.262 sec) -2022-05-01 16:39:01,563 - tensorflow - INFO - global_step/sec: 88.4446 -2022-05-01 16:39:01,563 - tensorflow - INFO - loss = 0.130778, step = 731600 (2.261 sec) -2022-05-01 16:39:03,826 - tensorflow - INFO - global_step/sec: 88.371 -2022-05-01 16:39:03,826 - tensorflow - INFO - loss = 0.004887106, step = 731800 (2.263 sec) -2022-05-01 16:39:06,090 - tensorflow - INFO - global_step/sec: 88.319 -2022-05-01 16:39:06,091 - tensorflow - INFO - loss = 0.012417823, step = 732000 (2.265 sec) -2022-05-01 16:39:08,350 - tensorflow - INFO - global_step/sec: 88.5021 -2022-05-01 16:39:08,351 - tensorflow - INFO - loss = 0.014312791, step = 732200 (2.260 sec) -2022-05-01 16:39:10,606 - tensorflow - INFO - global_step/sec: 88.676 -2022-05-01 16:39:10,606 - tensorflow - INFO - loss = 0.029261068, step = 732400 (2.255 sec) -2022-05-01 16:39:12,860 - tensorflow - INFO - global_step/sec: 88.7093 -2022-05-01 16:39:12,861 - tensorflow - INFO - loss = 0.18301973, step = 732600 (2.255 sec) -2022-05-01 16:39:15,119 - tensorflow - INFO - global_step/sec: 88.5591 -2022-05-01 16:39:15,119 - tensorflow - INFO - loss = 0.081953034, step = 732800 (2.258 sec) -2022-05-01 16:39:17,373 - tensorflow - INFO - global_step/sec: 88.6982 -2022-05-01 16:39:17,374 - tensorflow - INFO - loss = 0.014483898, step = 733000 (2.255 sec) -2022-05-01 16:39:19,627 - tensorflow - INFO - global_step/sec: 88.7559 -2022-05-01 16:39:19,627 - tensorflow - INFO - loss = 0.021055708, step = 733200 (2.253 sec) -2022-05-01 16:39:21,878 - tensorflow - INFO - global_step/sec: 88.8538 -2022-05-01 16:39:21,878 - tensorflow - INFO - loss = 0.044311155, step = 733400 (2.251 sec) -2022-05-01 16:39:24,133 - tensorflow - INFO - global_step/sec: 88.6963 -2022-05-01 16:39:24,133 - tensorflow - INFO - loss = 0.019267552, step = 733600 (2.255 sec) -2022-05-01 16:39:26,387 - tensorflow - INFO - global_step/sec: 88.7302 -2022-05-01 16:39:26,387 - tensorflow - INFO - loss = 0.052062158, step = 733800 (2.254 sec) -2022-05-01 16:39:28,645 - tensorflow - INFO - global_step/sec: 88.5681 -2022-05-01 16:39:28,645 - tensorflow - INFO - loss = 0.027188748, step = 734000 (2.258 sec) -2022-05-01 16:39:30,905 - tensorflow - INFO - global_step/sec: 88.5038 -2022-05-01 16:39:30,905 - tensorflow - INFO - loss = 0.036001444, step = 734200 (2.260 sec) -2022-05-01 16:39:33,167 - tensorflow - INFO - global_step/sec: 88.4102 -2022-05-01 16:39:33,167 - tensorflow - INFO - loss = 0.042178534, step = 734400 (2.262 sec) -2022-05-01 16:39:35,423 - tensorflow - INFO - global_step/sec: 88.6443 -2022-05-01 16:39:35,423 - tensorflow - INFO - loss = 0.05207739, step = 734600 (2.256 sec) -2022-05-01 16:39:37,677 - tensorflow - INFO - global_step/sec: 88.7151 -2022-05-01 16:39:37,678 - tensorflow - INFO - loss = 9.82839, step = 734800 (2.254 sec) -2022-05-01 16:39:39,932 - tensorflow - INFO - global_step/sec: 88.6921 -2022-05-01 16:39:39,933 - tensorflow - INFO - loss = 0.01811117, step = 735000 (2.255 sec) -2022-05-01 16:39:42,186 - tensorflow - INFO - global_step/sec: 88.73 -2022-05-01 16:39:42,187 - tensorflow - INFO - loss = 0.15514675, step = 735200 (2.254 sec) -2022-05-01 16:39:44,438 - tensorflow - INFO - global_step/sec: 88.8431 -2022-05-01 16:39:44,438 - tensorflow - INFO - loss = 0.02380142, step = 735400 (2.251 sec) -2022-05-01 16:39:46,691 - tensorflow - INFO - global_step/sec: 88.7591 -2022-05-01 16:39:46,691 - tensorflow - INFO - loss = 0.062118415, step = 735600 (2.253 sec) -2022-05-01 16:39:48,944 - tensorflow - INFO - global_step/sec: 88.7548 -2022-05-01 16:39:48,945 - tensorflow - INFO - loss = 0.13109383, step = 735800 (2.253 sec) -2022-05-01 16:39:51,200 - tensorflow - INFO - global_step/sec: 88.6642 -2022-05-01 16:39:51,200 - tensorflow - INFO - loss = 0.017995136, step = 736000 (2.256 sec) -2022-05-01 16:39:53,459 - tensorflow - INFO - global_step/sec: 88.5465 -2022-05-01 16:39:53,459 - tensorflow - INFO - loss = 0.09423657, step = 736200 (2.259 sec) -2022-05-01 16:39:55,713 - tensorflow - INFO - global_step/sec: 88.7268 -2022-05-01 16:39:55,713 - tensorflow - INFO - loss = 0.06658428, step = 736400 (2.254 sec) -2022-05-01 16:39:57,965 - tensorflow - INFO - global_step/sec: 88.8128 -2022-05-01 16:39:57,965 - tensorflow - INFO - loss = 0.030928845, step = 736600 (2.252 sec) -2022-05-01 16:40:00,216 - tensorflow - INFO - global_step/sec: 88.8191 -2022-05-01 16:40:00,217 - tensorflow - INFO - loss = 0.019541731, step = 736800 (2.252 sec) -2022-05-01 16:40:02,477 - tensorflow - INFO - global_step/sec: 88.4903 -2022-05-01 16:40:02,477 - tensorflow - INFO - loss = 0.20681232, step = 737000 (2.260 sec) -2022-05-01 16:40:04,731 - tensorflow - INFO - global_step/sec: 88.705 -2022-05-01 16:40:04,732 - tensorflow - INFO - loss = 0.04668199, step = 737200 (2.255 sec) -2022-05-01 16:40:06,992 - tensorflow - INFO - global_step/sec: 88.4479 -2022-05-01 16:40:06,993 - tensorflow - INFO - loss = 0.04145692, step = 737400 (2.261 sec) -2022-05-01 16:40:09,245 - tensorflow - INFO - global_step/sec: 88.7793 -2022-05-01 16:40:09,246 - tensorflow - INFO - loss = 0.028742775, step = 737600 (2.253 sec) -2022-05-01 16:40:11,498 - tensorflow - INFO - global_step/sec: 88.7839 -2022-05-01 16:40:11,498 - tensorflow - INFO - loss = 0.2889277, step = 737800 (2.253 sec) -2022-05-01 16:40:13,752 - tensorflow - INFO - global_step/sec: 88.7379 -2022-05-01 16:40:13,752 - tensorflow - INFO - loss = 0.048202626, step = 738000 (2.254 sec) -2022-05-01 16:40:16,003 - tensorflow - INFO - global_step/sec: 88.8509 -2022-05-01 16:40:16,003 - tensorflow - INFO - loss = 0.036064286, step = 738200 (2.251 sec) -2022-05-01 16:40:18,258 - tensorflow - INFO - global_step/sec: 88.692 -2022-05-01 16:40:18,258 - tensorflow - INFO - loss = 0.08762132, step = 738400 (2.255 sec) -2022-05-01 16:40:20,509 - tensorflow - INFO - global_step/sec: 88.8336 -2022-05-01 16:40:20,509 - tensorflow - INFO - loss = 0.015344279, step = 738600 (2.251 sec) -2022-05-01 16:40:22,763 - tensorflow - INFO - global_step/sec: 88.7503 -2022-05-01 16:40:22,763 - tensorflow - INFO - loss = 0.065214105, step = 738800 (2.253 sec) -2022-05-01 16:40:25,013 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.12965834, step = 740200 (2.256 sec) -2022-05-01 16:40:40,798 - tensorflow - INFO - global_step/sec: 88.5863 -2022-05-01 16:40:40,799 - tensorflow - INFO - loss = 0.012902705, step = 740400 (2.258 sec) -2022-05-01 16:40:43,051 - tensorflow - INFO - global_step/sec: 88.7989 -2022-05-01 16:40:43,051 - tensorflow - INFO - loss = 0.014853411, step = 740600 (2.252 sec) -2022-05-01 16:40:45,306 - tensorflow - INFO - global_step/sec: 88.6847 -2022-05-01 16:40:45,306 - tensorflow - INFO - loss = 0.15699339, step = 740800 (2.255 sec) -2022-05-01 16:40:47,553 - tensorflow - INFO - global_step/sec: 89.0141 -2022-05-01 16:40:47,553 - tensorflow - INFO - loss = 0.046699658, step = 741000 (2.247 sec) -2022-05-01 16:40:49,804 - tensorflow - INFO - global_step/sec: 88.8405 -2022-05-01 16:40:49,804 - tensorflow - INFO - loss = 0.048899673, step = 741200 (2.251 sec) -2022-05-01 16:40:52,056 - tensorflow - INFO - global_step/sec: 88.8089 -2022-05-01 16:40:52,056 - tensorflow - INFO - loss = 0.0370473, step = 741400 (2.252 sec) -2022-05-01 16:40:54,311 - tensorflow - INFO - global_step/sec: 88.6901 -2022-05-01 16:40:54,311 - tensorflow - INFO - loss = 0.03407522, step = 741600 (2.255 sec) -2022-05-01 16:40:56,573 - tensorflow - INFO - global_step/sec: 88.3954 -2022-05-01 16:40:56,574 - tensorflow - INFO - loss = 0.60129654, step = 741800 (2.263 sec) -2022-05-01 16:40:58,831 - tensorflow - INFO - global_step/sec: 88.5793 -2022-05-01 16:40:58,832 - tensorflow - INFO - loss = 0.07023975, step = 742000 (2.258 sec) -2022-05-01 16:41:01,086 - tensorflow - INFO - global_step/sec: 88.7148 -2022-05-01 16:41:01,086 - tensorflow - INFO - loss = 0.2038961, step = 742200 (2.254 sec) -2022-05-01 16:41:03,341 - tensorflow - INFO - global_step/sec: 88.6691 -2022-05-01 16:41:03,342 - tensorflow - INFO - loss = 0.023192236, step = 742400 (2.256 sec) -2022-05-01 16:41:05,598 - tensorflow - INFO - global_step/sec: 88.6189 -2022-05-01 16:41:05,599 - tensorflow - INFO - loss = 0.023544712, step = 742600 (2.257 sec) -2022-05-01 16:41:07,856 - tensorflow - INFO - global_step/sec: 88.5655 -2022-05-01 16:41:07,857 - tensorflow - INFO - loss = 0.036484316, step = 742800 (2.258 sec) -2022-05-01 16:41:10,113 - tensorflow - INFO - global_step/sec: 88.6428 -2022-05-01 16:41:10,113 - tensorflow - INFO - loss = 0.014574307, step = 743000 (2.256 sec) -2022-05-01 16:41:12,364 - tensorflow - INFO - global_step/sec: 88.8311 -2022-05-01 16:41:12,364 - tensorflow - INFO - loss = 1.627424, step = 743200 (2.252 sec) -2022-05-01 16:41:14,622 - tensorflow - INFO - global_step/sec: 88.5812 -2022-05-01 16:41:14,622 - tensorflow - INFO - loss = 0.028682247, step = 743400 (2.258 sec) -2022-05-01 16:41:16,878 - tensorflow - INFO - global_step/sec: 88.6651 -2022-05-01 16:41:16,878 - tensorflow - INFO - loss = 0.009794436, step = 743600 (2.256 sec) -2022-05-01 16:41:19,129 - tensorflow - INFO - global_step/sec: 88.8281 -2022-05-01 16:41:19,129 - tensorflow - INFO - loss = 0.04523033, step = 743800 (2.252 sec) -2022-05-01 16:41:21,391 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.07969646, step = 745200 (2.256 sec) -2022-05-01 16:41:37,203 - tensorflow - INFO - global_step/sec: 88.5365 -2022-05-01 16:41:37,203 - tensorflow - INFO - loss = 0.013269105, step = 745400 (2.259 sec) -2022-05-01 16:41:39,456 - tensorflow - INFO - global_step/sec: 88.7705 -2022-05-01 16:41:39,456 - tensorflow - INFO - loss = 0.094168976, step = 745600 (2.253 sec) -2022-05-01 16:41:41,709 - tensorflow - INFO - global_step/sec: 88.7424 -2022-05-01 16:41:41,710 - tensorflow - INFO - loss = 2.7729783, step = 745800 (2.254 sec) -2022-05-01 16:41:43,959 - tensorflow - INFO - global_step/sec: 88.9043 -2022-05-01 16:41:43,959 - tensorflow - INFO - loss = 0.05216115, step = 746000 (2.250 sec) -2022-05-01 16:41:46,215 - tensorflow - INFO - global_step/sec: 88.628 -2022-05-01 16:41:46,216 - tensorflow - INFO - loss = 0.06271626, step = 746200 (2.257 sec) -2022-05-01 16:41:48,471 - tensorflow - INFO - global_step/sec: 88.6778 -2022-05-01 16:41:48,471 - tensorflow - INFO - loss = 2.589942, step = 746400 (2.255 sec) -2022-05-01 16:41:50,724 - tensorflow - INFO - global_step/sec: 88.7647 -2022-05-01 16:41:50,724 - tensorflow - INFO - loss = 0.07205838, step = 746600 (2.253 sec) -2022-05-01 16:41:52,977 - tensorflow - INFO - global_step/sec: 88.7602 -2022-05-01 16:41:52,978 - tensorflow - INFO - loss = 0.013799798, step = 746800 (2.253 sec) -2022-05-01 16:41:55,234 - tensorflow - INFO - global_step/sec: 88.6087 -2022-05-01 16:41:55,235 - tensorflow - INFO - loss = 0.0339761, step = 747000 (2.257 sec) -2022-05-01 16:41:57,487 - tensorflow - INFO - global_step/sec: 88.7706 -2022-05-01 16:41:57,488 - tensorflow - INFO - loss = 0.08367941, step = 747200 (2.253 sec) -2022-05-01 16:41:59,748 - tensorflow - INFO - global_step/sec: 88.4724 -2022-05-01 16:41:59,748 - tensorflow - INFO - loss = 0.011904222, step = 747400 (2.261 sec) -2022-05-01 16:42:01,999 - tensorflow - INFO - global_step/sec: 88.8351 -2022-05-01 16:42:02,000 - tensorflow - INFO - loss = 0.035004426, step = 747600 (2.251 sec) -2022-05-01 16:42:04,258 - tensorflow - INFO - global_step/sec: 88.5354 -2022-05-01 16:42:04,259 - tensorflow - INFO - loss = 0.01963403, step = 747800 (2.259 sec) -2022-05-01 16:42:06,514 - tensorflow - INFO - global_step/sec: 88.6559 -2022-05-01 16:42:06,515 - tensorflow - INFO - loss = 0.020152962, step = 748000 (2.256 sec) -2022-05-01 16:42:08,766 - tensorflow - INFO - global_step/sec: 88.8282 -2022-05-01 16:42:08,766 - tensorflow - INFO - loss = 0.01514635, step = 748200 (2.252 sec) -2022-05-01 16:42:11,025 - tensorflow - INFO - global_step/sec: 88.5125 -2022-05-01 16:42:11,026 - tensorflow - INFO - loss = 0.025370374, step = 748400 (2.260 sec) -2022-05-01 16:42:13,296 - tensorflow - INFO - global_step/sec: 88.0874 -2022-05-01 16:42:13,296 - tensorflow - INFO - loss = 8.897505, step = 748600 (2.270 sec) -2022-05-01 16:42:15,558 - tensorflow - INFO - global_step/sec: 88.4005 -2022-05-01 16:42:15,559 - tensorflow - INFO - loss = 0.14021921, step = 748800 (2.262 sec) -2022-05-01 16:42:17,823 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.037792195, step = 750200 (2.268 sec) -2022-05-01 16:42:33,665 - tensorflow - INFO - global_step/sec: 88.358 -2022-05-01 16:42:33,665 - tensorflow - INFO - loss = 0.019121954, step = 750400 (2.264 sec) -2022-05-01 16:42:35,929 - tensorflow - INFO - global_step/sec: 88.3224 -2022-05-01 16:42:35,929 - tensorflow - INFO - loss = 0.071963236, step = 750600 (2.264 sec) -2022-05-01 16:42:38,189 - tensorflow - INFO - global_step/sec: 88.5129 -2022-05-01 16:42:38,189 - tensorflow - INFO - loss = 0.041974064, step = 750800 (2.260 sec) -2022-05-01 16:42:40,446 - tensorflow - INFO - global_step/sec: 88.5983 -2022-05-01 16:42:40,446 - tensorflow - INFO - loss = 0.024198234, step = 751000 (2.257 sec) -2022-05-01 16:42:42,699 - tensorflow - INFO - global_step/sec: 88.7831 -2022-05-01 16:42:42,699 - tensorflow - INFO - loss = 0.071568094, step = 751200 (2.253 sec) -2022-05-01 16:42:44,950 - tensorflow - INFO - global_step/sec: 88.8488 -2022-05-01 16:42:44,950 - tensorflow - INFO - loss = 0.030673733, step = 751400 (2.251 sec) -2022-05-01 16:42:47,203 - tensorflow - INFO - global_step/sec: 88.7587 -2022-05-01 16:42:47,203 - tensorflow - INFO - loss = 0.034062106, step = 751600 (2.253 sec) -2022-05-01 16:42:49,457 - tensorflow - INFO - global_step/sec: 88.7155 -2022-05-01 16:42:49,458 - tensorflow - INFO - loss = 0.020252142, step = 751800 (2.254 sec) -2022-05-01 16:42:51,714 - tensorflow - INFO - global_step/sec: 88.6465 -2022-05-01 16:42:51,714 - tensorflow - INFO - loss = 0.027839506, step = 752000 (2.256 sec) -2022-05-01 16:42:53,967 - tensorflow - INFO - global_step/sec: 88.7497 -2022-05-01 16:42:53,967 - tensorflow - INFO - loss = 0.118473224, step = 752200 (2.254 sec) -2022-05-01 16:42:56,228 - tensorflow - INFO - global_step/sec: 88.473 -2022-05-01 16:42:56,228 - tensorflow - INFO - loss = 0.03127812, step = 752400 (2.261 sec) -2022-05-01 16:42:58,484 - tensorflow - INFO - global_step/sec: 88.6423 -2022-05-01 16:42:58,484 - tensorflow - INFO - loss = 0.24062337, step = 752600 (2.256 sec) -2022-05-01 16:43:00,737 - tensorflow - INFO - global_step/sec: 88.7791 -2022-05-01 16:43:00,737 - tensorflow - INFO - loss = 0.02481427, step = 752800 (2.253 sec) -2022-05-01 16:43:02,997 - tensorflow - INFO - global_step/sec: 88.4808 -2022-05-01 16:43:02,997 - tensorflow - INFO - loss = 0.02572039, step = 753000 (2.260 sec) -2022-05-01 16:43:05,252 - tensorflow - INFO - global_step/sec: 88.679 -2022-05-01 16:43:05,253 - tensorflow - INFO - loss = 0.102261215, step = 753200 (2.255 sec) -2022-05-01 16:43:07,509 - tensorflow - INFO - global_step/sec: 88.6181 -2022-05-01 16:43:07,510 - tensorflow - INFO - loss = 0.032337368, step = 753400 (2.257 sec) -2022-05-01 16:43:09,768 - tensorflow - INFO - global_step/sec: 88.5433 -2022-05-01 16:43:09,768 - tensorflow - INFO - loss = 0.014850834, step = 753600 (2.259 sec) -2022-05-01 16:43:12,025 - tensorflow - INFO - global_step/sec: 88.6328 -2022-05-01 16:43:12,025 - tensorflow - INFO - loss = 0.02281077, step = 753800 (2.256 sec) -2022-05-01 16:43:14,278 - tensorflow - INFO - global_step/sec: 88.7447 -2022-05-01 16:43:14,279 - tensorflow - INFO - loss = 0.14529234, step = 754000 (2.254 sec) -2022-05-01 16:43:16,535 - tensorflow - INFO - global_step/sec: 88.6228 -2022-05-01 16:43:16,535 - tensorflow - INFO - loss = 0.07362701, step = 754200 (2.257 sec) -2022-05-01 16:43:18,792 - tensorflow - INFO - global_step/sec: 88.6072 -2022-05-01 16:43:18,793 - tensorflow - INFO - loss = 0.010719903, step = 754400 (2.257 sec) -2022-05-01 16:43:21,041 - tensorflow - INFO - global_step/sec: 88.9448 -2022-05-01 16:43:21,041 - tensorflow - INFO - loss = 8.220845, step = 754600 (2.249 sec) -2022-05-01 16:43:23,290 - tensorflow - INFO - global_step/sec: 88.9039 -2022-05-01 16:43:23,291 - tensorflow - INFO - loss = 0.07111853, step = 754800 (2.250 sec) -2022-05-01 16:43:25,541 - tensorflow - INFO - global_step/sec: 88.8543 -2022-05-01 16:43:25,542 - tensorflow - INFO - loss = 0.013378707, step = 755000 (2.251 sec) -2022-05-01 16:43:27,797 - tensorflow - INFO - global_step/sec: 88.6771 -2022-05-01 16:43:27,797 - tensorflow - INFO - loss = 1.4978168, step = 755200 (2.255 sec) -2022-05-01 16:43:30,059 - tensorflow - INFO - global_step/sec: 88.3996 -2022-05-01 16:43:30,059 - tensorflow - INFO - loss = 8.718706, step = 755400 (2.262 sec) -2022-05-01 16:43:32,316 - tensorflow - INFO - global_step/sec: 88.6187 -2022-05-01 16:43:32,316 - tensorflow - INFO - loss = 0.044593006, step = 755600 (2.257 sec) -2022-05-01 16:43:34,564 - tensorflow - INFO - global_step/sec: 88.9641 -2022-05-01 16:43:34,564 - tensorflow - INFO - loss = 0.0884004, step = 755800 (2.248 sec) -2022-05-01 16:43:36,813 - tensorflow - INFO - global_step/sec: 88.929 -2022-05-01 16:43:36,813 - tensorflow - INFO - loss = 0.083780736, step = 756000 (2.249 sec) -2022-05-01 16:43:39,067 - tensorflow - INFO - global_step/sec: 88.7169 -2022-05-01 16:43:39,068 - tensorflow - INFO - loss = 0.024215344, step = 756200 (2.254 sec) -2022-05-01 16:43:41,330 - tensorflow - INFO - global_step/sec: 88.4013 -2022-05-01 16:43:41,330 - tensorflow - INFO - loss = 0.036362167, step = 756400 (2.262 sec) -2022-05-01 16:43:43,585 - tensorflow - INFO - global_step/sec: 88.6992 -2022-05-01 16:43:43,585 - tensorflow - INFO - loss = 0.017386153, step = 756600 (2.255 sec) -2022-05-01 16:43:45,841 - tensorflow - INFO - global_step/sec: 88.6415 -2022-05-01 16:43:45,841 - tensorflow - INFO - loss = 0.036402073, step = 756800 (2.256 sec) -2022-05-01 16:43:48,095 - tensorflow - INFO - global_step/sec: 88.7388 -2022-05-01 16:43:48,095 - tensorflow - INFO - loss = 0.023358872, step = 757000 (2.254 sec) -2022-05-01 16:43:50,350 - tensorflow - INFO - global_step/sec: 88.686 -2022-05-01 16:43:50,350 - tensorflow - INFO - loss = 0.02112079, step = 757200 (2.255 sec) -2022-05-01 16:43:52,603 - tensorflow - INFO - global_step/sec: 88.7611 -2022-05-01 16:43:52,603 - tensorflow - INFO - loss = 0.07236029, step = 757400 (2.253 sec) -2022-05-01 16:43:54,863 - tensorflow - INFO - global_step/sec: 88.5102 -2022-05-01 16:43:54,863 - tensorflow - INFO - loss = 0.3196033, step = 757600 (2.260 sec) -2022-05-01 16:43:57,116 - tensorflow - INFO - global_step/sec: 88.7567 -2022-05-01 16:43:57,116 - tensorflow - INFO - loss = 0.09343986, step = 757800 (2.253 sec) -2022-05-01 16:43:59,372 - tensorflow - INFO - global_step/sec: 88.6528 -2022-05-01 16:43:59,372 - tensorflow - INFO - loss = 0.06432448, step = 758000 (2.256 sec) -2022-05-01 16:44:01,623 - tensorflow - INFO - global_step/sec: 88.8473 -2022-05-01 16:44:01,623 - tensorflow - INFO - loss = 0.038612396, step = 758200 (2.251 sec) -2022-05-01 16:44:03,877 - tensorflow - INFO - global_step/sec: 88.7272 -2022-05-01 16:44:03,878 - tensorflow - INFO - loss = 0.3299265, step = 758400 (2.254 sec) -2022-05-01 16:44:06,132 - tensorflow - INFO - global_step/sec: 88.7053 -2022-05-01 16:44:06,132 - tensorflow - INFO - loss = 0.034532495, step = 758600 (2.255 sec) -2022-05-01 16:44:08,386 - tensorflow - INFO - global_step/sec: 88.7216 -2022-05-01 16:44:08,386 - tensorflow - INFO - loss = 0.49787322, step = 758800 (2.254 sec) -2022-05-01 16:44:10,637 - tensorflow - INFO - global_step/sec: 88.8405 -2022-05-01 16:44:10,638 - tensorflow - INFO - loss = 0.0216678, step = 759000 (2.251 sec) -2022-05-01 16:44:12,894 - tensorflow - INFO - global_step/sec: 88.6138 -2022-05-01 16:44:12,895 - tensorflow - INFO - loss = 0.10780929, step = 759200 (2.257 sec) -2022-05-01 16:44:15,148 - tensorflow - INFO - global_step/sec: 88.7475 -2022-05-01 16:44:15,148 - tensorflow - INFO - loss = 0.03311348, step = 759400 (2.254 sec) -2022-05-01 16:44:17,400 - tensorflow - INFO - global_step/sec: 88.7849 -2022-05-01 16:44:17,401 - tensorflow - INFO - loss = 0.013074419, step = 759600 (2.253 sec) -2022-05-01 16:44:19,657 - tensorflow - INFO - global_step/sec: 88.6375 -2022-05-01 16:44:19,657 - tensorflow - INFO - loss = 0.054565508, step = 759800 (2.256 sec) -2022-05-01 16:44:21,907 - tensorflow - INFO - global_step/sec: 88.8788 -2022-05-01 16:44:21,907 - tensorflow - INFO - loss = 0.05146771, step = 760000 (2.250 sec) -2022-05-01 16:44:24,162 - tensorflow - INFO - global_step/sec: 88.6991 -2022-05-01 16:44:24,162 - tensorflow - INFO - loss = 0.024711419, step = 760200 (2.255 sec) -2022-05-01 16:44:26,413 - tensorflow - INFO - global_step/sec: 88.8594 -2022-05-01 16:44:26,413 - tensorflow - INFO - loss = 0.020534966, step = 760400 (2.251 sec) -2022-05-01 16:44:28,666 - tensorflow - INFO - global_step/sec: 88.7528 -2022-05-01 16:44:28,666 - tensorflow - INFO - loss = 0.01117647, step = 760600 (2.253 sec) -2022-05-01 16:44:30,926 - tensorflow - INFO - global_step/sec: 88.4921 -2022-05-01 16:44:30,927 - tensorflow - INFO - loss = 0.20856713, step = 760800 (2.260 sec) -2022-05-01 16:44:33,187 - tensorflow - INFO - global_step/sec: 88.4512 -2022-05-01 16:44:33,188 - tensorflow - INFO - loss = 0.027001064, step = 761000 (2.261 sec) -2022-05-01 16:44:35,441 - tensorflow - INFO - global_step/sec: 88.7496 -2022-05-01 16:44:35,441 - tensorflow - INFO - loss = 0.028856246, step = 761200 (2.254 sec) -2022-05-01 16:44:37,690 - tensorflow - INFO - global_step/sec: 88.9181 -2022-05-01 16:44:37,691 - tensorflow - INFO - loss = 0.016628332, step = 761400 (2.249 sec) -2022-05-01 16:44:39,942 - tensorflow - INFO - global_step/sec: 88.8153 -2022-05-01 16:44:39,942 - tensorflow - INFO - loss = 0.015344487, step = 761600 (2.252 sec) -2022-05-01 16:44:42,189 - tensorflow - INFO - global_step/sec: 88.9995 -2022-05-01 16:44:42,190 - tensorflow - INFO - loss = 0.06295501, step = 761800 (2.247 sec) -2022-05-01 16:44:44,450 - tensorflow - INFO - global_step/sec: 88.4536 -2022-05-01 16:44:44,451 - tensorflow - INFO - loss = 0.03487993, step = 762000 (2.261 sec) -2022-05-01 16:44:46,704 - tensorflow - INFO - global_step/sec: 88.7216 -2022-05-01 16:44:46,705 - tensorflow - INFO - loss = 8.466547, step = 762200 (2.254 sec) -2022-05-01 16:44:48,956 - tensorflow - INFO - global_step/sec: 88.8244 -2022-05-01 16:44:48,957 - tensorflow - INFO - loss = 0.09064051, step = 762400 (2.252 sec) -2022-05-01 16:44:51,215 - tensorflow - INFO - global_step/sec: 88.5358 -2022-05-01 16:44:51,216 - tensorflow - INFO - loss = 0.022429889, step = 762600 (2.259 sec) -2022-05-01 16:44:53,472 - tensorflow - INFO - global_step/sec: 88.6019 -2022-05-01 16:44:53,473 - tensorflow - INFO - loss = 0.010964585, step = 762800 (2.257 sec) -2022-05-01 16:44:55,728 - tensorflow - INFO - global_step/sec: 88.6826 -2022-05-01 16:44:55,728 - tensorflow - INFO - loss = 0.0625984, step = 763000 (2.255 sec) -2022-05-01 16:44:57,982 - tensorflow - INFO - global_step/sec: 88.712 -2022-05-01 16:44:57,983 - tensorflow - INFO - loss = 0.054418862, step = 763200 (2.255 sec) -2022-05-01 16:45:00,236 - tensorflow - INFO - global_step/sec: 88.7534 -2022-05-01 16:45:00,236 - tensorflow - INFO - loss = 0.03869965, step = 763400 (2.253 sec) -2022-05-01 16:45:02,489 - tensorflow - INFO - global_step/sec: 88.754 -2022-05-01 16:45:02,489 - tensorflow - INFO - loss = 0.012486335, step = 763600 (2.253 sec) -2022-05-01 16:45:04,745 - tensorflow - INFO - global_step/sec: 88.6332 -2022-05-01 16:45:04,746 - tensorflow - INFO - loss = 0.009464521, step = 763800 (2.256 sec) -2022-05-01 16:45:07,000 - tensorflow - INFO - global_step/sec: 88.7272 -2022-05-01 16:45:07,000 - tensorflow - INFO - loss = 1.9164569, step = 764000 (2.254 sec) -2022-05-01 16:45:09,255 - tensorflow - INFO - global_step/sec: 88.6732 -2022-05-01 16:45:09,255 - tensorflow - INFO - loss = 0.01771454, step = 764200 (2.256 sec) -2022-05-01 16:45:11,511 - tensorflow - INFO - global_step/sec: 88.6512 -2022-05-01 16:45:11,511 - tensorflow - INFO - loss = 0.049625978, step = 764400 (2.256 sec) -2022-05-01 16:45:13,766 - tensorflow - INFO - global_step/sec: 88.6851 -2022-05-01 16:45:13,767 - tensorflow - INFO - loss = 0.059604965, step = 764600 (2.255 sec) -2022-05-01 16:45:16,018 - tensorflow - INFO - global_step/sec: 88.818 -2022-05-01 16:45:16,018 - tensorflow - INFO - loss = 0.0255786, step = 764800 (2.252 sec) -2022-05-01 16:45:18,269 - tensorflow - INFO - global_step/sec: 88.8647 -2022-05-01 16:45:18,269 - tensorflow - INFO - loss = 0.030189814, step = 765000 (2.251 sec) -2022-05-01 16:45:20,520 - tensorflow - INFO - global_step/sec: 88.8312 -2022-05-01 16:45:20,520 - tensorflow - INFO - loss = 0.092219666, step = 765200 (2.251 sec) -2022-05-01 16:45:22,776 - tensorflow - INFO - global_step/sec: 88.6416 -2022-05-01 16:45:22,777 - tensorflow - INFO - loss = 0.090820804, step = 765400 (2.256 sec) -2022-05-01 16:45:25,031 - tensorflow - INFO - global_step/sec: 88.72 -2022-05-01 16:45:25,031 - tensorflow - INFO - loss = 0.055353288, step = 765600 (2.254 sec) -2022-05-01 16:45:27,289 - tensorflow - INFO - global_step/sec: 88.575 -2022-05-01 16:45:27,289 - tensorflow - INFO - loss = 0.11388934, step = 765800 (2.258 sec) -2022-05-01 16:45:29,538 - tensorflow - INFO - global_step/sec: 88.9327 -2022-05-01 16:45:29,538 - tensorflow - INFO - loss = 0.13925542, step = 766000 (2.249 sec) -2022-05-01 16:45:31,795 - tensorflow - INFO - global_step/sec: 88.6053 -2022-05-01 16:45:31,795 - tensorflow - INFO - loss = 0.052290387, step = 766200 (2.257 sec) -2022-05-01 16:45:34,051 - tensorflow - INFO - global_step/sec: 88.6556 -2022-05-01 16:45:34,051 - tensorflow - INFO - loss = 0.026844043, step = 766400 (2.256 sec) -2022-05-01 16:45:36,308 - tensorflow - INFO - global_step/sec: 88.5844 -2022-05-01 16:45:36,309 - tensorflow - INFO - loss = 0.104311466, step = 766600 (2.258 sec) -2022-05-01 16:45:38,568 - tensorflow - INFO - global_step/sec: 88.4912 -2022-05-01 16:45:38,569 - tensorflow - INFO - loss = 0.09069614, step = 766800 (2.260 sec) -2022-05-01 16:45:40,825 - tensorflow - INFO - global_step/sec: 88.6413 -2022-05-01 16:45:40,825 - tensorflow - INFO - loss = 0.07376866, step = 767000 (2.256 sec) -2022-05-01 16:45:43,085 - tensorflow - INFO - global_step/sec: 88.4942 -2022-05-01 16:45:43,085 - tensorflow - INFO - loss = 0.014613312, step = 767200 (2.260 sec) -2022-05-01 16:45:45,350 - tensorflow - INFO - global_step/sec: 88.2976 -2022-05-01 16:45:45,350 - tensorflow - INFO - loss = 0.03660327, step = 767400 (2.265 sec) -2022-05-01 16:45:47,611 - tensorflow - INFO - global_step/sec: 88.4373 -2022-05-01 16:45:47,612 - tensorflow - INFO - loss = 0.016163658, step = 767600 (2.261 sec) -2022-05-01 16:45:49,874 - tensorflow - INFO - global_step/sec: 88.397 -2022-05-01 16:45:49,874 - tensorflow - INFO - loss = 0.03184437, step = 767800 (2.263 sec) -2022-05-01 16:45:52,138 - tensorflow - INFO - global_step/sec: 88.318 -2022-05-01 16:45:52,139 - tensorflow - INFO - loss = 0.05155728, step = 768000 (2.265 sec) -2022-05-01 16:45:54,401 - tensorflow - INFO - global_step/sec: 88.3757 -2022-05-01 16:45:54,402 - tensorflow - INFO - loss = 0.16172525, step = 768200 (2.263 sec) -2022-05-01 16:45:56,664 - tensorflow - INFO - global_step/sec: 88.3868 -2022-05-01 16:45:56,665 - tensorflow - INFO - loss = 0.037523683, step = 768400 (2.263 sec) -2022-05-01 16:45:58,926 - tensorflow - INFO - global_step/sec: 88.434 -2022-05-01 16:45:58,926 - tensorflow - INFO - loss = 0.017480196, step = 768600 (2.262 sec) -2022-05-01 16:46:01,189 - tensorflow - INFO - global_step/sec: 88.3785 -2022-05-01 16:46:01,189 - tensorflow - INFO - loss = 0.054639865, step = 768800 (2.263 sec) -2022-05-01 16:46:03,452 - tensorflow - INFO - global_step/sec: 88.3836 -2022-05-01 16:46:03,452 - tensorflow - INFO - loss = 0.06187892, step = 769000 (2.263 sec) -2022-05-01 16:46:05,712 - tensorflow - INFO - global_step/sec: 88.4726 -2022-05-01 16:46:05,713 - tensorflow - INFO - loss = 0.040791094, step = 769200 (2.261 sec) -2022-05-01 16:46:07,962 - tensorflow - INFO - global_step/sec: 88.8823 -2022-05-01 16:46:07,963 - tensorflow - INFO - loss = 0.056501552, step = 769400 (2.250 sec) -2022-05-01 16:46:10,215 - tensorflow - INFO - global_step/sec: 88.7794 -2022-05-01 16:46:10,216 - tensorflow - INFO - loss = 0.07831244, step = 769600 (2.253 sec) -2022-05-01 16:46:12,467 - tensorflow - INFO - global_step/sec: 88.8335 -2022-05-01 16:46:12,467 - tensorflow - INFO - loss = 0.016525311, step = 769800 (2.251 sec) -2022-05-01 16:46:14,723 - tensorflow - INFO - global_step/sec: 88.6328 -2022-05-01 16:46:14,724 - tensorflow - INFO - loss = 16.363789, step = 770000 (2.256 sec) -2022-05-01 16:46:16,982 - tensorflow - INFO - global_step/sec: 88.5457 -2022-05-01 16:46:16,982 - tensorflow - INFO - loss = 0.023497995, step = 770200 (2.259 sec) -2022-05-01 16:46:19,239 - tensorflow - INFO - global_step/sec: 88.6167 -2022-05-01 16:46:19,239 - tensorflow - INFO - loss = 0.029995466, step = 770400 (2.257 sec) -2022-05-01 16:46:21,493 - tensorflow - INFO - global_step/sec: 88.7198 -2022-05-01 16:46:21,494 - tensorflow - INFO - loss = 0.09299415, step = 770600 (2.254 sec) -2022-05-01 16:46:23,745 - tensorflow - INFO - global_step/sec: 88.8217 -2022-05-01 16:46:23,745 - tensorflow - INFO - loss = 0.052417703, step = 770800 (2.252 sec) -2022-05-01 16:46:26,007 - tensorflow - INFO - global_step/sec: 88.4075 -2022-05-01 16:46:26,007 - tensorflow - INFO - loss = 0.057726912, step = 771000 (2.262 sec) -2022-05-01 16:46:28,261 - tensorflow - INFO - global_step/sec: 88.7314 -2022-05-01 16:46:28,261 - tensorflow - INFO - loss = 0.028460063, step = 771200 (2.254 sec) -2022-05-01 16:46:30,528 - tensorflow - INFO - global_step/sec: 88.2163 -2022-05-01 16:46:30,529 - tensorflow - INFO - loss = 5.1369634, step = 771400 (2.267 sec) -2022-05-01 16:46:32,783 - tensorflow - INFO - global_step/sec: 88.6818 -2022-05-01 16:46:32,784 - tensorflow - INFO - loss = 0.041760676, step = 771600 (2.255 sec) -2022-05-01 16:46:35,041 - tensorflow - INFO - global_step/sec: 88.582 -2022-05-01 16:46:35,042 - tensorflow - INFO - loss = 0.017105188, step = 771800 (2.258 sec) -2022-05-01 16:46:37,301 - tensorflow - INFO - global_step/sec: 88.5145 -2022-05-01 16:46:37,301 - tensorflow - INFO - loss = 0.035858568, step = 772000 (2.260 sec) -2022-05-01 16:46:39,556 - tensorflow - INFO - global_step/sec: 88.6948 -2022-05-01 16:46:39,556 - tensorflow - INFO - loss = 0.014652055, step = 772200 (2.255 sec) -2022-05-01 16:46:41,809 - tensorflow - INFO - global_step/sec: 88.7612 -2022-05-01 16:46:41,809 - tensorflow - INFO - loss = 0.5293211, step = 772400 (2.253 sec) -2022-05-01 16:46:44,062 - tensorflow - INFO - global_step/sec: 88.7716 -2022-05-01 16:46:44,062 - tensorflow - INFO - loss = 0.022106223, step = 772600 (2.253 sec) -2022-05-01 16:46:46,315 - tensorflow - INFO - global_step/sec: 88.7586 -2022-05-01 16:46:46,316 - tensorflow - INFO - loss = 0.07572379, step = 772800 (2.253 sec) -2022-05-01 16:46:48,572 - tensorflow - INFO - global_step/sec: 88.6369 -2022-05-01 16:46:48,572 - tensorflow - INFO - loss = 0.07716698, step = 773000 (2.256 sec) -2022-05-01 16:46:50,825 - tensorflow - INFO - global_step/sec: 88.7564 -2022-05-01 16:46:50,825 - tensorflow - INFO - loss = 0.14256883, step = 773200 (2.253 sec) -2022-05-01 16:46:53,084 - tensorflow - INFO - global_step/sec: 88.5203 -2022-05-01 16:46:53,085 - tensorflow - INFO - loss = 0.23689112, step = 773400 (2.259 sec) -2022-05-01 16:46:55,345 - tensorflow - INFO - global_step/sec: 88.4794 -2022-05-01 16:46:55,345 - tensorflow - INFO - loss = 0.0662338, step = 773600 (2.260 sec) -2022-05-01 16:46:57,603 - tensorflow - INFO - global_step/sec: 88.5429 -2022-05-01 16:46:57,604 - tensorflow - INFO - loss = 0.09745243, step = 773800 (2.259 sec) -2022-05-01 16:46:59,861 - tensorflow - INFO - global_step/sec: 88.6121 -2022-05-01 16:46:59,861 - tensorflow - INFO - loss = 0.021856584, step = 774000 (2.257 sec) -2022-05-01 16:47:02,116 - tensorflow - INFO - global_step/sec: 88.6761 -2022-05-01 16:47:02,116 - tensorflow - INFO - loss = 0.030670226, step = 774200 (2.255 sec) -2022-05-01 16:47:04,374 - tensorflow - INFO - global_step/sec: 88.5727 -2022-05-01 16:47:04,374 - tensorflow - INFO - loss = 0.05376959, step = 774400 (2.258 sec) -2022-05-01 16:47:06,625 - tensorflow - INFO - global_step/sec: 88.834 -2022-05-01 16:47:06,626 - tensorflow - INFO - loss = 0.029265922, step = 774600 (2.251 sec) -2022-05-01 16:47:08,882 - tensorflow - INFO - global_step/sec: 88.6314 -2022-05-01 16:47:08,882 - tensorflow - INFO - loss = 0.058595054, step = 774800 (2.257 sec) -2022-05-01 16:47:11,136 - tensorflow - INFO - global_step/sec: 88.7321 -2022-05-01 16:47:11,137 - tensorflow - INFO - loss = 0.0823068, step = 775000 (2.254 sec) -2022-05-01 16:47:13,390 - tensorflow - INFO - global_step/sec: 88.7379 -2022-05-01 16:47:13,390 - tensorflow - INFO - loss = 0.022349877, step = 775200 (2.253 sec) -2022-05-01 16:47:15,644 - tensorflow - INFO - global_step/sec: 88.7231 -2022-05-01 16:47:15,644 - tensorflow - INFO - loss = 0.03451605, step = 775400 (2.254 sec) -2022-05-01 16:47:17,895 - tensorflow - INFO - global_step/sec: 88.831 -2022-05-01 16:47:17,896 - tensorflow - INFO - loss = 0.032310568, step = 775600 (2.252 sec) -2022-05-01 16:47:20,154 - tensorflow - INFO - global_step/sec: 88.5512 -2022-05-01 16:47:20,154 - tensorflow - INFO - loss = 0.016294904, step = 775800 (2.259 sec) -2022-05-01 16:47:22,411 - tensorflow - INFO - global_step/sec: 88.6172 -2022-05-01 16:47:22,411 - tensorflow - INFO - loss = 0.027242772, step = 776000 (2.257 sec) -2022-05-01 16:47:24,666 - tensorflow - INFO - global_step/sec: 88.6885 -2022-05-01 16:47:24,666 - tensorflow - INFO - loss = 0.025471672, step = 776200 (2.255 sec) -2022-05-01 16:47:26,924 - tensorflow - INFO - global_step/sec: 88.5835 -2022-05-01 16:47:26,924 - tensorflow - INFO - loss = 0.025382172, step = 776400 (2.258 sec) -2022-05-01 16:47:29,179 - tensorflow - INFO - global_step/sec: 88.6749 -2022-05-01 16:47:29,180 - tensorflow - INFO - loss = 0.09946121, step = 776600 (2.255 sec) -2022-05-01 16:47:31,437 - tensorflow - INFO - global_step/sec: 88.5769 -2022-05-01 16:47:31,437 - tensorflow - INFO - loss = 0.036002457, step = 776800 (2.258 sec) -2022-05-01 16:47:33,692 - tensorflow - INFO - global_step/sec: 88.7097 -2022-05-01 16:47:33,692 - tensorflow - INFO - loss = 0.035741895, step = 777000 (2.255 sec) -2022-05-01 16:47:35,945 - tensorflow - INFO - global_step/sec: 88.7654 -2022-05-01 16:47:35,945 - tensorflow - INFO - loss = 0.0351523, step = 777200 (2.253 sec) -2022-05-01 16:47:38,199 - tensorflow - INFO - global_step/sec: 88.7048 -2022-05-01 16:47:38,200 - tensorflow - INFO - loss = 0.4478139, step = 777400 (2.255 sec) -2022-05-01 16:47:40,450 - tensorflow - INFO - global_step/sec: 88.8447 -2022-05-01 16:47:40,451 - tensorflow - INFO - loss = 0.031508293, step = 777600 (2.251 sec) -2022-05-01 16:47:42,705 - tensorflow - INFO - global_step/sec: 88.6946 -2022-05-01 16:47:42,706 - tensorflow - INFO - loss = 0.20118828, step = 777800 (2.255 sec) -2022-05-01 16:47:44,966 - tensorflow - INFO - global_step/sec: 88.4684 -2022-05-01 16:47:44,967 - tensorflow - INFO - loss = 0.021901006, step = 778000 (2.261 sec) -2022-05-01 16:47:47,221 - tensorflow - INFO - global_step/sec: 88.7071 -2022-05-01 16:47:47,221 - tensorflow - INFO - loss = 0.37587863, step = 778200 (2.255 sec) -2022-05-01 16:47:49,478 - tensorflow - INFO - global_step/sec: 88.6095 -2022-05-01 16:47:49,478 - tensorflow - INFO - loss = 8.8121195, step = 778400 (2.257 sec) -2022-05-01 16:47:51,731 - tensorflow - INFO - global_step/sec: 88.7569 -2022-05-01 16:47:51,732 - tensorflow - INFO - loss = 0.04143539, step = 778600 (2.253 sec) -2022-05-01 16:47:53,985 - tensorflow - INFO - global_step/sec: 88.7304 -2022-05-01 16:47:53,986 - tensorflow - INFO - loss = 0.018876223, step = 778800 (2.254 sec) -2022-05-01 16:47:56,245 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.7066 -2022-05-01 16:48:39,092 - tensorflow - INFO - loss = 0.0494501, step = 782800 (2.255 sec) -2022-05-01 16:48:41,343 - tensorflow - INFO - global_step/sec: 88.8366 -2022-05-01 16:48:41,344 - tensorflow - INFO - loss = 0.02963003, step = 783000 (2.251 sec) -2022-05-01 16:48:43,598 - tensorflow - INFO - global_step/sec: 88.6945 -2022-05-01 16:48:43,598 - tensorflow - INFO - loss = 0.12955765, step = 783200 (2.255 sec) -2022-05-01 16:48:45,855 - tensorflow - INFO - global_step/sec: 88.6205 -2022-05-01 16:48:45,855 - tensorflow - INFO - loss = 0.059161827, step = 783400 (2.257 sec) -2022-05-01 16:48:48,112 - tensorflow - INFO - global_step/sec: 88.6158 -2022-05-01 16:48:48,112 - tensorflow - INFO - loss = 0.050310932, step = 783600 (2.257 sec) -2022-05-01 16:48:50,368 - tensorflow - INFO - global_step/sec: 88.6584 -2022-05-01 16:48:50,368 - tensorflow - INFO - loss = 0.019565593, step = 783800 (2.256 sec) -2022-05-01 16:48:52,629 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.031977743, step = 785200 (2.254 sec) -2022-05-01 16:49:08,423 - tensorflow - INFO - global_step/sec: 88.5657 -2022-05-01 16:49:08,424 - tensorflow - INFO - loss = 0.11330609, step = 785400 (2.258 sec) -2022-05-01 16:49:10,687 - tensorflow - INFO - global_step/sec: 88.3329 -2022-05-01 16:49:10,688 - tensorflow - INFO - loss = 0.027367208, step = 785600 (2.264 sec) -2022-05-01 16:49:12,952 - tensorflow - INFO - global_step/sec: 88.2983 -2022-05-01 16:49:12,953 - tensorflow - INFO - loss = 0.18189652, step = 785800 (2.265 sec) -2022-05-01 16:49:15,212 - tensorflow - INFO - global_step/sec: 88.5153 -2022-05-01 16:49:15,212 - tensorflow - INFO - loss = 0.078452155, step = 786000 (2.259 sec) -2022-05-01 16:49:17,476 - tensorflow - INFO - global_step/sec: 88.319 -2022-05-01 16:49:17,477 - tensorflow - INFO - loss = 0.0397557, step = 786200 (2.265 sec) -2022-05-01 16:49:19,737 - tensorflow - INFO - global_step/sec: 88.4577 -2022-05-01 16:49:19,738 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.8113 -2022-05-01 16:49:35,574 - tensorflow - INFO - loss = 0.038253732, step = 787800 (2.252 sec) -2022-05-01 16:49:37,825 - tensorflow - INFO - global_step/sec: 88.8166 -2022-05-01 16:49:37,826 - tensorflow - INFO - loss = 0.09698951, step = 788000 (2.252 sec) -2022-05-01 16:49:40,084 - tensorflow - INFO - global_step/sec: 88.5559 -2022-05-01 16:49:40,084 - tensorflow - INFO - loss = 0.4601246, step = 788200 (2.258 sec) -2022-05-01 16:49:42,340 - tensorflow - INFO - global_step/sec: 88.625 -2022-05-01 16:49:42,341 - tensorflow - INFO - loss = 9.054163, step = 788400 (2.257 sec) -2022-05-01 16:49:44,591 - tensorflow - INFO - global_step/sec: 88.8459 -2022-05-01 16:49:44,592 - tensorflow - INFO - loss = 0.031948823, step = 788600 (2.251 sec) -2022-05-01 16:49:46,845 - tensorflow - INFO - global_step/sec: 88.7617 -2022-05-01 16:49:46,845 - tensorflow - INFO - loss = 0.083035044, step = 788800 (2.253 sec) -2022-05-01 16:49:49,096 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.015727483, step = 790200 (2.255 sec) -2022-05-01 16:50:04,897 - tensorflow - INFO - global_step/sec: 88.3484 -2022-05-01 16:50:04,897 - tensorflow - INFO - loss = 0.019167326, step = 790400 (2.264 sec) -2022-05-01 16:50:07,149 - tensorflow - INFO - global_step/sec: 88.7865 -2022-05-01 16:50:07,150 - tensorflow - INFO - loss = 0.11715488, step = 790600 (2.253 sec) -2022-05-01 16:50:09,403 - tensorflow - INFO - global_step/sec: 88.757 -2022-05-01 16:50:09,403 - tensorflow - INFO - loss = 0.01616585, step = 790800 (2.253 sec) -2022-05-01 16:50:11,659 - tensorflow - INFO - global_step/sec: 88.6453 -2022-05-01 16:50:11,659 - tensorflow - INFO - loss = 0.021459151, step = 791000 (2.256 sec) -2022-05-01 16:50:13,914 - tensorflow - INFO - global_step/sec: 88.6707 -2022-05-01 16:50:13,915 - tensorflow - INFO - loss = 0.0305481, step = 791200 (2.256 sec) -2022-05-01 16:50:16,170 - tensorflow - INFO - global_step/sec: 88.6709 -2022-05-01 16:50:16,170 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.5379 -2022-05-01 16:50:31,958 - tensorflow - INFO - loss = 0.01844433, step = 792800 (2.259 sec) -2022-05-01 16:50:34,210 - tensorflow - INFO - global_step/sec: 88.7669 -2022-05-01 16:50:34,211 - tensorflow - INFO - loss = 0.017058585, step = 793000 (2.253 sec) -2022-05-01 16:50:36,459 - tensorflow - INFO - global_step/sec: 88.9281 -2022-05-01 16:50:36,460 - tensorflow - INFO - loss = 0.055735964, step = 793200 (2.249 sec) -2022-05-01 16:50:38,712 - tensorflow - INFO - global_step/sec: 88.7662 -2022-05-01 16:50:38,713 - tensorflow - INFO - loss = 0.016033482, step = 793400 (2.253 sec) -2022-05-01 16:50:40,966 - tensorflow - INFO - global_step/sec: 88.7285 -2022-05-01 16:50:40,967 - tensorflow - INFO - loss = 0.2633359, step = 793600 (2.254 sec) -2022-05-01 16:50:43,223 - tensorflow - INFO - global_step/sec: 88.6167 -2022-05-01 16:50:43,224 - tensorflow - INFO - loss = 0.04098193, step = 793800 (2.257 sec) -2022-05-01 16:50:45,478 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.008105198, step = 795200 (2.258 sec) -2022-05-01 16:51:01,276 - tensorflow - INFO - global_step/sec: 88.8152 -2022-05-01 16:51:01,276 - tensorflow - INFO - loss = 0.09291974, step = 795400 (2.252 sec) -2022-05-01 16:51:03,537 - tensorflow - INFO - global_step/sec: 88.4481 -2022-05-01 16:51:03,537 - tensorflow - INFO - loss = 0.058347143, step = 795600 (2.261 sec) -2022-05-01 16:51:05,793 - tensorflow - INFO - global_step/sec: 88.6529 -2022-05-01 16:51:05,793 - tensorflow - INFO - loss = 0.030016731, step = 795800 (2.256 sec) -2022-05-01 16:51:08,052 - tensorflow - INFO - global_step/sec: 88.5311 -2022-05-01 16:51:08,052 - tensorflow - INFO - loss = 0.46277308, step = 796000 (2.259 sec) -2022-05-01 16:51:10,309 - tensorflow - INFO - global_step/sec: 88.6285 -2022-05-01 16:51:10,309 - tensorflow - INFO - loss = 0.10902999, step = 796200 (2.257 sec) -2022-05-01 16:51:12,566 - tensorflow - INFO - global_step/sec: 88.5918 -2022-05-01 16:51:12,567 - tensorflow - INFO - loss = 0.034445357, step = 796400 (2.258 sec) -2022-05-01 16:51:14,823 - tensorflow - INFO - global_step/sec: 88.6104 -2022-05-01 16:51:14,824 - tensorflow - INFO - loss = 0.19711475, step = 796600 (2.257 sec) -2022-05-01 16:51:17,077 - tensorflow - INFO - global_step/sec: 88.7387 -2022-05-01 16:51:17,078 - tensorflow - INFO - loss = 0.04598197, step = 796800 (2.254 sec) -2022-05-01 16:51:19,333 - tensorflow - INFO - global_step/sec: 88.6389 -2022-05-01 16:51:19,334 - tensorflow - INFO - loss = 0.02298907, step = 797000 (2.256 sec) -2022-05-01 16:51:21,592 - tensorflow - INFO - global_step/sec: 88.5418 -2022-05-01 16:51:21,593 - tensorflow - INFO - loss = 0.015141054, step = 797200 (2.259 sec) -2022-05-01 16:51:23,849 - tensorflow - INFO - global_step/sec: 88.625 -2022-05-01 16:51:23,849 - tensorflow - INFO - loss = 0.021146398, step = 797400 (2.257 sec) -2022-05-01 16:51:26,106 - tensorflow - INFO - global_step/sec: 88.6213 -2022-05-01 16:51:26,106 - tensorflow - INFO - loss = 0.084369704, step = 797600 (2.257 sec) -2022-05-01 16:51:28,365 - tensorflow - INFO - global_step/sec: 88.5264 -2022-05-01 16:51:28,365 - tensorflow - INFO - loss = 0.028809236, step = 797800 (2.259 sec) -2022-05-01 16:51:30,625 - tensorflow - INFO - global_step/sec: 88.5081 -2022-05-01 16:51:30,625 - tensorflow - INFO - loss = 0.016825045, step = 798000 (2.260 sec) -2022-05-01 16:51:32,883 - tensorflow - INFO - global_step/sec: 88.5496 -2022-05-01 16:51:32,884 - tensorflow - INFO - loss = 0.03811729, step = 798200 (2.259 sec) -2022-05-01 16:51:35,144 - tensorflow - INFO - global_step/sec: 88.453 -2022-05-01 16:51:35,145 - tensorflow - INFO - loss = 0.012519284, step = 798400 (2.261 sec) -2022-05-01 16:51:37,398 - tensorflow - INFO - global_step/sec: 88.753 -2022-05-01 16:51:37,398 - tensorflow - INFO - loss = 0.38044575, step = 798600 (2.253 sec) -2022-05-01 16:51:39,652 - tensorflow - INFO - global_step/sec: 88.7151 -2022-05-01 16:51:39,653 - tensorflow - INFO - loss = 1.1314454, step = 798800 (2.254 sec) -2022-05-01 16:51:41,902 - tensorflow - INFO - global_step/sec: 88.8894 -2022-05-01 16:51:41,903 - tensorflow - INFO - loss = 0.017083233, step = 799000 (2.250 sec) -2022-05-01 16:51:44,159 - tensorflow - INFO - global_step/sec: 88.6192 -2022-05-01 16:51:44,159 - tensorflow - INFO - loss = 0.042783007, step = 799200 (2.257 sec) -2022-05-01 16:51:46,421 - tensorflow - INFO - global_step/sec: 88.4185 -2022-05-01 16:51:46,421 - tensorflow - INFO - loss = 0.030692004, step = 799400 (2.262 sec) -2022-05-01 16:51:48,680 - tensorflow - INFO - global_step/sec: 88.5367 -2022-05-01 16:51:48,680 - tensorflow - INFO - loss = 0.041929305, step = 799600 (2.259 sec) -2022-05-01 16:51:50,932 - tensorflow - INFO - global_step/sec: 88.7945 -2022-05-01 16:51:50,933 - tensorflow - INFO - loss = 0.018519888, step = 799800 (2.252 sec) -2022-05-01 16:51:53,175 - tensorflow - INFO - Saving checkpoints for 800000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. -2022-05-01 16:51:53,191 - tensorflow - INFO - model.ckpt-800000 is not in all_model_checkpoint_paths. Manually adding it. -2022-05-01 16:51:53,709 - tensorflow - INFO - Calling model_fn. -2022-05-01 16:51:53,839 - tensorflow - INFO - Done calling model_fn. -2022-05-01 16:51:53,855 - tensorflow - INFO - Starting evaluation at 2022-05-01T16:51:53Z -2022-05-01 16:51:54,134 - tensorflow - INFO - Graph was finalized. -2022-05-01 16:51:54,137 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-800000 -2022-05-01 16:51:54,419 - tensorflow - INFO - Running local_init_op. -2022-05-01 16:51:54,527 - tensorflow - INFO - Done running local_init_op. -2022-05-01 16:52:04,691 - tensorflow - INFO - Evaluation [3990/39900] -2022-05-01 16:52:14,173 - tensorflow - INFO - Evaluation [7980/39900] -2022-05-01 16:52:23,645 - tensorflow - INFO - Evaluation [11970/39900] -2022-05-01 16:52:33,137 - tensorflow - INFO - Evaluation [15960/39900] -2022-05-01 16:52:42,626 - tensorflow - INFO - Evaluation [19950/39900] -2022-05-01 16:52:52,112 - tensorflow - INFO - Evaluation [23940/39900] -2022-05-01 16:53:01,615 - tensorflow - INFO - Evaluation [27930/39900] -2022-05-01 16:53:11,104 - tensorflow - INFO - Evaluation [31920/39900] -2022-05-01 16:53:20,576 - tensorflow - INFO - Evaluation [35910/39900] -2022-05-01 16:53:30,075 - tensorflow - INFO - Evaluation [39900/39900] -2022-05-01 16:53:30,215 - tensorflow - INFO - Finished evaluation at 2022-05-01-16:53:30 -2022-05-01 16:53:30,215 - tensorflow - INFO - Saving dict for global step 800000: global_step = 800000, loss = 0.026910517, mean_degree_err = 4.5198975 -2022-05-01 16:53:30,215 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 800000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-800000 -2022-05-01 16:53:30,229 - tensorflow - INFO - global_step/sec: 2.01416 -2022-05-01 16:53:30,230 - tensorflow - INFO - loss = 0.0899144, step = 800000 (99.297 sec) -2022-05-01 16:53:32,509 - tensorflow - INFO - global_step/sec: 87.7145 -2022-05-01 16:53:32,510 - tensorflow - INFO - loss = 0.010599776, step = 800200 (2.280 sec) -2022-05-01 16:53:34,761 - tensorflow - INFO - global_step/sec: 88.8073 -2022-05-01 16:53:34,762 - tensorflow - INFO - loss = 0.010874215, step = 800400 (2.252 sec) -2022-05-01 16:53:37,020 - tensorflow - INFO - global_step/sec: 88.5681 -2022-05-01 16:53:37,020 - tensorflow - INFO - loss = 0.041985117, step = 800600 (2.258 sec) -2022-05-01 16:53:39,271 - tensorflow - INFO - global_step/sec: 88.8123 -2022-05-01 16:53:39,272 - tensorflow - INFO - loss = 0.052029114, step = 800800 (2.252 sec) -2022-05-01 16:53:41,528 - tensorflow - INFO - global_step/sec: 88.6158 -2022-05-01 16:53:41,529 - tensorflow - INFO - loss = 0.103633836, step = 801000 (2.257 sec) -2022-05-01 16:53:43,789 - tensorflow - INFO - global_step/sec: 88.4821 -2022-05-01 16:53:43,789 - tensorflow - INFO - loss = 0.024310533, step = 801200 (2.260 sec) -2022-05-01 16:53:46,046 - tensorflow - INFO - global_step/sec: 88.6088 -2022-05-01 16:53:46,046 - tensorflow - INFO - loss = 0.07490441, step = 801400 (2.257 sec) -2022-05-01 16:53:48,299 - tensorflow - INFO - global_step/sec: 88.7689 -2022-05-01 16:53:48,299 - tensorflow - INFO - loss = 0.019317463, step = 801600 (2.253 sec) -2022-05-01 16:53:50,555 - tensorflow - INFO - global_step/sec: 88.636 -2022-05-01 16:53:50,556 - tensorflow - INFO - loss = 0.05756301, step = 801800 (2.256 sec) -2022-05-01 16:53:52,814 - tensorflow - INFO - global_step/sec: 88.5411 -2022-05-01 16:53:52,815 - tensorflow - INFO - loss = 2.24759, step = 802000 (2.259 sec) -2022-05-01 16:53:55,075 - tensorflow - INFO - global_step/sec: 88.4525 -2022-05-01 16:53:55,076 - tensorflow - INFO - loss = 0.10740603, step = 802200 (2.261 sec) -2022-05-01 16:53:57,332 - tensorflow - INFO - global_step/sec: 88.6125 -2022-05-01 16:53:57,333 - tensorflow - INFO - loss = 0.020427408, step = 802400 (2.257 sec) -2022-05-01 16:53:59,589 - tensorflow - INFO - global_step/sec: 88.6375 -2022-05-01 16:53:59,589 - tensorflow - INFO - loss = 0.019146318, step = 802600 (2.256 sec) -2022-05-01 16:54:01,845 - tensorflow - INFO - global_step/sec: 88.6373 -2022-05-01 16:54:01,845 - tensorflow - INFO - loss = 0.02906144, step = 802800 (2.256 sec) -2022-05-01 16:54:04,106 - tensorflow - INFO - global_step/sec: 88.4622 -2022-05-01 16:54:04,106 - tensorflow - INFO - loss = 0.036592133, step = 803000 (2.261 sec) -2022-05-01 16:54:06,359 - tensorflow - INFO - global_step/sec: 88.7492 -2022-05-01 16:54:06,360 - tensorflow - INFO - loss = 0.42497355, step = 803200 (2.254 sec) -2022-05-01 16:54:08,617 - tensorflow - INFO - global_step/sec: 88.5852 -2022-05-01 16:54:08,618 - tensorflow - INFO - loss = 0.1766534, step = 803400 (2.258 sec) -2022-05-01 16:54:10,872 - tensorflow - INFO - global_step/sec: 88.7036 -2022-05-01 16:54:10,872 - tensorflow - INFO - loss = 0.024735363, step = 803600 (2.255 sec) -2022-05-01 16:54:13,128 - tensorflow - INFO - global_step/sec: 88.6317 -2022-05-01 16:54:13,129 - tensorflow - INFO - loss = 0.030707162, step = 803800 (2.257 sec) -2022-05-01 16:54:15,383 - tensorflow - INFO - global_step/sec: 88.7 -2022-05-01 16:54:15,384 - tensorflow - INFO - loss = 0.016483106, step = 804000 (2.255 sec) -2022-05-01 16:54:17,638 - tensorflow - INFO - global_step/sec: 88.6855 -2022-05-01 16:54:17,639 - tensorflow - INFO - loss = 0.022009581, step = 804200 (2.255 sec) -2022-05-01 16:54:19,892 - tensorflow - INFO - global_step/sec: 88.7563 -2022-05-01 16:54:19,892 - tensorflow - INFO - loss = 0.029243818, step = 804400 (2.253 sec) -2022-05-01 16:54:22,151 - tensorflow - INFO - global_step/sec: 88.5227 -2022-05-01 16:54:22,151 - tensorflow - INFO - loss = 0.058547143, step = 804600 (2.259 sec) -2022-05-01 16:54:24,407 - tensorflow - INFO - global_step/sec: 88.6612 -2022-05-01 16:54:24,407 - tensorflow - INFO - loss = 0.031076716, step = 804800 (2.256 sec) -2022-05-01 16:54:26,662 - tensorflow - INFO - global_step/sec: 88.684 -2022-05-01 16:54:26,662 - tensorflow - INFO - loss = 0.031477463, step = 805000 (2.255 sec) -2022-05-01 16:54:28,920 - tensorflow - INFO - global_step/sec: 88.5908 -2022-05-01 16:54:28,920 - tensorflow - INFO - loss = 0.018799651, step = 805200 (2.258 sec) -2022-05-01 16:54:31,182 - tensorflow - INFO - global_step/sec: 88.3974 -2022-05-01 16:54:31,182 - tensorflow - INFO - loss = 0.028745413, step = 805400 (2.263 sec) -2022-05-01 16:54:33,443 - tensorflow - INFO - global_step/sec: 88.4767 -2022-05-01 16:54:33,443 - tensorflow - INFO - loss = 0.5449137, step = 805600 (2.261 sec) -2022-05-01 16:54:35,698 - tensorflow - INFO - global_step/sec: 88.6692 -2022-05-01 16:54:35,699 - tensorflow - INFO - loss = 0.24509883, step = 805800 (2.256 sec) -2022-05-01 16:54:37,954 - tensorflow - INFO - global_step/sec: 88.6504 -2022-05-01 16:54:37,955 - tensorflow - INFO - loss = 0.025008408, step = 806000 (2.256 sec) -2022-05-01 16:54:40,206 - tensorflow - INFO - global_step/sec: 88.8094 -2022-05-01 16:54:40,207 - tensorflow - INFO - loss = 0.03249572, step = 806200 (2.252 sec) -2022-05-01 16:54:42,461 - tensorflow - INFO - global_step/sec: 88.6961 -2022-05-01 16:54:42,462 - tensorflow - INFO - loss = 0.030577507, step = 806400 (2.255 sec) -2022-05-01 16:54:44,720 - tensorflow - INFO - global_step/sec: 88.5433 -2022-05-01 16:54:44,720 - tensorflow - INFO - loss = 0.020079143, step = 806600 (2.259 sec) -2022-05-01 16:54:46,975 - tensorflow - INFO - global_step/sec: 88.6837 -2022-05-01 16:54:46,975 - tensorflow - INFO - loss = 0.03394632, step = 806800 (2.255 sec) -2022-05-01 16:54:49,234 - tensorflow - INFO - global_step/sec: 88.5236 -2022-05-01 16:54:49,235 - tensorflow - INFO - loss = 0.7879622, step = 807000 (2.259 sec) -2022-05-01 16:54:51,489 - tensorflow - INFO - global_step/sec: 88.72 -2022-05-01 16:54:51,489 - tensorflow - INFO - loss = 0.049458347, step = 807200 (2.254 sec) -2022-05-01 16:54:53,747 - tensorflow - INFO - global_step/sec: 88.5466 -2022-05-01 16:54:53,748 - tensorflow - INFO - loss = 0.049946, step = 807400 (2.259 sec) -2022-05-01 16:54:56,006 - tensorflow - INFO - global_step/sec: 88.5631 -2022-05-01 16:54:56,006 - tensorflow - INFO - loss = 0.25674763, step = 807600 (2.258 sec) -2022-05-01 16:54:58,269 - tensorflow - INFO - global_step/sec: 88.3516 -2022-05-01 16:54:58,270 - tensorflow - INFO - loss = 0.063438654, step = 807800 (2.264 sec) -2022-05-01 16:55:00,527 - tensorflow - INFO - global_step/sec: 88.5901 -2022-05-01 16:55:00,527 - tensorflow - INFO - loss = 0.033711236, step = 808000 (2.258 sec) -2022-05-01 16:55:02,783 - tensorflow - INFO - global_step/sec: 88.628 -2022-05-01 16:55:02,784 - tensorflow - INFO - loss = 0.030713592, step = 808200 (2.257 sec) -2022-05-01 16:55:05,043 - tensorflow - INFO - global_step/sec: 88.5287 -2022-05-01 16:55:05,043 - tensorflow - INFO - loss = 0.3922224, step = 808400 (2.259 sec) -2022-05-01 16:55:07,305 - tensorflow - INFO - global_step/sec: 88.3993 -2022-05-01 16:55:07,306 - tensorflow - INFO - loss = 0.014256238, step = 808600 (2.262 sec) -2022-05-01 16:55:09,559 - tensorflow - INFO - global_step/sec: 88.7537 -2022-05-01 16:55:09,559 - tensorflow - INFO - loss = 0.048423983, step = 808800 (2.253 sec) -2022-05-01 16:55:11,810 - tensorflow - INFO - global_step/sec: 88.8171 -2022-05-01 16:55:11,811 - tensorflow - INFO - loss = 0.6235335, step = 809000 (2.252 sec) -2022-05-01 16:55:14,066 - tensorflow - INFO - global_step/sec: 88.6688 -2022-05-01 16:55:14,066 - tensorflow - INFO - loss = 0.040774103, step = 809200 (2.256 sec) -2022-05-01 16:55:16,325 - tensorflow - INFO - global_step/sec: 88.5454 -2022-05-01 16:55:16,325 - tensorflow - INFO - loss = 0.008105355, step = 809400 (2.259 sec) -2022-05-01 16:55:18,583 - tensorflow - INFO - global_step/sec: 88.5712 -2022-05-01 16:55:18,583 - tensorflow - INFO - loss = 0.21221718, step = 809600 (2.258 sec) -2022-05-01 16:55:20,838 - tensorflow - INFO - global_step/sec: 88.6648 -2022-05-01 16:55:20,839 - tensorflow - INFO - loss = 0.032146968, step = 809800 (2.256 sec) -2022-05-01 16:55:23,091 - tensorflow - INFO - global_step/sec: 88.7708 -2022-05-01 16:55:23,092 - tensorflow - INFO - loss = 0.034307744, step = 810000 (2.253 sec) -2022-05-01 16:55:25,342 - tensorflow - INFO - global_step/sec: 88.8554 -2022-05-01 16:55:25,343 - tensorflow - INFO - loss = 0.3609604, step = 810200 (2.251 sec) -2022-05-01 16:55:27,590 - tensorflow - INFO - global_step/sec: 88.9818 -2022-05-01 16:55:27,590 - tensorflow - INFO - loss = 0.09175968, step = 810400 (2.248 sec) -2022-05-01 16:55:29,840 - tensorflow - INFO - global_step/sec: 88.882 -2022-05-01 16:55:29,840 - tensorflow - INFO - loss = 0.026782908, step = 810600 (2.250 sec) -2022-05-01 16:55:32,097 - tensorflow - INFO - global_step/sec: 88.6085 -2022-05-01 16:55:32,098 - tensorflow - INFO - loss = 0.017560637, step = 810800 (2.257 sec) -2022-05-01 16:55:34,350 - tensorflow - INFO - global_step/sec: 88.7788 -2022-05-01 16:55:34,350 - tensorflow - INFO - loss = 0.03503574, step = 811000 (2.253 sec) -2022-05-01 16:55:36,609 - tensorflow - INFO - global_step/sec: 88.5262 -2022-05-01 16:55:36,610 - tensorflow - INFO - loss = 11.913107, step = 811200 (2.259 sec) -2022-05-01 16:55:38,864 - tensorflow - INFO - global_step/sec: 88.7198 -2022-05-01 16:55:38,864 - tensorflow - INFO - loss = 0.034631055, step = 811400 (2.254 sec) -2022-05-01 16:55:41,112 - tensorflow - INFO - global_step/sec: 88.9584 -2022-05-01 16:55:41,112 - tensorflow - INFO - loss = 0.019718375, step = 811600 (2.248 sec) -2022-05-01 16:55:43,364 - tensorflow - INFO - global_step/sec: 88.7959 -2022-05-01 16:55:43,365 - tensorflow - INFO - loss = 0.0121886395, step = 811800 (2.252 sec) -2022-05-01 16:55:45,619 - tensorflow - INFO - global_step/sec: 88.7142 -2022-05-01 16:55:45,619 - tensorflow - INFO - loss = 0.052457172, step = 812000 (2.254 sec) -2022-05-01 16:55:47,875 - tensorflow - INFO - global_step/sec: 88.6286 -2022-05-01 16:55:47,876 - tensorflow - INFO - loss = 0.039111532, step = 812200 (2.257 sec) -2022-05-01 16:55:50,129 - tensorflow - INFO - global_step/sec: 88.746 -2022-05-01 16:55:50,129 - tensorflow - INFO - loss = 0.1315316, step = 812400 (2.254 sec) -2022-05-01 16:55:52,388 - tensorflow - INFO - global_step/sec: 88.5362 -2022-05-01 16:55:52,388 - tensorflow - INFO - loss = 0.065420076, step = 812600 (2.259 sec) -2022-05-01 16:55:54,645 - tensorflow - INFO - global_step/sec: 88.5937 -2022-05-01 16:55:54,646 - tensorflow - INFO - loss = 0.22496642, step = 812800 (2.258 sec) -2022-05-01 16:55:56,898 - tensorflow - INFO - global_step/sec: 88.7743 -2022-05-01 16:55:56,899 - tensorflow - INFO - loss = 0.057431646, step = 813000 (2.253 sec) -2022-05-01 16:55:59,154 - tensorflow - INFO - global_step/sec: 88.672 -2022-05-01 16:55:59,154 - tensorflow - INFO - loss = 0.10027319, step = 813200 (2.255 sec) -2022-05-01 16:56:01,416 - tensorflow - INFO - global_step/sec: 88.4213 -2022-05-01 16:56:01,416 - tensorflow - INFO - loss = 0.017701805, step = 813400 (2.262 sec) -2022-05-01 16:56:03,668 - tensorflow - INFO - global_step/sec: 88.8079 -2022-05-01 16:56:03,668 - tensorflow - INFO - loss = 0.045442797, step = 813600 (2.252 sec) -2022-05-01 16:56:05,920 - tensorflow - INFO - global_step/sec: 88.7759 -2022-05-01 16:56:05,921 - tensorflow - INFO - loss = 0.059727803, step = 813800 (2.253 sec) -2022-05-01 16:56:08,179 - tensorflow - INFO - global_step/sec: 88.5478 -2022-05-01 16:56:08,180 - tensorflow - INFO - loss = 0.02758157, step = 814000 (2.259 sec) -2022-05-01 16:56:10,440 - tensorflow - INFO - global_step/sec: 88.4731 -2022-05-01 16:56:10,440 - tensorflow - INFO - loss = 0.17523694, step = 814200 (2.261 sec) -2022-05-01 16:56:12,701 - tensorflow - INFO - global_step/sec: 88.4638 -2022-05-01 16:56:12,701 - tensorflow - INFO - loss = 0.03142998, step = 814400 (2.261 sec) -2022-05-01 16:56:14,962 - tensorflow - INFO - global_step/sec: 88.4498 -2022-05-01 16:56:14,962 - tensorflow - INFO - loss = 0.25013742, step = 814600 (2.261 sec) -2022-05-01 16:56:17,222 - tensorflow - INFO - global_step/sec: 88.483 -2022-05-01 16:56:17,222 - tensorflow - INFO - loss = 0.24534044, step = 814800 (2.260 sec) -2022-05-01 16:56:19,482 - tensorflow - INFO - global_step/sec: 88.4985 -2022-05-01 16:56:19,482 - tensorflow - INFO - loss = 0.028317003, step = 815000 (2.260 sec) -2022-05-01 16:56:21,743 - tensorflow - INFO - global_step/sec: 88.4711 -2022-05-01 16:56:21,743 - tensorflow - INFO - loss = 0.035891015, step = 815200 (2.261 sec) -2022-05-01 16:56:24,004 - tensorflow - INFO - global_step/sec: 88.4282 -2022-05-01 16:56:24,005 - tensorflow - INFO - loss = 0.010115391, step = 815400 (2.262 sec) -2022-05-01 16:56:26,267 - tensorflow - INFO - global_step/sec: 88.3786 -2022-05-01 16:56:26,268 - tensorflow - INFO - loss = 0.07750321, step = 815600 (2.263 sec) -2022-05-01 16:56:28,526 - tensorflow - INFO - global_step/sec: 88.5364 -2022-05-01 16:56:28,527 - tensorflow - INFO - loss = 0.03354431, step = 815800 (2.259 sec) -2022-05-01 16:56:30,797 - tensorflow - INFO - global_step/sec: 88.0603 -2022-05-01 16:56:30,798 - tensorflow - INFO - loss = 0.025497619, step = 816000 (2.271 sec) -2022-05-01 16:56:33,058 - tensorflow - INFO - global_step/sec: 88.4668 -2022-05-01 16:56:33,059 - tensorflow - INFO - loss = 0.012306116, step = 816200 (2.261 sec) -2022-05-01 16:56:35,324 - tensorflow - INFO - global_step/sec: 88.2618 -2022-05-01 16:56:35,325 - tensorflow - 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tensorflow - INFO - loss = 0.044448182, step = 820200 (2.255 sec) -2022-05-01 16:57:20,448 - tensorflow - INFO - global_step/sec: 88.5737 -2022-05-01 16:57:20,448 - tensorflow - INFO - loss = 0.036466215, step = 820400 (2.258 sec) -2022-05-01 16:57:22,699 - tensorflow - INFO - global_step/sec: 88.8727 -2022-05-01 16:57:22,699 - tensorflow - INFO - loss = 0.016094217, step = 820600 (2.250 sec) -2022-05-01 16:57:24,947 - tensorflow - INFO - global_step/sec: 88.9303 -2022-05-01 16:57:24,948 - tensorflow - INFO - loss = 0.027211662, step = 820800 (2.249 sec) -2022-05-01 16:57:27,204 - tensorflow - INFO - global_step/sec: 88.6355 -2022-05-01 16:57:27,204 - tensorflow - INFO - loss = 0.020300074, step = 821000 (2.256 sec) -2022-05-01 16:57:29,457 - tensorflow - INFO - global_step/sec: 88.7849 -2022-05-01 16:57:29,457 - tensorflow - INFO - loss = 0.0277876, step = 821200 (2.253 sec) -2022-05-01 16:57:31,721 - tensorflow - INFO - global_step/sec: 88.3328 -2022-05-01 16:57:31,721 - tensorflow - INFO - loss = 0.019403651, step = 821400 (2.264 sec) -2022-05-01 16:57:33,976 - tensorflow - INFO - global_step/sec: 88.6777 -2022-05-01 16:57:33,977 - tensorflow - INFO - loss = 0.085541576, step = 821600 (2.255 sec) -2022-05-01 16:57:36,228 - tensorflow - INFO - global_step/sec: 88.8209 -2022-05-01 16:57:36,228 - tensorflow - INFO - loss = 0.16410998, step = 821800 (2.252 sec) -2022-05-01 16:57:38,483 - tensorflow - INFO - global_step/sec: 88.6798 -2022-05-01 16:57:38,483 - tensorflow - INFO - loss = 0.067582645, step = 822000 (2.255 sec) -2022-05-01 16:57:40,735 - tensorflow - INFO - global_step/sec: 88.8003 -2022-05-01 16:57:40,736 - tensorflow - INFO - loss = 0.025372028, step = 822200 (2.252 sec) -2022-05-01 16:57:42,989 - tensorflow - INFO - global_step/sec: 88.7311 -2022-05-01 16:57:42,990 - tensorflow - INFO - loss = 0.016275853, step = 822400 (2.254 sec) -2022-05-01 16:57:45,245 - tensorflow - INFO - global_step/sec: 88.6722 -2022-05-01 16:57:45,245 - tensorflow - INFO - loss = 0.37046713, step = 822600 (2.256 sec) -2022-05-01 16:57:47,494 - tensorflow - INFO - global_step/sec: 88.9097 -2022-05-01 16:57:47,495 - tensorflow - INFO - loss = 0.017730419, step = 822800 (2.249 sec) -2022-05-01 16:57:49,747 - tensorflow - INFO - global_step/sec: 88.7978 -2022-05-01 16:57:49,747 - tensorflow - INFO - loss = 0.093039796, step = 823000 (2.252 sec) -2022-05-01 16:57:52,002 - tensorflow - INFO - global_step/sec: 88.6594 -2022-05-01 16:57:52,003 - tensorflow - INFO - loss = 0.040588602, step = 823200 (2.256 sec) -2022-05-01 16:57:54,260 - tensorflow - INFO - global_step/sec: 88.5799 -2022-05-01 16:57:54,261 - tensorflow - INFO - loss = 0.39238918, step = 823400 (2.258 sec) -2022-05-01 16:57:56,517 - tensorflow - INFO - global_step/sec: 88.643 -2022-05-01 16:57:56,517 - tensorflow - INFO - loss = 0.01678184, step = 823600 (2.256 sec) -2022-05-01 16:57:58,779 - tensorflow - INFO - global_step/sec: 88.3877 -2022-05-01 16:57:58,780 - tensorflow - INFO - loss = 0.022689862, step = 823800 (2.263 sec) -2022-05-01 16:58:01,031 - tensorflow - INFO - global_step/sec: 88.8167 -2022-05-01 16:58:01,032 - tensorflow - INFO - loss = 0.11005012, step = 824000 (2.252 sec) -2022-05-01 16:58:03,290 - tensorflow - INFO - global_step/sec: 88.5512 -2022-05-01 16:58:03,290 - tensorflow - INFO - loss = 0.19351901, step = 824200 (2.259 sec) -2022-05-01 16:58:05,542 - tensorflow - INFO - global_step/sec: 88.7824 -2022-05-01 16:58:05,543 - tensorflow - INFO - loss = 0.026002258, step = 824400 (2.253 sec) -2022-05-01 16:58:07,794 - tensorflow - INFO - global_step/sec: 88.8161 -2022-05-01 16:58:07,795 - tensorflow - INFO - loss = 0.03154104, step = 824600 (2.252 sec) -2022-05-01 16:58:10,056 - tensorflow - INFO - global_step/sec: 88.4304 -2022-05-01 16:58:10,056 - tensorflow - INFO - loss = 0.048960663, step = 824800 (2.262 sec) -2022-05-01 16:58:12,312 - tensorflow - INFO - global_step/sec: 88.6479 -2022-05-01 16:58:12,313 - tensorflow - INFO - loss = 0.014210501, step = 825000 (2.256 sec) -2022-05-01 16:58:14,565 - tensorflow - INFO - global_step/sec: 88.7528 -2022-05-01 16:58:14,566 - tensorflow - INFO - loss = 0.034409862, step = 825200 (2.253 sec) -2022-05-01 16:58:16,814 - tensorflow - INFO - global_step/sec: 88.9362 -2022-05-01 16:58:16,815 - tensorflow - INFO - loss = 0.02997354, step = 825400 (2.249 sec) -2022-05-01 16:58:19,068 - tensorflow - INFO - global_step/sec: 88.7621 -2022-05-01 16:58:19,068 - tensorflow - INFO - loss = 14.25892, step = 825600 (2.253 sec) -2022-05-01 16:58:21,317 - tensorflow - INFO - global_step/sec: 88.9072 -2022-05-01 16:58:21,317 - tensorflow - INFO - loss = 0.23673823, step = 825800 (2.250 sec) -2022-05-01 16:58:23,574 - tensorflow - INFO - global_step/sec: 88.6337 -2022-05-01 16:58:23,574 - tensorflow - INFO - loss = 0.15329827, step = 826000 (2.256 sec) -2022-05-01 16:58:25,827 - tensorflow - INFO - global_step/sec: 88.7621 -2022-05-01 16:58:25,827 - tensorflow - INFO - loss = 0.29284167, step = 826200 (2.253 sec) -2022-05-01 16:58:28,080 - tensorflow - INFO - global_step/sec: 88.7688 -2022-05-01 16:58:28,080 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.7041 -2022-05-01 16:58:43,852 - tensorflow - INFO - loss = 0.030137088, step = 827800 (2.255 sec) -2022-05-01 16:58:46,108 - tensorflow - INFO - global_step/sec: 88.6432 -2022-05-01 16:58:46,108 - tensorflow - INFO - loss = 0.013250478, step = 828000 (2.256 sec) -2022-05-01 16:58:48,364 - tensorflow - INFO - global_step/sec: 88.6335 -2022-05-01 16:58:48,365 - tensorflow - INFO - loss = 0.08264479, step = 828200 (2.256 sec) -2022-05-01 16:58:50,616 - tensorflow - INFO - global_step/sec: 88.8343 -2022-05-01 16:58:50,616 - tensorflow - INFO - loss = 0.17820087, step = 828400 (2.251 sec) -2022-05-01 16:58:52,871 - tensorflow - INFO - global_step/sec: 88.6773 -2022-05-01 16:58:52,871 - tensorflow - INFO - loss = 0.41365913, step = 828600 (2.255 sec) -2022-05-01 16:58:55,127 - tensorflow - INFO - global_step/sec: 88.6339 -2022-05-01 16:58:55,128 - tensorflow - INFO - loss = 0.03269793, step = 828800 (2.257 sec) -2022-05-01 16:58:57,380 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.24388763, step = 830200 (2.252 sec) -2022-05-01 16:59:13,159 - tensorflow - INFO - global_step/sec: 88.6404 -2022-05-01 16:59:13,159 - tensorflow - INFO - loss = 0.010640362, step = 830400 (2.256 sec) -2022-05-01 16:59:15,410 - tensorflow - INFO - global_step/sec: 88.8544 -2022-05-01 16:59:15,410 - tensorflow - INFO - loss = 0.010333479, step = 830600 (2.251 sec) -2022-05-01 16:59:17,667 - tensorflow - INFO - global_step/sec: 88.6087 -2022-05-01 16:59:17,667 - tensorflow - INFO - loss = 0.12576565, step = 830800 (2.257 sec) -2022-05-01 16:59:19,921 - tensorflow - INFO - global_step/sec: 88.7294 -2022-05-01 16:59:19,921 - tensorflow - INFO - loss = 0.045042016, step = 831000 (2.254 sec) -2022-05-01 16:59:22,172 - tensorflow - INFO - global_step/sec: 88.8491 -2022-05-01 16:59:22,172 - tensorflow - INFO - loss = 0.037481226, step = 831200 (2.251 sec) -2022-05-01 16:59:24,427 - tensorflow - INFO - global_step/sec: 88.695 -2022-05-01 16:59:24,427 - tensorflow - INFO - loss = 0.04068721, step = 831400 (2.255 sec) -2022-05-01 16:59:26,681 - tensorflow - INFO - global_step/sec: 88.7158 -2022-05-01 16:59:26,682 - tensorflow - INFO - loss = 0.011278633, step = 831600 (2.254 sec) -2022-05-01 16:59:28,931 - tensorflow - INFO - global_step/sec: 88.8847 -2022-05-01 16:59:28,932 - tensorflow - INFO - loss = 0.026288154, step = 831800 (2.250 sec) -2022-05-01 16:59:31,189 - tensorflow - INFO - global_step/sec: 88.58 -2022-05-01 16:59:31,190 - tensorflow - INFO - loss = 0.026115807, step = 832000 (2.258 sec) -2022-05-01 16:59:33,440 - tensorflow - INFO - global_step/sec: 88.8708 -2022-05-01 16:59:33,440 - tensorflow - INFO - loss = 0.008827671, step = 832200 (2.250 sec) -2022-05-01 16:59:35,690 - tensorflow - INFO - global_step/sec: 88.893 -2022-05-01 16:59:35,690 - tensorflow - INFO - loss = 0.075771615, step = 832400 (2.250 sec) -2022-05-01 16:59:37,950 - tensorflow - INFO - global_step/sec: 88.5013 -2022-05-01 16:59:37,950 - tensorflow - INFO - loss = 0.030688344, step = 832600 (2.260 sec) -2022-05-01 16:59:40,211 - tensorflow - INFO - global_step/sec: 88.4329 -2022-05-01 16:59:40,212 - tensorflow - INFO - loss = 0.1068082, step = 832800 (2.262 sec) -2022-05-01 16:59:42,468 - tensorflow - INFO - global_step/sec: 88.622 -2022-05-01 16:59:42,468 - tensorflow - INFO - loss = 0.05456999, step = 833000 (2.257 sec) -2022-05-01 16:59:44,731 - tensorflow - INFO - global_step/sec: 88.3917 -2022-05-01 16:59:44,731 - tensorflow - INFO - loss = 0.029156476, step = 833200 (2.263 sec) -2022-05-01 16:59:46,990 - tensorflow - INFO - global_step/sec: 88.5054 -2022-05-01 16:59:46,991 - tensorflow - INFO - loss = 0.02217762, step = 833400 (2.260 sec) -2022-05-01 16:59:49,252 - tensorflow - INFO - global_step/sec: 88.422 -2022-05-01 16:59:49,253 - tensorflow - INFO - loss = 0.026112422, step = 833600 (2.262 sec) -2022-05-01 16:59:51,520 - tensorflow - INFO - global_step/sec: 88.1743 -2022-05-01 16:59:51,521 - tensorflow - INFO - loss = 0.025735682, step = 833800 (2.268 sec) -2022-05-01 16:59:53,783 - tensorflow - INFO - 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INFO - loss = 0.07826371, step = 856400 (2.254 sec) -2022-05-01 17:04:08,666 - tensorflow - INFO - global_step/sec: 88.5035 -2022-05-01 17:04:08,666 - tensorflow - INFO - loss = 0.027022364, step = 856600 (2.260 sec) -2022-05-01 17:04:10,922 - tensorflow - INFO - global_step/sec: 88.6372 -2022-05-01 17:04:10,923 - tensorflow - INFO - loss = 0.0835408, step = 856800 (2.256 sec) -2022-05-01 17:04:13,175 - tensorflow - INFO - global_step/sec: 88.766 -2022-05-01 17:04:13,176 - tensorflow - INFO - loss = 0.037885983, step = 857000 (2.253 sec) -2022-05-01 17:04:15,429 - tensorflow - INFO - global_step/sec: 88.7521 -2022-05-01 17:04:15,429 - tensorflow - INFO - loss = 0.023721894, step = 857200 (2.253 sec) -2022-05-01 17:04:17,681 - tensorflow - INFO - global_step/sec: 88.807 -2022-05-01 17:04:17,681 - tensorflow - INFO - loss = 0.017154926, step = 857400 (2.252 sec) -2022-05-01 17:04:19,932 - tensorflow - INFO - global_step/sec: 88.852 -2022-05-01 17:04:19,932 - tensorflow - INFO - loss = 0.26575536, step = 857600 (2.251 sec) -2022-05-01 17:04:22,186 - tensorflow - INFO - global_step/sec: 88.7241 -2022-05-01 17:04:22,186 - tensorflow - INFO - loss = 0.08691725, step = 857800 (2.254 sec) -2022-05-01 17:04:24,440 - tensorflow - INFO - global_step/sec: 88.7346 -2022-05-01 17:04:24,440 - tensorflow - INFO - loss = 0.020663533, step = 858000 (2.254 sec) -2022-05-01 17:04:26,695 - tensorflow - INFO - global_step/sec: 88.7001 -2022-05-01 17:04:26,695 - tensorflow - INFO - loss = 0.05000124, step = 858200 (2.255 sec) -2022-05-01 17:04:28,943 - tensorflow - INFO - global_step/sec: 88.9374 -2022-05-01 17:04:28,944 - tensorflow - INFO - loss = 0.20630419, step = 858400 (2.249 sec) -2022-05-01 17:04:31,200 - tensorflow - INFO - global_step/sec: 88.6236 -2022-05-01 17:04:31,201 - tensorflow - INFO - loss = 0.033283014, step = 858600 (2.257 sec) -2022-05-01 17:04:33,457 - tensorflow - INFO - global_step/sec: 88.6326 -2022-05-01 17:04:33,457 - tensorflow - INFO - loss = 0.041254193, step = 858800 (2.256 sec) -2022-05-01 17:04:35,711 - tensorflow - INFO - global_step/sec: 88.7054 -2022-05-01 17:04:35,712 - tensorflow - INFO - loss = 0.046876565, step = 859000 (2.255 sec) -2022-05-01 17:04:37,963 - tensorflow - INFO - global_step/sec: 88.8284 -2022-05-01 17:04:37,963 - tensorflow - INFO - loss = 0.02186305, step = 859200 (2.252 sec) -2022-05-01 17:04:40,214 - tensorflow - INFO - global_step/sec: 88.8545 -2022-05-01 17:04:40,214 - tensorflow - INFO - loss = 0.018246038, step = 859400 (2.251 sec) -2022-05-01 17:04:42,467 - tensorflow - INFO - global_step/sec: 88.7486 -2022-05-01 17:04:42,468 - tensorflow - INFO - loss = 0.019937154, step = 859600 (2.254 sec) -2022-05-01 17:04:44,729 - tensorflow - INFO - global_step/sec: 88.4255 -2022-05-01 17:04:44,730 - tensorflow - INFO - loss = 0.023076858, step = 859800 (2.262 sec) -2022-05-01 17:04:46,981 - tensorflow - INFO - global_step/sec: 88.8118 -2022-05-01 17:04:46,982 - tensorflow - INFO - loss = 0.026521694, step = 860000 (2.252 sec) -2022-05-01 17:04:49,231 - tensorflow - INFO - global_step/sec: 88.8829 -2022-05-01 17:04:49,232 - tensorflow - INFO - loss = 15.845024, step = 860200 (2.250 sec) -2022-05-01 17:04:51,482 - tensorflow - INFO - global_step/sec: 88.8534 -2022-05-01 17:04:51,483 - tensorflow - INFO - loss = 0.011186894, step = 860400 (2.251 sec) -2022-05-01 17:04:53,738 - tensorflow - INFO - global_step/sec: 88.6468 -2022-05-01 17:04:53,739 - tensorflow - INFO - loss = 0.06533711, step = 860600 (2.256 sec) -2022-05-01 17:04:55,995 - tensorflow - INFO - global_step/sec: 88.6324 -2022-05-01 17:04:55,995 - tensorflow - INFO - loss = 0.4109085, step = 860800 (2.256 sec) -2022-05-01 17:04:58,257 - tensorflow - INFO - global_step/sec: 88.3963 -2022-05-01 17:04:58,258 - tensorflow - INFO - loss = 0.034719594, step = 861000 (2.263 sec) -2022-05-01 17:05:00,515 - tensorflow - INFO - global_step/sec: 88.5983 -2022-05-01 17:05:00,515 - tensorflow - INFO - loss = 0.054027583, step = 861200 (2.257 sec) -2022-05-01 17:05:02,772 - tensorflow - INFO - global_step/sec: 88.5963 -2022-05-01 17:05:02,773 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.7862 -2022-05-01 17:05:18,554 - tensorflow - INFO - loss = 0.14712748, step = 862800 (2.253 sec) -2022-05-01 17:05:20,803 - tensorflow - INFO - global_step/sec: 88.8768 -2022-05-01 17:05:20,804 - tensorflow - INFO - loss = 0.031885386, step = 863000 (2.250 sec) -2022-05-01 17:05:23,058 - tensorflow - INFO - global_step/sec: 88.7211 -2022-05-01 17:05:23,058 - tensorflow - INFO - loss = 0.058298945, step = 863200 (2.254 sec) -2022-05-01 17:05:25,313 - tensorflow - INFO - global_step/sec: 88.6924 -2022-05-01 17:05:25,313 - tensorflow - INFO - loss = 0.060620643, step = 863400 (2.255 sec) -2022-05-01 17:05:27,567 - tensorflow - INFO - global_step/sec: 88.7219 -2022-05-01 17:05:27,567 - tensorflow - INFO - loss = 0.040557064, step = 863600 (2.254 sec) -2022-05-01 17:05:29,824 - tensorflow - INFO - global_step/sec: 88.6033 -2022-05-01 17:05:29,825 - tensorflow - INFO - loss = 0.070168525, step = 863800 (2.257 sec) -2022-05-01 17:05:32,088 - tensorflow - INFO - global_step/sec: 88.3626 -2022-05-01 17:05:32,088 - tensorflow - INFO - loss = 0.014815092, step = 864000 (2.263 sec) -2022-05-01 17:05:34,349 - tensorflow - INFO - global_step/sec: 88.4244 -2022-05-01 17:05:34,350 - tensorflow - INFO - loss = 0.020924546, step = 864200 (2.262 sec) -2022-05-01 17:05:36,600 - tensorflow - INFO - global_step/sec: 88.8727 -2022-05-01 17:05:36,600 - tensorflow - INFO - loss = 0.027764486, step = 864400 (2.250 sec) -2022-05-01 17:05:38,849 - tensorflow - INFO - global_step/sec: 88.9039 -2022-05-01 17:05:38,850 - tensorflow - INFO - loss = 0.65319145, step = 864600 (2.250 sec) -2022-05-01 17:05:41,100 - tensorflow - INFO - global_step/sec: 88.8741 -2022-05-01 17:05:41,100 - tensorflow - INFO - loss = 0.015839405, step = 864800 (2.250 sec) -2022-05-01 17:05:43,354 - tensorflow - INFO - global_step/sec: 88.7387 -2022-05-01 17:05:43,354 - tensorflow - INFO - loss = 0.017803952, step = 865000 (2.254 sec) -2022-05-01 17:05:45,605 - tensorflow - INFO - global_step/sec: 88.8128 -2022-05-01 17:05:45,606 - tensorflow - INFO - loss = 0.17650893, step = 865200 (2.252 sec) -2022-05-01 17:05:47,860 - tensorflow - INFO - global_step/sec: 88.7213 -2022-05-01 17:05:47,860 - tensorflow - INFO - loss = 0.034872018, step = 865400 (2.254 sec) -2022-05-01 17:05:50,117 - tensorflow - INFO - global_step/sec: 88.6071 -2022-05-01 17:05:50,117 - tensorflow - INFO - loss = 0.030002914, step = 865600 (2.257 sec) -2022-05-01 17:05:52,373 - tensorflow - INFO - global_step/sec: 88.6476 -2022-05-01 17:05:52,373 - tensorflow - INFO - loss = 0.0352073, step = 865800 (2.256 sec) -2022-05-01 17:05:54,623 - tensorflow - INFO - global_step/sec: 88.8698 -2022-05-01 17:05:54,624 - tensorflow - INFO - loss = 0.20045105, step = 866000 (2.250 sec) -2022-05-01 17:05:56,882 - tensorflow - INFO - global_step/sec: 88.5466 -2022-05-01 17:05:56,883 - tensorflow - INFO - loss = 0.085450634, step = 866200 (2.259 sec) -2022-05-01 17:05:59,141 - tensorflow - INFO - global_step/sec: 88.5423 -2022-05-01 17:05:59,141 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.8529 -2022-05-01 17:06:14,925 - tensorflow - INFO - loss = 0.0117491465, step = 867800 (2.251 sec) -2022-05-01 17:06:17,173 - tensorflow - INFO - global_step/sec: 88.9653 -2022-05-01 17:06:17,173 - tensorflow - INFO - loss = 0.079364665, step = 868000 (2.248 sec) -2022-05-01 17:06:19,427 - tensorflow - INFO - global_step/sec: 88.7452 -2022-05-01 17:06:19,427 - tensorflow - INFO - loss = 0.17102456, step = 868200 (2.254 sec) -2022-05-01 17:06:21,679 - tensorflow - INFO - global_step/sec: 88.7789 -2022-05-01 17:06:21,680 - tensorflow - INFO - loss = 0.1309104, step = 868400 (2.253 sec) -2022-05-01 17:06:23,942 - tensorflow - INFO - global_step/sec: 88.4089 -2022-05-01 17:06:23,942 - tensorflow - INFO - loss = 0.011843708, step = 868600 (2.262 sec) -2022-05-01 17:06:26,201 - tensorflow - INFO - global_step/sec: 88.5083 -2022-05-01 17:06:26,202 - tensorflow - INFO - loss = 0.076262355, step = 868800 (2.260 sec) -2022-05-01 17:06:28,459 - tensorflow - INFO - global_step/sec: 88.6012 -2022-05-01 17:06:28,459 - tensorflow - INFO - loss = 0.052073367, step = 869000 (2.257 sec) -2022-05-01 17:06:30,712 - tensorflow - INFO - global_step/sec: 88.7546 -2022-05-01 17:06:30,712 - tensorflow - INFO - loss = 0.036755126, step = 869200 (2.253 sec) -2022-05-01 17:06:32,968 - tensorflow - INFO - global_step/sec: 88.6492 -2022-05-01 17:06:32,968 - tensorflow - INFO - loss = 0.011701852, step = 869400 (2.256 sec) -2022-05-01 17:06:35,229 - tensorflow - INFO - global_step/sec: 88.4539 -2022-05-01 17:06:35,230 - tensorflow - INFO - loss = 0.022537433, step = 869600 (2.261 sec) -2022-05-01 17:06:37,494 - tensorflow - INFO - global_step/sec: 88.2918 -2022-05-01 17:06:37,495 - tensorflow - INFO - loss = 0.094644085, step = 869800 (2.265 sec) -2022-05-01 17:06:39,757 - tensorflow - INFO - global_step/sec: 88.4093 -2022-05-01 17:06:39,757 - tensorflow - INFO - loss = 0.2601124, step = 870000 (2.262 sec) -2022-05-01 17:06:42,014 - tensorflow - INFO - global_step/sec: 88.5748 -2022-05-01 17:06:42,015 - tensorflow - INFO - loss = 0.01299309, step = 870200 (2.258 sec) -2022-05-01 17:06:44,277 - tensorflow - INFO - global_step/sec: 88.3834 -2022-05-01 17:06:44,278 - tensorflow - INFO - loss = 0.01612484, step = 870400 (2.263 sec) -2022-05-01 17:06:46,539 - tensorflow - INFO - global_step/sec: 88.4181 -2022-05-01 17:06:46,540 - tensorflow - INFO - loss = 21.621862, step = 870600 (2.262 sec) -2022-05-01 17:06:48,801 - tensorflow - INFO - global_step/sec: 88.4474 -2022-05-01 17:06:48,801 - tensorflow - INFO - loss = 0.019373389, step = 870800 (2.261 sec) -2022-05-01 17:06:51,067 - tensorflow - INFO - global_step/sec: 88.2587 -2022-05-01 17:06:51,067 - tensorflow - INFO - loss = 0.010516878, step = 871000 (2.266 sec) -2022-05-01 17:06:53,330 - tensorflow - INFO - global_step/sec: 88.3772 -2022-05-01 17:06:53,330 - tensorflow - INFO - loss = 0.10557799, step = 871200 (2.263 sec) -2022-05-01 17:06:55,596 - tensorflow - INFO - global_step/sec: 88.2539 -2022-05-01 17:06:55,596 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.9656 -2022-05-01 17:07:11,413 - tensorflow - INFO - loss = 9.742434, step = 872800 (2.248 sec) -2022-05-01 17:07:13,663 - tensorflow - INFO - global_step/sec: 88.8998 -2022-05-01 17:07:13,663 - tensorflow - INFO - loss = 0.03104175, step = 873000 (2.250 sec) -2022-05-01 17:07:15,916 - tensorflow - INFO - global_step/sec: 88.7461 -2022-05-01 17:07:15,917 - tensorflow - INFO - loss = 0.021317285, step = 873200 (2.254 sec) -2022-05-01 17:07:18,171 - tensorflow - INFO - global_step/sec: 88.7069 -2022-05-01 17:07:18,171 - tensorflow - INFO - loss = 0.0664188, step = 873400 (2.255 sec) -2022-05-01 17:07:20,424 - tensorflow - INFO - global_step/sec: 88.7515 -2022-05-01 17:07:20,425 - tensorflow - INFO - loss = 0.83080256, step = 873600 (2.254 sec) -2022-05-01 17:07:22,676 - tensorflow - INFO - global_step/sec: 88.8352 -2022-05-01 17:07:22,676 - tensorflow - INFO - loss = 0.12817049, step = 873800 (2.251 sec) -2022-05-01 17:07:24,929 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.044056162, step = 875200 (2.253 sec) -2022-05-01 17:07:40,712 - tensorflow - INFO - global_step/sec: 88.8565 -2022-05-01 17:07:40,712 - tensorflow - INFO - loss = 0.021596733, step = 875400 (2.251 sec) -2022-05-01 17:07:42,968 - tensorflow - INFO - global_step/sec: 88.646 -2022-05-01 17:07:42,969 - tensorflow - INFO - loss = 0.06474429, step = 875600 (2.256 sec) -2022-05-01 17:07:45,224 - tensorflow - INFO - global_step/sec: 88.6436 -2022-05-01 17:07:45,225 - tensorflow - INFO - loss = 0.038283028, step = 875800 (2.256 sec) -2022-05-01 17:07:47,475 - tensorflow - INFO - global_step/sec: 88.8624 -2022-05-01 17:07:47,475 - tensorflow - INFO - loss = 0.03411206, step = 876000 (2.251 sec) -2022-05-01 17:07:49,727 - tensorflow - INFO - global_step/sec: 88.8071 -2022-05-01 17:07:49,728 - tensorflow - INFO - loss = 0.04376263, step = 876200 (2.252 sec) -2022-05-01 17:07:51,985 - tensorflow - INFO - global_step/sec: 88.5961 -2022-05-01 17:07:51,985 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.4732 -2022-05-01 17:08:07,773 - tensorflow - INFO - loss = 0.024417002, step = 877800 (2.261 sec) -2022-05-01 17:08:10,029 - tensorflow - INFO - global_step/sec: 88.6399 -2022-05-01 17:08:10,029 - tensorflow - INFO - loss = 0.016935084, step = 878000 (2.256 sec) -2022-05-01 17:08:12,281 - tensorflow - INFO - global_step/sec: 88.8034 -2022-05-01 17:08:12,281 - tensorflow - INFO - loss = 0.78828967, step = 878200 (2.252 sec) -2022-05-01 17:08:14,535 - tensorflow - INFO - global_step/sec: 88.7308 -2022-05-01 17:08:14,535 - tensorflow - INFO - loss = 0.037419386, step = 878400 (2.254 sec) -2022-05-01 17:08:16,787 - tensorflow - INFO - global_step/sec: 88.7924 -2022-05-01 17:08:16,788 - tensorflow - INFO - loss = 0.021398695, step = 878600 (2.252 sec) -2022-05-01 17:08:19,038 - tensorflow - INFO - global_step/sec: 88.8631 -2022-05-01 17:08:19,038 - tensorflow - INFO - loss = 0.021772686, step = 878800 (2.251 sec) -2022-05-01 17:08:21,294 - tensorflow - INFO - global_step/sec: 88.646 -2022-05-01 17:08:21,295 - tensorflow - INFO - loss = 0.12339576, step = 879000 (2.256 sec) -2022-05-01 17:08:23,549 - tensorflow - INFO - global_step/sec: 88.6844 -2022-05-01 17:08:23,550 - tensorflow - INFO - loss = 0.11540138, step = 879200 (2.255 sec) -2022-05-01 17:08:25,802 - tensorflow - INFO - global_step/sec: 88.8013 -2022-05-01 17:08:25,802 - tensorflow - INFO - loss = 0.04531049, step = 879400 (2.252 sec) -2022-05-01 17:08:28,054 - tensorflow - INFO - global_step/sec: 88.7858 -2022-05-01 17:08:28,055 - tensorflow - INFO - loss = 0.03590607, step = 879600 (2.253 sec) -2022-05-01 17:08:30,311 - tensorflow - INFO - global_step/sec: 88.6385 -2022-05-01 17:08:30,311 - tensorflow - INFO - loss = 0.015862115, step = 879800 (2.256 sec) -2022-05-01 17:08:32,572 - tensorflow - INFO - global_step/sec: 88.4284 -2022-05-01 17:08:32,573 - tensorflow - INFO - loss = 0.03887237, step = 880000 (2.262 sec) -2022-05-01 17:08:34,823 - tensorflow - INFO - global_step/sec: 88.8708 -2022-05-01 17:08:34,823 - 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INFO - loss = 0.0777564, step = 881400 (2.256 sec) -2022-05-01 17:08:50,609 - tensorflow - INFO - global_step/sec: 88.8476 -2022-05-01 17:08:50,610 - tensorflow - INFO - loss = 0.038037412, step = 881600 (2.251 sec) -2022-05-01 17:08:52,863 - tensorflow - INFO - global_step/sec: 88.7315 -2022-05-01 17:08:52,864 - tensorflow - INFO - loss = 0.08663983, step = 881800 (2.254 sec) -2022-05-01 17:08:55,118 - tensorflow - INFO - global_step/sec: 88.6907 -2022-05-01 17:08:55,119 - tensorflow - INFO - loss = 0.04738753, step = 882000 (2.255 sec) -2022-05-01 17:08:57,378 - tensorflow - INFO - global_step/sec: 88.5168 -2022-05-01 17:08:57,378 - tensorflow - INFO - loss = 0.034706645, step = 882200 (2.259 sec) -2022-05-01 17:08:59,632 - tensorflow - INFO - global_step/sec: 88.7143 -2022-05-01 17:08:59,633 - tensorflow - INFO - loss = 0.08204596, step = 882400 (2.254 sec) -2022-05-01 17:09:01,886 - tensorflow - INFO - global_step/sec: 88.7198 -2022-05-01 17:09:01,887 - tensorflow - INFO - loss = 0.017074298, step = 882600 (2.254 sec) -2022-05-01 17:09:04,147 - tensorflow - INFO - global_step/sec: 88.4616 -2022-05-01 17:09:04,148 - tensorflow - INFO - loss = 0.020894779, step = 882800 (2.261 sec) -2022-05-01 17:09:06,403 - tensorflow - INFO - global_step/sec: 88.6612 -2022-05-01 17:09:06,403 - tensorflow - INFO - loss = 0.25742978, step = 883000 (2.256 sec) -2022-05-01 17:09:08,660 - tensorflow - INFO - global_step/sec: 88.6066 -2022-05-01 17:09:08,661 - tensorflow - INFO - loss = 0.33435446, step = 883200 (2.257 sec) -2022-05-01 17:09:10,916 - tensorflow - INFO - global_step/sec: 88.6616 -2022-05-01 17:09:10,916 - tensorflow - INFO - loss = 0.030990891, step = 883400 (2.256 sec) -2022-05-01 17:09:13,168 - tensorflow - INFO - global_step/sec: 88.8028 -2022-05-01 17:09:13,169 - tensorflow - INFO - loss = 0.09165773, step = 883600 (2.252 sec) -2022-05-01 17:09:15,425 - tensorflow - INFO - global_step/sec: 88.6206 -2022-05-01 17:09:15,425 - tensorflow - INFO - loss = 0.043104094, step = 883800 (2.257 sec) -2022-05-01 17:09:17,684 - tensorflow - INFO - global_step/sec: 88.5475 -2022-05-01 17:09:17,684 - tensorflow - INFO - loss = 0.04267798, step = 884000 (2.259 sec) -2022-05-01 17:09:19,937 - tensorflow - INFO - global_step/sec: 88.7699 -2022-05-01 17:09:19,937 - tensorflow - INFO - loss = 0.011365008, step = 884200 (2.253 sec) -2022-05-01 17:09:22,191 - tensorflow - INFO - global_step/sec: 88.7002 -2022-05-01 17:09:22,192 - tensorflow - INFO - loss = 0.017686948, step = 884400 (2.255 sec) -2022-05-01 17:09:24,447 - tensorflow - INFO - global_step/sec: 88.6606 -2022-05-01 17:09:24,448 - tensorflow - INFO - loss = 0.01443425, step = 884600 (2.256 sec) -2022-05-01 17:09:26,701 - tensorflow - INFO - global_step/sec: 88.7299 -2022-05-01 17:09:26,702 - tensorflow - INFO - loss = 0.028009282, step = 884800 (2.254 sec) -2022-05-01 17:09:28,954 - tensorflow - INFO - global_step/sec: 88.7849 -2022-05-01 17:09:28,954 - tensorflow - INFO - loss = 0.03606085, step = 885000 (2.253 sec) -2022-05-01 17:09:31,215 - tensorflow - INFO - global_step/sec: 88.4532 -2022-05-01 17:09:31,215 - tensorflow - INFO - loss = 0.05631309, step = 885200 (2.261 sec) -2022-05-01 17:09:33,467 - tensorflow - INFO - global_step/sec: 88.8258 -2022-05-01 17:09:33,467 - tensorflow - INFO - loss = 0.018005019, step = 885400 (2.252 sec) -2022-05-01 17:09:35,726 - tensorflow - INFO - global_step/sec: 88.5009 -2022-05-01 17:09:35,727 - tensorflow - INFO - loss = 0.038840145, step = 885600 (2.260 sec) -2022-05-01 17:09:37,977 - tensorflow - INFO - global_step/sec: 88.8839 -2022-05-01 17:09:37,977 - tensorflow - INFO - loss = 0.013953901, step = 885800 (2.250 sec) -2022-05-01 17:09:40,231 - tensorflow - INFO - global_step/sec: 88.7118 -2022-05-01 17:09:40,231 - tensorflow - INFO - loss = 0.023950927, step = 886000 (2.255 sec) -2022-05-01 17:09:42,485 - tensorflow - INFO - global_step/sec: 88.7519 -2022-05-01 17:09:42,485 - tensorflow - INFO - loss = 0.022658564, step = 886200 (2.253 sec) -2022-05-01 17:09:44,738 - tensorflow - INFO - global_step/sec: 88.7628 -2022-05-01 17:09:44,738 - tensorflow - INFO - loss = 0.02205094, step = 886400 (2.253 sec) -2022-05-01 17:09:46,996 - tensorflow - INFO - global_step/sec: 88.5699 -2022-05-01 17:09:46,996 - tensorflow - INFO - loss = 0.012074009, step = 886600 (2.258 sec) -2022-05-01 17:09:49,256 - tensorflow - INFO - global_step/sec: 88.4804 -2022-05-01 17:09:49,257 - tensorflow - INFO - loss = 0.05748287, step = 886800 (2.260 sec) -2022-05-01 17:09:51,510 - tensorflow - INFO - global_step/sec: 88.7343 -2022-05-01 17:09:51,511 - tensorflow - INFO - loss = 0.0186936, step = 887000 (2.254 sec) -2022-05-01 17:09:53,766 - tensorflow - INFO - global_step/sec: 88.6744 -2022-05-01 17:09:53,766 - tensorflow - INFO - loss = 0.06944361, step = 887200 (2.255 sec) -2022-05-01 17:09:56,023 - tensorflow - INFO - global_step/sec: 88.6072 -2022-05-01 17:09:56,023 - tensorflow - INFO - loss = 0.031998947, step = 887400 (2.257 sec) -2022-05-01 17:09:58,275 - tensorflow - INFO - global_step/sec: 88.7897 -2022-05-01 17:09:58,276 - tensorflow - INFO - loss = 0.032064967, step = 887600 (2.253 sec) -2022-05-01 17:10:00,532 - tensorflow - INFO - global_step/sec: 88.6205 -2022-05-01 17:10:00,532 - tensorflow - INFO - loss = 0.05730361, step = 887800 (2.257 sec) -2022-05-01 17:10:02,792 - tensorflow - INFO - global_step/sec: 88.5096 -2022-05-01 17:10:02,792 - tensorflow - INFO - loss = 0.11221716, step = 888000 (2.260 sec) -2022-05-01 17:10:05,051 - tensorflow - INFO - global_step/sec: 88.5407 -2022-05-01 17:10:05,051 - tensorflow - INFO - loss = 0.010961418, step = 888200 (2.259 sec) -2022-05-01 17:10:07,310 - tensorflow - INFO - global_step/sec: 88.5327 -2022-05-01 17:10:07,310 - tensorflow - INFO - loss = 0.019681413, step = 888400 (2.259 sec) -2022-05-01 17:10:09,572 - tensorflow - INFO - global_step/sec: 88.4162 -2022-05-01 17:10:09,572 - tensorflow - INFO - loss = 0.023460643, step = 888600 (2.262 sec) -2022-05-01 17:10:11,833 - tensorflow - INFO - global_step/sec: 88.4609 -2022-05-01 17:10:11,833 - tensorflow - INFO - loss = 0.024240648, step = 888800 (2.261 sec) -2022-05-01 17:10:14,097 - tensorflow - INFO - global_step/sec: 88.3333 -2022-05-01 17:10:14,097 - tensorflow - INFO - loss = 0.05807086, step = 889000 (2.264 sec) -2022-05-01 17:10:16,359 - tensorflow - INFO - global_step/sec: 88.4001 -2022-05-01 17:10:16,360 - tensorflow - INFO - loss = 2.2288222, step = 889200 (2.262 sec) -2022-05-01 17:10:18,621 - tensorflow - INFO - global_step/sec: 88.4196 -2022-05-01 17:10:18,622 - tensorflow - INFO - loss = 0.01446765, step = 889400 (2.262 sec) -2022-05-01 17:10:20,886 - tensorflow - INFO - global_step/sec: 88.3086 -2022-05-01 17:10:20,886 - tensorflow - INFO - loss = 0.08652735, step = 889600 (2.265 sec) -2022-05-01 17:10:23,147 - tensorflow - INFO - global_step/sec: 88.451 -2022-05-01 17:10:23,147 - tensorflow - INFO - loss = 0.034366205, step = 889800 (2.261 sec) -2022-05-01 17:10:25,409 - tensorflow - INFO - global_step/sec: 88.4101 -2022-05-01 17:10:25,410 - tensorflow - INFO - loss = 0.025828442, step = 890000 (2.262 sec) -2022-05-01 17:10:27,672 - tensorflow - INFO - global_step/sec: 88.3771 -2022-05-01 17:10:27,673 - tensorflow - INFO - loss = 0.035792287, step = 890200 (2.263 sec) -2022-05-01 17:10:29,933 - tensorflow - INFO - global_step/sec: 88.4806 -2022-05-01 17:10:29,933 - tensorflow - INFO - loss = 0.07184704, step = 890400 (2.260 sec) -2022-05-01 17:10:32,204 - tensorflow - INFO - global_step/sec: 88.0624 -2022-05-01 17:10:32,204 - tensorflow - INFO - loss = 0.020343488, step = 890600 (2.271 sec) -2022-05-01 17:10:34,462 - tensorflow - INFO - global_step/sec: 88.5795 -2022-05-01 17:10:34,462 - tensorflow - INFO - loss = 0.14220776, step = 890800 (2.258 sec) -2022-05-01 17:10:36,715 - tensorflow - INFO - global_step/sec: 88.7423 -2022-05-01 17:10:36,716 - tensorflow - INFO - loss = 0.031225761, step = 891000 (2.254 sec) -2022-05-01 17:10:38,968 - tensorflow - INFO - global_step/sec: 88.7761 -2022-05-01 17:10:38,969 - tensorflow - INFO - loss = 0.06711307, step = 891200 (2.253 sec) -2022-05-01 17:10:41,229 - tensorflow - INFO - global_step/sec: 88.4802 -2022-05-01 17:10:41,229 - tensorflow - INFO - loss = 0.052210785, step = 891400 (2.260 sec) -2022-05-01 17:10:43,482 - tensorflow - INFO - global_step/sec: 88.7556 -2022-05-01 17:10:43,482 - tensorflow - INFO - loss = 0.017388288, step = 891600 (2.253 sec) -2022-05-01 17:10:45,740 - tensorflow - INFO - global_step/sec: 88.5706 -2022-05-01 17:10:45,740 - tensorflow - INFO - loss = 0.028012885, step = 891800 (2.258 sec) -2022-05-01 17:10:47,995 - tensorflow - INFO - global_step/sec: 88.708 -2022-05-01 17:10:47,995 - tensorflow - INFO - loss = 0.03236787, step = 892000 (2.255 sec) -2022-05-01 17:10:50,248 - tensorflow - INFO - global_step/sec: 88.7693 -2022-05-01 17:10:50,248 - tensorflow - INFO - loss = 0.07721909, step = 892200 (2.253 sec) -2022-05-01 17:10:52,506 - tensorflow - INFO - global_step/sec: 88.5659 -2022-05-01 17:10:52,506 - tensorflow - INFO - loss = 0.02358967, step = 892400 (2.258 sec) -2022-05-01 17:10:54,761 - tensorflow - INFO - global_step/sec: 88.6786 -2022-05-01 17:10:54,762 - tensorflow - INFO - loss = 0.016704123, step = 892600 (2.255 sec) -2022-05-01 17:10:57,017 - tensorflow - INFO - global_step/sec: 88.6653 -2022-05-01 17:10:57,017 - tensorflow - INFO - loss = 0.02639231, step = 892800 (2.256 sec) -2022-05-01 17:10:59,272 - tensorflow - INFO - global_step/sec: 88.7048 -2022-05-01 17:10:59,272 - tensorflow - INFO - loss = 0.58438075, step = 893000 (2.255 sec) -2022-05-01 17:11:01,525 - tensorflow - INFO - global_step/sec: 88.7504 -2022-05-01 17:11:01,525 - tensorflow - INFO - loss = 0.062124535, step = 893200 (2.254 sec) -2022-05-01 17:11:03,785 - tensorflow - INFO - global_step/sec: 88.5042 -2022-05-01 17:11:03,785 - tensorflow - INFO - loss = 0.120855875, step = 893400 (2.260 sec) -2022-05-01 17:11:06,043 - tensorflow - INFO - global_step/sec: 88.5804 -2022-05-01 17:11:06,043 - tensorflow - INFO - loss = 0.037611075, step = 893600 (2.258 sec) -2022-05-01 17:11:08,298 - tensorflow - INFO - global_step/sec: 88.6667 -2022-05-01 17:11:08,299 - tensorflow - INFO - loss = 0.025271785, step = 893800 (2.256 sec) -2022-05-01 17:11:10,548 - tensorflow - INFO - global_step/sec: 88.8843 -2022-05-01 17:11:10,549 - tensorflow - INFO - loss = 0.052211516, step = 894000 (2.250 sec) -2022-05-01 17:11:12,804 - tensorflow - INFO - global_step/sec: 88.6806 -2022-05-01 17:11:12,804 - tensorflow - INFO - loss = 0.023117388, step = 894200 (2.255 sec) -2022-05-01 17:11:15,063 - tensorflow - INFO - global_step/sec: 88.5046 -2022-05-01 17:11:15,064 - tensorflow - INFO - loss = 0.0851819, step = 894400 (2.260 sec) -2022-05-01 17:11:17,316 - tensorflow - INFO - global_step/sec: 88.7882 -2022-05-01 17:11:17,316 - tensorflow - INFO - loss = 0.03454585, step = 894600 (2.253 sec) -2022-05-01 17:11:19,576 - tensorflow - INFO - global_step/sec: 88.4988 -2022-05-01 17:11:19,576 - tensorflow - INFO - loss = 0.029106721, step = 894800 (2.260 sec) -2022-05-01 17:11:21,830 - tensorflow - INFO - global_step/sec: 88.7318 -2022-05-01 17:11:21,830 - tensorflow - INFO - loss = 0.02112896, step = 895000 (2.254 sec) -2022-05-01 17:11:24,089 - tensorflow - INFO - global_step/sec: 88.5157 -2022-05-01 17:11:24,090 - tensorflow - INFO - loss = 0.031981267, step = 895200 (2.260 sec) -2022-05-01 17:11:26,349 - tensorflow - INFO - global_step/sec: 88.495 -2022-05-01 17:11:26,350 - tensorflow - INFO - loss = 0.10848446, step = 895400 (2.260 sec) -2022-05-01 17:11:28,608 - tensorflow - INFO - global_step/sec: 88.5451 -2022-05-01 17:11:28,609 - tensorflow - INFO - loss = 0.48689383, step = 895600 (2.259 sec) -2022-05-01 17:11:30,874 - tensorflow - INFO - global_step/sec: 88.2549 -2022-05-01 17:11:30,875 - tensorflow - INFO - loss = 0.0074283807, step = 895800 (2.266 sec) -2022-05-01 17:11:33,139 - tensorflow - INFO - global_step/sec: 88.3263 -2022-05-01 17:11:33,139 - tensorflow - INFO - loss = 0.26092327, step = 896000 (2.264 sec) -2022-05-01 17:11:35,401 - tensorflow - INFO - global_step/sec: 88.4119 -2022-05-01 17:11:35,401 - tensorflow - INFO - loss = 0.026314523, step = 896200 (2.262 sec) -2022-05-01 17:11:37,650 - tensorflow - INFO - global_step/sec: 88.9025 -2022-05-01 17:11:37,651 - tensorflow - INFO - loss = 2.9744234, step = 896400 (2.250 sec) -2022-05-01 17:11:39,908 - tensorflow - INFO - global_step/sec: 88.6083 -2022-05-01 17:11:39,908 - tensorflow - INFO - loss = 0.064552605, step = 896600 (2.257 sec) -2022-05-01 17:11:42,161 - tensorflow - INFO - global_step/sec: 88.7661 -2022-05-01 17:11:42,161 - tensorflow - INFO - loss = 0.012820228, step = 896800 (2.253 sec) -2022-05-01 17:11:44,418 - tensorflow - INFO - global_step/sec: 88.6105 -2022-05-01 17:11:44,418 - tensorflow - INFO - loss = 0.19172603, step = 897000 (2.257 sec) -2022-05-01 17:11:46,673 - tensorflow - INFO - global_step/sec: 88.6802 -2022-05-01 17:11:46,674 - tensorflow - INFO - loss = 0.36441582, step = 897200 (2.255 sec) -2022-05-01 17:11:48,928 - tensorflow - INFO - global_step/sec: 88.6997 -2022-05-01 17:11:48,928 - tensorflow - INFO - loss = 0.22654104, step = 897400 (2.255 sec) -2022-05-01 17:11:51,184 - tensorflow - INFO - global_step/sec: 88.656 -2022-05-01 17:11:51,184 - tensorflow - INFO - loss = 0.014491557, step = 897600 (2.256 sec) -2022-05-01 17:11:53,435 - tensorflow - INFO - global_step/sec: 88.8386 -2022-05-01 17:11:53,435 - tensorflow - INFO - loss = 0.015789127, step = 897800 (2.251 sec) -2022-05-01 17:11:55,699 - tensorflow - INFO - global_step/sec: 88.3486 -2022-05-01 17:11:55,699 - tensorflow - INFO - loss = 0.028927404, step = 898000 (2.264 sec) -2022-05-01 17:11:57,953 - tensorflow - INFO - global_step/sec: 88.7079 -2022-05-01 17:11:57,954 - tensorflow - INFO - loss = 0.3525368, step = 898200 (2.255 sec) -2022-05-01 17:12:00,206 - tensorflow - INFO - global_step/sec: 88.8036 -2022-05-01 17:12:00,206 - tensorflow - INFO - loss = 0.094420716, step = 898400 (2.252 sec) -2022-05-01 17:12:02,463 - tensorflow - INFO - global_step/sec: 88.5853 -2022-05-01 17:12:02,464 - tensorflow - INFO - loss = 0.018449508, step = 898600 (2.258 sec) -2022-05-01 17:12:04,723 - tensorflow - INFO - global_step/sec: 88.5187 -2022-05-01 17:12:04,723 - tensorflow - INFO - loss = 0.027755884, step = 898800 (2.259 sec) -2022-05-01 17:12:06,977 - tensorflow - INFO - global_step/sec: 88.7219 -2022-05-01 17:12:06,977 - tensorflow - INFO - loss = 0.2651413, step = 899000 (2.254 sec) -2022-05-01 17:12:09,238 - tensorflow - INFO - global_step/sec: 88.4568 -2022-05-01 17:12:09,238 - tensorflow - INFO - loss = 0.113350786, step = 899200 (2.261 sec) -2022-05-01 17:12:11,490 - tensorflow - INFO - global_step/sec: 88.8042 -2022-05-01 17:12:11,490 - tensorflow - INFO - loss = 0.022238614, step = 899400 (2.252 sec) -2022-05-01 17:12:13,741 - tensorflow - INFO - global_step/sec: 88.8416 -2022-05-01 17:12:13,742 - tensorflow - INFO - loss = 0.06388148, step = 899600 (2.251 sec) -2022-05-01 17:12:15,993 - tensorflow - INFO - global_step/sec: 88.8187 -2022-05-01 17:12:15,993 - tensorflow - INFO - loss = 0.16148487, step = 899800 (2.252 sec) -2022-05-01 17:12:18,237 - tensorflow - INFO - Saving checkpoints for 900000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. -2022-05-01 17:12:18,255 - tensorflow - INFO - model.ckpt-900000 is not in all_model_checkpoint_paths. Manually adding it. -2022-05-01 17:12:18,789 - tensorflow - INFO - Calling model_fn. -2022-05-01 17:12:18,919 - tensorflow - INFO - Done calling model_fn. -2022-05-01 17:12:18,934 - tensorflow - INFO - Starting evaluation at 2022-05-01T17:12:18Z -2022-05-01 17:12:18,999 - tensorflow - INFO - Graph was finalized. -2022-05-01 17:12:19,002 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-900000 -2022-05-01 17:12:19,283 - tensorflow - INFO - Running local_init_op. -2022-05-01 17:12:19,392 - tensorflow - INFO - Done running local_init_op. -2022-05-01 17:12:29,492 - tensorflow - INFO - Evaluation [3990/39900] -2022-05-01 17:12:38,917 - tensorflow - INFO - Evaluation [7980/39900] -2022-05-01 17:12:48,325 - tensorflow - INFO - Evaluation [11970/39900] -2022-05-01 17:12:57,723 - tensorflow - INFO - Evaluation [15960/39900] -2022-05-01 17:13:07,119 - tensorflow - INFO - Evaluation [19950/39900] -2022-05-01 17:13:16,541 - tensorflow - INFO - Evaluation [23940/39900] -2022-05-01 17:13:25,933 - tensorflow - INFO - Evaluation [27930/39900] -2022-05-01 17:13:35,327 - tensorflow - INFO - Evaluation [31920/39900] -2022-05-01 17:13:44,745 - tensorflow - INFO - Evaluation [35910/39900] -2022-05-01 17:13:54,197 - tensorflow - INFO - Evaluation [39900/39900] -2022-05-01 17:13:54,334 - tensorflow - INFO - Finished evaluation at 2022-05-01-17:13:54 -2022-05-01 17:13:54,334 - tensorflow - INFO - Saving dict for global step 900000: global_step = 900000, loss = 0.035565972, mean_degree_err = 6.131773 -2022-05-01 17:13:54,336 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 900000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-900000 -2022-05-01 17:13:54,350 - tensorflow - INFO - global_step/sec: 2.03342 -2022-05-01 17:13:54,350 - tensorflow - INFO - loss = 0.013065057, step = 900000 (98.357 sec) -2022-05-01 17:13:56,636 - tensorflow - INFO - global_step/sec: 87.487 -2022-05-01 17:13:56,636 - tensorflow - INFO - loss = 0.012146402, step = 900200 (2.286 sec) -2022-05-01 17:13:58,898 - tensorflow - INFO - global_step/sec: 88.4111 -2022-05-01 17:13:58,898 - tensorflow - INFO - loss = 0.016029745, step = 900400 (2.262 sec) -2022-05-01 17:14:01,161 - tensorflow - INFO - global_step/sec: 88.3742 -2022-05-01 17:14:01,161 - tensorflow - INFO - loss = 0.031824853, step = 900600 (2.263 sec) -2022-05-01 17:14:03,421 - tensorflow - INFO - global_step/sec: 88.4699 -2022-05-01 17:14:03,422 - tensorflow - INFO - loss = 0.018616183, step = 900800 (2.261 sec) -2022-05-01 17:14:05,686 - tensorflow - INFO - global_step/sec: 88.3096 -2022-05-01 17:14:05,687 - tensorflow - INFO - loss = 0.027795551, step = 901000 (2.265 sec) -2022-05-01 17:14:07,942 - tensorflow - INFO - global_step/sec: 88.6682 -2022-05-01 17:14:07,942 - tensorflow - INFO - loss = 0.014406723, step = 901200 (2.256 sec) -2022-05-01 17:14:10,203 - tensorflow - INFO - global_step/sec: 88.4331 -2022-05-01 17:14:10,204 - tensorflow - INFO - loss = 0.017534908, step = 901400 (2.262 sec) -2022-05-01 17:14:12,455 - tensorflow - INFO - global_step/sec: 88.8078 -2022-05-01 17:14:12,456 - tensorflow - INFO - loss = 0.58057, step = 901600 (2.252 sec) -2022-05-01 17:14:14,714 - tensorflow - INFO - global_step/sec: 88.5464 -2022-05-01 17:14:14,715 - tensorflow - INFO - loss = 0.038886838, step = 901800 (2.259 sec) -2022-05-01 17:14:16,967 - tensorflow - INFO - global_step/sec: 88.7601 -2022-05-01 17:14:16,968 - tensorflow - INFO - loss = 0.14230952, step = 902000 (2.253 sec) -2022-05-01 17:14:19,222 - tensorflow - INFO - global_step/sec: 88.6934 -2022-05-01 17:14:19,223 - tensorflow - INFO - loss = 0.11496422, step = 902200 (2.255 sec) -2022-05-01 17:14:21,479 - tensorflow - INFO - global_step/sec: 88.6156 -2022-05-01 17:14:21,480 - tensorflow - INFO - loss = 0.027795611, step = 902400 (2.257 sec) -2022-05-01 17:14:23,735 - tensorflow - INFO - global_step/sec: 88.6611 -2022-05-01 17:14:23,736 - tensorflow - INFO - loss = 0.27380404, step = 902600 (2.256 sec) -2022-05-01 17:14:25,991 - tensorflow - INFO - global_step/sec: 88.6636 -2022-05-01 17:14:25,991 - tensorflow - INFO - loss = 0.018897396, step = 902800 (2.256 sec) -2022-05-01 17:14:28,242 - tensorflow - INFO - global_step/sec: 88.8552 -2022-05-01 17:14:28,242 - tensorflow - INFO - loss = 0.055229053, step = 903000 (2.251 sec) -2022-05-01 17:14:30,506 - tensorflow - INFO - global_step/sec: 88.3257 -2022-05-01 17:14:30,507 - tensorflow - INFO - loss = 0.03179595, step = 903200 (2.264 sec) -2022-05-01 17:14:32,758 - tensorflow - INFO - global_step/sec: 88.796 -2022-05-01 17:14:32,759 - tensorflow - INFO - loss = 0.036151156, step = 903400 (2.252 sec) -2022-05-01 17:14:35,012 - tensorflow - INFO - global_step/sec: 88.7354 -2022-05-01 17:14:35,013 - tensorflow - INFO - loss = 0.06902796, step = 903600 (2.254 sec) -2022-05-01 17:14:37,265 - tensorflow - INFO - global_step/sec: 88.7679 -2022-05-01 17:14:37,266 - tensorflow - INFO - loss = 0.04816742, step = 903800 (2.253 sec) -2022-05-01 17:14:39,520 - tensorflow - INFO - global_step/sec: 88.722 -2022-05-01 17:14:39,520 - tensorflow - INFO - loss = 0.020497266, step = 904000 (2.254 sec) -2022-05-01 17:14:41,776 - tensorflow - INFO - global_step/sec: 88.637 -2022-05-01 17:14:41,776 - tensorflow - INFO - loss = 0.57189643, step = 904200 (2.256 sec) -2022-05-01 17:14:44,029 - tensorflow - INFO - global_step/sec: 88.756 -2022-05-01 17:14:44,030 - tensorflow - INFO - loss = 0.0389892, step = 904400 (2.253 sec) -2022-05-01 17:14:46,287 - tensorflow - INFO - global_step/sec: 88.5882 -2022-05-01 17:14:46,287 - tensorflow - INFO - loss = 0.11031467, step = 904600 (2.258 sec) -2022-05-01 17:14:48,541 - tensorflow - INFO - global_step/sec: 88.7401 -2022-05-01 17:14:48,541 - tensorflow - INFO - loss = 0.07635092, step = 904800 (2.254 sec) -2022-05-01 17:14:50,792 - tensorflow - INFO - global_step/sec: 88.8347 -2022-05-01 17:14:50,793 - tensorflow - INFO - loss = 0.015127299, step = 905000 (2.251 sec) -2022-05-01 17:14:53,051 - tensorflow - INFO - global_step/sec: 88.5252 -2022-05-01 17:14:53,052 - tensorflow - INFO - loss = 0.21705827, step = 905200 (2.259 sec) -2022-05-01 17:14:55,312 - tensorflow - INFO - global_step/sec: 88.469 -2022-05-01 17:14:55,313 - tensorflow - INFO - loss = 0.3189776, step = 905400 (2.261 sec) -2022-05-01 17:14:57,565 - tensorflow - INFO - global_step/sec: 88.7576 -2022-05-01 17:14:57,566 - tensorflow - INFO - loss = 0.010876756, step = 905600 (2.253 sec) -2022-05-01 17:14:59,816 - tensorflow - INFO - global_step/sec: 88.8569 -2022-05-01 17:14:59,817 - tensorflow - INFO - loss = 0.5480637, step = 905800 (2.251 sec) -2022-05-01 17:15:02,075 - tensorflow - INFO - global_step/sec: 88.5619 -2022-05-01 17:15:02,075 - tensorflow - INFO - loss = 0.028712355, step = 906000 (2.258 sec) -2022-05-01 17:15:04,331 - tensorflow - INFO - global_step/sec: 88.6158 -2022-05-01 17:15:04,332 - tensorflow - INFO - loss = 0.03353673, step = 906200 (2.257 sec) -2022-05-01 17:15:06,589 - tensorflow - INFO - global_step/sec: 88.6025 -2022-05-01 17:15:06,589 - tensorflow - INFO - loss = 0.048440434, step = 906400 (2.257 sec) -2022-05-01 17:15:08,848 - tensorflow - INFO - global_step/sec: 88.5289 -2022-05-01 17:15:08,848 - tensorflow - INFO - loss = 0.11641862, step = 906600 (2.259 sec) -2022-05-01 17:15:11,106 - tensorflow - INFO - global_step/sec: 88.582 -2022-05-01 17:15:11,106 - tensorflow - INFO - loss = 0.04855759, step = 906800 (2.258 sec) -2022-05-01 17:15:13,361 - tensorflow - INFO - global_step/sec: 88.6868 -2022-05-01 17:15:13,361 - tensorflow - INFO - loss = 0.020095387, step = 907000 (2.255 sec) -2022-05-01 17:15:15,617 - tensorflow - INFO - global_step/sec: 88.6428 -2022-05-01 17:15:15,617 - tensorflow - INFO - loss = 0.021196015, step = 907200 (2.256 sec) -2022-05-01 17:15:17,879 - tensorflow - INFO - global_step/sec: 88.4285 -2022-05-01 17:15:17,879 - tensorflow - INFO - loss = 0.031916, step = 907400 (2.262 sec) -2022-05-01 17:15:20,145 - tensorflow - INFO - global_step/sec: 88.2537 -2022-05-01 17:15:20,145 - tensorflow - INFO - loss = 0.056354966, step = 907600 (2.266 sec) -2022-05-01 17:15:22,402 - tensorflow - INFO - global_step/sec: 88.6152 -2022-05-01 17:15:22,402 - tensorflow - INFO - loss = 0.05101075, step = 907800 (2.257 sec) -2022-05-01 17:15:24,656 - tensorflow - INFO - global_step/sec: 88.7127 -2022-05-01 17:15:24,657 - tensorflow - INFO - loss = 0.014117379, step = 908000 (2.254 sec) -2022-05-01 17:15:26,917 - tensorflow - INFO - global_step/sec: 88.4876 -2022-05-01 17:15:26,917 - tensorflow - INFO - loss = 0.026169278, step = 908200 (2.260 sec) -2022-05-01 17:15:29,171 - tensorflow - INFO - global_step/sec: 88.7315 -2022-05-01 17:15:29,171 - tensorflow - INFO - loss = 0.4392848, step = 908400 (2.254 sec) -2022-05-01 17:15:31,436 - tensorflow - INFO - global_step/sec: 88.2919 -2022-05-01 17:15:31,436 - tensorflow - INFO - loss = 0.010062726, step = 908600 (2.265 sec) -2022-05-01 17:15:33,691 - tensorflow - INFO - global_step/sec: 88.6653 -2022-05-01 17:15:33,692 - tensorflow - INFO - loss = 0.24155402, step = 908800 (2.256 sec) -2022-05-01 17:15:35,942 - tensorflow - INFO - global_step/sec: 88.8539 -2022-05-01 17:15:35,943 - tensorflow - INFO - loss = 2.5512338, step = 909000 (2.251 sec) -2022-05-01 17:15:38,199 - tensorflow - INFO - global_step/sec: 88.617 -2022-05-01 17:15:38,200 - tensorflow - INFO - loss = 0.021701274, step = 909200 (2.257 sec) -2022-05-01 17:15:40,454 - tensorflow - INFO - global_step/sec: 88.7107 -2022-05-01 17:15:40,454 - tensorflow - INFO - loss = 0.04103797, step = 909400 (2.254 sec) -2022-05-01 17:15:42,707 - tensorflow - INFO - global_step/sec: 88.7639 -2022-05-01 17:15:42,707 - tensorflow - INFO - loss = 0.011487696, step = 909600 (2.253 sec) -2022-05-01 17:15:44,972 - tensorflow - INFO - global_step/sec: 88.2904 -2022-05-01 17:15:44,973 - tensorflow - INFO - loss = 0.017504783, step = 909800 (2.265 sec) -2022-05-01 17:15:47,229 - tensorflow - INFO - global_step/sec: 88.6267 -2022-05-01 17:15:47,229 - tensorflow - INFO - loss = 0.057098113, step = 910000 (2.257 sec) -2022-05-01 17:15:49,484 - tensorflow - INFO - global_step/sec: 88.7044 -2022-05-01 17:15:49,484 - tensorflow - INFO - loss = 0.020508293, step = 910200 (2.255 sec) -2022-05-01 17:15:51,735 - tensorflow - INFO - global_step/sec: 88.8193 -2022-05-01 17:15:51,736 - tensorflow - INFO - loss = 0.008839138, step = 910400 (2.252 sec) -2022-05-01 17:15:53,990 - tensorflow - INFO - global_step/sec: 88.6945 -2022-05-01 17:15:53,991 - tensorflow - INFO - loss = 0.028127495, step = 910600 (2.255 sec) -2022-05-01 17:15:56,254 - tensorflow - INFO - global_step/sec: 88.3461 -2022-05-01 17:15:56,255 - tensorflow - INFO - loss = 0.049872562, step = 910800 (2.264 sec) -2022-05-01 17:15:58,513 - tensorflow - INFO - global_step/sec: 88.5308 -2022-05-01 17:15:58,514 - tensorflow - INFO - loss = 0.03336969, step = 911000 (2.259 sec) -2022-05-01 17:16:00,769 - tensorflow - INFO - global_step/sec: 88.6562 -2022-05-01 17:16:00,769 - tensorflow - INFO - loss = 0.028285317, step = 911200 (2.256 sec) -2022-05-01 17:16:03,030 - tensorflow - INFO - global_step/sec: 88.4384 -2022-05-01 17:16:03,031 - tensorflow - INFO - loss = 0.12840421, step = 911400 (2.261 sec) -2022-05-01 17:16:05,290 - tensorflow - INFO - global_step/sec: 88.5284 -2022-05-01 17:16:05,290 - tensorflow - INFO - loss = 0.016192503, step = 911600 (2.259 sec) -2022-05-01 17:16:07,550 - tensorflow - INFO - global_step/sec: 88.4985 -2022-05-01 17:16:07,550 - tensorflow - INFO - loss = 0.013111943, step = 911800 (2.260 sec) -2022-05-01 17:16:09,809 - tensorflow - INFO - global_step/sec: 88.506 -2022-05-01 17:16:09,810 - tensorflow - INFO - loss = 0.04875886, step = 912000 (2.260 sec) -2022-05-01 17:16:12,064 - tensorflow - INFO - global_step/sec: 88.7177 -2022-05-01 17:16:12,064 - tensorflow - INFO - loss = 0.0111401025, step = 912200 (2.254 sec) -2022-05-01 17:16:14,315 - tensorflow - INFO - global_step/sec: 88.8319 -2022-05-01 17:16:14,316 - tensorflow - INFO - loss = 0.04116901, step = 912400 (2.251 sec) -2022-05-01 17:16:16,572 - tensorflow - INFO - global_step/sec: 88.6078 -2022-05-01 17:16:16,573 - tensorflow - INFO - loss = 0.021799387, step = 912600 (2.257 sec) -2022-05-01 17:16:18,833 - tensorflow - INFO - global_step/sec: 88.4828 -2022-05-01 17:16:18,833 - tensorflow - INFO - loss = 0.02074817, step = 912800 (2.260 sec) -2022-05-01 17:16:21,089 - tensorflow - INFO - global_step/sec: 88.6219 -2022-05-01 17:16:21,090 - tensorflow - INFO - loss = 0.009668754, step = 913000 (2.257 sec) -2022-05-01 17:16:23,353 - tensorflow - INFO - global_step/sec: 88.3617 -2022-05-01 17:16:23,353 - tensorflow - INFO - loss = 0.15812874, step = 913200 (2.263 sec) -2022-05-01 17:16:25,610 - tensorflow - INFO - global_step/sec: 88.5979 -2022-05-01 17:16:25,611 - tensorflow - INFO - loss = 0.04642421, step = 913400 (2.257 sec) -2022-05-01 17:16:27,871 - tensorflow - INFO - global_step/sec: 88.4448 -2022-05-01 17:16:27,872 - tensorflow - INFO - loss = 0.01026053, step = 913600 (2.261 sec) -2022-05-01 17:16:30,134 - tensorflow - INFO - global_step/sec: 88.3798 -2022-05-01 17:16:30,135 - tensorflow - INFO - loss = 0.15093714, step = 913800 (2.263 sec) -2022-05-01 17:16:32,399 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.4686 -2022-05-01 17:21:01,122 - tensorflow - INFO - loss = 0.018456774, step = 937800 (2.261 sec) -2022-05-01 17:21:03,382 - tensorflow - INFO - global_step/sec: 88.4825 -2022-05-01 17:21:03,382 - tensorflow - INFO - loss = 0.025731664, step = 938000 (2.260 sec) -2022-05-01 17:21:05,641 - tensorflow - INFO - global_step/sec: 88.5555 -2022-05-01 17:21:05,641 - tensorflow - INFO - loss = 0.07282418, step = 938200 (2.258 sec) -2022-05-01 17:21:07,902 - tensorflow - INFO - global_step/sec: 88.4433 -2022-05-01 17:21:07,902 - tensorflow - INFO - loss = 0.07319632, step = 938400 (2.261 sec) -2022-05-01 17:21:10,159 - tensorflow - INFO - global_step/sec: 88.5926 -2022-05-01 17:21:10,160 - tensorflow - INFO - loss = 0.085002154, step = 938600 (2.258 sec) -2022-05-01 17:21:12,415 - tensorflow - INFO - global_step/sec: 88.6844 -2022-05-01 17:21:12,415 - tensorflow - INFO - loss = 0.05168, step = 938800 (2.255 sec) -2022-05-01 17:21:14,674 - tensorflow - INFO - 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INFO - global_step/sec: 88.5988 -2022-05-01 17:22:11,111 - tensorflow - INFO - loss = 0.01842107, step = 944000 (2.257 sec) -2022-05-01 17:22:13,367 - tensorflow - INFO - global_step/sec: 88.6394 -2022-05-01 17:22:13,368 - tensorflow - INFO - loss = 0.032064628, step = 944200 (2.256 sec) -2022-05-01 17:22:15,625 - tensorflow - INFO - global_step/sec: 88.5845 -2022-05-01 17:22:15,625 - tensorflow - INFO - loss = 0.02634815, step = 944400 (2.258 sec) -2022-05-01 17:22:17,880 - tensorflow - INFO - global_step/sec: 88.6875 -2022-05-01 17:22:17,881 - tensorflow - INFO - loss = 0.052733615, step = 944600 (2.255 sec) -2022-05-01 17:22:20,139 - tensorflow - INFO - global_step/sec: 88.5233 -2022-05-01 17:22:20,140 - tensorflow - INFO - loss = 0.15861678, step = 944800 (2.259 sec) -2022-05-01 17:22:22,390 - tensorflow - INFO - global_step/sec: 88.8645 -2022-05-01 17:22:22,390 - tensorflow - INFO - loss = 0.16137886, step = 945000 (2.251 sec) -2022-05-01 17:22:24,646 - tensorflow - INFO - global_step/sec: 88.6569 -2022-05-01 17:22:24,646 - 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INFO - global_step/sec: 88.5638 -2022-05-01 17:23:07,521 - tensorflow - INFO - loss = 0.029653884, step = 949000 (2.258 sec) -2022-05-01 17:23:09,778 - tensorflow - INFO - global_step/sec: 88.5779 -2022-05-01 17:23:09,779 - tensorflow - INFO - loss = 0.018251628, step = 949200 (2.258 sec) -2022-05-01 17:23:12,032 - tensorflow - INFO - global_step/sec: 88.7311 -2022-05-01 17:23:12,033 - tensorflow - INFO - loss = 0.022625616, step = 949400 (2.254 sec) -2022-05-01 17:23:14,286 - tensorflow - INFO - global_step/sec: 88.7528 -2022-05-01 17:23:14,286 - tensorflow - INFO - loss = 1.4423913, step = 949600 (2.253 sec) -2022-05-01 17:23:16,541 - tensorflow - INFO - global_step/sec: 88.6742 -2022-05-01 17:23:16,542 - tensorflow - INFO - loss = 0.064597204, step = 949800 (2.255 sec) -2022-05-01 17:23:18,795 - tensorflow - INFO - global_step/sec: 88.7497 -2022-05-01 17:23:18,796 - tensorflow - INFO - loss = 0.019017642, step = 950000 (2.254 sec) -2022-05-01 17:23:21,052 - tensorflow - INFO - global_step/sec: 88.6168 -2022-05-01 17:23:21,052 - 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tensorflow - INFO - global_step/sec: 88.6822 -2022-05-01 17:23:50,369 - tensorflow - INFO - loss = 0.04063668, step = 952800 (2.255 sec) -2022-05-01 17:23:52,629 - tensorflow - INFO - global_step/sec: 88.4835 -2022-05-01 17:23:52,629 - tensorflow - INFO - loss = 0.024519823, step = 953000 (2.260 sec) -2022-05-01 17:23:54,881 - tensorflow - INFO - global_step/sec: 88.7976 -2022-05-01 17:23:54,882 - tensorflow - INFO - loss = 0.056098625, step = 953200 (2.252 sec) -2022-05-01 17:23:57,137 - tensorflow - INFO - global_step/sec: 88.6485 -2022-05-01 17:23:57,138 - tensorflow - INFO - loss = 0.09465159, step = 953400 (2.256 sec) -2022-05-01 17:23:59,398 - tensorflow - INFO - global_step/sec: 88.4774 -2022-05-01 17:23:59,398 - tensorflow - INFO - loss = 0.027529754, step = 953600 (2.260 sec) -2022-05-01 17:24:01,658 - tensorflow - INFO - global_step/sec: 88.4694 -2022-05-01 17:24:01,659 - tensorflow - INFO - loss = 0.13809925, step = 953800 (2.261 sec) -2022-05-01 17:24:03,923 - tensorflow - INFO - global_step/sec: 88.3192 -2022-05-01 17:24:03,923 - tensorflow - INFO - loss = 0.033749137, step = 954000 (2.265 sec) -2022-05-01 17:24:06,187 - tensorflow - INFO - global_step/sec: 88.3423 -2022-05-01 17:24:06,187 - tensorflow - INFO - loss = 0.060991913, step = 954200 (2.264 sec) -2022-05-01 17:24:08,451 - tensorflow - INFO - global_step/sec: 88.3467 -2022-05-01 17:24:08,451 - tensorflow - INFO - loss = 17.136433, step = 954400 (2.264 sec) -2022-05-01 17:24:10,711 - tensorflow - INFO - global_step/sec: 88.4849 -2022-05-01 17:24:10,711 - tensorflow - INFO - loss = 0.30171964, step = 954600 (2.260 sec) -2022-05-01 17:24:12,976 - tensorflow - INFO - global_step/sec: 88.3141 -2022-05-01 17:24:12,976 - tensorflow - INFO - loss = 0.013845048, step = 954800 (2.265 sec) -2022-05-01 17:24:15,240 - tensorflow - INFO - global_step/sec: 88.3123 -2022-05-01 17:24:15,241 - tensorflow - INFO - loss = 0.023153916, step = 955000 (2.265 sec) -2022-05-01 17:24:17,502 - tensorflow - INFO - global_step/sec: 88.4209 -2022-05-01 17:24:17,503 - 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tensorflow - INFO - global_step/sec: 88.7297 -2022-05-01 17:24:46,884 - tensorflow - INFO - loss = 0.0199404, step = 957800 (2.254 sec) -2022-05-01 17:24:49,141 - tensorflow - INFO - global_step/sec: 88.6028 -2022-05-01 17:24:49,142 - tensorflow - INFO - loss = 0.03067346, step = 958000 (2.257 sec) -2022-05-01 17:24:51,398 - tensorflow - INFO - global_step/sec: 88.6148 -2022-05-01 17:24:51,399 - tensorflow - INFO - loss = 0.030765407, step = 958200 (2.257 sec) -2022-05-01 17:24:53,655 - tensorflow - INFO - global_step/sec: 88.6222 -2022-05-01 17:24:53,655 - tensorflow - INFO - loss = 0.00927158, step = 958400 (2.257 sec) -2022-05-01 17:24:55,909 - tensorflow - INFO - global_step/sec: 88.7448 -2022-05-01 17:24:55,909 - tensorflow - INFO - loss = 0.13562378, step = 958600 (2.254 sec) -2022-05-01 17:24:58,160 - tensorflow - INFO - global_step/sec: 88.8414 -2022-05-01 17:24:58,160 - tensorflow - INFO - loss = 0.032666013, step = 958800 (2.251 sec) -2022-05-01 17:25:00,416 - tensorflow - INFO - 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INFO - loss = 0.054105025, step = 961400 (2.257 sec) -2022-05-01 17:25:29,752 - tensorflow - INFO - global_step/sec: 88.5433 -2022-05-01 17:25:29,752 - tensorflow - INFO - loss = 0.0301522, step = 961600 (2.259 sec) -2022-05-01 17:25:32,005 - tensorflow - INFO - global_step/sec: 88.7831 -2022-05-01 17:25:32,005 - tensorflow - INFO - loss = 0.07410993, step = 961800 (2.253 sec) -2022-05-01 17:25:34,261 - tensorflow - INFO - global_step/sec: 88.6545 -2022-05-01 17:25:34,261 - tensorflow - INFO - loss = 1.5347927, step = 962000 (2.256 sec) -2022-05-01 17:25:36,513 - tensorflow - INFO - global_step/sec: 88.7754 -2022-05-01 17:25:36,514 - tensorflow - INFO - loss = 0.059033245, step = 962200 (2.253 sec) -2022-05-01 17:25:38,774 - tensorflow - INFO - global_step/sec: 88.4838 -2022-05-01 17:25:38,774 - tensorflow - INFO - loss = 0.015997827, step = 962400 (2.260 sec) -2022-05-01 17:25:41,030 - tensorflow - INFO - global_step/sec: 88.6449 -2022-05-01 17:25:41,030 - tensorflow - INFO - loss = 0.015084545, step = 962600 (2.256 sec) -2022-05-01 17:25:43,283 - tensorflow - INFO - global_step/sec: 88.7834 -2022-05-01 17:25:43,283 - tensorflow - INFO - loss = 0.03374805, step = 962800 (2.253 sec) -2022-05-01 17:25:45,534 - tensorflow - INFO - global_step/sec: 88.8375 -2022-05-01 17:25:45,534 - tensorflow - INFO - loss = 0.04450837, step = 963000 (2.251 sec) -2022-05-01 17:25:47,788 - tensorflow - INFO - global_step/sec: 88.7443 -2022-05-01 17:25:47,788 - tensorflow - INFO - loss = 0.027296763, step = 963200 (2.254 sec) -2022-05-01 17:25:50,042 - tensorflow - INFO - global_step/sec: 88.7116 -2022-05-01 17:25:50,043 - tensorflow - INFO - loss = 0.009122146, step = 963400 (2.255 sec) -2022-05-01 17:25:52,304 - tensorflow - INFO - global_step/sec: 88.436 -2022-05-01 17:25:52,304 - tensorflow - INFO - loss = 0.13918698, step = 963600 (2.261 sec) -2022-05-01 17:25:54,559 - tensorflow - INFO - global_step/sec: 88.6729 -2022-05-01 17:25:54,560 - tensorflow - INFO - loss = 0.04125909, step = 963800 (2.256 sec) -2022-05-01 17:25:56,819 - tensorflow - INFO - 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INFO - loss = 0.079419926, step = 966400 (2.260 sec) -2022-05-01 17:26:26,153 - tensorflow - INFO - global_step/sec: 88.4771 -2022-05-01 17:26:26,153 - tensorflow - INFO - loss = 0.015647521, step = 966600 (2.260 sec) -2022-05-01 17:26:28,412 - tensorflow - INFO - global_step/sec: 88.526 -2022-05-01 17:26:28,413 - tensorflow - INFO - loss = 0.08310799, step = 966800 (2.259 sec) -2022-05-01 17:26:30,686 - tensorflow - INFO - global_step/sec: 87.9628 -2022-05-01 17:26:30,686 - tensorflow - INFO - loss = 0.09947473, step = 967000 (2.274 sec) -2022-05-01 17:26:32,951 - tensorflow - INFO - global_step/sec: 88.3029 -2022-05-01 17:26:32,951 - tensorflow - INFO - loss = 0.050885316, step = 967200 (2.265 sec) -2022-05-01 17:26:35,208 - tensorflow - INFO - global_step/sec: 88.6192 -2022-05-01 17:26:35,208 - tensorflow - INFO - loss = 0.014569244, step = 967400 (2.257 sec) -2022-05-01 17:26:37,456 - tensorflow - INFO - global_step/sec: 88.9568 -2022-05-01 17:26:37,456 - tensorflow - INFO - loss = 0.0064915894, step = 967600 (2.248 sec) -2022-05-01 17:26:39,712 - 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tensorflow - INFO - global_step/sec: 88.2469 -2022-05-01 17:27:36,155 - tensorflow - INFO - loss = 1.0652466, step = 972800 (2.266 sec) -2022-05-01 17:27:38,420 - tensorflow - INFO - global_step/sec: 88.3081 -2022-05-01 17:27:38,420 - tensorflow - INFO - loss = 0.013959445, step = 973000 (2.265 sec) -2022-05-01 17:27:40,680 - tensorflow - INFO - global_step/sec: 88.4964 -2022-05-01 17:27:40,680 - tensorflow - INFO - loss = 0.037181213, step = 973200 (2.260 sec) -2022-05-01 17:27:42,945 - tensorflow - INFO - global_step/sec: 88.3081 -2022-05-01 17:27:42,945 - tensorflow - INFO - loss = 0.07719441, step = 973400 (2.265 sec) -2022-05-01 17:27:45,205 - tensorflow - INFO - global_step/sec: 88.4847 -2022-05-01 17:27:45,205 - tensorflow - INFO - loss = 0.08151187, step = 973600 (2.260 sec) -2022-05-01 17:27:47,468 - tensorflow - INFO - global_step/sec: 88.3531 -2022-05-01 17:27:47,469 - tensorflow - INFO - loss = 0.08388565, step = 973800 (2.264 sec) -2022-05-01 17:27:49,731 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.2051 -2022-05-01 17:28:32,858 - tensorflow - INFO - loss = 0.012920011, step = 977800 (2.267 sec) -2022-05-01 17:28:35,125 - tensorflow - INFO - global_step/sec: 88.202 -2022-05-01 17:28:35,125 - tensorflow - INFO - loss = 0.016317671, step = 978000 (2.268 sec) -2022-05-01 17:28:37,391 - tensorflow - INFO - global_step/sec: 88.2428 -2022-05-01 17:28:37,392 - tensorflow - INFO - loss = 0.07118274, step = 978200 (2.266 sec) -2022-05-01 17:28:39,661 - tensorflow - INFO - global_step/sec: 88.1176 -2022-05-01 17:28:39,661 - tensorflow - INFO - loss = 0.015032679, step = 978400 (2.270 sec) -2022-05-01 17:28:41,926 - tensorflow - INFO - global_step/sec: 88.2807 -2022-05-01 17:28:41,927 - tensorflow - INFO - loss = 0.012356233, step = 978600 (2.266 sec) -2022-05-01 17:28:44,198 - tensorflow - INFO - global_step/sec: 88.0509 -2022-05-01 17:28:44,198 - tensorflow - INFO - loss = 0.010050238, step = 978800 (2.271 sec) -2022-05-01 17:28:46,466 - tensorflow - INFO - global_step/sec: 88.1573 -2022-05-01 17:28:46,467 - tensorflow - INFO - loss = 0.013371667, step = 979000 (2.269 sec) -2022-05-01 17:28:48,733 - tensorflow - INFO - global_step/sec: 88.2271 -2022-05-01 17:28:48,734 - tensorflow - INFO - loss = 0.20692414, step = 979200 (2.267 sec) -2022-05-01 17:28:51,004 - tensorflow - INFO - global_step/sec: 88.0974 -2022-05-01 17:28:51,004 - tensorflow - INFO - loss = 0.014547777, step = 979400 (2.270 sec) -2022-05-01 17:28:53,276 - tensorflow - INFO - global_step/sec: 88.0222 -2022-05-01 17:28:53,276 - tensorflow - INFO - loss = 0.12706436, step = 979600 (2.272 sec) -2022-05-01 17:28:55,540 - tensorflow - INFO - global_step/sec: 88.344 -2022-05-01 17:28:55,540 - tensorflow - INFO - loss = 0.05867211, step = 979800 (2.264 sec) -2022-05-01 17:28:57,807 - tensorflow - INFO - global_step/sec: 88.1949 -2022-05-01 17:28:57,808 - tensorflow - INFO - loss = 0.054758377, step = 980000 (2.268 sec) -2022-05-01 17:29:00,074 - tensorflow - INFO - global_step/sec: 88.2393 -2022-05-01 17:29:00,074 - tensorflow - INFO - loss = 0.013110551, step = 980200 (2.267 sec) -2022-05-01 17:29:02,339 - tensorflow - INFO - global_step/sec: 88.2893 -2022-05-01 17:29:02,340 - tensorflow - INFO - loss = 0.015686195, step = 980400 (2.265 sec) -2022-05-01 17:29:04,614 - tensorflow - INFO - global_step/sec: 87.9188 -2022-05-01 17:29:04,614 - tensorflow - INFO - loss = 0.7616093, step = 980600 (2.275 sec) -2022-05-01 17:29:06,886 - tensorflow - INFO - global_step/sec: 88.04 -2022-05-01 17:29:06,886 - tensorflow - INFO - loss = 0.07527063, step = 980800 (2.272 sec) -2022-05-01 17:29:09,151 - tensorflow - INFO - global_step/sec: 88.2956 -2022-05-01 17:29:09,151 - tensorflow - INFO - loss = 0.01953073, step = 981000 (2.265 sec) -2022-05-01 17:29:11,418 - tensorflow - INFO - global_step/sec: 88.1979 -2022-05-01 17:29:11,419 - tensorflow - INFO - loss = 0.023897674, step = 981200 (2.268 sec) -2022-05-01 17:29:13,689 - tensorflow - INFO - global_step/sec: 88.1014 -2022-05-01 17:29:13,689 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.1484 -2022-05-01 17:29:29,567 - tensorflow - INFO - loss = 0.021682613, step = 982800 (2.269 sec) -2022-05-01 17:29:31,841 - tensorflow - INFO - global_step/sec: 87.9505 -2022-05-01 17:29:31,841 - tensorflow - INFO - loss = 0.065413214, step = 983000 (2.274 sec) -2022-05-01 17:29:34,107 - tensorflow - INFO - global_step/sec: 88.2625 -2022-05-01 17:29:34,107 - tensorflow - INFO - loss = 0.02960616, step = 983200 (2.266 sec) -2022-05-01 17:29:36,374 - tensorflow - INFO - global_step/sec: 88.2038 -2022-05-01 17:29:36,374 - tensorflow - INFO - loss = 0.009819341, step = 983400 (2.268 sec) -2022-05-01 17:29:38,643 - tensorflow - INFO - global_step/sec: 88.1329 -2022-05-01 17:29:38,644 - tensorflow - INFO - loss = 0.010089403, step = 983600 (2.269 sec) -2022-05-01 17:29:40,908 - tensorflow - INFO - global_step/sec: 88.3044 -2022-05-01 17:29:40,909 - tensorflow - INFO - loss = 0.080353625, step = 983800 (2.265 sec) -2022-05-01 17:29:43,180 - tensorflow - INFO - global_step/sec: 88.0331 -2022-05-01 17:29:43,181 - tensorflow - INFO - loss = 0.0091108745, step = 984000 (2.272 sec) -2022-05-01 17:29:45,447 - tensorflow - INFO - global_step/sec: 88.2278 -2022-05-01 17:29:45,447 - tensorflow - INFO - loss = 0.015508783, step = 984200 (2.267 sec) -2022-05-01 17:29:47,719 - tensorflow - INFO - global_step/sec: 88.0386 -2022-05-01 17:29:47,719 - tensorflow - INFO - loss = 0.036307346, step = 984400 (2.272 sec) -2022-05-01 17:29:49,983 - tensorflow - INFO - global_step/sec: 88.3255 -2022-05-01 17:29:49,983 - tensorflow - INFO - loss = 0.07458148, step = 984600 (2.264 sec) -2022-05-01 17:29:52,249 - tensorflow - INFO - global_step/sec: 88.2767 -2022-05-01 17:29:52,249 - tensorflow - INFO - loss = 0.025107179, step = 984800 (2.266 sec) -2022-05-01 17:29:54,520 - tensorflow - INFO - global_step/sec: 88.0421 -2022-05-01 17:29:54,521 - tensorflow - INFO - loss = 0.03554614, step = 985000 (2.272 sec) -2022-05-01 17:29:56,795 - tensorflow - INFO - global_step/sec: 87.9401 -2022-05-01 17:29:56,795 - 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tensorflow - INFO - global_step/sec: 88.2279 -2022-05-01 17:30:26,300 - tensorflow - INFO - loss = 0.04953441, step = 987800 (2.267 sec) -2022-05-01 17:30:28,574 - tensorflow - INFO - global_step/sec: 87.9615 -2022-05-01 17:30:28,574 - tensorflow - INFO - loss = 0.021212831, step = 988000 (2.274 sec) -2022-05-01 17:30:30,846 - tensorflow - INFO - global_step/sec: 88.0065 -2022-05-01 17:30:30,847 - tensorflow - INFO - loss = 0.0115836, step = 988200 (2.273 sec) -2022-05-01 17:30:33,117 - tensorflow - INFO - global_step/sec: 88.0964 -2022-05-01 17:30:33,117 - tensorflow - INFO - loss = 0.03893371, step = 988400 (2.270 sec) -2022-05-01 17:30:35,382 - tensorflow - INFO - global_step/sec: 88.2646 -2022-05-01 17:30:35,383 - tensorflow - INFO - loss = 0.011640495, step = 988600 (2.266 sec) -2022-05-01 17:30:37,643 - tensorflow - INFO - global_step/sec: 88.491 -2022-05-01 17:30:37,643 - tensorflow - INFO - loss = 0.07432926, step = 988800 (2.260 sec) -2022-05-01 17:30:39,908 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.021077337, step = 990200 (2.265 sec) -2022-05-01 17:30:55,789 - tensorflow - INFO - global_step/sec: 88.131 -2022-05-01 17:30:55,789 - tensorflow - INFO - loss = 0.046095267, step = 990400 (2.269 sec) -2022-05-01 17:30:58,058 - tensorflow - INFO - global_step/sec: 88.1344 -2022-05-01 17:30:58,058 - tensorflow - INFO - loss = 0.013050471, step = 990600 (2.269 sec) -2022-05-01 17:31:00,333 - tensorflow - INFO - global_step/sec: 87.9212 -2022-05-01 17:31:00,333 - tensorflow - INFO - loss = 0.04462536, step = 990800 (2.275 sec) -2022-05-01 17:31:02,605 - tensorflow - INFO - global_step/sec: 88.0144 -2022-05-01 17:31:02,605 - tensorflow - INFO - loss = 0.13494569, step = 991000 (2.272 sec) -2022-05-01 17:31:04,877 - tensorflow - INFO - global_step/sec: 88.022 -2022-05-01 17:31:04,878 - tensorflow - INFO - loss = 0.016361812, step = 991200 (2.272 sec) -2022-05-01 17:31:07,153 - tensorflow - INFO - global_step/sec: 87.8648 -2022-05-01 17:31:07,154 - tensorflow - INFO - loss = 0.14117768, step = 991400 (2.276 sec) -2022-05-01 17:31:09,430 - tensorflow - INFO - global_step/sec: 87.8558 -2022-05-01 17:31:09,430 - tensorflow - INFO - loss = 0.010469894, step = 991600 (2.276 sec) -2022-05-01 17:31:11,706 - tensorflow - INFO - global_step/sec: 87.8807 -2022-05-01 17:31:11,706 - tensorflow - INFO - loss = 0.11327028, step = 991800 (2.276 sec) -2022-05-01 17:31:13,984 - tensorflow - INFO - global_step/sec: 87.7696 -2022-05-01 17:31:13,985 - tensorflow - INFO - loss = 0.23409489, step = 992000 (2.279 sec) -2022-05-01 17:31:16,261 - tensorflow - INFO - global_step/sec: 87.862 -2022-05-01 17:31:16,261 - tensorflow - INFO - loss = 5.709214, step = 992200 (2.276 sec) -2022-05-01 17:31:18,536 - tensorflow - INFO - global_step/sec: 87.891 -2022-05-01 17:31:18,537 - tensorflow - INFO - loss = 0.02604986, step = 992400 (2.276 sec) -2022-05-01 17:31:20,809 - tensorflow - INFO - global_step/sec: 87.995 -2022-05-01 17:31:20,809 - tensorflow - INFO - loss = 0.020889275, step = 992600 (2.273 sec) -2022-05-01 17:31:23,086 - tensorflow - INFO - global_step/sec: 87.822 -2022-05-01 17:31:23,087 - tensorflow - INFO - loss = 0.041441254, step = 992800 (2.277 sec) -2022-05-01 17:31:25,366 - tensorflow - INFO - global_step/sec: 87.7491 -2022-05-01 17:31:25,366 - tensorflow - INFO - loss = 0.0638247, step = 993000 (2.279 sec) -2022-05-01 17:31:27,642 - tensorflow - INFO - global_step/sec: 87.8521 -2022-05-01 17:31:27,643 - tensorflow - INFO - loss = 0.09642565, step = 993200 (2.277 sec) -2022-05-01 17:31:29,918 - tensorflow - INFO - global_step/sec: 87.8951 -2022-05-01 17:31:29,918 - tensorflow - INFO - loss = 0.053129744, step = 993400 (2.275 sec) -2022-05-01 17:31:32,199 - tensorflow - INFO - global_step/sec: 87.6458 -2022-05-01 17:31:32,200 - tensorflow - INFO - loss = 0.06550279, step = 993600 (2.282 sec) -2022-05-01 17:31:34,469 - tensorflow - INFO - global_step/sec: 88.107 -2022-05-01 17:31:34,470 - tensorflow - INFO - loss = 0.01763028, step = 993800 (2.270 sec) -2022-05-01 17:31:36,738 - tensorflow - INFO - global_step/sec: 88.1738 -2022-05-01 17:31:36,738 - tensorflow - INFO - loss = 0.36183137, step = 994000 (2.268 sec) -2022-05-01 17:31:39,012 - tensorflow - INFO - global_step/sec: 87.9508 -2022-05-01 17:31:39,012 - tensorflow - INFO - loss = 0.048842005, step = 994200 (2.274 sec) -2022-05-01 17:31:41,284 - tensorflow - INFO - global_step/sec: 88.0298 -2022-05-01 17:31:41,284 - tensorflow - INFO - loss = 31.629017, step = 994400 (2.272 sec) -2022-05-01 17:31:43,550 - tensorflow - INFO - global_step/sec: 88.2431 -2022-05-01 17:31:43,551 - tensorflow - INFO - loss = 0.017132096, step = 994600 (2.267 sec) -2022-05-01 17:31:45,821 - tensorflow - INFO - global_step/sec: 88.0834 -2022-05-01 17:31:45,821 - tensorflow - INFO - loss = 0.04079227, step = 994800 (2.271 sec) -2022-05-01 17:31:48,087 - tensorflow - INFO - global_step/sec: 88.2552 -2022-05-01 17:31:48,087 - tensorflow - INFO - loss = 0.825424, step = 995000 (2.266 sec) -2022-05-01 17:31:50,356 - tensorflow - INFO - global_step/sec: 88.1421 -2022-05-01 17:31:50,356 - tensorflow - INFO - loss = 0.01457743, step = 995200 (2.269 sec) -2022-05-01 17:31:52,627 - tensorflow - INFO - global_step/sec: 88.0675 -2022-05-01 17:31:52,627 - tensorflow - INFO - loss = 0.08510444, step = 995400 (2.271 sec) -2022-05-01 17:31:54,894 - tensorflow - INFO - global_step/sec: 88.2252 -2022-05-01 17:31:54,894 - tensorflow - INFO - loss = 0.10075992, step = 995600 (2.267 sec) -2022-05-01 17:31:57,164 - tensorflow - INFO - global_step/sec: 88.1038 -2022-05-01 17:31:57,164 - tensorflow - INFO - loss = 0.5939552, step = 995800 (2.270 sec) -2022-05-01 17:31:59,431 - tensorflow - INFO - global_step/sec: 88.237 -2022-05-01 17:31:59,431 - tensorflow - INFO - loss = 0.023191817, step = 996000 (2.267 sec) -2022-05-01 17:32:01,698 - tensorflow - INFO - global_step/sec: 88.2096 -2022-05-01 17:32:01,698 - tensorflow - INFO - loss = 0.03370051, step = 996200 (2.267 sec) -2022-05-01 17:32:03,972 - tensorflow - INFO - global_step/sec: 87.941 -2022-05-01 17:32:03,972 - tensorflow - INFO - loss = 0.045667723, step = 996400 (2.274 sec) -2022-05-01 17:32:06,245 - tensorflow - INFO - global_step/sec: 88.0017 -2022-05-01 17:32:06,245 - tensorflow - INFO - loss = 5.847514, step = 996600 (2.273 sec) -2022-05-01 17:32:08,515 - tensorflow - INFO - global_step/sec: 88.1051 -2022-05-01 17:32:08,515 - tensorflow - INFO - loss = 0.0076303408, step = 996800 (2.270 sec) -2022-05-01 17:32:10,780 - tensorflow - INFO - global_step/sec: 88.3048 -2022-05-01 17:32:10,780 - tensorflow - INFO - loss = 0.014137331, step = 997000 (2.265 sec) -2022-05-01 17:32:13,053 - tensorflow - INFO - global_step/sec: 87.9674 -2022-05-01 17:32:13,054 - tensorflow - INFO - loss = 0.012826626, step = 997200 (2.274 sec) -2022-05-01 17:32:15,317 - tensorflow - INFO - global_step/sec: 88.3681 -2022-05-01 17:32:15,317 - tensorflow - INFO - loss = 0.027969304, step = 997400 (2.263 sec) -2022-05-01 17:32:17,589 - tensorflow - INFO - global_step/sec: 88.0105 -2022-05-01 17:32:17,589 - tensorflow - INFO - loss = 0.05405557, step = 997600 (2.272 sec) -2022-05-01 17:32:19,864 - tensorflow - INFO - global_step/sec: 87.9009 -2022-05-01 17:32:19,865 - tensorflow - INFO - loss = 0.095805936, step = 997800 (2.275 sec) -2022-05-01 17:32:22,134 - tensorflow - INFO - global_step/sec: 88.1227 -2022-05-01 17:32:22,134 - tensorflow - INFO - loss = 0.10985251, step = 998000 (2.270 sec) -2022-05-01 17:32:24,401 - tensorflow - INFO - global_step/sec: 88.229 -2022-05-01 17:32:24,401 - tensorflow - INFO - loss = 0.031816743, step = 998200 (2.267 sec) -2022-05-01 17:32:26,668 - tensorflow - INFO - global_step/sec: 88.2149 -2022-05-01 17:32:26,668 - tensorflow - INFO - loss = 0.28096992, step = 998400 (2.267 sec) -2022-05-01 17:32:28,937 - tensorflow - INFO - global_step/sec: 88.1398 -2022-05-01 17:32:28,937 - tensorflow - INFO - loss = 0.03208027, step = 998600 (2.269 sec) -2022-05-01 17:32:31,209 - tensorflow - INFO - global_step/sec: 88.0357 -2022-05-01 17:32:31,209 - tensorflow - INFO - loss = 0.11195407, step = 998800 (2.272 sec) -2022-05-01 17:32:33,473 - tensorflow - INFO - global_step/sec: 88.3346 -2022-05-01 17:32:33,473 - tensorflow - INFO - loss = 0.11846994, step = 999000 (2.264 sec) -2022-05-01 17:32:35,741 - tensorflow - INFO - global_step/sec: 88.1636 -2022-05-01 17:32:35,742 - tensorflow - INFO - loss = 0.01635918, step = 999200 (2.268 sec) -2022-05-01 17:32:38,008 - tensorflow - INFO - global_step/sec: 88.2422 -2022-05-01 17:32:38,008 - tensorflow - INFO - loss = 0.086311236, step = 999400 (2.267 sec) -2022-05-01 17:32:40,277 - tensorflow - INFO - global_step/sec: 88.1249 -2022-05-01 17:32:40,278 - tensorflow - INFO - loss = 0.028363196, step = 999600 (2.269 sec) -2022-05-01 17:32:42,543 - tensorflow - INFO - global_step/sec: 88.2577 -2022-05-01 17:32:42,544 - tensorflow - INFO - loss = 0.02159482, step = 999800 (2.266 sec) -2022-05-01 17:32:44,799 - tensorflow - INFO - Saving checkpoints for 1000000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. -2022-05-01 17:32:44,814 - tensorflow - INFO - model.ckpt-1000000 is not in all_model_checkpoint_paths. Manually adding it. -2022-05-01 17:32:45,367 - tensorflow - INFO - Calling model_fn. -2022-05-01 17:32:45,495 - tensorflow - INFO - Done calling model_fn. -2022-05-01 17:32:45,510 - tensorflow - INFO - Starting evaluation at 2022-05-01T17:32:45Z -2022-05-01 17:32:45,575 - tensorflow - INFO - Graph was finalized. -2022-05-01 17:32:45,578 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1000000 -2022-05-01 17:32:45,861 - tensorflow - INFO - Running local_init_op. -2022-05-01 17:32:45,969 - tensorflow - INFO - Done running local_init_op. -2022-05-01 17:32:56,249 - tensorflow - INFO - Evaluation [3990/39900] -2022-05-01 17:33:05,789 - tensorflow - INFO - Evaluation [7980/39900] -2022-05-01 17:33:15,364 - tensorflow - INFO - Evaluation [11970/39900] -2022-05-01 17:33:24,953 - tensorflow - INFO - Evaluation [15960/39900] -2022-05-01 17:33:34,512 - tensorflow - INFO - Evaluation [19950/39900] -2022-05-01 17:33:44,109 - tensorflow - INFO - Evaluation [23940/39900] -2022-05-01 17:33:53,662 - tensorflow - INFO - Evaluation [27930/39900] -2022-05-01 17:34:03,220 - tensorflow - INFO - Evaluation [31920/39900] -2022-05-01 17:34:12,787 - tensorflow - INFO - Evaluation [35910/39900] -2022-05-01 17:34:22,350 - tensorflow - INFO - Evaluation [39900/39900] -2022-05-01 17:34:22,488 - tensorflow - INFO - Finished evaluation at 2022-05-01-17:34:22 -2022-05-01 17:34:22,489 - tensorflow - INFO - Saving dict for global step 1000000: global_step = 1000000, loss = 0.024924062, mean_degree_err = 4.3142204 -2022-05-01 17:34:22,489 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 1000000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1000000 -2022-05-01 17:34:22,503 - tensorflow - INFO - global_step/sec: 2.00081 -2022-05-01 17:34:22,504 - tensorflow - INFO - loss = 0.026846617, step = 1000000 (99.960 sec) -2022-05-01 17:34:24,791 - tensorflow - INFO - global_step/sec: 87.4221 -2022-05-01 17:34:24,791 - tensorflow - INFO - loss = 0.062070694, step = 1000200 (2.287 sec) -2022-05-01 17:34:27,060 - tensorflow - INFO - global_step/sec: 88.1238 -2022-05-01 17:34:27,061 - tensorflow - INFO - loss = 0.008369792, step = 1000400 (2.270 sec) -2022-05-01 17:34:29,329 - tensorflow - INFO - global_step/sec: 88.1393 -2022-05-01 17:34:29,330 - tensorflow - INFO - loss = 0.012222083, step = 1000600 (2.269 sec) -2022-05-01 17:34:31,595 - tensorflow - INFO - global_step/sec: 88.2946 -2022-05-01 17:34:31,595 - tensorflow - INFO - loss = 0.17804483, step = 1000800 (2.265 sec) -2022-05-01 17:34:33,869 - tensorflow - INFO - global_step/sec: 87.9165 -2022-05-01 17:34:33,870 - tensorflow - INFO - loss = 0.070887625, step = 1001000 (2.275 sec) -2022-05-01 17:34:36,147 - tensorflow - INFO - global_step/sec: 87.8316 -2022-05-01 17:34:36,147 - tensorflow - INFO - loss = 0.099069044, step = 1001200 (2.277 sec) -2022-05-01 17:34:38,417 - tensorflow - INFO - global_step/sec: 88.087 -2022-05-01 17:34:38,417 - tensorflow - INFO - loss = 0.24533297, step = 1001400 (2.271 sec) -2022-05-01 17:34:40,688 - tensorflow - INFO - global_step/sec: 88.0499 -2022-05-01 17:34:40,689 - tensorflow - INFO - loss = 0.025633255, step = 1001600 (2.271 sec) -2022-05-01 17:34:42,965 - tensorflow - INFO - global_step/sec: 87.8484 -2022-05-01 17:34:42,966 - tensorflow - INFO - loss = 1.1007679, step = 1001800 (2.277 sec) -2022-05-01 17:34:45,238 - tensorflow - INFO - global_step/sec: 87.9866 -2022-05-01 17:34:45,239 - tensorflow - INFO - loss = 0.12545982, step = 1002000 (2.273 sec) -2022-05-01 17:34:47,510 - tensorflow - INFO - global_step/sec: 88.0195 -2022-05-01 17:34:47,511 - tensorflow - INFO - loss = 0.060006622, step = 1002200 (2.272 sec) -2022-05-01 17:34:49,789 - tensorflow - INFO - global_step/sec: 87.7842 -2022-05-01 17:34:49,789 - tensorflow - INFO - loss = 0.058975074, step = 1002400 (2.278 sec) -2022-05-01 17:34:52,063 - tensorflow - INFO - global_step/sec: 87.9484 -2022-05-01 17:34:52,063 - tensorflow - INFO - loss = 0.3131972, step = 1002600 (2.274 sec) -2022-05-01 17:34:54,336 - tensorflow - INFO - global_step/sec: 87.9905 -2022-05-01 17:34:54,336 - tensorflow - INFO - loss = 0.08788369, step = 1002800 (2.273 sec) -2022-05-01 17:34:56,611 - tensorflow - INFO - global_step/sec: 87.9106 -2022-05-01 17:34:56,611 - tensorflow - INFO - loss = 0.22353008, step = 1003000 (2.275 sec) -2022-05-01 17:34:58,886 - tensorflow - INFO - global_step/sec: 87.9241 -2022-05-01 17:34:58,886 - tensorflow - INFO - loss = 0.022893198, step = 1003200 (2.275 sec) -2022-05-01 17:35:01,154 - tensorflow - INFO - global_step/sec: 88.1526 -2022-05-01 17:35:01,155 - tensorflow - INFO - loss = 0.029260218, step = 1003400 (2.269 sec) -2022-05-01 17:35:03,426 - tensorflow - INFO - global_step/sec: 88.0377 -2022-05-01 17:35:03,426 - tensorflow - INFO - loss = 0.03587124, step = 1003600 (2.272 sec) -2022-05-01 17:35:05,693 - tensorflow - INFO - global_step/sec: 88.2093 -2022-05-01 17:35:05,694 - tensorflow - INFO - loss = 0.027963502, step = 1003800 (2.267 sec) -2022-05-01 17:35:07,958 - tensorflow - INFO - global_step/sec: 88.3015 -2022-05-01 17:35:07,959 - tensorflow - INFO - loss = 0.21320803, step = 1004000 (2.265 sec) -2022-05-01 17:35:10,224 - tensorflow - INFO - global_step/sec: 88.269 -2022-05-01 17:35:10,225 - tensorflow - INFO - loss = 0.010730086, step = 1004200 (2.266 sec) -2022-05-01 17:35:12,488 - tensorflow - INFO - global_step/sec: 88.3264 -2022-05-01 17:35:12,489 - tensorflow - INFO - loss = 0.020996207, step = 1004400 (2.264 sec) -2022-05-01 17:35:14,760 - tensorflow - INFO - global_step/sec: 88.0305 -2022-05-01 17:35:14,761 - tensorflow - INFO - loss = 0.014935322, step = 1004600 (2.272 sec) -2022-05-01 17:35:17,027 - tensorflow - INFO - global_step/sec: 88.238 -2022-05-01 17:35:17,027 - tensorflow - INFO - loss = 0.020521518, step = 1004800 (2.267 sec) -2022-05-01 17:35:19,299 - tensorflow - INFO - global_step/sec: 88.0149 -2022-05-01 17:35:19,300 - tensorflow - INFO - loss = 0.027829817, step = 1005000 (2.272 sec) -2022-05-01 17:35:21,569 - tensorflow - INFO - global_step/sec: 88.1069 -2022-05-01 17:35:21,570 - tensorflow - INFO - loss = 0.01738238, step = 1005200 (2.270 sec) -2022-05-01 17:35:23,837 - tensorflow - INFO - global_step/sec: 88.1844 -2022-05-01 17:35:23,838 - tensorflow - INFO - loss = 0.02370587, step = 1005400 (2.268 sec) -2022-05-01 17:35:26,101 - tensorflow - INFO - global_step/sec: 88.3633 -2022-05-01 17:35:26,101 - tensorflow - INFO - loss = 0.027154919, step = 1005600 (2.263 sec) -2022-05-01 17:35:28,371 - tensorflow - INFO - global_step/sec: 88.0921 -2022-05-01 17:35:28,371 - tensorflow - INFO - loss = 0.08494438, step = 1005800 (2.270 sec) -2022-05-01 17:35:30,638 - tensorflow - INFO - global_step/sec: 88.2119 -2022-05-01 17:35:30,639 - tensorflow - INFO - loss = 0.01766038, step = 1006000 (2.267 sec) -2022-05-01 17:35:32,904 - tensorflow - INFO - global_step/sec: 88.2707 -2022-05-01 17:35:32,904 - tensorflow - INFO - loss = 0.0381309, step = 1006200 (2.266 sec) -2022-05-01 17:35:35,171 - tensorflow - INFO - global_step/sec: 88.2355 -2022-05-01 17:35:35,171 - tensorflow - INFO - loss = 0.3142389, step = 1006400 (2.267 sec) -2022-05-01 17:35:37,437 - tensorflow - INFO - global_step/sec: 88.2609 -2022-05-01 17:35:37,437 - tensorflow - INFO - loss = 0.011612243, step = 1006600 (2.266 sec) -2022-05-01 17:35:39,705 - tensorflow - INFO - global_step/sec: 88.1593 -2022-05-01 17:35:39,706 - tensorflow - INFO - loss = 0.010148924, step = 1006800 (2.269 sec) -2022-05-01 17:35:41,969 - tensorflow - INFO - global_step/sec: 88.3549 -2022-05-01 17:35:41,969 - tensorflow - INFO - loss = 0.048177227, step = 1007000 (2.264 sec) -2022-05-01 17:35:44,237 - tensorflow - INFO - global_step/sec: 88.1994 -2022-05-01 17:35:44,237 - tensorflow - INFO - loss = 0.033830248, step = 1007200 (2.268 sec) -2022-05-01 17:35:46,503 - tensorflow - INFO - global_step/sec: 88.2257 -2022-05-01 17:35:46,504 - tensorflow - INFO - loss = 0.02257288, step = 1007400 (2.267 sec) -2022-05-01 17:35:48,772 - tensorflow - INFO - global_step/sec: 88.1676 -2022-05-01 17:35:48,772 - tensorflow - INFO - loss = 0.014890956, step = 1007600 (2.268 sec) -2022-05-01 17:35:51,043 - tensorflow - INFO - global_step/sec: 88.0437 -2022-05-01 17:35:51,044 - tensorflow - INFO - loss = 0.020477772, step = 1007800 (2.272 sec) -2022-05-01 17:35:53,317 - tensorflow - INFO - global_step/sec: 87.9613 -2022-05-01 17:35:53,318 - tensorflow - INFO - loss = 0.09701069, step = 1008000 (2.274 sec) -2022-05-01 17:35:55,584 - tensorflow - INFO - global_step/sec: 88.2138 -2022-05-01 17:35:55,585 - tensorflow - INFO - loss = 0.06696297, step = 1008200 (2.267 sec) -2022-05-01 17:35:57,854 - tensorflow - INFO - global_step/sec: 88.1281 -2022-05-01 17:35:57,854 - tensorflow - INFO - loss = 0.03343594, step = 1008400 (2.269 sec) -2022-05-01 17:36:00,118 - tensorflow - INFO - global_step/sec: 88.3367 -2022-05-01 17:36:00,118 - tensorflow - INFO - loss = 0.019149898, step = 1008600 (2.264 sec) -2022-05-01 17:36:02,386 - tensorflow - INFO - global_step/sec: 88.1685 -2022-05-01 17:36:02,387 - tensorflow - INFO - loss = 0.06799473, step = 1008800 (2.268 sec) -2022-05-01 17:36:04,654 - tensorflow - INFO - global_step/sec: 88.1859 -2022-05-01 17:36:04,655 - tensorflow - INFO - loss = 0.0144699365, step = 1009000 (2.268 sec) -2022-05-01 17:36:06,921 - tensorflow - INFO - global_step/sec: 88.231 -2022-05-01 17:36:06,921 - tensorflow - INFO - loss = 0.013778967, step = 1009200 (2.267 sec) -2022-05-01 17:36:09,190 - tensorflow - INFO - global_step/sec: 88.1347 -2022-05-01 17:36:09,191 - tensorflow - INFO - loss = 0.6359856, step = 1009400 (2.269 sec) -2022-05-01 17:36:11,455 - tensorflow - INFO - global_step/sec: 88.2936 -2022-05-01 17:36:11,456 - tensorflow - INFO - loss = 0.04642318, step = 1009600 (2.265 sec) -2022-05-01 17:36:13,720 - tensorflow - INFO - global_step/sec: 88.3184 -2022-05-01 17:36:13,720 - tensorflow - INFO - loss = 0.02470339, step = 1009800 (2.265 sec) -2022-05-01 17:36:15,986 - tensorflow - INFO - global_step/sec: 88.2754 -2022-05-01 17:36:15,986 - tensorflow - INFO - loss = 0.020680029, step = 1010000 (2.266 sec) -2022-05-01 17:36:18,257 - tensorflow - INFO - global_step/sec: 88.067 -2022-05-01 17:36:18,257 - tensorflow - INFO - loss = 0.3155625, step = 1010200 (2.271 sec) -2022-05-01 17:36:20,524 - tensorflow - INFO - global_step/sec: 88.211 -2022-05-01 17:36:20,524 - tensorflow - INFO - loss = 0.02587337, step = 1010400 (2.267 sec) -2022-05-01 17:36:22,793 - tensorflow - INFO - global_step/sec: 88.1232 -2022-05-01 17:36:22,794 - tensorflow - INFO - loss = 0.0048405537, step = 1010600 (2.270 sec) -2022-05-01 17:36:25,065 - tensorflow - INFO - global_step/sec: 88.043 -2022-05-01 17:36:25,066 - tensorflow - INFO - loss = 0.16252938, step = 1010800 (2.272 sec) -2022-05-01 17:36:27,337 - tensorflow - INFO - global_step/sec: 88.0333 -2022-05-01 17:36:27,337 - tensorflow - INFO - loss = 0.07259081, step = 1011000 (2.272 sec) -2022-05-01 17:36:29,613 - tensorflow - INFO - global_step/sec: 87.8746 -2022-05-01 17:36:29,613 - tensorflow - INFO - loss = 0.079295605, step = 1011200 (2.276 sec) -2022-05-01 17:36:31,889 - tensorflow - INFO - global_step/sec: 87.8681 -2022-05-01 17:36:31,889 - tensorflow - INFO - loss = 0.13707778, step = 1011400 (2.276 sec) -2022-05-01 17:36:34,155 - tensorflow - INFO - global_step/sec: 88.2678 -2022-05-01 17:36:34,155 - tensorflow - INFO - loss = 0.15011756, step = 1011600 (2.266 sec) -2022-05-01 17:36:36,418 - tensorflow - INFO - global_step/sec: 88.3756 -2022-05-01 17:36:36,418 - tensorflow - INFO - loss = 0.026561353, step = 1011800 (2.263 sec) -2022-05-01 17:36:38,686 - tensorflow - INFO - global_step/sec: 88.1862 -2022-05-01 17:36:38,686 - tensorflow - INFO - loss = 0.01753925, step = 1012000 (2.268 sec) -2022-05-01 17:36:40,950 - tensorflow - INFO - global_step/sec: 88.334 -2022-05-01 17:36:40,950 - tensorflow - INFO - loss = 0.06748611, step = 1012200 (2.264 sec) -2022-05-01 17:36:43,215 - tensorflow - INFO - global_step/sec: 88.2923 -2022-05-01 17:36:43,216 - tensorflow - INFO - loss = 0.04561261, step = 1012400 (2.265 sec) -2022-05-01 17:36:45,484 - tensorflow - INFO - global_step/sec: 88.1397 -2022-05-01 17:36:45,485 - 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INFO - loss = 0.42224547, step = 1068400 (2.267 sec) -2022-05-01 17:47:20,884 - tensorflow - INFO - global_step/sec: 88.2765 -2022-05-01 17:47:20,884 - tensorflow - INFO - loss = 0.034962434, step = 1068600 (2.266 sec) -2022-05-01 17:47:23,150 - tensorflow - INFO - global_step/sec: 88.2516 -2022-05-01 17:47:23,150 - tensorflow - INFO - loss = 0.015111672, step = 1068800 (2.266 sec) -2022-05-01 17:47:25,415 - tensorflow - INFO - global_step/sec: 88.2953 -2022-05-01 17:47:25,416 - tensorflow - INFO - loss = 1.1196761, step = 1069000 (2.265 sec) -2022-05-01 17:47:27,684 - tensorflow - INFO - global_step/sec: 88.152 -2022-05-01 17:47:27,684 - tensorflow - INFO - loss = 0.052162483, step = 1069200 (2.269 sec) -2022-05-01 17:47:29,951 - tensorflow - INFO - global_step/sec: 88.2073 -2022-05-01 17:47:29,952 - tensorflow - INFO - loss = 0.017754044, step = 1069400 (2.267 sec) -2022-05-01 17:47:32,229 - tensorflow - INFO - global_step/sec: 87.7978 -2022-05-01 17:47:32,230 - tensorflow - INFO - loss = 0.0082748365, step = 1069600 (2.278 sec) -2022-05-01 17:47:34,493 - 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INFO - loss = 0.080452554, step = 1089600 (2.271 sec) -2022-05-01 17:51:21,410 - tensorflow - INFO - global_step/sec: 88.0926 -2022-05-01 17:51:21,411 - tensorflow - INFO - loss = 0.03880716, step = 1089800 (2.270 sec) -2022-05-01 17:51:23,679 - tensorflow - INFO - global_step/sec: 88.1435 -2022-05-01 17:51:23,680 - tensorflow - INFO - loss = 0.008254628, step = 1090000 (2.269 sec) -2022-05-01 17:51:25,951 - tensorflow - INFO - global_step/sec: 88.0392 -2022-05-01 17:51:25,951 - tensorflow - INFO - loss = 0.00797873, step = 1090200 (2.272 sec) -2022-05-01 17:51:28,220 - tensorflow - INFO - global_step/sec: 88.1287 -2022-05-01 17:51:28,221 - tensorflow - INFO - loss = 0.20207301, step = 1090400 (2.269 sec) -2022-05-01 17:51:30,497 - tensorflow - INFO - global_step/sec: 87.8379 -2022-05-01 17:51:30,498 - tensorflow - INFO - loss = 0.05177628, step = 1090600 (2.277 sec) -2022-05-01 17:51:32,779 - tensorflow - INFO - global_step/sec: 87.675 -2022-05-01 17:51:32,779 - tensorflow - INFO - loss = 0.14082907, step = 1090800 (2.281 sec) -2022-05-01 17:51:35,050 - tensorflow - INFO - global_step/sec: 88.0333 -2022-05-01 17:51:35,051 - tensorflow - INFO - loss = 0.38474733, step = 1091000 (2.272 sec) -2022-05-01 17:51:37,317 - tensorflow - INFO - global_step/sec: 88.2365 -2022-05-01 17:51:37,317 - tensorflow - INFO - loss = 0.025618026, step = 1091200 (2.267 sec) -2022-05-01 17:51:39,581 - tensorflow - INFO - global_step/sec: 88.3333 -2022-05-01 17:51:39,582 - tensorflow - INFO - loss = 0.035239328, step = 1091400 (2.264 sec) -2022-05-01 17:51:41,845 - tensorflow - INFO - global_step/sec: 88.339 -2022-05-01 17:51:41,846 - tensorflow - INFO - loss = 0.026621893, step = 1091600 (2.264 sec) -2022-05-01 17:51:44,119 - tensorflow - INFO - global_step/sec: 87.9404 -2022-05-01 17:51:44,120 - tensorflow - INFO - loss = 0.012498661, step = 1091800 (2.274 sec) -2022-05-01 17:51:46,388 - tensorflow - INFO - global_step/sec: 88.1492 -2022-05-01 17:51:46,389 - tensorflow - INFO - loss = 0.07283859, step = 1092000 (2.269 sec) -2022-05-01 17:51:48,657 - tensorflow - INFO - global_step/sec: 88.1626 -2022-05-01 17:51:48,657 - tensorflow - INFO - loss = 0.023764364, step = 1092200 (2.269 sec) -2022-05-01 17:51:50,922 - tensorflow - INFO - global_step/sec: 88.2985 -2022-05-01 17:51:50,922 - tensorflow - INFO - loss = 0.013630393, step = 1092400 (2.265 sec) -2022-05-01 17:51:53,192 - tensorflow - INFO - global_step/sec: 88.1159 -2022-05-01 17:51:53,192 - tensorflow - INFO - loss = 0.05786172, step = 1092600 (2.270 sec) -2022-05-01 17:51:55,455 - tensorflow - INFO - global_step/sec: 88.3758 -2022-05-01 17:51:55,455 - tensorflow - INFO - loss = 0.019399472, step = 1092800 (2.263 sec) -2022-05-01 17:51:57,732 - tensorflow - INFO - global_step/sec: 87.8392 -2022-05-01 17:51:57,732 - tensorflow - INFO - loss = 0.008102739, step = 1093000 (2.277 sec) -2022-05-01 17:51:59,999 - tensorflow - INFO - global_step/sec: 88.2145 -2022-05-01 17:51:59,999 - tensorflow - INFO - loss = 0.014448153, step = 1093200 (2.267 sec) -2022-05-01 17:52:02,269 - tensorflow - INFO - global_step/sec: 88.1059 -2022-05-01 17:52:02,269 - tensorflow - INFO - loss = 0.07463558, step = 1093400 (2.270 sec) -2022-05-01 17:52:04,539 - tensorflow - INFO - global_step/sec: 88.094 -2022-05-01 17:52:04,540 - tensorflow - INFO - loss = 0.029204894, step = 1093600 (2.270 sec) -2022-05-01 17:52:06,810 - tensorflow - INFO - global_step/sec: 88.0569 -2022-05-01 17:52:06,811 - tensorflow - INFO - loss = 0.095928356, step = 1093800 (2.271 sec) -2022-05-01 17:52:09,082 - tensorflow - INFO - global_step/sec: 88.0486 -2022-05-01 17:52:09,082 - tensorflow - INFO - loss = 0.008705968, step = 1094000 (2.272 sec) -2022-05-01 17:52:11,356 - tensorflow - INFO - global_step/sec: 87.9548 -2022-05-01 17:52:11,356 - tensorflow - INFO - loss = 0.02741694, step = 1094200 (2.274 sec) -2022-05-01 17:52:13,631 - tensorflow - INFO - global_step/sec: 87.8973 -2022-05-01 17:52:13,632 - tensorflow - INFO - loss = 0.026683709, step = 1094400 (2.275 sec) -2022-05-01 17:52:15,904 - tensorflow - INFO - global_step/sec: 87.9759 -2022-05-01 17:52:15,905 - tensorflow - INFO - loss = 0.039245937, step = 1094600 (2.273 sec) -2022-05-01 17:52:18,182 - tensorflow - INFO - global_step/sec: 87.8312 -2022-05-01 17:52:18,182 - tensorflow - INFO - loss = 0.03418677, step = 1094800 (2.277 sec) -2022-05-01 17:52:20,458 - tensorflow - INFO - global_step/sec: 87.8518 -2022-05-01 17:52:20,459 - tensorflow - INFO - loss = 0.018989494, step = 1095000 (2.277 sec) -2022-05-01 17:52:22,733 - tensorflow - INFO - global_step/sec: 87.904 -2022-05-01 17:52:22,734 - tensorflow - INFO - loss = 0.010458937, step = 1095200 (2.275 sec) -2022-05-01 17:52:25,010 - tensorflow - INFO - global_step/sec: 87.8573 -2022-05-01 17:52:25,010 - tensorflow - INFO - loss = 0.96352756, step = 1095400 (2.276 sec) -2022-05-01 17:52:27,283 - tensorflow - INFO - global_step/sec: 87.9611 -2022-05-01 17:52:27,284 - tensorflow - INFO - loss = 0.06753484, step = 1095600 (2.274 sec) -2022-05-01 17:52:29,557 - tensorflow - INFO - global_step/sec: 87.9567 -2022-05-01 17:52:29,558 - tensorflow - INFO - loss = 0.015580781, step = 1095800 (2.274 sec) -2022-05-01 17:52:31,831 - tensorflow - INFO - global_step/sec: 87.962 -2022-05-01 17:52:31,831 - tensorflow - INFO - loss = 0.15739396, step = 1096000 (2.274 sec) -2022-05-01 17:52:34,108 - tensorflow - INFO - global_step/sec: 87.8395 -2022-05-01 17:52:34,108 - tensorflow - INFO - loss = 0.05162877, step = 1096200 (2.277 sec) -2022-05-01 17:52:36,376 - tensorflow - INFO - global_step/sec: 88.1794 -2022-05-01 17:52:36,376 - tensorflow - INFO - loss = 0.011576347, step = 1096400 (2.268 sec) -2022-05-01 17:52:38,645 - tensorflow - INFO - global_step/sec: 88.1411 -2022-05-01 17:52:38,646 - tensorflow - INFO - loss = 0.010396266, step = 1096600 (2.269 sec) -2022-05-01 17:52:40,912 - tensorflow - INFO - global_step/sec: 88.2174 -2022-05-01 17:52:40,913 - tensorflow - INFO - loss = 0.022482887, step = 1096800 (2.267 sec) -2022-05-01 17:52:43,179 - tensorflow - INFO - global_step/sec: 88.2341 -2022-05-01 17:52:43,179 - tensorflow - INFO - loss = 0.010558477, step = 1097000 (2.267 sec) -2022-05-01 17:52:45,446 - tensorflow - INFO - global_step/sec: 88.2151 -2022-05-01 17:52:45,447 - tensorflow - INFO - loss = 0.036490947, step = 1097200 (2.267 sec) -2022-05-01 17:52:47,709 - tensorflow - INFO - global_step/sec: 88.3911 -2022-05-01 17:52:47,709 - tensorflow - INFO - loss = 0.15203127, step = 1097400 (2.263 sec) -2022-05-01 17:52:49,982 - tensorflow - INFO - global_step/sec: 87.9806 -2022-05-01 17:52:49,982 - tensorflow - INFO - loss = 0.05722553, step = 1097600 (2.273 sec) -2022-05-01 17:52:52,254 - tensorflow - INFO - global_step/sec: 88.0291 -2022-05-01 17:52:52,254 - tensorflow - INFO - loss = 0.051573113, step = 1097800 (2.272 sec) -2022-05-01 17:52:54,519 - tensorflow - INFO - global_step/sec: 88.3146 -2022-05-01 17:52:54,519 - tensorflow - INFO - loss = 0.042725034, step = 1098000 (2.265 sec) -2022-05-01 17:52:56,791 - tensorflow - INFO - global_step/sec: 88.0275 -2022-05-01 17:52:56,791 - tensorflow - INFO - loss = 0.2444915, step = 1098200 (2.272 sec) -2022-05-01 17:52:59,057 - tensorflow - INFO - global_step/sec: 88.2664 -2022-05-01 17:52:59,057 - tensorflow - INFO - loss = 0.01457686, step = 1098400 (2.266 sec) -2022-05-01 17:53:01,327 - tensorflow - INFO - global_step/sec: 88.0678 -2022-05-01 17:53:01,328 - tensorflow - INFO - loss = 0.008405423, step = 1098600 (2.271 sec) -2022-05-01 17:53:03,599 - tensorflow - INFO - global_step/sec: 88.0387 -2022-05-01 17:53:03,600 - tensorflow - INFO - loss = 0.023712724, step = 1098800 (2.272 sec) -2022-05-01 17:53:05,868 - tensorflow - INFO - global_step/sec: 88.1347 -2022-05-01 17:53:05,869 - tensorflow - INFO - loss = 0.017779306, step = 1099000 (2.269 sec) -2022-05-01 17:53:08,139 - tensorflow - INFO - global_step/sec: 88.0815 -2022-05-01 17:53:08,139 - tensorflow - INFO - loss = 0.03620185, step = 1099200 (2.271 sec) -2022-05-01 17:53:10,414 - tensorflow - INFO - global_step/sec: 87.921 -2022-05-01 17:53:10,414 - tensorflow - INFO - loss = 0.00687285, step = 1099400 (2.275 sec) -2022-05-01 17:53:12,683 - tensorflow - INFO - global_step/sec: 88.141 -2022-05-01 17:53:12,683 - tensorflow - INFO - loss = 0.015671916, step = 1099600 (2.269 sec) -2022-05-01 17:53:14,957 - tensorflow - INFO - global_step/sec: 87.9438 -2022-05-01 17:53:14,958 - tensorflow - INFO - loss = 0.021432642, step = 1099800 (2.274 sec) -2022-05-01 17:53:17,213 - tensorflow - INFO - Saving checkpoints for 1100000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. -2022-05-01 17:53:17,228 - tensorflow - INFO - model.ckpt-1100000 is not in all_model_checkpoint_paths. Manually adding it. -2022-05-01 17:53:17,753 - tensorflow - INFO - Calling model_fn. -2022-05-01 17:53:17,880 - tensorflow - INFO - Done calling model_fn. -2022-05-01 17:53:17,895 - tensorflow - INFO - Starting evaluation at 2022-05-01T17:53:17Z -2022-05-01 17:53:17,959 - tensorflow - INFO - Graph was finalized. -2022-05-01 17:53:17,962 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1100000 -2022-05-01 17:53:18,242 - tensorflow - INFO - Running local_init_op. -2022-05-01 17:53:18,350 - tensorflow - INFO - Done running local_init_op. -2022-05-01 17:53:28,495 - tensorflow - INFO - Evaluation [3990/39900] -2022-05-01 17:53:37,950 - tensorflow - INFO - Evaluation [7980/39900] -2022-05-01 17:53:47,393 - tensorflow - INFO - Evaluation [11970/39900] -2022-05-01 17:53:56,838 - tensorflow - INFO - Evaluation [15960/39900] -2022-05-01 17:54:06,279 - tensorflow - INFO - Evaluation [19950/39900] -2022-05-01 17:54:15,733 - tensorflow - INFO - Evaluation [23940/39900] -2022-05-01 17:54:25,160 - tensorflow - INFO - Evaluation [27930/39900] -2022-05-01 17:54:34,614 - tensorflow - INFO - Evaluation [31920/39900] -2022-05-01 17:54:44,055 - tensorflow - INFO - Evaluation [35910/39900] -2022-05-01 17:54:53,494 - tensorflow - INFO - Evaluation [39900/39900] -2022-05-01 17:54:53,631 - tensorflow - INFO - Finished evaluation at 2022-05-01-17:54:53 -2022-05-01 17:54:53,632 - tensorflow - INFO - Saving dict for global step 1100000: global_step = 1100000, loss = 0.025317876, mean_degree_err = 4.4118333 -2022-05-01 17:54:53,632 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 1100000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1100000 -2022-05-01 17:54:53,646 - tensorflow - INFO - global_step/sec: 2.02658 -2022-05-01 17:54:53,647 - tensorflow - INFO - loss = 5.8303533, step = 1100000 (98.689 sec) -2022-05-01 17:54:55,928 - tensorflow - INFO - global_step/sec: 87.6514 -2022-05-01 17:54:55,928 - tensorflow - INFO - loss = 0.017083686, step = 1100200 (2.281 sec) -2022-05-01 17:54:58,192 - tensorflow - INFO - global_step/sec: 88.3137 -2022-05-01 17:54:58,193 - tensorflow - INFO - loss = 0.029804125, step = 1100400 (2.265 sec) -2022-05-01 17:55:00,461 - tensorflow - INFO - global_step/sec: 88.1546 -2022-05-01 17:55:00,461 - tensorflow - INFO - loss = 0.16030154, step = 1100600 (2.269 sec) -2022-05-01 17:55:02,731 - tensorflow - INFO - global_step/sec: 88.1041 -2022-05-01 17:55:02,731 - tensorflow - INFO - loss = 0.014746798, step = 1100800 (2.270 sec) -2022-05-01 17:55:05,001 - tensorflow - INFO - global_step/sec: 88.1035 -2022-05-01 17:55:05,001 - tensorflow - INFO - loss = 0.018318102, step = 1101000 (2.270 sec) -2022-05-01 17:55:07,275 - tensorflow - INFO - global_step/sec: 87.959 -2022-05-01 17:55:07,275 - tensorflow - INFO - loss = 0.026991412, step = 1101200 (2.274 sec) -2022-05-01 17:55:09,546 - tensorflow - INFO - global_step/sec: 88.0721 -2022-05-01 17:55:09,546 - tensorflow - INFO - loss = 0.03456292, step = 1101400 (2.271 sec) -2022-05-01 17:55:11,813 - tensorflow - INFO - global_step/sec: 88.2073 -2022-05-01 17:55:11,813 - tensorflow - INFO - loss = 0.007666119, step = 1101600 (2.267 sec) -2022-05-01 17:55:14,081 - tensorflow - INFO - global_step/sec: 88.1868 -2022-05-01 17:55:14,081 - tensorflow - INFO - loss = 0.019554604, step = 1101800 (2.268 sec) -2022-05-01 17:55:16,350 - tensorflow - INFO - global_step/sec: 88.1564 -2022-05-01 17:55:16,350 - tensorflow - INFO - loss = 0.013642142, step = 1102000 (2.269 sec) -2022-05-01 17:55:18,618 - tensorflow - INFO - global_step/sec: 88.17 -2022-05-01 17:55:18,618 - tensorflow - INFO - loss = 0.01439528, step = 1102200 (2.268 sec) -2022-05-01 17:55:20,890 - tensorflow - INFO - global_step/sec: 88.0217 -2022-05-01 17:55:20,891 - tensorflow - INFO - loss = 0.015880037, step = 1102400 (2.272 sec) -2022-05-01 17:55:23,160 - tensorflow - INFO - global_step/sec: 88.1192 -2022-05-01 17:55:23,160 - tensorflow - INFO - loss = 0.048466884, step = 1102600 (2.270 sec) -2022-05-01 17:55:25,429 - tensorflow - INFO - global_step/sec: 88.1244 -2022-05-01 17:55:25,430 - tensorflow - INFO - loss = 0.049483653, step = 1102800 (2.270 sec) -2022-05-01 17:55:27,694 - tensorflow - INFO - global_step/sec: 88.3191 -2022-05-01 17:55:27,694 - tensorflow - INFO - loss = 0.3012721, step = 1103000 (2.265 sec) -2022-05-01 17:55:29,964 - tensorflow - INFO - global_step/sec: 88.0985 -2022-05-01 17:55:29,964 - tensorflow - INFO - loss = 0.019714927, step = 1103200 (2.270 sec) -2022-05-01 17:55:32,245 - tensorflow - INFO - global_step/sec: 87.6843 -2022-05-01 17:55:32,245 - tensorflow - INFO - loss = 0.022536471, step = 1103400 (2.281 sec) -2022-05-01 17:55:34,522 - tensorflow - INFO - global_step/sec: 87.8411 -2022-05-01 17:55:34,522 - tensorflow - INFO - loss = 0.04687898, step = 1103600 (2.277 sec) -2022-05-01 17:55:36,798 - tensorflow - INFO - global_step/sec: 87.8777 -2022-05-01 17:55:36,798 - tensorflow - INFO - loss = 0.058065794, step = 1103800 (2.276 sec) -2022-05-01 17:55:39,072 - tensorflow - INFO - global_step/sec: 87.9228 -2022-05-01 17:55:39,073 - tensorflow - INFO - loss = 0.45963365, step = 1104000 (2.275 sec) -2022-05-01 17:55:41,350 - tensorflow - INFO - global_step/sec: 87.8175 -2022-05-01 17:55:41,350 - tensorflow - INFO - loss = 0.6161173, step = 1104200 (2.277 sec) -2022-05-01 17:55:43,623 - tensorflow - INFO - global_step/sec: 87.9808 -2022-05-01 17:55:43,623 - tensorflow - INFO - loss = 0.025974642, step = 1104400 (2.273 sec) -2022-05-01 17:55:45,900 - tensorflow - INFO - global_step/sec: 87.8351 -2022-05-01 17:55:45,901 - tensorflow - INFO - loss = 0.04511058, step = 1104600 (2.277 sec) -2022-05-01 17:55:48,179 - tensorflow - INFO - global_step/sec: 87.7429 -2022-05-01 17:55:48,180 - tensorflow - INFO - loss = 0.14503525, step = 1104800 (2.279 sec) -2022-05-01 17:55:50,453 - tensorflow - INFO - global_step/sec: 87.965 -2022-05-01 17:55:50,454 - tensorflow - INFO - loss = 0.030037086, step = 1105000 (2.274 sec) -2022-05-01 17:55:52,733 - tensorflow - INFO - global_step/sec: 87.7381 -2022-05-01 17:55:52,733 - tensorflow - INFO - loss = 0.044755362, step = 1105200 (2.279 sec) -2022-05-01 17:55:55,006 - tensorflow - INFO - global_step/sec: 87.9747 -2022-05-01 17:55:55,006 - tensorflow - INFO - loss = 0.01589223, step = 1105400 (2.273 sec) -2022-05-01 17:55:57,288 - tensorflow - INFO - global_step/sec: 87.658 -2022-05-01 17:55:57,288 - tensorflow - INFO - loss = 1.0562906, step = 1105600 (2.282 sec) -2022-05-01 17:55:59,564 - tensorflow - INFO - global_step/sec: 87.867 -2022-05-01 17:55:59,564 - tensorflow - INFO - loss = 0.12573439, step = 1105800 (2.276 sec) -2022-05-01 17:56:01,843 - tensorflow - INFO - global_step/sec: 87.747 -2022-05-01 17:56:01,843 - tensorflow - INFO - loss = 0.018375054, step = 1106000 (2.279 sec) -2022-05-01 17:56:04,112 - tensorflow - INFO - global_step/sec: 88.1355 -2022-05-01 17:56:04,113 - tensorflow - INFO - loss = 0.018151755, step = 1106200 (2.269 sec) -2022-05-01 17:56:06,383 - tensorflow - INFO - global_step/sec: 88.0759 -2022-05-01 17:56:06,383 - tensorflow - INFO - loss = 0.021065243, step = 1106400 (2.271 sec) -2022-05-01 17:56:08,650 - tensorflow - INFO - global_step/sec: 88.2391 -2022-05-01 17:56:08,650 - tensorflow - INFO - loss = 0.3877352, step = 1106600 (2.267 sec) -2022-05-01 17:56:10,919 - tensorflow - INFO - global_step/sec: 88.138 -2022-05-01 17:56:10,919 - tensorflow - INFO - loss = 0.005250844, step = 1106800 (2.269 sec) -2022-05-01 17:56:13,192 - tensorflow - INFO - global_step/sec: 87.9927 -2022-05-01 17:56:13,192 - tensorflow - INFO - loss = 0.01313768, step = 1107000 (2.273 sec) -2022-05-01 17:56:15,463 - tensorflow - INFO - global_step/sec: 88.0475 -2022-05-01 17:56:15,464 - tensorflow - INFO - loss = 0.11005046, step = 1107200 (2.271 sec) -2022-05-01 17:56:17,733 - tensorflow - INFO - global_step/sec: 88.1181 -2022-05-01 17:56:17,733 - tensorflow - INFO - loss = 0.060840447, step = 1107400 (2.270 sec) -2022-05-01 17:56:20,001 - tensorflow - INFO - global_step/sec: 88.1591 -2022-05-01 17:56:20,002 - tensorflow - INFO - loss = 0.008830101, step = 1107600 (2.269 sec) -2022-05-01 17:56:22,270 - tensorflow - INFO - global_step/sec: 88.167 -2022-05-01 17:56:22,270 - tensorflow - INFO - loss = 0.044792574, step = 1107800 (2.268 sec) -2022-05-01 17:56:24,542 - tensorflow - INFO - global_step/sec: 88.0255 -2022-05-01 17:56:24,542 - tensorflow - INFO - loss = 0.024858685, step = 1108000 (2.272 sec) -2022-05-01 17:56:26,810 - tensorflow - INFO - global_step/sec: 88.1793 -2022-05-01 17:56:26,810 - tensorflow - INFO - loss = 0.010348566, step = 1108200 (2.268 sec) -2022-05-01 17:56:29,085 - tensorflow - INFO - global_step/sec: 87.9299 -2022-05-01 17:56:29,085 - tensorflow - INFO - loss = 0.031716585, step = 1108400 (2.275 sec) -2022-05-01 17:56:31,361 - tensorflow - INFO - global_step/sec: 87.8529 -2022-05-01 17:56:31,362 - tensorflow - INFO - loss = 0.013905285, step = 1108600 (2.277 sec) -2022-05-01 17:56:33,628 - tensorflow - INFO - global_step/sec: 88.23 -2022-05-01 17:56:33,628 - tensorflow - INFO - loss = 0.020705432, step = 1108800 (2.267 sec) -2022-05-01 17:56:35,895 - tensorflow - INFO - global_step/sec: 88.2283 -2022-05-01 17:56:35,895 - tensorflow - INFO - loss = 0.1813573, step = 1109000 (2.267 sec) -2022-05-01 17:56:38,160 - tensorflow - INFO - global_step/sec: 88.3103 -2022-05-01 17:56:38,160 - tensorflow - INFO - loss = 0.016116481, step = 1109200 (2.265 sec) -2022-05-01 17:56:40,431 - tensorflow - INFO - global_step/sec: 88.034 -2022-05-01 17:56:40,432 - tensorflow - INFO - loss = 0.11028355, step = 1109400 (2.272 sec) -2022-05-01 17:56:42,700 - tensorflow - INFO - global_step/sec: 88.1522 -2022-05-01 17:56:42,701 - tensorflow - INFO - loss = 0.0069679697, step = 1109600 (2.269 sec) -2022-05-01 17:56:44,962 - tensorflow - INFO - global_step/sec: 88.4366 -2022-05-01 17:56:44,962 - tensorflow - INFO - loss = 0.054119132, step = 1109800 (2.262 sec) -2022-05-01 17:56:47,230 - tensorflow - INFO - global_step/sec: 88.1574 -2022-05-01 17:56:47,231 - tensorflow - INFO - loss = 0.05626864, step = 1110000 (2.269 sec) -2022-05-01 17:56:49,496 - tensorflow - INFO - global_step/sec: 88.2617 -2022-05-01 17:56:49,497 - 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INFO - global_step/sec: 88.1881 -2022-05-01 17:57:46,183 - tensorflow - INFO - loss = 0.4434392, step = 1115200 (2.268 sec) -2022-05-01 17:57:48,444 - tensorflow - INFO - global_step/sec: 88.446 -2022-05-01 17:57:48,444 - tensorflow - INFO - loss = 0.015171867, step = 1115400 (2.261 sec) -2022-05-01 17:57:50,717 - tensorflow - INFO - global_step/sec: 87.9781 -2022-05-01 17:57:50,717 - tensorflow - INFO - loss = 1.0668705, step = 1115600 (2.273 sec) -2022-05-01 17:57:52,985 - tensorflow - INFO - global_step/sec: 88.2044 -2022-05-01 17:57:52,985 - tensorflow - INFO - loss = 0.009059138, step = 1115800 (2.267 sec) -2022-05-01 17:57:55,255 - tensorflow - INFO - global_step/sec: 88.1046 -2022-05-01 17:57:55,255 - tensorflow - INFO - loss = 0.011908996, step = 1116000 (2.270 sec) -2022-05-01 17:57:57,523 - tensorflow - INFO - global_step/sec: 88.1727 -2022-05-01 17:57:57,523 - tensorflow - INFO - loss = 0.046205234, step = 1116200 (2.268 sec) -2022-05-01 17:57:59,792 - tensorflow - INFO - global_step/sec: 88.1376 -2022-05-01 17:57:59,792 - 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tensorflow - INFO - global_step/sec: 88.2979 -2022-05-01 17:58:29,267 - tensorflow - INFO - loss = 0.02196571, step = 1119000 (2.265 sec) -2022-05-01 17:58:31,536 - tensorflow - INFO - global_step/sec: 88.1376 -2022-05-01 17:58:31,536 - tensorflow - INFO - loss = 0.006170545, step = 1119200 (2.269 sec) -2022-05-01 17:58:33,805 - tensorflow - INFO - global_step/sec: 88.157 -2022-05-01 17:58:33,805 - tensorflow - INFO - loss = 0.017200092, step = 1119400 (2.269 sec) -2022-05-01 17:58:36,073 - tensorflow - INFO - global_step/sec: 88.1544 -2022-05-01 17:58:36,074 - tensorflow - INFO - loss = 0.017230386, step = 1119600 (2.269 sec) -2022-05-01 17:58:38,336 - tensorflow - INFO - global_step/sec: 88.4133 -2022-05-01 17:58:38,336 - tensorflow - INFO - loss = 0.027107479, step = 1119800 (2.262 sec) -2022-05-01 17:58:40,599 - tensorflow - INFO - global_step/sec: 88.3789 -2022-05-01 17:58:40,599 - tensorflow - INFO - loss = 0.030391566, step = 1120000 (2.263 sec) -2022-05-01 17:58:42,865 - tensorflow - INFO - global_step/sec: 88.2343 -2022-05-01 17:58:42,866 - tensorflow - INFO - loss = 0.15300664, step = 1120200 (2.267 sec) -2022-05-01 17:58:45,135 - tensorflow - INFO - global_step/sec: 88.1265 -2022-05-01 17:58:45,135 - tensorflow - INFO - loss = 0.03331274, step = 1120400 (2.269 sec) -2022-05-01 17:58:47,405 - tensorflow - INFO - global_step/sec: 88.0974 -2022-05-01 17:58:47,405 - tensorflow - INFO - loss = 0.027307924, step = 1120600 (2.270 sec) -2022-05-01 17:58:49,676 - tensorflow - INFO - global_step/sec: 88.0676 -2022-05-01 17:58:49,676 - tensorflow - INFO - loss = 0.017618783, step = 1120800 (2.271 sec) -2022-05-01 17:58:51,939 - tensorflow - INFO - global_step/sec: 88.3901 -2022-05-01 17:58:51,939 - tensorflow - INFO - loss = 0.007736385, step = 1121000 (2.263 sec) -2022-05-01 17:58:54,205 - tensorflow - INFO - global_step/sec: 88.2279 -2022-05-01 17:58:54,206 - tensorflow - INFO - loss = 0.06589486, step = 1121200 (2.267 sec) -2022-05-01 17:58:56,473 - tensorflow - INFO - 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INFO - loss = 0.0068932925, step = 1123800 (2.270 sec) -2022-05-01 17:59:26,014 - tensorflow - INFO - global_step/sec: 87.9387 -2022-05-01 17:59:26,014 - tensorflow - INFO - loss = 0.019140247, step = 1124000 (2.274 sec) -2022-05-01 17:59:28,289 - tensorflow - INFO - global_step/sec: 87.9109 -2022-05-01 17:59:28,289 - tensorflow - INFO - loss = 0.039606776, step = 1124200 (2.275 sec) -2022-05-01 17:59:30,566 - tensorflow - INFO - global_step/sec: 87.8217 -2022-05-01 17:59:30,566 - tensorflow - INFO - loss = 0.11960248, step = 1124400 (2.277 sec) -2022-05-01 17:59:32,825 - tensorflow - INFO - global_step/sec: 88.515 -2022-05-01 17:59:32,826 - tensorflow - INFO - loss = 0.019091856, step = 1124600 (2.259 sec) -2022-05-01 17:59:35,091 - tensorflow - INFO - global_step/sec: 88.2854 -2022-05-01 17:59:35,091 - tensorflow - INFO - loss = 0.033167087, step = 1124800 (2.265 sec) -2022-05-01 17:59:37,351 - tensorflow - INFO - global_step/sec: 88.4847 -2022-05-01 17:59:37,352 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.0752 -2022-05-01 17:59:53,230 - tensorflow - INFO - loss = 0.046166666, step = 1126400 (2.271 sec) -2022-05-01 17:59:55,494 - tensorflow - INFO - global_step/sec: 88.3034 -2022-05-01 17:59:55,494 - tensorflow - INFO - loss = 0.11403679, step = 1126600 (2.265 sec) -2022-05-01 17:59:57,761 - tensorflow - INFO - global_step/sec: 88.2225 -2022-05-01 17:59:57,761 - tensorflow - INFO - loss = 0.020823315, step = 1126800 (2.267 sec) -2022-05-01 18:00:00,027 - tensorflow - INFO - global_step/sec: 88.2785 -2022-05-01 18:00:00,027 - tensorflow - INFO - loss = 0.023803, step = 1127000 (2.266 sec) -2022-05-01 18:00:02,297 - tensorflow - INFO - global_step/sec: 88.0914 -2022-05-01 18:00:02,297 - tensorflow - INFO - loss = 0.012753614, step = 1127200 (2.270 sec) -2022-05-01 18:00:04,571 - tensorflow - INFO - global_step/sec: 87.9508 -2022-05-01 18:00:04,571 - tensorflow - INFO - loss = 0.16387165, step = 1127400 (2.274 sec) -2022-05-01 18:00:06,838 - tensorflow - 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INFO - loss = 0.019315578, step = 1158600 (2.262 sec) -2022-05-01 18:05:59,900 - tensorflow - INFO - global_step/sec: 88.3495 -2022-05-01 18:05:59,901 - tensorflow - INFO - loss = 0.025300844, step = 1158800 (2.264 sec) -2022-05-01 18:06:02,164 - tensorflow - INFO - global_step/sec: 88.3524 -2022-05-01 18:06:02,165 - tensorflow - INFO - loss = 0.032918695, step = 1159000 (2.264 sec) -2022-05-01 18:06:04,427 - tensorflow - INFO - global_step/sec: 88.3916 -2022-05-01 18:06:04,427 - tensorflow - INFO - loss = 0.03392154, step = 1159200 (2.263 sec) -2022-05-01 18:06:06,690 - tensorflow - INFO - global_step/sec: 88.3814 -2022-05-01 18:06:06,690 - tensorflow - INFO - loss = 0.017178094, step = 1159400 (2.263 sec) -2022-05-01 18:06:08,952 - tensorflow - INFO - global_step/sec: 88.4104 -2022-05-01 18:06:08,952 - tensorflow - INFO - loss = 0.014831856, step = 1159600 (2.262 sec) -2022-05-01 18:06:11,216 - tensorflow - INFO - global_step/sec: 88.3349 -2022-05-01 18:06:11,216 - tensorflow - INFO - 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INFO - loss = 0.052462846, step = 1166000 (2.259 sec) -2022-05-01 18:07:23,478 - tensorflow - INFO - global_step/sec: 88.6444 -2022-05-01 18:07:23,479 - tensorflow - INFO - loss = 0.046224374, step = 1166200 (2.256 sec) -2022-05-01 18:07:25,731 - tensorflow - INFO - global_step/sec: 88.7964 -2022-05-01 18:07:25,731 - tensorflow - INFO - loss = 0.024157666, step = 1166400 (2.252 sec) -2022-05-01 18:07:27,984 - tensorflow - INFO - global_step/sec: 88.741 -2022-05-01 18:07:27,985 - tensorflow - INFO - loss = 0.028098095, step = 1166600 (2.254 sec) -2022-05-01 18:07:30,243 - tensorflow - INFO - global_step/sec: 88.5541 -2022-05-01 18:07:30,243 - tensorflow - INFO - loss = 0.012415642, step = 1166800 (2.259 sec) -2022-05-01 18:07:32,505 - tensorflow - INFO - global_step/sec: 88.4272 -2022-05-01 18:07:32,505 - tensorflow - INFO - loss = 0.04531084, step = 1167000 (2.262 sec) -2022-05-01 18:07:34,764 - tensorflow - INFO - global_step/sec: 88.5168 -2022-05-01 18:07:34,764 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.8064 -2022-05-01 18:07:50,538 - tensorflow - INFO - loss = 0.013101386, step = 1168600 (2.252 sec) -2022-05-01 18:07:52,793 - tensorflow - INFO - global_step/sec: 88.6936 -2022-05-01 18:07:52,793 - tensorflow - INFO - loss = 0.008198092, step = 1168800 (2.255 sec) -2022-05-01 18:07:55,051 - tensorflow - INFO - global_step/sec: 88.5533 -2022-05-01 18:07:55,052 - tensorflow - INFO - loss = 0.013200732, step = 1169000 (2.259 sec) -2022-05-01 18:07:57,311 - tensorflow - INFO - global_step/sec: 88.4882 -2022-05-01 18:07:57,312 - tensorflow - INFO - loss = 0.0046111504, step = 1169200 (2.260 sec) -2022-05-01 18:07:59,574 - tensorflow - INFO - global_step/sec: 88.4093 -2022-05-01 18:07:59,574 - tensorflow - INFO - loss = 0.11613696, step = 1169400 (2.262 sec) -2022-05-01 18:08:01,827 - tensorflow - INFO - global_step/sec: 88.7733 -2022-05-01 18:08:01,827 - tensorflow - INFO - loss = 0.043632142, step = 1169600 (2.253 sec) -2022-05-01 18:08:04,087 - tensorflow - INFO - global_step/sec: 88.4688 -2022-05-01 18:08:04,088 - tensorflow - INFO - loss = 0.51394105, step = 1169800 (2.261 sec) -2022-05-01 18:08:06,343 - tensorflow - INFO - global_step/sec: 88.6518 -2022-05-01 18:08:06,344 - tensorflow - INFO - loss = 0.015525489, step = 1170000 (2.256 sec) -2022-05-01 18:08:08,597 - tensorflow - INFO - global_step/sec: 88.7342 -2022-05-01 18:08:08,598 - tensorflow - INFO - loss = 0.12603165, step = 1170200 (2.254 sec) -2022-05-01 18:08:10,859 - tensorflow - INFO - global_step/sec: 88.4158 -2022-05-01 18:08:10,860 - tensorflow - INFO - loss = 0.04159105, step = 1170400 (2.262 sec) -2022-05-01 18:08:13,115 - tensorflow - INFO - global_step/sec: 88.6477 -2022-05-01 18:08:13,116 - tensorflow - INFO - loss = 0.080631554, step = 1170600 (2.256 sec) -2022-05-01 18:08:15,376 - tensorflow - INFO - global_step/sec: 88.4528 -2022-05-01 18:08:15,377 - tensorflow - INFO - loss = 0.013049016, step = 1170800 (2.261 sec) -2022-05-01 18:08:17,627 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.014415514, step = 1172200 (2.259 sec) -2022-05-01 18:08:33,417 - tensorflow - INFO - global_step/sec: 88.5383 -2022-05-01 18:08:33,417 - tensorflow - INFO - loss = 0.13393927, step = 1172400 (2.259 sec) -2022-05-01 18:08:35,674 - tensorflow - INFO - global_step/sec: 88.6092 -2022-05-01 18:08:35,674 - tensorflow - INFO - loss = 0.017891834, step = 1172600 (2.257 sec) -2022-05-01 18:08:37,931 - tensorflow - INFO - global_step/sec: 88.5828 -2022-05-01 18:08:37,932 - tensorflow - INFO - loss = 0.053806677, step = 1172800 (2.258 sec) -2022-05-01 18:08:40,185 - tensorflow - INFO - global_step/sec: 88.7598 -2022-05-01 18:08:40,185 - tensorflow - INFO - loss = 0.018386256, step = 1173000 (2.253 sec) -2022-05-01 18:08:42,440 - tensorflow - INFO - global_step/sec: 88.6645 -2022-05-01 18:08:42,441 - tensorflow - INFO - loss = 0.012489137, step = 1173200 (2.256 sec) -2022-05-01 18:08:44,696 - tensorflow - INFO - global_step/sec: 88.6586 -2022-05-01 18:08:44,697 - tensorflow - INFO - loss = 0.0048879846, step = 1173400 (2.256 sec) -2022-05-01 18:08:46,949 - tensorflow - INFO - global_step/sec: 88.7866 -2022-05-01 18:08:46,949 - tensorflow - INFO - loss = 0.021216586, step = 1173600 (2.253 sec) -2022-05-01 18:08:49,210 - tensorflow - INFO - global_step/sec: 88.4403 -2022-05-01 18:08:49,211 - tensorflow - INFO - loss = 0.03790966, step = 1173800 (2.261 sec) -2022-05-01 18:08:51,464 - tensorflow - INFO - global_step/sec: 88.7269 -2022-05-01 18:08:51,465 - tensorflow - INFO - loss = 0.015610474, step = 1174000 (2.254 sec) -2022-05-01 18:08:53,717 - tensorflow - INFO - global_step/sec: 88.7694 -2022-05-01 18:08:53,718 - tensorflow - INFO - loss = 0.0103667835, step = 1174200 (2.253 sec) -2022-05-01 18:08:55,978 - tensorflow - INFO - global_step/sec: 88.4637 -2022-05-01 18:08:55,979 - tensorflow - INFO - loss = 0.01396005, step = 1174400 (2.261 sec) -2022-05-01 18:08:58,233 - tensorflow - INFO - global_step/sec: 88.7032 -2022-05-01 18:08:58,233 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.4805 -2022-05-01 18:09:14,035 - tensorflow - INFO - loss = 0.025212659, step = 1176000 (2.260 sec) -2022-05-01 18:09:16,291 - tensorflow - INFO - global_step/sec: 88.6625 -2022-05-01 18:09:16,291 - tensorflow - INFO - loss = 0.02352048, step = 1176200 (2.256 sec) -2022-05-01 18:09:18,546 - tensorflow - INFO - global_step/sec: 88.6881 -2022-05-01 18:09:18,546 - tensorflow - INFO - loss = 0.07429921, step = 1176400 (2.255 sec) -2022-05-01 18:09:20,799 - tensorflow - INFO - global_step/sec: 88.7814 -2022-05-01 18:09:20,799 - tensorflow - INFO - loss = 0.09907514, step = 1176600 (2.253 sec) -2022-05-01 18:09:23,055 - tensorflow - INFO - global_step/sec: 88.649 -2022-05-01 18:09:23,055 - tensorflow - INFO - loss = 0.017612314, step = 1176800 (2.256 sec) -2022-05-01 18:09:25,314 - tensorflow - INFO - global_step/sec: 88.5069 -2022-05-01 18:09:25,315 - tensorflow - INFO - loss = 0.036762282, step = 1177000 (2.260 sec) -2022-05-01 18:09:27,573 - tensorflow - INFO - global_step/sec: 88.5352 -2022-05-01 18:09:27,574 - tensorflow - INFO - loss = 0.11830039, step = 1177200 (2.259 sec) -2022-05-01 18:09:29,828 - tensorflow - INFO - global_step/sec: 88.712 -2022-05-01 18:09:29,828 - tensorflow - INFO - loss = 0.023711583, step = 1177400 (2.254 sec) -2022-05-01 18:09:32,089 - tensorflow - INFO - global_step/sec: 88.4599 -2022-05-01 18:09:32,089 - tensorflow - INFO - loss = 0.013508679, step = 1177600 (2.261 sec) -2022-05-01 18:09:34,355 - tensorflow - INFO - global_step/sec: 88.2524 -2022-05-01 18:09:34,355 - tensorflow - INFO - loss = 0.011801957, step = 1177800 (2.266 sec) -2022-05-01 18:09:36,620 - tensorflow - INFO - global_step/sec: 88.3141 -2022-05-01 18:09:36,620 - tensorflow - INFO - loss = 0.011798587, step = 1178000 (2.265 sec) -2022-05-01 18:09:38,887 - tensorflow - INFO - global_step/sec: 88.2272 -2022-05-01 18:09:38,887 - tensorflow - INFO - loss = 0.08090085, step = 1178200 (2.267 sec) -2022-05-01 18:09:41,149 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.024037143, step = 1179600 (2.266 sec) -2022-05-01 18:09:57,006 - tensorflow - INFO - global_step/sec: 88.178 -2022-05-01 18:09:57,007 - tensorflow - INFO - loss = 0.100833595, step = 1179800 (2.268 sec) -2022-05-01 18:09:59,272 - tensorflow - INFO - global_step/sec: 88.2797 -2022-05-01 18:09:59,272 - tensorflow - INFO - loss = 0.13181756, step = 1180000 (2.265 sec) -2022-05-01 18:10:01,529 - tensorflow - INFO - global_step/sec: 88.6203 -2022-05-01 18:10:01,529 - tensorflow - INFO - loss = 0.04202525, step = 1180200 (2.257 sec) -2022-05-01 18:10:03,783 - tensorflow - INFO - global_step/sec: 88.7326 -2022-05-01 18:10:03,783 - tensorflow - INFO - loss = 0.022234075, step = 1180400 (2.254 sec) -2022-05-01 18:10:06,041 - tensorflow - INFO - global_step/sec: 88.5698 -2022-05-01 18:10:06,041 - tensorflow - INFO - loss = 0.01398593, step = 1180600 (2.258 sec) -2022-05-01 18:10:08,302 - tensorflow - INFO - global_step/sec: 88.4474 -2022-05-01 18:10:08,302 - tensorflow - INFO - loss = 0.08817243, step = 1180800 (2.261 sec) -2022-05-01 18:10:10,560 - tensorflow - INFO - global_step/sec: 88.58 -2022-05-01 18:10:10,560 - tensorflow - INFO - loss = 0.012736602, step = 1181000 (2.258 sec) -2022-05-01 18:10:12,815 - tensorflow - INFO - global_step/sec: 88.6696 -2022-05-01 18:10:12,816 - tensorflow - INFO - loss = 0.07885161, step = 1181200 (2.256 sec) -2022-05-01 18:10:15,066 - tensorflow - INFO - global_step/sec: 88.8567 -2022-05-01 18:10:15,067 - tensorflow - INFO - loss = 0.0152314175, step = 1181400 (2.251 sec) -2022-05-01 18:10:17,317 - tensorflow - INFO - global_step/sec: 88.8601 -2022-05-01 18:10:17,317 - tensorflow - INFO - loss = 1.0100908, step = 1181600 (2.251 sec) -2022-05-01 18:10:19,572 - tensorflow - INFO - global_step/sec: 88.6878 -2022-05-01 18:10:19,572 - tensorflow - INFO - loss = 0.0416034, step = 1181800 (2.255 sec) -2022-05-01 18:10:21,835 - tensorflow - INFO - global_step/sec: 88.364 -2022-05-01 18:10:21,836 - tensorflow - INFO - loss = 0.020663407, step = 1182000 (2.263 sec) -2022-05-01 18:10:24,087 - tensorflow - INFO - global_step/sec: 88.8179 -2022-05-01 18:10:24,088 - tensorflow - INFO - loss = 0.013663379, step = 1182200 (2.252 sec) -2022-05-01 18:10:26,340 - tensorflow - INFO - global_step/sec: 88.7946 -2022-05-01 18:10:26,340 - tensorflow - INFO - loss = 0.010723071, step = 1182400 (2.252 sec) -2022-05-01 18:10:28,594 - tensorflow - INFO - global_step/sec: 88.6936 -2022-05-01 18:10:28,595 - tensorflow - INFO - loss = 0.020838728, step = 1182600 (2.255 sec) -2022-05-01 18:10:30,855 - tensorflow - INFO - global_step/sec: 88.4827 -2022-05-01 18:10:30,855 - tensorflow - INFO - loss = 0.010693238, step = 1182800 (2.260 sec) -2022-05-01 18:10:33,112 - tensorflow - INFO - global_step/sec: 88.6114 -2022-05-01 18:10:33,112 - tensorflow - INFO - loss = 0.005051244, step = 1183000 (2.257 sec) -2022-05-01 18:10:35,368 - tensorflow - INFO - global_step/sec: 88.6634 -2022-05-01 18:10:35,368 - tensorflow - INFO - loss = 0.34863693, step = 1183200 (2.256 sec) -2022-05-01 18:10:37,622 - tensorflow - INFO - global_step/sec: 88.7199 -2022-05-01 18:10:37,622 - tensorflow - INFO - loss = 7.8540344, step = 1183400 (2.254 sec) -2022-05-01 18:10:39,878 - tensorflow - INFO - global_step/sec: 88.6312 -2022-05-01 18:10:39,879 - tensorflow - INFO - loss = 0.02740361, step = 1183600 (2.257 sec) -2022-05-01 18:10:42,136 - tensorflow - INFO - global_step/sec: 88.5754 -2022-05-01 18:10:42,137 - tensorflow - INFO - loss = 0.034917794, step = 1183800 (2.258 sec) -2022-05-01 18:10:44,385 - tensorflow - INFO - global_step/sec: 88.933 -2022-05-01 18:10:44,386 - tensorflow - INFO - loss = 0.038255285, step = 1184000 (2.249 sec) -2022-05-01 18:10:46,643 - tensorflow - INFO - global_step/sec: 88.5861 -2022-05-01 18:10:46,643 - tensorflow - INFO - loss = 0.008987745, step = 1184200 (2.258 sec) -2022-05-01 18:10:48,893 - tensorflow - INFO - global_step/sec: 88.8941 -2022-05-01 18:10:48,893 - tensorflow - INFO - loss = 0.019046573, step = 1184400 (2.250 sec) -2022-05-01 18:10:51,153 - tensorflow - INFO - global_step/sec: 88.4934 -2022-05-01 18:10:51,153 - tensorflow - INFO - loss = 0.015017347, step = 1184600 (2.260 sec) -2022-05-01 18:10:53,404 - tensorflow - INFO - global_step/sec: 88.8355 -2022-05-01 18:10:53,405 - tensorflow - INFO - loss = 0.020354364, step = 1184800 (2.251 sec) -2022-05-01 18:10:55,658 - tensorflow - INFO - global_step/sec: 88.7297 -2022-05-01 18:10:55,659 - tensorflow - INFO - loss = 0.023749266, step = 1185000 (2.254 sec) -2022-05-01 18:10:57,919 - tensorflow - INFO - global_step/sec: 88.4582 -2022-05-01 18:10:57,920 - tensorflow - INFO - loss = 0.042302318, step = 1185200 (2.261 sec) -2022-05-01 18:11:00,176 - tensorflow - INFO - global_step/sec: 88.6145 -2022-05-01 18:11:00,177 - tensorflow - INFO - loss = 0.2269616, step = 1185400 (2.257 sec) -2022-05-01 18:11:02,430 - tensorflow - INFO - global_step/sec: 88.7532 -2022-05-01 18:11:02,430 - tensorflow - INFO - loss = 0.023176527, step = 1185600 (2.254 sec) -2022-05-01 18:11:04,686 - tensorflow - INFO - global_step/sec: 88.6265 -2022-05-01 18:11:04,687 - tensorflow - INFO - loss = 0.044826522, step = 1185800 (2.257 sec) -2022-05-01 18:11:06,943 - tensorflow - INFO - global_step/sec: 88.6347 -2022-05-01 18:11:06,943 - tensorflow - INFO - loss = 0.03250031, step = 1186000 (2.256 sec) -2022-05-01 18:11:09,199 - tensorflow - INFO - global_step/sec: 88.6546 -2022-05-01 18:11:09,199 - tensorflow - INFO - loss = 0.062487267, step = 1186200 (2.256 sec) -2022-05-01 18:11:11,462 - tensorflow - INFO - global_step/sec: 88.3802 -2022-05-01 18:11:11,462 - tensorflow - INFO - loss = 0.011299405, step = 1186400 (2.263 sec) -2022-05-01 18:11:13,720 - tensorflow - INFO - global_step/sec: 88.558 -2022-05-01 18:11:13,720 - tensorflow - INFO - loss = 0.35987207, step = 1186600 (2.258 sec) -2022-05-01 18:11:15,977 - tensorflow - INFO - global_step/sec: 88.629 -2022-05-01 18:11:15,977 - tensorflow - INFO - loss = 0.48028773, step = 1186800 (2.257 sec) -2022-05-01 18:11:18,230 - tensorflow - INFO - global_step/sec: 88.7715 -2022-05-01 18:11:18,230 - tensorflow - INFO - loss = 0.019366598, step = 1187000 (2.253 sec) -2022-05-01 18:11:20,484 - tensorflow - INFO - global_step/sec: 88.7232 -2022-05-01 18:11:20,484 - tensorflow - INFO - loss = 0.04313879, step = 1187200 (2.254 sec) -2022-05-01 18:11:22,742 - tensorflow - INFO - global_step/sec: 88.5613 -2022-05-01 18:11:22,743 - tensorflow - INFO - loss = 0.007858479, step = 1187400 (2.258 sec) -2022-05-01 18:11:25,003 - tensorflow - INFO - global_step/sec: 88.4462 -2022-05-01 18:11:25,004 - tensorflow - INFO - loss = 0.113287, step = 1187600 (2.261 sec) -2022-05-01 18:11:27,261 - tensorflow - INFO - global_step/sec: 88.5974 -2022-05-01 18:11:27,261 - tensorflow - INFO - loss = 0.010161064, step = 1187800 (2.257 sec) -2022-05-01 18:11:29,518 - tensorflow - INFO - global_step/sec: 88.6209 -2022-05-01 18:11:29,518 - tensorflow - INFO - loss = 0.018867392, step = 1188000 (2.257 sec) -2022-05-01 18:11:31,780 - tensorflow - INFO - global_step/sec: 88.4049 -2022-05-01 18:11:31,780 - tensorflow - INFO - loss = 0.061028652, step = 1188200 (2.262 sec) -2022-05-01 18:11:34,039 - tensorflow - INFO - global_step/sec: 88.5441 -2022-05-01 18:11:34,039 - tensorflow - INFO - loss = 0.03321492, step = 1188400 (2.259 sec) -2022-05-01 18:11:36,301 - tensorflow - INFO - global_step/sec: 88.4174 -2022-05-01 18:11:36,301 - tensorflow - INFO - loss = 0.066412434, step = 1188600 (2.262 sec) -2022-05-01 18:11:38,550 - tensorflow - INFO - global_step/sec: 88.9089 -2022-05-01 18:11:38,551 - tensorflow - INFO - loss = 0.017991543, step = 1188800 (2.250 sec) -2022-05-01 18:11:40,807 - tensorflow - INFO - global_step/sec: 88.6189 -2022-05-01 18:11:40,807 - tensorflow - INFO - loss = 0.027934484, step = 1189000 (2.257 sec) -2022-05-01 18:11:43,058 - tensorflow - INFO - global_step/sec: 88.8359 -2022-05-01 18:11:43,059 - tensorflow - INFO - loss = 0.014947686, step = 1189200 (2.251 sec) -2022-05-01 18:11:45,312 - tensorflow - INFO - global_step/sec: 88.7526 -2022-05-01 18:11:45,312 - tensorflow - INFO - loss = 0.019492371, step = 1189400 (2.253 sec) -2022-05-01 18:11:47,565 - tensorflow - INFO - global_step/sec: 88.7467 -2022-05-01 18:11:47,566 - tensorflow - INFO - loss = 0.036995653, step = 1189600 (2.254 sec) -2022-05-01 18:11:49,825 - tensorflow - INFO - global_step/sec: 88.5181 -2022-05-01 18:11:49,825 - tensorflow - INFO - loss = 0.09084523, step = 1189800 (2.259 sec) -2022-05-01 18:11:52,078 - tensorflow - INFO - global_step/sec: 88.748 -2022-05-01 18:11:52,079 - tensorflow - INFO - loss = 0.10673227, step = 1190000 (2.254 sec) -2022-05-01 18:11:54,329 - tensorflow - INFO - global_step/sec: 88.8723 -2022-05-01 18:11:54,329 - tensorflow - INFO - loss = 0.0142885335, step = 1190200 (2.250 sec) -2022-05-01 18:11:56,583 - tensorflow - INFO - global_step/sec: 88.7303 -2022-05-01 18:11:56,583 - tensorflow - INFO - loss = 0.43347728, step = 1190400 (2.254 sec) -2022-05-01 18:11:58,840 - tensorflow - INFO - global_step/sec: 88.6206 -2022-05-01 18:11:58,840 - tensorflow - INFO - loss = 0.030480046, step = 1190600 (2.257 sec) -2022-05-01 18:12:01,093 - tensorflow - INFO - global_step/sec: 88.7518 -2022-05-01 18:12:01,094 - tensorflow - INFO - loss = 0.08956727, step = 1190800 (2.253 sec) -2022-05-01 18:12:03,353 - tensorflow - INFO - global_step/sec: 88.4922 -2022-05-01 18:12:03,354 - tensorflow - INFO - loss = 0.034298472, step = 1191000 (2.260 sec) -2022-05-01 18:12:05,608 - tensorflow - INFO - global_step/sec: 88.6941 -2022-05-01 18:12:05,609 - tensorflow - INFO - loss = 0.04235449, step = 1191200 (2.255 sec) -2022-05-01 18:12:07,865 - tensorflow - INFO - global_step/sec: 88.6266 -2022-05-01 18:12:07,865 - tensorflow - INFO - loss = 0.018376255, step = 1191400 (2.257 sec) -2022-05-01 18:12:10,118 - tensorflow - INFO - global_step/sec: 88.7677 -2022-05-01 18:12:10,118 - tensorflow - INFO - loss = 0.03255583, step = 1191600 (2.253 sec) -2022-05-01 18:12:12,377 - tensorflow - INFO - global_step/sec: 88.5139 -2022-05-01 18:12:12,378 - tensorflow - INFO - loss = 0.012742184, step = 1191800 (2.260 sec) -2022-05-01 18:12:14,641 - tensorflow - INFO - global_step/sec: 88.3719 -2022-05-01 18:12:14,641 - tensorflow - INFO - loss = 0.010812651, step = 1192000 (2.263 sec) -2022-05-01 18:12:16,898 - tensorflow - INFO - global_step/sec: 88.6071 -2022-05-01 18:12:16,898 - tensorflow - INFO - loss = 0.12380754, step = 1192200 (2.257 sec) -2022-05-01 18:12:19,146 - tensorflow - INFO - global_step/sec: 88.9405 -2022-05-01 18:12:19,147 - tensorflow - INFO - loss = 0.017856931, step = 1192400 (2.249 sec) -2022-05-01 18:12:21,399 - tensorflow - INFO - global_step/sec: 88.8056 -2022-05-01 18:12:21,399 - tensorflow - INFO - loss = 0.006084865, step = 1192600 (2.252 sec) -2022-05-01 18:12:23,656 - tensorflow - INFO - global_step/sec: 88.5832 -2022-05-01 18:12:23,657 - tensorflow - INFO - loss = 0.019839931, step = 1192800 (2.258 sec) -2022-05-01 18:12:25,910 - tensorflow - INFO - global_step/sec: 88.747 -2022-05-01 18:12:25,910 - tensorflow - INFO - loss = 0.057857707, step = 1193000 (2.254 sec) -2022-05-01 18:12:28,167 - tensorflow - INFO - global_step/sec: 88.6283 -2022-05-01 18:12:28,167 - tensorflow - INFO - loss = 0.026098432, step = 1193200 (2.257 sec) -2022-05-01 18:12:30,425 - tensorflow - INFO - global_step/sec: 88.5675 -2022-05-01 18:12:30,425 - tensorflow - INFO - loss = 0.017955255, step = 1193400 (2.258 sec) -2022-05-01 18:12:32,676 - tensorflow - INFO - global_step/sec: 88.8284 -2022-05-01 18:12:32,677 - tensorflow - INFO - loss = 14.991538, step = 1193600 (2.251 sec) -2022-05-01 18:12:34,933 - tensorflow - INFO - global_step/sec: 88.6301 -2022-05-01 18:12:34,933 - tensorflow - INFO - loss = 0.021420287, step = 1193800 (2.257 sec) -2022-05-01 18:12:37,184 - tensorflow - INFO - global_step/sec: 88.8432 -2022-05-01 18:12:37,184 - tensorflow - INFO - loss = 0.025775535, step = 1194000 (2.251 sec) -2022-05-01 18:12:39,442 - tensorflow - INFO - global_step/sec: 88.5622 -2022-05-01 18:12:39,443 - tensorflow - INFO - loss = 0.016893541, step = 1194200 (2.258 sec) -2022-05-01 18:12:41,697 - tensorflow - INFO - global_step/sec: 88.713 -2022-05-01 18:12:41,697 - tensorflow - INFO - loss = 0.033774905, step = 1194400 (2.254 sec) -2022-05-01 18:12:43,948 - tensorflow - INFO - global_step/sec: 88.8447 -2022-05-01 18:12:43,948 - tensorflow - INFO - loss = 0.009096233, step = 1194600 (2.251 sec) -2022-05-01 18:12:46,203 - tensorflow - INFO - global_step/sec: 88.6816 -2022-05-01 18:12:46,204 - tensorflow - INFO - loss = 0.061590552, step = 1194800 (2.255 sec) -2022-05-01 18:12:48,458 - tensorflow - INFO - global_step/sec: 88.6798 -2022-05-01 18:12:48,459 - tensorflow - INFO - loss = 0.56076455, step = 1195000 (2.255 sec) -2022-05-01 18:12:50,717 - tensorflow - INFO - global_step/sec: 88.5356 -2022-05-01 18:12:50,718 - tensorflow - INFO - loss = 0.047898788, step = 1195200 (2.259 sec) -2022-05-01 18:12:52,976 - tensorflow - INFO - global_step/sec: 88.5673 -2022-05-01 18:12:52,976 - tensorflow - INFO - loss = 0.037085086, step = 1195400 (2.258 sec) -2022-05-01 18:12:55,230 - tensorflow - INFO - global_step/sec: 88.716 -2022-05-01 18:12:55,230 - tensorflow - INFO - loss = 0.056205664, step = 1195600 (2.254 sec) -2022-05-01 18:12:57,491 - tensorflow - INFO - global_step/sec: 88.4358 -2022-05-01 18:12:57,492 - tensorflow - INFO - loss = 0.03376495, step = 1195800 (2.261 sec) -2022-05-01 18:12:59,749 - tensorflow - INFO - global_step/sec: 88.5909 -2022-05-01 18:12:59,749 - tensorflow - INFO - loss = 0.0069675804, step = 1196000 (2.258 sec) -2022-05-01 18:13:02,015 - tensorflow - INFO - global_step/sec: 88.2758 -2022-05-01 18:13:02,015 - tensorflow - INFO - loss = 0.09626718, step = 1196200 (2.266 sec) -2022-05-01 18:13:04,278 - tensorflow - INFO - global_step/sec: 88.384 -2022-05-01 18:13:04,278 - tensorflow - INFO - loss = 0.28406236, step = 1196400 (2.263 sec) -2022-05-01 18:13:06,539 - tensorflow - INFO - global_step/sec: 88.4227 -2022-05-01 18:13:06,540 - tensorflow - INFO - loss = 0.013516074, step = 1196600 (2.262 sec) -2022-05-01 18:13:08,799 - tensorflow - INFO - global_step/sec: 88.5036 -2022-05-01 18:13:08,800 - tensorflow - INFO - loss = 0.037272662, step = 1196800 (2.260 sec) -2022-05-01 18:13:11,060 - tensorflow - INFO - global_step/sec: 88.4786 -2022-05-01 18:13:11,060 - tensorflow - INFO - loss = 0.020124855, step = 1197000 (2.260 sec) -2022-05-01 18:13:13,325 - tensorflow - INFO - global_step/sec: 88.2711 -2022-05-01 18:13:13,326 - tensorflow - INFO - loss = 0.03263738, step = 1197200 (2.266 sec) -2022-05-01 18:13:15,591 - tensorflow - INFO - global_step/sec: 88.2575 -2022-05-01 18:13:15,592 - tensorflow - INFO - loss = 0.21277907, step = 1197400 (2.266 sec) -2022-05-01 18:13:17,858 - tensorflow - INFO - global_step/sec: 88.2431 -2022-05-01 18:13:17,858 - tensorflow - INFO - loss = 0.011050388, step = 1197600 (2.266 sec) -2022-05-01 18:13:20,124 - tensorflow - INFO - global_step/sec: 88.2652 -2022-05-01 18:13:20,124 - tensorflow - INFO - loss = 0.029462758, step = 1197800 (2.266 sec) -2022-05-01 18:13:22,385 - tensorflow - INFO - global_step/sec: 88.4637 -2022-05-01 18:13:22,385 - tensorflow - INFO - loss = 0.016916715, step = 1198000 (2.261 sec) -2022-05-01 18:13:24,649 - tensorflow - INFO - global_step/sec: 88.3252 -2022-05-01 18:13:24,649 - tensorflow - INFO - loss = 0.01425764, step = 1198200 (2.264 sec) -2022-05-01 18:13:26,914 - tensorflow - INFO - global_step/sec: 88.3187 -2022-05-01 18:13:26,914 - tensorflow - INFO - loss = 0.08497985, step = 1198400 (2.265 sec) -2022-05-01 18:13:29,178 - tensorflow - INFO - global_step/sec: 88.3126 -2022-05-01 18:13:29,179 - tensorflow - INFO - loss = 0.0450411, step = 1198600 (2.265 sec) -2022-05-01 18:13:31,448 - tensorflow - INFO - global_step/sec: 88.1132 -2022-05-01 18:13:31,448 - tensorflow - INFO - loss = 0.033562914, step = 1198800 (2.270 sec) -2022-05-01 18:13:33,706 - tensorflow - INFO - global_step/sec: 88.5748 -2022-05-01 18:13:33,706 - tensorflow - INFO - loss = 0.082803875, step = 1199000 (2.258 sec) -2022-05-01 18:13:35,964 - tensorflow - INFO - global_step/sec: 88.5552 -2022-05-01 18:13:35,965 - tensorflow - INFO - loss = 1.5781188, step = 1199200 (2.258 sec) -2022-05-01 18:13:38,215 - tensorflow - INFO - global_step/sec: 88.8648 -2022-05-01 18:13:38,215 - tensorflow - INFO - loss = 0.0135265775, step = 1199400 (2.251 sec) -2022-05-01 18:13:40,467 - tensorflow - INFO - global_step/sec: 88.8301 -2022-05-01 18:13:40,467 - tensorflow - INFO - loss = 0.009275254, step = 1199600 (2.252 sec) -2022-05-01 18:13:42,718 - tensorflow - INFO - global_step/sec: 88.8346 -2022-05-01 18:13:42,718 - tensorflow - INFO - loss = 0.029307913, step = 1199800 (2.251 sec) -2022-05-01 18:13:44,962 - tensorflow - INFO - Saving checkpoints for 1200000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. -2022-05-01 18:13:44,978 - tensorflow - INFO - model.ckpt-1200000 is not in all_model_checkpoint_paths. Manually adding it. -2022-05-01 18:13:45,518 - tensorflow - INFO - Calling model_fn. -2022-05-01 18:13:45,645 - tensorflow - INFO - Done calling model_fn. -2022-05-01 18:13:45,660 - tensorflow - INFO - Starting evaluation at 2022-05-01T18:13:45Z -2022-05-01 18:13:45,724 - tensorflow - INFO - Graph was finalized. -2022-05-01 18:13:45,727 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1200000 -2022-05-01 18:13:46,008 - tensorflow - INFO - Running local_init_op. -2022-05-01 18:13:46,116 - tensorflow - INFO - Done running local_init_op. -2022-05-01 18:13:56,323 - tensorflow - INFO - Evaluation [3990/39900] -2022-05-01 18:14:05,837 - tensorflow - INFO - Evaluation [7980/39900] -2022-05-01 18:14:15,350 - tensorflow - INFO - Evaluation [11970/39900] -2022-05-01 18:14:24,848 - tensorflow - INFO - Evaluation [15960/39900] -2022-05-01 18:14:34,349 - tensorflow - INFO - Evaluation [19950/39900] -2022-05-01 18:14:43,851 - tensorflow - INFO - Evaluation [23940/39900] -2022-05-01 18:14:53,368 - tensorflow - INFO - Evaluation [27930/39900] -2022-05-01 18:15:02,897 - tensorflow - INFO - Evaluation [31920/39900] -2022-05-01 18:15:12,412 - tensorflow - INFO - Evaluation [35910/39900] -2022-05-01 18:15:21,938 - tensorflow - INFO - Evaluation [39900/39900] -2022-05-01 18:15:22,073 - tensorflow - INFO - Finished evaluation at 2022-05-01-18:15:22 -2022-05-01 18:15:22,074 - tensorflow - INFO - Saving dict for global step 1200000: global_step = 1200000, loss = 0.049551684, mean_degree_err = 8.192199 -2022-05-01 18:15:22,074 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 1200000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1200000 -2022-05-01 18:15:22,088 - tensorflow - INFO - global_step/sec: 2.01268 -2022-05-01 18:15:22,089 - tensorflow - INFO - loss = 0.10029067, step = 1200000 (99.370 sec) -2022-05-01 18:15:24,354 - tensorflow - INFO - global_step/sec: 88.256 -2022-05-01 18:15:24,354 - tensorflow - INFO - loss = 0.03690188, step = 1200200 (2.266 sec) -2022-05-01 18:15:26,611 - tensorflow - INFO - global_step/sec: 88.6113 -2022-05-01 18:15:26,611 - tensorflow - INFO - loss = 0.033221673, step = 1200400 (2.257 sec) -2022-05-01 18:15:28,864 - tensorflow - INFO - global_step/sec: 88.7596 -2022-05-01 18:15:28,865 - tensorflow - INFO - loss = 0.021254009, step = 1200600 (2.253 sec) -2022-05-01 18:15:31,122 - tensorflow - INFO - global_step/sec: 88.5909 -2022-05-01 18:15:31,122 - tensorflow - INFO - loss = 0.03441968, step = 1200800 (2.258 sec) -2022-05-01 18:15:33,376 - tensorflow - INFO - global_step/sec: 88.7153 -2022-05-01 18:15:33,377 - tensorflow - INFO - loss = 0.030785479, step = 1201000 (2.254 sec) -2022-05-01 18:15:35,629 - tensorflow - INFO - global_step/sec: 88.7627 -2022-05-01 18:15:35,630 - tensorflow - INFO - loss = 0.008263586, step = 1201200 (2.253 sec) -2022-05-01 18:15:37,886 - tensorflow - INFO - global_step/sec: 88.6402 -2022-05-01 18:15:37,886 - tensorflow - INFO - loss = 0.08718412, step = 1201400 (2.256 sec) -2022-05-01 18:15:40,141 - tensorflow - INFO - global_step/sec: 88.6754 -2022-05-01 18:15:40,142 - tensorflow - INFO - loss = 0.06642119, step = 1201600 (2.255 sec) -2022-05-01 18:15:42,395 - tensorflow - INFO - global_step/sec: 88.7366 -2022-05-01 18:15:42,395 - tensorflow - INFO - loss = 0.05555537, step = 1201800 (2.254 sec) -2022-05-01 18:15:44,645 - tensorflow - INFO - global_step/sec: 88.9064 -2022-05-01 18:15:44,645 - tensorflow - INFO - loss = 1.1072648, step = 1202000 (2.250 sec) -2022-05-01 18:15:46,905 - tensorflow - INFO - global_step/sec: 88.478 -2022-05-01 18:15:46,905 - tensorflow - INFO - loss = 0.06818071, step = 1202200 (2.260 sec) -2022-05-01 18:15:49,162 - tensorflow - INFO - global_step/sec: 88.6274 -2022-05-01 18:15:49,162 - tensorflow - INFO - loss = 0.02435386, step = 1202400 (2.257 sec) -2022-05-01 18:15:51,425 - tensorflow - INFO - global_step/sec: 88.3848 -2022-05-01 18:15:51,425 - tensorflow - INFO - loss = 0.031496737, step = 1202600 (2.263 sec) -2022-05-01 18:15:53,680 - tensorflow - INFO - global_step/sec: 88.6802 -2022-05-01 18:15:53,680 - tensorflow - INFO - loss = 0.22101256, step = 1202800 (2.255 sec) -2022-05-01 18:15:55,937 - tensorflow - INFO - global_step/sec: 88.6014 -2022-05-01 18:15:55,938 - tensorflow - INFO - loss = 0.010230656, step = 1203000 (2.257 sec) -2022-05-01 18:15:58,194 - tensorflow - INFO - global_step/sec: 88.5991 -2022-05-01 18:15:58,195 - tensorflow - INFO - loss = 2.6677594, step = 1203200 (2.257 sec) -2022-05-01 18:16:00,449 - tensorflow - INFO - global_step/sec: 88.7291 -2022-05-01 18:16:00,449 - tensorflow - INFO - loss = 0.14728445, step = 1203400 (2.254 sec) -2022-05-01 18:16:02,714 - tensorflow - INFO - global_step/sec: 88.2869 -2022-05-01 18:16:02,714 - tensorflow - INFO - loss = 0.03147973, step = 1203600 (2.265 sec) -2022-05-01 18:16:04,972 - tensorflow - INFO - global_step/sec: 88.5771 -2022-05-01 18:16:04,972 - tensorflow - INFO - loss = 0.089283906, step = 1203800 (2.258 sec) -2022-05-01 18:16:07,227 - tensorflow - INFO - global_step/sec: 88.6778 -2022-05-01 18:16:07,228 - tensorflow - INFO - loss = 0.05661044, step = 1204000 (2.255 sec) -2022-05-01 18:16:09,482 - tensorflow - INFO - global_step/sec: 88.6908 -2022-05-01 18:16:09,483 - tensorflow - INFO - loss = 0.018297628, step = 1204200 (2.255 sec) -2022-05-01 18:16:11,740 - tensorflow - INFO - global_step/sec: 88.5849 -2022-05-01 18:16:11,740 - tensorflow - INFO - loss = 0.025900912, step = 1204400 (2.258 sec) -2022-05-01 18:16:14,000 - tensorflow - INFO - global_step/sec: 88.4942 -2022-05-01 18:16:14,000 - tensorflow - INFO - loss = 0.034806952, step = 1204600 (2.260 sec) -2022-05-01 18:16:16,267 - tensorflow - INFO - global_step/sec: 88.2319 -2022-05-01 18:16:16,267 - tensorflow - INFO - loss = 0.018693745, step = 1204800 (2.267 sec) -2022-05-01 18:16:18,518 - tensorflow - INFO - global_step/sec: 88.8386 -2022-05-01 18:16:18,518 - tensorflow - INFO - loss = 0.034952633, step = 1205000 (2.251 sec) -2022-05-01 18:16:20,778 - tensorflow - INFO - global_step/sec: 88.4784 -2022-05-01 18:16:20,779 - tensorflow - INFO - loss = 0.013241578, step = 1205200 (2.260 sec) -2022-05-01 18:16:23,041 - tensorflow - INFO - global_step/sec: 88.3991 -2022-05-01 18:16:23,041 - tensorflow - INFO - loss = 0.011428614, step = 1205400 (2.262 sec) -2022-05-01 18:16:25,300 - tensorflow - INFO - global_step/sec: 88.5366 -2022-05-01 18:16:25,300 - tensorflow - INFO - loss = 0.56577766, step = 1205600 (2.259 sec) -2022-05-01 18:16:27,560 - tensorflow - INFO - global_step/sec: 88.4861 -2022-05-01 18:16:27,560 - tensorflow - INFO - loss = 0.01962918, step = 1205800 (2.260 sec) -2022-05-01 18:16:29,818 - tensorflow - INFO - global_step/sec: 88.5815 -2022-05-01 18:16:29,818 - tensorflow - INFO - loss = 0.077739194, step = 1206000 (2.258 sec) -2022-05-01 18:16:32,082 - tensorflow - INFO - global_step/sec: 88.3441 -2022-05-01 18:16:32,082 - tensorflow - INFO - loss = 0.030087005, step = 1206200 (2.264 sec) -2022-05-01 18:16:34,344 - tensorflow - INFO - global_step/sec: 88.3936 -2022-05-01 18:16:34,345 - tensorflow - INFO - loss = 0.029479984, step = 1206400 (2.263 sec) -2022-05-01 18:16:36,608 - tensorflow - INFO - global_step/sec: 88.3688 -2022-05-01 18:16:36,608 - tensorflow - INFO - loss = 0.13650742, step = 1206600 (2.263 sec) -2022-05-01 18:16:38,871 - tensorflow - INFO - global_step/sec: 88.3564 -2022-05-01 18:16:38,872 - tensorflow - INFO - loss = 0.025181746, step = 1206800 (2.264 sec) -2022-05-01 18:16:41,139 - tensorflow - INFO - global_step/sec: 88.2039 -2022-05-01 18:16:41,139 - tensorflow - INFO - loss = 0.10817782, step = 1207000 (2.268 sec) -2022-05-01 18:16:43,404 - tensorflow - INFO - global_step/sec: 88.2996 -2022-05-01 18:16:43,404 - tensorflow - INFO - loss = 0.024515321, step = 1207200 (2.265 sec) -2022-05-01 18:16:45,668 - tensorflow - INFO - global_step/sec: 88.3078 -2022-05-01 18:16:45,669 - tensorflow - INFO - loss = 0.008551151, step = 1207400 (2.265 sec) -2022-05-01 18:16:47,931 - tensorflow - INFO - global_step/sec: 88.4017 -2022-05-01 18:16:47,931 - tensorflow - 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INFO - global_step/sec: 88.6143 -2022-05-01 18:23:20,660 - tensorflow - INFO - loss = 0.011611406, step = 1242400 (2.257 sec) -2022-05-01 18:23:22,913 - tensorflow - INFO - global_step/sec: 88.7342 -2022-05-01 18:23:22,913 - tensorflow - INFO - loss = 0.015167651, step = 1242600 (2.254 sec) -2022-05-01 18:23:25,170 - tensorflow - INFO - global_step/sec: 88.6087 -2022-05-01 18:23:25,171 - tensorflow - INFO - loss = 0.010836497, step = 1242800 (2.257 sec) -2022-05-01 18:23:27,436 - tensorflow - INFO - global_step/sec: 88.2604 -2022-05-01 18:23:27,437 - tensorflow - INFO - loss = 0.044162713, step = 1243000 (2.266 sec) -2022-05-01 18:23:29,693 - tensorflow - INFO - global_step/sec: 88.6233 -2022-05-01 18:23:29,693 - tensorflow - INFO - loss = 0.08167398, step = 1243200 (2.257 sec) -2022-05-01 18:23:31,963 - tensorflow - INFO - global_step/sec: 88.0938 -2022-05-01 18:23:31,964 - tensorflow - INFO - loss = 0.027459173, step = 1243400 (2.270 sec) -2022-05-01 18:23:34,225 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.5468 -2022-05-01 18:31:16,815 - tensorflow - INFO - loss = 0.03547059, step = 1284600 (2.259 sec) -2022-05-01 18:31:19,072 - tensorflow - INFO - global_step/sec: 88.581 -2022-05-01 18:31:19,073 - tensorflow - INFO - loss = 0.010466327, step = 1284800 (2.258 sec) -2022-05-01 18:31:21,332 - tensorflow - INFO - global_step/sec: 88.5187 -2022-05-01 18:31:21,332 - tensorflow - INFO - loss = 0.013831692, step = 1285000 (2.259 sec) -2022-05-01 18:31:23,589 - tensorflow - INFO - global_step/sec: 88.5789 -2022-05-01 18:31:23,590 - tensorflow - INFO - loss = 0.019581486, step = 1285200 (2.258 sec) -2022-05-01 18:31:25,847 - tensorflow - INFO - global_step/sec: 88.5897 -2022-05-01 18:31:25,847 - tensorflow - INFO - loss = 0.04874866, step = 1285400 (2.258 sec) -2022-05-01 18:31:28,107 - tensorflow - INFO - global_step/sec: 88.5069 -2022-05-01 18:31:28,107 - tensorflow - INFO - loss = 0.036323167, step = 1285600 (2.260 sec) -2022-05-01 18:31:30,366 - tensorflow - INFO - global_step/sec: 88.5367 -2022-05-01 18:31:30,366 - tensorflow - INFO - loss = 0.01787388, step = 1285800 (2.259 sec) -2022-05-01 18:31:32,626 - tensorflow - INFO - global_step/sec: 88.5028 -2022-05-01 18:31:32,626 - tensorflow - INFO - loss = 0.2014732, step = 1286000 (2.260 sec) -2022-05-01 18:31:34,886 - tensorflow - INFO - global_step/sec: 88.4615 -2022-05-01 18:31:34,887 - tensorflow - INFO - loss = 0.010692698, step = 1286200 (2.261 sec) -2022-05-01 18:31:37,139 - tensorflow - INFO - global_step/sec: 88.7775 -2022-05-01 18:31:37,140 - tensorflow - INFO - loss = 0.026695687, step = 1286400 (2.253 sec) -2022-05-01 18:31:39,391 - tensorflow - INFO - global_step/sec: 88.813 -2022-05-01 18:31:39,392 - tensorflow - INFO - loss = 0.051970016, step = 1286600 (2.252 sec) -2022-05-01 18:31:41,645 - tensorflow - INFO - global_step/sec: 88.7399 -2022-05-01 18:31:41,645 - tensorflow - INFO - loss = 0.06618852, step = 1286800 (2.254 sec) -2022-05-01 18:31:43,897 - tensorflow - INFO - global_step/sec: 88.8046 -2022-05-01 18:31:43,897 - tensorflow - INFO - loss = 0.024169106, step = 1287000 (2.252 sec) -2022-05-01 18:31:46,153 - tensorflow - INFO - global_step/sec: 88.6677 -2022-05-01 18:31:46,153 - tensorflow - INFO - loss = 0.009108191, step = 1287200 (2.256 sec) -2022-05-01 18:31:48,406 - tensorflow - INFO - global_step/sec: 88.76 -2022-05-01 18:31:48,406 - tensorflow - INFO - loss = 0.08247757, step = 1287400 (2.253 sec) -2022-05-01 18:31:50,658 - tensorflow - INFO - global_step/sec: 88.82 -2022-05-01 18:31:50,658 - tensorflow - INFO - loss = 0.010382421, step = 1287600 (2.252 sec) -2022-05-01 18:31:52,908 - tensorflow - INFO - global_step/sec: 88.8904 -2022-05-01 18:31:52,908 - tensorflow - INFO - loss = 0.4617443, step = 1287800 (2.250 sec) -2022-05-01 18:31:55,155 - tensorflow - INFO - global_step/sec: 88.9815 -2022-05-01 18:31:55,156 - tensorflow - INFO - loss = 0.018690221, step = 1288000 (2.248 sec) -2022-05-01 18:31:57,423 - tensorflow - INFO - global_step/sec: 88.1855 -2022-05-01 18:31:57,424 - tensorflow - INFO - loss = 0.006660033, step = 1288200 (2.268 sec) -2022-05-01 18:31:59,680 - tensorflow - INFO - global_step/sec: 88.626 -2022-05-01 18:31:59,680 - tensorflow - INFO - loss = 0.08108465, step = 1288400 (2.257 sec) -2022-05-01 18:32:01,939 - tensorflow - INFO - global_step/sec: 88.548 -2022-05-01 18:32:01,939 - tensorflow - INFO - loss = 0.14775956, step = 1288600 (2.259 sec) -2022-05-01 18:32:04,193 - tensorflow - INFO - global_step/sec: 88.7121 -2022-05-01 18:32:04,193 - tensorflow - INFO - loss = 0.024420854, step = 1288800 (2.254 sec) -2022-05-01 18:32:06,444 - tensorflow - INFO - global_step/sec: 88.8523 -2022-05-01 18:32:06,444 - tensorflow - INFO - loss = 2.4564295, step = 1289000 (2.251 sec) -2022-05-01 18:32:08,694 - tensorflow - INFO - global_step/sec: 88.8765 -2022-05-01 18:32:08,695 - tensorflow - INFO - loss = 0.037567124, step = 1289200 (2.250 sec) -2022-05-01 18:32:10,951 - tensorflow - INFO - global_step/sec: 88.6394 -2022-05-01 18:32:10,951 - tensorflow - INFO - loss = 0.020364668, step = 1289400 (2.256 sec) -2022-05-01 18:32:13,204 - tensorflow - INFO - global_step/sec: 88.744 -2022-05-01 18:32:13,205 - tensorflow - INFO - loss = 0.0064563705, step = 1289600 (2.254 sec) -2022-05-01 18:32:15,464 - tensorflow - INFO - global_step/sec: 88.4911 -2022-05-01 18:32:15,465 - tensorflow - INFO - loss = 0.022302406, step = 1289800 (2.260 sec) -2022-05-01 18:32:17,713 - tensorflow - INFO - global_step/sec: 88.9568 -2022-05-01 18:32:17,713 - tensorflow - INFO - loss = 0.03229746, step = 1290000 (2.248 sec) -2022-05-01 18:32:19,963 - tensorflow - INFO - global_step/sec: 88.8878 -2022-05-01 18:32:19,963 - tensorflow - INFO - loss = 0.012207605, step = 1290200 (2.250 sec) -2022-05-01 18:32:22,218 - tensorflow - INFO - global_step/sec: 88.6647 -2022-05-01 18:32:22,219 - tensorflow - INFO - loss = 2.3386588, step = 1290400 (2.256 sec) -2022-05-01 18:32:24,474 - tensorflow - INFO - global_step/sec: 88.6881 -2022-05-01 18:32:24,474 - tensorflow - INFO - loss = 0.012496609, step = 1290600 (2.255 sec) -2022-05-01 18:32:26,729 - tensorflow - INFO - global_step/sec: 88.6877 -2022-05-01 18:32:26,729 - tensorflow - INFO - loss = 0.07285924, step = 1290800 (2.255 sec) -2022-05-01 18:32:28,980 - tensorflow - INFO - global_step/sec: 88.8342 -2022-05-01 18:32:28,980 - tensorflow - INFO - loss = 0.29961962, step = 1291000 (2.251 sec) -2022-05-01 18:32:31,243 - tensorflow - INFO - global_step/sec: 88.3785 -2022-05-01 18:32:31,243 - tensorflow - INFO - loss = 0.15170337, step = 1291200 (2.263 sec) -2022-05-01 18:32:33,499 - tensorflow - INFO - global_step/sec: 88.6536 -2022-05-01 18:32:33,499 - tensorflow - INFO - loss = 0.038761783, step = 1291400 (2.256 sec) -2022-05-01 18:32:35,754 - tensorflow - INFO - global_step/sec: 88.6969 -2022-05-01 18:32:35,754 - tensorflow - INFO - loss = 0.09981961, step = 1291600 (2.255 sec) -2022-05-01 18:32:38,002 - tensorflow - INFO - global_step/sec: 88.9676 -2022-05-01 18:32:38,002 - tensorflow - INFO - loss = 0.2219835, step = 1291800 (2.248 sec) -2022-05-01 18:32:40,252 - tensorflow - INFO - global_step/sec: 88.8749 -2022-05-01 18:32:40,253 - tensorflow - INFO - loss = 0.005805756, step = 1292000 (2.250 sec) -2022-05-01 18:32:42,510 - tensorflow - INFO - global_step/sec: 88.5711 -2022-05-01 18:32:42,511 - tensorflow - INFO - loss = 0.029483391, step = 1292200 (2.258 sec) -2022-05-01 18:32:44,761 - tensorflow - INFO - global_step/sec: 88.8757 -2022-05-01 18:32:44,761 - tensorflow - INFO - loss = 0.014592943, step = 1292400 (2.250 sec) -2022-05-01 18:32:47,018 - tensorflow - INFO - global_step/sec: 88.5939 -2022-05-01 18:32:47,018 - tensorflow - INFO - loss = 0.014249931, step = 1292600 (2.258 sec) -2022-05-01 18:32:49,275 - tensorflow - INFO - global_step/sec: 88.6192 -2022-05-01 18:32:49,275 - tensorflow - INFO - loss = 0.017477775, step = 1292800 (2.257 sec) -2022-05-01 18:32:51,527 - tensorflow - INFO - global_step/sec: 88.8213 -2022-05-01 18:32:51,527 - tensorflow - INFO - loss = 0.011953859, step = 1293000 (2.252 sec) -2022-05-01 18:32:53,778 - tensorflow - INFO - global_step/sec: 88.8499 -2022-05-01 18:32:53,778 - tensorflow - INFO - loss = 0.011802371, step = 1293200 (2.251 sec) -2022-05-01 18:32:56,028 - tensorflow - INFO - global_step/sec: 88.8638 -2022-05-01 18:32:56,029 - tensorflow - INFO - loss = 0.015671462, step = 1293400 (2.251 sec) -2022-05-01 18:32:58,283 - tensorflow - INFO - global_step/sec: 88.709 -2022-05-01 18:32:58,283 - tensorflow - INFO - loss = 0.010309601, step = 1293600 (2.255 sec) -2022-05-01 18:33:00,540 - tensorflow - INFO - global_step/sec: 88.6174 -2022-05-01 18:33:00,540 - tensorflow - INFO - loss = 0.5059539, step = 1293800 (2.257 sec) -2022-05-01 18:33:02,795 - tensorflow - INFO - global_step/sec: 88.6678 -2022-05-01 18:33:02,796 - tensorflow - INFO - loss = 0.029848307, step = 1294000 (2.256 sec) -2022-05-01 18:33:05,054 - tensorflow - INFO - global_step/sec: 88.566 -2022-05-01 18:33:05,054 - tensorflow - INFO - loss = 0.008277052, step = 1294200 (2.258 sec) -2022-05-01 18:33:07,311 - tensorflow - INFO - global_step/sec: 88.5882 -2022-05-01 18:33:07,312 - tensorflow - INFO - loss = 0.20894733, step = 1294400 (2.258 sec) -2022-05-01 18:33:09,563 - tensorflow - INFO - global_step/sec: 88.8015 -2022-05-01 18:33:09,564 - tensorflow - INFO - loss = 0.15793276, step = 1294600 (2.252 sec) -2022-05-01 18:33:11,819 - tensorflow - INFO - global_step/sec: 88.6602 -2022-05-01 18:33:11,820 - tensorflow - INFO - loss = 0.010115347, step = 1294800 (2.256 sec) -2022-05-01 18:33:14,067 - tensorflow - INFO - global_step/sec: 88.9644 -2022-05-01 18:33:14,068 - tensorflow - INFO - loss = 0.01433547, step = 1295000 (2.248 sec) -2022-05-01 18:33:16,320 - tensorflow - INFO - global_step/sec: 88.7784 -2022-05-01 18:33:16,320 - tensorflow - INFO - loss = 0.05959379, step = 1295200 (2.253 sec) -2022-05-01 18:33:18,574 - tensorflow - INFO - global_step/sec: 88.7471 -2022-05-01 18:33:18,574 - tensorflow - INFO - loss = 0.009571571, step = 1295400 (2.254 sec) -2022-05-01 18:33:20,829 - tensorflow - INFO - global_step/sec: 88.688 -2022-05-01 18:33:20,829 - tensorflow - INFO - loss = 0.013059959, step = 1295600 (2.255 sec) -2022-05-01 18:33:23,086 - tensorflow - INFO - global_step/sec: 88.6207 -2022-05-01 18:33:23,086 - tensorflow - INFO - loss = 0.019144017, step = 1295800 (2.257 sec) -2022-05-01 18:33:25,348 - tensorflow - INFO - global_step/sec: 88.4099 -2022-05-01 18:33:25,348 - tensorflow - INFO - loss = 0.012158981, step = 1296000 (2.262 sec) -2022-05-01 18:33:27,601 - tensorflow - INFO - global_step/sec: 88.7429 -2022-05-01 18:33:27,602 - tensorflow - INFO - loss = 0.012564669, step = 1296200 (2.254 sec) -2022-05-01 18:33:29,853 - tensorflow - INFO - global_step/sec: 88.816 -2022-05-01 18:33:29,854 - tensorflow - INFO - loss = 6.0557833, step = 1296400 (2.252 sec) -2022-05-01 18:33:32,110 - tensorflow - INFO - global_step/sec: 88.6116 -2022-05-01 18:33:32,111 - tensorflow - INFO - loss = 0.022484478, step = 1296600 (2.257 sec) -2022-05-01 18:33:34,367 - tensorflow - INFO - global_step/sec: 88.6347 -2022-05-01 18:33:34,367 - tensorflow - INFO - loss = 0.05494872, step = 1296800 (2.256 sec) -2022-05-01 18:33:36,624 - tensorflow - INFO - global_step/sec: 88.5898 -2022-05-01 18:33:36,625 - tensorflow - INFO - loss = 0.01839782, step = 1297000 (2.258 sec) -2022-05-01 18:33:38,880 - tensorflow - INFO - global_step/sec: 88.654 -2022-05-01 18:33:38,881 - tensorflow - INFO - loss = 0.029880244, step = 1297200 (2.256 sec) -2022-05-01 18:33:41,131 - tensorflow - INFO - global_step/sec: 88.8782 -2022-05-01 18:33:41,131 - tensorflow - INFO - loss = 0.018286102, step = 1297400 (2.250 sec) -2022-05-01 18:33:43,382 - tensorflow - INFO - global_step/sec: 88.8519 -2022-05-01 18:33:43,382 - tensorflow - INFO - loss = 0.25781596, step = 1297600 (2.251 sec) -2022-05-01 18:33:45,635 - tensorflow - INFO - global_step/sec: 88.742 -2022-05-01 18:33:45,636 - tensorflow - INFO - loss = 0.016293323, step = 1297800 (2.254 sec) -2022-05-01 18:33:47,887 - tensorflow - INFO - global_step/sec: 88.8093 -2022-05-01 18:33:47,888 - tensorflow - INFO - loss = 0.0564257, step = 1298000 (2.252 sec) -2022-05-01 18:33:50,142 - tensorflow - INFO - global_step/sec: 88.7104 -2022-05-01 18:33:50,142 - tensorflow - INFO - loss = 0.01472992, step = 1298200 (2.255 sec) -2022-05-01 18:33:52,399 - tensorflow - INFO - global_step/sec: 88.6091 -2022-05-01 18:33:52,399 - tensorflow - INFO - loss = 0.21787146, step = 1298400 (2.257 sec) -2022-05-01 18:33:54,656 - tensorflow - INFO - global_step/sec: 88.6034 -2022-05-01 18:33:54,657 - tensorflow - INFO - loss = 0.030389164, step = 1298600 (2.257 sec) -2022-05-01 18:33:56,921 - tensorflow - INFO - global_step/sec: 88.3192 -2022-05-01 18:33:56,921 - tensorflow - INFO - loss = 0.014049642, step = 1298800 (2.264 sec) -2022-05-01 18:33:59,178 - tensorflow - INFO - global_step/sec: 88.611 -2022-05-01 18:33:59,178 - tensorflow - INFO - loss = 0.07121677, step = 1299000 (2.257 sec) -2022-05-01 18:34:01,441 - tensorflow - INFO - global_step/sec: 88.3786 -2022-05-01 18:34:01,441 - tensorflow - INFO - loss = 0.011796599, step = 1299200 (2.263 sec) -2022-05-01 18:34:03,706 - tensorflow - INFO - global_step/sec: 88.3047 -2022-05-01 18:34:03,706 - tensorflow - INFO - loss = 0.02658301, step = 1299400 (2.265 sec) -2022-05-01 18:34:05,964 - tensorflow - INFO - global_step/sec: 88.5573 -2022-05-01 18:34:05,964 - tensorflow - INFO - loss = 0.020927524, step = 1299600 (2.258 sec) -2022-05-01 18:34:08,224 - tensorflow - INFO - global_step/sec: 88.4939 -2022-05-01 18:34:08,225 - tensorflow - INFO - loss = 0.08239248, step = 1299800 (2.260 sec) -2022-05-01 18:34:10,473 - tensorflow - INFO - Saving checkpoints for 1300000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. -2022-05-01 18:34:10,488 - tensorflow - INFO - model.ckpt-1300000 is not in all_model_checkpoint_paths. Manually adding it. -2022-05-01 18:34:11,011 - tensorflow - INFO - Calling model_fn. -2022-05-01 18:34:11,139 - tensorflow - INFO - Done calling model_fn. -2022-05-01 18:34:11,154 - tensorflow - INFO - Starting evaluation at 2022-05-01T18:34:11Z -2022-05-01 18:34:11,218 - tensorflow - INFO - Graph was finalized. -2022-05-01 18:34:11,221 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1300000 -2022-05-01 18:34:11,503 - tensorflow - INFO - Running local_init_op. -2022-05-01 18:34:11,611 - tensorflow - INFO - Done running local_init_op. -2022-05-01 18:34:21,788 - tensorflow - INFO - Evaluation [3990/39900] -2022-05-01 18:34:31,243 - tensorflow - INFO - Evaluation [7980/39900] -2022-05-01 18:34:40,667 - tensorflow - INFO - Evaluation [11970/39900] -2022-05-01 18:34:50,117 - tensorflow - INFO - Evaluation [15960/39900] -2022-05-01 18:34:59,573 - tensorflow - INFO - Evaluation [19950/39900] -2022-05-01 18:35:09,037 - tensorflow - INFO - Evaluation [23940/39900] -2022-05-01 18:35:18,472 - tensorflow - INFO - Evaluation [27930/39900] -2022-05-01 18:35:27,938 - tensorflow - INFO - Evaluation [31920/39900] -2022-05-01 18:35:37,388 - tensorflow - INFO - Evaluation [35910/39900] -2022-05-01 18:35:46,832 - tensorflow - INFO - Evaluation [39900/39900] -2022-05-01 18:35:46,968 - tensorflow - INFO - Finished evaluation at 2022-05-01-18:35:46 -2022-05-01 18:35:46,969 - tensorflow - INFO - Saving dict for global step 1300000: global_step = 1300000, loss = 0.037376873, mean_degree_err = 5.5991697 -2022-05-01 18:35:46,969 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 1300000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1300000 -2022-05-01 18:35:46,983 - tensorflow - INFO - global_step/sec: 2.02513 -2022-05-01 18:35:46,984 - tensorflow - INFO - loss = 0.030853275, step = 1300000 (98.759 sec) -2022-05-01 18:35:49,254 - tensorflow - INFO - global_step/sec: 88.0857 -2022-05-01 18:35:49,254 - tensorflow - INFO - loss = 0.024246916, step = 1300200 (2.270 sec) -2022-05-01 18:35:51,503 - tensorflow - INFO - global_step/sec: 88.8944 -2022-05-01 18:35:51,504 - tensorflow - INFO - loss = 4.38457, step = 1300400 (2.250 sec) -2022-05-01 18:35:53,758 - tensorflow - INFO - global_step/sec: 88.7213 -2022-05-01 18:35:53,758 - tensorflow - INFO - loss = 0.03259698, step = 1300600 (2.254 sec) -2022-05-01 18:35:56,012 - tensorflow - INFO - global_step/sec: 88.7204 -2022-05-01 18:35:56,012 - tensorflow - INFO - loss = 0.026033219, step = 1300800 (2.254 sec) -2022-05-01 18:35:58,264 - tensorflow - INFO - global_step/sec: 88.8051 -2022-05-01 18:35:58,265 - tensorflow - INFO - loss = 0.03283344, step = 1301000 (2.252 sec) -2022-05-01 18:36:00,523 - tensorflow - INFO - global_step/sec: 88.5379 -2022-05-01 18:36:00,523 - tensorflow - INFO - loss = 0.012986472, step = 1301200 (2.259 sec) -2022-05-01 18:36:02,785 - tensorflow - INFO - global_step/sec: 88.4277 -2022-05-01 18:36:02,785 - tensorflow - INFO - loss = 0.078854054, step = 1301400 (2.262 sec) -2022-05-01 18:36:05,037 - tensorflow - INFO - global_step/sec: 88.801 -2022-05-01 18:36:05,037 - tensorflow - INFO - loss = 0.11142617, step = 1301600 (2.252 sec) -2022-05-01 18:36:07,299 - tensorflow - INFO - global_step/sec: 88.4314 -2022-05-01 18:36:07,299 - tensorflow - INFO - loss = 0.04029717, step = 1301800 (2.262 sec) -2022-05-01 18:36:09,551 - tensorflow - INFO - global_step/sec: 88.7994 -2022-05-01 18:36:09,551 - tensorflow - INFO - loss = 0.014742036, step = 1302000 (2.252 sec) -2022-05-01 18:36:11,808 - tensorflow - INFO - global_step/sec: 88.6242 -2022-05-01 18:36:11,808 - tensorflow - INFO - loss = 0.015955841, step = 1302200 (2.257 sec) -2022-05-01 18:36:14,065 - tensorflow - INFO - global_step/sec: 88.6135 -2022-05-01 18:36:14,065 - tensorflow - INFO - loss = 0.013542844, step = 1302400 (2.257 sec) -2022-05-01 18:36:16,323 - tensorflow - INFO - global_step/sec: 88.5507 -2022-05-01 18:36:16,324 - tensorflow - INFO - loss = 0.26178345, step = 1302600 (2.259 sec) -2022-05-01 18:36:18,576 - tensorflow - INFO - global_step/sec: 88.7931 -2022-05-01 18:36:18,576 - tensorflow - INFO - loss = 0.03694377, step = 1302800 (2.252 sec) -2022-05-01 18:36:20,827 - tensorflow - INFO - global_step/sec: 88.8155 -2022-05-01 18:36:20,828 - tensorflow - INFO - loss = 0.15881334, step = 1303000 (2.252 sec) -2022-05-01 18:36:23,081 - tensorflow - INFO - global_step/sec: 88.7524 -2022-05-01 18:36:23,081 - tensorflow - INFO - loss = 0.016012246, step = 1303200 (2.254 sec) -2022-05-01 18:36:25,341 - tensorflow - INFO - global_step/sec: 88.4896 -2022-05-01 18:36:25,342 - tensorflow - INFO - loss = 0.0563306, step = 1303400 (2.260 sec) -2022-05-01 18:36:27,600 - tensorflow - INFO - global_step/sec: 88.5233 -2022-05-01 18:36:27,601 - tensorflow - INFO - loss = 0.00582133, step = 1303600 (2.259 sec) -2022-05-01 18:36:29,857 - tensorflow - INFO - global_step/sec: 88.6167 -2022-05-01 18:36:29,858 - tensorflow - INFO - loss = 0.018234067, step = 1303800 (2.257 sec) -2022-05-01 18:36:32,120 - tensorflow - INFO - global_step/sec: 88.3989 -2022-05-01 18:36:32,120 - tensorflow - INFO - loss = 0.020625934, step = 1304000 (2.262 sec) -2022-05-01 18:36:34,377 - tensorflow - INFO - global_step/sec: 88.6045 -2022-05-01 18:36:34,377 - tensorflow - INFO - loss = 0.03280647, step = 1304200 (2.257 sec) -2022-05-01 18:36:36,635 - tensorflow - INFO - global_step/sec: 88.5891 -2022-05-01 18:36:36,635 - tensorflow - INFO - loss = 0.024327138, step = 1304400 (2.258 sec) -2022-05-01 18:36:38,889 - tensorflow - INFO - global_step/sec: 88.7012 -2022-05-01 18:36:38,890 - tensorflow - INFO - loss = 0.029814724, step = 1304600 (2.255 sec) -2022-05-01 18:36:41,148 - tensorflow - INFO - global_step/sec: 88.5416 -2022-05-01 18:36:41,149 - tensorflow - INFO - loss = 0.5983901, step = 1304800 (2.259 sec) -2022-05-01 18:36:43,405 - tensorflow - INFO - global_step/sec: 88.611 -2022-05-01 18:36:43,406 - tensorflow - INFO - loss = 0.0073133297, step = 1305000 (2.257 sec) -2022-05-01 18:36:45,661 - tensorflow - 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tensorflow - INFO - global_step/sec: 88.6336 -2022-05-01 18:41:52,459 - tensorflow - INFO - loss = 0.85027575, step = 1332400 (2.257 sec) -2022-05-01 18:41:54,719 - tensorflow - INFO - global_step/sec: 88.5084 -2022-05-01 18:41:54,719 - tensorflow - INFO - loss = 0.010502579, step = 1332600 (2.260 sec) -2022-05-01 18:41:56,973 - tensorflow - INFO - global_step/sec: 88.7171 -2022-05-01 18:41:56,973 - tensorflow - INFO - loss = 0.054889064, step = 1332800 (2.254 sec) -2022-05-01 18:41:59,221 - tensorflow - INFO - global_step/sec: 88.9608 -2022-05-01 18:41:59,221 - tensorflow - INFO - loss = 0.013105155, step = 1333000 (2.248 sec) -2022-05-01 18:42:01,472 - tensorflow - INFO - global_step/sec: 88.8684 -2022-05-01 18:42:01,472 - tensorflow - INFO - loss = 0.14711675, step = 1333200 (2.251 sec) -2022-05-01 18:42:03,726 - tensorflow - INFO - global_step/sec: 88.7069 -2022-05-01 18:42:03,727 - tensorflow - INFO - loss = 0.013106503, step = 1333400 (2.255 sec) -2022-05-01 18:42:05,984 - tensorflow - INFO - global_step/sec: 88.5852 -2022-05-01 18:42:05,984 - tensorflow - INFO - loss = 0.013921598, step = 1333600 (2.258 sec) -2022-05-01 18:42:08,240 - tensorflow - INFO - global_step/sec: 88.6409 -2022-05-01 18:42:08,241 - tensorflow - INFO - loss = 0.018557567, step = 1333800 (2.256 sec) -2022-05-01 18:42:10,494 - tensorflow - INFO - global_step/sec: 88.7404 -2022-05-01 18:42:10,494 - tensorflow - INFO - loss = 0.048707053, step = 1334000 (2.254 sec) -2022-05-01 18:42:12,745 - tensorflow - INFO - global_step/sec: 88.8631 -2022-05-01 18:42:12,745 - tensorflow - INFO - loss = 0.071592085, step = 1334200 (2.251 sec) -2022-05-01 18:42:14,997 - tensorflow - INFO - global_step/sec: 88.8115 -2022-05-01 18:42:14,997 - tensorflow - INFO - loss = 0.036467098, step = 1334400 (2.252 sec) -2022-05-01 18:42:17,246 - tensorflow - INFO - global_step/sec: 88.8944 -2022-05-01 18:42:17,247 - tensorflow - INFO - loss = 0.011111818, step = 1334600 (2.250 sec) -2022-05-01 18:42:19,505 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.8989 -2022-05-01 18:43:02,334 - tensorflow - INFO - loss = 0.57656294, step = 1338600 (2.250 sec) -2022-05-01 18:43:04,588 - tensorflow - INFO - global_step/sec: 88.7255 -2022-05-01 18:43:04,589 - tensorflow - INFO - loss = 0.022323834, step = 1338800 (2.254 sec) -2022-05-01 18:43:06,844 - tensorflow - INFO - global_step/sec: 88.645 -2022-05-01 18:43:06,845 - tensorflow - INFO - loss = 0.030625707, step = 1339000 (2.256 sec) -2022-05-01 18:43:09,105 - tensorflow - INFO - global_step/sec: 88.4828 -2022-05-01 18:43:09,105 - tensorflow - INFO - loss = 0.105094716, step = 1339200 (2.260 sec) -2022-05-01 18:43:11,352 - tensorflow - INFO - global_step/sec: 88.9951 -2022-05-01 18:43:11,352 - tensorflow - INFO - loss = 0.08304466, step = 1339400 (2.247 sec) -2022-05-01 18:43:13,604 - tensorflow - INFO - global_step/sec: 88.8167 -2022-05-01 18:43:13,604 - tensorflow - INFO - loss = 0.1481662, step = 1339600 (2.252 sec) -2022-05-01 18:43:15,852 - tensorflow - INFO - global_step/sec: 88.9591 -2022-05-01 18:43:15,853 - tensorflow - INFO - loss = 0.041512046, step = 1339800 (2.248 sec) -2022-05-01 18:43:18,102 - tensorflow - INFO - global_step/sec: 88.8808 -2022-05-01 18:43:18,103 - tensorflow - INFO - loss = 0.0433067, step = 1340000 (2.250 sec) -2022-05-01 18:43:20,353 - tensorflow - INFO - global_step/sec: 88.845 -2022-05-01 18:43:20,354 - tensorflow - INFO - loss = 0.044409916, step = 1340200 (2.251 sec) -2022-05-01 18:43:22,612 - tensorflow - INFO - global_step/sec: 88.5381 -2022-05-01 18:43:22,613 - tensorflow - INFO - loss = 0.018954748, step = 1340400 (2.259 sec) -2022-05-01 18:43:24,870 - tensorflow - INFO - global_step/sec: 88.5734 -2022-05-01 18:43:24,871 - tensorflow - INFO - loss = 0.008331417, step = 1340600 (2.258 sec) -2022-05-01 18:43:27,123 - tensorflow - INFO - global_step/sec: 88.7859 -2022-05-01 18:43:27,123 - tensorflow - INFO - loss = 0.011661217, step = 1340800 (2.253 sec) -2022-05-01 18:43:29,376 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.011366306, step = 1381800 (2.252 sec) -2022-05-01 18:51:11,692 - tensorflow - INFO - global_step/sec: 88.831 -2022-05-01 18:51:11,693 - tensorflow - INFO - loss = 0.016377091, step = 1382000 (2.251 sec) -2022-05-01 18:51:13,945 - tensorflow - INFO - global_step/sec: 88.77 -2022-05-01 18:51:13,946 - tensorflow - INFO - loss = 0.026458615, step = 1382200 (2.253 sec) -2022-05-01 18:51:16,201 - tensorflow - INFO - global_step/sec: 88.655 -2022-05-01 18:51:16,202 - tensorflow - INFO - loss = 0.009050167, step = 1382400 (2.256 sec) -2022-05-01 18:51:18,461 - tensorflow - INFO - global_step/sec: 88.514 -2022-05-01 18:51:18,461 - tensorflow - INFO - loss = 0.01643644, step = 1382600 (2.260 sec) -2022-05-01 18:51:20,716 - tensorflow - INFO - global_step/sec: 88.6844 -2022-05-01 18:51:20,717 - tensorflow - INFO - loss = 0.048719812, step = 1382800 (2.255 sec) -2022-05-01 18:51:22,974 - tensorflow - INFO - global_step/sec: 88.5929 -2022-05-01 18:51:22,974 - tensorflow - INFO - loss = 0.065892175, step = 1383000 (2.257 sec) -2022-05-01 18:51:25,233 - tensorflow - INFO - global_step/sec: 88.5119 -2022-05-01 18:51:25,234 - tensorflow - INFO - loss = 0.023639807, step = 1383200 (2.260 sec) -2022-05-01 18:51:27,491 - tensorflow - INFO - global_step/sec: 88.5873 -2022-05-01 18:51:27,491 - tensorflow - INFO - loss = 0.09788959, step = 1383400 (2.258 sec) -2022-05-01 18:51:29,749 - tensorflow - INFO - global_step/sec: 88.5779 -2022-05-01 18:51:29,749 - tensorflow - INFO - loss = 0.31161013, step = 1383600 (2.258 sec) -2022-05-01 18:51:32,014 - tensorflow - INFO - global_step/sec: 88.2782 -2022-05-01 18:51:32,015 - tensorflow - INFO - loss = 0.018368604, step = 1383800 (2.266 sec) -2022-05-01 18:51:34,270 - tensorflow - INFO - global_step/sec: 88.6773 -2022-05-01 18:51:34,270 - tensorflow - INFO - loss = 0.03938148, step = 1384000 (2.255 sec) -2022-05-01 18:51:36,528 - tensorflow - INFO - global_step/sec: 88.5622 -2022-05-01 18:51:36,528 - tensorflow - INFO - loss = 0.023210814, step = 1384200 (2.258 sec) -2022-05-01 18:51:38,778 - tensorflow - INFO - global_step/sec: 88.8731 -2022-05-01 18:51:38,779 - tensorflow - INFO - loss = 0.009314157, step = 1384400 (2.250 sec) -2022-05-01 18:51:41,030 - tensorflow - INFO - global_step/sec: 88.8453 -2022-05-01 18:51:41,030 - tensorflow - INFO - loss = 0.014635728, step = 1384600 (2.251 sec) -2022-05-01 18:51:43,289 - tensorflow - INFO - global_step/sec: 88.5026 -2022-05-01 18:51:43,290 - tensorflow - INFO - loss = 0.03272478, step = 1384800 (2.260 sec) -2022-05-01 18:51:45,542 - tensorflow - INFO - global_step/sec: 88.7836 -2022-05-01 18:51:45,542 - tensorflow - INFO - loss = 0.17609331, step = 1385000 (2.253 sec) -2022-05-01 18:51:47,805 - tensorflow - INFO - global_step/sec: 88.3729 -2022-05-01 18:51:47,806 - tensorflow - INFO - loss = 0.15155299, step = 1385200 (2.263 sec) -2022-05-01 18:51:50,070 - tensorflow - INFO - global_step/sec: 88.3249 -2022-05-01 18:51:50,070 - tensorflow - INFO - loss = 0.0067518665, step = 1385400 (2.264 sec) -2022-05-01 18:51:52,329 - tensorflow - INFO - global_step/sec: 88.5203 -2022-05-01 18:51:52,329 - tensorflow - INFO - loss = 0.0189787, step = 1385600 (2.259 sec) -2022-05-01 18:51:54,587 - tensorflow - INFO - global_step/sec: 88.5633 -2022-05-01 18:51:54,588 - tensorflow - INFO - loss = 0.02249827, step = 1385800 (2.258 sec) -2022-05-01 18:51:56,853 - tensorflow - INFO - global_step/sec: 88.2833 -2022-05-01 18:51:56,853 - tensorflow - INFO - loss = 0.013075041, step = 1386000 (2.265 sec) -2022-05-01 18:51:59,116 - tensorflow - INFO - global_step/sec: 88.3614 -2022-05-01 18:51:59,116 - tensorflow - INFO - loss = 0.008851925, step = 1386200 (2.263 sec) -2022-05-01 18:52:01,375 - tensorflow - INFO - global_step/sec: 88.5203 -2022-05-01 18:52:01,376 - tensorflow - INFO - loss = 0.109118104, step = 1386400 (2.259 sec) -2022-05-01 18:52:03,636 - tensorflow - INFO - global_step/sec: 88.4737 -2022-05-01 18:52:03,636 - tensorflow - INFO - loss = 0.011091237, step = 1386600 (2.261 sec) -2022-05-01 18:52:05,895 - tensorflow - INFO - global_step/sec: 88.5509 -2022-05-01 18:52:05,895 - tensorflow - INFO - loss = 0.023800887, step = 1386800 (2.259 sec) -2022-05-01 18:52:08,152 - tensorflow - INFO - global_step/sec: 88.6082 -2022-05-01 18:52:08,152 - tensorflow - INFO - loss = 0.011461074, step = 1387000 (2.257 sec) -2022-05-01 18:52:10,415 - tensorflow - INFO - global_step/sec: 88.3837 -2022-05-01 18:52:10,415 - tensorflow - INFO - loss = 0.027946133, step = 1387200 (2.263 sec) -2022-05-01 18:52:12,675 - tensorflow - INFO - global_step/sec: 88.4788 -2022-05-01 18:52:12,675 - tensorflow - INFO - loss = 0.028160535, step = 1387400 (2.260 sec) -2022-05-01 18:52:14,936 - tensorflow - INFO - global_step/sec: 88.4406 -2022-05-01 18:52:14,937 - tensorflow - INFO - loss = 0.037239272, step = 1387600 (2.261 sec) -2022-05-01 18:52:17,193 - tensorflow - INFO - global_step/sec: 88.6221 -2022-05-01 18:52:17,194 - tensorflow - INFO - loss = 0.009626794, step = 1387800 (2.257 sec) -2022-05-01 18:52:19,457 - tensorflow - INFO - global_step/sec: 88.3497 -2022-05-01 18:52:19,457 - tensorflow - INFO - loss = 0.0589523, step = 1388000 (2.264 sec) -2022-05-01 18:52:21,711 - tensorflow - INFO - global_step/sec: 88.7277 -2022-05-01 18:52:21,711 - tensorflow - INFO - loss = 0.069719814, step = 1388200 (2.254 sec) -2022-05-01 18:52:23,963 - tensorflow - INFO - global_step/sec: 88.8262 -2022-05-01 18:52:23,963 - tensorflow - INFO - loss = 0.014002705, step = 1388400 (2.252 sec) -2022-05-01 18:52:26,215 - tensorflow - INFO - global_step/sec: 88.7766 -2022-05-01 18:52:26,216 - tensorflow - INFO - loss = 0.04540629, step = 1388600 (2.253 sec) -2022-05-01 18:52:28,468 - tensorflow - INFO - global_step/sec: 88.7994 -2022-05-01 18:52:28,468 - tensorflow - INFO - loss = 0.02488325, step = 1388800 (2.252 sec) -2022-05-01 18:52:30,723 - tensorflow - INFO - global_step/sec: 88.6864 -2022-05-01 18:52:30,723 - tensorflow - INFO - loss = 0.012708927, step = 1389000 (2.255 sec) -2022-05-01 18:52:32,978 - tensorflow - INFO - global_step/sec: 88.6978 -2022-05-01 18:52:32,978 - tensorflow - INFO - loss = 0.015943076, step = 1389200 (2.255 sec) -2022-05-01 18:52:35,228 - tensorflow - INFO - global_step/sec: 88.8893 -2022-05-01 18:52:35,228 - tensorflow - INFO - loss = 0.20571089, step = 1389400 (2.250 sec) -2022-05-01 18:52:37,486 - tensorflow - INFO - global_step/sec: 88.5603 -2022-05-01 18:52:37,486 - tensorflow - INFO - loss = 0.05094683, step = 1389600 (2.258 sec) -2022-05-01 18:52:39,737 - tensorflow - INFO - global_step/sec: 88.8359 -2022-05-01 18:52:39,738 - tensorflow - INFO - loss = 0.007741022, step = 1389800 (2.251 sec) -2022-05-01 18:52:41,988 - tensorflow - INFO - global_step/sec: 88.845 -2022-05-01 18:52:41,989 - tensorflow - INFO - loss = 0.27258632, step = 1390000 (2.251 sec) -2022-05-01 18:52:44,242 - tensorflow - INFO - global_step/sec: 88.7345 -2022-05-01 18:52:44,243 - tensorflow - INFO - loss = 0.00971758, step = 1390200 (2.254 sec) -2022-05-01 18:52:46,495 - tensorflow - INFO - global_step/sec: 88.799 -2022-05-01 18:52:46,495 - tensorflow - INFO - loss = 0.020727014, step = 1390400 (2.252 sec) -2022-05-01 18:52:48,749 - tensorflow - INFO - global_step/sec: 88.7312 -2022-05-01 18:52:48,749 - tensorflow - INFO - loss = 0.03944362, step = 1390600 (2.254 sec) -2022-05-01 18:52:51,008 - tensorflow - INFO - global_step/sec: 88.5047 -2022-05-01 18:52:51,009 - tensorflow - INFO - loss = 0.041285716, step = 1390800 (2.260 sec) -2022-05-01 18:52:53,265 - tensorflow - INFO - global_step/sec: 88.6425 -2022-05-01 18:52:53,265 - tensorflow - INFO - loss = 0.010349192, step = 1391000 (2.256 sec) -2022-05-01 18:52:55,517 - tensorflow - INFO - global_step/sec: 88.8059 -2022-05-01 18:52:55,517 - tensorflow - INFO - loss = 0.012859952, step = 1391200 (2.252 sec) -2022-05-01 18:52:57,770 - tensorflow - INFO - global_step/sec: 88.7646 -2022-05-01 18:52:57,770 - tensorflow - INFO - loss = 0.017221091, step = 1391400 (2.253 sec) -2022-05-01 18:53:00,022 - tensorflow - INFO - global_step/sec: 88.8126 -2022-05-01 18:53:00,022 - tensorflow - INFO - loss = 0.016328134, step = 1391600 (2.252 sec) -2022-05-01 18:53:02,277 - tensorflow - INFO - global_step/sec: 88.6749 -2022-05-01 18:53:02,278 - tensorflow - INFO - loss = 0.051186584, step = 1391800 (2.255 sec) -2022-05-01 18:53:04,529 - tensorflow - INFO - global_step/sec: 88.8071 -2022-05-01 18:53:04,530 - tensorflow - INFO - loss = 0.07615837, step = 1392000 (2.252 sec) -2022-05-01 18:53:06,783 - tensorflow - INFO - global_step/sec: 88.7597 -2022-05-01 18:53:06,783 - tensorflow - INFO - loss = 0.33934033, step = 1392200 (2.253 sec) -2022-05-01 18:53:09,037 - tensorflow - INFO - global_step/sec: 88.734 -2022-05-01 18:53:09,037 - tensorflow - INFO - loss = 0.030979685, step = 1392400 (2.254 sec) -2022-05-01 18:53:11,287 - tensorflow - INFO - global_step/sec: 88.8638 -2022-05-01 18:53:11,288 - tensorflow - INFO - loss = 0.06840564, step = 1392600 (2.251 sec) -2022-05-01 18:53:13,540 - tensorflow - INFO - global_step/sec: 88.768 -2022-05-01 18:53:13,541 - tensorflow - INFO - loss = 0.06952453, step = 1392800 (2.253 sec) -2022-05-01 18:53:15,797 - tensorflow - INFO - global_step/sec: 88.6284 -2022-05-01 18:53:15,797 - tensorflow - INFO - loss = 0.08090894, step = 1393000 (2.257 sec) -2022-05-01 18:53:18,051 - tensorflow - INFO - global_step/sec: 88.7182 -2022-05-01 18:53:18,052 - tensorflow - INFO - loss = 0.049930155, step = 1393200 (2.254 sec) -2022-05-01 18:53:20,305 - tensorflow - INFO - global_step/sec: 88.7516 -2022-05-01 18:53:20,305 - tensorflow - INFO - loss = 0.007678329, step = 1393400 (2.253 sec) -2022-05-01 18:53:22,556 - tensorflow - INFO - global_step/sec: 88.844 -2022-05-01 18:53:22,556 - tensorflow - INFO - loss = 3.3933983, step = 1393600 (2.251 sec) -2022-05-01 18:53:24,812 - tensorflow - INFO - global_step/sec: 88.6503 -2022-05-01 18:53:24,812 - tensorflow - INFO - loss = 0.50099397, step = 1393800 (2.256 sec) -2022-05-01 18:53:27,063 - tensorflow - INFO - global_step/sec: 88.8246 -2022-05-01 18:53:27,064 - tensorflow - INFO - loss = 0.016014893, step = 1394000 (2.252 sec) -2022-05-01 18:53:29,320 - tensorflow - INFO - global_step/sec: 88.627 -2022-05-01 18:53:29,321 - tensorflow - INFO - loss = 0.6599004, step = 1394200 (2.257 sec) -2022-05-01 18:53:31,580 - tensorflow - INFO - global_step/sec: 88.5192 -2022-05-01 18:53:31,580 - tensorflow - INFO - loss = 0.04345376, step = 1394400 (2.259 sec) -2022-05-01 18:53:33,829 - tensorflow - INFO - global_step/sec: 88.9041 -2022-05-01 18:53:33,830 - tensorflow - INFO - loss = 0.44049037, step = 1394600 (2.250 sec) -2022-05-01 18:53:36,081 - tensorflow - INFO - global_step/sec: 88.7955 -2022-05-01 18:53:36,082 - tensorflow - INFO - loss = 0.012496112, step = 1394800 (2.252 sec) -2022-05-01 18:53:38,329 - tensorflow - INFO - global_step/sec: 88.9786 -2022-05-01 18:53:38,330 - tensorflow - INFO - loss = 0.03480921, step = 1395000 (2.248 sec) -2022-05-01 18:53:40,590 - tensorflow - INFO - global_step/sec: 88.4503 -2022-05-01 18:53:40,591 - tensorflow - INFO - loss = 0.010541502, step = 1395200 (2.261 sec) -2022-05-01 18:53:42,839 - tensorflow - INFO - global_step/sec: 88.9279 -2022-05-01 18:53:42,840 - tensorflow - INFO - loss = 0.020792527, step = 1395400 (2.249 sec) -2022-05-01 18:53:45,087 - tensorflow - INFO - global_step/sec: 88.9803 -2022-05-01 18:53:45,088 - tensorflow - INFO - loss = 0.26173452, step = 1395600 (2.248 sec) -2022-05-01 18:53:47,335 - tensorflow - INFO - global_step/sec: 88.962 -2022-05-01 18:53:47,336 - tensorflow - INFO - loss = 0.01936509, step = 1395800 (2.248 sec) -2022-05-01 18:53:49,590 - tensorflow - INFO - global_step/sec: 88.7004 -2022-05-01 18:53:49,590 - tensorflow - INFO - loss = 0.016364845, step = 1396000 (2.255 sec) -2022-05-01 18:53:51,847 - tensorflow - INFO - global_step/sec: 88.6169 -2022-05-01 18:53:51,847 - tensorflow - INFO - loss = 0.0135764945, step = 1396200 (2.257 sec) -2022-05-01 18:53:54,100 - tensorflow - INFO - global_step/sec: 88.7631 -2022-05-01 18:53:54,101 - tensorflow - INFO - loss = 0.019440118, step = 1396400 (2.253 sec) -2022-05-01 18:53:56,351 - tensorflow - INFO - global_step/sec: 88.8551 -2022-05-01 18:53:56,351 - tensorflow - INFO - loss = 0.024875076, step = 1396600 (2.251 sec) -2022-05-01 18:53:58,600 - tensorflow - INFO - global_step/sec: 88.939 -2022-05-01 18:53:58,600 - tensorflow - INFO - loss = 0.008331047, step = 1396800 (2.249 sec) -2022-05-01 18:54:00,856 - tensorflow - INFO - global_step/sec: 88.6235 -2022-05-01 18:54:00,857 - tensorflow - INFO - loss = 0.009838301, step = 1397000 (2.257 sec) -2022-05-01 18:54:03,110 - tensorflow - INFO - global_step/sec: 88.7341 -2022-05-01 18:54:03,111 - tensorflow - INFO - loss = 0.011992238, step = 1397200 (2.254 sec) -2022-05-01 18:54:05,369 - tensorflow - INFO - global_step/sec: 88.5512 -2022-05-01 18:54:05,369 - tensorflow - INFO - loss = 0.37778002, step = 1397400 (2.259 sec) -2022-05-01 18:54:07,626 - tensorflow - INFO - global_step/sec: 88.6144 -2022-05-01 18:54:07,626 - tensorflow - INFO - loss = 0.020134173, step = 1397600 (2.257 sec) -2022-05-01 18:54:09,882 - tensorflow - INFO - global_step/sec: 88.6686 -2022-05-01 18:54:09,882 - tensorflow - INFO - loss = 0.07450393, step = 1397800 (2.256 sec) -2022-05-01 18:54:12,133 - tensorflow - INFO - global_step/sec: 88.8287 -2022-05-01 18:54:12,133 - tensorflow - INFO - loss = 0.04988601, step = 1398000 (2.252 sec) -2022-05-01 18:54:14,384 - tensorflow - INFO - global_step/sec: 88.8409 -2022-05-01 18:54:14,385 - tensorflow - INFO - loss = 0.022269156, step = 1398200 (2.251 sec) -2022-05-01 18:54:16,641 - tensorflow - INFO - global_step/sec: 88.631 -2022-05-01 18:54:16,641 - tensorflow - INFO - loss = 0.025311477, step = 1398400 (2.257 sec) -2022-05-01 18:54:18,898 - tensorflow - INFO - global_step/sec: 88.5862 -2022-05-01 18:54:18,899 - tensorflow - INFO - loss = 0.43566298, step = 1398600 (2.258 sec) -2022-05-01 18:54:21,153 - tensorflow - INFO - global_step/sec: 88.7096 -2022-05-01 18:54:21,153 - tensorflow - INFO - loss = 0.011970665, step = 1398800 (2.255 sec) -2022-05-01 18:54:23,404 - tensorflow - INFO - global_step/sec: 88.8583 -2022-05-01 18:54:23,404 - tensorflow - INFO - loss = 0.060460646, step = 1399000 (2.251 sec) -2022-05-01 18:54:25,659 - tensorflow - INFO - global_step/sec: 88.698 -2022-05-01 18:54:25,659 - tensorflow - INFO - loss = 0.024868513, step = 1399200 (2.255 sec) -2022-05-01 18:54:27,915 - tensorflow - INFO - global_step/sec: 88.6522 -2022-05-01 18:54:27,915 - tensorflow - INFO - loss = 0.11175607, step = 1399400 (2.256 sec) -2022-05-01 18:54:30,173 - tensorflow - INFO - global_step/sec: 88.5635 -2022-05-01 18:54:30,173 - tensorflow - INFO - loss = 0.008654011, step = 1399600 (2.258 sec) -2022-05-01 18:54:32,427 - tensorflow - INFO - global_step/sec: 88.7413 -2022-05-01 18:54:32,427 - tensorflow - INFO - loss = 0.053722493, step = 1399800 (2.254 sec) -2022-05-01 18:54:34,666 - tensorflow - INFO - Saving checkpoints for 1400000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. -2022-05-01 18:54:34,682 - tensorflow - INFO - model.ckpt-1400000 is not in all_model_checkpoint_paths. Manually adding it. -2022-05-01 18:54:35,205 - tensorflow - INFO - Calling model_fn. -2022-05-01 18:54:35,331 - tensorflow - INFO - Done calling model_fn. -2022-05-01 18:54:35,346 - tensorflow - INFO - Starting evaluation at 2022-05-01T18:54:35Z -2022-05-01 18:54:35,409 - tensorflow - INFO - Graph was finalized. -2022-05-01 18:54:35,412 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1400000 -2022-05-01 18:54:35,695 - tensorflow - INFO - Running local_init_op. -2022-05-01 18:54:35,802 - tensorflow - INFO - Done running local_init_op. -2022-05-01 18:54:45,951 - tensorflow - INFO - Evaluation [3990/39900] -2022-05-01 18:54:55,477 - tensorflow - INFO - Evaluation [7980/39900] -2022-05-01 18:55:04,953 - tensorflow - INFO - Evaluation [11970/39900] -2022-05-01 18:55:14,432 - tensorflow - INFO - Evaluation [15960/39900] -2022-05-01 18:55:23,916 - tensorflow - INFO - Evaluation [19950/39900] -2022-05-01 18:55:33,443 - tensorflow - INFO - Evaluation [23940/39900] -2022-05-01 18:55:42,942 - tensorflow - INFO - Evaluation [27930/39900] -2022-05-01 18:55:52,441 - tensorflow - INFO - Evaluation [31920/39900] -2022-05-01 18:56:01,914 - tensorflow - INFO - Evaluation [35910/39900] -2022-05-01 18:56:11,413 - tensorflow - INFO - Evaluation [39900/39900] -2022-05-01 18:56:11,550 - tensorflow - INFO - Finished evaluation at 2022-05-01-18:56:11 -2022-05-01 18:56:11,550 - tensorflow - INFO - Saving dict for global step 1400000: global_step = 1400000, loss = 0.022675576, mean_degree_err = 4.711262 -2022-05-01 18:56:11,551 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 1400000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1400000 -2022-05-01 18:56:11,564 - tensorflow - INFO - global_step/sec: 2.01739 -2022-05-01 18:56:11,565 - tensorflow - INFO - loss = 1.4852409, step = 1400000 (99.138 sec) -2022-05-01 18:56:13,836 - tensorflow - INFO - global_step/sec: 88.0279 -2022-05-01 18:56:13,837 - tensorflow - INFO - loss = 0.014152582, step = 1400200 (2.272 sec) -2022-05-01 18:56:16,096 - tensorflow - INFO - global_step/sec: 88.4963 -2022-05-01 18:56:16,097 - tensorflow - INFO - loss = 0.034330446, step = 1400400 (2.260 sec) -2022-05-01 18:56:18,349 - tensorflow - INFO - global_step/sec: 88.7976 -2022-05-01 18:56:18,349 - tensorflow - INFO - loss = 0.03530475, step = 1400600 (2.252 sec) -2022-05-01 18:56:20,604 - tensorflow - INFO - global_step/sec: 88.694 -2022-05-01 18:56:20,604 - tensorflow - INFO - loss = 0.029222818, step = 1400800 (2.255 sec) -2022-05-01 18:56:22,862 - tensorflow - INFO - global_step/sec: 88.5766 -2022-05-01 18:56:22,862 - tensorflow - INFO - loss = 0.11862981, step = 1401000 (2.258 sec) -2022-05-01 18:56:25,120 - tensorflow - INFO - global_step/sec: 88.5508 -2022-05-01 18:56:25,121 - tensorflow - INFO - loss = 0.037320793, step = 1401200 (2.259 sec) -2022-05-01 18:56:27,376 - tensorflow - INFO - global_step/sec: 88.6684 -2022-05-01 18:56:27,376 - tensorflow - INFO - loss = 0.3678997, step = 1401400 (2.256 sec) -2022-05-01 18:56:29,631 - tensorflow - INFO - global_step/sec: 88.6882 -2022-05-01 18:56:29,631 - tensorflow - INFO - loss = 0.16894914, step = 1401600 (2.255 sec) -2022-05-01 18:56:31,899 - tensorflow - INFO - global_step/sec: 88.1598 -2022-05-01 18:56:31,900 - tensorflow - INFO - loss = 0.02190847, step = 1401800 (2.269 sec) -2022-05-01 18:56:34,158 - tensorflow - INFO - global_step/sec: 88.5499 -2022-05-01 18:56:34,158 - tensorflow - INFO - loss = 0.0119854715, step = 1402000 (2.259 sec) -2022-05-01 18:56:36,409 - tensorflow - INFO - global_step/sec: 88.8583 -2022-05-01 18:56:36,409 - tensorflow - INFO - loss = 0.020928359, step = 1402200 (2.251 sec) -2022-05-01 18:56:38,663 - tensorflow - INFO - global_step/sec: 88.7287 -2022-05-01 18:56:38,663 - tensorflow - INFO - loss = 0.01655328, step = 1402400 (2.254 sec) -2022-05-01 18:56:40,918 - tensorflow - INFO - global_step/sec: 88.7054 -2022-05-01 18:56:40,918 - tensorflow - INFO - loss = 0.012418087, step = 1402600 (2.255 sec) -2022-05-01 18:56:43,171 - 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INFO - global_step/sec: 88.7635 -2022-05-01 19:02:03,385 - tensorflow - INFO - loss = 0.024886461, step = 1431200 (2.253 sec) -2022-05-01 19:02:05,642 - tensorflow - INFO - global_step/sec: 88.5942 -2022-05-01 19:02:05,643 - tensorflow - INFO - loss = 0.021281244, step = 1431400 (2.258 sec) -2022-05-01 19:02:07,897 - tensorflow - INFO - global_step/sec: 88.7173 -2022-05-01 19:02:07,897 - tensorflow - INFO - loss = 0.057097122, step = 1431600 (2.254 sec) -2022-05-01 19:02:10,154 - tensorflow - INFO - global_step/sec: 88.6021 -2022-05-01 19:02:10,154 - tensorflow - INFO - loss = 0.06532149, step = 1431800 (2.257 sec) -2022-05-01 19:02:12,405 - tensorflow - INFO - global_step/sec: 88.8382 -2022-05-01 19:02:12,406 - tensorflow - INFO - loss = 0.032092087, step = 1432000 (2.251 sec) -2022-05-01 19:02:14,660 - tensorflow - INFO - global_step/sec: 88.7057 -2022-05-01 19:02:14,660 - tensorflow - INFO - loss = 0.065582216, step = 1432200 (2.255 sec) -2022-05-01 19:02:16,914 - tensorflow - INFO - 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INFO - loss = 0.016357116, step = 1480600 (2.256 sec) -2022-05-01 19:11:22,857 - tensorflow - INFO - global_step/sec: 88.592 -2022-05-01 19:11:22,857 - tensorflow - INFO - loss = 0.021436311, step = 1480800 (2.258 sec) -2022-05-01 19:11:25,112 - tensorflow - INFO - global_step/sec: 88.6889 -2022-05-01 19:11:25,112 - tensorflow - INFO - loss = 0.0068689487, step = 1481000 (2.255 sec) -2022-05-01 19:11:27,374 - tensorflow - INFO - global_step/sec: 88.3918 -2022-05-01 19:11:27,375 - tensorflow - INFO - loss = 0.10025742, step = 1481200 (2.263 sec) -2022-05-01 19:11:29,627 - tensorflow - INFO - global_step/sec: 88.7898 -2022-05-01 19:11:29,627 - tensorflow - INFO - loss = 0.019031854, step = 1481400 (2.252 sec) -2022-05-01 19:11:31,890 - tensorflow - INFO - global_step/sec: 88.3739 -2022-05-01 19:11:31,891 - tensorflow - INFO - loss = 0.116369985, step = 1481600 (2.263 sec) -2022-05-01 19:11:34,149 - tensorflow - INFO - global_step/sec: 88.5285 -2022-05-01 19:11:34,150 - tensorflow - INFO - loss = 1.6864299, step = 1481800 (2.259 sec) -2022-05-01 19:11:36,404 - tensorflow - INFO - global_step/sec: 88.6846 -2022-05-01 19:11:36,405 - tensorflow - INFO - loss = 0.38291132, step = 1482000 (2.255 sec) -2022-05-01 19:11:38,658 - tensorflow - INFO - global_step/sec: 88.7496 -2022-05-01 19:11:38,658 - tensorflow - INFO - loss = 0.02460587, step = 1482200 (2.254 sec) -2022-05-01 19:11:40,915 - tensorflow - INFO - global_step/sec: 88.6252 -2022-05-01 19:11:40,915 - tensorflow - INFO - loss = 0.012626898, step = 1482400 (2.257 sec) -2022-05-01 19:11:43,167 - tensorflow - INFO - global_step/sec: 88.8111 -2022-05-01 19:11:43,167 - tensorflow - INFO - loss = 0.047014453, step = 1482600 (2.252 sec) -2022-05-01 19:11:45,420 - tensorflow - INFO - global_step/sec: 88.7618 -2022-05-01 19:11:45,420 - tensorflow - INFO - loss = 0.027172279, step = 1482800 (2.253 sec) -2022-05-01 19:11:47,670 - tensorflow - INFO - global_step/sec: 88.9013 -2022-05-01 19:11:47,670 - tensorflow - INFO - loss = 0.042673394, step = 1483000 (2.250 sec) -2022-05-01 19:11:49,921 - tensorflow - INFO - global_step/sec: 88.8368 -2022-05-01 19:11:49,921 - tensorflow - INFO - loss = 0.01118323, step = 1483200 (2.251 sec) -2022-05-01 19:11:52,175 - tensorflow - INFO - global_step/sec: 88.7255 -2022-05-01 19:11:52,175 - tensorflow - INFO - loss = 0.009533244, step = 1483400 (2.254 sec) -2022-05-01 19:11:54,432 - tensorflow - INFO - global_step/sec: 88.6159 -2022-05-01 19:11:54,432 - tensorflow - INFO - loss = 0.038333315, step = 1483600 (2.257 sec) -2022-05-01 19:11:56,686 - tensorflow - INFO - global_step/sec: 88.709 -2022-05-01 19:11:56,687 - tensorflow - INFO - loss = 0.017258832, step = 1483800 (2.255 sec) -2022-05-01 19:11:58,942 - tensorflow - INFO - global_step/sec: 88.6808 -2022-05-01 19:11:58,942 - tensorflow - INFO - loss = 0.023304895, step = 1484000 (2.255 sec) -2022-05-01 19:12:01,199 - tensorflow - INFO - global_step/sec: 88.6191 -2022-05-01 19:12:01,199 - tensorflow - INFO - loss = 0.016935058, step = 1484200 (2.257 sec) -2022-05-01 19:12:03,454 - tensorflow - INFO - global_step/sec: 88.6904 -2022-05-01 19:12:03,454 - tensorflow - INFO - loss = 0.017758131, step = 1484400 (2.255 sec) -2022-05-01 19:12:05,713 - tensorflow - INFO - global_step/sec: 88.5057 -2022-05-01 19:12:05,714 - tensorflow - INFO - loss = 0.012710876, step = 1484600 (2.260 sec) -2022-05-01 19:12:07,969 - tensorflow - INFO - global_step/sec: 88.6751 -2022-05-01 19:12:07,969 - tensorflow - INFO - loss = 0.01151262, step = 1484800 (2.255 sec) -2022-05-01 19:12:10,225 - tensorflow - INFO - global_step/sec: 88.6448 -2022-05-01 19:12:10,225 - tensorflow - INFO - loss = 0.018936135, step = 1485000 (2.256 sec) -2022-05-01 19:12:12,478 - tensorflow - INFO - global_step/sec: 88.7749 -2022-05-01 19:12:12,478 - tensorflow - INFO - loss = 0.012820639, step = 1485200 (2.253 sec) -2022-05-01 19:12:14,732 - tensorflow - INFO - global_step/sec: 88.7397 -2022-05-01 19:12:14,732 - tensorflow - INFO - loss = 0.018086525, step = 1485400 (2.254 sec) -2022-05-01 19:12:16,987 - tensorflow - INFO - global_step/sec: 88.6785 -2022-05-01 19:12:16,987 - tensorflow - INFO - loss = 0.09597358, step = 1485600 (2.255 sec) -2022-05-01 19:12:19,241 - tensorflow - INFO - global_step/sec: 88.7245 -2022-05-01 19:12:19,242 - tensorflow - INFO - loss = 0.012371361, step = 1485800 (2.254 sec) -2022-05-01 19:12:21,495 - tensorflow - INFO - global_step/sec: 88.7441 -2022-05-01 19:12:21,495 - tensorflow - INFO - loss = 0.01012472, step = 1486000 (2.254 sec) -2022-05-01 19:12:23,750 - tensorflow - INFO - global_step/sec: 88.6763 -2022-05-01 19:12:23,751 - tensorflow - INFO - loss = 0.019150168, step = 1486200 (2.255 sec) -2022-05-01 19:12:26,003 - tensorflow - INFO - global_step/sec: 88.7715 -2022-05-01 19:12:26,004 - tensorflow - INFO - loss = 0.0027024292, step = 1486400 (2.253 sec) -2022-05-01 19:12:28,254 - tensorflow - INFO - global_step/sec: 88.8734 -2022-05-01 19:12:28,254 - tensorflow - INFO - loss = 0.0192898, step = 1486600 (2.250 sec) -2022-05-01 19:12:30,519 - tensorflow - INFO - global_step/sec: 88.291 -2022-05-01 19:12:30,519 - tensorflow - INFO - loss = 0.01304977, step = 1486800 (2.265 sec) -2022-05-01 19:12:32,773 - tensorflow - INFO - global_step/sec: 88.7179 -2022-05-01 19:12:32,774 - tensorflow - INFO - loss = 0.026753342, step = 1487000 (2.254 sec) -2022-05-01 19:12:35,024 - tensorflow - INFO - global_step/sec: 88.841 -2022-05-01 19:12:35,025 - tensorflow - INFO - loss = 0.016424507, step = 1487200 (2.251 sec) -2022-05-01 19:12:37,280 - tensorflow - INFO - global_step/sec: 88.6709 -2022-05-01 19:12:37,280 - tensorflow - INFO - loss = 0.11279587, step = 1487400 (2.256 sec) -2022-05-01 19:12:39,536 - tensorflow - INFO - global_step/sec: 88.6624 -2022-05-01 19:12:39,536 - tensorflow - INFO - loss = 0.11770095, step = 1487600 (2.256 sec) -2022-05-01 19:12:41,788 - tensorflow - INFO - global_step/sec: 88.806 -2022-05-01 19:12:41,788 - tensorflow - INFO - loss = 0.054372743, step = 1487800 (2.252 sec) -2022-05-01 19:12:44,047 - tensorflow - INFO - global_step/sec: 88.5252 -2022-05-01 19:12:44,047 - tensorflow - INFO - loss = 4.644326, step = 1488000 (2.259 sec) -2022-05-01 19:12:46,298 - tensorflow - INFO - global_step/sec: 88.8402 -2022-05-01 19:12:46,299 - tensorflow - INFO - loss = 0.033585217, step = 1488200 (2.251 sec) -2022-05-01 19:12:48,552 - tensorflow - INFO - global_step/sec: 88.757 -2022-05-01 19:12:48,552 - tensorflow - INFO - loss = 0.011081589, step = 1488400 (2.253 sec) -2022-05-01 19:12:50,807 - tensorflow - INFO - global_step/sec: 88.6627 -2022-05-01 19:12:50,808 - tensorflow - INFO - loss = 0.011284853, step = 1488600 (2.256 sec) -2022-05-01 19:12:53,062 - tensorflow - INFO - global_step/sec: 88.698 -2022-05-01 19:12:53,063 - tensorflow - INFO - loss = 0.0142420065, step = 1488800 (2.255 sec) -2022-05-01 19:12:55,319 - tensorflow - INFO - global_step/sec: 88.6163 -2022-05-01 19:12:55,320 - tensorflow - INFO - loss = 0.009824946, step = 1489000 (2.257 sec) -2022-05-01 19:12:57,577 - tensorflow - INFO - global_step/sec: 88.5776 -2022-05-01 19:12:57,577 - tensorflow - INFO - loss = 0.043002818, step = 1489200 (2.258 sec) -2022-05-01 19:12:59,827 - tensorflow - INFO - global_step/sec: 88.8722 -2022-05-01 19:12:59,828 - tensorflow - INFO - loss = 0.04212212, step = 1489400 (2.250 sec) -2022-05-01 19:13:02,081 - tensorflow - INFO - global_step/sec: 88.7289 -2022-05-01 19:13:02,082 - tensorflow - INFO - loss = 0.076076016, step = 1489600 (2.254 sec) -2022-05-01 19:13:04,337 - tensorflow - INFO - global_step/sec: 88.6653 -2022-05-01 19:13:04,338 - tensorflow - INFO - loss = 0.018436994, step = 1489800 (2.256 sec) -2022-05-01 19:13:06,593 - tensorflow - INFO - global_step/sec: 88.6461 -2022-05-01 19:13:06,594 - tensorflow - INFO - loss = 0.03337249, step = 1490000 (2.256 sec) -2022-05-01 19:13:08,851 - tensorflow - INFO - global_step/sec: 88.5903 -2022-05-01 19:13:08,851 - tensorflow - INFO - loss = 0.0954804, step = 1490200 (2.258 sec) -2022-05-01 19:13:11,115 - tensorflow - INFO - global_step/sec: 88.326 -2022-05-01 19:13:11,116 - tensorflow - INFO - loss = 0.038690947, step = 1490400 (2.264 sec) -2022-05-01 19:13:13,376 - tensorflow - INFO - global_step/sec: 88.4515 -2022-05-01 19:13:13,377 - tensorflow - INFO - loss = 0.022575928, step = 1490600 (2.261 sec) -2022-05-01 19:13:15,636 - tensorflow - INFO - global_step/sec: 88.5299 -2022-05-01 19:13:15,636 - tensorflow - INFO - loss = 0.04049495, step = 1490800 (2.259 sec) -2022-05-01 19:13:17,892 - tensorflow - INFO - global_step/sec: 88.6267 -2022-05-01 19:13:17,893 - tensorflow - INFO - loss = 0.053369366, step = 1491000 (2.257 sec) -2022-05-01 19:13:20,155 - tensorflow - INFO - global_step/sec: 88.3868 -2022-05-01 19:13:20,155 - tensorflow - INFO - loss = 0.0073466636, step = 1491200 (2.263 sec) -2022-05-01 19:13:22,417 - tensorflow - INFO - global_step/sec: 88.4281 -2022-05-01 19:13:22,417 - tensorflow - INFO - loss = 0.01530133, step = 1491400 (2.262 sec) -2022-05-01 19:13:24,679 - tensorflow - INFO - global_step/sec: 88.3877 -2022-05-01 19:13:24,680 - tensorflow - INFO - loss = 0.040279463, step = 1491600 (2.263 sec) -2022-05-01 19:13:26,939 - tensorflow - INFO - global_step/sec: 88.5227 -2022-05-01 19:13:26,939 - tensorflow - INFO - loss = 0.019282173, step = 1491800 (2.259 sec) -2022-05-01 19:13:29,198 - tensorflow - INFO - global_step/sec: 88.533 -2022-05-01 19:13:29,198 - tensorflow - INFO - loss = 0.008762198, step = 1492000 (2.259 sec) -2022-05-01 19:13:31,468 - tensorflow - INFO - global_step/sec: 88.1147 -2022-05-01 19:13:31,468 - tensorflow - INFO - loss = 0.56280273, step = 1492200 (2.270 sec) -2022-05-01 19:13:33,724 - tensorflow - INFO - global_step/sec: 88.644 -2022-05-01 19:13:33,724 - tensorflow - INFO - loss = 0.019971997, step = 1492400 (2.256 sec) -2022-05-01 19:13:35,979 - tensorflow - INFO - global_step/sec: 88.6862 -2022-05-01 19:13:35,979 - tensorflow - INFO - loss = 0.017949276, step = 1492600 (2.255 sec) -2022-05-01 19:13:38,232 - tensorflow - INFO - global_step/sec: 88.7758 -2022-05-01 19:13:38,232 - tensorflow - INFO - loss = 0.024088422, step = 1492800 (2.253 sec) -2022-05-01 19:13:40,482 - tensorflow - INFO - global_step/sec: 88.8973 -2022-05-01 19:13:40,482 - tensorflow - INFO - loss = 0.01847333, step = 1493000 (2.250 sec) -2022-05-01 19:13:42,738 - tensorflow - INFO - global_step/sec: 88.6168 -2022-05-01 19:13:42,739 - tensorflow - INFO - loss = 0.0061604944, step = 1493200 (2.257 sec) -2022-05-01 19:13:44,991 - tensorflow - INFO - global_step/sec: 88.8081 -2022-05-01 19:13:44,991 - tensorflow - INFO - loss = 0.031813048, step = 1493400 (2.252 sec) -2022-05-01 19:13:47,246 - tensorflow - INFO - global_step/sec: 88.6719 -2022-05-01 19:13:47,246 - tensorflow - INFO - loss = 0.011895972, step = 1493600 (2.255 sec) -2022-05-01 19:13:49,505 - tensorflow - INFO - global_step/sec: 88.5243 -2022-05-01 19:13:49,506 - tensorflow - INFO - loss = 0.03530208, step = 1493800 (2.259 sec) -2022-05-01 19:13:51,757 - tensorflow - INFO - global_step/sec: 88.8307 -2022-05-01 19:13:51,757 - tensorflow - INFO - loss = 0.042276334, step = 1494000 (2.251 sec) -2022-05-01 19:13:54,003 - tensorflow - INFO - global_step/sec: 89.0302 -2022-05-01 19:13:54,004 - tensorflow - INFO - loss = 0.05104211, step = 1494200 (2.246 sec) -2022-05-01 19:13:56,255 - tensorflow - INFO - global_step/sec: 88.829 -2022-05-01 19:13:56,255 - tensorflow - INFO - loss = 0.044423785, step = 1494400 (2.251 sec) -2022-05-01 19:13:58,512 - tensorflow - INFO - global_step/sec: 88.6044 -2022-05-01 19:13:58,512 - tensorflow - INFO - loss = 0.020210141, step = 1494600 (2.257 sec) -2022-05-01 19:14:00,765 - tensorflow - INFO - global_step/sec: 88.7685 -2022-05-01 19:14:00,765 - tensorflow - INFO - loss = 0.045281194, step = 1494800 (2.253 sec) -2022-05-01 19:14:03,026 - tensorflow - INFO - global_step/sec: 88.4692 -2022-05-01 19:14:03,026 - tensorflow - INFO - loss = 0.015739895, step = 1495000 (2.261 sec) -2022-05-01 19:14:05,279 - tensorflow - INFO - global_step/sec: 88.7514 -2022-05-01 19:14:05,280 - tensorflow - INFO - loss = 0.010020976, step = 1495200 (2.254 sec) -2022-05-01 19:14:07,533 - tensorflow - INFO - global_step/sec: 88.7238 -2022-05-01 19:14:07,534 - tensorflow - INFO - loss = 0.01750209, step = 1495400 (2.254 sec) -2022-05-01 19:14:09,787 - tensorflow - INFO - global_step/sec: 88.7341 -2022-05-01 19:14:09,788 - tensorflow - INFO - loss = 0.06548672, step = 1495600 (2.254 sec) -2022-05-01 19:14:12,040 - tensorflow - INFO - global_step/sec: 88.7937 -2022-05-01 19:14:12,040 - tensorflow - INFO - loss = 0.21525896, step = 1495800 (2.252 sec) -2022-05-01 19:14:14,296 - tensorflow - INFO - global_step/sec: 88.62 -2022-05-01 19:14:14,297 - tensorflow - INFO - loss = 0.014747351, step = 1496000 (2.257 sec) -2022-05-01 19:14:16,555 - tensorflow - INFO - global_step/sec: 88.5362 -2022-05-01 19:14:16,556 - tensorflow - INFO - loss = 0.18117778, step = 1496200 (2.259 sec) -2022-05-01 19:14:18,809 - tensorflow - INFO - global_step/sec: 88.7606 -2022-05-01 19:14:18,809 - tensorflow - INFO - loss = 0.032358292, step = 1496400 (2.253 sec) -2022-05-01 19:14:21,061 - tensorflow - INFO - global_step/sec: 88.7971 -2022-05-01 19:14:21,062 - tensorflow - INFO - loss = 0.012879986, step = 1496600 (2.252 sec) -2022-05-01 19:14:23,316 - tensorflow - INFO - global_step/sec: 88.6747 -2022-05-01 19:14:23,317 - tensorflow - INFO - loss = 0.014577984, step = 1496800 (2.255 sec) -2022-05-01 19:14:25,577 - tensorflow - INFO - global_step/sec: 88.4653 -2022-05-01 19:14:25,578 - tensorflow - INFO - loss = 1.6467451, step = 1497000 (2.261 sec) -2022-05-01 19:14:27,830 - tensorflow - INFO - global_step/sec: 88.7919 -2022-05-01 19:14:27,830 - tensorflow - INFO - loss = 0.04715134, step = 1497200 (2.252 sec) -2022-05-01 19:14:30,079 - tensorflow - INFO - global_step/sec: 88.8975 -2022-05-01 19:14:30,080 - tensorflow - INFO - loss = 0.03892113, step = 1497400 (2.250 sec) -2022-05-01 19:14:32,343 - tensorflow - INFO - global_step/sec: 88.377 -2022-05-01 19:14:32,343 - tensorflow - INFO - loss = 0.034063794, step = 1497600 (2.263 sec) -2022-05-01 19:14:34,602 - tensorflow - INFO - global_step/sec: 88.5231 -2022-05-01 19:14:34,602 - tensorflow - INFO - loss = 0.033480745, step = 1497800 (2.259 sec) -2022-05-01 19:14:36,849 - tensorflow - INFO - global_step/sec: 88.9951 -2022-05-01 19:14:36,850 - tensorflow - INFO - loss = 0.010095269, step = 1498000 (2.247 sec) -2022-05-01 19:14:39,107 - tensorflow - INFO - global_step/sec: 88.5728 -2022-05-01 19:14:39,108 - tensorflow - INFO - loss = 0.02026205, step = 1498200 (2.258 sec) -2022-05-01 19:14:41,358 - tensorflow - INFO - global_step/sec: 88.8423 -2022-05-01 19:14:41,359 - tensorflow - INFO - loss = 0.011833824, step = 1498400 (2.251 sec) -2022-05-01 19:14:43,616 - tensorflow - INFO - global_step/sec: 88.5942 -2022-05-01 19:14:43,616 - tensorflow - INFO - loss = 0.025549715, step = 1498600 (2.258 sec) -2022-05-01 19:14:45,866 - tensorflow - INFO - global_step/sec: 88.871 -2022-05-01 19:14:45,867 - tensorflow - INFO - loss = 0.014869514, step = 1498800 (2.250 sec) -2022-05-01 19:14:48,118 - tensorflow - INFO - global_step/sec: 88.814 -2022-05-01 19:14:48,119 - tensorflow - INFO - loss = 0.013517971, step = 1499000 (2.252 sec) -2022-05-01 19:14:50,376 - tensorflow - INFO - global_step/sec: 88.5686 -2022-05-01 19:14:50,377 - tensorflow - INFO - loss = 0.009027958, step = 1499200 (2.258 sec) -2022-05-01 19:14:52,636 - tensorflow - INFO - global_step/sec: 88.5199 -2022-05-01 19:14:52,636 - tensorflow - INFO - loss = 0.017943226, step = 1499400 (2.259 sec) -2022-05-01 19:14:54,887 - tensorflow - INFO - global_step/sec: 88.83 -2022-05-01 19:14:54,888 - tensorflow - INFO - loss = 19.609617, step = 1499600 (2.252 sec) -2022-05-01 19:14:57,142 - tensorflow - INFO - global_step/sec: 88.7111 -2022-05-01 19:14:57,142 - tensorflow - INFO - loss = 0.034245685, step = 1499800 (2.254 sec) -2022-05-01 19:14:59,381 - tensorflow - INFO - Saving checkpoints for 1500000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. -2022-05-01 19:14:59,397 - tensorflow - INFO - model.ckpt-1500000 is not in all_model_checkpoint_paths. Manually adding it. -2022-05-01 19:14:59,914 - tensorflow - INFO - Calling model_fn. -2022-05-01 19:15:00,041 - tensorflow - INFO - Done calling model_fn. -2022-05-01 19:15:00,056 - tensorflow - INFO - Starting evaluation at 2022-05-01T19:15:00Z -2022-05-01 19:15:00,120 - tensorflow - INFO - Graph was finalized. -2022-05-01 19:15:00,123 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1500000 -2022-05-01 19:15:00,404 - tensorflow - INFO - Running local_init_op. -2022-05-01 19:15:00,511 - tensorflow - INFO - Done running local_init_op. -2022-05-01 19:15:10,766 - tensorflow - INFO - Evaluation [3990/39900] -2022-05-01 19:15:20,318 - tensorflow - INFO - Evaluation [7980/39900] -2022-05-01 19:15:29,860 - tensorflow - INFO - Evaluation [11970/39900] -2022-05-01 19:15:39,412 - tensorflow - INFO - Evaluation [15960/39900] -2022-05-01 19:15:48,935 - tensorflow - INFO - Evaluation [19950/39900] -2022-05-01 19:15:58,483 - tensorflow - INFO - Evaluation [23940/39900] -2022-05-01 19:16:08,019 - tensorflow - INFO - Evaluation [27930/39900] -2022-05-01 19:16:17,585 - tensorflow - INFO - Evaluation [31920/39900] -2022-05-01 19:16:27,118 - tensorflow - INFO - Evaluation [35910/39900] -2022-05-01 19:16:36,684 - tensorflow - INFO - Evaluation [39900/39900] -2022-05-01 19:16:36,821 - tensorflow - INFO - Finished evaluation at 2022-05-01-19:16:36 -2022-05-01 19:16:36,821 - tensorflow - INFO - Saving dict for global step 1500000: global_step = 1500000, loss = 0.019688489, mean_degree_err = 3.836875 -2022-05-01 19:16:36,822 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 1500000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1500000 -2022-05-01 19:16:36,835 - tensorflow - INFO - global_step/sec: 2.00614 -2022-05-01 19:16:36,836 - tensorflow - INFO - loss = 0.016405582, step = 1500000 (99.694 sec) -2022-05-01 19:16:39,110 - tensorflow - INFO - global_step/sec: 87.9407 -2022-05-01 19:16:39,110 - tensorflow - INFO - loss = 0.03980479, step = 1500200 (2.274 sec) -2022-05-01 19:16:41,368 - tensorflow - INFO - global_step/sec: 88.5673 -2022-05-01 19:16:41,368 - tensorflow - INFO - loss = 0.03160281, step = 1500400 (2.258 sec) -2022-05-01 19:16:43,626 - tensorflow - INFO - global_step/sec: 88.5718 -2022-05-01 19:16:43,626 - tensorflow - INFO - loss = 0.010400597, step = 1500600 (2.258 sec) -2022-05-01 19:16:45,885 - tensorflow - INFO - global_step/sec: 88.548 -2022-05-01 19:16:45,885 - tensorflow - INFO - loss = 0.018450955, step = 1500800 (2.259 sec) -2022-05-01 19:16:48,145 - tensorflow - INFO - global_step/sec: 88.4955 -2022-05-01 19:16:48,145 - tensorflow - INFO - loss = 0.039949965, step = 1501000 (2.260 sec) -2022-05-01 19:16:50,403 - tensorflow - INFO - global_step/sec: 88.5761 -2022-05-01 19:16:50,403 - tensorflow - INFO - loss = 0.012375185, step = 1501200 (2.258 sec) -2022-05-01 19:16:52,665 - tensorflow - INFO - global_step/sec: 88.387 -2022-05-01 19:16:52,666 - tensorflow - INFO - loss = 0.013643623, step = 1501400 (2.263 sec) -2022-05-01 19:16:54,927 - tensorflow - INFO - global_step/sec: 88.4466 -2022-05-01 19:16:54,927 - tensorflow - INFO - loss = 0.039375756, step = 1501600 (2.261 sec) -2022-05-01 19:16:57,186 - tensorflow - INFO - global_step/sec: 88.536 -2022-05-01 19:16:57,186 - tensorflow - INFO - loss = 0.0076917754, step = 1501800 (2.259 sec) -2022-05-01 19:16:59,445 - tensorflow - INFO - global_step/sec: 88.5146 -2022-05-01 19:16:59,445 - tensorflow - INFO - loss = 0.009893475, step = 1502000 (2.260 sec) -2022-05-01 19:17:01,705 - tensorflow - INFO - global_step/sec: 88.4975 -2022-05-01 19:17:01,705 - tensorflow - INFO - loss = 0.047156, step = 1502200 (2.260 sec) -2022-05-01 19:17:03,962 - tensorflow - INFO - global_step/sec: 88.5943 -2022-05-01 19:17:03,963 - tensorflow - INFO - loss = 0.021427762, step = 1502400 (2.258 sec) -2022-05-01 19:17:06,219 - tensorflow - INFO - global_step/sec: 88.6152 -2022-05-01 19:17:06,220 - tensorflow - INFO - loss = 0.0681486, step = 1502600 (2.257 sec) -2022-05-01 19:17:08,473 - tensorflow - INFO - global_step/sec: 88.751 -2022-05-01 19:17:08,473 - tensorflow - INFO - loss = 0.028135728, step = 1502800 (2.254 sec) -2022-05-01 19:17:10,724 - tensorflow - INFO - global_step/sec: 88.8541 -2022-05-01 19:17:10,724 - tensorflow - INFO - loss = 0.009688498, step = 1503000 (2.251 sec) -2022-05-01 19:17:12,973 - tensorflow - INFO - global_step/sec: 88.9314 -2022-05-01 19:17:12,973 - tensorflow - INFO - loss = 0.009663465, step = 1503200 (2.249 sec) -2022-05-01 19:17:15,228 - tensorflow - INFO - global_step/sec: 88.6792 -2022-05-01 19:17:15,228 - tensorflow - INFO - loss = 0.010690352, step = 1503400 (2.255 sec) -2022-05-01 19:17:17,479 - tensorflow - INFO - global_step/sec: 88.8556 -2022-05-01 19:17:17,479 - tensorflow - INFO - loss = 0.01446222, step = 1503600 (2.251 sec) -2022-05-01 19:17:19,730 - tensorflow - INFO - global_step/sec: 88.8299 -2022-05-01 19:17:19,731 - tensorflow - INFO - loss = 0.16363442, step = 1503800 (2.251 sec) -2022-05-01 19:17:21,983 - tensorflow - INFO - global_step/sec: 88.7991 -2022-05-01 19:17:21,983 - tensorflow - INFO - loss = 0.24157685, step = 1504000 (2.252 sec) -2022-05-01 19:17:24,237 - tensorflow - INFO - global_step/sec: 88.7346 -2022-05-01 19:17:24,237 - tensorflow - INFO - loss = 0.037840072, step = 1504200 (2.254 sec) -2022-05-01 19:17:26,496 - tensorflow - INFO - global_step/sec: 88.5264 -2022-05-01 19:17:26,496 - tensorflow - INFO - loss = 0.013430537, step = 1504400 (2.259 sec) -2022-05-01 19:17:28,747 - tensorflow - INFO - global_step/sec: 88.8359 -2022-05-01 19:17:28,748 - tensorflow - INFO - loss = 0.0102399755, step = 1504600 (2.251 sec) -2022-05-01 19:17:31,003 - tensorflow - INFO - global_step/sec: 88.6629 -2022-05-01 19:17:31,003 - tensorflow - INFO - loss = 0.47035003, step = 1504800 (2.256 sec) -2022-05-01 19:17:33,258 - tensorflow - INFO - global_step/sec: 88.6973 -2022-05-01 19:17:33,258 - tensorflow - INFO - loss = 0.035697296, step = 1505000 (2.255 sec) -2022-05-01 19:17:35,510 - tensorflow - INFO - global_step/sec: 88.8108 -2022-05-01 19:17:35,510 - tensorflow - INFO - loss = 0.01537087, step = 1505200 (2.252 sec) -2022-05-01 19:17:37,760 - tensorflow - INFO - global_step/sec: 88.86 -2022-05-01 19:17:37,761 - tensorflow - INFO - loss = 0.0061171902, step = 1505400 (2.251 sec) -2022-05-01 19:17:40,012 - tensorflow - INFO - global_step/sec: 88.8074 -2022-05-01 19:17:40,013 - tensorflow - INFO - loss = 0.008986438, step = 1505600 (2.252 sec) -2022-05-01 19:17:42,265 - tensorflow - INFO - global_step/sec: 88.7722 -2022-05-01 19:17:42,266 - tensorflow - INFO - loss = 0.0074248677, step = 1505800 (2.253 sec) -2022-05-01 19:17:44,519 - tensorflow - INFO - global_step/sec: 88.7331 -2022-05-01 19:17:44,520 - tensorflow - INFO - loss = 0.045062054, step = 1506000 (2.254 sec) -2022-05-01 19:17:46,775 - tensorflow - INFO - global_step/sec: 88.6578 -2022-05-01 19:17:46,776 - tensorflow - INFO - loss = 0.049057905, step = 1506200 (2.256 sec) -2022-05-01 19:17:49,031 - tensorflow - INFO - global_step/sec: 88.6698 -2022-05-01 19:17:49,031 - tensorflow - INFO - loss = 9.571878, step = 1506400 (2.256 sec) -2022-05-01 19:17:51,279 - tensorflow - INFO - global_step/sec: 88.9518 -2022-05-01 19:17:51,280 - tensorflow - INFO - loss = 0.010163815, step = 1506600 (2.248 sec) -2022-05-01 19:17:53,537 - tensorflow - INFO - global_step/sec: 88.5769 -2022-05-01 19:17:53,538 - tensorflow - INFO - loss = 0.037897877, step = 1506800 (2.258 sec) -2022-05-01 19:17:55,785 - tensorflow - INFO - global_step/sec: 88.9572 -2022-05-01 19:17:55,786 - tensorflow - INFO - loss = 0.011446608, step = 1507000 (2.248 sec) -2022-05-01 19:17:58,047 - tensorflow - INFO - global_step/sec: 88.4464 -2022-05-01 19:17:58,047 - tensorflow - INFO - loss = 0.030028125, step = 1507200 (2.261 sec) -2022-05-01 19:18:00,304 - tensorflow - INFO - global_step/sec: 88.5904 -2022-05-01 19:18:00,305 - tensorflow - INFO - loss = 0.37124282, step = 1507400 (2.258 sec) -2022-05-01 19:18:02,558 - tensorflow - INFO - global_step/sec: 88.7302 -2022-05-01 19:18:02,559 - tensorflow - INFO - loss = 0.0071982597, step = 1507600 (2.254 sec) -2022-05-01 19:18:04,817 - tensorflow - INFO - global_step/sec: 88.5361 -2022-05-01 19:18:04,818 - tensorflow - INFO - loss = 0.026802769, step = 1507800 (2.259 sec) -2022-05-01 19:18:07,076 - tensorflow - INFO - global_step/sec: 88.5585 -2022-05-01 19:18:07,076 - tensorflow - INFO - loss = 0.01104089, step = 1508000 (2.258 sec) -2022-05-01 19:18:09,330 - tensorflow - INFO - global_step/sec: 88.7186 -2022-05-01 19:18:09,330 - tensorflow - INFO - loss = 0.026884561, step = 1508200 (2.254 sec) -2022-05-01 19:18:11,580 - tensorflow - INFO - global_step/sec: 88.8738 -2022-05-01 19:18:11,581 - tensorflow - INFO - loss = 0.04371132, step = 1508400 (2.250 sec) -2022-05-01 19:18:13,832 - tensorflow - INFO - global_step/sec: 88.8071 -2022-05-01 19:18:13,833 - tensorflow - INFO - loss = 0.35379708, step = 1508600 (2.252 sec) -2022-05-01 19:18:16,083 - tensorflow - INFO - global_step/sec: 88.8594 -2022-05-01 19:18:16,084 - tensorflow - INFO - loss = 0.01931915, step = 1508800 (2.251 sec) -2022-05-01 19:18:18,341 - tensorflow - INFO - global_step/sec: 88.5654 -2022-05-01 19:18:18,342 - tensorflow - INFO - loss = 0.009167142, step = 1509000 (2.258 sec) -2022-05-01 19:18:20,596 - tensorflow - INFO - global_step/sec: 88.6953 -2022-05-01 19:18:20,597 - tensorflow - INFO - loss = 0.010578865, step = 1509200 (2.255 sec) -2022-05-01 19:18:22,845 - tensorflow - INFO - global_step/sec: 88.9425 -2022-05-01 19:18:22,845 - tensorflow - INFO - loss = 0.013854955, step = 1509400 (2.249 sec) -2022-05-01 19:18:25,094 - tensorflow - INFO - global_step/sec: 88.9234 -2022-05-01 19:18:25,095 - tensorflow - INFO - loss = 0.06622584, step = 1509600 (2.249 sec) -2022-05-01 19:18:27,348 - tensorflow - INFO - global_step/sec: 88.7279 -2022-05-01 19:18:27,349 - tensorflow - INFO - loss = 0.8782365, step = 1509800 (2.254 sec) -2022-05-01 19:18:29,604 - tensorflow - INFO - global_step/sec: 88.6551 -2022-05-01 19:18:29,605 - tensorflow - INFO - loss = 0.041533154, step = 1510000 (2.256 sec) -2022-05-01 19:18:31,865 - tensorflow - INFO - global_step/sec: 88.4552 -2022-05-01 19:18:31,866 - tensorflow - INFO - loss = 0.048824005, step = 1510200 (2.261 sec) -2022-05-01 19:18:34,119 - tensorflow - INFO - global_step/sec: 88.75 -2022-05-01 19:18:34,119 - tensorflow - INFO - loss = 0.06452929, step = 1510400 (2.253 sec) -2022-05-01 19:18:36,373 - tensorflow - INFO - global_step/sec: 88.6973 -2022-05-01 19:18:36,374 - tensorflow - INFO - loss = 0.021103691, step = 1510600 (2.255 sec) -2022-05-01 19:18:38,626 - tensorflow - INFO - global_step/sec: 88.7726 -2022-05-01 19:18:38,627 - tensorflow - INFO - loss = 0.03738752, step = 1510800 (2.253 sec) -2022-05-01 19:18:40,874 - tensorflow - INFO - global_step/sec: 89.0037 -2022-05-01 19:18:40,874 - tensorflow - INFO - loss = 0.020102445, step = 1511000 (2.247 sec) -2022-05-01 19:18:43,130 - tensorflow - INFO - global_step/sec: 88.6228 -2022-05-01 19:18:43,131 - tensorflow - INFO - loss = 0.014712809, step = 1511200 (2.257 sec) -2022-05-01 19:18:45,387 - tensorflow - INFO - global_step/sec: 88.6175 -2022-05-01 19:18:45,388 - tensorflow - INFO - loss = 0.115707554, step = 1511400 (2.257 sec) -2022-05-01 19:18:47,637 - tensorflow - INFO - global_step/sec: 88.8917 -2022-05-01 19:18:47,638 - tensorflow - INFO - loss = 0.022547714, step = 1511600 (2.250 sec) -2022-05-01 19:18:49,890 - tensorflow - INFO - global_step/sec: 88.7748 -2022-05-01 19:18:49,890 - tensorflow - INFO - loss = 0.04035278, step = 1511800 (2.253 sec) -2022-05-01 19:18:52,142 - tensorflow - INFO - global_step/sec: 88.8283 -2022-05-01 19:18:52,142 - tensorflow - INFO - loss = 0.011679567, step = 1512000 (2.252 sec) -2022-05-01 19:18:54,395 - tensorflow - INFO - global_step/sec: 88.7433 -2022-05-01 19:18:54,396 - tensorflow - INFO - loss = 0.026710436, step = 1512200 (2.254 sec) -2022-05-01 19:18:56,651 - tensorflow - INFO - global_step/sec: 88.6618 -2022-05-01 19:18:56,651 - tensorflow - INFO - loss = 0.017468259, step = 1512400 (2.256 sec) -2022-05-01 19:18:58,904 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.961452, step = 1513800 (2.248 sec) -2022-05-01 19:19:14,675 - tensorflow - INFO - global_step/sec: 88.8292 -2022-05-01 19:19:14,676 - tensorflow - INFO - loss = 0.005127412, step = 1514000 (2.252 sec) -2022-05-01 19:19:16,930 - tensorflow - INFO - global_step/sec: 88.7034 -2022-05-01 19:19:16,930 - tensorflow - INFO - loss = 0.013860978, step = 1514200 (2.255 sec) -2022-05-01 19:19:19,186 - tensorflow - INFO - global_step/sec: 88.647 -2022-05-01 19:19:19,186 - tensorflow - INFO - loss = 0.009205536, step = 1514400 (2.256 sec) -2022-05-01 19:19:21,441 - tensorflow - INFO - global_step/sec: 88.708 -2022-05-01 19:19:21,441 - tensorflow - INFO - loss = 0.0066144904, step = 1514600 (2.255 sec) -2022-05-01 19:19:23,700 - tensorflow - INFO - global_step/sec: 88.5165 -2022-05-01 19:19:23,701 - tensorflow - INFO - loss = 0.04965475, step = 1514800 (2.259 sec) -2022-05-01 19:19:25,954 - tensorflow - INFO - global_step/sec: 88.7295 -2022-05-01 19:19:25,955 - tensorflow - INFO - loss = 0.017812291, step = 1515000 (2.254 sec) -2022-05-01 19:19:28,204 - tensorflow - INFO - global_step/sec: 88.8944 -2022-05-01 19:19:28,204 - tensorflow - INFO - loss = 0.027830694, step = 1515200 (2.250 sec) -2022-05-01 19:19:30,466 - tensorflow - INFO - global_step/sec: 88.4032 -2022-05-01 19:19:30,467 - tensorflow - INFO - loss = 0.027515944, step = 1515400 (2.262 sec) -2022-05-01 19:19:32,726 - tensorflow - INFO - global_step/sec: 88.5016 -2022-05-01 19:19:32,727 - tensorflow - INFO - loss = 0.11355199, step = 1515600 (2.260 sec) -2022-05-01 19:19:34,977 - tensorflow - INFO - global_step/sec: 88.8434 -2022-05-01 19:19:34,978 - tensorflow - INFO - loss = 0.014939457, step = 1515800 (2.251 sec) -2022-05-01 19:19:37,231 - tensorflow - INFO - global_step/sec: 88.7384 -2022-05-01 19:19:37,232 - tensorflow - INFO - loss = 0.020215426, step = 1516000 (2.254 sec) -2022-05-01 19:19:39,484 - tensorflow - INFO - global_step/sec: 88.7715 -2022-05-01 19:19:39,485 - tensorflow - INFO - loss = 0.01886259, step = 1516200 (2.253 sec) -2022-05-01 19:19:41,740 - tensorflow - INFO - global_step/sec: 88.6547 -2022-05-01 19:19:41,741 - tensorflow - INFO - loss = 0.16277888, step = 1516400 (2.256 sec) -2022-05-01 19:19:43,992 - tensorflow - INFO - global_step/sec: 88.8261 -2022-05-01 19:19:43,992 - tensorflow - INFO - loss = 0.012797175, step = 1516600 (2.252 sec) -2022-05-01 19:19:46,250 - tensorflow - INFO - global_step/sec: 88.5513 -2022-05-01 19:19:46,251 - tensorflow - INFO - loss = 0.011014702, step = 1516800 (2.259 sec) -2022-05-01 19:19:48,500 - tensorflow - INFO - global_step/sec: 88.8827 -2022-05-01 19:19:48,501 - tensorflow - INFO - loss = 0.026667975, step = 1517000 (2.250 sec) -2022-05-01 19:19:50,753 - tensorflow - INFO - global_step/sec: 88.7997 -2022-05-01 19:19:50,753 - tensorflow - INFO - loss = 0.011713488, step = 1517200 (2.252 sec) -2022-05-01 19:19:53,006 - tensorflow - INFO - global_step/sec: 88.7603 -2022-05-01 19:19:53,006 - tensorflow - INFO - loss = 0.028351596, step = 1517400 (2.253 sec) -2022-05-01 19:19:55,254 - tensorflow - INFO - global_step/sec: 88.9736 -2022-05-01 19:19:55,254 - tensorflow - INFO - loss = 0.008203949, step = 1517600 (2.248 sec) -2022-05-01 19:19:57,514 - tensorflow - INFO - global_step/sec: 88.5006 -2022-05-01 19:19:57,514 - tensorflow - INFO - loss = 0.4390188, step = 1517800 (2.260 sec) -2022-05-01 19:19:59,772 - tensorflow - INFO - global_step/sec: 88.5811 -2022-05-01 19:19:59,772 - tensorflow - INFO - loss = 0.016573153, step = 1518000 (2.258 sec) -2022-05-01 19:20:02,026 - tensorflow - INFO - global_step/sec: 88.7131 -2022-05-01 19:20:02,026 - tensorflow - INFO - loss = 0.029199861, step = 1518200 (2.254 sec) -2022-05-01 19:20:04,284 - tensorflow - INFO - global_step/sec: 88.5768 -2022-05-01 19:20:04,284 - tensorflow - INFO - loss = 0.012956169, step = 1518400 (2.258 sec) -2022-05-01 19:20:06,534 - tensorflow - INFO - global_step/sec: 88.8654 -2022-05-01 19:20:06,535 - tensorflow - INFO - loss = 0.028454738, step = 1518600 (2.251 sec) -2022-05-01 19:20:08,792 - tensorflow - 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INFO - global_step/sec: 88.7263 -2022-05-01 19:24:19,237 - tensorflow - INFO - loss = 0.020975178, step = 1541000 (2.254 sec) -2022-05-01 19:24:21,488 - tensorflow - INFO - global_step/sec: 88.8172 -2022-05-01 19:24:21,489 - tensorflow - INFO - loss = 0.028373608, step = 1541200 (2.252 sec) -2022-05-01 19:24:23,742 - tensorflow - INFO - global_step/sec: 88.7457 -2022-05-01 19:24:23,742 - tensorflow - INFO - loss = 0.029991167, step = 1541400 (2.254 sec) -2022-05-01 19:24:25,996 - tensorflow - INFO - global_step/sec: 88.7159 -2022-05-01 19:24:25,997 - tensorflow - INFO - loss = 0.06427478, step = 1541600 (2.254 sec) -2022-05-01 19:24:28,248 - tensorflow - INFO - global_step/sec: 88.8243 -2022-05-01 19:24:28,248 - tensorflow - INFO - loss = 0.009837613, step = 1541800 (2.252 sec) -2022-05-01 19:24:30,509 - tensorflow - INFO - global_step/sec: 88.4659 -2022-05-01 19:24:30,509 - tensorflow - INFO - loss = 0.011918984, step = 1542000 (2.261 sec) -2022-05-01 19:24:32,766 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.7448 -2022-05-01 19:25:15,604 - tensorflow - INFO - loss = 0.020859353, step = 1546000 (2.254 sec) -2022-05-01 19:25:17,854 - tensorflow - INFO - global_step/sec: 88.8677 -2022-05-01 19:25:17,854 - tensorflow - INFO - loss = 0.8780155, step = 1546200 (2.251 sec) -2022-05-01 19:25:20,107 - tensorflow - INFO - global_step/sec: 88.7496 -2022-05-01 19:25:20,108 - tensorflow - INFO - loss = 0.020770505, step = 1546400 (2.254 sec) -2022-05-01 19:25:22,365 - tensorflow - INFO - global_step/sec: 88.5836 -2022-05-01 19:25:22,365 - tensorflow - INFO - loss = 0.02166292, step = 1546600 (2.258 sec) -2022-05-01 19:25:24,619 - tensorflow - INFO - global_step/sec: 88.7166 -2022-05-01 19:25:24,620 - tensorflow - INFO - loss = 0.05862613, step = 1546800 (2.254 sec) -2022-05-01 19:25:26,875 - tensorflow - INFO - global_step/sec: 88.6816 -2022-05-01 19:25:26,875 - tensorflow - INFO - loss = 0.01882706, step = 1547000 (2.255 sec) -2022-05-01 19:25:29,133 - tensorflow - INFO - global_step/sec: 88.569 -2022-05-01 19:25:29,133 - tensorflow - INFO - loss = 0.04019544, step = 1547200 (2.258 sec) -2022-05-01 19:25:31,394 - tensorflow - INFO - global_step/sec: 88.4471 -2022-05-01 19:25:31,394 - tensorflow - INFO - loss = 0.30397993, step = 1547400 (2.261 sec) -2022-05-01 19:25:33,652 - tensorflow - INFO - global_step/sec: 88.5754 -2022-05-01 19:25:33,652 - tensorflow - INFO - loss = 0.10902523, step = 1547600 (2.258 sec) -2022-05-01 19:25:35,907 - tensorflow - INFO - global_step/sec: 88.6866 -2022-05-01 19:25:35,907 - tensorflow - INFO - loss = 0.037281685, step = 1547800 (2.255 sec) -2022-05-01 19:25:38,156 - tensorflow - INFO - global_step/sec: 88.9134 -2022-05-01 19:25:38,157 - tensorflow - INFO - loss = 1.4373074, step = 1548000 (2.249 sec) -2022-05-01 19:25:40,413 - tensorflow - INFO - global_step/sec: 88.6115 -2022-05-01 19:25:40,414 - tensorflow - INFO - loss = 0.019181658, step = 1548200 (2.257 sec) -2022-05-01 19:25:42,666 - tensorflow - INFO - global_step/sec: 88.7797 -2022-05-01 19:25:42,667 - tensorflow - INFO - loss = 0.04345332, step = 1548400 (2.253 sec) -2022-05-01 19:25:44,921 - tensorflow - INFO - global_step/sec: 88.7074 -2022-05-01 19:25:44,921 - tensorflow - INFO - loss = 1.7033477, step = 1548600 (2.255 sec) -2022-05-01 19:25:47,181 - tensorflow - INFO - global_step/sec: 88.5033 -2022-05-01 19:25:47,181 - tensorflow - INFO - loss = 0.022207957, step = 1548800 (2.260 sec) -2022-05-01 19:25:49,433 - tensorflow - INFO - global_step/sec: 88.7784 -2022-05-01 19:25:49,434 - tensorflow - INFO - loss = 0.05524181, step = 1549000 (2.253 sec) -2022-05-01 19:25:51,687 - tensorflow - INFO - global_step/sec: 88.7576 -2022-05-01 19:25:51,687 - tensorflow - INFO - loss = 0.008339899, step = 1549200 (2.253 sec) -2022-05-01 19:25:53,938 - tensorflow - INFO - global_step/sec: 88.8375 -2022-05-01 19:25:53,938 - tensorflow - INFO - loss = 0.09378567, step = 1549400 (2.251 sec) -2022-05-01 19:25:56,194 - tensorflow - INFO - global_step/sec: 88.6386 -2022-05-01 19:25:56,195 - tensorflow - INFO - loss = 0.027930196, step = 1549600 (2.256 sec) -2022-05-01 19:25:58,454 - tensorflow - INFO - global_step/sec: 88.5092 -2022-05-01 19:25:58,455 - tensorflow - INFO - loss = 0.3648109, step = 1549800 (2.260 sec) -2022-05-01 19:26:00,712 - tensorflow - INFO - global_step/sec: 88.5662 -2022-05-01 19:26:00,713 - tensorflow - INFO - loss = 0.02041462, step = 1550000 (2.259 sec) -2022-05-01 19:26:02,966 - tensorflow - INFO - global_step/sec: 88.7315 -2022-05-01 19:26:02,967 - tensorflow - INFO - loss = 1.336118, step = 1550200 (2.254 sec) -2022-05-01 19:26:05,222 - tensorflow - INFO - global_step/sec: 88.6473 -2022-05-01 19:26:05,223 - tensorflow - INFO - loss = 0.04982681, step = 1550400 (2.256 sec) -2022-05-01 19:26:07,478 - tensorflow - INFO - global_step/sec: 88.6657 -2022-05-01 19:26:07,478 - tensorflow - INFO - loss = 0.06480853, step = 1550600 (2.256 sec) -2022-05-01 19:26:09,731 - tensorflow - INFO - global_step/sec: 88.7566 -2022-05-01 19:26:09,732 - tensorflow - INFO - loss = 0.015623615, step = 1550800 (2.253 sec) -2022-05-01 19:26:11,991 - tensorflow - INFO - global_step/sec: 88.5312 -2022-05-01 19:26:11,991 - tensorflow - INFO - loss = 0.0297763, step = 1551000 (2.259 sec) -2022-05-01 19:26:14,254 - tensorflow - INFO - global_step/sec: 88.3703 -2022-05-01 19:26:14,254 - tensorflow - INFO - loss = 0.01136495, step = 1551200 (2.263 sec) -2022-05-01 19:26:16,505 - tensorflow - INFO - global_step/sec: 88.8248 -2022-05-01 19:26:16,506 - tensorflow - INFO - loss = 0.010518458, step = 1551400 (2.252 sec) -2022-05-01 19:26:18,758 - tensorflow - INFO - global_step/sec: 88.7683 -2022-05-01 19:26:18,759 - tensorflow - INFO - loss = 0.014938101, step = 1551600 (2.253 sec) -2022-05-01 19:26:21,009 - tensorflow - INFO - global_step/sec: 88.8808 -2022-05-01 19:26:21,009 - tensorflow - INFO - loss = 0.009529099, step = 1551800 (2.250 sec) -2022-05-01 19:26:23,260 - tensorflow - INFO - global_step/sec: 88.8393 -2022-05-01 19:26:23,260 - tensorflow - INFO - loss = 0.032692857, step = 1552000 (2.251 sec) -2022-05-01 19:26:25,520 - tensorflow - INFO - global_step/sec: 88.4978 -2022-05-01 19:26:25,520 - tensorflow - INFO - loss = 0.022069555, step = 1552200 (2.260 sec) -2022-05-01 19:26:27,778 - tensorflow - INFO - global_step/sec: 88.5814 -2022-05-01 19:26:27,778 - tensorflow - INFO - loss = 0.028814282, step = 1552400 (2.258 sec) -2022-05-01 19:26:30,036 - tensorflow - INFO - global_step/sec: 88.5769 -2022-05-01 19:26:30,036 - tensorflow - INFO - loss = 0.0140770385, step = 1552600 (2.258 sec) -2022-05-01 19:26:32,299 - tensorflow - INFO - global_step/sec: 88.3689 -2022-05-01 19:26:32,299 - tensorflow - INFO - loss = 0.011250533, step = 1552800 (2.263 sec) -2022-05-01 19:26:34,553 - tensorflow - INFO - global_step/sec: 88.7143 -2022-05-01 19:26:34,554 - tensorflow - INFO - loss = 0.010542143, step = 1553000 (2.254 sec) -2022-05-01 19:26:36,814 - tensorflow - INFO - global_step/sec: 88.4834 -2022-05-01 19:26:36,814 - tensorflow - INFO - loss = 0.50717986, step = 1553200 (2.260 sec) -2022-05-01 19:26:39,065 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.035712287, step = 1554600 (2.250 sec) -2022-05-01 19:26:54,850 - tensorflow - INFO - global_step/sec: 88.5326 -2022-05-01 19:26:54,850 - tensorflow - INFO - loss = 0.41522735, step = 1554800 (2.259 sec) -2022-05-01 19:26:57,106 - tensorflow - INFO - global_step/sec: 88.647 -2022-05-01 19:26:57,106 - tensorflow - INFO - loss = 0.030813845, step = 1555000 (2.256 sec) -2022-05-01 19:26:59,361 - tensorflow - INFO - global_step/sec: 88.6715 -2022-05-01 19:26:59,362 - tensorflow - INFO - loss = 0.016018137, step = 1555200 (2.255 sec) -2022-05-01 19:27:01,615 - tensorflow - INFO - global_step/sec: 88.7456 -2022-05-01 19:27:01,615 - tensorflow - INFO - loss = 0.0454354, step = 1555400 (2.254 sec) -2022-05-01 19:27:03,874 - tensorflow - INFO - global_step/sec: 88.5274 -2022-05-01 19:27:03,875 - tensorflow - INFO - loss = 0.009947162, step = 1555600 (2.259 sec) -2022-05-01 19:27:06,128 - tensorflow - INFO - global_step/sec: 88.7262 -2022-05-01 19:27:06,129 - tensorflow - INFO - loss = 0.014390573, step = 1555800 (2.254 sec) -2022-05-01 19:27:08,384 - tensorflow - INFO - global_step/sec: 88.6759 -2022-05-01 19:27:08,384 - tensorflow - INFO - loss = 0.029519245, step = 1556000 (2.255 sec) -2022-05-01 19:27:10,646 - tensorflow - INFO - global_step/sec: 88.4069 -2022-05-01 19:27:10,646 - tensorflow - INFO - loss = 0.020814005, step = 1556200 (2.262 sec) -2022-05-01 19:27:12,905 - tensorflow - INFO - global_step/sec: 88.5466 -2022-05-01 19:27:12,905 - tensorflow - INFO - loss = 0.04717438, step = 1556400 (2.259 sec) -2022-05-01 19:27:15,170 - tensorflow - INFO - global_step/sec: 88.3023 -2022-05-01 19:27:15,170 - tensorflow - INFO - loss = 0.007176837, step = 1556600 (2.265 sec) -2022-05-01 19:27:17,432 - tensorflow - INFO - global_step/sec: 88.3971 -2022-05-01 19:27:17,433 - tensorflow - INFO - loss = 0.023968074, step = 1556800 (2.263 sec) -2022-05-01 19:27:19,695 - tensorflow - INFO - global_step/sec: 88.3919 -2022-05-01 19:27:19,695 - tensorflow - INFO - 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INFO - global_step/sec: 88.7717 -2022-05-01 19:31:18,817 - tensorflow - INFO - loss = 0.18352231, step = 1578200 (2.253 sec) -2022-05-01 19:31:21,074 - tensorflow - INFO - global_step/sec: 88.6043 -2022-05-01 19:31:21,075 - tensorflow - INFO - loss = 0.31989393, step = 1578400 (2.257 sec) -2022-05-01 19:31:23,336 - tensorflow - INFO - global_step/sec: 88.4351 -2022-05-01 19:31:23,336 - tensorflow - INFO - loss = 0.012910461, step = 1578600 (2.262 sec) -2022-05-01 19:31:25,592 - tensorflow - INFO - global_step/sec: 88.6243 -2022-05-01 19:31:25,593 - tensorflow - INFO - loss = 0.024716763, step = 1578800 (2.257 sec) -2022-05-01 19:31:27,849 - tensorflow - INFO - global_step/sec: 88.6312 -2022-05-01 19:31:27,849 - tensorflow - INFO - loss = 0.016229302, step = 1579000 (2.257 sec) -2022-05-01 19:31:30,106 - tensorflow - INFO - global_step/sec: 88.6216 -2022-05-01 19:31:30,106 - tensorflow - INFO - loss = 0.019719245, step = 1579200 (2.257 sec) -2022-05-01 19:31:32,369 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.07760454, step = 1580600 (2.250 sec) -2022-05-01 19:31:48,160 - tensorflow - INFO - global_step/sec: 88.4801 -2022-05-01 19:31:48,160 - tensorflow - INFO - loss = 0.06351081, step = 1580800 (2.260 sec) -2022-05-01 19:31:50,411 - tensorflow - INFO - global_step/sec: 88.8325 -2022-05-01 19:31:50,412 - tensorflow - INFO - loss = 0.0094411755, step = 1581000 (2.251 sec) -2022-05-01 19:31:52,662 - tensorflow - INFO - global_step/sec: 88.8538 -2022-05-01 19:31:52,663 - tensorflow - INFO - loss = 0.017369913, step = 1581200 (2.251 sec) -2022-05-01 19:31:54,918 - tensorflow - INFO - global_step/sec: 88.6613 -2022-05-01 19:31:54,919 - tensorflow - INFO - loss = 0.03887278, step = 1581400 (2.256 sec) -2022-05-01 19:31:57,178 - tensorflow - INFO - global_step/sec: 88.5103 -2022-05-01 19:31:57,178 - tensorflow - INFO - loss = 0.19254777, step = 1581600 (2.260 sec) -2022-05-01 19:31:59,432 - tensorflow - INFO - global_step/sec: 88.7072 -2022-05-01 19:31:59,433 - tensorflow - INFO - loss = 0.0091712065, step = 1581800 (2.255 sec) -2022-05-01 19:32:01,687 - tensorflow - INFO - global_step/sec: 88.7115 -2022-05-01 19:32:01,687 - tensorflow - INFO - loss = 0.8167502, step = 1582000 (2.255 sec) -2022-05-01 19:32:03,937 - tensorflow - INFO - global_step/sec: 88.8941 -2022-05-01 19:32:03,937 - tensorflow - INFO - loss = 0.015261, step = 1582200 (2.250 sec) -2022-05-01 19:32:06,191 - tensorflow - INFO - global_step/sec: 88.7356 -2022-05-01 19:32:06,191 - tensorflow - INFO - loss = 0.07977207, step = 1582400 (2.254 sec) -2022-05-01 19:32:08,443 - tensorflow - INFO - global_step/sec: 88.7754 -2022-05-01 19:32:08,444 - tensorflow - INFO - loss = 0.026426725, step = 1582600 (2.253 sec) -2022-05-01 19:32:10,695 - tensorflow - INFO - global_step/sec: 88.838 -2022-05-01 19:32:10,695 - tensorflow - INFO - loss = 0.035139702, step = 1582800 (2.251 sec) -2022-05-01 19:32:12,955 - tensorflow - INFO - global_step/sec: 88.4909 -2022-05-01 19:32:12,955 - tensorflow - INFO - loss = 0.014947243, step = 1583000 (2.260 sec) -2022-05-01 19:32:15,213 - tensorflow - INFO - global_step/sec: 88.5803 -2022-05-01 19:32:15,213 - tensorflow - INFO - loss = 0.08532808, step = 1583200 (2.258 sec) -2022-05-01 19:32:17,463 - tensorflow - INFO - global_step/sec: 88.8682 -2022-05-01 19:32:17,464 - tensorflow - INFO - loss = 0.020720141, step = 1583400 (2.250 sec) -2022-05-01 19:32:19,721 - tensorflow - INFO - global_step/sec: 88.5699 -2022-05-01 19:32:19,722 - tensorflow - INFO - loss = 0.0035738472, step = 1583600 (2.258 sec) -2022-05-01 19:32:21,972 - tensorflow - INFO - global_step/sec: 88.849 -2022-05-01 19:32:21,973 - tensorflow - INFO - loss = 2.649255, step = 1583800 (2.251 sec) -2022-05-01 19:32:24,224 - tensorflow - INFO - global_step/sec: 88.8165 -2022-05-01 19:32:24,225 - tensorflow - INFO - loss = 0.02071458, step = 1584000 (2.252 sec) -2022-05-01 19:32:26,484 - tensorflow - INFO - global_step/sec: 88.4871 -2022-05-01 19:32:26,485 - tensorflow - INFO - loss = 0.18882996, step = 1584200 (2.260 sec) -2022-05-01 19:32:28,736 - tensorflow - INFO - global_step/sec: 88.8337 -2022-05-01 19:32:28,736 - tensorflow - INFO - loss = 0.0497748, step = 1584400 (2.251 sec) -2022-05-01 19:32:30,990 - tensorflow - INFO - global_step/sec: 88.7308 -2022-05-01 19:32:30,990 - tensorflow - INFO - loss = 1.3267566, step = 1584600 (2.254 sec) -2022-05-01 19:32:33,243 - tensorflow - INFO - global_step/sec: 88.7704 -2022-05-01 19:32:33,243 - tensorflow - INFO - loss = 0.027151179, step = 1584800 (2.253 sec) -2022-05-01 19:32:35,497 - tensorflow - INFO - global_step/sec: 88.7239 -2022-05-01 19:32:35,497 - tensorflow - INFO - loss = 0.04791772, step = 1585000 (2.254 sec) -2022-05-01 19:32:37,749 - tensorflow - INFO - global_step/sec: 88.8205 -2022-05-01 19:32:37,749 - tensorflow - INFO - loss = 0.09944801, step = 1585200 (2.252 sec) -2022-05-01 19:32:40,003 - tensorflow - INFO - global_step/sec: 88.7124 -2022-05-01 19:32:40,004 - tensorflow - INFO - loss = 0.021462023, step = 1585400 (2.254 sec) -2022-05-01 19:32:42,256 - tensorflow - INFO - global_step/sec: 88.7972 -2022-05-01 19:32:42,256 - tensorflow - INFO - loss = 0.03806497, step = 1585600 (2.252 sec) -2022-05-01 19:32:44,510 - tensorflow - INFO - global_step/sec: 88.7148 -2022-05-01 19:32:44,510 - tensorflow - INFO - loss = 0.026090106, step = 1585800 (2.254 sec) -2022-05-01 19:32:46,762 - tensorflow - INFO - global_step/sec: 88.8133 -2022-05-01 19:32:46,762 - tensorflow - INFO - loss = 0.0069897245, step = 1586000 (2.252 sec) -2022-05-01 19:32:49,017 - tensorflow - INFO - global_step/sec: 88.6905 -2022-05-01 19:32:49,017 - tensorflow - INFO - loss = 0.013601289, step = 1586200 (2.255 sec) -2022-05-01 19:32:51,271 - tensorflow - INFO - global_step/sec: 88.7082 -2022-05-01 19:32:51,272 - tensorflow - INFO - loss = 0.006317136, step = 1586400 (2.255 sec) -2022-05-01 19:32:53,527 - tensorflow - INFO - global_step/sec: 88.6746 -2022-05-01 19:32:53,527 - tensorflow - INFO - loss = 0.008498697, step = 1586600 (2.255 sec) -2022-05-01 19:32:55,781 - tensorflow - INFO - global_step/sec: 88.7223 -2022-05-01 19:32:55,782 - tensorflow - INFO - loss = 0.013440279, step = 1586800 (2.254 sec) -2022-05-01 19:32:58,032 - tensorflow - INFO - global_step/sec: 88.8497 -2022-05-01 19:32:58,033 - tensorflow - INFO - loss = 0.026137035, step = 1587000 (2.251 sec) -2022-05-01 19:33:00,284 - tensorflow - INFO - global_step/sec: 88.8006 -2022-05-01 19:33:00,285 - tensorflow - INFO - loss = 0.010146251, step = 1587200 (2.252 sec) -2022-05-01 19:33:02,539 - tensorflow - INFO - global_step/sec: 88.6921 -2022-05-01 19:33:02,540 - tensorflow - INFO - loss = 0.13608116, step = 1587400 (2.255 sec) -2022-05-01 19:33:04,799 - tensorflow - INFO - global_step/sec: 88.527 -2022-05-01 19:33:04,799 - tensorflow - INFO - loss = 0.06195675, step = 1587600 (2.259 sec) -2022-05-01 19:33:07,049 - tensorflow - INFO - global_step/sec: 88.8633 -2022-05-01 19:33:07,050 - tensorflow - INFO - loss = 0.006039193, step = 1587800 (2.251 sec) -2022-05-01 19:33:09,303 - tensorflow - INFO - global_step/sec: 88.7286 -2022-05-01 19:33:09,304 - tensorflow - INFO - loss = 0.0078074397, step = 1588000 (2.254 sec) -2022-05-01 19:33:11,550 - tensorflow - INFO - global_step/sec: 89.0298 -2022-05-01 19:33:11,550 - tensorflow - INFO - loss = 0.020005397, step = 1588200 (2.246 sec) -2022-05-01 19:33:13,803 - tensorflow - INFO - global_step/sec: 88.7499 -2022-05-01 19:33:13,804 - tensorflow - INFO - loss = 0.023765197, step = 1588400 (2.254 sec) -2022-05-01 19:33:16,056 - tensorflow - INFO - global_step/sec: 88.7924 -2022-05-01 19:33:16,056 - tensorflow - INFO - loss = 0.046877913, step = 1588600 (2.252 sec) -2022-05-01 19:33:18,307 - tensorflow - INFO - global_step/sec: 88.8261 -2022-05-01 19:33:18,308 - tensorflow - INFO - loss = 0.028097004, step = 1588800 (2.252 sec) -2022-05-01 19:33:20,564 - tensorflow - INFO - global_step/sec: 88.6399 -2022-05-01 19:33:20,564 - tensorflow - INFO - loss = 0.057888266, step = 1589000 (2.256 sec) -2022-05-01 19:33:22,816 - tensorflow - INFO - global_step/sec: 88.8022 -2022-05-01 19:33:22,816 - tensorflow - INFO - loss = 0.05316, step = 1589200 (2.252 sec) -2022-05-01 19:33:25,064 - tensorflow - INFO - global_step/sec: 88.9413 -2022-05-01 19:33:25,065 - tensorflow - INFO - loss = 0.101530254, step = 1589400 (2.249 sec) -2022-05-01 19:33:27,315 - tensorflow - INFO - global_step/sec: 88.8613 -2022-05-01 19:33:27,316 - tensorflow - INFO - loss = 0.055434287, step = 1589600 (2.251 sec) -2022-05-01 19:33:29,567 - tensorflow - INFO - global_step/sec: 88.8333 -2022-05-01 19:33:29,567 - tensorflow - INFO - loss = 0.015367792, step = 1589800 (2.251 sec) -2022-05-01 19:33:31,822 - tensorflow - INFO - global_step/sec: 88.676 -2022-05-01 19:33:31,822 - tensorflow - INFO - loss = 0.07796347, step = 1590000 (2.255 sec) -2022-05-01 19:33:34,075 - tensorflow - INFO - global_step/sec: 88.7751 -2022-05-01 19:33:34,075 - tensorflow - INFO - loss = 0.024588708, step = 1590200 (2.253 sec) -2022-05-01 19:33:36,330 - tensorflow - INFO - global_step/sec: 88.6726 -2022-05-01 19:33:36,331 - tensorflow - INFO - loss = 0.009122549, step = 1590400 (2.256 sec) -2022-05-01 19:33:38,585 - tensorflow - INFO - global_step/sec: 88.7176 -2022-05-01 19:33:38,585 - tensorflow - INFO - loss = 0.01820556, step = 1590600 (2.254 sec) -2022-05-01 19:33:40,838 - tensorflow - INFO - global_step/sec: 88.7469 -2022-05-01 19:33:40,839 - tensorflow - INFO - loss = 0.13771403, step = 1590800 (2.254 sec) -2022-05-01 19:33:43,097 - tensorflow - INFO - global_step/sec: 88.5529 -2022-05-01 19:33:43,097 - tensorflow - INFO - loss = 0.01528587, step = 1591000 (2.259 sec) -2022-05-01 19:33:45,344 - tensorflow - INFO - global_step/sec: 89.003 -2022-05-01 19:33:45,344 - tensorflow - INFO - loss = 0.11659643, step = 1591200 (2.247 sec) -2022-05-01 19:33:47,595 - tensorflow - INFO - global_step/sec: 88.846 -2022-05-01 19:33:47,595 - tensorflow - INFO - loss = 0.016851345, step = 1591400 (2.251 sec) -2022-05-01 19:33:49,846 - tensorflow - INFO - global_step/sec: 88.8568 -2022-05-01 19:33:49,846 - tensorflow - INFO - loss = 0.013559494, step = 1591600 (2.251 sec) -2022-05-01 19:33:52,102 - tensorflow - INFO - global_step/sec: 88.6615 -2022-05-01 19:33:52,102 - tensorflow - INFO - loss = 0.04798165, step = 1591800 (2.256 sec) -2022-05-01 19:33:54,358 - tensorflow - INFO - global_step/sec: 88.6353 -2022-05-01 19:33:54,358 - tensorflow - INFO - loss = 0.018655952, step = 1592000 (2.256 sec) -2022-05-01 19:33:56,620 - tensorflow - INFO - global_step/sec: 88.4314 -2022-05-01 19:33:56,620 - tensorflow - INFO - loss = 0.03391049, step = 1592200 (2.262 sec) -2022-05-01 19:33:58,869 - tensorflow - INFO - global_step/sec: 88.9314 -2022-05-01 19:33:58,869 - tensorflow - INFO - loss = 0.069250464, step = 1592400 (2.249 sec) -2022-05-01 19:34:01,118 - tensorflow - INFO - global_step/sec: 88.9064 -2022-05-01 19:34:01,119 - tensorflow - INFO - loss = 0.035049617, step = 1592600 (2.250 sec) -2022-05-01 19:34:03,371 - tensorflow - INFO - global_step/sec: 88.7631 -2022-05-01 19:34:03,372 - tensorflow - INFO - loss = 0.009838936, step = 1592800 (2.253 sec) -2022-05-01 19:34:05,623 - tensorflow - INFO - global_step/sec: 88.8397 -2022-05-01 19:34:05,623 - tensorflow - INFO - loss = 0.047494456, step = 1593000 (2.251 sec) -2022-05-01 19:34:07,880 - tensorflow - INFO - global_step/sec: 88.5972 -2022-05-01 19:34:07,880 - tensorflow - INFO - loss = 0.09272047, step = 1593200 (2.257 sec) -2022-05-01 19:34:10,136 - tensorflow - INFO - global_step/sec: 88.6715 -2022-05-01 19:34:10,136 - tensorflow - INFO - loss = 0.0153245935, step = 1593400 (2.256 sec) -2022-05-01 19:34:12,394 - tensorflow - INFO - global_step/sec: 88.5618 -2022-05-01 19:34:12,394 - tensorflow - INFO - loss = 1.4519277, step = 1593600 (2.258 sec) -2022-05-01 19:34:14,657 - tensorflow - INFO - global_step/sec: 88.37 -2022-05-01 19:34:14,658 - tensorflow - INFO - loss = 0.58025074, step = 1593800 (2.263 sec) -2022-05-01 19:34:16,918 - tensorflow - INFO - global_step/sec: 88.4433 -2022-05-01 19:34:16,919 - tensorflow - INFO - loss = 0.018323332, step = 1594000 (2.261 sec) -2022-05-01 19:34:19,181 - tensorflow - INFO - global_step/sec: 88.3756 -2022-05-01 19:34:19,182 - tensorflow - INFO - loss = 0.027733069, step = 1594200 (2.263 sec) -2022-05-01 19:34:21,442 - tensorflow - INFO - global_step/sec: 88.4649 -2022-05-01 19:34:21,443 - tensorflow - INFO - loss = 0.021599734, step = 1594400 (2.261 sec) -2022-05-01 19:34:23,709 - tensorflow - INFO - global_step/sec: 88.2266 -2022-05-01 19:34:23,710 - tensorflow - INFO - loss = 0.020881085, step = 1594600 (2.267 sec) -2022-05-01 19:34:25,971 - tensorflow - INFO - global_step/sec: 88.4053 -2022-05-01 19:34:25,972 - tensorflow - INFO - loss = 0.034342658, step = 1594800 (2.262 sec) -2022-05-01 19:34:28,234 - tensorflow - INFO - global_step/sec: 88.3982 -2022-05-01 19:34:28,234 - tensorflow - INFO - loss = 0.025880713, step = 1595000 (2.262 sec) -2022-05-01 19:34:30,498 - tensorflow - INFO - global_step/sec: 88.3272 -2022-05-01 19:34:30,499 - tensorflow - INFO - loss = 0.130944, step = 1595200 (2.264 sec) -2022-05-01 19:34:32,759 - tensorflow - INFO - global_step/sec: 88.4758 -2022-05-01 19:34:32,759 - tensorflow - INFO - loss = 0.010540167, step = 1595400 (2.261 sec) -2022-05-01 19:34:35,021 - tensorflow - INFO - global_step/sec: 88.4221 -2022-05-01 19:34:35,021 - tensorflow - INFO - loss = 0.007979, step = 1595600 (2.262 sec) -2022-05-01 19:34:37,283 - tensorflow - INFO - global_step/sec: 88.4207 -2022-05-01 19:34:37,283 - tensorflow - INFO - loss = 0.018332234, step = 1595800 (2.262 sec) -2022-05-01 19:34:39,544 - tensorflow - INFO - global_step/sec: 88.4203 -2022-05-01 19:34:39,545 - tensorflow - INFO - loss = 0.029539935, step = 1596000 (2.262 sec) -2022-05-01 19:34:41,798 - tensorflow - INFO - global_step/sec: 88.7637 -2022-05-01 19:34:41,798 - tensorflow - INFO - loss = 0.010827591, step = 1596200 (2.253 sec) -2022-05-01 19:34:44,052 - tensorflow - INFO - global_step/sec: 88.709 -2022-05-01 19:34:44,053 - tensorflow - INFO - loss = 0.02367578, step = 1596400 (2.255 sec) -2022-05-01 19:34:46,302 - tensorflow - INFO - global_step/sec: 88.8811 -2022-05-01 19:34:46,303 - tensorflow - INFO - loss = 0.029965963, step = 1596600 (2.250 sec) -2022-05-01 19:34:48,561 - tensorflow - INFO - global_step/sec: 88.5344 -2022-05-01 19:34:48,562 - tensorflow - INFO - loss = 0.0040046135, step = 1596800 (2.259 sec) -2022-05-01 19:34:50,816 - tensorflow - INFO - global_step/sec: 88.7208 -2022-05-01 19:34:50,816 - tensorflow - INFO - loss = 0.013771693, step = 1597000 (2.254 sec) -2022-05-01 19:34:53,066 - tensorflow - INFO - global_step/sec: 88.8668 -2022-05-01 19:34:53,067 - tensorflow - INFO - loss = 0.0055685453, step = 1597200 (2.251 sec) -2022-05-01 19:34:55,317 - tensorflow - INFO - global_step/sec: 88.8493 -2022-05-01 19:34:55,318 - tensorflow - INFO - loss = 0.29111502, step = 1597400 (2.251 sec) -2022-05-01 19:34:57,567 - tensorflow - INFO - global_step/sec: 88.9108 -2022-05-01 19:34:57,567 - tensorflow - INFO - loss = 0.034654275, step = 1597600 (2.249 sec) -2022-05-01 19:34:59,819 - tensorflow - INFO - global_step/sec: 88.7871 -2022-05-01 19:34:59,820 - tensorflow - INFO - loss = 0.045142382, step = 1597800 (2.253 sec) -2022-05-01 19:35:02,077 - tensorflow - INFO - global_step/sec: 88.5762 -2022-05-01 19:35:02,078 - tensorflow - INFO - loss = 0.02174133, step = 1598000 (2.258 sec) -2022-05-01 19:35:04,335 - tensorflow - INFO - global_step/sec: 88.5995 -2022-05-01 19:35:04,335 - tensorflow - INFO - loss = 0.034647133, step = 1598200 (2.257 sec) -2022-05-01 19:35:06,584 - tensorflow - INFO - global_step/sec: 88.8987 -2022-05-01 19:35:06,585 - tensorflow - INFO - loss = 0.048062347, step = 1598400 (2.250 sec) -2022-05-01 19:35:08,836 - tensorflow - INFO - global_step/sec: 88.808 -2022-05-01 19:35:08,837 - tensorflow - INFO - loss = 0.06000845, step = 1598600 (2.252 sec) -2022-05-01 19:35:11,090 - tensorflow - INFO - global_step/sec: 88.738 -2022-05-01 19:35:11,091 - tensorflow - INFO - loss = 0.014896672, step = 1598800 (2.254 sec) -2022-05-01 19:35:13,348 - tensorflow - INFO - global_step/sec: 88.5689 -2022-05-01 19:35:13,349 - tensorflow - INFO - loss = 0.11341639, step = 1599000 (2.258 sec) -2022-05-01 19:35:15,607 - tensorflow - INFO - global_step/sec: 88.5451 -2022-05-01 19:35:15,607 - tensorflow - INFO - loss = 0.017968118, step = 1599200 (2.259 sec) -2022-05-01 19:35:17,859 - tensorflow - INFO - global_step/sec: 88.7948 -2022-05-01 19:35:17,860 - tensorflow - INFO - loss = 0.02949712, step = 1599400 (2.252 sec) -2022-05-01 19:35:20,112 - tensorflow - INFO - global_step/sec: 88.7764 -2022-05-01 19:35:20,113 - tensorflow - INFO - loss = 0.016649557, step = 1599600 (2.253 sec) -2022-05-01 19:35:22,367 - tensorflow - INFO - global_step/sec: 88.6975 -2022-05-01 19:35:22,368 - tensorflow - INFO - loss = 0.019204346, step = 1599800 (2.255 sec) -2022-05-01 19:35:24,612 - tensorflow - INFO - Saving checkpoints for 1600000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. -2022-05-01 19:35:24,629 - tensorflow - INFO - model.ckpt-1600000 is not in all_model_checkpoint_paths. Manually adding it. -2022-05-01 19:35:25,147 - tensorflow - INFO - Calling model_fn. -2022-05-01 19:35:25,275 - tensorflow - INFO - Done calling model_fn. -2022-05-01 19:35:25,290 - tensorflow - INFO - Starting evaluation at 2022-05-01T19:35:25Z -2022-05-01 19:35:25,354 - tensorflow - INFO - Graph was finalized. -2022-05-01 19:35:25,357 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1600000 -2022-05-01 19:35:25,639 - tensorflow - INFO - Running local_init_op. -2022-05-01 19:35:25,747 - tensorflow - INFO - Done running local_init_op. -2022-05-01 19:35:35,989 - tensorflow - INFO - Evaluation [3990/39900] -2022-05-01 19:35:45,552 - tensorflow - INFO - Evaluation [7980/39900] -2022-05-01 19:35:55,109 - tensorflow - INFO - Evaluation [11970/39900] -2022-05-01 19:36:04,674 - tensorflow - INFO - Evaluation [15960/39900] -2022-05-01 19:36:14,213 - tensorflow - INFO - Evaluation [19950/39900] -2022-05-01 19:36:23,777 - tensorflow - INFO - Evaluation [23940/39900] -2022-05-01 19:36:33,343 - tensorflow - INFO - Evaluation [27930/39900] -2022-05-01 19:36:42,896 - tensorflow - INFO - Evaluation [31920/39900] -2022-05-01 19:36:52,449 - tensorflow - INFO - Evaluation [35910/39900] -2022-05-01 19:37:02,022 - tensorflow - INFO - Evaluation [39900/39900] -2022-05-01 19:37:02,157 - tensorflow - INFO - Finished evaluation at 2022-05-01-19:37:02 -2022-05-01 19:37:02,157 - tensorflow - INFO - Saving dict for global step 1600000: global_step = 1600000, loss = 0.0288886, mean_degree_err = 5.0725417 -2022-05-01 19:37:02,157 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 1600000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1600000 -2022-05-01 19:37:02,171 - tensorflow - INFO - global_step/sec: 2.00392 -2022-05-01 19:37:02,172 - tensorflow - INFO - loss = 0.036867063, step = 1600000 (99.805 sec) -2022-05-01 19:37:04,446 - tensorflow - INFO - global_step/sec: 87.9391 -2022-05-01 19:37:04,446 - tensorflow - INFO - loss = 0.040967178, step = 1600200 (2.274 sec) -2022-05-01 19:37:06,704 - tensorflow - INFO - global_step/sec: 88.5445 -2022-05-01 19:37:06,705 - tensorflow - INFO - loss = 0.22369778, step = 1600400 (2.259 sec) -2022-05-01 19:37:08,963 - tensorflow - INFO - global_step/sec: 88.5558 -2022-05-01 19:37:08,963 - tensorflow - INFO - loss = 0.04972737, step = 1600600 (2.258 sec) -2022-05-01 19:37:11,221 - tensorflow - INFO - global_step/sec: 88.5523 -2022-05-01 19:37:11,222 - tensorflow - INFO - loss = 0.012088019, step = 1600800 (2.259 sec) -2022-05-01 19:37:13,476 - tensorflow - INFO - global_step/sec: 88.7227 -2022-05-01 19:37:13,476 - tensorflow - INFO - loss = 0.027309652, step = 1601000 (2.254 sec) -2022-05-01 19:37:15,733 - tensorflow - INFO - global_step/sec: 88.6028 -2022-05-01 19:37:15,733 - tensorflow - INFO - loss = 0.010206912, step = 1601200 (2.257 sec) -2022-05-01 19:37:17,986 - tensorflow - INFO - global_step/sec: 88.7572 -2022-05-01 19:37:17,987 - tensorflow - INFO - loss = 0.005925677, step = 1601400 (2.253 sec) -2022-05-01 19:37:20,241 - tensorflow - INFO - global_step/sec: 88.712 -2022-05-01 19:37:20,241 - tensorflow - INFO - loss = 0.01557181, step = 1601600 (2.254 sec) -2022-05-01 19:37:22,502 - tensorflow - INFO - global_step/sec: 88.4272 -2022-05-01 19:37:22,503 - tensorflow - INFO - loss = 0.012669121, step = 1601800 (2.262 sec) -2022-05-01 19:37:24,760 - tensorflow - INFO - global_step/sec: 88.6035 -2022-05-01 19:37:24,760 - tensorflow - INFO - loss = 0.010645354, step = 1602000 (2.257 sec) -2022-05-01 19:37:27,014 - tensorflow - INFO - global_step/sec: 88.7156 -2022-05-01 19:37:27,014 - tensorflow - INFO - loss = 0.017084083, step = 1602200 (2.254 sec) -2022-05-01 19:37:29,268 - tensorflow - INFO - global_step/sec: 88.7194 -2022-05-01 19:37:29,269 - tensorflow - INFO - loss = 0.025699474, step = 1602400 (2.254 sec) -2022-05-01 19:37:31,527 - tensorflow - INFO - global_step/sec: 88.5463 -2022-05-01 19:37:31,527 - tensorflow - INFO - loss = 0.022008544, step = 1602600 (2.259 sec) -2022-05-01 19:37:33,783 - tensorflow - INFO - global_step/sec: 88.6539 -2022-05-01 19:37:33,783 - tensorflow - INFO - loss = 0.010230547, step = 1602800 (2.256 sec) -2022-05-01 19:37:36,036 - tensorflow - INFO - global_step/sec: 88.7676 -2022-05-01 19:37:36,037 - tensorflow - INFO - loss = 0.029947232, step = 1603000 (2.253 sec) -2022-05-01 19:37:38,289 - tensorflow - INFO - global_step/sec: 88.7648 -2022-05-01 19:37:38,290 - tensorflow - INFO - loss = 0.007518686, step = 1603200 (2.253 sec) -2022-05-01 19:37:40,547 - tensorflow - INFO - global_step/sec: 88.585 -2022-05-01 19:37:40,547 - tensorflow - INFO - loss = 0.0097355675, step = 1603400 (2.258 sec) -2022-05-01 19:37:42,809 - tensorflow - INFO - global_step/sec: 88.4294 -2022-05-01 19:37:42,809 - tensorflow - INFO - loss = 0.074278854, step = 1603600 (2.262 sec) -2022-05-01 19:37:45,073 - tensorflow - INFO - global_step/sec: 88.3324 -2022-05-01 19:37:45,073 - tensorflow - INFO - loss = 0.017599702, step = 1603800 (2.264 sec) -2022-05-01 19:37:47,338 - tensorflow - INFO - global_step/sec: 88.3053 -2022-05-01 19:37:47,338 - tensorflow - INFO - loss = 0.06725284, step = 1604000 (2.265 sec) -2022-05-01 19:37:49,600 - tensorflow - INFO - global_step/sec: 88.4228 -2022-05-01 19:37:49,600 - tensorflow - INFO - loss = 0.06788803, step = 1604200 (2.262 sec) -2022-05-01 19:37:51,865 - tensorflow - INFO - global_step/sec: 88.2895 -2022-05-01 19:37:51,865 - tensorflow - INFO - loss = 0.024817625, step = 1604400 (2.265 sec) -2022-05-01 19:37:54,130 - tensorflow - INFO - global_step/sec: 88.3108 -2022-05-01 19:37:54,130 - tensorflow - INFO - loss = 0.011394728, step = 1604600 (2.265 sec) -2022-05-01 19:37:56,394 - tensorflow - INFO - global_step/sec: 88.3047 -2022-05-01 19:37:56,395 - tensorflow - INFO - loss = 0.07237558, step = 1604800 (2.265 sec) -2022-05-01 19:37:58,658 - tensorflow - INFO - global_step/sec: 88.3465 -2022-05-01 19:37:58,659 - tensorflow - INFO - loss = 0.08910976, step = 1605000 (2.264 sec) -2022-05-01 19:38:00,919 - tensorflow - INFO - global_step/sec: 88.4754 -2022-05-01 19:38:00,919 - tensorflow - INFO - loss = 0.10602189, step = 1605200 (2.261 sec) -2022-05-01 19:38:03,187 - tensorflow - INFO - global_step/sec: 88.1663 -2022-05-01 19:38:03,188 - tensorflow - INFO - loss = 0.35692906, step = 1605400 (2.268 sec) -2022-05-01 19:38:05,451 - tensorflow - INFO - global_step/sec: 88.3521 -2022-05-01 19:38:05,451 - tensorflow - INFO - loss = 0.025508648, step = 1605600 (2.264 sec) -2022-05-01 19:38:07,710 - tensorflow - INFO - global_step/sec: 88.531 -2022-05-01 19:38:07,710 - tensorflow - INFO - loss = 0.020784743, step = 1605800 (2.259 sec) -2022-05-01 19:38:09,971 - tensorflow - INFO - global_step/sec: 88.4562 -2022-05-01 19:38:09,971 - tensorflow - INFO - loss = 0.009046527, step = 1606000 (2.261 sec) -2022-05-01 19:38:12,230 - tensorflow - INFO - global_step/sec: 88.5493 -2022-05-01 19:38:12,230 - tensorflow - INFO - loss = 0.12052645, step = 1606200 (2.259 sec) -2022-05-01 19:38:14,487 - tensorflow - INFO - global_step/sec: 88.6104 -2022-05-01 19:38:14,487 - tensorflow - INFO - loss = 0.010093826, step = 1606400 (2.257 sec) -2022-05-01 19:38:16,748 - tensorflow - INFO - global_step/sec: 88.4417 -2022-05-01 19:38:16,748 - tensorflow - INFO - loss = 0.060307868, step = 1606600 (2.261 sec) -2022-05-01 19:38:19,003 - tensorflow - INFO - global_step/sec: 88.7118 -2022-05-01 19:38:19,003 - tensorflow - INFO - loss = 0.0069079367, step = 1606800 (2.255 sec) -2022-05-01 19:38:21,254 - tensorflow - INFO - global_step/sec: 88.8224 -2022-05-01 19:38:21,255 - tensorflow - INFO - loss = 0.040427912, step = 1607000 (2.252 sec) -2022-05-01 19:38:23,512 - tensorflow - INFO - global_step/sec: 88.6025 -2022-05-01 19:38:23,512 - tensorflow - INFO - loss = 0.04088958, step = 1607200 (2.257 sec) -2022-05-01 19:38:25,767 - tensorflow - INFO - global_step/sec: 88.6759 -2022-05-01 19:38:25,767 - tensorflow - INFO - loss = 0.4712106, step = 1607400 (2.255 sec) -2022-05-01 19:38:28,028 - tensorflow - INFO - global_step/sec: 88.4476 -2022-05-01 19:38:28,029 - tensorflow - INFO - loss = 0.11803105, step = 1607600 (2.261 sec) -2022-05-01 19:38:30,286 - tensorflow - INFO - global_step/sec: 88.5953 -2022-05-01 19:38:30,286 - tensorflow - INFO - loss = 0.0077702906, step = 1607800 (2.257 sec) -2022-05-01 19:38:32,542 - tensorflow - INFO - global_step/sec: 88.6321 -2022-05-01 19:38:32,543 - tensorflow - INFO - loss = 0.07963283, step = 1608000 (2.256 sec) -2022-05-01 19:38:34,799 - tensorflow - INFO - global_step/sec: 88.6339 -2022-05-01 19:38:34,799 - tensorflow - INFO - loss = 0.28873613, step = 1608200 (2.256 sec) -2022-05-01 19:38:37,053 - tensorflow - INFO - global_step/sec: 88.7161 -2022-05-01 19:38:37,053 - tensorflow - INFO - loss = 0.05215706, step = 1608400 (2.254 sec) -2022-05-01 19:38:39,307 - tensorflow - INFO - global_step/sec: 88.7456 -2022-05-01 19:38:39,307 - tensorflow - INFO - loss = 0.0728301, step = 1608600 (2.254 sec) -2022-05-01 19:38:41,563 - tensorflow - INFO - global_step/sec: 88.6317 -2022-05-01 19:38:41,564 - tensorflow - INFO - loss = 0.05030317, step = 1608800 (2.257 sec) -2022-05-01 19:38:43,819 - tensorflow - INFO - global_step/sec: 88.6564 -2022-05-01 19:38:43,819 - tensorflow - INFO - loss = 0.013282448, step = 1609000 (2.256 sec) -2022-05-01 19:38:46,071 - tensorflow - INFO - global_step/sec: 88.8167 -2022-05-01 19:38:46,071 - tensorflow - INFO - loss = 0.019527066, step = 1609200 (2.252 sec) -2022-05-01 19:38:48,327 - tensorflow - INFO - global_step/sec: 88.6506 -2022-05-01 19:38:48,327 - tensorflow - INFO - loss = 0.009463694, step = 1609400 (2.256 sec) -2022-05-01 19:38:50,578 - tensorflow - INFO - global_step/sec: 88.849 -2022-05-01 19:38:50,578 - tensorflow - INFO - loss = 0.014914087, step = 1609600 (2.251 sec) -2022-05-01 19:38:52,834 - tensorflow - INFO - global_step/sec: 88.6385 -2022-05-01 19:38:52,835 - tensorflow - INFO - loss = 0.03069091, step = 1609800 (2.256 sec) -2022-05-01 19:38:55,093 - tensorflow - INFO - global_step/sec: 88.5442 -2022-05-01 19:38:55,093 - tensorflow - INFO - loss = 0.015568502, step = 1610000 (2.259 sec) -2022-05-01 19:38:57,350 - tensorflow - INFO - global_step/sec: 88.6284 -2022-05-01 19:38:57,350 - tensorflow - INFO - loss = 0.026304193, step = 1610200 (2.257 sec) -2022-05-01 19:38:59,600 - tensorflow - INFO - global_step/sec: 88.8798 -2022-05-01 19:38:59,600 - tensorflow - INFO - loss = 0.01577914, step = 1610400 (2.250 sec) -2022-05-01 19:39:01,853 - tensorflow - INFO - global_step/sec: 88.7517 -2022-05-01 19:39:01,854 - tensorflow - INFO - loss = 0.010001397, step = 1610600 (2.253 sec) -2022-05-01 19:39:04,111 - tensorflow - INFO - global_step/sec: 88.6022 -2022-05-01 19:39:04,111 - tensorflow - INFO - loss = 0.16669104, step = 1610800 (2.257 sec) -2022-05-01 19:39:06,372 - tensorflow - INFO - global_step/sec: 88.4573 -2022-05-01 19:39:06,372 - tensorflow - INFO - loss = 0.086048484, step = 1611000 (2.261 sec) -2022-05-01 19:39:08,626 - tensorflow - INFO - global_step/sec: 88.7357 -2022-05-01 19:39:08,626 - tensorflow - INFO - loss = 0.015150867, step = 1611200 (2.254 sec) -2022-05-01 19:39:10,874 - tensorflow - INFO - global_step/sec: 88.9594 -2022-05-01 19:39:10,874 - tensorflow - INFO - loss = 0.029532706, step = 1611400 (2.248 sec) -2022-05-01 19:39:13,123 - tensorflow - INFO - global_step/sec: 88.9277 -2022-05-01 19:39:13,123 - tensorflow - INFO - loss = 0.0062228935, step = 1611600 (2.249 sec) -2022-05-01 19:39:15,379 - tensorflow - INFO - global_step/sec: 88.631 -2022-05-01 19:39:15,380 - tensorflow - INFO - loss = 0.02429897, step = 1611800 (2.257 sec) -2022-05-01 19:39:17,634 - tensorflow - INFO - global_step/sec: 88.7114 -2022-05-01 19:39:17,634 - tensorflow - INFO - loss = 0.012944584, step = 1612000 (2.254 sec) -2022-05-01 19:39:19,890 - tensorflow - INFO - global_step/sec: 88.6536 -2022-05-01 19:39:19,890 - tensorflow - INFO - loss = 0.12568352, step = 1612200 (2.256 sec) -2022-05-01 19:39:22,144 - tensorflow - INFO - global_step/sec: 88.7101 -2022-05-01 19:39:22,145 - tensorflow - INFO - loss = 0.5938471, step = 1612400 (2.255 sec) -2022-05-01 19:39:24,396 - tensorflow - INFO - global_step/sec: 88.8153 -2022-05-01 19:39:24,397 - tensorflow - INFO - loss = 0.034583516, step = 1612600 (2.252 sec) -2022-05-01 19:39:26,652 - tensorflow - INFO - global_step/sec: 88.6671 -2022-05-01 19:39:26,652 - tensorflow - INFO - loss = 0.022513185, step = 1612800 (2.256 sec) -2022-05-01 19:39:28,906 - tensorflow - INFO - global_step/sec: 88.7386 -2022-05-01 19:39:28,906 - tensorflow - INFO - loss = 0.017508483, step = 1613000 (2.254 sec) -2022-05-01 19:39:31,167 - tensorflow - INFO - global_step/sec: 88.4312 -2022-05-01 19:39:31,168 - tensorflow - INFO - loss = 0.004814804, step = 1613200 (2.262 sec) -2022-05-01 19:39:33,421 - tensorflow - INFO - global_step/sec: 88.7257 -2022-05-01 19:39:33,422 - tensorflow - INFO - loss = 0.02977703, step = 1613400 (2.254 sec) -2022-05-01 19:39:35,669 - tensorflow - INFO - global_step/sec: 88.9721 -2022-05-01 19:39:35,670 - tensorflow - INFO - loss = 0.026092917, step = 1613600 (2.248 sec) -2022-05-01 19:39:37,918 - tensorflow - INFO - global_step/sec: 88.9591 -2022-05-01 19:39:37,918 - tensorflow - INFO - loss = 0.022681203, step = 1613800 (2.248 sec) -2022-05-01 19:39:40,174 - tensorflow - INFO - global_step/sec: 88.6215 -2022-05-01 19:39:40,175 - tensorflow - INFO - loss = 0.031323865, step = 1614000 (2.257 sec) -2022-05-01 19:39:42,425 - tensorflow - INFO - global_step/sec: 88.8421 -2022-05-01 19:39:42,426 - tensorflow - INFO - loss = 0.010317453, step = 1614200 (2.251 sec) -2022-05-01 19:39:44,681 - tensorflow - INFO - global_step/sec: 88.6888 -2022-05-01 19:39:44,681 - tensorflow - INFO - loss = 0.053331293, step = 1614400 (2.255 sec) -2022-05-01 19:39:46,930 - tensorflow - INFO - global_step/sec: 88.8947 -2022-05-01 19:39:46,931 - tensorflow - INFO - loss = 0.0056943605, step = 1614600 (2.250 sec) -2022-05-01 19:39:49,187 - tensorflow - INFO - global_step/sec: 88.6119 -2022-05-01 19:39:49,188 - tensorflow - INFO - loss = 0.042760827, step = 1614800 (2.257 sec) -2022-05-01 19:39:51,442 - tensorflow - INFO - global_step/sec: 88.7208 -2022-05-01 19:39:51,442 - tensorflow - INFO - loss = 0.09538596, step = 1615000 (2.254 sec) -2022-05-01 19:39:53,698 - tensorflow - INFO - global_step/sec: 88.6245 -2022-05-01 19:39:53,699 - tensorflow - INFO - loss = 0.0061489423, step = 1615200 (2.257 sec) -2022-05-01 19:39:55,959 - tensorflow - INFO - global_step/sec: 88.4757 -2022-05-01 19:39:55,959 - tensorflow - INFO - loss = 0.025436368, step = 1615400 (2.261 sec) -2022-05-01 19:39:58,211 - tensorflow - INFO - global_step/sec: 88.8139 -2022-05-01 19:39:58,211 - tensorflow - INFO - loss = 0.019615395, step = 1615600 (2.252 sec) -2022-05-01 19:40:00,468 - tensorflow - INFO - global_step/sec: 88.6208 -2022-05-01 19:40:00,468 - tensorflow - INFO - loss = 0.008789796, step = 1615800 (2.257 sec) -2022-05-01 19:40:02,720 - tensorflow - INFO - global_step/sec: 88.7873 -2022-05-01 19:40:02,721 - tensorflow - INFO - loss = 0.014913684, step = 1616000 (2.253 sec) -2022-05-01 19:40:04,974 - tensorflow - INFO - global_step/sec: 88.7429 -2022-05-01 19:40:04,974 - tensorflow - INFO - loss = 0.07278446, step = 1616200 (2.254 sec) -2022-05-01 19:40:07,226 - tensorflow - INFO - global_step/sec: 88.8158 -2022-05-01 19:40:07,226 - tensorflow - INFO - loss = 0.12501572, step = 1616400 (2.252 sec) -2022-05-01 19:40:09,481 - tensorflow - INFO - global_step/sec: 88.6723 -2022-05-01 19:40:09,482 - tensorflow - INFO - loss = 0.28377822, step = 1616600 (2.256 sec) -2022-05-01 19:40:11,732 - tensorflow - INFO - global_step/sec: 88.8551 -2022-05-01 19:40:11,733 - tensorflow - INFO - loss = 0.021106012, step = 1616800 (2.251 sec) -2022-05-01 19:40:13,987 - tensorflow - INFO - global_step/sec: 88.7023 -2022-05-01 19:40:13,987 - tensorflow - INFO - loss = 8.388082, step = 1617000 (2.255 sec) -2022-05-01 19:40:16,238 - tensorflow - INFO - global_step/sec: 88.8266 -2022-05-01 19:40:16,239 - tensorflow - INFO - loss = 0.019565396, step = 1617200 (2.252 sec) -2022-05-01 19:40:18,491 - tensorflow - INFO - global_step/sec: 88.7973 -2022-05-01 19:40:18,491 - tensorflow - INFO - loss = 0.03616175, step = 1617400 (2.252 sec) -2022-05-01 19:40:20,743 - tensorflow - INFO - global_step/sec: 88.807 -2022-05-01 19:40:20,743 - tensorflow - INFO - loss = 0.05423862, step = 1617600 (2.252 sec) -2022-05-01 19:40:22,997 - tensorflow - INFO - global_step/sec: 88.7125 -2022-05-01 19:40:22,998 - tensorflow - INFO - loss = 0.042066444, step = 1617800 (2.255 sec) -2022-05-01 19:40:25,250 - tensorflow - INFO - global_step/sec: 88.7986 -2022-05-01 19:40:25,250 - tensorflow - INFO - loss = 0.011787547, step = 1618000 (2.252 sec) -2022-05-01 19:40:27,505 - tensorflow - INFO - global_step/sec: 88.6943 -2022-05-01 19:40:27,505 - tensorflow - INFO - loss = 0.04883994, step = 1618200 (2.255 sec) -2022-05-01 19:40:29,760 - tensorflow - INFO - global_step/sec: 88.677 -2022-05-01 19:40:29,760 - tensorflow - INFO - loss = 0.012506776, step = 1618400 (2.255 sec) -2022-05-01 19:40:32,022 - tensorflow - INFO - global_step/sec: 88.4313 -2022-05-01 19:40:32,022 - tensorflow - INFO - loss = 0.025670322, step = 1618600 (2.262 sec) -2022-05-01 19:40:34,279 - tensorflow - INFO - global_step/sec: 88.5886 -2022-05-01 19:40:34,280 - tensorflow - INFO - loss = 0.04210098, step = 1618800 (2.258 sec) -2022-05-01 19:40:36,528 - tensorflow - INFO - global_step/sec: 88.9197 -2022-05-01 19:40:36,529 - tensorflow - INFO - loss = 0.016680093, step = 1619000 (2.249 sec) -2022-05-01 19:40:38,775 - tensorflow - INFO - global_step/sec: 89.0187 -2022-05-01 19:40:38,775 - tensorflow - INFO - loss = 0.46585515, step = 1619200 (2.247 sec) -2022-05-01 19:40:41,031 - tensorflow - INFO - global_step/sec: 88.6695 -2022-05-01 19:40:41,031 - tensorflow - INFO - loss = 0.023283059, step = 1619400 (2.256 sec) -2022-05-01 19:40:43,283 - tensorflow - INFO - global_step/sec: 88.7917 -2022-05-01 19:40:43,284 - tensorflow - INFO - loss = 0.015406001, step = 1619600 (2.252 sec) -2022-05-01 19:40:45,537 - tensorflow - INFO - global_step/sec: 88.7295 -2022-05-01 19:40:45,538 - tensorflow - INFO - loss = 0.021344887, step = 1619800 (2.254 sec) -2022-05-01 19:40:47,795 - tensorflow - INFO - global_step/sec: 88.5796 -2022-05-01 19:40:47,795 - tensorflow - 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INFO - loss = 0.009885623, step = 1698000 (2.259 sec) -2022-05-01 19:55:29,711 - tensorflow - INFO - global_step/sec: 88.2778 -2022-05-01 19:55:29,712 - tensorflow - INFO - loss = 0.02006259, step = 1698200 (2.266 sec) -2022-05-01 19:55:31,979 - tensorflow - INFO - global_step/sec: 88.188 -2022-05-01 19:55:31,980 - tensorflow - INFO - loss = 0.0032196615, step = 1698400 (2.268 sec) -2022-05-01 19:55:34,236 - tensorflow - INFO - global_step/sec: 88.6414 -2022-05-01 19:55:34,236 - tensorflow - INFO - loss = 0.010056755, step = 1698600 (2.256 sec) -2022-05-01 19:55:36,494 - tensorflow - INFO - global_step/sec: 88.5485 -2022-05-01 19:55:36,495 - tensorflow - INFO - loss = 0.053509187, step = 1698800 (2.259 sec) -2022-05-01 19:55:38,753 - tensorflow - INFO - global_step/sec: 88.5453 -2022-05-01 19:55:38,753 - tensorflow - INFO - loss = 0.08257486, step = 1699000 (2.259 sec) -2022-05-01 19:55:41,011 - tensorflow - INFO - global_step/sec: 88.5622 -2022-05-01 19:55:41,012 - tensorflow - INFO - loss = 0.037976272, step = 1699200 (2.258 sec) -2022-05-01 19:55:43,270 - tensorflow - INFO - global_step/sec: 88.554 -2022-05-01 19:55:43,270 - tensorflow - INFO - loss = 0.017978225, step = 1699400 (2.258 sec) -2022-05-01 19:55:45,528 - tensorflow - INFO - global_step/sec: 88.5821 -2022-05-01 19:55:45,528 - tensorflow - INFO - loss = 0.012758279, step = 1699600 (2.258 sec) -2022-05-01 19:55:47,786 - tensorflow - INFO - global_step/sec: 88.5669 -2022-05-01 19:55:47,786 - tensorflow - INFO - loss = 0.022172417, step = 1699800 (2.258 sec) -2022-05-01 19:55:50,034 - tensorflow - INFO - Saving checkpoints for 1700000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. -2022-05-01 19:55:50,049 - tensorflow - INFO - model.ckpt-1700000 is not in all_model_checkpoint_paths. Manually adding it. -2022-05-01 19:55:50,567 - tensorflow - INFO - Calling model_fn. -2022-05-01 19:55:50,695 - tensorflow - INFO - Done calling model_fn. -2022-05-01 19:55:50,710 - tensorflow - INFO - Starting evaluation at 2022-05-01T19:55:50Z -2022-05-01 19:55:50,773 - tensorflow - INFO - Graph was finalized. -2022-05-01 19:55:50,777 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1700000 -2022-05-01 19:55:51,056 - tensorflow - INFO - Running local_init_op. -2022-05-01 19:55:51,164 - tensorflow - INFO - Done running local_init_op. -2022-05-01 19:56:01,425 - tensorflow - INFO - Evaluation [3990/39900] -2022-05-01 19:56:10,995 - tensorflow - INFO - Evaluation [7980/39900] -2022-05-01 19:56:20,542 - tensorflow - INFO - Evaluation [11970/39900] -2022-05-01 19:56:30,094 - tensorflow - INFO - Evaluation [15960/39900] -2022-05-01 19:56:39,656 - tensorflow - INFO - Evaluation [19950/39900] -2022-05-01 19:56:49,226 - tensorflow - INFO - Evaluation [23940/39900] -2022-05-01 19:56:58,785 - tensorflow - INFO - Evaluation [27930/39900] -2022-05-01 19:57:08,350 - tensorflow - INFO - Evaluation [31920/39900] -2022-05-01 19:57:17,900 - tensorflow - INFO - Evaluation [35910/39900] -2022-05-01 19:57:27,483 - tensorflow - INFO - Evaluation [39900/39900] -2022-05-01 19:57:27,620 - tensorflow - INFO - Finished evaluation at 2022-05-01-19:57:27 -2022-05-01 19:57:27,620 - tensorflow - INFO - Saving dict for global step 1700000: global_step = 1700000, loss = 0.016528208, mean_degree_err = 3.3223944 -2022-05-01 19:57:27,621 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 1700000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1700000 -2022-05-01 19:57:27,635 - tensorflow - INFO - global_step/sec: 2.00302 -2022-05-01 19:57:27,635 - tensorflow - INFO - loss = 0.018213827, step = 1700000 (99.849 sec) -2022-05-01 19:57:29,903 - tensorflow - INFO - global_step/sec: 88.1838 -2022-05-01 19:57:29,903 - tensorflow - INFO - loss = 0.021835033, step = 1700200 (2.268 sec) -2022-05-01 19:57:32,156 - tensorflow - INFO - global_step/sec: 88.7664 -2022-05-01 19:57:32,156 - tensorflow - INFO - loss = 0.041633718, step = 1700400 (2.253 sec) -2022-05-01 19:57:34,411 - tensorflow - INFO - global_step/sec: 88.6858 -2022-05-01 19:57:34,411 - tensorflow - INFO - loss = 0.10517413, step = 1700600 (2.255 sec) -2022-05-01 19:57:36,664 - tensorflow - INFO - global_step/sec: 88.7511 -2022-05-01 19:57:36,665 - tensorflow - INFO - loss = 0.037491966, step = 1700800 (2.254 sec) -2022-05-01 19:57:38,916 - tensorflow - INFO - global_step/sec: 88.8348 -2022-05-01 19:57:38,916 - tensorflow - INFO - loss = 0.031109095, step = 1701000 (2.251 sec) -2022-05-01 19:57:41,173 - tensorflow - INFO - global_step/sec: 88.623 -2022-05-01 19:57:41,173 - tensorflow - INFO - loss = 0.040808327, step = 1701200 (2.257 sec) -2022-05-01 19:57:43,428 - tensorflow - INFO - global_step/sec: 88.6723 -2022-05-01 19:57:43,428 - tensorflow - INFO - loss = 0.47118276, step = 1701400 (2.256 sec) -2022-05-01 19:57:45,677 - tensorflow - INFO - global_step/sec: 88.942 -2022-05-01 19:57:45,677 - tensorflow - INFO - loss = 0.040171977, step = 1701600 (2.249 sec) -2022-05-01 19:57:47,929 - tensorflow - INFO - global_step/sec: 88.8062 -2022-05-01 19:57:47,929 - tensorflow - INFO - loss = 0.01732021, step = 1701800 (2.252 sec) -2022-05-01 19:57:50,183 - tensorflow - INFO - global_step/sec: 88.7345 -2022-05-01 19:57:50,183 - tensorflow - INFO - loss = 0.013448552, step = 1702000 (2.254 sec) -2022-05-01 19:57:52,438 - tensorflow - INFO - global_step/sec: 88.6815 -2022-05-01 19:57:52,438 - tensorflow - INFO - loss = 0.017138971, step = 1702200 (2.255 sec) -2022-05-01 19:57:54,692 - tensorflow - INFO - global_step/sec: 88.7224 -2022-05-01 19:57:54,693 - tensorflow - INFO - loss = 0.01174594, step = 1702400 (2.254 sec) -2022-05-01 19:57:56,946 - tensorflow - INFO - global_step/sec: 88.7225 -2022-05-01 19:57:56,947 - tensorflow - INFO - loss = 0.041543134, step = 1702600 (2.254 sec) -2022-05-01 19:57:59,199 - tensorflow - INFO - global_step/sec: 88.7761 -2022-05-01 19:57:59,200 - tensorflow - INFO - loss = 0.048404578, step = 1702800 (2.253 sec) -2022-05-01 19:58:01,454 - tensorflow - INFO - global_step/sec: 88.7005 -2022-05-01 19:58:01,454 - tensorflow - INFO - loss = 0.14215983, step = 1703000 (2.255 sec) -2022-05-01 19:58:03,707 - tensorflow - INFO - global_step/sec: 88.7618 -2022-05-01 19:58:03,708 - tensorflow - INFO - loss = 0.027555201, step = 1703200 (2.253 sec) -2022-05-01 19:58:05,972 - tensorflow - INFO - global_step/sec: 88.3134 -2022-05-01 19:58:05,972 - tensorflow - INFO - loss = 0.045765974, step = 1703400 (2.265 sec) -2022-05-01 19:58:08,230 - tensorflow - INFO - global_step/sec: 88.5631 -2022-05-01 19:58:08,231 - tensorflow - INFO - loss = 0.037620254, step = 1703600 (2.258 sec) -2022-05-01 19:58:10,487 - tensorflow - INFO - global_step/sec: 88.638 -2022-05-01 19:58:10,487 - tensorflow - INFO - loss = 0.03932178, step = 1703800 (2.256 sec) -2022-05-01 19:58:12,738 - tensorflow - INFO - global_step/sec: 88.8331 -2022-05-01 19:58:12,738 - tensorflow - INFO - loss = 0.024784284, step = 1704000 (2.251 sec) -2022-05-01 19:58:14,990 - tensorflow - INFO - global_step/sec: 88.8245 -2022-05-01 19:58:14,990 - tensorflow - INFO - loss = 0.03867591, step = 1704200 (2.252 sec) -2022-05-01 19:58:17,242 - tensorflow - INFO - global_step/sec: 88.8119 -2022-05-01 19:58:17,242 - tensorflow - INFO - loss = 0.17101954, step = 1704400 (2.252 sec) -2022-05-01 19:58:19,502 - tensorflow - INFO - global_step/sec: 88.4969 -2022-05-01 19:58:19,502 - tensorflow - INFO - loss = 0.12228063, step = 1704600 (2.260 sec) -2022-05-01 19:58:21,751 - tensorflow - INFO - global_step/sec: 88.8995 -2022-05-01 19:58:21,752 - tensorflow - INFO - loss = 0.020781545, step = 1704800 (2.250 sec) -2022-05-01 19:58:24,001 - tensorflow - INFO - global_step/sec: 88.8852 -2022-05-01 19:58:24,002 - tensorflow - INFO - loss = 0.016060911, step = 1705000 (2.250 sec) -2022-05-01 19:58:26,257 - tensorflow - INFO - global_step/sec: 88.6791 -2022-05-01 19:58:26,257 - tensorflow - INFO - loss = 0.013135418, step = 1705200 (2.255 sec) -2022-05-01 19:58:28,508 - tensorflow - INFO - global_step/sec: 88.8238 -2022-05-01 19:58:28,509 - tensorflow - INFO - loss = 0.033608403, step = 1705400 (2.252 sec) -2022-05-01 19:58:30,774 - tensorflow - INFO - global_step/sec: 88.2905 -2022-05-01 19:58:30,774 - tensorflow - INFO - loss = 0.02455812, step = 1705600 (2.265 sec) -2022-05-01 19:58:33,024 - tensorflow - INFO - global_step/sec: 88.8609 -2022-05-01 19:58:33,025 - tensorflow - INFO - loss = 0.73054725, step = 1705800 (2.251 sec) -2022-05-01 19:58:35,271 - tensorflow - INFO - global_step/sec: 89.0251 -2022-05-01 19:58:35,271 - tensorflow - INFO - loss = 0.46992564, step = 1706000 (2.247 sec) -2022-05-01 19:58:37,521 - tensorflow - INFO - global_step/sec: 88.8733 -2022-05-01 19:58:37,522 - tensorflow - INFO - loss = 0.008819725, step = 1706200 (2.250 sec) -2022-05-01 19:58:39,774 - tensorflow - INFO - global_step/sec: 88.7783 -2022-05-01 19:58:39,774 - tensorflow - INFO - loss = 0.05270659, step = 1706400 (2.253 sec) -2022-05-01 19:58:42,030 - tensorflow - INFO - global_step/sec: 88.6664 -2022-05-01 19:58:42,030 - tensorflow - INFO - loss = 0.06409759, step = 1706600 (2.256 sec) -2022-05-01 19:58:44,289 - tensorflow - INFO - global_step/sec: 88.5222 -2022-05-01 19:58:44,289 - tensorflow - INFO - loss = 0.017197635, step = 1706800 (2.259 sec) -2022-05-01 19:58:46,544 - tensorflow - INFO - global_step/sec: 88.7045 -2022-05-01 19:58:46,544 - tensorflow - INFO - loss = 0.0068109254, step = 1707000 (2.255 sec) -2022-05-01 19:58:48,793 - tensorflow - INFO - global_step/sec: 88.9021 -2022-05-01 19:58:48,794 - tensorflow - INFO - loss = 0.005135089, step = 1707200 (2.250 sec) -2022-05-01 19:58:51,047 - tensorflow - INFO - global_step/sec: 88.7387 -2022-05-01 19:58:51,048 - tensorflow - INFO - loss = 0.015555227, step = 1707400 (2.254 sec) -2022-05-01 19:58:53,301 - tensorflow - INFO - global_step/sec: 88.7398 -2022-05-01 19:58:53,301 - tensorflow - INFO - loss = 0.008056469, step = 1707600 (2.254 sec) -2022-05-01 19:58:55,556 - tensorflow - INFO - global_step/sec: 88.6893 -2022-05-01 19:58:55,556 - tensorflow - INFO - loss = 11.810549, step = 1707800 (2.255 sec) -2022-05-01 19:58:57,820 - tensorflow - INFO - global_step/sec: 88.3409 -2022-05-01 19:58:57,820 - tensorflow - INFO - loss = 0.63033855, step = 1708000 (2.264 sec) -2022-05-01 19:59:00,084 - tensorflow - INFO - global_step/sec: 88.3282 -2022-05-01 19:59:00,085 - tensorflow - INFO - loss = 0.02858428, step = 1708200 (2.264 sec) -2022-05-01 19:59:02,342 - tensorflow - INFO - global_step/sec: 88.5962 -2022-05-01 19:59:02,342 - tensorflow - INFO - loss = 0.028509991, step = 1708400 (2.257 sec) -2022-05-01 19:59:04,599 - tensorflow - INFO - global_step/sec: 88.5975 -2022-05-01 19:59:04,599 - tensorflow - INFO - loss = 0.043081976, step = 1708600 (2.257 sec) -2022-05-01 19:59:06,856 - tensorflow - INFO - global_step/sec: 88.6067 -2022-05-01 19:59:06,857 - tensorflow - INFO - loss = 0.6489007, step = 1708800 (2.257 sec) -2022-05-01 19:59:09,115 - tensorflow - INFO - global_step/sec: 88.5532 -2022-05-01 19:59:09,115 - tensorflow - INFO - loss = 0.06143674, step = 1709000 (2.259 sec) -2022-05-01 19:59:11,374 - tensorflow - INFO - global_step/sec: 88.5094 -2022-05-01 19:59:11,375 - tensorflow - INFO - loss = 0.014285991, step = 1709200 (2.260 sec) -2022-05-01 19:59:13,635 - tensorflow - INFO - global_step/sec: 88.482 -2022-05-01 19:59:13,635 - tensorflow - INFO - loss = 0.02394065, step = 1709400 (2.260 sec) -2022-05-01 19:59:15,894 - tensorflow - INFO - global_step/sec: 88.521 -2022-05-01 19:59:15,895 - tensorflow - INFO - loss = 0.040852383, step = 1709600 (2.259 sec) -2022-05-01 19:59:18,155 - tensorflow - INFO - global_step/sec: 88.4788 -2022-05-01 19:59:18,155 - tensorflow - INFO - loss = 0.020867106, step = 1709800 (2.260 sec) -2022-05-01 19:59:20,416 - tensorflow - INFO - global_step/sec: 88.444 -2022-05-01 19:59:20,416 - tensorflow - INFO - loss = 0.015351091, step = 1710000 (2.261 sec) -2022-05-01 19:59:22,678 - tensorflow - INFO - global_step/sec: 88.4161 -2022-05-01 19:59:22,678 - tensorflow - INFO - loss = 0.008525239, step = 1710200 (2.262 sec) -2022-05-01 19:59:24,939 - tensorflow - INFO - global_step/sec: 88.4628 -2022-05-01 19:59:24,939 - tensorflow - INFO - loss = 0.041885566, step = 1710400 (2.261 sec) -2022-05-01 19:59:27,199 - tensorflow - INFO - global_step/sec: 88.4711 -2022-05-01 19:59:27,200 - tensorflow - INFO - loss = 0.018942935, step = 1710600 (2.261 sec) -2022-05-01 19:59:29,452 - tensorflow - INFO - global_step/sec: 88.8002 -2022-05-01 19:59:29,452 - tensorflow - INFO - loss = 0.008305543, step = 1710800 (2.252 sec) -2022-05-01 19:59:31,714 - tensorflow - INFO - global_step/sec: 88.3904 -2022-05-01 19:59:31,715 - tensorflow - INFO - loss = 0.02622455, step = 1711000 (2.263 sec) -2022-05-01 19:59:33,966 - tensorflow - INFO - global_step/sec: 88.8314 -2022-05-01 19:59:33,966 - tensorflow - INFO - loss = 0.03469228, step = 1711200 (2.251 sec) -2022-05-01 19:59:36,219 - tensorflow - INFO - global_step/sec: 88.7587 -2022-05-01 19:59:36,219 - tensorflow - INFO - loss = 0.012422364, step = 1711400 (2.253 sec) -2022-05-01 19:59:38,473 - tensorflow - INFO - global_step/sec: 88.7485 -2022-05-01 19:59:38,473 - tensorflow - INFO - loss = 0.012812477, step = 1711600 (2.254 sec) -2022-05-01 19:59:40,725 - tensorflow - INFO - global_step/sec: 88.7892 -2022-05-01 19:59:40,726 - tensorflow - INFO - loss = 0.028063836, step = 1711800 (2.253 sec) -2022-05-01 19:59:42,975 - tensorflow - INFO - global_step/sec: 88.9101 -2022-05-01 19:59:42,975 - tensorflow - INFO - loss = 0.010674957, step = 1712000 (2.249 sec) -2022-05-01 19:59:45,226 - tensorflow - INFO - global_step/sec: 88.832 -2022-05-01 19:59:45,226 - tensorflow - INFO - loss = 0.020355092, step = 1712200 (2.251 sec) -2022-05-01 19:59:47,477 - tensorflow - INFO - global_step/sec: 88.8343 -2022-05-01 19:59:47,478 - tensorflow - INFO - loss = 0.029279368, step = 1712400 (2.251 sec) -2022-05-01 19:59:49,735 - tensorflow - INFO - global_step/sec: 88.6006 -2022-05-01 19:59:49,735 - tensorflow - INFO - loss = 0.006299399, step = 1712600 (2.257 sec) -2022-05-01 19:59:51,983 - tensorflow - INFO - global_step/sec: 88.9729 -2022-05-01 19:59:51,983 - tensorflow - INFO - loss = 0.0369786, step = 1712800 (2.248 sec) -2022-05-01 19:59:54,232 - tensorflow - INFO - global_step/sec: 88.9232 -2022-05-01 19:59:54,232 - tensorflow - INFO - loss = 0.052752845, step = 1713000 (2.249 sec) -2022-05-01 19:59:56,482 - tensorflow - INFO - global_step/sec: 88.8903 -2022-05-01 19:59:56,482 - tensorflow - INFO - loss = 0.022305928, step = 1713200 (2.250 sec) -2022-05-01 19:59:58,729 - tensorflow - INFO - global_step/sec: 88.9813 -2022-05-01 19:59:58,730 - tensorflow - INFO - loss = 0.102901265, step = 1713400 (2.248 sec) -2022-05-01 20:00:00,985 - tensorflow - INFO - global_step/sec: 88.6524 -2022-05-01 20:00:00,986 - tensorflow - INFO - loss = 0.012466687, step = 1713600 (2.256 sec) -2022-05-01 20:00:03,245 - tensorflow - INFO - global_step/sec: 88.4958 -2022-05-01 20:00:03,246 - tensorflow - INFO - loss = 0.041081473, step = 1713800 (2.260 sec) -2022-05-01 20:00:05,498 - tensorflow - INFO - global_step/sec: 88.8039 -2022-05-01 20:00:05,498 - tensorflow - INFO - loss = 0.021235213, step = 1714000 (2.252 sec) -2022-05-01 20:00:07,749 - tensorflow - INFO - global_step/sec: 88.8408 -2022-05-01 20:00:07,749 - tensorflow - INFO - loss = 0.10852687, step = 1714200 (2.251 sec) -2022-05-01 20:00:10,004 - tensorflow - INFO - global_step/sec: 88.693 -2022-05-01 20:00:10,004 - tensorflow - INFO - loss = 0.14167748, step = 1714400 (2.255 sec) -2022-05-01 20:00:12,255 - tensorflow - INFO - global_step/sec: 88.8499 -2022-05-01 20:00:12,255 - tensorflow - INFO - loss = 0.012458287, step = 1714600 (2.251 sec) -2022-05-01 20:00:14,507 - tensorflow - INFO - global_step/sec: 88.7877 -2022-05-01 20:00:14,508 - tensorflow - INFO - loss = 0.15571158, step = 1714800 (2.253 sec) -2022-05-01 20:00:16,765 - tensorflow - INFO - global_step/sec: 88.5666 -2022-05-01 20:00:16,766 - tensorflow - INFO - loss = 0.019827556, step = 1715000 (2.258 sec) -2022-05-01 20:00:19,014 - tensorflow - INFO - global_step/sec: 88.9583 -2022-05-01 20:00:19,014 - tensorflow - INFO - loss = 3.7223036, step = 1715200 (2.248 sec) -2022-05-01 20:00:21,263 - tensorflow - INFO - global_step/sec: 88.9078 -2022-05-01 20:00:21,264 - tensorflow - INFO - loss = 0.014220778, step = 1715400 (2.249 sec) -2022-05-01 20:00:23,512 - tensorflow - INFO - global_step/sec: 88.9291 -2022-05-01 20:00:23,513 - tensorflow - INFO - loss = 0.034245614, step = 1715600 (2.249 sec) -2022-05-01 20:00:25,766 - tensorflow - INFO - global_step/sec: 88.7206 -2022-05-01 20:00:25,767 - tensorflow - INFO - loss = 0.025689885, step = 1715800 (2.254 sec) -2022-05-01 20:00:28,026 - tensorflow - INFO - global_step/sec: 88.531 -2022-05-01 20:00:28,026 - tensorflow - INFO - loss = 0.012042551, step = 1716000 (2.259 sec) -2022-05-01 20:00:30,281 - tensorflow - INFO - global_step/sec: 88.6762 -2022-05-01 20:00:30,281 - tensorflow - INFO - loss = 0.011883922, step = 1716200 (2.255 sec) -2022-05-01 20:00:32,541 - tensorflow - INFO - global_step/sec: 88.4955 -2022-05-01 20:00:32,541 - tensorflow - INFO - loss = 0.038108338, step = 1716400 (2.260 sec) -2022-05-01 20:00:34,790 - tensorflow - INFO - global_step/sec: 88.9393 -2022-05-01 20:00:34,790 - tensorflow - INFO - loss = 0.021661714, step = 1716600 (2.249 sec) -2022-05-01 20:00:37,047 - tensorflow - INFO - global_step/sec: 88.586 -2022-05-01 20:00:37,048 - tensorflow - INFO - loss = 0.0092684915, step = 1716800 (2.258 sec) -2022-05-01 20:00:39,299 - tensorflow - INFO - global_step/sec: 88.8071 -2022-05-01 20:00:39,300 - tensorflow - INFO - loss = 0.07145426, step = 1717000 (2.252 sec) -2022-05-01 20:00:41,551 - tensorflow - INFO - global_step/sec: 88.8133 -2022-05-01 20:00:41,552 - tensorflow - INFO - loss = 0.011701191, step = 1717200 (2.252 sec) -2022-05-01 20:00:43,805 - tensorflow - INFO - global_step/sec: 88.7525 -2022-05-01 20:00:43,805 - tensorflow - INFO - loss = 0.038168073, step = 1717400 (2.253 sec) -2022-05-01 20:00:46,059 - tensorflow - INFO - 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INFO - global_step/sec: 88.6817 -2022-05-01 20:09:35,854 - tensorflow - INFO - loss = 0.051668994, step = 1764600 (2.255 sec) -2022-05-01 20:09:38,110 - tensorflow - INFO - global_step/sec: 88.6415 -2022-05-01 20:09:38,110 - tensorflow - INFO - loss = 0.015650654, step = 1764800 (2.256 sec) -2022-05-01 20:09:40,369 - tensorflow - INFO - global_step/sec: 88.5277 -2022-05-01 20:09:40,369 - tensorflow - INFO - loss = 0.009082341, step = 1765000 (2.259 sec) -2022-05-01 20:09:42,626 - tensorflow - INFO - global_step/sec: 88.5888 -2022-05-01 20:09:42,627 - tensorflow - INFO - loss = 0.019312901, step = 1765200 (2.258 sec) -2022-05-01 20:09:44,888 - tensorflow - INFO - global_step/sec: 88.4305 -2022-05-01 20:09:44,888 - tensorflow - INFO - loss = 0.015152554, step = 1765400 (2.262 sec) -2022-05-01 20:09:47,148 - tensorflow - INFO - global_step/sec: 88.4924 -2022-05-01 20:09:47,149 - tensorflow - INFO - loss = 0.046553165, step = 1765600 (2.260 sec) -2022-05-01 20:09:49,405 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.7209 -2022-05-01 20:11:55,692 - tensorflow - INFO - loss = 0.0155015765, step = 1777000 (2.254 sec) -2022-05-01 20:11:57,946 - tensorflow - INFO - global_step/sec: 88.7392 -2022-05-01 20:11:57,946 - tensorflow - INFO - loss = 0.14066681, step = 1777200 (2.254 sec) -2022-05-01 20:12:00,196 - tensorflow - INFO - global_step/sec: 88.8755 -2022-05-01 20:12:00,196 - tensorflow - INFO - loss = 0.019969832, step = 1777400 (2.250 sec) -2022-05-01 20:12:02,449 - tensorflow - INFO - global_step/sec: 88.7876 -2022-05-01 20:12:02,449 - tensorflow - INFO - loss = 0.01277157, step = 1777600 (2.253 sec) -2022-05-01 20:12:04,704 - tensorflow - INFO - global_step/sec: 88.6616 -2022-05-01 20:12:04,705 - tensorflow - INFO - loss = 0.015029792, step = 1777800 (2.256 sec) -2022-05-01 20:12:06,956 - tensorflow - INFO - global_step/sec: 88.8082 -2022-05-01 20:12:06,957 - tensorflow - INFO - loss = 0.034628175, step = 1778000 (2.252 sec) -2022-05-01 20:12:09,210 - tensorflow - INFO - global_step/sec: 88.7385 -2022-05-01 20:12:09,211 - tensorflow - INFO - loss = 0.020785347, step = 1778200 (2.254 sec) -2022-05-01 20:12:11,462 - tensorflow - INFO - global_step/sec: 88.8232 -2022-05-01 20:12:11,462 - tensorflow - INFO - loss = 0.014245909, step = 1778400 (2.252 sec) -2022-05-01 20:12:13,711 - tensorflow - INFO - global_step/sec: 88.9194 -2022-05-01 20:12:13,712 - tensorflow - INFO - loss = 0.017461497, step = 1778600 (2.249 sec) -2022-05-01 20:12:15,969 - tensorflow - INFO - global_step/sec: 88.593 -2022-05-01 20:12:15,969 - tensorflow - INFO - loss = 0.04171947, step = 1778800 (2.258 sec) -2022-05-01 20:12:18,225 - tensorflow - INFO - global_step/sec: 88.6212 -2022-05-01 20:12:18,226 - tensorflow - INFO - loss = 0.053099677, step = 1779000 (2.257 sec) -2022-05-01 20:12:20,478 - tensorflow - INFO - global_step/sec: 88.787 -2022-05-01 20:12:20,478 - tensorflow - INFO - loss = 0.010349859, step = 1779200 (2.253 sec) -2022-05-01 20:12:22,729 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.022772396, step = 1780600 (2.253 sec) -2022-05-01 20:12:38,520 - tensorflow - INFO - global_step/sec: 88.7063 -2022-05-01 20:12:38,520 - tensorflow - INFO - loss = 0.013392142, step = 1780800 (2.255 sec) -2022-05-01 20:12:40,769 - tensorflow - INFO - global_step/sec: 88.9085 -2022-05-01 20:12:40,770 - tensorflow - INFO - loss = 0.021318369, step = 1781000 (2.249 sec) -2022-05-01 20:12:43,033 - tensorflow - INFO - global_step/sec: 88.3635 -2022-05-01 20:12:43,033 - tensorflow - INFO - loss = 0.030087322, step = 1781200 (2.263 sec) -2022-05-01 20:12:45,288 - tensorflow - INFO - global_step/sec: 88.6784 -2022-05-01 20:12:45,288 - tensorflow - INFO - loss = 0.02462101, step = 1781400 (2.255 sec) -2022-05-01 20:12:47,542 - tensorflow - INFO - global_step/sec: 88.7298 -2022-05-01 20:12:47,543 - tensorflow - INFO - loss = 0.07925145, step = 1781600 (2.254 sec) -2022-05-01 20:12:49,800 - tensorflow - INFO - global_step/sec: 88.5814 -2022-05-01 20:12:49,800 - tensorflow - INFO - loss = 0.008063761, step = 1781800 (2.258 sec) -2022-05-01 20:12:52,055 - tensorflow - INFO - global_step/sec: 88.7008 -2022-05-01 20:12:52,055 - tensorflow - INFO - loss = 0.018648414, step = 1782000 (2.255 sec) -2022-05-01 20:12:54,313 - tensorflow - INFO - global_step/sec: 88.5547 -2022-05-01 20:12:54,314 - tensorflow - INFO - loss = 0.009507858, step = 1782200 (2.259 sec) -2022-05-01 20:12:56,565 - tensorflow - INFO - global_step/sec: 88.8153 -2022-05-01 20:12:56,565 - tensorflow - INFO - loss = 0.013911311, step = 1782400 (2.252 sec) -2022-05-01 20:12:58,815 - tensorflow - INFO - global_step/sec: 88.8714 -2022-05-01 20:12:58,816 - tensorflow - INFO - loss = 0.015068855, step = 1782600 (2.250 sec) -2022-05-01 20:13:01,068 - tensorflow - INFO - global_step/sec: 88.7709 -2022-05-01 20:13:01,069 - tensorflow - INFO - loss = 0.021220945, step = 1782800 (2.253 sec) -2022-05-01 20:13:03,329 - tensorflow - INFO - global_step/sec: 88.4902 -2022-05-01 20:13:03,329 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.3074 -2022-05-01 20:13:19,143 - tensorflow - INFO - loss = 0.012970069, step = 1784400 (2.265 sec) -2022-05-01 20:13:21,408 - tensorflow - INFO - global_step/sec: 88.2976 -2022-05-01 20:13:21,408 - tensorflow - INFO - loss = 0.008985808, step = 1784600 (2.265 sec) -2022-05-01 20:13:23,670 - tensorflow - INFO - global_step/sec: 88.4183 -2022-05-01 20:13:23,670 - tensorflow - INFO - loss = 0.036576655, step = 1784800 (2.262 sec) -2022-05-01 20:13:25,930 - tensorflow - INFO - global_step/sec: 88.4635 -2022-05-01 20:13:25,931 - tensorflow - INFO - loss = 0.026095899, step = 1785000 (2.261 sec) -2022-05-01 20:13:28,188 - tensorflow - INFO - global_step/sec: 88.5763 -2022-05-01 20:13:28,189 - tensorflow - INFO - loss = 0.039014556, step = 1785200 (2.258 sec) -2022-05-01 20:13:30,452 - tensorflow - INFO - global_step/sec: 88.3553 -2022-05-01 20:13:30,452 - tensorflow - INFO - loss = 0.010799349, step = 1785400 (2.264 sec) -2022-05-01 20:13:32,714 - tensorflow - INFO - global_step/sec: 88.3979 -2022-05-01 20:13:32,715 - tensorflow - INFO - loss = 0.07767479, step = 1785600 (2.263 sec) -2022-05-01 20:13:34,977 - tensorflow - INFO - global_step/sec: 88.3776 -2022-05-01 20:13:34,978 - tensorflow - INFO - loss = 0.013945446, step = 1785800 (2.263 sec) -2022-05-01 20:13:37,242 - tensorflow - INFO - global_step/sec: 88.3042 -2022-05-01 20:13:37,243 - tensorflow - INFO - loss = 0.007430071, step = 1786000 (2.265 sec) -2022-05-01 20:13:39,502 - tensorflow - INFO - global_step/sec: 88.5204 -2022-05-01 20:13:39,502 - tensorflow - INFO - loss = 0.07640466, step = 1786200 (2.259 sec) -2022-05-01 20:13:41,756 - tensorflow - INFO - global_step/sec: 88.7186 -2022-05-01 20:13:41,756 - tensorflow - INFO - loss = 0.009463412, step = 1786400 (2.254 sec) -2022-05-01 20:13:44,011 - tensorflow - INFO - global_step/sec: 88.6931 -2022-05-01 20:13:44,011 - tensorflow - INFO - loss = 0.11465584, step = 1786600 (2.255 sec) -2022-05-01 20:13:46,264 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.05434043, step = 1788000 (2.250 sec) -2022-05-01 20:14:02,062 - tensorflow - INFO - global_step/sec: 88.506 -2022-05-01 20:14:02,063 - tensorflow - INFO - loss = 0.02158013, step = 1788200 (2.260 sec) -2022-05-01 20:14:04,318 - tensorflow - INFO - global_step/sec: 88.672 -2022-05-01 20:14:04,318 - tensorflow - INFO - loss = 0.036688764, step = 1788400 (2.255 sec) -2022-05-01 20:14:06,567 - tensorflow - INFO - global_step/sec: 88.9208 -2022-05-01 20:14:06,567 - tensorflow - INFO - loss = 0.036543354, step = 1788600 (2.249 sec) -2022-05-01 20:14:08,825 - tensorflow - INFO - global_step/sec: 88.5745 -2022-05-01 20:14:08,825 - tensorflow - INFO - loss = 4.63417, step = 1788800 (2.258 sec) -2022-05-01 20:14:11,082 - tensorflow - INFO - global_step/sec: 88.6162 -2022-05-01 20:14:11,082 - tensorflow - INFO - loss = 0.0054372754, step = 1789000 (2.257 sec) -2022-05-01 20:14:13,337 - tensorflow - INFO - global_step/sec: 88.6694 -2022-05-01 20:14:13,338 - tensorflow - INFO - loss = 0.07487969, step = 1789200 (2.256 sec) -2022-05-01 20:14:15,595 - tensorflow - INFO - global_step/sec: 88.5954 -2022-05-01 20:14:15,595 - tensorflow - INFO - loss = 0.018776996, step = 1789400 (2.257 sec) -2022-05-01 20:14:17,852 - tensorflow - INFO - global_step/sec: 88.595 -2022-05-01 20:14:17,853 - tensorflow - INFO - loss = 0.022459345, step = 1789600 (2.257 sec) -2022-05-01 20:14:20,104 - tensorflow - INFO - global_step/sec: 88.8163 -2022-05-01 20:14:20,104 - tensorflow - INFO - loss = 0.05700142, step = 1789800 (2.252 sec) -2022-05-01 20:14:22,361 - tensorflow - INFO - global_step/sec: 88.597 -2022-05-01 20:14:22,362 - tensorflow - INFO - loss = 0.024788637, step = 1790000 (2.257 sec) -2022-05-01 20:14:24,617 - tensorflow - INFO - global_step/sec: 88.6738 -2022-05-01 20:14:24,617 - tensorflow - INFO - loss = 0.089785844, step = 1790200 (2.255 sec) -2022-05-01 20:14:26,878 - tensorflow - INFO - global_step/sec: 88.4581 -2022-05-01 20:14:26,878 - tensorflow - INFO - loss = 0.47366673, step = 1790400 (2.261 sec) -2022-05-01 20:14:29,133 - tensorflow - INFO - global_step/sec: 88.6972 -2022-05-01 20:14:29,133 - tensorflow - INFO - loss = 0.030992618, step = 1790600 (2.255 sec) -2022-05-01 20:14:31,390 - tensorflow - INFO - global_step/sec: 88.5982 -2022-05-01 20:14:31,391 - tensorflow - INFO - loss = 0.034015767, step = 1790800 (2.257 sec) -2022-05-01 20:14:33,644 - tensorflow - INFO - global_step/sec: 88.731 -2022-05-01 20:14:33,645 - tensorflow - INFO - loss = 0.071194686, step = 1791000 (2.254 sec) -2022-05-01 20:14:35,895 - tensorflow - INFO - global_step/sec: 88.8645 -2022-05-01 20:14:35,895 - tensorflow - INFO - loss = 0.008461665, step = 1791200 (2.251 sec) -2022-05-01 20:14:38,149 - tensorflow - INFO - global_step/sec: 88.7362 -2022-05-01 20:14:38,149 - tensorflow - INFO - loss = 0.040716104, step = 1791400 (2.254 sec) -2022-05-01 20:14:40,402 - tensorflow - INFO - global_step/sec: 88.7425 -2022-05-01 20:14:40,403 - tensorflow - INFO - loss = 0.053414673, step = 1791600 (2.254 sec) -2022-05-01 20:14:42,654 - tensorflow - INFO - global_step/sec: 88.8393 -2022-05-01 20:14:42,654 - tensorflow - INFO - loss = 0.016931232, step = 1791800 (2.251 sec) -2022-05-01 20:14:44,906 - tensorflow - INFO - global_step/sec: 88.7935 -2022-05-01 20:14:44,906 - tensorflow - INFO - loss = 0.037635088, step = 1792000 (2.252 sec) -2022-05-01 20:14:47,157 - tensorflow - INFO - global_step/sec: 88.8472 -2022-05-01 20:14:47,158 - tensorflow - INFO - loss = 0.621138, step = 1792200 (2.251 sec) -2022-05-01 20:14:49,409 - tensorflow - INFO - global_step/sec: 88.826 -2022-05-01 20:14:49,409 - tensorflow - INFO - loss = 0.011683818, step = 1792400 (2.252 sec) -2022-05-01 20:14:51,668 - tensorflow - INFO - global_step/sec: 88.5339 -2022-05-01 20:14:51,668 - tensorflow - INFO - loss = 0.06398842, step = 1792600 (2.259 sec) -2022-05-01 20:14:53,926 - tensorflow - INFO - global_step/sec: 88.5539 -2022-05-01 20:14:53,927 - tensorflow - INFO - loss = 0.0077382503, step = 1792800 (2.259 sec) -2022-05-01 20:14:56,178 - tensorflow - INFO - global_step/sec: 88.8049 -2022-05-01 20:14:56,179 - tensorflow - INFO - loss = 0.0059882104, step = 1793000 (2.252 sec) -2022-05-01 20:14:58,433 - tensorflow - INFO - global_step/sec: 88.7048 -2022-05-01 20:14:58,433 - tensorflow - INFO - loss = 0.0121996235, step = 1793200 (2.255 sec) -2022-05-01 20:15:00,692 - tensorflow - INFO - global_step/sec: 88.5164 -2022-05-01 20:15:00,693 - tensorflow - INFO - loss = 0.013352605, step = 1793400 (2.259 sec) -2022-05-01 20:15:02,947 - tensorflow - INFO - global_step/sec: 88.7274 -2022-05-01 20:15:02,947 - tensorflow - INFO - loss = 0.35117516, step = 1793600 (2.254 sec) -2022-05-01 20:15:05,204 - tensorflow - INFO - global_step/sec: 88.6128 -2022-05-01 20:15:05,204 - tensorflow - INFO - loss = 0.017232269, step = 1793800 (2.257 sec) -2022-05-01 20:15:07,457 - tensorflow - INFO - global_step/sec: 88.76 -2022-05-01 20:15:07,457 - tensorflow - INFO - loss = 0.06693153, step = 1794000 (2.253 sec) -2022-05-01 20:15:09,709 - tensorflow - INFO - global_step/sec: 88.7869 -2022-05-01 20:15:09,710 - tensorflow - INFO - loss = 0.056630254, step = 1794200 (2.253 sec) -2022-05-01 20:15:11,960 - tensorflow - INFO - global_step/sec: 88.8683 -2022-05-01 20:15:11,960 - tensorflow - INFO - loss = 0.031748325, step = 1794400 (2.251 sec) -2022-05-01 20:15:14,210 - tensorflow - INFO - global_step/sec: 88.8958 -2022-05-01 20:15:14,210 - tensorflow - INFO - loss = 0.021562433, step = 1794600 (2.250 sec) -2022-05-01 20:15:16,462 - tensorflow - INFO - global_step/sec: 88.7856 -2022-05-01 20:15:16,463 - tensorflow - INFO - loss = 0.014114676, step = 1794800 (2.253 sec) -2022-05-01 20:15:18,724 - tensorflow - INFO - global_step/sec: 88.441 -2022-05-01 20:15:18,724 - tensorflow - INFO - loss = 0.017012898, step = 1795000 (2.261 sec) -2022-05-01 20:15:20,976 - tensorflow - INFO - global_step/sec: 88.7909 -2022-05-01 20:15:20,977 - tensorflow - INFO - loss = 0.3222133, step = 1795200 (2.252 sec) -2022-05-01 20:15:23,231 - tensorflow - INFO - global_step/sec: 88.7104 -2022-05-01 20:15:23,231 - tensorflow - INFO - loss = 0.019055504, step = 1795400 (2.255 sec) -2022-05-01 20:15:25,485 - tensorflow - INFO - global_step/sec: 88.7088 -2022-05-01 20:15:25,486 - tensorflow - INFO - loss = 0.009157332, step = 1795600 (2.255 sec) -2022-05-01 20:15:27,738 - tensorflow - INFO - global_step/sec: 88.8014 -2022-05-01 20:15:27,738 - tensorflow - INFO - loss = 0.014342883, step = 1795800 (2.252 sec) -2022-05-01 20:15:29,991 - tensorflow - INFO - global_step/sec: 88.7719 -2022-05-01 20:15:29,991 - tensorflow - INFO - loss = 0.013180383, step = 1796000 (2.253 sec) -2022-05-01 20:15:32,253 - tensorflow - INFO - global_step/sec: 88.4086 -2022-05-01 20:15:32,253 - tensorflow - INFO - loss = 0.0068212757, step = 1796200 (2.262 sec) -2022-05-01 20:15:34,515 - tensorflow - INFO - global_step/sec: 88.4121 -2022-05-01 20:15:34,515 - tensorflow - INFO - loss = 0.017810522, step = 1796400 (2.262 sec) -2022-05-01 20:15:36,763 - tensorflow - INFO - global_step/sec: 88.9589 -2022-05-01 20:15:36,764 - tensorflow - INFO - loss = 0.06250075, step = 1796600 (2.248 sec) -2022-05-01 20:15:39,023 - tensorflow - INFO - global_step/sec: 88.4817 -2022-05-01 20:15:39,024 - tensorflow - INFO - loss = 0.010002321, step = 1796800 (2.260 sec) -2022-05-01 20:15:41,274 - tensorflow - INFO - global_step/sec: 88.8512 -2022-05-01 20:15:41,275 - tensorflow - INFO - loss = 0.004910208, step = 1797000 (2.251 sec) -2022-05-01 20:15:43,528 - tensorflow - INFO - global_step/sec: 88.745 -2022-05-01 20:15:43,529 - tensorflow - INFO - loss = 0.96549255, step = 1797200 (2.254 sec) -2022-05-01 20:15:45,778 - tensorflow - INFO - global_step/sec: 88.9052 -2022-05-01 20:15:45,778 - tensorflow - INFO - loss = 0.012416754, step = 1797400 (2.250 sec) -2022-05-01 20:15:48,032 - tensorflow - INFO - global_step/sec: 88.7304 -2022-05-01 20:15:48,032 - tensorflow - INFO - loss = 0.25109956, step = 1797600 (2.254 sec) -2022-05-01 20:15:50,285 - tensorflow - INFO - global_step/sec: 88.7773 -2022-05-01 20:15:50,285 - tensorflow - INFO - loss = 0.017483495, step = 1797800 (2.253 sec) -2022-05-01 20:15:52,541 - tensorflow - INFO - global_step/sec: 88.6378 -2022-05-01 20:15:52,541 - tensorflow - INFO - loss = 0.028559688, step = 1798000 (2.256 sec) -2022-05-01 20:15:54,800 - tensorflow - INFO - global_step/sec: 88.5397 -2022-05-01 20:15:54,800 - tensorflow - INFO - loss = 1.5846872, step = 1798200 (2.259 sec) -2022-05-01 20:15:57,053 - tensorflow - INFO - global_step/sec: 88.7447 -2022-05-01 20:15:57,054 - tensorflow - INFO - loss = 0.008915515, step = 1798400 (2.254 sec) -2022-05-01 20:15:59,308 - tensorflow - INFO - global_step/sec: 88.7048 -2022-05-01 20:15:59,308 - tensorflow - INFO - loss = 0.01911306, step = 1798600 (2.255 sec) -2022-05-01 20:16:01,564 - tensorflow - INFO - global_step/sec: 88.6598 -2022-05-01 20:16:01,564 - tensorflow - INFO - loss = 0.0072809146, step = 1798800 (2.256 sec) -2022-05-01 20:16:03,820 - tensorflow - INFO - global_step/sec: 88.6346 -2022-05-01 20:16:03,821 - tensorflow - INFO - loss = 0.044288322, step = 1799000 (2.256 sec) -2022-05-01 20:16:06,074 - tensorflow - INFO - global_step/sec: 88.7646 -2022-05-01 20:16:06,074 - tensorflow - INFO - loss = 0.23373622, step = 1799200 (2.253 sec) -2022-05-01 20:16:08,332 - tensorflow - INFO - global_step/sec: 88.5687 -2022-05-01 20:16:08,332 - tensorflow - INFO - loss = 0.05972874, step = 1799400 (2.258 sec) -2022-05-01 20:16:10,588 - tensorflow - INFO - global_step/sec: 88.6542 -2022-05-01 20:16:10,588 - tensorflow - INFO - loss = 0.061447047, step = 1799600 (2.256 sec) -2022-05-01 20:16:12,838 - tensorflow - INFO - global_step/sec: 88.8724 -2022-05-01 20:16:12,838 - tensorflow - INFO - loss = 0.01870015, step = 1799800 (2.250 sec) -2022-05-01 20:16:15,078 - tensorflow - INFO - Saving checkpoints for 1800000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. -2022-05-01 20:16:15,094 - tensorflow - INFO - model.ckpt-1800000 is not in all_model_checkpoint_paths. Manually adding it. -2022-05-01 20:16:15,614 - tensorflow - INFO - Calling model_fn. -2022-05-01 20:16:15,741 - tensorflow - INFO - Done calling model_fn. -2022-05-01 20:16:15,756 - tensorflow - INFO - Starting evaluation at 2022-05-01T20:16:15Z -2022-05-01 20:16:15,820 - tensorflow - INFO - Graph was finalized. -2022-05-01 20:16:15,823 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1800000 -2022-05-01 20:16:16,107 - tensorflow - INFO - Running local_init_op. -2022-05-01 20:16:16,216 - tensorflow - INFO - Done running local_init_op. -2022-05-01 20:16:26,353 - tensorflow - INFO - Evaluation [3990/39900] -2022-05-01 20:16:35,826 - tensorflow - INFO - Evaluation [7980/39900] -2022-05-01 20:16:45,282 - tensorflow - INFO - Evaluation [11970/39900] -2022-05-01 20:16:54,752 - tensorflow - INFO - Evaluation [15960/39900] -2022-05-01 20:17:04,229 - tensorflow - INFO - Evaluation [19950/39900] -2022-05-01 20:17:13,709 - tensorflow - INFO - Evaluation [23940/39900] -2022-05-01 20:17:23,164 - tensorflow - INFO - Evaluation [27930/39900] -2022-05-01 20:17:32,617 - tensorflow - INFO - Evaluation [31920/39900] -2022-05-01 20:17:42,064 - tensorflow - INFO - Evaluation [35910/39900] -2022-05-01 20:17:51,527 - tensorflow - INFO - Evaluation [39900/39900] -2022-05-01 20:17:51,666 - tensorflow - INFO - Finished evaluation at 2022-05-01-20:17:51 -2022-05-01 20:17:51,666 - tensorflow - INFO - Saving dict for global step 1800000: global_step = 1800000, loss = 0.016763782, mean_degree_err = 3.5563521 -2022-05-01 20:17:51,667 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 1800000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1800000 -2022-05-01 20:17:51,681 - tensorflow - INFO - global_step/sec: 2.02342 -2022-05-01 20:17:51,682 - tensorflow - INFO - loss = 0.027914403, step = 1800000 (98.843 sec) -2022-05-01 20:17:53,950 - tensorflow - INFO - global_step/sec: 88.1532 -2022-05-01 20:17:53,950 - tensorflow - INFO - loss = 0.021869771, step = 1800200 (2.268 sec) -2022-05-01 20:17:56,207 - tensorflow - INFO - global_step/sec: 88.5779 -2022-05-01 20:17:56,208 - tensorflow - INFO - loss = 0.092984244, step = 1800400 (2.258 sec) -2022-05-01 20:17:58,460 - tensorflow - INFO - global_step/sec: 88.8035 -2022-05-01 20:17:58,460 - tensorflow - INFO - loss = 0.013734039, step = 1800600 (2.252 sec) -2022-05-01 20:18:00,715 - tensorflow - INFO - global_step/sec: 88.671 -2022-05-01 20:18:00,716 - tensorflow - INFO - loss = 0.024841797, step = 1800800 (2.256 sec) -2022-05-01 20:18:02,971 - tensorflow - INFO - global_step/sec: 88.6527 -2022-05-01 20:18:02,972 - tensorflow - INFO - loss = 0.022523532, step = 1801000 (2.256 sec) -2022-05-01 20:18:05,226 - tensorflow - INFO - global_step/sec: 88.6917 -2022-05-01 20:18:05,227 - tensorflow - INFO - loss = 0.019885017, step = 1801200 (2.255 sec) -2022-05-01 20:18:07,485 - tensorflow - INFO - global_step/sec: 88.5244 -2022-05-01 20:18:07,486 - tensorflow - INFO - loss = 0.035318907, step = 1801400 (2.259 sec) -2022-05-01 20:18:09,741 - tensorflow - INFO - global_step/sec: 88.6836 -2022-05-01 20:18:09,741 - tensorflow - INFO - loss = 0.010549978, step = 1801600 (2.255 sec) -2022-05-01 20:18:12,000 - tensorflow - INFO - global_step/sec: 88.5235 -2022-05-01 20:18:12,000 - tensorflow - INFO - loss = 0.30921438, step = 1801800 (2.259 sec) -2022-05-01 20:18:14,257 - tensorflow - INFO - global_step/sec: 88.6201 -2022-05-01 20:18:14,257 - tensorflow - INFO - loss = 0.060837887, step = 1802000 (2.257 sec) -2022-05-01 20:18:16,514 - tensorflow - INFO - global_step/sec: 88.6091 -2022-05-01 20:18:16,514 - tensorflow - INFO - loss = 0.073248446, step = 1802200 (2.257 sec) -2022-05-01 20:18:18,769 - tensorflow - INFO - global_step/sec: 88.7003 -2022-05-01 20:18:18,769 - tensorflow - INFO - loss = 0.020106304, step = 1802400 (2.255 sec) -2022-05-01 20:18:21,026 - tensorflow - INFO - global_step/sec: 88.609 -2022-05-01 20:18:21,026 - tensorflow - INFO - loss = 0.34301686, step = 1802600 (2.257 sec) -2022-05-01 20:18:23,277 - tensorflow - INFO - global_step/sec: 88.851 -2022-05-01 20:18:23,277 - tensorflow - INFO - loss = 0.03422773, step = 1802800 (2.251 sec) -2022-05-01 20:18:25,536 - tensorflow - INFO - global_step/sec: 88.5365 -2022-05-01 20:18:25,536 - tensorflow - INFO - loss = 0.010858849, step = 1803000 (2.259 sec) -2022-05-01 20:18:27,794 - tensorflow - INFO - global_step/sec: 88.5571 -2022-05-01 20:18:27,794 - tensorflow - INFO - loss = 0.28296545, step = 1803200 (2.258 sec) -2022-05-01 20:18:30,056 - tensorflow - INFO - global_step/sec: 88.4263 -2022-05-01 20:18:30,056 - tensorflow - INFO - loss = 0.04658611, step = 1803400 (2.262 sec) -2022-05-01 20:18:32,319 - tensorflow - INFO - global_step/sec: 88.3909 -2022-05-01 20:18:32,319 - tensorflow - INFO - loss = 0.048911583, step = 1803600 (2.263 sec) -2022-05-01 20:18:34,577 - tensorflow - INFO - global_step/sec: 88.5663 -2022-05-01 20:18:34,577 - tensorflow - INFO - loss = 0.01163725, step = 1803800 (2.258 sec) -2022-05-01 20:18:36,830 - tensorflow - INFO - global_step/sec: 88.7513 -2022-05-01 20:18:36,831 - tensorflow - INFO - loss = 9.297604, step = 1804000 (2.254 sec) -2022-05-01 20:18:39,083 - tensorflow - INFO - global_step/sec: 88.7746 -2022-05-01 20:18:39,084 - tensorflow - INFO - loss = 0.0068070027, step = 1804200 (2.253 sec) -2022-05-01 20:18:41,337 - tensorflow - INFO - global_step/sec: 88.7427 -2022-05-01 20:18:41,337 - tensorflow - INFO - loss = 0.015604509, step = 1804400 (2.254 sec) -2022-05-01 20:18:43,593 - tensorflow - INFO - global_step/sec: 88.6336 -2022-05-01 20:18:43,594 - tensorflow - INFO - loss = 0.0144014135, step = 1804600 (2.256 sec) -2022-05-01 20:18:45,845 - tensorflow - INFO - global_step/sec: 88.8138 -2022-05-01 20:18:45,846 - tensorflow - INFO - loss = 0.02853755, step = 1804800 (2.252 sec) -2022-05-01 20:18:48,096 - tensorflow - INFO - global_step/sec: 88.8374 -2022-05-01 20:18:48,097 - tensorflow - INFO - loss = 0.008257154, step = 1805000 (2.251 sec) -2022-05-01 20:18:50,349 - tensorflow - INFO - global_step/sec: 88.7729 -2022-05-01 20:18:50,350 - tensorflow - INFO - loss = 0.02656951, step = 1805200 (2.253 sec) -2022-05-01 20:18:52,603 - tensorflow - INFO - global_step/sec: 88.755 -2022-05-01 20:18:52,603 - tensorflow - INFO - loss = 0.01831774, step = 1805400 (2.253 sec) -2022-05-01 20:18:54,856 - tensorflow - INFO - global_step/sec: 88.7477 -2022-05-01 20:18:54,857 - tensorflow - INFO - loss = 0.023801386, step = 1805600 (2.254 sec) -2022-05-01 20:18:57,118 - tensorflow - INFO - global_step/sec: 88.4459 -2022-05-01 20:18:57,118 - tensorflow - INFO - loss = 0.65763414, step = 1805800 (2.261 sec) -2022-05-01 20:18:59,372 - tensorflow - INFO - global_step/sec: 88.7027 -2022-05-01 20:18:59,373 - tensorflow - INFO - loss = 0.101669826, step = 1806000 (2.255 sec) -2022-05-01 20:19:01,624 - tensorflow - INFO - global_step/sec: 88.82 -2022-05-01 20:19:01,625 - tensorflow - INFO - loss = 0.012000534, step = 1806200 (2.252 sec) -2022-05-01 20:19:03,880 - tensorflow - INFO - global_step/sec: 88.6715 -2022-05-01 20:19:03,880 - tensorflow - INFO - loss = 0.026554633, step = 1806400 (2.255 sec) -2022-05-01 20:19:06,133 - tensorflow - INFO - global_step/sec: 88.7724 -2022-05-01 20:19:06,133 - tensorflow - INFO - loss = 0.013799883, step = 1806600 (2.253 sec) -2022-05-01 20:19:08,392 - tensorflow - INFO - global_step/sec: 88.5263 -2022-05-01 20:19:08,392 - tensorflow - INFO - loss = 0.30768314, step = 1806800 (2.259 sec) -2022-05-01 20:19:10,646 - tensorflow - INFO - global_step/sec: 88.7053 -2022-05-01 20:19:10,647 - tensorflow - INFO - loss = 0.025185607, step = 1807000 (2.255 sec) -2022-05-01 20:19:12,902 - tensorflow - INFO - global_step/sec: 88.6882 -2022-05-01 20:19:12,902 - tensorflow - INFO - loss = 0.008936843, step = 1807200 (2.255 sec) -2022-05-01 20:19:15,156 - tensorflow - INFO - global_step/sec: 88.7325 -2022-05-01 20:19:15,156 - tensorflow - INFO - loss = 0.036230564, step = 1807400 (2.254 sec) -2022-05-01 20:19:17,411 - tensorflow - INFO - global_step/sec: 88.6543 -2022-05-01 20:19:17,412 - tensorflow - INFO - loss = 0.011483357, step = 1807600 (2.256 sec) -2022-05-01 20:19:19,671 - tensorflow - INFO - global_step/sec: 88.5007 -2022-05-01 20:19:19,672 - tensorflow - INFO - loss = 0.024457522, step = 1807800 (2.260 sec) -2022-05-01 20:19:21,933 - tensorflow - INFO - global_step/sec: 88.4353 -2022-05-01 20:19:21,933 - tensorflow - INFO - loss = 0.00525497, step = 1808000 (2.262 sec) -2022-05-01 20:19:24,185 - tensorflow - INFO - global_step/sec: 88.8016 -2022-05-01 20:19:24,186 - tensorflow - INFO - loss = 0.012021458, step = 1808200 (2.252 sec) -2022-05-01 20:19:26,438 - tensorflow - INFO - global_step/sec: 88.7816 -2022-05-01 20:19:26,438 - tensorflow - INFO - loss = 0.025556244, step = 1808400 (2.253 sec) -2022-05-01 20:19:28,695 - tensorflow - INFO - global_step/sec: 88.6239 -2022-05-01 20:19:28,695 - tensorflow - INFO - loss = 0.05274012, step = 1808600 (2.257 sec) -2022-05-01 20:19:30,950 - tensorflow - INFO - global_step/sec: 88.6846 -2022-05-01 20:19:30,950 - tensorflow - INFO - loss = 0.10206439, step = 1808800 (2.255 sec) -2022-05-01 20:19:33,206 - tensorflow - INFO - global_step/sec: 88.6274 -2022-05-01 20:19:33,207 - tensorflow - INFO - loss = 0.061892245, step = 1809000 (2.257 sec) -2022-05-01 20:19:35,468 - tensorflow - INFO - global_step/sec: 88.4147 -2022-05-01 20:19:35,469 - tensorflow - INFO - loss = 0.010666244, step = 1809200 (2.262 sec) -2022-05-01 20:19:37,716 - tensorflow - INFO - global_step/sec: 88.9914 -2022-05-01 20:19:37,716 - tensorflow - INFO - loss = 0.059726246, step = 1809400 (2.247 sec) -2022-05-01 20:19:39,964 - tensorflow - INFO - global_step/sec: 88.9669 -2022-05-01 20:19:39,964 - tensorflow - INFO - loss = 0.015109569, step = 1809600 (2.248 sec) -2022-05-01 20:19:42,218 - tensorflow - INFO - global_step/sec: 88.7319 -2022-05-01 20:19:42,218 - tensorflow - INFO - loss = 0.010054525, step = 1809800 (2.254 sec) -2022-05-01 20:19:44,470 - tensorflow - INFO - global_step/sec: 88.7908 -2022-05-01 20:19:44,471 - tensorflow - INFO - loss = 0.018031895, step = 1810000 (2.252 sec) -2022-05-01 20:19:46,722 - tensorflow - INFO - global_step/sec: 88.8368 -2022-05-01 20:19:46,722 - tensorflow - INFO - loss = 0.016368955, step = 1810200 (2.251 sec) -2022-05-01 20:19:48,981 - tensorflow - INFO - global_step/sec: 88.5389 -2022-05-01 20:19:48,981 - tensorflow - INFO - loss = 0.007853529, step = 1810400 (2.259 sec) -2022-05-01 20:19:51,232 - tensorflow - INFO - global_step/sec: 88.8182 -2022-05-01 20:19:51,233 - tensorflow - INFO - loss = 0.0068646725, step = 1810600 (2.252 sec) -2022-05-01 20:19:53,485 - tensorflow - INFO - global_step/sec: 88.767 -2022-05-01 20:19:53,486 - tensorflow - INFO - loss = 0.21444012, step = 1810800 (2.253 sec) -2022-05-01 20:19:55,739 - tensorflow - INFO - global_step/sec: 88.7615 -2022-05-01 20:19:55,739 - tensorflow - INFO - loss = 0.049558554, step = 1811000 (2.253 sec) -2022-05-01 20:19:57,990 - tensorflow - INFO - global_step/sec: 88.8386 -2022-05-01 20:19:57,990 - tensorflow - INFO - loss = 0.0171601, step = 1811200 (2.251 sec) -2022-05-01 20:20:00,245 - tensorflow - INFO - global_step/sec: 88.685 -2022-05-01 20:20:00,246 - tensorflow - INFO - loss = 0.01656272, step = 1811400 (2.255 sec) -2022-05-01 20:20:02,500 - tensorflow - INFO - global_step/sec: 88.694 -2022-05-01 20:20:02,500 - tensorflow - INFO - loss = 0.006385114, step = 1811600 (2.255 sec) -2022-05-01 20:20:04,751 - tensorflow - INFO - global_step/sec: 88.8453 -2022-05-01 20:20:04,752 - tensorflow - INFO - loss = 0.009432913, step = 1811800 (2.251 sec) -2022-05-01 20:20:07,009 - tensorflow - INFO - global_step/sec: 88.5886 -2022-05-01 20:20:07,009 - tensorflow - INFO - loss = 0.04209045, step = 1812000 (2.258 sec) -2022-05-01 20:20:09,258 - tensorflow - INFO - global_step/sec: 88.915 -2022-05-01 20:20:09,259 - tensorflow - INFO - loss = 0.009847219, step = 1812200 (2.249 sec) -2022-05-01 20:20:11,510 - tensorflow - INFO - global_step/sec: 88.8152 -2022-05-01 20:20:11,510 - tensorflow - INFO - loss = 0.051155195, step = 1812400 (2.252 sec) -2022-05-01 20:20:13,773 - tensorflow - INFO - global_step/sec: 88.3861 -2022-05-01 20:20:13,773 - tensorflow - INFO - loss = 0.010624302, step = 1812600 (2.263 sec) -2022-05-01 20:20:16,034 - tensorflow - INFO - global_step/sec: 88.4405 -2022-05-01 20:20:16,035 - tensorflow - INFO - loss = 0.007253981, step = 1812800 (2.261 sec) -2022-05-01 20:20:18,291 - tensorflow - INFO - global_step/sec: 88.6121 -2022-05-01 20:20:18,292 - tensorflow - INFO - loss = 0.006397931, step = 1813000 (2.257 sec) -2022-05-01 20:20:20,552 - tensorflow - INFO - global_step/sec: 88.4662 -2022-05-01 20:20:20,552 - tensorflow - INFO - loss = 0.038472924, step = 1813200 (2.261 sec) -2022-05-01 20:20:22,814 - tensorflow - INFO - global_step/sec: 88.4175 -2022-05-01 20:20:22,814 - tensorflow - INFO - loss = 0.025957687, step = 1813400 (2.262 sec) -2022-05-01 20:20:25,078 - tensorflow - INFO - global_step/sec: 88.3259 -2022-05-01 20:20:25,079 - tensorflow - INFO - loss = 0.01257656, step = 1813600 (2.264 sec) -2022-05-01 20:20:27,343 - tensorflow - INFO - global_step/sec: 88.2963 -2022-05-01 20:20:27,344 - tensorflow - INFO - loss = 0.0098339375, step = 1813800 (2.265 sec) -2022-05-01 20:20:29,605 - tensorflow - INFO - global_step/sec: 88.452 -2022-05-01 20:20:29,605 - tensorflow - INFO - loss = 0.003876243, step = 1814000 (2.261 sec) -2022-05-01 20:20:31,866 - tensorflow - INFO - global_step/sec: 88.4305 -2022-05-01 20:20:31,867 - tensorflow - INFO - loss = 4.306312, step = 1814200 (2.262 sec) -2022-05-01 20:20:34,129 - tensorflow - INFO - global_step/sec: 88.37 -2022-05-01 20:20:34,130 - tensorflow - INFO - loss = 0.016175754, step = 1814400 (2.263 sec) -2022-05-01 20:20:36,390 - tensorflow - INFO - global_step/sec: 88.4749 -2022-05-01 20:20:36,390 - tensorflow - INFO - loss = 0.031760797, step = 1814600 (2.261 sec) -2022-05-01 20:20:38,654 - tensorflow - INFO - global_step/sec: 88.336 -2022-05-01 20:20:38,654 - tensorflow - INFO - loss = 0.013849026, step = 1814800 (2.264 sec) -2022-05-01 20:20:40,916 - tensorflow - INFO - global_step/sec: 88.4109 -2022-05-01 20:20:40,917 - tensorflow - INFO - loss = 0.014158366, step = 1815000 (2.262 sec) -2022-05-01 20:20:43,176 - tensorflow - INFO - global_step/sec: 88.4899 -2022-05-01 20:20:43,177 - tensorflow - INFO - loss = 0.18197498, step = 1815200 (2.260 sec) -2022-05-01 20:20:45,427 - tensorflow - INFO - global_step/sec: 88.8548 -2022-05-01 20:20:45,428 - tensorflow - INFO - loss = 0.01803931, step = 1815400 (2.251 sec) -2022-05-01 20:20:47,680 - tensorflow - INFO - global_step/sec: 88.7659 -2022-05-01 20:20:47,681 - tensorflow - INFO - loss = 0.023806624, step = 1815600 (2.253 sec) -2022-05-01 20:20:49,936 - tensorflow - INFO - global_step/sec: 88.6643 -2022-05-01 20:20:49,936 - tensorflow - INFO - loss = 0.008687656, step = 1815800 (2.256 sec) -2022-05-01 20:20:52,192 - tensorflow - INFO - global_step/sec: 88.6655 -2022-05-01 20:20:52,192 - tensorflow - INFO - loss = 0.018411316, step = 1816000 (2.256 sec) -2022-05-01 20:20:54,451 - tensorflow - INFO - global_step/sec: 88.5323 -2022-05-01 20:20:54,451 - tensorflow - INFO - loss = 0.013286571, step = 1816200 (2.259 sec) -2022-05-01 20:20:56,707 - tensorflow - 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tensorflow - INFO - global_step/sec: 88.7831 -2022-05-01 20:24:26,490 - tensorflow - INFO - loss = 0.01560775, step = 1835000 (2.253 sec) -2022-05-01 20:24:28,742 - tensorflow - INFO - global_step/sec: 88.8244 -2022-05-01 20:24:28,742 - tensorflow - INFO - loss = 0.022258969, step = 1835200 (2.252 sec) -2022-05-01 20:24:31,006 - tensorflow - INFO - global_step/sec: 88.3116 -2022-05-01 20:24:31,007 - tensorflow - INFO - loss = 0.0053165057, step = 1835400 (2.265 sec) -2022-05-01 20:24:33,261 - tensorflow - INFO - global_step/sec: 88.7107 -2022-05-01 20:24:33,261 - tensorflow - INFO - loss = 0.06763758, step = 1835600 (2.255 sec) -2022-05-01 20:24:35,515 - tensorflow - INFO - global_step/sec: 88.7253 -2022-05-01 20:24:35,515 - tensorflow - INFO - loss = 0.015103186, step = 1835800 (2.254 sec) -2022-05-01 20:24:37,766 - tensorflow - INFO - global_step/sec: 88.8569 -2022-05-01 20:24:37,766 - tensorflow - INFO - loss = 0.03623424, step = 1836000 (2.251 sec) -2022-05-01 20:24:40,020 - tensorflow - INFO - global_step/sec: 88.7192 -2022-05-01 20:24:40,021 - tensorflow - INFO - loss = 0.032539867, step = 1836200 (2.254 sec) -2022-05-01 20:24:42,272 - tensorflow - INFO - global_step/sec: 88.8284 -2022-05-01 20:24:42,272 - tensorflow - INFO - loss = 0.06612409, step = 1836400 (2.252 sec) -2022-05-01 20:24:44,529 - tensorflow - INFO - global_step/sec: 88.5875 -2022-05-01 20:24:44,530 - tensorflow - INFO - loss = 0.007893974, step = 1836600 (2.258 sec) -2022-05-01 20:24:46,785 - tensorflow - INFO - global_step/sec: 88.6538 -2022-05-01 20:24:46,786 - tensorflow - INFO - loss = 0.014295866, step = 1836800 (2.256 sec) -2022-05-01 20:24:49,035 - tensorflow - INFO - global_step/sec: 88.8826 -2022-05-01 20:24:49,036 - tensorflow - INFO - loss = 0.02057194, step = 1837000 (2.250 sec) -2022-05-01 20:24:51,289 - tensorflow - INFO - global_step/sec: 88.7366 -2022-05-01 20:24:51,290 - tensorflow - INFO - loss = 0.046822, step = 1837200 (2.254 sec) -2022-05-01 20:24:53,546 - tensorflow - INFO - global_step/sec: 88.6409 -2022-05-01 20:24:53,546 - tensorflow - INFO - loss = 0.012756139, step = 1837400 (2.256 sec) -2022-05-01 20:24:55,801 - tensorflow - INFO - global_step/sec: 88.6943 -2022-05-01 20:24:55,801 - tensorflow - INFO - loss = 0.007280142, step = 1837600 (2.255 sec) -2022-05-01 20:24:58,057 - tensorflow - INFO - global_step/sec: 88.6528 -2022-05-01 20:24:58,057 - tensorflow - INFO - loss = 0.0045169657, step = 1837800 (2.256 sec) -2022-05-01 20:25:00,309 - tensorflow - INFO - global_step/sec: 88.791 -2022-05-01 20:25:00,309 - tensorflow - INFO - loss = 0.010652699, step = 1838000 (2.252 sec) -2022-05-01 20:25:02,561 - tensorflow - INFO - global_step/sec: 88.8252 -2022-05-01 20:25:02,561 - tensorflow - INFO - loss = 0.026743425, step = 1838200 (2.252 sec) -2022-05-01 20:25:04,817 - tensorflow - INFO - global_step/sec: 88.6541 -2022-05-01 20:25:04,817 - tensorflow - INFO - loss = 0.07407653, step = 1838400 (2.256 sec) -2022-05-01 20:25:07,069 - tensorflow - INFO - global_step/sec: 88.8024 -2022-05-01 20:25:07,069 - tensorflow - INFO - loss = 4.821314, step = 1838600 (2.252 sec) -2022-05-01 20:25:09,329 - tensorflow - INFO - global_step/sec: 88.4997 -2022-05-01 20:25:09,329 - tensorflow - INFO - loss = 0.056913167, step = 1838800 (2.260 sec) -2022-05-01 20:25:11,580 - tensorflow - INFO - global_step/sec: 88.843 -2022-05-01 20:25:11,580 - tensorflow - INFO - loss = 0.018655833, step = 1839000 (2.251 sec) -2022-05-01 20:25:13,831 - tensorflow - INFO - global_step/sec: 88.8633 -2022-05-01 20:25:13,831 - tensorflow - INFO - loss = 0.010940998, step = 1839200 (2.251 sec) -2022-05-01 20:25:16,086 - tensorflow - INFO - global_step/sec: 88.6838 -2022-05-01 20:25:16,086 - tensorflow - INFO - loss = 0.011907165, step = 1839400 (2.255 sec) -2022-05-01 20:25:18,343 - tensorflow - INFO - global_step/sec: 88.612 -2022-05-01 20:25:18,343 - tensorflow - INFO - loss = 0.011977665, step = 1839600 (2.257 sec) -2022-05-01 20:25:20,597 - tensorflow - INFO - global_step/sec: 88.7328 -2022-05-01 20:25:20,597 - tensorflow - INFO - loss = 0.006268484, step = 1839800 (2.254 sec) -2022-05-01 20:25:22,854 - tensorflow - INFO - global_step/sec: 88.5894 -2022-05-01 20:25:22,855 - tensorflow - INFO - loss = 0.015248191, step = 1840000 (2.258 sec) -2022-05-01 20:25:25,109 - tensorflow - INFO - global_step/sec: 88.7118 -2022-05-01 20:25:25,109 - tensorflow - INFO - loss = 0.016756605, step = 1840200 (2.255 sec) -2022-05-01 20:25:27,364 - tensorflow - INFO - global_step/sec: 88.6919 -2022-05-01 20:25:27,364 - tensorflow - INFO - loss = 0.11232264, step = 1840400 (2.255 sec) -2022-05-01 20:25:29,616 - tensorflow - INFO - global_step/sec: 88.7934 -2022-05-01 20:25:29,617 - tensorflow - INFO - loss = 0.018364131, step = 1840600 (2.252 sec) -2022-05-01 20:25:31,872 - tensorflow - INFO - global_step/sec: 88.6449 -2022-05-01 20:25:31,873 - tensorflow - INFO - loss = 0.006125507, step = 1840800 (2.256 sec) -2022-05-01 20:25:34,131 - tensorflow - INFO - global_step/sec: 88.5571 -2022-05-01 20:25:34,131 - tensorflow - INFO - loss = 0.005172074, step = 1841000 (2.258 sec) -2022-05-01 20:25:36,388 - tensorflow - INFO - global_step/sec: 88.5967 -2022-05-01 20:25:36,389 - tensorflow - INFO - loss = 0.005981467, step = 1841200 (2.257 sec) -2022-05-01 20:25:38,639 - tensorflow - INFO - global_step/sec: 88.8647 -2022-05-01 20:25:38,639 - tensorflow - INFO - loss = 0.039633546, step = 1841400 (2.251 sec) -2022-05-01 20:25:40,897 - tensorflow - INFO - global_step/sec: 88.5703 -2022-05-01 20:25:40,897 - tensorflow - INFO - loss = 0.054717924, step = 1841600 (2.258 sec) -2022-05-01 20:25:43,149 - tensorflow - INFO - global_step/sec: 88.8282 -2022-05-01 20:25:43,149 - tensorflow - INFO - loss = 0.015850564, step = 1841800 (2.252 sec) -2022-05-01 20:25:45,399 - tensorflow - INFO - global_step/sec: 88.8804 -2022-05-01 20:25:45,399 - tensorflow - INFO - loss = 0.009141061, step = 1842000 (2.250 sec) -2022-05-01 20:25:47,654 - tensorflow - INFO - global_step/sec: 88.6671 -2022-05-01 20:25:47,655 - tensorflow - INFO - loss = 0.013469338, step = 1842200 (2.256 sec) -2022-05-01 20:25:49,906 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.0918156, step = 1843600 (2.250 sec) -2022-05-01 20:26:05,676 - tensorflow - INFO - global_step/sec: 88.6945 -2022-05-01 20:26:05,677 - tensorflow - INFO - loss = 0.025549937, step = 1843800 (2.255 sec) -2022-05-01 20:26:07,932 - tensorflow - INFO - global_step/sec: 88.6866 -2022-05-01 20:26:07,932 - tensorflow - INFO - loss = 0.027432416, step = 1844000 (2.255 sec) -2022-05-01 20:26:10,188 - tensorflow - INFO - global_step/sec: 88.6408 -2022-05-01 20:26:10,188 - tensorflow - INFO - loss = 0.011867199, step = 1844200 (2.256 sec) -2022-05-01 20:26:12,447 - tensorflow - INFO - global_step/sec: 88.5122 -2022-05-01 20:26:12,448 - tensorflow - INFO - loss = 0.012199122, step = 1844400 (2.260 sec) -2022-05-01 20:26:14,700 - tensorflow - INFO - global_step/sec: 88.788 -2022-05-01 20:26:14,700 - tensorflow - INFO - loss = 0.00509081, step = 1844600 (2.253 sec) -2022-05-01 20:26:16,952 - tensorflow - INFO - global_step/sec: 88.8005 -2022-05-01 20:26:16,953 - tensorflow - INFO - loss = 0.78227735, step = 1844800 (2.252 sec) -2022-05-01 20:26:19,206 - tensorflow - INFO - global_step/sec: 88.7443 -2022-05-01 20:26:19,206 - tensorflow - INFO - loss = 0.0076343045, step = 1845000 (2.254 sec) -2022-05-01 20:26:21,458 - tensorflow - INFO - global_step/sec: 88.8079 -2022-05-01 20:26:21,458 - tensorflow - INFO - loss = 0.00985547, step = 1845200 (2.252 sec) -2022-05-01 20:26:23,722 - tensorflow - INFO - global_step/sec: 88.338 -2022-05-01 20:26:23,722 - tensorflow - INFO - loss = 0.025111364, step = 1845400 (2.264 sec) -2022-05-01 20:26:25,982 - tensorflow - INFO - global_step/sec: 88.4837 -2022-05-01 20:26:25,983 - tensorflow - INFO - loss = 0.08325562, step = 1845600 (2.260 sec) -2022-05-01 20:26:28,242 - tensorflow - INFO - global_step/sec: 88.5013 -2022-05-01 20:26:28,243 - tensorflow - INFO - loss = 0.086955115, step = 1845800 (2.260 sec) -2022-05-01 20:26:30,504 - tensorflow - INFO - global_step/sec: 88.4293 -2022-05-01 20:26:30,504 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.8645 -2022-05-01 20:26:46,300 - tensorflow - INFO - loss = 0.0078629125, step = 1847400 (2.251 sec) -2022-05-01 20:26:48,554 - tensorflow - INFO - global_step/sec: 88.7161 -2022-05-01 20:26:48,555 - tensorflow - INFO - loss = 21.127949, step = 1847600 (2.254 sec) -2022-05-01 20:26:50,813 - tensorflow - INFO - global_step/sec: 88.5408 -2022-05-01 20:26:50,814 - tensorflow - INFO - loss = 0.010781923, step = 1847800 (2.259 sec) -2022-05-01 20:26:53,063 - tensorflow - INFO - global_step/sec: 88.8844 -2022-05-01 20:26:53,064 - tensorflow - INFO - loss = 0.0041205594, step = 1848000 (2.250 sec) -2022-05-01 20:26:55,322 - tensorflow - INFO - global_step/sec: 88.5485 -2022-05-01 20:26:55,322 - tensorflow - INFO - loss = 0.011492386, step = 1848200 (2.259 sec) -2022-05-01 20:26:57,577 - tensorflow - INFO - global_step/sec: 88.683 -2022-05-01 20:26:57,578 - tensorflow - INFO - loss = 0.011655846, step = 1848400 (2.255 sec) -2022-05-01 20:26:59,830 - tensorflow - INFO - global_step/sec: 88.7821 -2022-05-01 20:26:59,830 - tensorflow - INFO - loss = 0.0073967315, step = 1848600 (2.253 sec) -2022-05-01 20:27:02,092 - tensorflow - INFO - global_step/sec: 88.4243 -2022-05-01 20:27:02,092 - tensorflow - INFO - loss = 0.123996615, step = 1848800 (2.262 sec) -2022-05-01 20:27:04,348 - tensorflow - INFO - global_step/sec: 88.6468 -2022-05-01 20:27:04,348 - tensorflow - INFO - loss = 0.013393193, step = 1849000 (2.256 sec) -2022-05-01 20:27:06,602 - tensorflow - INFO - global_step/sec: 88.7481 -2022-05-01 20:27:06,602 - tensorflow - INFO - loss = 0.03099203, step = 1849200 (2.254 sec) -2022-05-01 20:27:08,854 - tensorflow - INFO - global_step/sec: 88.7932 -2022-05-01 20:27:08,854 - tensorflow - INFO - loss = 0.02929867, step = 1849400 (2.252 sec) -2022-05-01 20:27:11,110 - tensorflow - INFO - global_step/sec: 88.6343 -2022-05-01 20:27:11,111 - tensorflow - INFO - loss = 0.013980811, step = 1849600 (2.256 sec) -2022-05-01 20:27:13,367 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.15137419, step = 1851000 (2.265 sec) -2022-05-01 20:27:29,210 - tensorflow - INFO - global_step/sec: 88.3522 -2022-05-01 20:27:29,211 - tensorflow - INFO - loss = 0.0065481705, step = 1851200 (2.264 sec) -2022-05-01 20:27:31,478 - tensorflow - INFO - global_step/sec: 88.213 -2022-05-01 20:27:31,478 - tensorflow - INFO - loss = 0.019686084, step = 1851400 (2.267 sec) -2022-05-01 20:27:33,739 - tensorflow - INFO - global_step/sec: 88.4619 -2022-05-01 20:27:33,739 - tensorflow - INFO - loss = 0.004503956, step = 1851600 (2.261 sec) -2022-05-01 20:27:36,001 - tensorflow - INFO - global_step/sec: 88.3995 -2022-05-01 20:27:36,001 - tensorflow - INFO - loss = 0.08106224, step = 1851800 (2.262 sec) -2022-05-01 20:27:38,261 - tensorflow - INFO - global_step/sec: 88.4841 -2022-05-01 20:27:38,262 - tensorflow - INFO - loss = 0.028569568, step = 1852000 (2.260 sec) -2022-05-01 20:27:40,525 - tensorflow - INFO - global_step/sec: 88.3676 -2022-05-01 20:27:40,525 - tensorflow - INFO - loss = 0.012765411, step = 1852200 (2.263 sec) -2022-05-01 20:27:42,787 - tensorflow - INFO - global_step/sec: 88.3998 -2022-05-01 20:27:42,788 - tensorflow - INFO - loss = 0.02419498, step = 1852400 (2.262 sec) -2022-05-01 20:27:45,045 - tensorflow - INFO - global_step/sec: 88.5608 -2022-05-01 20:27:45,046 - tensorflow - INFO - loss = 0.07054446, step = 1852600 (2.258 sec) -2022-05-01 20:27:47,296 - tensorflow - INFO - global_step/sec: 88.8835 -2022-05-01 20:27:47,296 - tensorflow - INFO - loss = 0.011235343, step = 1852800 (2.250 sec) -2022-05-01 20:27:49,549 - tensorflow - INFO - global_step/sec: 88.7393 -2022-05-01 20:27:49,550 - tensorflow - INFO - loss = 0.09467631, step = 1853000 (2.254 sec) -2022-05-01 20:27:51,806 - tensorflow - INFO - global_step/sec: 88.6149 -2022-05-01 20:27:51,807 - tensorflow - INFO - loss = 0.022562038, step = 1853200 (2.257 sec) -2022-05-01 20:27:54,059 - tensorflow - INFO - global_step/sec: 88.7968 -2022-05-01 20:27:54,059 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.6778 -2022-05-01 20:28:09,848 - tensorflow - INFO - loss = 1.252865, step = 1854800 (2.255 sec) -2022-05-01 20:28:12,100 - tensorflow - INFO - global_step/sec: 88.7969 -2022-05-01 20:28:12,101 - tensorflow - INFO - loss = 0.027452521, step = 1855000 (2.252 sec) -2022-05-01 20:28:14,354 - tensorflow - INFO - global_step/sec: 88.7332 -2022-05-01 20:28:14,354 - tensorflow - INFO - loss = 0.01079275, step = 1855200 (2.254 sec) -2022-05-01 20:28:16,604 - tensorflow - INFO - global_step/sec: 88.8892 -2022-05-01 20:28:16,604 - tensorflow - INFO - loss = 0.0138835525, step = 1855400 (2.250 sec) -2022-05-01 20:28:18,858 - tensorflow - INFO - global_step/sec: 88.7494 -2022-05-01 20:28:18,858 - tensorflow - INFO - loss = 0.002622583, step = 1855600 (2.254 sec) -2022-05-01 20:28:21,118 - tensorflow - INFO - global_step/sec: 88.472 -2022-05-01 20:28:21,119 - tensorflow - INFO - loss = 0.27811888, step = 1855800 (2.261 sec) -2022-05-01 20:28:23,367 - tensorflow - INFO - global_step/sec: 88.9264 -2022-05-01 20:28:23,368 - tensorflow - INFO - loss = 0.038571924, step = 1856000 (2.249 sec) -2022-05-01 20:28:25,620 - tensorflow - INFO - global_step/sec: 88.7633 -2022-05-01 20:28:25,621 - tensorflow - INFO - loss = 0.008832814, step = 1856200 (2.253 sec) -2022-05-01 20:28:27,869 - tensorflow - INFO - global_step/sec: 88.9429 -2022-05-01 20:28:27,869 - tensorflow - INFO - loss = 0.012104606, step = 1856400 (2.249 sec) -2022-05-01 20:28:30,129 - tensorflow - INFO - global_step/sec: 88.5071 -2022-05-01 20:28:30,129 - tensorflow - INFO - loss = 0.2690666, step = 1856600 (2.260 sec) -2022-05-01 20:28:32,384 - tensorflow - INFO - global_step/sec: 88.6681 -2022-05-01 20:28:32,385 - tensorflow - INFO - loss = 0.012544476, step = 1856800 (2.256 sec) -2022-05-01 20:28:34,637 - tensorflow - INFO - global_step/sec: 88.8032 -2022-05-01 20:28:34,637 - tensorflow - INFO - loss = 0.033771485, step = 1857000 (2.252 sec) -2022-05-01 20:28:36,887 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.6166 -2022-05-01 20:29:33,247 - tensorflow - INFO - loss = 0.025657427, step = 1862200 (2.257 sec) -2022-05-01 20:29:35,504 - tensorflow - INFO - global_step/sec: 88.5842 -2022-05-01 20:29:35,505 - tensorflow - INFO - loss = 0.037618686, step = 1862400 (2.258 sec) -2022-05-01 20:29:37,760 - tensorflow - INFO - global_step/sec: 88.6551 -2022-05-01 20:29:37,761 - tensorflow - INFO - loss = 0.0073780203, step = 1862600 (2.256 sec) -2022-05-01 20:29:40,012 - tensorflow - INFO - global_step/sec: 88.8074 -2022-05-01 20:29:40,013 - tensorflow - INFO - loss = 0.0057529695, step = 1862800 (2.252 sec) -2022-05-01 20:29:42,260 - tensorflow - INFO - global_step/sec: 88.9563 -2022-05-01 20:29:42,261 - tensorflow - INFO - loss = 0.061805457, step = 1863000 (2.248 sec) -2022-05-01 20:29:44,511 - tensorflow - INFO - global_step/sec: 88.8643 -2022-05-01 20:29:44,512 - tensorflow - INFO - loss = 0.14968938, step = 1863200 (2.251 sec) -2022-05-01 20:29:46,764 - tensorflow - INFO - global_step/sec: 88.7697 -2022-05-01 20:29:46,765 - tensorflow - INFO - loss = 0.38842642, step = 1863400 (2.253 sec) -2022-05-01 20:29:49,022 - tensorflow - INFO - global_step/sec: 88.5753 -2022-05-01 20:29:49,023 - tensorflow - INFO - loss = 0.04474082, step = 1863600 (2.258 sec) -2022-05-01 20:29:51,280 - tensorflow - INFO - global_step/sec: 88.5699 -2022-05-01 20:29:51,281 - tensorflow - INFO - loss = 0.100732185, step = 1863800 (2.258 sec) -2022-05-01 20:29:53,530 - tensorflow - INFO - global_step/sec: 88.9017 -2022-05-01 20:29:53,530 - tensorflow - INFO - loss = 0.01562722, step = 1864000 (2.250 sec) -2022-05-01 20:29:55,784 - tensorflow - INFO - global_step/sec: 88.7161 -2022-05-01 20:29:55,785 - tensorflow - INFO - loss = 0.009844961, step = 1864200 (2.254 sec) -2022-05-01 20:29:58,041 - tensorflow - INFO - global_step/sec: 88.63 -2022-05-01 20:29:58,041 - tensorflow - INFO - loss = 0.019376265, step = 1864400 (2.257 sec) -2022-05-01 20:30:00,297 - tensorflow - INFO - global_step/sec: 88.6414 -2022-05-01 20:30:00,298 - tensorflow - INFO - loss = 0.025746921, step = 1864600 (2.256 sec) -2022-05-01 20:30:02,561 - tensorflow - INFO - global_step/sec: 88.337 -2022-05-01 20:30:02,562 - tensorflow - INFO - loss = 0.016607694, step = 1864800 (2.264 sec) -2022-05-01 20:30:04,817 - tensorflow - INFO - global_step/sec: 88.6651 -2022-05-01 20:30:04,817 - tensorflow - INFO - loss = 0.006056394, step = 1865000 (2.256 sec) -2022-05-01 20:30:07,075 - tensorflow - INFO - global_step/sec: 88.5722 -2022-05-01 20:30:07,075 - tensorflow - INFO - loss = 0.012430912, step = 1865200 (2.258 sec) -2022-05-01 20:30:09,325 - tensorflow - INFO - global_step/sec: 88.8686 -2022-05-01 20:30:09,326 - tensorflow - INFO - loss = 0.014980311, step = 1865400 (2.251 sec) -2022-05-01 20:30:11,579 - tensorflow - INFO - global_step/sec: 88.7415 -2022-05-01 20:30:11,580 - tensorflow - INFO - loss = 0.022223497, step = 1865600 (2.254 sec) -2022-05-01 20:30:13,829 - tensorflow - INFO - global_step/sec: 88.88 -2022-05-01 20:30:13,830 - tensorflow - INFO - loss = 0.006417212, step = 1865800 (2.250 sec) -2022-05-01 20:30:16,086 - tensorflow - INFO - global_step/sec: 88.6469 -2022-05-01 20:30:16,086 - tensorflow - INFO - loss = 0.022596888, step = 1866000 (2.256 sec) -2022-05-01 20:30:18,340 - tensorflow - INFO - global_step/sec: 88.7191 -2022-05-01 20:30:18,340 - tensorflow - INFO - loss = 0.019118773, step = 1866200 (2.254 sec) -2022-05-01 20:30:20,593 - tensorflow - INFO - global_step/sec: 88.7686 -2022-05-01 20:30:20,593 - tensorflow - INFO - loss = 0.08572295, step = 1866400 (2.253 sec) -2022-05-01 20:30:22,845 - tensorflow - INFO - global_step/sec: 88.8008 -2022-05-01 20:30:22,846 - tensorflow - INFO - loss = 0.018673077, step = 1866600 (2.252 sec) -2022-05-01 20:30:25,105 - tensorflow - INFO - global_step/sec: 88.5006 -2022-05-01 20:30:25,105 - tensorflow - INFO - loss = 0.08109714, step = 1866800 (2.260 sec) -2022-05-01 20:30:27,358 - tensorflow - INFO - global_step/sec: 88.7613 -2022-05-01 20:30:27,359 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.7237 -2022-05-01 20:30:43,145 - tensorflow - INFO - loss = 0.32599005, step = 1868400 (2.254 sec) -2022-05-01 20:30:45,396 - tensorflow - INFO - global_step/sec: 88.8409 -2022-05-01 20:30:45,396 - tensorflow - INFO - loss = 0.011387359, step = 1868600 (2.251 sec) -2022-05-01 20:30:47,658 - tensorflow - INFO - global_step/sec: 88.4094 -2022-05-01 20:30:47,658 - tensorflow - INFO - loss = 0.03894508, step = 1868800 (2.262 sec) -2022-05-01 20:30:49,923 - tensorflow - INFO - global_step/sec: 88.2932 -2022-05-01 20:30:49,923 - tensorflow - INFO - loss = 0.030715233, step = 1869000 (2.265 sec) -2022-05-01 20:30:52,186 - tensorflow - INFO - global_step/sec: 88.3924 -2022-05-01 20:30:52,186 - tensorflow - INFO - loss = 0.007998239, step = 1869200 (2.263 sec) -2022-05-01 20:30:54,451 - tensorflow - INFO - global_step/sec: 88.2957 -2022-05-01 20:30:54,451 - tensorflow - INFO - loss = 0.011327565, step = 1869400 (2.265 sec) -2022-05-01 20:30:56,716 - tensorflow - INFO - global_step/sec: 88.3105 -2022-05-01 20:30:56,716 - tensorflow - INFO - loss = 0.0057827244, step = 1869600 (2.265 sec) -2022-05-01 20:30:58,983 - tensorflow - INFO - global_step/sec: 88.2174 -2022-05-01 20:30:58,983 - tensorflow - INFO - loss = 0.013923317, step = 1869800 (2.267 sec) -2022-05-01 20:31:01,246 - tensorflow - INFO - global_step/sec: 88.3611 -2022-05-01 20:31:01,247 - tensorflow - INFO - loss = 0.0124671925, step = 1870000 (2.263 sec) -2022-05-01 20:31:03,512 - tensorflow - INFO - global_step/sec: 88.2563 -2022-05-01 20:31:03,513 - tensorflow - INFO - loss = 0.029464753, step = 1870200 (2.266 sec) -2022-05-01 20:31:05,775 - tensorflow - INFO - global_step/sec: 88.3983 -2022-05-01 20:31:05,775 - tensorflow - INFO - loss = 0.12631345, step = 1870400 (2.262 sec) -2022-05-01 20:31:08,038 - tensorflow - INFO - global_step/sec: 88.3824 -2022-05-01 20:31:08,038 - tensorflow - INFO - loss = 0.019793203, step = 1870600 (2.263 sec) -2022-05-01 20:31:10,304 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.67305064, step = 1894200 (2.256 sec) -2022-05-01 20:35:36,582 - tensorflow - INFO - global_step/sec: 88.5272 -2022-05-01 20:35:36,583 - tensorflow - INFO - loss = 0.098160475, step = 1894400 (2.259 sec) -2022-05-01 20:35:38,836 - tensorflow - INFO - global_step/sec: 88.7334 -2022-05-01 20:35:38,837 - tensorflow - INFO - loss = 0.008926311, step = 1894600 (2.254 sec) -2022-05-01 20:35:41,086 - tensorflow - INFO - global_step/sec: 88.8908 -2022-05-01 20:35:41,087 - tensorflow - INFO - loss = 0.010282107, step = 1894800 (2.250 sec) -2022-05-01 20:35:43,338 - tensorflow - INFO - global_step/sec: 88.8349 -2022-05-01 20:35:43,338 - tensorflow - INFO - loss = 0.025060002, step = 1895000 (2.251 sec) -2022-05-01 20:35:45,592 - tensorflow - INFO - global_step/sec: 88.7108 -2022-05-01 20:35:45,593 - tensorflow - INFO - loss = 0.030853648, step = 1895200 (2.254 sec) -2022-05-01 20:35:47,849 - tensorflow - INFO - global_step/sec: 88.6236 -2022-05-01 20:35:47,849 - tensorflow - INFO - loss = 0.24151732, step = 1895400 (2.257 sec) -2022-05-01 20:35:50,105 - tensorflow - INFO - global_step/sec: 88.6622 -2022-05-01 20:35:50,105 - tensorflow - INFO - loss = 0.026341088, step = 1895600 (2.256 sec) -2022-05-01 20:35:52,356 - tensorflow - INFO - global_step/sec: 88.8508 -2022-05-01 20:35:52,356 - tensorflow - INFO - loss = 0.019398818, step = 1895800 (2.251 sec) -2022-05-01 20:35:54,609 - tensorflow - INFO - global_step/sec: 88.7598 -2022-05-01 20:35:54,609 - tensorflow - INFO - loss = 0.018978981, step = 1896000 (2.253 sec) -2022-05-01 20:35:56,861 - tensorflow - INFO - global_step/sec: 88.8227 -2022-05-01 20:35:56,861 - tensorflow - INFO - loss = 0.015217569, step = 1896200 (2.252 sec) -2022-05-01 20:35:59,111 - tensorflow - INFO - global_step/sec: 88.8691 -2022-05-01 20:35:59,111 - tensorflow - INFO - loss = 0.018522281, step = 1896400 (2.251 sec) -2022-05-01 20:36:01,371 - tensorflow - INFO - global_step/sec: 88.4987 -2022-05-01 20:36:01,371 - tensorflow - INFO - loss = 0.035829056, step = 1896600 (2.260 sec) -2022-05-01 20:36:03,624 - tensorflow - INFO - global_step/sec: 88.7635 -2022-05-01 20:36:03,625 - tensorflow - INFO - loss = 0.01608836, step = 1896800 (2.253 sec) -2022-05-01 20:36:05,882 - tensorflow - INFO - global_step/sec: 88.5936 -2022-05-01 20:36:05,882 - tensorflow - INFO - loss = 0.025285728, step = 1897000 (2.258 sec) -2022-05-01 20:36:08,132 - tensorflow - INFO - global_step/sec: 88.8569 -2022-05-01 20:36:08,133 - tensorflow - INFO - loss = 0.00848588, step = 1897200 (2.251 sec) -2022-05-01 20:36:10,387 - tensorflow - INFO - global_step/sec: 88.7181 -2022-05-01 20:36:10,387 - tensorflow - INFO - loss = 0.013154034, step = 1897400 (2.254 sec) -2022-05-01 20:36:12,640 - tensorflow - INFO - global_step/sec: 88.7697 -2022-05-01 20:36:12,640 - tensorflow - INFO - loss = 0.0072760545, step = 1897600 (2.253 sec) -2022-05-01 20:36:14,900 - tensorflow - INFO - global_step/sec: 88.4853 -2022-05-01 20:36:14,901 - tensorflow - INFO - loss = 0.00759117, step = 1897800 (2.260 sec) -2022-05-01 20:36:17,156 - tensorflow - INFO - global_step/sec: 88.667 -2022-05-01 20:36:17,156 - tensorflow - INFO - loss = 0.05357273, step = 1898000 (2.256 sec) -2022-05-01 20:36:19,405 - tensorflow - INFO - global_step/sec: 88.9256 -2022-05-01 20:36:19,405 - tensorflow - INFO - loss = 0.013738202, step = 1898200 (2.249 sec) -2022-05-01 20:36:21,661 - tensorflow - INFO - global_step/sec: 88.6619 -2022-05-01 20:36:21,661 - tensorflow - INFO - loss = 0.6070651, step = 1898400 (2.256 sec) -2022-05-01 20:36:23,918 - tensorflow - INFO - global_step/sec: 88.5968 -2022-05-01 20:36:23,918 - tensorflow - INFO - loss = 0.014128826, step = 1898600 (2.257 sec) -2022-05-01 20:36:26,177 - tensorflow - INFO - global_step/sec: 88.521 -2022-05-01 20:36:26,178 - tensorflow - INFO - loss = 0.0088235345, step = 1898800 (2.259 sec) -2022-05-01 20:36:28,436 - tensorflow - INFO - global_step/sec: 88.5464 -2022-05-01 20:36:28,436 - tensorflow - INFO - loss = 0.13586804, step = 1899000 (2.259 sec) -2022-05-01 20:36:30,696 - tensorflow - INFO - global_step/sec: 88.5006 -2022-05-01 20:36:30,696 - tensorflow - INFO - loss = 0.010024523, step = 1899200 (2.260 sec) -2022-05-01 20:36:32,957 - tensorflow - INFO - global_step/sec: 88.4388 -2022-05-01 20:36:32,958 - tensorflow - INFO - loss = 0.011715906, step = 1899400 (2.261 sec) -2022-05-01 20:36:35,209 - tensorflow - INFO - global_step/sec: 88.8165 -2022-05-01 20:36:35,210 - tensorflow - INFO - loss = 0.030111665, step = 1899600 (2.252 sec) -2022-05-01 20:36:37,466 - tensorflow - INFO - global_step/sec: 88.6293 -2022-05-01 20:36:37,466 - tensorflow - INFO - loss = 0.03254773, step = 1899800 (2.257 sec) -2022-05-01 20:36:39,716 - tensorflow - INFO - Saving checkpoints for 1900000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. -2022-05-01 20:36:39,731 - tensorflow - INFO - model.ckpt-1900000 is not in all_model_checkpoint_paths. Manually adding it. -2022-05-01 20:36:40,255 - tensorflow - INFO - Calling model_fn. -2022-05-01 20:36:40,383 - tensorflow - INFO - Done calling model_fn. -2022-05-01 20:36:40,398 - tensorflow - INFO - Starting evaluation at 2022-05-01T20:36:40Z -2022-05-01 20:36:40,461 - tensorflow - INFO - Graph was finalized. -2022-05-01 20:36:40,465 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1900000 -2022-05-01 20:36:40,746 - tensorflow - INFO - Running local_init_op. -2022-05-01 20:36:40,853 - tensorflow - INFO - Done running local_init_op. -2022-05-01 20:36:50,996 - tensorflow - INFO - Evaluation [3990/39900] -2022-05-01 20:37:00,456 - tensorflow - INFO - Evaluation [7980/39900] -2022-05-01 20:37:09,895 - tensorflow - INFO - Evaluation [11970/39900] -2022-05-01 20:37:19,351 - tensorflow - INFO - Evaluation [15960/39900] -2022-05-01 20:37:28,792 - tensorflow - INFO - Evaluation [19950/39900] -2022-05-01 20:37:38,253 - tensorflow - INFO - Evaluation [23940/39900] -2022-05-01 20:37:47,688 - tensorflow - INFO - Evaluation [27930/39900] -2022-05-01 20:37:57,152 - tensorflow - INFO - Evaluation [31920/39900] -2022-05-01 20:38:06,602 - tensorflow - INFO - Evaluation [35910/39900] -2022-05-01 20:38:16,082 - tensorflow - INFO - Evaluation [39900/39900] -2022-05-01 20:38:16,222 - tensorflow - INFO - Finished evaluation at 2022-05-01-20:38:16 -2022-05-01 20:38:16,222 - tensorflow - INFO - Saving dict for global step 1900000: global_step = 1900000, loss = 0.021781396, mean_degree_err = 3.919044 -2022-05-01 20:38:16,223 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 1900000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1900000 -2022-05-01 20:38:16,236 - tensorflow - INFO - global_step/sec: 2.0249 -2022-05-01 20:38:16,237 - tensorflow - INFO - loss = 0.010318757, step = 1900000 (98.771 sec) -2022-05-01 20:38:18,518 - tensorflow - INFO - global_step/sec: 87.665 -2022-05-01 20:38:18,518 - tensorflow - INFO - loss = 0.05919086, step = 1900200 (2.281 sec) -2022-05-01 20:38:20,775 - tensorflow - INFO - global_step/sec: 88.6049 -2022-05-01 20:38:20,775 - tensorflow - INFO - loss = 0.0043990593, step = 1900400 (2.257 sec) -2022-05-01 20:38:23,037 - tensorflow - INFO - global_step/sec: 88.411 -2022-05-01 20:38:23,038 - tensorflow - INFO - loss = 0.014942108, step = 1900600 (2.262 sec) -2022-05-01 20:38:25,292 - tensorflow - INFO - global_step/sec: 88.6773 -2022-05-01 20:38:25,293 - tensorflow - INFO - loss = 0.018904645, step = 1900800 (2.255 sec) -2022-05-01 20:38:27,548 - tensorflow - INFO - global_step/sec: 88.656 -2022-05-01 20:38:27,549 - tensorflow - INFO - loss = 0.014355497, step = 1901000 (2.256 sec) -2022-05-01 20:38:29,801 - tensorflow - INFO - global_step/sec: 88.7702 -2022-05-01 20:38:29,802 - tensorflow - INFO - loss = 0.022026083, step = 1901200 (2.253 sec) -2022-05-01 20:38:32,061 - tensorflow - INFO - global_step/sec: 88.511 -2022-05-01 20:38:32,061 - tensorflow - INFO - loss = 0.026987147, step = 1901400 (2.260 sec) -2022-05-01 20:38:34,317 - tensorflow - INFO - global_step/sec: 88.64 -2022-05-01 20:38:34,318 - tensorflow - INFO - loss = 0.016082386, step = 1901600 (2.256 sec) -2022-05-01 20:38:36,573 - tensorflow - INFO - global_step/sec: 88.6813 -2022-05-01 20:38:36,573 - tensorflow - INFO - loss = 0.021116573, step = 1901800 (2.255 sec) -2022-05-01 20:38:38,827 - tensorflow - INFO - global_step/sec: 88.7107 -2022-05-01 20:38:38,828 - tensorflow - INFO - loss = 0.17873903, step = 1902000 (2.255 sec) -2022-05-01 20:38:41,079 - tensorflow - INFO - global_step/sec: 88.8149 -2022-05-01 20:38:41,079 - tensorflow - INFO - loss = 0.010280648, step = 1902200 (2.252 sec) -2022-05-01 20:38:43,339 - tensorflow - INFO - global_step/sec: 88.4946 -2022-05-01 20:38:43,339 - tensorflow - INFO - loss = 0.007372737, step = 1902400 (2.260 sec) -2022-05-01 20:38:45,596 - tensorflow - INFO - global_step/sec: 88.6306 -2022-05-01 20:38:45,596 - tensorflow - INFO - loss = 0.069900736, step = 1902600 (2.257 sec) -2022-05-01 20:38:47,853 - tensorflow - INFO - global_step/sec: 88.5961 -2022-05-01 20:38:47,853 - tensorflow - INFO - loss = 0.025056195, step = 1902800 (2.257 sec) -2022-05-01 20:38:50,112 - tensorflow - INFO - global_step/sec: 88.5546 -2022-05-01 20:38:50,112 - tensorflow - INFO - loss = 0.09052631, step = 1903000 (2.258 sec) -2022-05-01 20:38:52,368 - tensorflow - INFO - global_step/sec: 88.614 -2022-05-01 20:38:52,369 - tensorflow - INFO - loss = 0.006703171, step = 1903200 (2.257 sec) -2022-05-01 20:38:54,621 - tensorflow - INFO - global_step/sec: 88.7818 -2022-05-01 20:38:54,622 - tensorflow - INFO - loss = 1.4233687, step = 1903400 (2.253 sec) -2022-05-01 20:38:56,877 - tensorflow - INFO - global_step/sec: 88.6577 -2022-05-01 20:38:56,877 - tensorflow - INFO - loss = 0.008456467, step = 1903600 (2.256 sec) -2022-05-01 20:38:59,128 - tensorflow - INFO - global_step/sec: 88.8706 -2022-05-01 20:38:59,128 - tensorflow - INFO - loss = 0.0235586, step = 1903800 (2.250 sec) -2022-05-01 20:39:01,390 - tensorflow - INFO - global_step/sec: 88.3861 -2022-05-01 20:39:01,391 - tensorflow - INFO - loss = 0.028131448, step = 1904000 (2.263 sec) -2022-05-01 20:39:03,646 - tensorflow - INFO - global_step/sec: 88.6654 -2022-05-01 20:39:03,646 - tensorflow - INFO - loss = 0.3278071, step = 1904200 (2.256 sec) -2022-05-01 20:39:05,902 - tensorflow - INFO - global_step/sec: 88.6647 -2022-05-01 20:39:05,902 - tensorflow - INFO - loss = 0.061649084, step = 1904400 (2.256 sec) -2022-05-01 20:39:08,156 - tensorflow - INFO - global_step/sec: 88.7094 -2022-05-01 20:39:08,157 - tensorflow - INFO - loss = 0.011733083, step = 1904600 (2.255 sec) -2022-05-01 20:39:10,409 - tensorflow - INFO - global_step/sec: 88.7938 -2022-05-01 20:39:10,409 - tensorflow - INFO - loss = 0.09543198, step = 1904800 (2.252 sec) -2022-05-01 20:39:12,669 - tensorflow - INFO - global_step/sec: 88.4762 -2022-05-01 20:39:12,670 - tensorflow - INFO - loss = 0.011331211, step = 1905000 (2.261 sec) -2022-05-01 20:39:14,932 - tensorflow - INFO - global_step/sec: 88.3948 -2022-05-01 20:39:14,932 - tensorflow - INFO - loss = 0.14373742, step = 1905200 (2.262 sec) -2022-05-01 20:39:17,182 - tensorflow - INFO - global_step/sec: 88.8763 -2022-05-01 20:39:17,182 - tensorflow - INFO - loss = 0.059360716, step = 1905400 (2.250 sec) -2022-05-01 20:39:19,435 - tensorflow - INFO - global_step/sec: 88.7831 -2022-05-01 20:39:19,435 - tensorflow - INFO - loss = 0.010062706, step = 1905600 (2.253 sec) -2022-05-01 20:39:21,692 - tensorflow - INFO - global_step/sec: 88.6162 -2022-05-01 20:39:21,692 - tensorflow - INFO - loss = 0.032864153, step = 1905800 (2.257 sec) -2022-05-01 20:39:23,948 - tensorflow - INFO - global_step/sec: 88.6232 -2022-05-01 20:39:23,949 - tensorflow - INFO - loss = 0.003971881, step = 1906000 (2.257 sec) -2022-05-01 20:39:26,206 - tensorflow - INFO - global_step/sec: 88.6007 -2022-05-01 20:39:26,206 - tensorflow - INFO - loss = 0.04123966, step = 1906200 (2.257 sec) -2022-05-01 20:39:28,463 - tensorflow - INFO - global_step/sec: 88.5905 -2022-05-01 20:39:28,464 - tensorflow - INFO - loss = 0.014023264, step = 1906400 (2.258 sec) -2022-05-01 20:39:30,727 - tensorflow - INFO - global_step/sec: 88.3625 -2022-05-01 20:39:30,727 - tensorflow - INFO - loss = 0.019056926, step = 1906600 (2.263 sec) -2022-05-01 20:39:32,987 - tensorflow - INFO - global_step/sec: 88.4871 -2022-05-01 20:39:32,987 - tensorflow - INFO - loss = 0.03874336, step = 1906800 (2.260 sec) -2022-05-01 20:39:35,239 - tensorflow - INFO - global_step/sec: 88.7957 -2022-05-01 20:39:35,240 - tensorflow - INFO - loss = 0.011484627, step = 1907000 (2.252 sec) -2022-05-01 20:39:37,493 - tensorflow - INFO - global_step/sec: 88.7349 -2022-05-01 20:39:37,494 - tensorflow - INFO - loss = 0.40345308, step = 1907200 (2.254 sec) -2022-05-01 20:39:39,751 - tensorflow - INFO - global_step/sec: 88.5682 -2022-05-01 20:39:39,752 - tensorflow - INFO - loss = 0.2545933, step = 1907400 (2.258 sec) -2022-05-01 20:39:42,002 - tensorflow - INFO - global_step/sec: 88.8538 -2022-05-01 20:39:42,003 - tensorflow - INFO - loss = 0.030244552, step = 1907600 (2.251 sec) -2022-05-01 20:39:44,256 - tensorflow - INFO - global_step/sec: 88.752 -2022-05-01 20:39:44,256 - tensorflow - INFO - loss = 0.07354349, step = 1907800 (2.253 sec) -2022-05-01 20:39:46,511 - tensorflow - INFO - global_step/sec: 88.6774 -2022-05-01 20:39:46,511 - tensorflow - INFO - loss = 0.19981001, step = 1908000 (2.255 sec) -2022-05-01 20:39:48,768 - tensorflow - INFO - global_step/sec: 88.6124 -2022-05-01 20:39:48,768 - tensorflow - INFO - loss = 0.01842366, step = 1908200 (2.257 sec) -2022-05-01 20:39:51,025 - tensorflow - INFO - global_step/sec: 88.61 -2022-05-01 20:39:51,026 - tensorflow - INFO - loss = 0.029061416, step = 1908400 (2.257 sec) -2022-05-01 20:39:53,274 - tensorflow - INFO - global_step/sec: 88.9391 -2022-05-01 20:39:53,274 - tensorflow - INFO - loss = 0.49992847, step = 1908600 (2.249 sec) -2022-05-01 20:39:55,530 - tensorflow - INFO - global_step/sec: 88.6647 -2022-05-01 20:39:55,530 - tensorflow - INFO - loss = 0.0052447515, step = 1908800 (2.256 sec) -2022-05-01 20:39:57,782 - tensorflow - INFO - global_step/sec: 88.7969 -2022-05-01 20:39:57,782 - tensorflow - INFO - loss = 0.05780475, step = 1909000 (2.252 sec) -2022-05-01 20:40:00,037 - tensorflow - INFO - global_step/sec: 88.701 -2022-05-01 20:40:00,037 - tensorflow - INFO - loss = 0.014669808, step = 1909200 (2.255 sec) -2022-05-01 20:40:02,288 - tensorflow - INFO - global_step/sec: 88.83 -2022-05-01 20:40:02,289 - tensorflow - INFO - loss = 0.021751998, step = 1909400 (2.251 sec) -2022-05-01 20:40:04,547 - tensorflow - INFO - global_step/sec: 88.5593 -2022-05-01 20:40:04,547 - tensorflow - INFO - loss = 0.09459678, step = 1909600 (2.258 sec) -2022-05-01 20:40:06,803 - tensorflow - INFO - global_step/sec: 88.6219 -2022-05-01 20:40:06,804 - tensorflow - INFO - loss = 0.09258586, step = 1909800 (2.257 sec) -2022-05-01 20:40:09,053 - tensorflow - INFO - global_step/sec: 88.8807 -2022-05-01 20:40:09,054 - tensorflow - INFO - loss = 0.039172817, step = 1910000 (2.250 sec) -2022-05-01 20:40:11,304 - tensorflow - INFO - global_step/sec: 88.879 -2022-05-01 20:40:11,304 - tensorflow - INFO - loss = 0.0074850977, step = 1910200 (2.250 sec) -2022-05-01 20:40:13,556 - tensorflow - INFO - global_step/sec: 88.7955 -2022-05-01 20:40:13,557 - tensorflow - INFO - loss = 0.048138943, step = 1910400 (2.252 sec) -2022-05-01 20:40:15,812 - tensorflow - INFO - global_step/sec: 88.6453 -2022-05-01 20:40:15,813 - tensorflow - INFO - loss = 0.025071843, step = 1910600 (2.256 sec) -2022-05-01 20:40:18,067 - tensorflow - INFO - global_step/sec: 88.6936 -2022-05-01 20:40:18,068 - tensorflow - INFO - loss = 0.021888126, step = 1910800 (2.255 sec) -2022-05-01 20:40:20,321 - tensorflow - INFO - global_step/sec: 88.7523 -2022-05-01 20:40:20,321 - tensorflow - INFO - loss = 0.022394136, step = 1911000 (2.253 sec) -2022-05-01 20:40:22,574 - tensorflow - INFO - global_step/sec: 88.7484 -2022-05-01 20:40:22,575 - tensorflow - INFO - loss = 0.011828977, step = 1911200 (2.254 sec) -2022-05-01 20:40:24,823 - tensorflow - INFO - global_step/sec: 88.9248 -2022-05-01 20:40:24,824 - tensorflow - INFO - loss = 0.02373368, step = 1911400 (2.249 sec) -2022-05-01 20:40:27,075 - tensorflow - INFO - global_step/sec: 88.8402 -2022-05-01 20:40:27,075 - tensorflow - INFO - loss = 0.035408095, step = 1911600 (2.251 sec) -2022-05-01 20:40:29,330 - tensorflow - INFO - global_step/sec: 88.6673 -2022-05-01 20:40:29,331 - tensorflow - INFO - loss = 0.016631411, step = 1911800 (2.256 sec) -2022-05-01 20:40:31,587 - tensorflow - INFO - global_step/sec: 88.64 -2022-05-01 20:40:31,587 - tensorflow - INFO - loss = 0.0070046624, step = 1912000 (2.256 sec) -2022-05-01 20:40:33,841 - tensorflow - INFO - global_step/sec: 88.7216 -2022-05-01 20:40:33,841 - tensorflow - INFO - loss = 0.06245377, step = 1912200 (2.254 sec) -2022-05-01 20:40:36,094 - tensorflow - INFO - global_step/sec: 88.7758 -2022-05-01 20:40:36,094 - tensorflow - INFO - loss = 0.013396024, step = 1912400 (2.253 sec) -2022-05-01 20:40:38,353 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.01927559, step = 1913800 (2.249 sec) -2022-05-01 20:40:54,126 - tensorflow - INFO - global_step/sec: 88.4858 -2022-05-01 20:40:54,127 - tensorflow - INFO - loss = 9.369586, step = 1914000 (2.260 sec) -2022-05-01 20:40:56,381 - tensorflow - INFO - global_step/sec: 88.7036 -2022-05-01 20:40:56,382 - tensorflow - INFO - loss = 0.01684592, step = 1914200 (2.255 sec) -2022-05-01 20:40:58,637 - tensorflow - INFO - global_step/sec: 88.6603 -2022-05-01 20:40:58,637 - tensorflow - INFO - loss = 0.101945385, step = 1914400 (2.256 sec) -2022-05-01 20:41:00,890 - tensorflow - INFO - global_step/sec: 88.7815 -2022-05-01 20:41:00,890 - tensorflow - INFO - loss = 0.0064888103, step = 1914600 (2.253 sec) -2022-05-01 20:41:03,142 - tensorflow - INFO - global_step/sec: 88.7776 -2022-05-01 20:41:03,143 - tensorflow - INFO - loss = 0.021903522, step = 1914800 (2.253 sec) -2022-05-01 20:41:05,398 - tensorflow - INFO - global_step/sec: 88.6585 -2022-05-01 20:41:05,399 - tensorflow - 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tensorflow - INFO - loss = 0.0121766105, step = 1997800 (2.254 sec) -2022-05-01 20:56:41,465 - tensorflow - INFO - global_step/sec: 88.6415 -2022-05-01 20:56:41,465 - tensorflow - INFO - loss = 0.047288876, step = 1998000 (2.256 sec) -2022-05-01 20:56:43,718 - tensorflow - INFO - global_step/sec: 88.7616 -2022-05-01 20:56:43,719 - tensorflow - INFO - loss = 0.062814854, step = 1998200 (2.253 sec) -2022-05-01 20:56:45,978 - tensorflow - INFO - global_step/sec: 88.4877 -2022-05-01 20:56:45,979 - tensorflow - INFO - loss = 0.020641323, step = 1998400 (2.260 sec) -2022-05-01 20:56:48,228 - tensorflow - INFO - global_step/sec: 88.8852 -2022-05-01 20:56:48,229 - tensorflow - INFO - loss = 16.469042, step = 1998600 (2.250 sec) -2022-05-01 20:56:50,483 - tensorflow - INFO - global_step/sec: 88.7054 -2022-05-01 20:56:50,483 - tensorflow - INFO - loss = 0.011845652, step = 1998800 (2.255 sec) -2022-05-01 20:56:52,740 - tensorflow - INFO - global_step/sec: 88.6063 -2022-05-01 20:56:52,741 - tensorflow - INFO - loss = 0.0357652, step = 1999000 (2.257 sec) -2022-05-01 20:56:54,994 - tensorflow - INFO - global_step/sec: 88.7291 -2022-05-01 20:56:54,995 - tensorflow - INFO - loss = 0.07935603, step = 1999200 (2.254 sec) -2022-05-01 20:56:57,256 - tensorflow - INFO - global_step/sec: 88.4201 -2022-05-01 20:56:57,257 - tensorflow - INFO - loss = 0.012287976, step = 1999400 (2.262 sec) -2022-05-01 20:56:59,509 - tensorflow - INFO - global_step/sec: 88.7909 -2022-05-01 20:56:59,509 - tensorflow - INFO - loss = 0.0061325766, step = 1999600 (2.252 sec) -2022-05-01 20:57:01,759 - tensorflow - INFO - global_step/sec: 88.8596 -2022-05-01 20:57:01,760 - tensorflow - INFO - loss = 0.0115417605, step = 1999800 (2.251 sec) -2022-05-01 20:57:04,005 - tensorflow - INFO - Saving checkpoints for 2000000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. -2022-05-01 20:57:04,023 - tensorflow - INFO - model.ckpt-2000000 is not in all_model_checkpoint_paths. Manually adding it. -2022-05-01 20:57:04,551 - tensorflow - INFO - Calling model_fn. -2022-05-01 20:57:04,678 - tensorflow - INFO - Done calling model_fn. -2022-05-01 20:57:04,693 - tensorflow - INFO - Starting evaluation at 2022-05-01T20:57:04Z -2022-05-01 20:57:05,038 - tensorflow - INFO - Graph was finalized. -2022-05-01 20:57:05,041 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-2000000 -2022-05-01 20:57:05,323 - tensorflow - INFO - Running local_init_op. -2022-05-01 20:57:05,432 - tensorflow - INFO - Done running local_init_op. -2022-05-01 20:57:15,574 - tensorflow - INFO - Evaluation [3990/39900] -2022-05-01 20:57:25,021 - tensorflow - INFO - Evaluation [7980/39900] -2022-05-01 20:57:34,462 - tensorflow - INFO - Evaluation [11970/39900] -2022-05-01 20:57:43,894 - tensorflow - INFO - Evaluation [15960/39900] -2022-05-01 20:57:53,335 - tensorflow - INFO - Evaluation [19950/39900] -2022-05-01 20:58:02,783 - tensorflow - INFO - Evaluation [23940/39900] -2022-05-01 20:58:12,227 - tensorflow - INFO - Evaluation [27930/39900] -2022-05-01 20:58:21,686 - tensorflow - INFO - Evaluation [31920/39900] -2022-05-01 20:58:31,130 - tensorflow - INFO - Evaluation [35910/39900] -2022-05-01 20:58:40,568 - tensorflow - INFO - Evaluation [39900/39900] -2022-05-01 20:58:40,705 - tensorflow - INFO - Finished evaluation at 2022-05-01-20:58:40 -2022-05-01 20:58:40,706 - tensorflow - INFO - Saving dict for global step 2000000: global_step = 2000000, loss = 0.015859876, mean_degree_err = 3.1788845 -2022-05-01 20:58:40,706 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 2000000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-2000000 -2022-05-01 20:58:40,720 - tensorflow - INFO - global_step/sec: 2.02101 -2022-05-01 20:58:40,721 - tensorflow - INFO - loss = 0.01467032, step = 2000000 (98.961 sec) -2022-05-01 20:58:42,981 - tensorflow - INFO - global_step/sec: 88.4545 -2022-05-01 20:58:42,982 - tensorflow - INFO - loss = 0.013274206, step = 2000200 (2.261 sec) -2022-05-01 20:58:45,239 - tensorflow - INFO - global_step/sec: 88.558 -2022-05-01 20:58:45,240 - tensorflow - INFO - loss = 0.026724704, step = 2000400 (2.258 sec) -2022-05-01 20:58:47,492 - tensorflow - INFO - global_step/sec: 88.7754 -2022-05-01 20:58:47,493 - tensorflow - INFO - loss = 0.007345394, step = 2000600 (2.253 sec) -2022-05-01 20:58:49,751 - tensorflow - INFO - global_step/sec: 88.5343 -2022-05-01 20:58:49,752 - tensorflow - INFO - loss = 0.008276798, step = 2000800 (2.259 sec) -2022-05-01 20:58:52,005 - tensorflow - INFO - global_step/sec: 88.7607 -2022-05-01 20:58:52,005 - tensorflow - INFO - loss = 0.037647825, step = 2001000 (2.253 sec) -2022-05-01 20:58:54,264 - tensorflow - INFO - global_step/sec: 88.5243 -2022-05-01 20:58:54,264 - tensorflow - INFO - loss = 0.014356632, step = 2001200 (2.259 sec) -2022-05-01 20:58:56,527 - tensorflow - INFO - global_step/sec: 88.3761 -2022-05-01 20:58:56,527 - tensorflow - INFO - loss = 0.018045077, step = 2001400 (2.263 sec) -2022-05-01 20:58:58,788 - tensorflow - INFO - global_step/sec: 88.4435 -2022-05-01 20:58:58,789 - tensorflow - INFO - loss = 0.25831372, step = 2001600 (2.261 sec) -2022-05-01 20:59:01,052 - tensorflow - INFO - global_step/sec: 88.3513 -2022-05-01 20:59:01,052 - tensorflow - INFO - loss = 0.0193472, step = 2001800 (2.264 sec) -2022-05-01 20:59:03,311 - tensorflow - INFO - global_step/sec: 88.5515 -2022-05-01 20:59:03,311 - tensorflow - INFO - loss = 0.039867893, step = 2002000 (2.259 sec) -2022-05-01 20:59:05,575 - tensorflow - INFO - global_step/sec: 88.3249 -2022-05-01 20:59:05,575 - tensorflow - INFO - loss = 0.021221787, step = 2002200 (2.264 sec) -2022-05-01 20:59:07,836 - tensorflow - INFO - global_step/sec: 88.4694 -2022-05-01 20:59:07,836 - tensorflow - INFO - loss = 0.04830868, step = 2002400 (2.261 sec) -2022-05-01 20:59:10,096 - tensorflow - INFO - global_step/sec: 88.4924 -2022-05-01 20:59:10,096 - tensorflow - INFO - loss = 1.0361093, step = 2002600 (2.260 sec) -2022-05-01 20:59:12,354 - tensorflow - INFO - global_step/sec: 88.5539 -2022-05-01 20:59:12,355 - tensorflow - INFO - loss = 0.018668309, step = 2002800 (2.259 sec) -2022-05-01 20:59:14,616 - tensorflow - INFO - global_step/sec: 88.4391 -2022-05-01 20:59:14,616 - tensorflow - INFO - loss = 0.014582523, step = 2003000 (2.261 sec) -2022-05-01 20:59:16,875 - tensorflow - INFO - global_step/sec: 88.5287 -2022-05-01 20:59:16,875 - tensorflow - INFO - loss = 0.34214815, step = 2003200 (2.259 sec) -2022-05-01 20:59:19,141 - tensorflow - INFO - global_step/sec: 88.2529 -2022-05-01 20:59:19,141 - tensorflow - INFO - loss = 0.027201526, step = 2003400 (2.266 sec) -2022-05-01 20:59:21,399 - tensorflow - INFO - global_step/sec: 88.5729 -2022-05-01 20:59:21,399 - tensorflow - INFO - loss = 0.017191805, step = 2003600 (2.258 sec) -2022-05-01 20:59:23,659 - tensorflow - INFO - global_step/sec: 88.5133 -2022-05-01 20:59:23,659 - tensorflow - INFO - loss = 0.040490415, step = 2003800 (2.260 sec) -2022-05-01 20:59:25,911 - tensorflow - INFO - global_step/sec: 88.8008 -2022-05-01 20:59:25,911 - tensorflow - INFO - loss = 0.019516265, step = 2004000 (2.252 sec) -2022-05-01 20:59:28,164 - tensorflow - INFO - global_step/sec: 88.7503 -2022-05-01 20:59:28,165 - tensorflow - INFO - loss = 0.025298692, step = 2004200 (2.254 sec) -2022-05-01 20:59:30,427 - tensorflow - INFO - global_step/sec: 88.3838 -2022-05-01 20:59:30,428 - tensorflow - INFO - loss = 0.02187489, step = 2004400 (2.263 sec) -2022-05-01 20:59:32,683 - tensorflow - INFO - global_step/sec: 88.6602 -2022-05-01 20:59:32,683 - tensorflow - INFO - loss = 0.008246376, step = 2004600 (2.256 sec) -2022-05-01 20:59:34,936 - tensorflow - INFO - global_step/sec: 88.7644 -2022-05-01 20:59:34,936 - tensorflow - INFO - loss = 0.00793772, step = 2004800 (2.253 sec) -2022-05-01 20:59:37,186 - tensorflow - INFO - global_step/sec: 88.9049 -2022-05-01 20:59:37,186 - tensorflow - INFO - loss = 0.009742752, step = 2005000 (2.250 sec) -2022-05-01 20:59:39,439 - tensorflow - INFO - global_step/sec: 88.7513 -2022-05-01 20:59:39,440 - tensorflow - INFO - loss = 0.019376889, step = 2005200 (2.253 sec) -2022-05-01 20:59:41,694 - tensorflow - INFO - global_step/sec: 88.6935 -2022-05-01 20:59:41,695 - tensorflow - INFO - loss = 0.0048106383, step = 2005400 (2.255 sec) -2022-05-01 20:59:43,958 - tensorflow - INFO - global_step/sec: 88.3361 -2022-05-01 20:59:43,959 - tensorflow - INFO - loss = 0.13213453, step = 2005600 (2.264 sec) -2022-05-01 20:59:46,211 - tensorflow - INFO - global_step/sec: 88.7763 -2022-05-01 20:59:46,211 - tensorflow - INFO - loss = 0.050713982, step = 2005800 (2.253 sec) -2022-05-01 20:59:48,461 - tensorflow - INFO - global_step/sec: 88.8766 -2022-05-01 20:59:48,462 - tensorflow - INFO - loss = 0.027069667, step = 2006000 (2.250 sec) -2022-05-01 20:59:50,712 - tensorflow - INFO - global_step/sec: 88.8684 -2022-05-01 20:59:50,712 - tensorflow - INFO - loss = 0.2192508, step = 2006200 (2.251 sec) -2022-05-01 20:59:52,968 - tensorflow - INFO - global_step/sec: 88.629 -2022-05-01 20:59:52,969 - tensorflow - INFO - loss = 0.02215605, step = 2006400 (2.257 sec) -2022-05-01 20:59:55,224 - tensorflow - INFO - global_step/sec: 88.688 -2022-05-01 20:59:55,224 - tensorflow - INFO - loss = 0.015829494, step = 2006600 (2.255 sec) -2022-05-01 20:59:57,483 - tensorflow - INFO - global_step/sec: 88.5101 -2022-05-01 20:59:57,484 - tensorflow - INFO - loss = 0.020551346, step = 2006800 (2.260 sec) -2022-05-01 20:59:59,737 - tensorflow - INFO - global_step/sec: 88.7463 -2022-05-01 20:59:59,737 - tensorflow - INFO - loss = 2.006907, step = 2007000 (2.254 sec) -2022-05-01 21:00:01,992 - tensorflow - INFO - global_step/sec: 88.6747 -2022-05-01 21:00:01,993 - tensorflow - INFO - loss = 0.009550948, step = 2007200 (2.255 sec) -2022-05-01 21:00:04,245 - tensorflow - INFO - global_step/sec: 88.7846 -2022-05-01 21:00:04,245 - tensorflow - INFO - loss = 0.03207432, step = 2007400 (2.253 sec) -2022-05-01 21:00:06,496 - tensorflow - INFO - global_step/sec: 88.8523 -2022-05-01 21:00:06,496 - tensorflow - INFO - loss = 0.008671386, step = 2007600 (2.251 sec) -2022-05-01 21:00:08,752 - tensorflow - INFO - global_step/sec: 88.6274 -2022-05-01 21:00:08,753 - tensorflow - INFO - loss = 0.081116244, step = 2007800 (2.257 sec) -2022-05-01 21:00:11,003 - tensorflow - INFO - global_step/sec: 88.8742 -2022-05-01 21:00:11,003 - tensorflow - INFO - loss = 0.024560489, step = 2008000 (2.250 sec) -2022-05-01 21:00:13,260 - tensorflow - INFO - global_step/sec: 88.625 -2022-05-01 21:00:13,260 - tensorflow - INFO - loss = 0.054812565, step = 2008200 (2.257 sec) -2022-05-01 21:00:15,514 - tensorflow - INFO - global_step/sec: 88.7045 -2022-05-01 21:00:15,515 - tensorflow - INFO - loss = 0.021981124, step = 2008400 (2.255 sec) -2022-05-01 21:00:17,765 - tensorflow - INFO - global_step/sec: 88.8768 -2022-05-01 21:00:17,765 - tensorflow - INFO - loss = 0.024330994, step = 2008600 (2.250 sec) -2022-05-01 21:00:20,021 - tensorflow - INFO - global_step/sec: 88.6422 -2022-05-01 21:00:20,021 - tensorflow - INFO - loss = 0.121265806, step = 2008800 (2.256 sec) -2022-05-01 21:00:22,279 - tensorflow - INFO - global_step/sec: 88.5661 -2022-05-01 21:00:22,279 - tensorflow - INFO - loss = 0.013724765, step = 2009000 (2.258 sec) -2022-05-01 21:00:24,529 - tensorflow - INFO - global_step/sec: 88.8898 -2022-05-01 21:00:24,529 - tensorflow - INFO - loss = 0.07820247, step = 2009200 (2.250 sec) -2022-05-01 21:00:26,781 - tensorflow - INFO - global_step/sec: 88.8091 -2022-05-01 21:00:26,781 - tensorflow - INFO - loss = 0.069547355, step = 2009400 (2.252 sec) -2022-05-01 21:00:29,039 - tensorflow - INFO - global_step/sec: 88.5597 -2022-05-01 21:00:29,040 - tensorflow - INFO - loss = 0.032171834, step = 2009600 (2.258 sec) -2022-05-01 21:00:31,300 - tensorflow - INFO - global_step/sec: 88.4701 -2022-05-01 21:00:31,300 - tensorflow - INFO - loss = 0.074208364, step = 2009800 (2.261 sec) -2022-05-01 21:00:33,551 - tensorflow - INFO - global_step/sec: 88.8576 -2022-05-01 21:00:33,551 - tensorflow - INFO - loss = 0.014253901, step = 2010000 (2.251 sec) -2022-05-01 21:00:35,801 - tensorflow - INFO - global_step/sec: 88.8754 -2022-05-01 21:00:35,802 - tensorflow - INFO - loss = 0.107017614, step = 2010200 (2.250 sec) -2022-05-01 21:00:38,052 - tensorflow - INFO - global_step/sec: 88.8646 -2022-05-01 21:00:38,052 - tensorflow - INFO - loss = 0.02333238, step = 2010400 (2.251 sec) -2022-05-01 21:00:40,304 - tensorflow - INFO - global_step/sec: 88.8074 -2022-05-01 21:00:40,304 - tensorflow - INFO - loss = 0.013060355, step = 2010600 (2.252 sec) -2022-05-01 21:00:42,550 - tensorflow - INFO - global_step/sec: 89.0439 -2022-05-01 21:00:42,550 - tensorflow - INFO - loss = 0.05624281, step = 2010800 (2.246 sec) -2022-05-01 21:00:44,804 - tensorflow - INFO - global_step/sec: 88.7119 -2022-05-01 21:00:44,805 - tensorflow - INFO - loss = 0.09383938, step = 2011000 (2.254 sec) -2022-05-01 21:00:47,063 - tensorflow - INFO - global_step/sec: 88.5444 -2022-05-01 21:00:47,064 - tensorflow - INFO - loss = 0.026484538, step = 2011200 (2.259 sec) -2022-05-01 21:00:49,314 - tensorflow - INFO - global_step/sec: 88.8738 -2022-05-01 21:00:49,314 - tensorflow - INFO - loss = 0.010374699, step = 2011400 (2.250 sec) -2022-05-01 21:00:51,568 - tensorflow - INFO - global_step/sec: 88.7215 -2022-05-01 21:00:51,568 - tensorflow - INFO - loss = 0.11623212, step = 2011600 (2.254 sec) -2022-05-01 21:00:53,815 - tensorflow - INFO - global_step/sec: 89.0112 -2022-05-01 21:00:53,815 - tensorflow - INFO - loss = 0.0196031, step = 2011800 (2.247 sec) -2022-05-01 21:00:56,068 - tensorflow - INFO - global_step/sec: 88.7529 -2022-05-01 21:00:56,069 - tensorflow - INFO - loss = 0.024542011, step = 2012000 (2.253 sec) -2022-05-01 21:00:58,320 - tensorflow - INFO - global_step/sec: 88.8249 -2022-05-01 21:00:58,320 - tensorflow - INFO - loss = 0.008934006, step = 2012200 (2.252 sec) -2022-05-01 21:01:00,578 - tensorflow - INFO - global_step/sec: 88.5485 -2022-05-01 21:01:00,579 - tensorflow - INFO - loss = 0.014190808, step = 2012400 (2.259 sec) -2022-05-01 21:01:02,830 - tensorflow - INFO - global_step/sec: 88.8435 -2022-05-01 21:01:02,830 - tensorflow - INFO - loss = 0.011069354, step = 2012600 (2.251 sec) -2022-05-01 21:01:05,082 - tensorflow - INFO - global_step/sec: 88.7971 -2022-05-01 21:01:05,082 - tensorflow - INFO - loss = 0.0054627806, step = 2012800 (2.252 sec) -2022-05-01 21:01:07,332 - tensorflow - INFO - global_step/sec: 88.8756 -2022-05-01 21:01:07,333 - tensorflow - INFO - loss = 0.03997647, step = 2013000 (2.250 sec) -2022-05-01 21:01:09,580 - tensorflow - INFO - global_step/sec: 88.9896 -2022-05-01 21:01:09,580 - tensorflow - INFO - loss = 0.07273039, step = 2013200 (2.247 sec) -2022-05-01 21:01:11,836 - tensorflow - INFO - global_step/sec: 88.6371 -2022-05-01 21:01:11,836 - tensorflow - INFO - loss = 0.07716078, step = 2013400 (2.256 sec) -2022-05-01 21:01:14,090 - tensorflow - INFO - global_step/sec: 88.7507 -2022-05-01 21:01:14,090 - tensorflow - INFO - loss = 0.00919323, step = 2013600 (2.253 sec) -2022-05-01 21:01:16,345 - tensorflow - INFO - global_step/sec: 88.6708 -2022-05-01 21:01:16,345 - tensorflow - INFO - loss = 0.006531185, step = 2013800 (2.256 sec) -2022-05-01 21:01:18,593 - tensorflow - INFO - global_step/sec: 88.9757 -2022-05-01 21:01:18,593 - tensorflow - INFO - loss = 0.009378989, step = 2014000 (2.248 sec) -2022-05-01 21:01:20,847 - tensorflow - INFO - global_step/sec: 88.7404 -2022-05-01 21:01:20,847 - tensorflow - INFO - loss = 0.010182643, step = 2014200 (2.254 sec) -2022-05-01 21:01:23,103 - tensorflow - INFO - global_step/sec: 88.6379 -2022-05-01 21:01:23,103 - tensorflow - INFO - loss = 0.03052396, step = 2014400 (2.256 sec) -2022-05-01 21:01:25,362 - tensorflow - INFO - global_step/sec: 88.5363 -2022-05-01 21:01:25,362 - tensorflow - INFO - loss = 0.021367367, step = 2014600 (2.259 sec) -2022-05-01 21:01:27,618 - tensorflow - INFO - global_step/sec: 88.632 -2022-05-01 21:01:27,619 - tensorflow - INFO - loss = 0.03744563, step = 2014800 (2.256 sec) -2022-05-01 21:01:29,875 - tensorflow - INFO - global_step/sec: 88.6486 -2022-05-01 21:01:29,875 - tensorflow - 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tensorflow - INFO - global_step/sec: 88.7349 -2022-05-01 21:01:45,653 - tensorflow - INFO - loss = 0.048359454, step = 2016400 (2.254 sec) -2022-05-01 21:01:47,908 - tensorflow - INFO - global_step/sec: 88.6848 -2022-05-01 21:01:47,908 - tensorflow - INFO - loss = 0.7631538, step = 2016600 (2.255 sec) -2022-05-01 21:01:50,160 - tensorflow - INFO - global_step/sec: 88.798 -2022-05-01 21:01:50,161 - tensorflow - INFO - loss = 0.010982657, step = 2016800 (2.252 sec) -2022-05-01 21:01:52,414 - tensorflow - INFO - global_step/sec: 88.7327 -2022-05-01 21:01:52,415 - tensorflow - INFO - loss = 0.015912972, step = 2017000 (2.254 sec) -2022-05-01 21:01:54,670 - tensorflow - INFO - global_step/sec: 88.6593 -2022-05-01 21:01:54,671 - tensorflow - INFO - loss = 0.047597427, step = 2017200 (2.256 sec) -2022-05-01 21:01:56,924 - tensorflow - INFO - global_step/sec: 88.7278 -2022-05-01 21:01:56,925 - tensorflow - INFO - loss = 0.005005228, step = 2017400 (2.254 sec) -2022-05-01 21:01:59,174 - tensorflow - 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INFO - global_step/sec: 88.5894 -2022-05-01 21:09:25,430 - tensorflow - INFO - loss = 0.009835939, step = 2057200 (2.258 sec) -2022-05-01 21:09:27,691 - tensorflow - INFO - global_step/sec: 88.4177 -2022-05-01 21:09:27,692 - tensorflow - INFO - loss = 0.06272012, step = 2057400 (2.262 sec) -2022-05-01 21:09:29,956 - tensorflow - INFO - global_step/sec: 88.3026 -2022-05-01 21:09:29,957 - tensorflow - INFO - loss = 0.03090753, step = 2057600 (2.265 sec) -2022-05-01 21:09:32,224 - tensorflow - INFO - global_step/sec: 88.1899 -2022-05-01 21:09:32,224 - tensorflow - INFO - loss = 0.01705765, step = 2057800 (2.268 sec) -2022-05-01 21:09:34,486 - tensorflow - INFO - global_step/sec: 88.4149 -2022-05-01 21:09:34,486 - tensorflow - INFO - loss = 0.014951571, step = 2058000 (2.262 sec) -2022-05-01 21:09:36,748 - tensorflow - INFO - global_step/sec: 88.3998 -2022-05-01 21:09:36,749 - tensorflow - INFO - loss = 0.12008022, step = 2058200 (2.262 sec) -2022-05-01 21:09:39,007 - tensorflow - INFO - global_step/sec: 88.539 -2022-05-01 21:09:39,008 - 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tensorflow - INFO - global_step/sec: 88.7619 -2022-05-01 21:10:08,350 - tensorflow - INFO - loss = 0.019503139, step = 2061000 (2.253 sec) -2022-05-01 21:10:10,605 - tensorflow - INFO - global_step/sec: 88.6836 -2022-05-01 21:10:10,605 - tensorflow - INFO - loss = 0.014946849, step = 2061200 (2.255 sec) -2022-05-01 21:10:12,856 - tensorflow - INFO - global_step/sec: 88.8291 -2022-05-01 21:10:12,857 - tensorflow - INFO - loss = 0.06617184, step = 2061400 (2.252 sec) -2022-05-01 21:10:15,105 - tensorflow - INFO - global_step/sec: 88.9473 -2022-05-01 21:10:15,105 - tensorflow - INFO - loss = 0.017059457, step = 2061600 (2.249 sec) -2022-05-01 21:10:17,356 - tensorflow - INFO - global_step/sec: 88.8569 -2022-05-01 21:10:17,356 - tensorflow - INFO - loss = 0.01815191, step = 2061800 (2.251 sec) -2022-05-01 21:10:19,608 - tensorflow - INFO - global_step/sec: 88.7956 -2022-05-01 21:10:19,608 - tensorflow - INFO - loss = 0.013575856, step = 2062000 (2.252 sec) -2022-05-01 21:10:21,863 - tensorflow - 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INFO - loss = 0.009364031, step = 2065800 (2.254 sec) -2022-05-01 21:11:04,653 - tensorflow - INFO - global_step/sec: 88.9472 -2022-05-01 21:11:04,654 - tensorflow - INFO - loss = 0.031356715, step = 2066000 (2.249 sec) -2022-05-01 21:11:06,904 - tensorflow - INFO - global_step/sec: 88.8554 -2022-05-01 21:11:06,905 - tensorflow - INFO - loss = 0.028149718, step = 2066200 (2.251 sec) -2022-05-01 21:11:09,151 - tensorflow - INFO - global_step/sec: 89.0089 -2022-05-01 21:11:09,152 - tensorflow - INFO - loss = 0.023787953, step = 2066400 (2.247 sec) -2022-05-01 21:11:11,405 - tensorflow - INFO - global_step/sec: 88.7357 -2022-05-01 21:11:11,406 - tensorflow - INFO - loss = 0.036262065, step = 2066600 (2.254 sec) -2022-05-01 21:11:13,659 - tensorflow - INFO - global_step/sec: 88.7255 -2022-05-01 21:11:13,660 - tensorflow - INFO - loss = 0.013351564, step = 2066800 (2.254 sec) -2022-05-01 21:11:15,913 - tensorflow - INFO - global_step/sec: 88.7623 -2022-05-01 21:11:15,913 - tensorflow - INFO - 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INFO - global_step/sec: 88.8301 -2022-05-01 21:11:45,214 - tensorflow - INFO - loss = 0.5638597, step = 2069600 (2.251 sec) -2022-05-01 21:11:47,465 - tensorflow - INFO - global_step/sec: 88.8383 -2022-05-01 21:11:47,465 - tensorflow - INFO - loss = 0.043907017, step = 2069800 (2.251 sec) -2022-05-01 21:11:49,719 - tensorflow - INFO - global_step/sec: 88.7031 -2022-05-01 21:11:49,720 - tensorflow - INFO - loss = 0.015858615, step = 2070000 (2.255 sec) -2022-05-01 21:11:51,978 - tensorflow - INFO - global_step/sec: 88.5345 -2022-05-01 21:11:51,979 - tensorflow - INFO - loss = 0.013738412, step = 2070200 (2.259 sec) -2022-05-01 21:11:54,228 - tensorflow - INFO - global_step/sec: 88.8952 -2022-05-01 21:11:54,229 - tensorflow - INFO - loss = 0.01958806, step = 2070400 (2.250 sec) -2022-05-01 21:11:56,485 - tensorflow - INFO - global_step/sec: 88.6396 -2022-05-01 21:11:56,485 - tensorflow - INFO - loss = 0.2860499, step = 2070600 (2.256 sec) -2022-05-01 21:11:58,737 - tensorflow - INFO - global_step/sec: 88.7878 -2022-05-01 21:11:58,738 - tensorflow - INFO - loss = 0.011893651, step = 2070800 (2.253 sec) -2022-05-01 21:12:00,986 - tensorflow - INFO - global_step/sec: 88.9309 -2022-05-01 21:12:00,987 - tensorflow - INFO - loss = 0.01323004, step = 2071000 (2.249 sec) -2022-05-01 21:12:03,243 - tensorflow - INFO - global_step/sec: 88.6124 -2022-05-01 21:12:03,244 - tensorflow - INFO - loss = 0.0049747634, step = 2071200 (2.257 sec) -2022-05-01 21:12:05,494 - tensorflow - INFO - global_step/sec: 88.8724 -2022-05-01 21:12:05,494 - tensorflow - INFO - loss = 0.007261668, step = 2071400 (2.250 sec) -2022-05-01 21:12:07,749 - tensorflow - INFO - global_step/sec: 88.6939 -2022-05-01 21:12:07,749 - tensorflow - INFO - loss = 0.02347025, step = 2071600 (2.255 sec) -2022-05-01 21:12:10,000 - tensorflow - INFO - global_step/sec: 88.8393 -2022-05-01 21:12:10,000 - tensorflow - INFO - loss = 0.021576278, step = 2071800 (2.251 sec) -2022-05-01 21:12:12,250 - tensorflow - INFO - global_step/sec: 88.8652 -2022-05-01 21:12:12,251 - tensorflow - INFO - loss = 0.13531554, step = 2072000 (2.251 sec) -2022-05-01 21:12:14,505 - tensorflow - INFO - global_step/sec: 88.7201 -2022-05-01 21:12:14,505 - tensorflow - INFO - loss = 0.089086235, step = 2072200 (2.254 sec) -2022-05-01 21:12:16,758 - tensorflow - INFO - global_step/sec: 88.7744 -2022-05-01 21:12:16,758 - tensorflow - INFO - loss = 0.014256991, step = 2072400 (2.253 sec) -2022-05-01 21:12:19,005 - tensorflow - INFO - global_step/sec: 88.9976 -2022-05-01 21:12:19,005 - tensorflow - INFO - loss = 0.019302256, step = 2072600 (2.247 sec) -2022-05-01 21:12:21,257 - tensorflow - INFO - global_step/sec: 88.7845 -2022-05-01 21:12:21,258 - tensorflow - INFO - loss = 0.02729341, step = 2072800 (2.253 sec) -2022-05-01 21:12:23,507 - tensorflow - INFO - global_step/sec: 88.8893 -2022-05-01 21:12:23,508 - tensorflow - INFO - loss = 0.0073949606, step = 2073000 (2.250 sec) -2022-05-01 21:12:25,766 - tensorflow - INFO - global_step/sec: 88.5397 -2022-05-01 21:12:25,767 - tensorflow - INFO - 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INFO - global_step/sec: 88.7154 -2022-05-01 21:12:55,046 - tensorflow - INFO - loss = 0.46384215, step = 2075800 (2.254 sec) -2022-05-01 21:12:57,316 - tensorflow - INFO - global_step/sec: 88.0912 -2022-05-01 21:12:57,316 - tensorflow - INFO - loss = 0.01076333, step = 2076000 (2.270 sec) -2022-05-01 21:12:59,575 - tensorflow - INFO - global_step/sec: 88.5378 -2022-05-01 21:12:59,575 - tensorflow - INFO - loss = 0.014261659, step = 2076200 (2.259 sec) -2022-05-01 21:13:01,839 - tensorflow - INFO - global_step/sec: 88.3265 -2022-05-01 21:13:01,839 - tensorflow - INFO - loss = 0.008098021, step = 2076400 (2.264 sec) -2022-05-01 21:13:04,101 - tensorflow - INFO - global_step/sec: 88.3948 -2022-05-01 21:13:04,102 - tensorflow - INFO - loss = 0.01590208, step = 2076600 (2.263 sec) -2022-05-01 21:13:06,354 - tensorflow - INFO - global_step/sec: 88.805 -2022-05-01 21:13:06,354 - tensorflow - INFO - loss = 0.031782128, step = 2076800 (2.252 sec) -2022-05-01 21:13:08,609 - tensorflow - INFO - global_step/sec: 88.6621 -2022-05-01 21:13:08,610 - 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INFO - global_step/sec: 88.5093 -2022-05-01 21:16:24,656 - tensorflow - INFO - loss = 0.01545664, step = 2094400 (2.260 sec) -2022-05-01 21:16:26,912 - tensorflow - INFO - global_step/sec: 88.6031 -2022-05-01 21:16:26,913 - tensorflow - INFO - loss = 0.023175605, step = 2094600 (2.257 sec) -2022-05-01 21:16:29,174 - tensorflow - INFO - global_step/sec: 88.4327 -2022-05-01 21:16:29,174 - tensorflow - INFO - loss = 0.24491048, step = 2094800 (2.262 sec) -2022-05-01 21:16:31,445 - tensorflow - INFO - global_step/sec: 88.0511 -2022-05-01 21:16:31,446 - tensorflow - INFO - loss = 0.01627911, step = 2095000 (2.271 sec) -2022-05-01 21:16:33,716 - tensorflow - INFO - global_step/sec: 88.0973 -2022-05-01 21:16:33,716 - tensorflow - INFO - loss = 0.07918907, step = 2095200 (2.270 sec) -2022-05-01 21:16:35,974 - tensorflow - INFO - global_step/sec: 88.5521 -2022-05-01 21:16:35,975 - tensorflow - INFO - loss = 0.012776617, step = 2095400 (2.259 sec) -2022-05-01 21:16:38,239 - tensorflow - INFO - global_step/sec: 88.3085 -2022-05-01 21:16:38,239 - tensorflow - INFO - loss = 0.026352443, step = 2095600 (2.265 sec) -2022-05-01 21:16:40,497 - tensorflow - INFO - global_step/sec: 88.555 -2022-05-01 21:16:40,498 - tensorflow - INFO - loss = 0.012043992, step = 2095800 (2.259 sec) -2022-05-01 21:16:42,759 - tensorflow - INFO - global_step/sec: 88.4462 -2022-05-01 21:16:42,759 - tensorflow - INFO - loss = 0.023362797, step = 2096000 (2.261 sec) -2022-05-01 21:16:45,021 - tensorflow - INFO - global_step/sec: 88.4093 -2022-05-01 21:16:45,021 - tensorflow - INFO - loss = 0.03033696, step = 2096200 (2.262 sec) -2022-05-01 21:16:47,281 - tensorflow - INFO - global_step/sec: 88.499 -2022-05-01 21:16:47,281 - tensorflow - INFO - loss = 0.0076375394, step = 2096400 (2.260 sec) -2022-05-01 21:16:49,542 - tensorflow - INFO - global_step/sec: 88.4654 -2022-05-01 21:16:49,542 - tensorflow - INFO - loss = 0.008694179, step = 2096600 (2.261 sec) -2022-05-01 21:16:51,800 - tensorflow - INFO - global_step/sec: 88.5724 -2022-05-01 21:16:51,800 - tensorflow - INFO - loss = 0.037878785, step = 2096800 (2.258 sec) -2022-05-01 21:16:54,059 - tensorflow - INFO - global_step/sec: 88.5216 -2022-05-01 21:16:54,059 - tensorflow - INFO - loss = 0.12967177, step = 2097000 (2.259 sec) -2022-05-01 21:16:56,321 - tensorflow - INFO - global_step/sec: 88.4241 -2022-05-01 21:16:56,321 - tensorflow - INFO - loss = 0.017660087, step = 2097200 (2.262 sec) -2022-05-01 21:16:58,580 - tensorflow - INFO - global_step/sec: 88.5298 -2022-05-01 21:16:58,580 - tensorflow - INFO - loss = 0.010364544, step = 2097400 (2.259 sec) -2022-05-01 21:17:00,841 - tensorflow - INFO - global_step/sec: 88.4503 -2022-05-01 21:17:00,842 - tensorflow - INFO - loss = 0.01831447, step = 2097600 (2.261 sec) -2022-05-01 21:17:03,098 - tensorflow - INFO - global_step/sec: 88.6275 -2022-05-01 21:17:03,098 - tensorflow - INFO - loss = 0.04949817, step = 2097800 (2.257 sec) -2022-05-01 21:17:05,350 - tensorflow - INFO - global_step/sec: 88.8195 -2022-05-01 21:17:05,350 - tensorflow - INFO - loss = 0.04070931, step = 2098000 (2.252 sec) -2022-05-01 21:17:07,604 - tensorflow - INFO - global_step/sec: 88.7045 -2022-05-01 21:17:07,605 - tensorflow - INFO - loss = 0.035523713, step = 2098200 (2.255 sec) -2022-05-01 21:17:09,859 - tensorflow - INFO - global_step/sec: 88.6824 -2022-05-01 21:17:09,860 - tensorflow - INFO - loss = 0.021236856, step = 2098400 (2.255 sec) -2022-05-01 21:17:12,119 - tensorflow - INFO - global_step/sec: 88.5007 -2022-05-01 21:17:12,120 - tensorflow - INFO - loss = 0.016438108, step = 2098600 (2.260 sec) -2022-05-01 21:17:14,369 - tensorflow - INFO - global_step/sec: 88.8847 -2022-05-01 21:17:14,370 - tensorflow - INFO - loss = 0.00890751, step = 2098800 (2.250 sec) -2022-05-01 21:17:16,623 - tensorflow - INFO - global_step/sec: 88.7402 -2022-05-01 21:17:16,624 - tensorflow - INFO - loss = 0.005814164, step = 2099000 (2.254 sec) -2022-05-01 21:17:18,879 - tensorflow - INFO - global_step/sec: 88.6581 -2022-05-01 21:17:18,879 - tensorflow - INFO - loss = 0.009963096, step = 2099200 (2.256 sec) -2022-05-01 21:17:21,129 - tensorflow - INFO - global_step/sec: 88.8753 -2022-05-01 21:17:21,130 - tensorflow - INFO - loss = 0.008472566, step = 2099400 (2.250 sec) -2022-05-01 21:17:23,383 - tensorflow - INFO - global_step/sec: 88.7272 -2022-05-01 21:17:23,384 - tensorflow - INFO - loss = 0.008451465, step = 2099600 (2.254 sec) -2022-05-01 21:17:25,635 - tensorflow - INFO - global_step/sec: 88.8399 -2022-05-01 21:17:25,635 - tensorflow - INFO - loss = 0.028972732, step = 2099800 (2.251 sec) -2022-05-01 21:17:27,870 - tensorflow - INFO - Saving checkpoints for 2100000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. -2022-05-01 21:17:27,885 - tensorflow - INFO - model.ckpt-2100000 is not in all_model_checkpoint_paths. Manually adding it. -2022-05-01 21:17:28,408 - tensorflow - INFO - Calling model_fn. -2022-05-01 21:17:28,535 - tensorflow - INFO - Done calling model_fn. -2022-05-01 21:17:28,550 - tensorflow - INFO - Starting evaluation at 2022-05-01T21:17:28Z -2022-05-01 21:17:28,618 - tensorflow - INFO - Graph was finalized. -2022-05-01 21:17:28,621 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-2100000 -2022-05-01 21:17:28,903 - tensorflow - INFO - Running local_init_op. -2022-05-01 21:17:29,010 - tensorflow - INFO - Done running local_init_op. -2022-05-01 21:17:39,236 - tensorflow - INFO - Evaluation [3990/39900] -2022-05-01 21:17:48,745 - tensorflow - INFO - Evaluation [7980/39900] -2022-05-01 21:17:58,242 - tensorflow - INFO - Evaluation [11970/39900] -2022-05-01 21:18:07,766 - tensorflow - INFO - Evaluation [15960/39900] -2022-05-01 21:18:17,302 - tensorflow - INFO - Evaluation [19950/39900] -2022-05-01 21:18:26,848 - tensorflow - INFO - Evaluation [23940/39900] -2022-05-01 21:18:36,360 - tensorflow - INFO - Evaluation [27930/39900] -2022-05-01 21:18:45,842 - tensorflow - INFO - Evaluation [31920/39900] -2022-05-01 21:18:55,344 - tensorflow - INFO - Evaluation [35910/39900] -2022-05-01 21:19:04,884 - tensorflow - INFO - Evaluation [39900/39900] -2022-05-01 21:19:05,022 - tensorflow - INFO - Finished evaluation at 2022-05-01-21:19:05 -2022-05-01 21:19:05,022 - tensorflow - INFO - Saving dict for global step 2100000: global_step = 2100000, loss = 0.016458333, mean_degree_err = 3.3069553 -2022-05-01 21:19:05,022 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 2100000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-2100000 -2022-05-01 21:19:05,036 - tensorflow - INFO - global_step/sec: 2.01204 -2022-05-01 21:19:05,037 - tensorflow - INFO - loss = 0.05436354, step = 2100000 (99.402 sec) -2022-05-01 21:19:07,310 - tensorflow - INFO - global_step/sec: 87.9633 -2022-05-01 21:19:07,310 - tensorflow - INFO - loss = 0.038885124, step = 2100200 (2.273 sec) -2022-05-01 21:19:09,564 - tensorflow - INFO - global_step/sec: 88.7438 -2022-05-01 21:19:09,564 - tensorflow - INFO - loss = 0.0110758245, step = 2100400 (2.254 sec) -2022-05-01 21:19:11,816 - tensorflow - INFO - global_step/sec: 88.8034 -2022-05-01 21:19:11,816 - tensorflow - INFO - loss = 0.02910995, step = 2100600 (2.252 sec) -2022-05-01 21:19:14,067 - tensorflow - INFO - global_step/sec: 88.8318 -2022-05-01 21:19:14,068 - tensorflow - INFO - loss = 0.019992223, step = 2100800 (2.251 sec) -2022-05-01 21:19:16,325 - tensorflow - INFO - global_step/sec: 88.5787 -2022-05-01 21:19:16,325 - tensorflow - INFO - loss = 0.010165205, step = 2101000 (2.258 sec) -2022-05-01 21:19:18,576 - tensorflow - INFO - global_step/sec: 88.8618 -2022-05-01 21:19:18,576 - tensorflow - INFO - loss = 0.013112518, step = 2101200 (2.251 sec) -2022-05-01 21:19:20,828 - tensorflow - INFO - global_step/sec: 88.8129 -2022-05-01 21:19:20,828 - tensorflow - INFO - loss = 0.01900612, step = 2101400 (2.252 sec) -2022-05-01 21:19:23,079 - tensorflow - INFO - global_step/sec: 88.8353 -2022-05-01 21:19:23,079 - tensorflow - INFO - loss = 0.05495621, step = 2101600 (2.251 sec) -2022-05-01 21:19:25,329 - tensorflow - INFO - global_step/sec: 88.9022 -2022-05-01 21:19:25,329 - tensorflow - INFO - loss = 0.05240432, step = 2101800 (2.250 sec) -2022-05-01 21:19:27,584 - tensorflow - INFO - global_step/sec: 88.69 -2022-05-01 21:19:27,584 - tensorflow - INFO - loss = 0.0105040055, step = 2102000 (2.255 sec) -2022-05-01 21:19:29,834 - tensorflow - INFO - global_step/sec: 88.86 -2022-05-01 21:19:29,835 - tensorflow - INFO - loss = 0.070999, step = 2102200 (2.251 sec) -2022-05-01 21:19:32,095 - tensorflow - INFO - global_step/sec: 88.4786 -2022-05-01 21:19:32,095 - tensorflow - INFO - loss = 0.016450152, step = 2102400 (2.260 sec) -2022-05-01 21:19:34,353 - tensorflow - INFO - global_step/sec: 88.5747 -2022-05-01 21:19:34,353 - tensorflow - INFO - loss = 0.009405328, step = 2102600 (2.258 sec) -2022-05-01 21:19:36,604 - tensorflow - INFO - global_step/sec: 88.8532 -2022-05-01 21:19:36,604 - tensorflow - INFO - loss = 0.09677004, step = 2102800 (2.251 sec) -2022-05-01 21:19:38,856 - tensorflow - INFO - global_step/sec: 88.8127 -2022-05-01 21:19:38,856 - tensorflow - INFO - loss = 0.010431, step = 2103000 (2.252 sec) -2022-05-01 21:19:41,114 - tensorflow - INFO - global_step/sec: 88.5512 -2022-05-01 21:19:41,115 - tensorflow - INFO - loss = 0.009252382, step = 2103200 (2.259 sec) -2022-05-01 21:19:43,362 - tensorflow - INFO - global_step/sec: 88.9678 -2022-05-01 21:19:43,363 - tensorflow - INFO - loss = 0.010826778, step = 2103400 (2.248 sec) -2022-05-01 21:19:45,612 - tensorflow - INFO - global_step/sec: 88.8826 -2022-05-01 21:19:45,613 - tensorflow - INFO - loss = 0.042679265, step = 2103600 (2.250 sec) -2022-05-01 21:19:47,860 - tensorflow - INFO - global_step/sec: 88.9817 -2022-05-01 21:19:47,860 - tensorflow - INFO - loss = 0.00809348, step = 2103800 (2.248 sec) -2022-05-01 21:19:50,115 - tensorflow - INFO - global_step/sec: 88.6784 -2022-05-01 21:19:50,116 - tensorflow - INFO - loss = 0.0068497146, step = 2104000 (2.255 sec) -2022-05-01 21:19:52,372 - tensorflow - INFO - global_step/sec: 88.617 -2022-05-01 21:19:52,373 - tensorflow - INFO - loss = 0.010443541, step = 2104200 (2.257 sec) -2022-05-01 21:19:54,626 - tensorflow - INFO - global_step/sec: 88.7574 -2022-05-01 21:19:54,626 - tensorflow - INFO - loss = 0.02146333, step = 2104400 (2.253 sec) -2022-05-01 21:19:56,883 - tensorflow - INFO - global_step/sec: 88.6164 -2022-05-01 21:19:56,883 - tensorflow - INFO - loss = 0.006963673, step = 2104600 (2.257 sec) -2022-05-01 21:19:59,136 - tensorflow - INFO - global_step/sec: 88.7392 -2022-05-01 21:19:59,137 - tensorflow - INFO - loss = 0.05600545, step = 2104800 (2.254 sec) -2022-05-01 21:20:01,392 - tensorflow - INFO - global_step/sec: 88.6644 -2022-05-01 21:20:01,392 - tensorflow - INFO - loss = 0.07515754, step = 2105000 (2.256 sec) -2022-05-01 21:20:03,651 - tensorflow - INFO - global_step/sec: 88.5435 -2022-05-01 21:20:03,651 - tensorflow - INFO - loss = 0.010558774, step = 2105200 (2.259 sec) -2022-05-01 21:20:05,915 - tensorflow - INFO - global_step/sec: 88.3282 -2022-05-01 21:20:05,916 - tensorflow - INFO - loss = 0.014063058, step = 2105400 (2.264 sec) -2022-05-01 21:20:08,178 - tensorflow - INFO - global_step/sec: 88.3788 -2022-05-01 21:20:08,179 - tensorflow - INFO - loss = 0.037848994, step = 2105600 (2.263 sec) -2022-05-01 21:20:10,439 - tensorflow - INFO - global_step/sec: 88.4571 -2022-05-01 21:20:10,439 - tensorflow - INFO - loss = 0.009343641, step = 2105800 (2.261 sec) -2022-05-01 21:20:12,700 - tensorflow - INFO - global_step/sec: 88.4639 -2022-05-01 21:20:12,700 - tensorflow - INFO - loss = 0.020438509, step = 2106000 (2.261 sec) -2022-05-01 21:20:14,959 - tensorflow - INFO - global_step/sec: 88.5247 -2022-05-01 21:20:14,960 - tensorflow - INFO - loss = 0.0067908484, step = 2106200 (2.259 sec) -2022-05-01 21:20:17,222 - tensorflow - INFO - global_step/sec: 88.3955 -2022-05-01 21:20:17,222 - tensorflow - INFO - loss = 0.10189227, step = 2106400 (2.263 sec) -2022-05-01 21:20:19,477 - tensorflow - INFO - global_step/sec: 88.6961 -2022-05-01 21:20:19,477 - tensorflow - INFO - loss = 0.029760987, step = 2106600 (2.255 sec) -2022-05-01 21:20:21,742 - tensorflow - INFO - global_step/sec: 88.2739 -2022-05-01 21:20:21,743 - tensorflow - INFO - loss = 0.034703635, step = 2106800 (2.266 sec) -2022-05-01 21:20:24,005 - tensorflow - INFO - global_step/sec: 88.3958 -2022-05-01 21:20:24,005 - tensorflow - INFO - loss = 0.017966729, step = 2107000 (2.263 sec) -2022-05-01 21:20:26,265 - tensorflow - INFO - global_step/sec: 88.4732 -2022-05-01 21:20:26,266 - tensorflow - INFO - loss = 0.019259527, step = 2107200 (2.261 sec) -2022-05-01 21:20:28,528 - tensorflow - INFO - global_step/sec: 88.4109 -2022-05-01 21:20:28,528 - tensorflow - INFO - loss = 0.010687232, step = 2107400 (2.262 sec) -2022-05-01 21:20:30,790 - tensorflow - INFO - global_step/sec: 88.4049 -2022-05-01 21:20:30,790 - tensorflow - INFO - loss = 0.03140063, step = 2107600 (2.262 sec) -2022-05-01 21:20:33,050 - tensorflow - INFO - global_step/sec: 88.4724 -2022-05-01 21:20:33,051 - tensorflow - INFO - loss = 0.0077216662, step = 2107800 (2.261 sec) -2022-05-01 21:20:35,303 - tensorflow - INFO - global_step/sec: 88.7959 -2022-05-01 21:20:35,303 - tensorflow - INFO - loss = 0.058895633, step = 2108000 (2.252 sec) -2022-05-01 21:20:37,555 - tensorflow - INFO - global_step/sec: 88.79 -2022-05-01 21:20:37,556 - tensorflow - INFO - loss = 0.2672078, step = 2108200 (2.252 sec) -2022-05-01 21:20:39,809 - tensorflow - INFO - global_step/sec: 88.7541 -2022-05-01 21:20:39,809 - tensorflow - INFO - loss = 0.0117831575, step = 2108400 (2.253 sec) -2022-05-01 21:20:42,062 - tensorflow - INFO - global_step/sec: 88.7505 -2022-05-01 21:20:42,063 - tensorflow - INFO - loss = 0.028337851, step = 2108600 (2.254 sec) -2022-05-01 21:20:44,316 - tensorflow - INFO - global_step/sec: 88.7299 -2022-05-01 21:20:44,317 - tensorflow - INFO - loss = 0.067769766, step = 2108800 (2.254 sec) -2022-05-01 21:20:46,570 - tensorflow - INFO - global_step/sec: 88.7534 -2022-05-01 21:20:46,570 - tensorflow - INFO - loss = 0.16715415, step = 2109000 (2.253 sec) -2022-05-01 21:20:48,816 - tensorflow - INFO - global_step/sec: 89.0504 -2022-05-01 21:20:48,816 - tensorflow - INFO - loss = 0.2685939, step = 2109200 (2.246 sec) -2022-05-01 21:20:51,065 - tensorflow - INFO - global_step/sec: 88.9068 -2022-05-01 21:20:51,066 - tensorflow - INFO - loss = 0.50303036, step = 2109400 (2.250 sec) -2022-05-01 21:20:53,315 - tensorflow - INFO - global_step/sec: 88.8834 -2022-05-01 21:20:53,316 - tensorflow - INFO - loss = 0.015402426, step = 2109600 (2.250 sec) -2022-05-01 21:20:55,571 - tensorflow - INFO - global_step/sec: 88.6581 -2022-05-01 21:20:55,572 - tensorflow - INFO - loss = 0.019315388, step = 2109800 (2.256 sec) -2022-05-01 21:20:57,832 - tensorflow - INFO - global_step/sec: 88.4478 -2022-05-01 21:20:57,833 - tensorflow - INFO - loss = 0.02295776, step = 2110000 (2.261 sec) -2022-05-01 21:21:00,090 - tensorflow - INFO - global_step/sec: 88.5985 -2022-05-01 21:21:00,090 - tensorflow - INFO - loss = 0.006488925, step = 2110200 (2.257 sec) -2022-05-01 21:21:02,341 - tensorflow - INFO - global_step/sec: 88.8574 -2022-05-01 21:21:02,341 - tensorflow - INFO - loss = 0.021128904, step = 2110400 (2.251 sec) -2022-05-01 21:21:04,590 - tensorflow - INFO - global_step/sec: 88.9032 -2022-05-01 21:21:04,591 - tensorflow - INFO - loss = 2.7757092, step = 2110600 (2.250 sec) -2022-05-01 21:21:06,843 - tensorflow - INFO - global_step/sec: 88.7744 -2022-05-01 21:21:06,844 - tensorflow - INFO - loss = 0.04770029, step = 2110800 (2.253 sec) -2022-05-01 21:21:09,096 - tensorflow - INFO - global_step/sec: 88.784 -2022-05-01 21:21:09,096 - tensorflow - INFO - loss = 0.26345018, step = 2111000 (2.253 sec) -2022-05-01 21:21:11,358 - tensorflow - INFO - global_step/sec: 88.4278 -2022-05-01 21:21:11,358 - tensorflow - INFO - loss = 0.006933829, step = 2111200 (2.262 sec) -2022-05-01 21:21:13,614 - tensorflow - INFO - global_step/sec: 88.6263 -2022-05-01 21:21:13,615 - tensorflow - INFO - loss = 0.03251392, step = 2111400 (2.257 sec) -2022-05-01 21:21:15,865 - tensorflow - INFO - global_step/sec: 88.8368 -2022-05-01 21:21:15,866 - tensorflow - INFO - loss = 0.0056138346, step = 2111600 (2.251 sec) -2022-05-01 21:21:18,114 - tensorflow - INFO - global_step/sec: 88.9652 -2022-05-01 21:21:18,114 - tensorflow - INFO - loss = 0.047357827, step = 2111800 (2.248 sec) -2022-05-01 21:21:20,365 - tensorflow - INFO - global_step/sec: 88.8384 -2022-05-01 21:21:20,365 - tensorflow - INFO - loss = 0.018748086, step = 2112000 (2.251 sec) -2022-05-01 21:21:22,621 - tensorflow - INFO - global_step/sec: 88.6269 -2022-05-01 21:21:22,622 - tensorflow - INFO - loss = 0.04558868, step = 2112200 (2.257 sec) -2022-05-01 21:21:24,885 - tensorflow - INFO - global_step/sec: 88.3769 -2022-05-01 21:21:24,885 - tensorflow - INFO - loss = 0.010202223, step = 2112400 (2.263 sec) -2022-05-01 21:21:27,139 - tensorflow - INFO - global_step/sec: 88.7074 -2022-05-01 21:21:27,140 - tensorflow - INFO - loss = 0.017460253, step = 2112600 (2.255 sec) -2022-05-01 21:21:29,397 - tensorflow - INFO - global_step/sec: 88.581 -2022-05-01 21:21:29,397 - tensorflow - INFO - loss = 0.012965627, step = 2112800 (2.258 sec) -2022-05-01 21:21:31,655 - tensorflow - INFO - global_step/sec: 88.5678 -2022-05-01 21:21:31,656 - tensorflow - INFO - loss = 0.0047509475, step = 2113000 (2.258 sec) -2022-05-01 21:21:33,915 - tensorflow - INFO - global_step/sec: 88.4957 -2022-05-01 21:21:33,915 - tensorflow - INFO - loss = 0.018873725, step = 2113200 (2.260 sec) -2022-05-01 21:21:36,166 - tensorflow - INFO - global_step/sec: 88.8707 -2022-05-01 21:21:36,166 - tensorflow - INFO - loss = 0.036171153, step = 2113400 (2.251 sec) -2022-05-01 21:21:38,424 - tensorflow - INFO - global_step/sec: 88.54 -2022-05-01 21:21:38,425 - tensorflow - INFO - loss = 0.031342074, step = 2113600 (2.259 sec) -2022-05-01 21:21:40,670 - tensorflow - INFO - global_step/sec: 89.0725 -2022-05-01 21:21:40,670 - tensorflow - INFO - loss = 0.076487854, step = 2113800 (2.245 sec) -2022-05-01 21:21:42,922 - tensorflow - INFO - global_step/sec: 88.8055 -2022-05-01 21:21:42,922 - tensorflow - INFO - loss = 0.008787336, step = 2114000 (2.252 sec) -2022-05-01 21:21:45,178 - tensorflow - INFO - global_step/sec: 88.659 -2022-05-01 21:21:45,178 - tensorflow - INFO - loss = 0.07260836, step = 2114200 (2.256 sec) -2022-05-01 21:21:47,429 - tensorflow - INFO - global_step/sec: 88.8457 -2022-05-01 21:21:47,429 - tensorflow - INFO - loss = 0.0040681423, step = 2114400 (2.251 sec) -2022-05-01 21:21:49,683 - tensorflow - INFO - global_step/sec: 88.7175 -2022-05-01 21:21:49,684 - tensorflow - INFO - loss = 0.015764127, step = 2114600 (2.254 sec) -2022-05-01 21:21:51,940 - tensorflow - INFO - global_step/sec: 88.6369 -2022-05-01 21:21:51,940 - tensorflow - INFO - loss = 0.021431446, step = 2114800 (2.256 sec) -2022-05-01 21:21:54,189 - tensorflow - INFO - global_step/sec: 88.9288 -2022-05-01 21:21:54,189 - tensorflow - 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INFO - global_step/sec: 88.4057 -2022-05-01 21:23:46,972 - tensorflow - INFO - loss = 0.0226475, step = 2125000 (2.263 sec) -2022-05-01 21:23:49,231 - tensorflow - INFO - global_step/sec: 88.4982 -2022-05-01 21:23:49,231 - tensorflow - INFO - loss = 0.005193279, step = 2125200 (2.260 sec) -2022-05-01 21:23:51,497 - tensorflow - INFO - global_step/sec: 88.2605 -2022-05-01 21:23:51,497 - tensorflow - INFO - loss = 0.0506152, step = 2125400 (2.266 sec) -2022-05-01 21:23:53,758 - tensorflow - INFO - global_step/sec: 88.4408 -2022-05-01 21:23:53,759 - tensorflow - INFO - loss = 0.07199786, step = 2125600 (2.261 sec) -2022-05-01 21:23:56,023 - tensorflow - INFO - global_step/sec: 88.3033 -2022-05-01 21:23:56,024 - tensorflow - INFO - loss = 0.008661807, step = 2125800 (2.265 sec) -2022-05-01 21:23:58,289 - tensorflow - INFO - global_step/sec: 88.2882 -2022-05-01 21:23:58,289 - tensorflow - INFO - loss = 0.025556296, step = 2126000 (2.265 sec) -2022-05-01 21:24:00,542 - tensorflow - INFO - global_step/sec: 88.7625 -2022-05-01 21:24:00,542 - 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tensorflow - INFO - global_step/sec: 88.8146 -2022-05-01 21:24:43,362 - tensorflow - INFO - loss = 0.016302548, step = 2130000 (2.252 sec) -2022-05-01 21:24:45,612 - tensorflow - INFO - global_step/sec: 88.8898 -2022-05-01 21:24:45,612 - tensorflow - INFO - loss = 0.019901492, step = 2130200 (2.250 sec) -2022-05-01 21:24:47,871 - tensorflow - INFO - global_step/sec: 88.5235 -2022-05-01 21:24:47,872 - tensorflow - INFO - loss = 0.016128661, step = 2130400 (2.259 sec) -2022-05-01 21:24:50,126 - tensorflow - INFO - global_step/sec: 88.6914 -2022-05-01 21:24:50,127 - tensorflow - INFO - loss = 0.005283707, step = 2130600 (2.255 sec) -2022-05-01 21:24:52,385 - tensorflow - INFO - global_step/sec: 88.5514 -2022-05-01 21:24:52,385 - tensorflow - INFO - loss = 0.098066896, step = 2130800 (2.259 sec) -2022-05-01 21:24:54,635 - tensorflow - INFO - global_step/sec: 88.8872 -2022-05-01 21:24:54,635 - tensorflow - INFO - loss = 0.068171926, step = 2131000 (2.250 sec) -2022-05-01 21:24:56,894 - tensorflow - INFO - global_step/sec: 88.5439 -2022-05-01 21:24:56,894 - tensorflow - INFO - loss = 0.032036074, step = 2131200 (2.259 sec) -2022-05-01 21:24:59,148 - tensorflow - INFO - global_step/sec: 88.7262 -2022-05-01 21:24:59,148 - tensorflow - INFO - loss = 1.1063821, step = 2131400 (2.254 sec) -2022-05-01 21:25:01,405 - tensorflow - INFO - global_step/sec: 88.6084 -2022-05-01 21:25:01,405 - tensorflow - INFO - loss = 0.18810025, step = 2131600 (2.257 sec) -2022-05-01 21:25:03,659 - tensorflow - INFO - global_step/sec: 88.7325 -2022-05-01 21:25:03,659 - tensorflow - INFO - loss = 0.0030217841, step = 2131800 (2.254 sec) -2022-05-01 21:25:05,910 - tensorflow - INFO - global_step/sec: 88.8345 -2022-05-01 21:25:05,911 - tensorflow - INFO - loss = 0.013679769, step = 2132000 (2.251 sec) -2022-05-01 21:25:08,162 - tensorflow - INFO - global_step/sec: 88.8263 -2022-05-01 21:25:08,162 - tensorflow - INFO - loss = 0.009654799, step = 2132200 (2.252 sec) -2022-05-01 21:25:10,415 - tensorflow - INFO - 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INFO - global_step/sec: 88.6723 -2022-05-01 21:26:20,289 - tensorflow - INFO - loss = 0.01930915, step = 2138600 (2.255 sec) -2022-05-01 21:26:22,544 - tensorflow - INFO - global_step/sec: 88.668 -2022-05-01 21:26:22,545 - tensorflow - INFO - loss = 0.43397862, step = 2138800 (2.256 sec) -2022-05-01 21:26:24,796 - tensorflow - INFO - global_step/sec: 88.8055 -2022-05-01 21:26:24,797 - tensorflow - INFO - loss = 0.008880379, step = 2139000 (2.252 sec) -2022-05-01 21:26:27,054 - tensorflow - INFO - global_step/sec: 88.5911 -2022-05-01 21:26:27,054 - tensorflow - INFO - loss = 0.021737663, step = 2139200 (2.258 sec) -2022-05-01 21:26:29,313 - tensorflow - INFO - global_step/sec: 88.5524 -2022-05-01 21:26:29,313 - tensorflow - INFO - loss = 0.017832357, step = 2139400 (2.258 sec) -2022-05-01 21:26:31,573 - tensorflow - INFO - global_step/sec: 88.4781 -2022-05-01 21:26:31,573 - tensorflow - INFO - loss = 0.008692442, step = 2139600 (2.260 sec) -2022-05-01 21:26:33,827 - tensorflow - INFO - 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INFO - loss = 0.053463686, step = 2142200 (2.261 sec) -2022-05-01 21:27:03,140 - tensorflow - INFO - global_step/sec: 88.4919 -2022-05-01 21:27:03,141 - tensorflow - INFO - loss = 0.013493532, step = 2142400 (2.260 sec) -2022-05-01 21:27:05,396 - tensorflow - INFO - global_step/sec: 88.6661 -2022-05-01 21:27:05,396 - tensorflow - INFO - loss = 0.014751355, step = 2142600 (2.256 sec) -2022-05-01 21:27:07,659 - tensorflow - INFO - global_step/sec: 88.3626 -2022-05-01 21:27:07,660 - tensorflow - INFO - loss = 0.015253401, step = 2142800 (2.263 sec) -2022-05-01 21:27:09,917 - tensorflow - INFO - global_step/sec: 88.5915 -2022-05-01 21:27:09,917 - tensorflow - INFO - loss = 0.030651044, step = 2143000 (2.258 sec) -2022-05-01 21:27:12,177 - tensorflow - INFO - global_step/sec: 88.5046 -2022-05-01 21:27:12,178 - tensorflow - INFO - loss = 0.010999289, step = 2143200 (2.261 sec) -2022-05-01 21:27:14,435 - tensorflow - INFO - global_step/sec: 88.5682 -2022-05-01 21:27:14,435 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.4172 -2022-05-01 21:27:30,264 - tensorflow - INFO - loss = 0.007630715, step = 2144800 (2.262 sec) -2022-05-01 21:27:32,529 - tensorflow - INFO - global_step/sec: 88.3023 -2022-05-01 21:27:32,529 - tensorflow - INFO - loss = 0.02547647, step = 2145000 (2.265 sec) -2022-05-01 21:27:34,782 - tensorflow - INFO - global_step/sec: 88.7504 -2022-05-01 21:27:34,783 - tensorflow - INFO - loss = 0.03039402, step = 2145200 (2.253 sec) -2022-05-01 21:27:37,037 - tensorflow - INFO - global_step/sec: 88.6971 -2022-05-01 21:27:37,038 - tensorflow - INFO - loss = 0.010505091, step = 2145400 (2.255 sec) -2022-05-01 21:27:39,292 - tensorflow - INFO - global_step/sec: 88.6855 -2022-05-01 21:27:39,293 - tensorflow - INFO - loss = 0.018424356, step = 2145600 (2.255 sec) -2022-05-01 21:27:41,545 - tensorflow - INFO - global_step/sec: 88.7705 -2022-05-01 21:27:41,546 - tensorflow - INFO - loss = 0.009579858, step = 2145800 (2.253 sec) -2022-05-01 21:27:43,799 - tensorflow - INFO - global_step/sec: 88.74 -2022-05-01 21:27:43,800 - tensorflow - INFO - loss = 0.013186602, step = 2146000 (2.254 sec) -2022-05-01 21:27:46,051 - tensorflow - INFO - global_step/sec: 88.8089 -2022-05-01 21:27:46,052 - tensorflow - INFO - loss = 0.13267243, step = 2146200 (2.252 sec) -2022-05-01 21:27:48,306 - tensorflow - INFO - global_step/sec: 88.7174 -2022-05-01 21:27:48,306 - tensorflow - INFO - loss = 0.01090483, step = 2146400 (2.254 sec) -2022-05-01 21:27:50,558 - tensorflow - INFO - global_step/sec: 88.7801 -2022-05-01 21:27:50,559 - tensorflow - INFO - loss = 0.021057162, step = 2146600 (2.253 sec) -2022-05-01 21:27:52,810 - tensorflow - INFO - global_step/sec: 88.8242 -2022-05-01 21:27:52,810 - tensorflow - INFO - loss = 0.006197462, step = 2146800 (2.252 sec) -2022-05-01 21:27:55,063 - tensorflow - INFO - global_step/sec: 88.7603 -2022-05-01 21:27:55,064 - tensorflow - INFO - loss = 0.039617598, step = 2147000 (2.253 sec) -2022-05-01 21:27:57,320 - tensorflow - INFO - global_step/sec: 88.6118 -2022-05-01 21:27:57,321 - tensorflow - INFO - loss = 0.020207167, step = 2147200 (2.257 sec) -2022-05-01 21:27:59,574 - tensorflow - INFO - global_step/sec: 88.7511 -2022-05-01 21:27:59,574 - tensorflow - INFO - loss = 0.013427195, step = 2147400 (2.254 sec) -2022-05-01 21:28:01,833 - tensorflow - INFO - global_step/sec: 88.5078 -2022-05-01 21:28:01,834 - tensorflow - INFO - loss = 0.017240942, step = 2147600 (2.260 sec) -2022-05-01 21:28:04,088 - tensorflow - INFO - global_step/sec: 88.7135 -2022-05-01 21:28:04,088 - tensorflow - INFO - loss = 0.010838108, step = 2147800 (2.254 sec) -2022-05-01 21:28:06,344 - tensorflow - INFO - global_step/sec: 88.63 -2022-05-01 21:28:06,345 - tensorflow - INFO - loss = 0.015569428, step = 2148000 (2.257 sec) -2022-05-01 21:28:08,600 - tensorflow - INFO - global_step/sec: 88.6835 -2022-05-01 21:28:08,600 - tensorflow - INFO - loss = 0.010146435, step = 2148200 (2.255 sec) -2022-05-01 21:28:10,852 - tensorflow - INFO - global_step/sec: 88.7926 -2022-05-01 21:28:10,853 - tensorflow - 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tensorflow - INFO - global_step/sec: 88.647 -2022-05-01 21:28:40,153 - tensorflow - INFO - loss = 0.0049577425, step = 2151000 (2.256 sec) -2022-05-01 21:28:42,408 - tensorflow - INFO - global_step/sec: 88.6683 -2022-05-01 21:28:42,408 - tensorflow - INFO - loss = 0.009653422, step = 2151200 (2.256 sec) -2022-05-01 21:28:44,663 - tensorflow - INFO - global_step/sec: 88.6838 -2022-05-01 21:28:44,663 - tensorflow - INFO - loss = 0.0048831427, step = 2151400 (2.255 sec) -2022-05-01 21:28:46,914 - tensorflow - INFO - global_step/sec: 88.8404 -2022-05-01 21:28:46,915 - tensorflow - INFO - loss = 0.007938244, step = 2151600 (2.251 sec) -2022-05-01 21:28:49,167 - tensorflow - INFO - global_step/sec: 88.7756 -2022-05-01 21:28:49,167 - tensorflow - INFO - loss = 0.004820142, step = 2151800 (2.253 sec) -2022-05-01 21:28:51,424 - tensorflow - INFO - global_step/sec: 88.6287 -2022-05-01 21:28:51,424 - tensorflow - INFO - loss = 0.01035807, step = 2152000 (2.257 sec) -2022-05-01 21:28:53,680 - tensorflow - 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INFO - global_step/sec: 88.5602 -2022-05-01 21:31:27,024 - tensorflow - INFO - loss = 0.010295693, step = 2165800 (2.258 sec) -2022-05-01 21:31:29,280 - tensorflow - INFO - global_step/sec: 88.6396 -2022-05-01 21:31:29,281 - tensorflow - INFO - loss = 0.025834952, step = 2166000 (2.256 sec) -2022-05-01 21:31:31,541 - tensorflow - INFO - global_step/sec: 88.4723 -2022-05-01 21:31:31,541 - tensorflow - INFO - loss = 0.009997468, step = 2166200 (2.261 sec) -2022-05-01 21:31:33,800 - tensorflow - INFO - global_step/sec: 88.5112 -2022-05-01 21:31:33,801 - tensorflow - INFO - loss = 0.1724355, step = 2166400 (2.260 sec) -2022-05-01 21:31:36,060 - tensorflow - INFO - global_step/sec: 88.5197 -2022-05-01 21:31:36,060 - tensorflow - INFO - loss = 0.012560392, step = 2166600 (2.259 sec) -2022-05-01 21:31:38,314 - tensorflow - INFO - global_step/sec: 88.734 -2022-05-01 21:31:38,314 - tensorflow - INFO - loss = 0.013011185, step = 2166800 (2.254 sec) -2022-05-01 21:31:40,572 - tensorflow - INFO - 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loss = 0.015364771, step = 2191600 (2.261 sec) -2022-05-01 21:36:20,185 - tensorflow - INFO - global_step/sec: 88.6835 -2022-05-01 21:36:20,186 - tensorflow - INFO - loss = 0.13073684, step = 2191800 (2.255 sec) -2022-05-01 21:36:22,446 - tensorflow - INFO - global_step/sec: 88.4874 -2022-05-01 21:36:22,446 - tensorflow - INFO - loss = 0.05943086, step = 2192000 (2.260 sec) -2022-05-01 21:36:24,701 - tensorflow - INFO - global_step/sec: 88.6829 -2022-05-01 21:36:24,701 - tensorflow - INFO - loss = 0.010933407, step = 2192200 (2.255 sec) -2022-05-01 21:36:26,954 - tensorflow - INFO - global_step/sec: 88.7558 -2022-05-01 21:36:26,955 - tensorflow - INFO - loss = 0.005767065, step = 2192400 (2.253 sec) -2022-05-01 21:36:29,206 - tensorflow - INFO - global_step/sec: 88.8305 -2022-05-01 21:36:29,206 - tensorflow - INFO - loss = 0.07748546, step = 2192600 (2.251 sec) -2022-05-01 21:36:31,470 - tensorflow - INFO - global_step/sec: 88.3346 -2022-05-01 21:36:31,470 - tensorflow - INFO - loss = 0.030347891, step = 2192800 (2.264 sec) -2022-05-01 21:36:33,726 - tensorflow - INFO - global_step/sec: 88.6374 -2022-05-01 21:36:33,727 - tensorflow - INFO - loss = 0.0073875445, step = 2193000 (2.256 sec) -2022-05-01 21:36:35,984 - tensorflow - INFO - global_step/sec: 88.58 -2022-05-01 21:36:35,985 - tensorflow - INFO - loss = 0.020786371, step = 2193200 (2.258 sec) -2022-05-01 21:36:38,232 - tensorflow - INFO - global_step/sec: 88.9812 -2022-05-01 21:36:38,232 - tensorflow - INFO - loss = 0.047285732, step = 2193400 (2.248 sec) -2022-05-01 21:36:40,484 - tensorflow - INFO - global_step/sec: 88.8151 -2022-05-01 21:36:40,484 - tensorflow - INFO - loss = 0.014292076, step = 2193600 (2.252 sec) -2022-05-01 21:36:42,739 - tensorflow - INFO - global_step/sec: 88.6817 -2022-05-01 21:36:42,739 - tensorflow - INFO - loss = 0.025393961, step = 2193800 (2.255 sec) -2022-05-01 21:36:44,993 - tensorflow - INFO - global_step/sec: 88.7232 -2022-05-01 21:36:44,994 - tensorflow - INFO - loss = 0.023196246, step = 2194000 (2.254 sec) -2022-05-01 21:36:47,244 - tensorflow - INFO - global_step/sec: 88.8493 -2022-05-01 21:36:47,245 - tensorflow - INFO - loss = 0.016532872, step = 2194200 (2.251 sec) -2022-05-01 21:36:49,500 - tensorflow - INFO - global_step/sec: 88.6702 -2022-05-01 21:36:49,500 - tensorflow - INFO - loss = 0.005099818, step = 2194400 (2.256 sec) -2022-05-01 21:36:51,756 - tensorflow - INFO - global_step/sec: 88.6475 -2022-05-01 21:36:51,756 - tensorflow - INFO - loss = 0.014568393, step = 2194600 (2.256 sec) -2022-05-01 21:36:54,003 - tensorflow - INFO - global_step/sec: 89.0025 -2022-05-01 21:36:54,003 - tensorflow - INFO - loss = 0.00928764, step = 2194800 (2.247 sec) -2022-05-01 21:36:56,258 - tensorflow - INFO - global_step/sec: 88.6935 -2022-05-01 21:36:56,258 - tensorflow - INFO - loss = 0.0219753, step = 2195000 (2.255 sec) -2022-05-01 21:36:58,511 - tensorflow - INFO - global_step/sec: 88.779 -2022-05-01 21:36:58,511 - tensorflow - INFO - loss = 1.0612338, step = 2195200 (2.253 sec) -2022-05-01 21:37:00,761 - tensorflow - INFO - global_step/sec: 88.8767 -2022-05-01 21:37:00,761 - tensorflow - INFO - loss = 0.31271794, step = 2195400 (2.250 sec) -2022-05-01 21:37:03,013 - tensorflow - INFO - global_step/sec: 88.8259 -2022-05-01 21:37:03,013 - tensorflow - INFO - loss = 0.07092461, step = 2195600 (2.252 sec) -2022-05-01 21:37:05,266 - tensorflow - INFO - global_step/sec: 88.766 -2022-05-01 21:37:05,266 - tensorflow - INFO - loss = 0.02657806, step = 2195800 (2.253 sec) -2022-05-01 21:37:07,522 - tensorflow - INFO - global_step/sec: 88.624 -2022-05-01 21:37:07,523 - tensorflow - INFO - loss = 0.012601734, step = 2196000 (2.257 sec) -2022-05-01 21:37:09,780 - tensorflow - INFO - global_step/sec: 88.5875 -2022-05-01 21:37:09,780 - tensorflow - INFO - loss = 0.010346203, step = 2196200 (2.258 sec) -2022-05-01 21:37:12,029 - tensorflow - INFO - global_step/sec: 88.94 -2022-05-01 21:37:12,029 - tensorflow - INFO - loss = 0.0631614, step = 2196400 (2.249 sec) -2022-05-01 21:37:14,278 - tensorflow - INFO - global_step/sec: 88.9297 -2022-05-01 21:37:14,278 - tensorflow - INFO - loss = 0.02143442, step = 2196600 (2.249 sec) -2022-05-01 21:37:16,528 - tensorflow - INFO - global_step/sec: 88.8974 -2022-05-01 21:37:16,528 - tensorflow - INFO - loss = 0.013591796, step = 2196800 (2.250 sec) -2022-05-01 21:37:18,783 - tensorflow - INFO - global_step/sec: 88.6885 -2022-05-01 21:37:18,783 - tensorflow - INFO - loss = 0.009670453, step = 2197000 (2.255 sec) -2022-05-01 21:37:21,040 - tensorflow - INFO - global_step/sec: 88.618 -2022-05-01 21:37:21,040 - tensorflow - INFO - loss = 0.012457259, step = 2197200 (2.257 sec) -2022-05-01 21:37:23,288 - tensorflow - INFO - global_step/sec: 88.938 -2022-05-01 21:37:23,289 - tensorflow - INFO - loss = 0.026262462, step = 2197400 (2.249 sec) -2022-05-01 21:37:25,540 - tensorflow - INFO - global_step/sec: 88.8215 -2022-05-01 21:37:25,540 - tensorflow - INFO - loss = 0.0077347336, step = 2197600 (2.252 sec) -2022-05-01 21:37:27,791 - tensorflow - INFO - global_step/sec: 88.8591 -2022-05-01 21:37:27,791 - tensorflow - INFO - loss = 0.01425202, step = 2197800 (2.251 sec) -2022-05-01 21:37:30,051 - tensorflow - INFO - global_step/sec: 88.4816 -2022-05-01 21:37:30,052 - tensorflow - INFO - loss = 0.07255714, step = 2198000 (2.260 sec) -2022-05-01 21:37:32,311 - tensorflow - INFO - global_step/sec: 88.4938 -2022-05-01 21:37:32,312 - tensorflow - INFO - loss = 0.20931415, step = 2198200 (2.260 sec) -2022-05-01 21:37:34,571 - tensorflow - INFO - global_step/sec: 88.4917 -2022-05-01 21:37:34,572 - tensorflow - INFO - loss = 0.037890192, step = 2198400 (2.260 sec) -2022-05-01 21:37:36,839 - tensorflow - INFO - global_step/sec: 88.1986 -2022-05-01 21:37:36,839 - tensorflow - INFO - loss = 0.037103213, step = 2198600 (2.268 sec) -2022-05-01 21:37:39,100 - tensorflow - INFO - global_step/sec: 88.4457 -2022-05-01 21:37:39,101 - tensorflow - INFO - loss = 0.021963807, step = 2198800 (2.261 sec) -2022-05-01 21:37:41,359 - tensorflow - INFO - global_step/sec: 88.5571 -2022-05-01 21:37:41,359 - tensorflow - INFO - loss = 0.017868139, step = 2199000 (2.258 sec) -2022-05-01 21:37:43,620 - tensorflow - INFO - global_step/sec: 88.4404 -2022-05-01 21:37:43,620 - tensorflow - INFO - loss = 0.08600532, step = 2199200 (2.261 sec) -2022-05-01 21:37:45,884 - tensorflow - INFO - global_step/sec: 88.3505 -2022-05-01 21:37:45,884 - tensorflow - INFO - loss = 0.014971986, step = 2199400 (2.264 sec) -2022-05-01 21:37:48,149 - tensorflow - INFO - global_step/sec: 88.2993 -2022-05-01 21:37:48,149 - tensorflow - INFO - loss = 0.009885784, step = 2199600 (2.265 sec) -2022-05-01 21:37:50,409 - tensorflow - INFO - global_step/sec: 88.4647 -2022-05-01 21:37:50,410 - tensorflow - INFO - loss = 0.16105652, step = 2199800 (2.261 sec) -2022-05-01 21:37:52,664 - tensorflow - INFO - Saving checkpoints for 2200000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. -2022-05-01 21:37:52,680 - tensorflow - INFO - model.ckpt-2200000 is not in all_model_checkpoint_paths. Manually adding it. -2022-05-01 21:37:53,206 - tensorflow - INFO - Calling model_fn. -2022-05-01 21:37:53,334 - tensorflow - INFO - Done calling model_fn. -2022-05-01 21:37:53,349 - tensorflow - INFO - Starting evaluation at 2022-05-01T21:37:53Z -2022-05-01 21:37:53,417 - tensorflow - INFO - Graph was finalized. -2022-05-01 21:37:53,421 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-2200000 -2022-05-01 21:37:53,703 - tensorflow - INFO - Running local_init_op. -2022-05-01 21:37:53,812 - tensorflow - INFO - Done running local_init_op. -2022-05-01 21:38:04,130 - tensorflow - INFO - Evaluation [3990/39900] -2022-05-01 21:38:13,663 - tensorflow - INFO - Evaluation [7980/39900] -2022-05-01 21:38:23,220 - tensorflow - INFO - Evaluation [11970/39900] -2022-05-01 21:38:32,772 - tensorflow - INFO - Evaluation [15960/39900] -2022-05-01 21:38:42,320 - tensorflow - INFO - Evaluation [19950/39900] -2022-05-01 21:38:51,892 - tensorflow - INFO - Evaluation [23940/39900] -2022-05-01 21:39:01,423 - tensorflow - INFO - Evaluation [27930/39900] -2022-05-01 21:39:10,981 - tensorflow - INFO - Evaluation [31920/39900] -2022-05-01 21:39:20,545 - tensorflow - INFO - Evaluation [35910/39900] -2022-05-01 21:39:30,119 - tensorflow - INFO - Evaluation [39900/39900] -2022-05-01 21:39:30,258 - tensorflow - INFO - Finished evaluation at 2022-05-01-21:39:30 -2022-05-01 21:39:30,259 - tensorflow - INFO - Saving dict for global step 2200000: global_step = 2200000, loss = 0.015515498, mean_degree_err = 3.1324177 -2022-05-01 21:39:30,259 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 2200000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-2200000 -2022-05-01 21:39:30,273 - tensorflow - INFO - global_step/sec: 2.00273 -2022-05-01 21:39:30,274 - tensorflow - INFO - loss = 0.016591474, step = 2200000 (99.864 sec) -2022-05-01 21:39:32,548 - tensorflow - INFO - global_step/sec: 87.916 -2022-05-01 21:39:32,548 - tensorflow - INFO - loss = 0.04369278, step = 2200200 (2.274 sec) -2022-05-01 21:39:34,804 - tensorflow - INFO - global_step/sec: 88.6638 -2022-05-01 21:39:34,804 - tensorflow - INFO - loss = 0.035334364, step = 2200400 (2.256 sec) -2022-05-01 21:39:37,057 - tensorflow - INFO - global_step/sec: 88.7553 -2022-05-01 21:39:37,057 - tensorflow - INFO - loss = 0.0069875494, step = 2200600 (2.253 sec) -2022-05-01 21:39:39,309 - tensorflow - INFO - global_step/sec: 88.8105 -2022-05-01 21:39:39,309 - tensorflow - INFO - loss = 0.041061778, step = 2200800 (2.252 sec) -2022-05-01 21:39:41,567 - tensorflow - INFO - global_step/sec: 88.579 -2022-05-01 21:39:41,567 - tensorflow - INFO - loss = 0.011567708, step = 2201000 (2.258 sec) -2022-05-01 21:39:43,822 - tensorflow - INFO - global_step/sec: 88.6912 -2022-05-01 21:39:43,822 - tensorflow - INFO - loss = 0.014715085, step = 2201200 (2.255 sec) -2022-05-01 21:39:46,073 - tensorflow - INFO - global_step/sec: 88.8445 -2022-05-01 21:39:46,073 - tensorflow - INFO - loss = 0.009093689, step = 2201400 (2.251 sec) -2022-05-01 21:39:48,322 - tensorflow - INFO - global_step/sec: 88.9168 -2022-05-01 21:39:48,323 - tensorflow - INFO - loss = 1.0612983, step = 2201600 (2.249 sec) -2022-05-01 21:39:50,575 - tensorflow - INFO - global_step/sec: 88.7781 -2022-05-01 21:39:50,575 - tensorflow - INFO - loss = 0.009138137, step = 2201800 (2.253 sec) -2022-05-01 21:39:52,827 - tensorflow - INFO - global_step/sec: 88.8019 -2022-05-01 21:39:52,828 - tensorflow - INFO - loss = 0.010925783, step = 2202000 (2.252 sec) -2022-05-01 21:39:55,085 - tensorflow - INFO - global_step/sec: 88.5793 -2022-05-01 21:39:55,086 - tensorflow - INFO - loss = 0.014196128, step = 2202200 (2.258 sec) -2022-05-01 21:39:57,344 - tensorflow - INFO - global_step/sec: 88.5409 -2022-05-01 21:39:57,344 - tensorflow - INFO - loss = 0.1022256, step = 2202400 (2.259 sec) -2022-05-01 21:39:59,595 - tensorflow - INFO - global_step/sec: 88.8293 -2022-05-01 21:39:59,596 - tensorflow - INFO - loss = 0.12285152, step = 2202600 (2.252 sec) -2022-05-01 21:40:01,849 - tensorflow - INFO - global_step/sec: 88.7609 -2022-05-01 21:40:01,849 - tensorflow - INFO - loss = 0.00926359, step = 2202800 (2.253 sec) -2022-05-01 21:40:04,101 - tensorflow - INFO - global_step/sec: 88.7822 -2022-05-01 21:40:04,102 - tensorflow - INFO - loss = 0.15393083, step = 2203000 (2.253 sec) -2022-05-01 21:40:06,353 - tensorflow - INFO - global_step/sec: 88.8288 -2022-05-01 21:40:06,353 - tensorflow - INFO - loss = 0.01870555, step = 2203200 (2.252 sec) -2022-05-01 21:40:08,613 - tensorflow - INFO - global_step/sec: 88.5019 -2022-05-01 21:40:08,613 - tensorflow - INFO - loss = 0.020982817, step = 2203400 (2.260 sec) -2022-05-01 21:40:10,864 - tensorflow - INFO - global_step/sec: 88.8411 -2022-05-01 21:40:10,864 - tensorflow - INFO - loss = 0.03838037, step = 2203600 (2.251 sec) -2022-05-01 21:40:13,117 - tensorflow - INFO - global_step/sec: 88.769 -2022-05-01 21:40:13,117 - tensorflow - INFO - loss = 0.019582264, step = 2203800 (2.253 sec) -2022-05-01 21:40:15,369 - tensorflow - INFO - global_step/sec: 88.8261 -2022-05-01 21:40:15,369 - tensorflow - INFO - loss = 0.0038917055, step = 2204000 (2.252 sec) -2022-05-01 21:40:17,623 - tensorflow - INFO - global_step/sec: 88.7092 -2022-05-01 21:40:17,624 - tensorflow - INFO - loss = 0.012383769, step = 2204200 (2.255 sec) -2022-05-01 21:40:19,879 - tensorflow - INFO - global_step/sec: 88.6498 -2022-05-01 21:40:19,880 - tensorflow - INFO - loss = 0.006668681, step = 2204400 (2.256 sec) -2022-05-01 21:40:22,129 - tensorflow - INFO - global_step/sec: 88.9148 -2022-05-01 21:40:22,129 - tensorflow - INFO - loss = 0.008942205, step = 2204600 (2.249 sec) -2022-05-01 21:40:24,380 - tensorflow - INFO - global_step/sec: 88.8245 -2022-05-01 21:40:24,381 - tensorflow - INFO - loss = 0.10828018, step = 2204800 (2.252 sec) -2022-05-01 21:40:26,635 - tensorflow - INFO - global_step/sec: 88.7117 -2022-05-01 21:40:26,635 - tensorflow - INFO - loss = 0.026557386, step = 2205000 (2.255 sec) -2022-05-01 21:40:28,884 - tensorflow - INFO - global_step/sec: 88.9343 -2022-05-01 21:40:28,884 - tensorflow - INFO - loss = 0.009144509, step = 2205200 (2.249 sec) -2022-05-01 21:40:31,141 - tensorflow - INFO - global_step/sec: 88.6109 -2022-05-01 21:40:31,141 - tensorflow - INFO - loss = 0.018995715, step = 2205400 (2.257 sec) -2022-05-01 21:40:33,394 - tensorflow - INFO - global_step/sec: 88.7683 -2022-05-01 21:40:33,394 - tensorflow - INFO - loss = 0.02026128, step = 2205600 (2.253 sec) -2022-05-01 21:40:35,649 - tensorflow - INFO - global_step/sec: 88.6789 -2022-05-01 21:40:35,649 - tensorflow - INFO - loss = 0.052717946, step = 2205800 (2.255 sec) -2022-05-01 21:40:37,899 - tensorflow - INFO - global_step/sec: 88.9058 -2022-05-01 21:40:37,899 - tensorflow - INFO - loss = 0.11664444, step = 2206000 (2.250 sec) -2022-05-01 21:40:40,151 - tensorflow - INFO - global_step/sec: 88.7921 -2022-05-01 21:40:40,151 - tensorflow - INFO - loss = 0.0046029845, step = 2206200 (2.252 sec) -2022-05-01 21:40:42,401 - tensorflow - INFO - global_step/sec: 88.8739 -2022-05-01 21:40:42,402 - tensorflow - INFO - loss = 0.04805688, step = 2206400 (2.250 sec) -2022-05-01 21:40:44,653 - tensorflow - INFO - global_step/sec: 88.8337 -2022-05-01 21:40:44,653 - tensorflow - INFO - loss = 0.006403135, step = 2206600 (2.251 sec) -2022-05-01 21:40:46,909 - tensorflow - INFO - global_step/sec: 88.6538 -2022-05-01 21:40:46,909 - tensorflow - INFO - loss = 0.11942297, step = 2206800 (2.256 sec) -2022-05-01 21:40:49,163 - tensorflow - INFO - global_step/sec: 88.7151 -2022-05-01 21:40:49,164 - tensorflow - INFO - loss = 18.514679, step = 2207000 (2.254 sec) -2022-05-01 21:40:51,414 - tensorflow - INFO - global_step/sec: 88.8687 -2022-05-01 21:40:51,414 - tensorflow - INFO - loss = 0.026035413, step = 2207200 (2.251 sec) -2022-05-01 21:40:53,661 - tensorflow - INFO - global_step/sec: 88.9766 -2022-05-01 21:40:53,662 - tensorflow - INFO - loss = 0.9963044, step = 2207400 (2.248 sec) -2022-05-01 21:40:55,918 - tensorflow - INFO - global_step/sec: 88.6148 -2022-05-01 21:40:55,919 - tensorflow - INFO - loss = 0.063365005, step = 2207600 (2.257 sec) -2022-05-01 21:40:58,179 - tensorflow - INFO - global_step/sec: 88.4572 -2022-05-01 21:40:58,180 - tensorflow - INFO - loss = 0.17814486, step = 2207800 (2.261 sec) -2022-05-01 21:41:00,433 - tensorflow - INFO - global_step/sec: 88.7593 -2022-05-01 21:41:00,433 - tensorflow - INFO - loss = 0.010942705, step = 2208000 (2.253 sec) -2022-05-01 21:41:02,690 - tensorflow - INFO - global_step/sec: 88.5825 -2022-05-01 21:41:02,691 - tensorflow - INFO - loss = 0.014380009, step = 2208200 (2.258 sec) -2022-05-01 21:41:04,950 - tensorflow - INFO - global_step/sec: 88.5144 -2022-05-01 21:41:04,950 - tensorflow - INFO - loss = 0.0478812, step = 2208400 (2.259 sec) -2022-05-01 21:41:07,209 - tensorflow - INFO - global_step/sec: 88.5299 -2022-05-01 21:41:07,210 - tensorflow - INFO - loss = 0.07858298, step = 2208600 (2.259 sec) -2022-05-01 21:41:09,468 - tensorflow - INFO - global_step/sec: 88.5227 -2022-05-01 21:41:09,469 - tensorflow - INFO - loss = 0.021009544, step = 2208800 (2.259 sec) -2022-05-01 21:41:11,730 - tensorflow - INFO - global_step/sec: 88.417 -2022-05-01 21:41:11,731 - tensorflow - INFO - loss = 0.020054996, step = 2209000 (2.262 sec) -2022-05-01 21:41:13,986 - tensorflow - INFO - global_step/sec: 88.6608 -2022-05-01 21:41:13,987 - tensorflow - INFO - loss = 0.008389406, step = 2209200 (2.256 sec) -2022-05-01 21:41:16,247 - tensorflow - INFO - global_step/sec: 88.4481 -2022-05-01 21:41:16,248 - tensorflow - INFO - loss = 0.013282923, step = 2209400 (2.261 sec) -2022-05-01 21:41:18,505 - tensorflow - INFO - global_step/sec: 88.5847 -2022-05-01 21:41:18,506 - tensorflow - INFO - loss = 0.010178466, step = 2209600 (2.258 sec) -2022-05-01 21:41:20,769 - tensorflow - INFO - global_step/sec: 88.3552 -2022-05-01 21:41:20,769 - tensorflow - INFO - loss = 0.013363117, step = 2209800 (2.264 sec) -2022-05-01 21:41:23,029 - tensorflow - INFO - global_step/sec: 88.4893 -2022-05-01 21:41:23,029 - tensorflow - INFO - loss = 0.035251763, step = 2210000 (2.260 sec) -2022-05-01 21:41:25,289 - tensorflow - INFO - global_step/sec: 88.4995 -2022-05-01 21:41:25,289 - tensorflow - INFO - loss = 0.0061641047, step = 2210200 (2.260 sec) -2022-05-01 21:41:27,552 - tensorflow - INFO - global_step/sec: 88.3811 -2022-05-01 21:41:27,552 - tensorflow - INFO - loss = 0.045407366, step = 2210400 (2.263 sec) -2022-05-01 21:41:29,817 - tensorflow - INFO - global_step/sec: 88.3095 -2022-05-01 21:41:29,817 - tensorflow - INFO - loss = 0.023515439, step = 2210600 (2.265 sec) -2022-05-01 21:41:32,083 - tensorflow - INFO - global_step/sec: 88.2607 -2022-05-01 21:41:32,083 - tensorflow - INFO - loss = 0.009892464, step = 2210800 (2.266 sec) -2022-05-01 21:41:34,340 - tensorflow - INFO - global_step/sec: 88.6008 -2022-05-01 21:41:34,340 - tensorflow - INFO - loss = 0.055669792, step = 2211000 (2.257 sec) -2022-05-01 21:41:36,601 - tensorflow - INFO - global_step/sec: 88.4679 -2022-05-01 21:41:36,601 - tensorflow - INFO - loss = 0.005568575, step = 2211200 (2.261 sec) -2022-05-01 21:41:38,861 - tensorflow - INFO - 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INFO - global_step/sec: 88.606 -2022-05-01 21:49:32,387 - tensorflow - INFO - loss = 0.032762483, step = 2253400 (2.257 sec) -2022-05-01 21:49:34,643 - tensorflow - INFO - global_step/sec: 88.6362 -2022-05-01 21:49:34,644 - tensorflow - INFO - loss = 0.07266872, step = 2253600 (2.256 sec) -2022-05-01 21:49:36,896 - tensorflow - INFO - global_step/sec: 88.7893 -2022-05-01 21:49:36,896 - tensorflow - INFO - loss = 0.016507518, step = 2253800 (2.253 sec) -2022-05-01 21:49:39,154 - tensorflow - INFO - global_step/sec: 88.5677 -2022-05-01 21:49:39,154 - tensorflow - INFO - loss = 0.018191723, step = 2254000 (2.258 sec) -2022-05-01 21:49:41,411 - tensorflow - INFO - global_step/sec: 88.6125 -2022-05-01 21:49:41,411 - tensorflow - INFO - loss = 0.0047383253, step = 2254200 (2.257 sec) -2022-05-01 21:49:43,664 - tensorflow - INFO - global_step/sec: 88.7601 -2022-05-01 21:49:43,665 - tensorflow - INFO - loss = 0.013006965, step = 2254400 (2.253 sec) -2022-05-01 21:49:45,916 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.77 -2022-05-01 21:50:28,749 - tensorflow - INFO - loss = 0.022165522, step = 2258400 (2.253 sec) -2022-05-01 21:50:31,006 - tensorflow - INFO - global_step/sec: 88.589 -2022-05-01 21:50:31,006 - tensorflow - INFO - loss = 0.02005813, step = 2258600 (2.258 sec) -2022-05-01 21:50:33,257 - tensorflow - INFO - global_step/sec: 88.8548 -2022-05-01 21:50:33,257 - tensorflow - INFO - loss = 0.0063831746, step = 2258800 (2.251 sec) -2022-05-01 21:50:35,513 - tensorflow - INFO - global_step/sec: 88.6299 -2022-05-01 21:50:35,514 - tensorflow - INFO - loss = 0.005107364, step = 2259000 (2.257 sec) -2022-05-01 21:50:37,767 - tensorflow - INFO - global_step/sec: 88.7591 -2022-05-01 21:50:37,767 - tensorflow - INFO - loss = 0.046872757, step = 2259200 (2.253 sec) -2022-05-01 21:50:40,022 - tensorflow - INFO - global_step/sec: 88.6742 -2022-05-01 21:50:40,023 - tensorflow - INFO - loss = 0.01797883, step = 2259400 (2.256 sec) -2022-05-01 21:50:42,274 - tensorflow - INFO - global_step/sec: 88.8139 -2022-05-01 21:50:42,274 - tensorflow - INFO - loss = 0.025002211, step = 2259600 (2.252 sec) -2022-05-01 21:50:44,526 - tensorflow - INFO - global_step/sec: 88.8089 -2022-05-01 21:50:44,526 - tensorflow - INFO - loss = 0.021525549, step = 2259800 (2.252 sec) -2022-05-01 21:50:46,780 - tensorflow - INFO - global_step/sec: 88.7497 -2022-05-01 21:50:46,780 - tensorflow - INFO - loss = 0.009225631, step = 2260000 (2.254 sec) -2022-05-01 21:50:49,037 - tensorflow - INFO - global_step/sec: 88.6101 -2022-05-01 21:50:49,037 - tensorflow - INFO - loss = 0.022248156, step = 2260200 (2.257 sec) -2022-05-01 21:50:51,292 - tensorflow - INFO - global_step/sec: 88.6772 -2022-05-01 21:50:51,292 - tensorflow - INFO - loss = 0.0394319, step = 2260400 (2.255 sec) -2022-05-01 21:50:53,545 - tensorflow - INFO - global_step/sec: 88.7701 -2022-05-01 21:50:53,545 - tensorflow - INFO - loss = 0.03016043, step = 2260600 (2.253 sec) -2022-05-01 21:50:55,802 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.011728222, step = 2262000 (2.251 sec) -2022-05-01 21:51:11,581 - tensorflow - INFO - global_step/sec: 88.717 -2022-05-01 21:51:11,582 - tensorflow - INFO - loss = 0.17968774, step = 2262200 (2.254 sec) -2022-05-01 21:51:13,838 - tensorflow - INFO - global_step/sec: 88.6033 -2022-05-01 21:51:13,839 - tensorflow - INFO - loss = 0.022172717, step = 2262400 (2.257 sec) -2022-05-01 21:51:16,088 - tensorflow - INFO - global_step/sec: 88.9105 -2022-05-01 21:51:16,088 - tensorflow - INFO - loss = 0.012855254, step = 2262600 (2.249 sec) -2022-05-01 21:51:18,346 - tensorflow - INFO - global_step/sec: 88.5533 -2022-05-01 21:51:18,347 - tensorflow - INFO - loss = 0.029036885, step = 2262800 (2.259 sec) -2022-05-01 21:51:20,603 - tensorflow - INFO - global_step/sec: 88.6229 -2022-05-01 21:51:20,603 - tensorflow - INFO - loss = 0.017841196, step = 2263000 (2.257 sec) -2022-05-01 21:51:22,860 - tensorflow - INFO - global_step/sec: 88.623 -2022-05-01 21:51:22,860 - tensorflow - INFO - loss = 0.01587489, step = 2263200 (2.257 sec) -2022-05-01 21:51:25,119 - tensorflow - INFO - global_step/sec: 88.5378 -2022-05-01 21:51:25,119 - tensorflow - INFO - loss = 0.007913903, step = 2263400 (2.259 sec) -2022-05-01 21:51:27,372 - tensorflow - INFO - global_step/sec: 88.7433 -2022-05-01 21:51:27,373 - tensorflow - INFO - loss = 0.012880603, step = 2263600 (2.254 sec) -2022-05-01 21:51:29,632 - tensorflow - INFO - global_step/sec: 88.5223 -2022-05-01 21:51:29,632 - tensorflow - INFO - loss = 0.003497982, step = 2263800 (2.259 sec) -2022-05-01 21:51:31,894 - tensorflow - INFO - global_step/sec: 88.4241 -2022-05-01 21:51:31,894 - tensorflow - INFO - loss = 0.022918161, step = 2264000 (2.262 sec) -2022-05-01 21:51:34,150 - tensorflow - INFO - global_step/sec: 88.6285 -2022-05-01 21:51:34,151 - tensorflow - INFO - loss = 0.13521606, step = 2264200 (2.257 sec) -2022-05-01 21:51:36,407 - tensorflow - INFO - global_step/sec: 88.6375 -2022-05-01 21:51:36,407 - tensorflow - INFO - 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INFO - global_step/sec: 88.4546 -2022-05-01 21:52:05,799 - tensorflow - INFO - loss = 0.0666922, step = 2267000 (2.261 sec) -2022-05-01 21:52:08,060 - tensorflow - INFO - global_step/sec: 88.4182 -2022-05-01 21:52:08,061 - tensorflow - INFO - loss = 0.01974915, step = 2267200 (2.262 sec) -2022-05-01 21:52:10,316 - tensorflow - INFO - global_step/sec: 88.6516 -2022-05-01 21:52:10,317 - tensorflow - INFO - loss = 0.93610466, step = 2267400 (2.256 sec) -2022-05-01 21:52:12,574 - tensorflow - INFO - global_step/sec: 88.6105 -2022-05-01 21:52:12,574 - tensorflow - INFO - loss = 0.022423476, step = 2267600 (2.257 sec) -2022-05-01 21:52:14,825 - tensorflow - INFO - global_step/sec: 88.8362 -2022-05-01 21:52:14,825 - tensorflow - INFO - loss = 0.011413089, step = 2267800 (2.251 sec) -2022-05-01 21:52:17,077 - tensorflow - INFO - global_step/sec: 88.8095 -2022-05-01 21:52:17,077 - tensorflow - INFO - loss = 0.013983531, step = 2268000 (2.252 sec) -2022-05-01 21:52:19,330 - tensorflow - INFO - global_step/sec: 88.7678 -2022-05-01 21:52:19,330 - tensorflow - INFO - loss = 0.0028574385, step = 2268200 (2.253 sec) -2022-05-01 21:52:21,582 - tensorflow - INFO - global_step/sec: 88.7979 -2022-05-01 21:52:21,583 - tensorflow - INFO - loss = 0.04333497, step = 2268400 (2.252 sec) -2022-05-01 21:52:23,831 - tensorflow - INFO - global_step/sec: 88.9502 -2022-05-01 21:52:23,831 - tensorflow - INFO - loss = 0.33113492, step = 2268600 (2.248 sec) -2022-05-01 21:52:26,083 - tensorflow - INFO - global_step/sec: 88.7816 -2022-05-01 21:52:26,084 - tensorflow - INFO - loss = 0.02726505, step = 2268800 (2.253 sec) -2022-05-01 21:52:28,335 - tensorflow - INFO - global_step/sec: 88.8254 -2022-05-01 21:52:28,335 - tensorflow - INFO - loss = 0.011160085, step = 2269000 (2.252 sec) -2022-05-01 21:52:30,595 - tensorflow - INFO - global_step/sec: 88.4921 -2022-05-01 21:52:30,596 - tensorflow - INFO - loss = 0.11342676, step = 2269200 (2.260 sec) -2022-05-01 21:52:32,850 - tensorflow - INFO - global_step/sec: 88.7093 -2022-05-01 21:52:32,850 - tensorflow - INFO - loss = 0.032502107, step = 2269400 (2.255 sec) -2022-05-01 21:52:35,103 - tensorflow - INFO - global_step/sec: 88.7527 -2022-05-01 21:52:35,103 - tensorflow - INFO - loss = 0.010498369, step = 2269600 (2.253 sec) -2022-05-01 21:52:37,350 - tensorflow - INFO - global_step/sec: 89.014 -2022-05-01 21:52:37,350 - tensorflow - INFO - loss = 0.04888901, step = 2269800 (2.247 sec) -2022-05-01 21:52:39,605 - tensorflow - INFO - global_step/sec: 88.6936 -2022-05-01 21:52:39,605 - tensorflow - INFO - loss = 0.032412484, step = 2270000 (2.255 sec) -2022-05-01 21:52:41,856 - tensorflow - INFO - global_step/sec: 88.8539 -2022-05-01 21:52:41,856 - tensorflow - INFO - loss = 0.009196581, step = 2270200 (2.251 sec) -2022-05-01 21:52:44,117 - tensorflow - INFO - global_step/sec: 88.4408 -2022-05-01 21:52:44,118 - tensorflow - INFO - loss = 0.07453966, step = 2270400 (2.261 sec) -2022-05-01 21:52:46,371 - tensorflow - INFO - global_step/sec: 88.7549 -2022-05-01 21:52:46,371 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.862 -2022-05-01 21:53:02,151 - tensorflow - INFO - loss = 0.016846657, step = 2272000 (2.251 sec) -2022-05-01 21:53:04,405 - tensorflow - INFO - global_step/sec: 88.7363 -2022-05-01 21:53:04,405 - tensorflow - INFO - loss = 0.060905166, step = 2272200 (2.254 sec) -2022-05-01 21:53:06,656 - tensorflow - INFO - global_step/sec: 88.8552 -2022-05-01 21:53:06,656 - tensorflow - INFO - loss = 0.012017032, step = 2272400 (2.251 sec) -2022-05-01 21:53:08,915 - tensorflow - INFO - global_step/sec: 88.5177 -2022-05-01 21:53:08,915 - tensorflow - INFO - loss = 0.0040350785, step = 2272600 (2.259 sec) -2022-05-01 21:53:11,169 - tensorflow - INFO - global_step/sec: 88.7429 -2022-05-01 21:53:11,169 - tensorflow - INFO - loss = 0.02077328, step = 2272800 (2.254 sec) -2022-05-01 21:53:13,423 - tensorflow - INFO - global_step/sec: 88.7306 -2022-05-01 21:53:13,423 - tensorflow - INFO - loss = 0.015841708, step = 2273000 (2.254 sec) -2022-05-01 21:53:15,674 - tensorflow - INFO - global_step/sec: 88.8567 -2022-05-01 21:53:15,674 - tensorflow - INFO - loss = 0.019090988, step = 2273200 (2.251 sec) -2022-05-01 21:53:17,926 - tensorflow - INFO - global_step/sec: 88.8015 -2022-05-01 21:53:17,926 - tensorflow - INFO - loss = 0.035035364, step = 2273400 (2.252 sec) -2022-05-01 21:53:20,181 - tensorflow - INFO - global_step/sec: 88.6723 -2022-05-01 21:53:20,182 - tensorflow - INFO - loss = 0.009551028, step = 2273600 (2.255 sec) -2022-05-01 21:53:22,438 - tensorflow - INFO - global_step/sec: 88.6157 -2022-05-01 21:53:22,439 - tensorflow - INFO - loss = 0.4296293, step = 2273800 (2.257 sec) -2022-05-01 21:53:24,693 - tensorflow - INFO - global_step/sec: 88.7129 -2022-05-01 21:53:24,693 - tensorflow - INFO - loss = 0.017614042, step = 2274000 (2.255 sec) -2022-05-01 21:53:26,943 - tensorflow - INFO - global_step/sec: 88.8563 -2022-05-01 21:53:26,944 - tensorflow - INFO - loss = 0.021202384, step = 2274200 (2.251 sec) -2022-05-01 21:53:29,202 - tensorflow - INFO - 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INFO - loss = 0.02268545, step = 2276800 (2.259 sec) -2022-05-01 21:53:58,493 - tensorflow - INFO - global_step/sec: 88.6152 -2022-05-01 21:53:58,494 - tensorflow - INFO - loss = 0.0313809, step = 2277000 (2.257 sec) -2022-05-01 21:54:00,748 - tensorflow - INFO - global_step/sec: 88.7119 -2022-05-01 21:54:00,748 - tensorflow - INFO - loss = 0.010058243, step = 2277200 (2.254 sec) -2022-05-01 21:54:03,000 - tensorflow - INFO - global_step/sec: 88.7937 -2022-05-01 21:54:03,001 - tensorflow - INFO - loss = 0.04645252, step = 2277400 (2.252 sec) -2022-05-01 21:54:05,253 - tensorflow - INFO - global_step/sec: 88.7785 -2022-05-01 21:54:05,253 - tensorflow - INFO - loss = 0.010454596, step = 2277600 (2.253 sec) -2022-05-01 21:54:07,509 - tensorflow - INFO - global_step/sec: 88.6569 -2022-05-01 21:54:07,509 - tensorflow - INFO - loss = 0.050151728, step = 2277800 (2.256 sec) -2022-05-01 21:54:09,762 - tensorflow - INFO - global_step/sec: 88.7449 -2022-05-01 21:54:09,763 - tensorflow - INFO - loss = 0.017808698, step = 2278000 (2.254 sec) -2022-05-01 21:54:12,017 - tensorflow - INFO - global_step/sec: 88.6915 -2022-05-01 21:54:12,018 - tensorflow - INFO - loss = 0.4646956, step = 2278200 (2.255 sec) -2022-05-01 21:54:14,270 - tensorflow - INFO - global_step/sec: 88.7932 -2022-05-01 21:54:14,270 - tensorflow - INFO - loss = 0.04662407, step = 2278400 (2.252 sec) -2022-05-01 21:54:16,526 - tensorflow - INFO - global_step/sec: 88.6609 -2022-05-01 21:54:16,526 - tensorflow - INFO - loss = 0.009046311, step = 2278600 (2.256 sec) -2022-05-01 21:54:18,775 - tensorflow - INFO - global_step/sec: 88.8987 -2022-05-01 21:54:18,776 - tensorflow - INFO - loss = 0.007356795, step = 2278800 (2.250 sec) -2022-05-01 21:54:21,026 - tensorflow - INFO - global_step/sec: 88.8807 -2022-05-01 21:54:21,026 - tensorflow - INFO - loss = 0.14606391, step = 2279000 (2.250 sec) -2022-05-01 21:54:23,281 - tensorflow - INFO - global_step/sec: 88.6939 -2022-05-01 21:54:23,281 - tensorflow - INFO - loss = 0.041655574, step = 2279200 (2.255 sec) -2022-05-01 21:54:25,532 - tensorflow - INFO - global_step/sec: 88.8313 -2022-05-01 21:54:25,532 - tensorflow - INFO - loss = 0.010438031, step = 2279400 (2.251 sec) -2022-05-01 21:54:27,788 - tensorflow - INFO - global_step/sec: 88.6481 -2022-05-01 21:54:27,789 - tensorflow - INFO - loss = 0.023894245, step = 2279600 (2.256 sec) -2022-05-01 21:54:30,045 - tensorflow - INFO - global_step/sec: 88.6244 -2022-05-01 21:54:30,045 - tensorflow - INFO - loss = 0.03804998, step = 2279800 (2.257 sec) -2022-05-01 21:54:32,303 - tensorflow - INFO - global_step/sec: 88.5512 -2022-05-01 21:54:32,304 - tensorflow - INFO - loss = 0.012431679, step = 2280000 (2.259 sec) -2022-05-01 21:54:34,554 - tensorflow - INFO - global_step/sec: 88.8645 -2022-05-01 21:54:34,555 - tensorflow - INFO - loss = 0.009730356, step = 2280200 (2.251 sec) -2022-05-01 21:54:36,809 - tensorflow - INFO - global_step/sec: 88.6889 -2022-05-01 21:54:36,810 - tensorflow - INFO - loss = 0.012002792, step = 2280400 (2.255 sec) -2022-05-01 21:54:39,056 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.02516003, step = 2281800 (2.251 sec) -2022-05-01 21:54:54,826 - tensorflow - INFO - global_step/sec: 88.991 -2022-05-01 21:54:54,826 - tensorflow - INFO - loss = 0.007950349, step = 2282000 (2.247 sec) -2022-05-01 21:54:57,081 - tensorflow - INFO - global_step/sec: 88.6804 -2022-05-01 21:54:57,081 - tensorflow - INFO - loss = 0.03578655, step = 2282200 (2.255 sec) -2022-05-01 21:54:59,334 - tensorflow - INFO - global_step/sec: 88.7636 -2022-05-01 21:54:59,334 - tensorflow - INFO - loss = 0.0075575593, step = 2282400 (2.253 sec) -2022-05-01 21:55:01,591 - tensorflow - INFO - global_step/sec: 88.6215 -2022-05-01 21:55:01,591 - tensorflow - INFO - loss = 0.0053380663, step = 2282600 (2.257 sec) -2022-05-01 21:55:03,851 - tensorflow - INFO - global_step/sec: 88.4811 -2022-05-01 21:55:03,852 - tensorflow - INFO - loss = 0.007805185, step = 2282800 (2.260 sec) -2022-05-01 21:55:06,104 - tensorflow - INFO - global_step/sec: 88.7876 -2022-05-01 21:55:06,104 - tensorflow - INFO - loss = 0.0065984176, step = 2283000 (2.253 sec) -2022-05-01 21:55:08,358 - tensorflow - INFO - global_step/sec: 88.738 -2022-05-01 21:55:08,358 - tensorflow - INFO - loss = 0.07137847, step = 2283200 (2.254 sec) -2022-05-01 21:55:10,615 - tensorflow - INFO - global_step/sec: 88.5968 -2022-05-01 21:55:10,615 - tensorflow - INFO - loss = 0.012754681, step = 2283400 (2.257 sec) -2022-05-01 21:55:12,874 - tensorflow - INFO - global_step/sec: 88.5431 -2022-05-01 21:55:12,874 - tensorflow - INFO - loss = 0.03999576, step = 2283600 (2.259 sec) -2022-05-01 21:55:15,138 - tensorflow - INFO - global_step/sec: 88.3463 -2022-05-01 21:55:15,138 - tensorflow - INFO - loss = 0.019726895, step = 2283800 (2.264 sec) -2022-05-01 21:55:17,395 - tensorflow - INFO - global_step/sec: 88.5942 -2022-05-01 21:55:17,395 - tensorflow - INFO - loss = 0.012661542, step = 2284000 (2.257 sec) -2022-05-01 21:55:19,655 - tensorflow - INFO - global_step/sec: 88.5009 -2022-05-01 21:55:19,655 - tensorflow - INFO - 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INFO - global_step/sec: 88.7376 -2022-05-01 21:55:49,024 - tensorflow - INFO - loss = 0.049257375, step = 2286800 (2.254 sec) -2022-05-01 21:55:51,275 - tensorflow - INFO - global_step/sec: 88.8473 -2022-05-01 21:55:51,275 - tensorflow - INFO - loss = 0.022474559, step = 2287000 (2.251 sec) -2022-05-01 21:55:53,529 - tensorflow - INFO - global_step/sec: 88.718 -2022-05-01 21:55:53,530 - tensorflow - INFO - loss = 0.015099445, step = 2287200 (2.254 sec) -2022-05-01 21:55:55,790 - tensorflow - INFO - global_step/sec: 88.4526 -2022-05-01 21:55:55,791 - tensorflow - INFO - loss = 0.0758746, step = 2287400 (2.261 sec) -2022-05-01 21:55:58,047 - tensorflow - INFO - global_step/sec: 88.6278 -2022-05-01 21:55:58,047 - tensorflow - INFO - loss = 0.33961636, step = 2287600 (2.257 sec) -2022-05-01 21:56:00,298 - tensorflow - INFO - global_step/sec: 88.8317 -2022-05-01 21:56:00,299 - tensorflow - INFO - loss = 0.049325325, step = 2287800 (2.251 sec) -2022-05-01 21:56:02,553 - tensorflow - INFO - global_step/sec: 88.7139 -2022-05-01 21:56:02,553 - tensorflow - INFO - loss = 0.008577246, step = 2288000 (2.254 sec) -2022-05-01 21:56:04,808 - tensorflow - INFO - global_step/sec: 88.6691 -2022-05-01 21:56:04,809 - tensorflow - INFO - loss = 0.004305575, step = 2288200 (2.256 sec) -2022-05-01 21:56:07,061 - tensorflow - INFO - global_step/sec: 88.7987 -2022-05-01 21:56:07,061 - tensorflow - INFO - loss = 0.05146283, step = 2288400 (2.252 sec) -2022-05-01 21:56:09,314 - tensorflow - INFO - global_step/sec: 88.7583 -2022-05-01 21:56:09,314 - tensorflow - INFO - loss = 0.007753142, step = 2288600 (2.253 sec) -2022-05-01 21:56:11,572 - tensorflow - INFO - global_step/sec: 88.5821 -2022-05-01 21:56:11,572 - tensorflow - INFO - loss = 0.03586986, step = 2288800 (2.258 sec) -2022-05-01 21:56:13,823 - tensorflow - INFO - global_step/sec: 88.8218 -2022-05-01 21:56:13,824 - tensorflow - INFO - loss = 0.0688177, step = 2289000 (2.252 sec) -2022-05-01 21:56:16,075 - tensorflow - INFO - global_step/sec: 88.8439 -2022-05-01 21:56:16,075 - tensorflow - INFO - loss = 0.00816809, step = 2289200 (2.251 sec) -2022-05-01 21:56:18,327 - tensorflow - INFO - global_step/sec: 88.81 -2022-05-01 21:56:18,327 - tensorflow - INFO - loss = 0.07801333, step = 2289400 (2.252 sec) -2022-05-01 21:56:20,581 - tensorflow - INFO - global_step/sec: 88.718 -2022-05-01 21:56:20,581 - tensorflow - INFO - loss = 0.048330776, step = 2289600 (2.254 sec) -2022-05-01 21:56:22,843 - tensorflow - INFO - global_step/sec: 88.4233 -2022-05-01 21:56:22,843 - tensorflow - INFO - loss = 0.05577566, step = 2289800 (2.262 sec) -2022-05-01 21:56:25,099 - tensorflow - INFO - global_step/sec: 88.6608 -2022-05-01 21:56:25,099 - tensorflow - INFO - loss = 0.042707544, step = 2290000 (2.256 sec) -2022-05-01 21:56:27,358 - tensorflow - INFO - global_step/sec: 88.534 -2022-05-01 21:56:27,358 - tensorflow - INFO - loss = 0.007859388, step = 2290200 (2.259 sec) -2022-05-01 21:56:29,618 - tensorflow - INFO - global_step/sec: 88.4927 -2022-05-01 21:56:29,618 - tensorflow - INFO - loss = 0.022069693, step = 2290400 (2.260 sec) -2022-05-01 21:56:31,886 - tensorflow - INFO - global_step/sec: 88.1805 -2022-05-01 21:56:31,886 - tensorflow - INFO - loss = 0.02007738, step = 2290600 (2.268 sec) -2022-05-01 21:56:34,146 - tensorflow - INFO - global_step/sec: 88.4982 -2022-05-01 21:56:34,146 - tensorflow - INFO - loss = 0.006746944, step = 2290800 (2.260 sec) -2022-05-01 21:56:36,398 - tensorflow - INFO - global_step/sec: 88.8038 -2022-05-01 21:56:36,398 - tensorflow - INFO - loss = 0.005979812, step = 2291000 (2.252 sec) -2022-05-01 21:56:38,648 - tensorflow - INFO - global_step/sec: 88.8831 -2022-05-01 21:56:38,648 - tensorflow - INFO - loss = 0.007480228, step = 2291200 (2.250 sec) -2022-05-01 21:56:40,901 - tensorflow - INFO - global_step/sec: 88.7637 -2022-05-01 21:56:40,902 - tensorflow - INFO - loss = 0.04220979, step = 2291400 (2.253 sec) -2022-05-01 21:56:43,148 - tensorflow - INFO - global_step/sec: 89.0311 -2022-05-01 21:56:43,148 - tensorflow - INFO - loss = 0.037302103, step = 2291600 (2.246 sec) -2022-05-01 21:56:45,405 - tensorflow - INFO - global_step/sec: 88.5843 -2022-05-01 21:56:45,406 - tensorflow - INFO - loss = 0.2169662, step = 2291800 (2.258 sec) -2022-05-01 21:56:47,662 - tensorflow - INFO - global_step/sec: 88.6128 -2022-05-01 21:56:47,663 - tensorflow - INFO - loss = 0.07506015, step = 2292000 (2.257 sec) -2022-05-01 21:56:49,917 - tensorflow - INFO - global_step/sec: 88.7082 -2022-05-01 21:56:49,917 - tensorflow - INFO - loss = 0.011692755, step = 2292200 (2.255 sec) -2022-05-01 21:56:52,167 - tensorflow - INFO - global_step/sec: 88.9001 -2022-05-01 21:56:52,167 - tensorflow - INFO - loss = 0.044270314, step = 2292400 (2.250 sec) -2022-05-01 21:56:54,418 - tensorflow - INFO - global_step/sec: 88.8406 -2022-05-01 21:56:54,418 - tensorflow - INFO - loss = 0.0096690245, step = 2292600 (2.251 sec) -2022-05-01 21:56:56,671 - tensorflow - INFO - global_step/sec: 88.7609 -2022-05-01 21:56:56,671 - tensorflow - INFO - loss = 0.015168619, step = 2292800 (2.253 sec) -2022-05-01 21:56:58,929 - tensorflow - INFO - global_step/sec: 88.5879 -2022-05-01 21:56:58,929 - tensorflow - INFO - loss = 0.0038347745, step = 2293000 (2.258 sec) -2022-05-01 21:57:01,179 - tensorflow - INFO - global_step/sec: 88.8582 -2022-05-01 21:57:01,180 - tensorflow - INFO - loss = 0.014080549, step = 2293200 (2.251 sec) -2022-05-01 21:57:03,439 - tensorflow - INFO - global_step/sec: 88.5001 -2022-05-01 21:57:03,440 - tensorflow - INFO - loss = 0.0055178367, step = 2293400 (2.260 sec) -2022-05-01 21:57:05,691 - tensorflow - INFO - global_step/sec: 88.8284 -2022-05-01 21:57:05,691 - tensorflow - INFO - loss = 0.031844318, step = 2293600 (2.252 sec) -2022-05-01 21:57:07,944 - tensorflow - INFO - global_step/sec: 88.7622 -2022-05-01 21:57:07,945 - tensorflow - INFO - loss = 1.3870256, step = 2293800 (2.253 sec) -2022-05-01 21:57:10,202 - tensorflow - INFO - global_step/sec: 88.5595 -2022-05-01 21:57:10,203 - tensorflow - INFO - loss = 0.010294217, step = 2294000 (2.258 sec) -2022-05-01 21:57:12,453 - tensorflow - INFO - global_step/sec: 88.8624 -2022-05-01 21:57:12,454 - tensorflow - INFO - loss = 0.014056441, step = 2294200 (2.251 sec) -2022-05-01 21:57:14,700 - tensorflow - INFO - global_step/sec: 89.0314 -2022-05-01 21:57:14,700 - tensorflow - INFO - loss = 0.028425688, step = 2294400 (2.246 sec) -2022-05-01 21:57:16,956 - tensorflow - INFO - global_step/sec: 88.6322 -2022-05-01 21:57:16,956 - tensorflow - INFO - loss = 0.14513147, step = 2294600 (2.257 sec) -2022-05-01 21:57:19,212 - tensorflow - INFO - global_step/sec: 88.6421 -2022-05-01 21:57:19,213 - tensorflow - INFO - loss = 0.018518772, step = 2294800 (2.256 sec) -2022-05-01 21:57:21,462 - tensorflow - INFO - global_step/sec: 88.8915 -2022-05-01 21:57:21,463 - tensorflow - INFO - loss = 0.0112281665, step = 2295000 (2.250 sec) -2022-05-01 21:57:23,724 - tensorflow - INFO - global_step/sec: 88.4416 -2022-05-01 21:57:23,724 - tensorflow - INFO - loss = 0.033915814, step = 2295200 (2.261 sec) -2022-05-01 21:57:25,974 - tensorflow - INFO - global_step/sec: 88.8701 -2022-05-01 21:57:25,975 - tensorflow - INFO - loss = 0.045164473, step = 2295400 (2.250 sec) -2022-05-01 21:57:28,221 - tensorflow - INFO - global_step/sec: 88.998 -2022-05-01 21:57:28,222 - tensorflow - INFO - loss = 0.015295318, step = 2295600 (2.247 sec) -2022-05-01 21:57:30,477 - tensorflow - INFO - global_step/sec: 88.6528 -2022-05-01 21:57:30,478 - tensorflow - INFO - loss = 0.055552155, step = 2295800 (2.256 sec) -2022-05-01 21:57:32,728 - tensorflow - INFO - global_step/sec: 88.8457 -2022-05-01 21:57:32,729 - tensorflow - INFO - loss = 0.007291389, step = 2296000 (2.251 sec) -2022-05-01 21:57:34,982 - tensorflow - INFO - global_step/sec: 88.7465 -2022-05-01 21:57:34,983 - tensorflow - INFO - loss = 0.009181721, step = 2296200 (2.254 sec) -2022-05-01 21:57:37,241 - tensorflow - INFO - global_step/sec: 88.5505 -2022-05-01 21:57:37,241 - tensorflow - INFO - loss = 0.015211635, step = 2296400 (2.259 sec) -2022-05-01 21:57:39,493 - tensorflow - INFO - global_step/sec: 88.8017 -2022-05-01 21:57:39,493 - tensorflow - INFO - loss = 0.07218133, step = 2296600 (2.252 sec) -2022-05-01 21:57:41,741 - tensorflow - INFO - global_step/sec: 88.98 -2022-05-01 21:57:41,741 - tensorflow - INFO - loss = 0.2888913, step = 2296800 (2.248 sec) -2022-05-01 21:57:43,991 - tensorflow - INFO - global_step/sec: 88.8873 -2022-05-01 21:57:43,991 - tensorflow - INFO - loss = 0.011046013, step = 2297000 (2.250 sec) -2022-05-01 21:57:46,238 - tensorflow - INFO - global_step/sec: 88.9803 -2022-05-01 21:57:46,239 - tensorflow - INFO - loss = 0.14288104, step = 2297200 (2.248 sec) -2022-05-01 21:57:48,495 - tensorflow - INFO - global_step/sec: 88.6299 -2022-05-01 21:57:48,495 - tensorflow - INFO - loss = 0.010061863, step = 2297400 (2.257 sec) -2022-05-01 21:57:50,747 - tensorflow - INFO - global_step/sec: 88.7933 -2022-05-01 21:57:50,748 - tensorflow - INFO - loss = 0.011489116, step = 2297600 (2.252 sec) -2022-05-01 21:57:52,996 - tensorflow - INFO - global_step/sec: 88.9238 -2022-05-01 21:57:52,997 - tensorflow - INFO - loss = 0.004584601, step = 2297800 (2.249 sec) -2022-05-01 21:57:55,247 - tensorflow - INFO - global_step/sec: 88.8742 -2022-05-01 21:57:55,247 - tensorflow - INFO - loss = 0.010511144, step = 2298000 (2.250 sec) -2022-05-01 21:57:57,500 - tensorflow - INFO - global_step/sec: 88.7639 -2022-05-01 21:57:57,500 - tensorflow - INFO - loss = 0.0056808935, step = 2298200 (2.253 sec) -2022-05-01 21:57:59,753 - tensorflow - INFO - global_step/sec: 88.7811 -2022-05-01 21:57:59,753 - tensorflow - INFO - loss = 0.01763389, step = 2298400 (2.253 sec) -2022-05-01 21:58:02,004 - tensorflow - INFO - global_step/sec: 88.8373 -2022-05-01 21:58:02,004 - tensorflow - INFO - loss = 0.11997159, step = 2298600 (2.251 sec) -2022-05-01 21:58:04,258 - tensorflow - INFO - global_step/sec: 88.7125 -2022-05-01 21:58:04,259 - tensorflow - INFO - loss = 0.06352675, step = 2298800 (2.254 sec) -2022-05-01 21:58:06,511 - tensorflow - INFO - global_step/sec: 88.7991 -2022-05-01 21:58:06,511 - tensorflow - INFO - loss = 0.18617159, step = 2299000 (2.252 sec) -2022-05-01 21:58:08,764 - tensorflow - INFO - global_step/sec: 88.7447 -2022-05-01 21:58:08,765 - tensorflow - INFO - loss = 0.010075515, step = 2299200 (2.254 sec) -2022-05-01 21:58:11,020 - tensorflow - INFO - global_step/sec: 88.6623 -2022-05-01 21:58:11,021 - tensorflow - INFO - loss = 0.07672944, step = 2299400 (2.256 sec) -2022-05-01 21:58:13,274 - tensorflow - INFO - global_step/sec: 88.7209 -2022-05-01 21:58:13,275 - tensorflow - INFO - loss = 0.012490427, step = 2299600 (2.254 sec) -2022-05-01 21:58:15,521 - tensorflow - INFO - global_step/sec: 89.0263 -2022-05-01 21:58:15,521 - tensorflow - INFO - loss = 0.084134504, step = 2299800 (2.246 sec) -2022-05-01 21:58:17,758 - tensorflow - INFO - Saving checkpoints for 2300000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. -2022-05-01 21:58:17,774 - tensorflow - INFO - model.ckpt-2300000 is not in all_model_checkpoint_paths. Manually adding it. -2022-05-01 21:58:18,309 - tensorflow - INFO - Calling model_fn. -2022-05-01 21:58:18,436 - tensorflow - INFO - Done calling model_fn. -2022-05-01 21:58:18,451 - tensorflow - INFO - Starting evaluation at 2022-05-01T21:58:18Z -2022-05-01 21:58:18,514 - tensorflow - INFO - Graph was finalized. -2022-05-01 21:58:18,517 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-2300000 -2022-05-01 21:58:18,797 - tensorflow - INFO - Running local_init_op. -2022-05-01 21:58:18,905 - tensorflow - INFO - Done running local_init_op. -2022-05-01 21:58:29,046 - tensorflow - INFO - Evaluation [3990/39900] -2022-05-01 21:58:38,480 - tensorflow - INFO - Evaluation [7980/39900] -2022-05-01 21:58:47,929 - tensorflow - INFO - Evaluation [11970/39900] -2022-05-01 21:58:57,409 - tensorflow - INFO - Evaluation [15960/39900] -2022-05-01 21:59:06,885 - tensorflow - INFO - Evaluation [19950/39900] -2022-05-01 21:59:16,344 - tensorflow - INFO - Evaluation [23940/39900] -2022-05-01 21:59:25,817 - tensorflow - INFO - Evaluation [27930/39900] -2022-05-01 21:59:35,288 - tensorflow - INFO - Evaluation [31920/39900] -2022-05-01 21:59:44,735 - tensorflow - INFO - Evaluation [35910/39900] -2022-05-01 21:59:54,202 - tensorflow - INFO - Evaluation [39900/39900] -2022-05-01 21:59:54,339 - tensorflow - INFO - Finished evaluation at 2022-05-01-21:59:54 -2022-05-01 21:59:54,339 - tensorflow - INFO - Saving dict for global step 2300000: global_step = 2300000, loss = 0.017903524, mean_degree_err = 3.449092 -2022-05-01 21:59:54,340 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 2300000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-2300000 -2022-05-01 21:59:54,353 - tensorflow - INFO - global_step/sec: 2.02363 -2022-05-01 21:59:54,354 - tensorflow - INFO - loss = 0.0091372905, step = 2300000 (98.833 sec) -2022-05-01 21:59:56,633 - tensorflow - INFO - global_step/sec: 87.7322 -2022-05-01 21:59:56,633 - tensorflow - INFO - loss = 0.024899326, step = 2300200 (2.279 sec) -2022-05-01 21:59:58,886 - tensorflow - INFO - global_step/sec: 88.7735 -2022-05-01 21:59:58,886 - tensorflow - INFO - loss = 0.016124004, step = 2300400 (2.253 sec) -2022-05-01 22:00:01,143 - tensorflow - INFO - global_step/sec: 88.6166 -2022-05-01 22:00:01,143 - tensorflow - INFO - loss = 0.01588392, step = 2300600 (2.257 sec) -2022-05-01 22:00:03,397 - tensorflow - INFO - global_step/sec: 88.7324 -2022-05-01 22:00:03,397 - tensorflow - INFO - loss = 0.010000843, step = 2300800 (2.254 sec) -2022-05-01 22:00:05,651 - tensorflow - INFO - global_step/sec: 88.7368 -2022-05-01 22:00:05,651 - tensorflow - INFO - loss = 0.059633113, step = 2301000 (2.254 sec) -2022-05-01 22:00:07,911 - tensorflow - INFO - global_step/sec: 88.4922 -2022-05-01 22:00:07,911 - tensorflow - INFO - loss = 0.0062330198, step = 2301200 (2.260 sec) -2022-05-01 22:00:10,169 - tensorflow - INFO - global_step/sec: 88.568 -2022-05-01 22:00:10,169 - tensorflow - INFO - loss = 0.23845582, step = 2301400 (2.258 sec) -2022-05-01 22:00:12,422 - tensorflow - INFO - global_step/sec: 88.7789 -2022-05-01 22:00:12,422 - tensorflow - INFO - loss = 0.011025475, step = 2301600 (2.253 sec) -2022-05-01 22:00:14,674 - tensorflow - INFO - global_step/sec: 88.8013 -2022-05-01 22:00:14,674 - tensorflow - INFO - loss = 0.0045896634, step = 2301800 (2.252 sec) -2022-05-01 22:00:16,926 - tensorflow - INFO - global_step/sec: 88.8024 -2022-05-01 22:00:16,927 - tensorflow - INFO - loss = 0.0063096797, step = 2302000 (2.252 sec) -2022-05-01 22:00:19,178 - tensorflow - INFO - global_step/sec: 88.8262 -2022-05-01 22:00:19,178 - tensorflow - INFO - loss = 0.006863452, step = 2302200 (2.252 sec) -2022-05-01 22:00:21,436 - tensorflow - INFO - global_step/sec: 88.5556 -2022-05-01 22:00:21,437 - tensorflow - INFO - loss = 0.048675433, step = 2302400 (2.259 sec) -2022-05-01 22:00:23,687 - tensorflow - INFO - global_step/sec: 88.8532 -2022-05-01 22:00:23,687 - tensorflow - INFO - loss = 0.0055150455, step = 2302600 (2.251 sec) -2022-05-01 22:00:25,941 - tensorflow - INFO - global_step/sec: 88.7483 -2022-05-01 22:00:25,941 - tensorflow - INFO - loss = 0.01271037, step = 2302800 (2.254 sec) -2022-05-01 22:00:28,193 - tensorflow - INFO - global_step/sec: 88.7814 -2022-05-01 22:00:28,194 - tensorflow - INFO - loss = 0.009561626, step = 2303000 (2.253 sec) -2022-05-01 22:00:30,452 - tensorflow - INFO - global_step/sec: 88.5499 -2022-05-01 22:00:30,452 - tensorflow - INFO - loss = 0.10002799, step = 2303200 (2.259 sec) -2022-05-01 22:00:32,708 - tensorflow - INFO - global_step/sec: 88.6695 -2022-05-01 22:00:32,708 - tensorflow - INFO - loss = 0.005737627, step = 2303400 (2.256 sec) -2022-05-01 22:00:34,966 - tensorflow - INFO - global_step/sec: 88.5713 -2022-05-01 22:00:34,966 - tensorflow - INFO - loss = 0.04569764, step = 2303600 (2.258 sec) -2022-05-01 22:00:37,215 - tensorflow - INFO - global_step/sec: 88.9007 -2022-05-01 22:00:37,216 - tensorflow - INFO - loss = 0.004944335, step = 2303800 (2.250 sec) -2022-05-01 22:00:39,464 - tensorflow - INFO - global_step/sec: 88.9471 -2022-05-01 22:00:39,464 - tensorflow - INFO - loss = 0.009186769, step = 2304000 (2.249 sec) -2022-05-01 22:00:41,715 - tensorflow - INFO - global_step/sec: 88.8285 -2022-05-01 22:00:41,716 - tensorflow - INFO - loss = 0.1479854, step = 2304200 (2.252 sec) -2022-05-01 22:00:43,969 - tensorflow - INFO - global_step/sec: 88.7419 -2022-05-01 22:00:43,969 - tensorflow - INFO - loss = 0.022605823, step = 2304400 (2.254 sec) -2022-05-01 22:00:46,227 - tensorflow - INFO - global_step/sec: 88.5717 -2022-05-01 22:00:46,228 - tensorflow - INFO - loss = 0.021883419, step = 2304600 (2.258 sec) -2022-05-01 22:00:48,478 - tensorflow - INFO - global_step/sec: 88.8408 -2022-05-01 22:00:48,479 - tensorflow - INFO - loss = 0.009133572, step = 2304800 (2.251 sec) -2022-05-01 22:00:50,734 - tensorflow - INFO - global_step/sec: 88.6823 -2022-05-01 22:00:50,734 - tensorflow - INFO - loss = 0.011011441, step = 2305000 (2.255 sec) -2022-05-01 22:00:52,985 - tensorflow - INFO - global_step/sec: 88.8334 -2022-05-01 22:00:52,985 - tensorflow - INFO - loss = 0.021263834, step = 2305200 (2.251 sec) -2022-05-01 22:00:55,239 - tensorflow - INFO - global_step/sec: 88.7191 -2022-05-01 22:00:55,240 - tensorflow - INFO - loss = 3.0099537, step = 2305400 (2.254 sec) -2022-05-01 22:00:57,496 - tensorflow - INFO - global_step/sec: 88.6135 -2022-05-01 22:00:57,497 - tensorflow - INFO - loss = 0.00849932, step = 2305600 (2.257 sec) -2022-05-01 22:00:59,751 - tensorflow - INFO - global_step/sec: 88.7239 -2022-05-01 22:00:59,751 - tensorflow - INFO - loss = 0.009330539, step = 2305800 (2.254 sec) -2022-05-01 22:01:02,003 - tensorflow - INFO - global_step/sec: 88.8097 -2022-05-01 22:01:02,003 - tensorflow - INFO - loss = 0.25501916, step = 2306000 (2.252 sec) -2022-05-01 22:01:04,259 - tensorflow - INFO - global_step/sec: 88.6387 -2022-05-01 22:01:04,259 - tensorflow - INFO - loss = 0.0076217586, step = 2306200 (2.256 sec) -2022-05-01 22:01:06,512 - tensorflow - INFO - global_step/sec: 88.7569 -2022-05-01 22:01:06,513 - tensorflow - INFO - loss = 0.2942369, step = 2306400 (2.253 sec) -2022-05-01 22:01:08,766 - tensorflow - INFO - global_step/sec: 88.7346 -2022-05-01 22:01:08,767 - tensorflow - INFO - loss = 0.007971099, step = 2306600 (2.254 sec) -2022-05-01 22:01:11,026 - tensorflow - INFO - global_step/sec: 88.4867 -2022-05-01 22:01:11,027 - tensorflow - INFO - loss = 0.0067938194, step = 2306800 (2.260 sec) -2022-05-01 22:01:13,279 - tensorflow - INFO - global_step/sec: 88.7929 -2022-05-01 22:01:13,279 - tensorflow - INFO - loss = 0.024233544, step = 2307000 (2.252 sec) -2022-05-01 22:01:15,529 - tensorflow - INFO - global_step/sec: 88.8989 -2022-05-01 22:01:15,529 - tensorflow - INFO - loss = 0.030882917, step = 2307200 (2.250 sec) -2022-05-01 22:01:17,782 - tensorflow - INFO - global_step/sec: 88.7576 -2022-05-01 22:01:17,782 - tensorflow - INFO - loss = 0.39104134, step = 2307400 (2.253 sec) -2022-05-01 22:01:20,035 - tensorflow - INFO - global_step/sec: 88.7829 -2022-05-01 22:01:20,035 - tensorflow - INFO - loss = 0.02334808, step = 2307600 (2.253 sec) -2022-05-01 22:01:22,294 - tensorflow - INFO - global_step/sec: 88.5144 -2022-05-01 22:01:22,294 - tensorflow - INFO - loss = 0.017138243, step = 2307800 (2.260 sec) -2022-05-01 22:01:24,555 - tensorflow - INFO - global_step/sec: 88.4762 -2022-05-01 22:01:24,555 - tensorflow - INFO - loss = 0.090016745, step = 2308000 (2.261 sec) -2022-05-01 22:01:26,813 - tensorflow - INFO - global_step/sec: 88.5436 -2022-05-01 22:01:26,814 - tensorflow - INFO - loss = 0.030420732, step = 2308200 (2.259 sec) -2022-05-01 22:01:29,070 - tensorflow - INFO - global_step/sec: 88.6409 -2022-05-01 22:01:29,070 - tensorflow - INFO - loss = 0.09480525, step = 2308400 (2.256 sec) -2022-05-01 22:01:31,332 - tensorflow - INFO - global_step/sec: 88.3843 -2022-05-01 22:01:31,333 - tensorflow - INFO - loss = 0.009265394, step = 2308600 (2.263 sec) -2022-05-01 22:01:33,594 - tensorflow - INFO - global_step/sec: 88.446 -2022-05-01 22:01:33,594 - tensorflow - INFO - loss = 0.0055280607, step = 2308800 (2.261 sec) -2022-05-01 22:01:35,854 - tensorflow - INFO - global_step/sec: 88.4888 -2022-05-01 22:01:35,854 - tensorflow - INFO - loss = 0.081085324, step = 2309000 (2.260 sec) -2022-05-01 22:01:38,103 - tensorflow - INFO - global_step/sec: 88.918 -2022-05-01 22:01:38,104 - tensorflow - INFO - loss = 0.026840352, step = 2309200 (2.249 sec) -2022-05-01 22:01:40,356 - tensorflow - INFO - global_step/sec: 88.7619 -2022-05-01 22:01:40,357 - tensorflow - INFO - loss = 0.010899147, step = 2309400 (2.253 sec) -2022-05-01 22:01:42,612 - tensorflow - INFO - global_step/sec: 88.6616 -2022-05-01 22:01:42,613 - tensorflow - INFO - loss = 0.030948, step = 2309600 (2.256 sec) -2022-05-01 22:01:44,864 - tensorflow - INFO - global_step/sec: 88.8096 -2022-05-01 22:01:44,865 - tensorflow - INFO - loss = 0.071116395, step = 2309800 (2.252 sec) -2022-05-01 22:01:47,115 - tensorflow - INFO - global_step/sec: 88.8501 -2022-05-01 22:01:47,116 - tensorflow - INFO - loss = 0.012341282, step = 2310000 (2.251 sec) -2022-05-01 22:01:49,374 - tensorflow - INFO - global_step/sec: 88.5234 -2022-05-01 22:01:49,375 - tensorflow - INFO - loss = 0.05269678, step = 2310200 (2.259 sec) -2022-05-01 22:01:51,632 - tensorflow - INFO - global_step/sec: 88.5832 -2022-05-01 22:01:51,633 - tensorflow - INFO - loss = 0.012764389, step = 2310400 (2.258 sec) -2022-05-01 22:01:53,879 - tensorflow - INFO - global_step/sec: 89.0218 -2022-05-01 22:01:53,879 - tensorflow - INFO - loss = 0.2302305, step = 2310600 (2.247 sec) -2022-05-01 22:01:56,133 - tensorflow - INFO - global_step/sec: 88.7124 -2022-05-01 22:01:56,134 - tensorflow - INFO - loss = 0.17189549, step = 2310800 (2.255 sec) -2022-05-01 22:01:58,387 - tensorflow - INFO - global_step/sec: 88.7537 -2022-05-01 22:01:58,387 - tensorflow - INFO - loss = 0.007894407, step = 2311000 (2.253 sec) -2022-05-01 22:02:00,641 - tensorflow - INFO - global_step/sec: 88.705 -2022-05-01 22:02:00,642 - tensorflow - INFO - loss = 0.18904224, step = 2311200 (2.255 sec) -2022-05-01 22:02:02,902 - 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INFO - global_step/sec: 88.6366 -2022-05-01 22:05:32,620 - tensorflow - INFO - loss = 0.037553925, step = 2330000 (2.256 sec) -2022-05-01 22:05:34,872 - tensorflow - INFO - global_step/sec: 88.8133 -2022-05-01 22:05:34,872 - tensorflow - INFO - loss = 0.008088087, step = 2330200 (2.252 sec) -2022-05-01 22:05:37,123 - tensorflow - INFO - global_step/sec: 88.834 -2022-05-01 22:05:37,124 - tensorflow - INFO - loss = 0.00979968, step = 2330400 (2.251 sec) -2022-05-01 22:05:39,374 - tensorflow - INFO - global_step/sec: 88.8699 -2022-05-01 22:05:39,374 - tensorflow - INFO - loss = 0.015699673, step = 2330600 (2.250 sec) -2022-05-01 22:05:41,630 - tensorflow - INFO - global_step/sec: 88.6432 -2022-05-01 22:05:41,630 - tensorflow - INFO - loss = 0.044774663, step = 2330800 (2.256 sec) -2022-05-01 22:05:43,894 - tensorflow - INFO - global_step/sec: 88.3547 -2022-05-01 22:05:43,894 - tensorflow - INFO - loss = 0.060779974, step = 2331000 (2.264 sec) -2022-05-01 22:05:46,155 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.3842 -2022-05-01 22:06:29,067 - tensorflow - INFO - loss = 0.01186622, step = 2335000 (2.263 sec) -2022-05-01 22:06:31,329 - tensorflow - INFO - global_step/sec: 88.4146 -2022-05-01 22:06:31,329 - tensorflow - INFO - loss = 0.08158864, step = 2335200 (2.262 sec) -2022-05-01 22:06:33,582 - tensorflow - INFO - global_step/sec: 88.778 -2022-05-01 22:06:33,582 - tensorflow - INFO - loss = 0.016213449, step = 2335400 (2.253 sec) -2022-05-01 22:06:35,837 - tensorflow - INFO - global_step/sec: 88.671 -2022-05-01 22:06:35,838 - tensorflow - INFO - loss = 0.1896997, step = 2335600 (2.256 sec) -2022-05-01 22:06:38,092 - tensorflow - INFO - global_step/sec: 88.7013 -2022-05-01 22:06:38,092 - tensorflow - INFO - loss = 0.039793912, step = 2335800 (2.255 sec) -2022-05-01 22:06:40,346 - tensorflow - INFO - global_step/sec: 88.7092 -2022-05-01 22:06:40,347 - tensorflow - INFO - loss = 0.5908584, step = 2336000 (2.255 sec) -2022-05-01 22:06:42,607 - tensorflow - INFO - global_step/sec: 88.4873 -2022-05-01 22:06:42,607 - tensorflow - INFO - loss = 0.009805529, step = 2336200 (2.260 sec) -2022-05-01 22:06:44,856 - tensorflow - INFO - global_step/sec: 88.9053 -2022-05-01 22:06:44,857 - tensorflow - INFO - loss = 0.019799821, step = 2336400 (2.250 sec) -2022-05-01 22:06:47,109 - tensorflow - INFO - global_step/sec: 88.7871 -2022-05-01 22:06:47,109 - tensorflow - INFO - loss = 0.011163163, step = 2336600 (2.253 sec) -2022-05-01 22:06:49,359 - tensorflow - INFO - global_step/sec: 88.8939 -2022-05-01 22:06:49,359 - tensorflow - INFO - loss = 0.057771083, step = 2336800 (2.250 sec) -2022-05-01 22:06:51,612 - tensorflow - INFO - global_step/sec: 88.7643 -2022-05-01 22:06:51,612 - tensorflow - INFO - loss = 0.009727901, step = 2337000 (2.253 sec) -2022-05-01 22:06:53,868 - tensorflow - INFO - global_step/sec: 88.6472 -2022-05-01 22:06:53,868 - tensorflow - INFO - loss = 0.0070720967, step = 2337200 (2.256 sec) -2022-05-01 22:06:56,126 - tensorflow - INFO - 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tensorflow - INFO - loss = 11.913254, step = 2338600 (2.255 sec) -2022-05-01 22:07:11,903 - tensorflow - INFO - global_step/sec: 88.6866 -2022-05-01 22:07:11,903 - tensorflow - INFO - loss = 0.0029664468, step = 2338800 (2.255 sec) -2022-05-01 22:07:14,156 - tensorflow - INFO - global_step/sec: 88.7648 -2022-05-01 22:07:14,156 - tensorflow - INFO - loss = 0.0143400775, step = 2339000 (2.253 sec) -2022-05-01 22:07:16,408 - tensorflow - INFO - global_step/sec: 88.8178 -2022-05-01 22:07:16,408 - tensorflow - INFO - loss = 0.023873892, step = 2339200 (2.252 sec) -2022-05-01 22:07:18,665 - tensorflow - INFO - global_step/sec: 88.6026 -2022-05-01 22:07:18,666 - tensorflow - INFO - loss = 0.01951231, step = 2339400 (2.257 sec) -2022-05-01 22:07:20,921 - tensorflow - INFO - global_step/sec: 88.662 -2022-05-01 22:07:20,921 - tensorflow - INFO - loss = 0.033171035, step = 2339600 (2.256 sec) -2022-05-01 22:07:23,172 - tensorflow - INFO - global_step/sec: 88.8626 -2022-05-01 22:07:23,172 - tensorflow - INFO - loss = 0.06758325, step = 2339800 (2.251 sec) -2022-05-01 22:07:25,422 - tensorflow - INFO - global_step/sec: 88.8592 -2022-05-01 22:07:25,423 - tensorflow - INFO - loss = 0.054566626, step = 2340000 (2.251 sec) -2022-05-01 22:07:27,673 - tensorflow - INFO - global_step/sec: 88.8646 -2022-05-01 22:07:27,673 - tensorflow - INFO - loss = 0.018394904, step = 2340200 (2.251 sec) -2022-05-01 22:07:29,926 - tensorflow - INFO - global_step/sec: 88.7599 -2022-05-01 22:07:29,927 - tensorflow - INFO - loss = 0.012832569, step = 2340400 (2.253 sec) -2022-05-01 22:07:32,189 - tensorflow - INFO - global_step/sec: 88.39 -2022-05-01 22:07:32,189 - tensorflow - INFO - loss = 0.005976406, step = 2340600 (2.263 sec) -2022-05-01 22:07:34,444 - tensorflow - INFO - global_step/sec: 88.6942 -2022-05-01 22:07:34,444 - tensorflow - INFO - loss = 0.003158088, step = 2340800 (2.255 sec) -2022-05-01 22:07:36,692 - tensorflow - INFO - global_step/sec: 88.9451 -2022-05-01 22:07:36,693 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.6672 -2022-05-01 22:07:52,474 - tensorflow - INFO - loss = 0.005737345, step = 2342400 (2.256 sec) -2022-05-01 22:07:54,726 - tensorflow - INFO - global_step/sec: 88.8181 -2022-05-01 22:07:54,726 - tensorflow - INFO - loss = 0.33173925, step = 2342600 (2.252 sec) -2022-05-01 22:07:56,988 - tensorflow - INFO - global_step/sec: 88.4059 -2022-05-01 22:07:56,988 - tensorflow - INFO - loss = 0.0030213078, step = 2342800 (2.262 sec) -2022-05-01 22:07:59,238 - tensorflow - INFO - global_step/sec: 88.8644 -2022-05-01 22:07:59,239 - tensorflow - INFO - loss = 0.019566808, step = 2343000 (2.251 sec) -2022-05-01 22:08:01,489 - tensorflow - INFO - global_step/sec: 88.8676 -2022-05-01 22:08:01,490 - tensorflow - INFO - loss = 0.04868641, step = 2343200 (2.251 sec) -2022-05-01 22:08:03,747 - tensorflow - INFO - global_step/sec: 88.5743 -2022-05-01 22:08:03,747 - tensorflow - INFO - loss = 0.02816845, step = 2343400 (2.258 sec) -2022-05-01 22:08:06,009 - tensorflow - INFO - global_step/sec: 88.4288 -2022-05-01 22:08:06,009 - tensorflow - INFO - loss = 0.0069576884, step = 2343600 (2.262 sec) -2022-05-01 22:08:08,262 - tensorflow - INFO - global_step/sec: 88.7771 -2022-05-01 22:08:08,262 - tensorflow - INFO - loss = 0.059992794, step = 2343800 (2.253 sec) -2022-05-01 22:08:10,518 - tensorflow - INFO - global_step/sec: 88.6328 -2022-05-01 22:08:10,518 - tensorflow - INFO - loss = 0.021908706, step = 2344000 (2.257 sec) -2022-05-01 22:08:12,771 - tensorflow - INFO - global_step/sec: 88.7547 -2022-05-01 22:08:12,772 - tensorflow - INFO - loss = 0.029149998, step = 2344200 (2.253 sec) -2022-05-01 22:08:15,026 - tensorflow - INFO - global_step/sec: 88.692 -2022-05-01 22:08:15,027 - tensorflow - INFO - loss = 0.0133520365, step = 2344400 (2.255 sec) -2022-05-01 22:08:17,276 - tensorflow - INFO - global_step/sec: 88.8985 -2022-05-01 22:08:17,277 - tensorflow - INFO - loss = 0.026598422, step = 2344600 (2.250 sec) -2022-05-01 22:08:19,526 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.00861906, step = 2346000 (2.253 sec) -2022-05-01 22:08:35,320 - tensorflow - INFO - global_step/sec: 88.3982 -2022-05-01 22:08:35,321 - tensorflow - INFO - loss = 0.0022816355, step = 2346200 (2.262 sec) -2022-05-01 22:08:37,574 - tensorflow - INFO - global_step/sec: 88.7508 -2022-05-01 22:08:37,574 - tensorflow - INFO - loss = 0.04020985, step = 2346400 (2.253 sec) -2022-05-01 22:08:39,830 - tensorflow - INFO - global_step/sec: 88.6572 -2022-05-01 22:08:39,830 - tensorflow - INFO - loss = 0.010818541, step = 2346600 (2.256 sec) -2022-05-01 22:08:42,080 - tensorflow - INFO - global_step/sec: 88.8779 -2022-05-01 22:08:42,080 - tensorflow - INFO - loss = 0.033512853, step = 2346800 (2.250 sec) -2022-05-01 22:08:44,335 - tensorflow - INFO - global_step/sec: 88.6902 -2022-05-01 22:08:44,335 - tensorflow - INFO - loss = 0.040175766, step = 2347000 (2.255 sec) -2022-05-01 22:08:46,585 - tensorflow - INFO - global_step/sec: 88.8838 -2022-05-01 22:08:46,586 - tensorflow - INFO - loss = 0.04513892, step = 2347200 (2.250 sec) -2022-05-01 22:08:48,844 - tensorflow - INFO - global_step/sec: 88.5486 -2022-05-01 22:08:48,844 - tensorflow - INFO - loss = 0.006276906, step = 2347400 (2.259 sec) -2022-05-01 22:08:51,100 - tensorflow - INFO - global_step/sec: 88.6439 -2022-05-01 22:08:51,100 - tensorflow - INFO - loss = 0.012032225, step = 2347600 (2.256 sec) -2022-05-01 22:08:53,355 - tensorflow - INFO - global_step/sec: 88.6893 -2022-05-01 22:08:53,356 - tensorflow - INFO - loss = 0.032703087, step = 2347800 (2.255 sec) -2022-05-01 22:08:55,609 - tensorflow - INFO - global_step/sec: 88.7573 -2022-05-01 22:08:55,609 - tensorflow - INFO - loss = 0.025609188, step = 2348000 (2.253 sec) -2022-05-01 22:08:57,869 - tensorflow - INFO - global_step/sec: 88.4827 -2022-05-01 22:08:57,869 - tensorflow - INFO - loss = 0.009650145, step = 2348200 (2.260 sec) -2022-05-01 22:09:00,123 - tensorflow - INFO - global_step/sec: 88.7084 -2022-05-01 22:09:00,124 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.3017 -2022-05-01 22:09:15,930 - tensorflow - INFO - loss = 0.017859217, step = 2349800 (2.265 sec) -2022-05-01 22:09:18,193 - tensorflow - INFO - global_step/sec: 88.363 -2022-05-01 22:09:18,193 - tensorflow - INFO - loss = 0.045248006, step = 2350000 (2.264 sec) -2022-05-01 22:09:20,452 - tensorflow - INFO - global_step/sec: 88.5298 -2022-05-01 22:09:20,452 - tensorflow - INFO - loss = 0.006212191, step = 2350200 (2.259 sec) -2022-05-01 22:09:22,716 - tensorflow - INFO - global_step/sec: 88.3369 -2022-05-01 22:09:22,716 - tensorflow - INFO - loss = 0.013777234, step = 2350400 (2.264 sec) -2022-05-01 22:09:24,982 - tensorflow - INFO - global_step/sec: 88.2669 -2022-05-01 22:09:24,982 - tensorflow - INFO - loss = 0.05128285, step = 2350600 (2.266 sec) -2022-05-01 22:09:27,241 - tensorflow - INFO - global_step/sec: 88.528 -2022-05-01 22:09:27,241 - tensorflow - INFO - loss = 0.0340871, step = 2350800 (2.259 sec) -2022-05-01 22:09:29,502 - tensorflow - INFO - global_step/sec: 88.4397 -2022-05-01 22:09:29,503 - tensorflow - INFO - loss = 0.0099432655, step = 2351000 (2.261 sec) -2022-05-01 22:09:31,770 - tensorflow - INFO - global_step/sec: 88.1938 -2022-05-01 22:09:31,770 - tensorflow - INFO - loss = 0.008760823, step = 2351200 (2.268 sec) -2022-05-01 22:09:34,030 - tensorflow - INFO - global_step/sec: 88.4807 -2022-05-01 22:09:34,031 - tensorflow - INFO - loss = 0.0542563, step = 2351400 (2.260 sec) -2022-05-01 22:09:36,289 - tensorflow - INFO - global_step/sec: 88.5482 -2022-05-01 22:09:36,290 - tensorflow - INFO - loss = 0.007148968, step = 2351600 (2.259 sec) -2022-05-01 22:09:38,550 - tensorflow - INFO - global_step/sec: 88.4466 -2022-05-01 22:09:38,551 - tensorflow - INFO - loss = 0.02096405, step = 2351800 (2.261 sec) -2022-05-01 22:09:40,814 - tensorflow - INFO - global_step/sec: 88.345 -2022-05-01 22:09:40,815 - tensorflow - INFO - loss = 0.0086912485, step = 2352000 (2.264 sec) -2022-05-01 22:09:43,074 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.030029457, step = 2353400 (2.254 sec) -2022-05-01 22:09:58,850 - tensorflow - INFO - global_step/sec: 88.7795 -2022-05-01 22:09:58,851 - tensorflow - INFO - loss = 0.0619104, step = 2353600 (2.253 sec) -2022-05-01 22:10:01,105 - tensorflow - INFO - global_step/sec: 88.7066 -2022-05-01 22:10:01,105 - tensorflow - INFO - loss = 0.007638214, step = 2353800 (2.255 sec) -2022-05-01 22:10:03,363 - tensorflow - INFO - global_step/sec: 88.5804 -2022-05-01 22:10:03,363 - tensorflow - INFO - loss = 0.117859095, step = 2354000 (2.258 sec) -2022-05-01 22:10:05,620 - tensorflow - INFO - global_step/sec: 88.6156 -2022-05-01 22:10:05,620 - tensorflow - INFO - loss = 0.052432656, step = 2354200 (2.257 sec) -2022-05-01 22:10:07,880 - tensorflow - INFO - global_step/sec: 88.4916 -2022-05-01 22:10:07,880 - tensorflow - INFO - loss = 0.0035383592, step = 2354400 (2.260 sec) -2022-05-01 22:10:10,132 - tensorflow - INFO - global_step/sec: 88.813 -2022-05-01 22:10:10,132 - tensorflow - INFO - loss = 0.028265294, step = 2354600 (2.252 sec) -2022-05-01 22:10:12,383 - tensorflow - INFO - global_step/sec: 88.8454 -2022-05-01 22:10:12,383 - tensorflow - INFO - loss = 0.0070477324, step = 2354800 (2.251 sec) -2022-05-01 22:10:14,633 - tensorflow - INFO - global_step/sec: 88.8861 -2022-05-01 22:10:14,633 - tensorflow - INFO - loss = 0.036895104, step = 2355000 (2.250 sec) -2022-05-01 22:10:16,889 - tensorflow - INFO - global_step/sec: 88.6651 -2022-05-01 22:10:16,889 - tensorflow - INFO - loss = 0.011889763, step = 2355200 (2.256 sec) -2022-05-01 22:10:19,146 - tensorflow - INFO - global_step/sec: 88.5931 -2022-05-01 22:10:19,146 - tensorflow - INFO - loss = 0.03548288, step = 2355400 (2.257 sec) -2022-05-01 22:10:21,402 - tensorflow - INFO - global_step/sec: 88.6655 -2022-05-01 22:10:21,402 - tensorflow - INFO - loss = 0.019216398, step = 2355600 (2.256 sec) -2022-05-01 22:10:23,658 - tensorflow - INFO - global_step/sec: 88.6541 -2022-05-01 22:10:23,658 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.5 -2022-05-01 22:10:39,449 - tensorflow - INFO - loss = 0.019714009, step = 2357200 (2.260 sec) -2022-05-01 22:10:41,703 - tensorflow - INFO - global_step/sec: 88.7033 -2022-05-01 22:10:41,704 - tensorflow - INFO - loss = 0.0466834, step = 2357400 (2.255 sec) -2022-05-01 22:10:43,957 - tensorflow - INFO - global_step/sec: 88.742 -2022-05-01 22:10:43,957 - tensorflow - INFO - loss = 0.01898101, step = 2357600 (2.254 sec) -2022-05-01 22:10:46,212 - tensorflow - INFO - global_step/sec: 88.6683 -2022-05-01 22:10:46,213 - tensorflow - INFO - loss = 0.016786704, step = 2357800 (2.256 sec) -2022-05-01 22:10:48,463 - tensorflow - INFO - global_step/sec: 88.8727 -2022-05-01 22:10:48,463 - tensorflow - INFO - loss = 0.067788795, step = 2358000 (2.250 sec) -2022-05-01 22:10:50,720 - tensorflow - INFO - global_step/sec: 88.6091 -2022-05-01 22:10:50,720 - tensorflow - INFO - loss = 0.021722503, step = 2358200 (2.257 sec) -2022-05-01 22:10:52,975 - tensorflow - INFO - global_step/sec: 88.6863 -2022-05-01 22:10:52,975 - tensorflow - INFO - loss = 0.033839982, step = 2358400 (2.255 sec) -2022-05-01 22:10:55,232 - tensorflow - INFO - global_step/sec: 88.6125 -2022-05-01 22:10:55,233 - tensorflow - INFO - loss = 0.08963957, step = 2358600 (2.257 sec) -2022-05-01 22:10:57,491 - tensorflow - INFO - global_step/sec: 88.5485 -2022-05-01 22:10:57,491 - tensorflow - INFO - loss = 0.009970225, step = 2358800 (2.259 sec) -2022-05-01 22:10:59,744 - tensorflow - INFO - global_step/sec: 88.7576 -2022-05-01 22:10:59,744 - tensorflow - INFO - loss = 0.01552359, step = 2359000 (2.253 sec) -2022-05-01 22:11:01,993 - tensorflow - INFO - global_step/sec: 88.9441 -2022-05-01 22:11:01,993 - tensorflow - INFO - loss = 0.11977928, step = 2359200 (2.249 sec) -2022-05-01 22:11:04,248 - tensorflow - INFO - global_step/sec: 88.686 -2022-05-01 22:11:04,248 - tensorflow - INFO - loss = 0.009839378, step = 2359400 (2.255 sec) -2022-05-01 22:11:06,501 - tensorflow - INFO - global_step/sec: 88.7465 -2022-05-01 22:11:06,502 - tensorflow - INFO - loss = 0.03091337, step = 2359600 (2.254 sec) -2022-05-01 22:11:08,759 - tensorflow - INFO - global_step/sec: 88.6063 -2022-05-01 22:11:08,759 - tensorflow - INFO - loss = 0.0106901685, step = 2359800 (2.257 sec) -2022-05-01 22:11:11,012 - tensorflow - INFO - global_step/sec: 88.7435 -2022-05-01 22:11:11,013 - tensorflow - INFO - loss = 0.14742582, step = 2360000 (2.254 sec) -2022-05-01 22:11:13,267 - tensorflow - INFO - global_step/sec: 88.7031 -2022-05-01 22:11:13,267 - tensorflow - INFO - loss = 0.009636853, step = 2360200 (2.255 sec) -2022-05-01 22:11:15,517 - tensorflow - INFO - global_step/sec: 88.892 -2022-05-01 22:11:15,517 - tensorflow - INFO - loss = 0.010644963, step = 2360400 (2.250 sec) -2022-05-01 22:11:17,774 - tensorflow - INFO - global_step/sec: 88.6064 -2022-05-01 22:11:17,775 - tensorflow - INFO - loss = 0.051432684, step = 2360600 (2.257 sec) -2022-05-01 22:11:20,027 - tensorflow - INFO - global_step/sec: 88.7765 -2022-05-01 22:11:20,027 - tensorflow - INFO - loss = 0.061627805, step = 2360800 (2.253 sec) -2022-05-01 22:11:22,283 - tensorflow - INFO - global_step/sec: 88.6341 -2022-05-01 22:11:22,284 - tensorflow - INFO - loss = 0.09752448, step = 2361000 (2.257 sec) -2022-05-01 22:11:24,541 - tensorflow - INFO - global_step/sec: 88.578 -2022-05-01 22:11:24,542 - tensorflow - INFO - loss = 0.021298578, step = 2361200 (2.258 sec) -2022-05-01 22:11:26,800 - tensorflow - INFO - global_step/sec: 88.5334 -2022-05-01 22:11:26,801 - tensorflow - INFO - loss = 0.01599053, step = 2361400 (2.259 sec) -2022-05-01 22:11:29,061 - tensorflow - INFO - global_step/sec: 88.479 -2022-05-01 22:11:29,061 - tensorflow - INFO - loss = 0.019439112, step = 2361600 (2.260 sec) -2022-05-01 22:11:31,325 - tensorflow - INFO - global_step/sec: 88.3495 -2022-05-01 22:11:31,325 - tensorflow - INFO - loss = 0.011619351, step = 2361800 (2.264 sec) -2022-05-01 22:11:33,584 - tensorflow - INFO - global_step/sec: 88.5199 -2022-05-01 22:11:33,584 - tensorflow - INFO - loss = 0.01157747, step = 2362000 (2.259 sec) -2022-05-01 22:11:35,843 - tensorflow - INFO - global_step/sec: 88.5389 -2022-05-01 22:11:35,843 - tensorflow - INFO - loss = 0.012894679, step = 2362200 (2.259 sec) -2022-05-01 22:11:38,096 - tensorflow - INFO - global_step/sec: 88.777 -2022-05-01 22:11:38,096 - tensorflow - INFO - loss = 0.119117185, step = 2362400 (2.253 sec) -2022-05-01 22:11:40,350 - tensorflow - INFO - global_step/sec: 88.7025 -2022-05-01 22:11:40,351 - tensorflow - INFO - loss = 0.01409577, step = 2362600 (2.255 sec) -2022-05-01 22:11:42,605 - tensorflow - INFO - global_step/sec: 88.6949 -2022-05-01 22:11:42,606 - tensorflow - INFO - loss = 0.029172141, step = 2362800 (2.255 sec) -2022-05-01 22:11:44,859 - tensorflow - INFO - global_step/sec: 88.7246 -2022-05-01 22:11:44,860 - tensorflow - INFO - loss = 0.012926551, step = 2363000 (2.254 sec) -2022-05-01 22:11:47,116 - tensorflow - INFO - global_step/sec: 88.6195 -2022-05-01 22:11:47,117 - tensorflow - INFO - loss = 0.019551968, step = 2363200 (2.257 sec) -2022-05-01 22:11:49,370 - tensorflow - INFO - global_step/sec: 88.7435 -2022-05-01 22:11:49,370 - tensorflow - INFO - loss = 0.0049020173, step = 2363400 (2.254 sec) -2022-05-01 22:11:51,621 - tensorflow - INFO - global_step/sec: 88.853 -2022-05-01 22:11:51,621 - tensorflow - INFO - loss = 0.026439264, step = 2363600 (2.251 sec) -2022-05-01 22:11:53,871 - tensorflow - INFO - global_step/sec: 88.8866 -2022-05-01 22:11:53,871 - tensorflow - INFO - loss = 0.010113217, step = 2363800 (2.250 sec) -2022-05-01 22:11:56,129 - tensorflow - INFO - global_step/sec: 88.559 -2022-05-01 22:11:56,130 - tensorflow - INFO - loss = 0.021224327, step = 2364000 (2.258 sec) -2022-05-01 22:11:58,377 - tensorflow - INFO - global_step/sec: 88.9714 -2022-05-01 22:11:58,378 - tensorflow - INFO - loss = 0.18730974, step = 2364200 (2.248 sec) -2022-05-01 22:12:00,636 - tensorflow - INFO - global_step/sec: 88.5341 -2022-05-01 22:12:00,637 - tensorflow - INFO - loss = 0.09812194, step = 2364400 (2.259 sec) -2022-05-01 22:12:02,890 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.03492358, step = 2365800 (2.254 sec) -2022-05-01 22:12:18,670 - tensorflow - INFO - global_step/sec: 88.6344 -2022-05-01 22:12:18,671 - tensorflow - INFO - loss = 0.10256855, step = 2366000 (2.256 sec) -2022-05-01 22:12:20,927 - tensorflow - INFO - global_step/sec: 88.6174 -2022-05-01 22:12:20,928 - tensorflow - INFO - loss = 0.0069527263, step = 2366200 (2.257 sec) -2022-05-01 22:12:23,185 - tensorflow - INFO - global_step/sec: 88.6066 -2022-05-01 22:12:23,185 - tensorflow - INFO - loss = 0.006643961, step = 2366400 (2.257 sec) -2022-05-01 22:12:25,442 - tensorflow - INFO - global_step/sec: 88.577 -2022-05-01 22:12:25,443 - tensorflow - INFO - loss = 0.023996007, step = 2366600 (2.258 sec) -2022-05-01 22:12:27,692 - tensorflow - INFO - global_step/sec: 88.8936 -2022-05-01 22:12:27,693 - tensorflow - INFO - loss = 0.017353734, step = 2366800 (2.250 sec) -2022-05-01 22:12:29,943 - tensorflow - INFO - global_step/sec: 88.8612 -2022-05-01 22:12:29,943 - tensorflow - INFO - loss = 0.012825327, step = 2367000 (2.251 sec) -2022-05-01 22:12:32,201 - tensorflow - INFO - global_step/sec: 88.5717 -2022-05-01 22:12:32,201 - tensorflow - INFO - loss = 0.0131017435, step = 2367200 (2.258 sec) -2022-05-01 22:12:34,457 - tensorflow - INFO - global_step/sec: 88.6408 -2022-05-01 22:12:34,458 - tensorflow - INFO - loss = 0.072256595, step = 2367400 (2.256 sec) -2022-05-01 22:12:36,713 - tensorflow - INFO - global_step/sec: 88.674 -2022-05-01 22:12:36,713 - tensorflow - INFO - loss = 0.014600789, step = 2367600 (2.255 sec) -2022-05-01 22:12:38,966 - tensorflow - INFO - global_step/sec: 88.7686 -2022-05-01 22:12:38,966 - tensorflow - INFO - loss = 0.019045288, step = 2367800 (2.253 sec) -2022-05-01 22:12:41,217 - tensorflow - INFO - global_step/sec: 88.8284 -2022-05-01 22:12:41,218 - tensorflow - INFO - loss = 0.0063287104, step = 2368000 (2.252 sec) -2022-05-01 22:12:43,480 - tensorflow - INFO - global_step/sec: 88.4123 -2022-05-01 22:12:43,480 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.4426 -2022-05-01 22:12:59,326 - tensorflow - INFO - loss = 0.0074239303, step = 2369600 (2.261 sec) -2022-05-01 22:13:01,588 - tensorflow - INFO - global_step/sec: 88.3978 -2022-05-01 22:13:01,588 - tensorflow - INFO - loss = 0.05968262, step = 2369800 (2.262 sec) -2022-05-01 22:13:03,846 - tensorflow - INFO - global_step/sec: 88.5701 -2022-05-01 22:13:03,846 - tensorflow - INFO - loss = 0.0046462216, step = 2370000 (2.258 sec) -2022-05-01 22:13:06,108 - tensorflow - INFO - global_step/sec: 88.3953 -2022-05-01 22:13:06,109 - tensorflow - INFO - loss = 0.0052085514, step = 2370200 (2.263 sec) -2022-05-01 22:13:08,373 - tensorflow - INFO - global_step/sec: 88.3005 -2022-05-01 22:13:08,374 - tensorflow - INFO - loss = 0.009080168, step = 2370400 (2.265 sec) -2022-05-01 22:13:10,635 - tensorflow - INFO - global_step/sec: 88.4213 -2022-05-01 22:13:10,636 - tensorflow - INFO - loss = 0.012946348, step = 2370600 (2.262 sec) -2022-05-01 22:13:12,898 - tensorflow - INFO - global_step/sec: 88.3653 -2022-05-01 22:13:12,899 - tensorflow - INFO - loss = 0.02769855, step = 2370800 (2.263 sec) -2022-05-01 22:13:15,158 - tensorflow - INFO - global_step/sec: 88.5008 -2022-05-01 22:13:15,159 - tensorflow - INFO - loss = 0.010072215, step = 2371000 (2.260 sec) -2022-05-01 22:13:17,412 - tensorflow - INFO - global_step/sec: 88.7519 -2022-05-01 22:13:17,412 - tensorflow - INFO - loss = 0.008546825, step = 2371200 (2.253 sec) -2022-05-01 22:13:19,670 - tensorflow - INFO - global_step/sec: 88.5802 -2022-05-01 22:13:19,670 - tensorflow - INFO - loss = 0.008905073, step = 2371400 (2.258 sec) -2022-05-01 22:13:21,920 - tensorflow - INFO - global_step/sec: 88.8881 -2022-05-01 22:13:21,920 - tensorflow - INFO - loss = 0.03151849, step = 2371600 (2.250 sec) -2022-05-01 22:13:24,172 - tensorflow - INFO - global_step/sec: 88.8046 -2022-05-01 22:13:24,172 - tensorflow - INFO - loss = 0.052477755, step = 2371800 (2.252 sec) -2022-05-01 22:13:26,423 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.040376134, step = 2373200 (2.252 sec) -2022-05-01 22:13:42,208 - tensorflow - INFO - global_step/sec: 88.6747 -2022-05-01 22:13:42,209 - tensorflow - INFO - loss = 0.67771184, step = 2373400 (2.255 sec) -2022-05-01 22:13:44,472 - tensorflow - INFO - global_step/sec: 88.3397 -2022-05-01 22:13:44,473 - tensorflow - INFO - loss = 0.03562181, step = 2373600 (2.264 sec) -2022-05-01 22:13:46,730 - tensorflow - INFO - global_step/sec: 88.5723 -2022-05-01 22:13:46,731 - tensorflow - INFO - loss = 0.0781373, step = 2373800 (2.258 sec) -2022-05-01 22:13:48,979 - tensorflow - INFO - global_step/sec: 88.937 -2022-05-01 22:13:48,980 - tensorflow - INFO - loss = 0.007866849, step = 2374000 (2.249 sec) -2022-05-01 22:13:51,238 - tensorflow - INFO - global_step/sec: 88.546 -2022-05-01 22:13:51,238 - tensorflow - INFO - loss = 0.10566403, step = 2374200 (2.259 sec) -2022-05-01 22:13:53,492 - tensorflow - INFO - global_step/sec: 88.7318 -2022-05-01 22:13:53,492 - tensorflow - INFO - loss = 0.012510798, step = 2374400 (2.254 sec) -2022-05-01 22:13:55,751 - tensorflow - INFO - global_step/sec: 88.5141 -2022-05-01 22:13:55,752 - tensorflow - INFO - loss = 0.21246701, step = 2374600 (2.260 sec) -2022-05-01 22:13:58,004 - tensorflow - INFO - global_step/sec: 88.7723 -2022-05-01 22:13:58,005 - tensorflow - INFO - loss = 0.008742791, step = 2374800 (2.253 sec) -2022-05-01 22:14:00,254 - tensorflow - INFO - global_step/sec: 88.8976 -2022-05-01 22:14:00,255 - tensorflow - INFO - loss = 0.012201543, step = 2375000 (2.250 sec) -2022-05-01 22:14:02,508 - tensorflow - INFO - global_step/sec: 88.7503 -2022-05-01 22:14:02,508 - tensorflow - INFO - loss = 0.2981624, step = 2375200 (2.253 sec) -2022-05-01 22:14:04,764 - tensorflow - INFO - global_step/sec: 88.6274 -2022-05-01 22:14:04,765 - tensorflow - INFO - loss = 0.31821147, step = 2375400 (2.257 sec) -2022-05-01 22:14:07,018 - tensorflow - INFO - global_step/sec: 88.7507 -2022-05-01 22:14:07,018 - tensorflow - INFO - loss = 0.036498245, step = 2375600 (2.254 sec) -2022-05-01 22:14:09,276 - tensorflow - INFO - global_step/sec: 88.5602 -2022-05-01 22:14:09,276 - tensorflow - INFO - loss = 0.017628154, step = 2375800 (2.258 sec) -2022-05-01 22:14:11,525 - tensorflow - INFO - global_step/sec: 88.9153 -2022-05-01 22:14:11,526 - tensorflow - INFO - loss = 0.017715462, step = 2376000 (2.249 sec) -2022-05-01 22:14:13,779 - tensorflow - INFO - global_step/sec: 88.7279 -2022-05-01 22:14:13,780 - tensorflow - INFO - loss = 0.02486781, step = 2376200 (2.254 sec) -2022-05-01 22:14:16,036 - tensorflow - INFO - global_step/sec: 88.6359 -2022-05-01 22:14:16,036 - tensorflow - INFO - loss = 0.009574259, step = 2376400 (2.256 sec) -2022-05-01 22:14:18,295 - tensorflow - INFO - global_step/sec: 88.5367 -2022-05-01 22:14:18,295 - tensorflow - INFO - loss = 0.037036, step = 2376600 (2.259 sec) -2022-05-01 22:14:20,557 - tensorflow - INFO - global_step/sec: 88.4025 -2022-05-01 22:14:20,558 - tensorflow - INFO - loss = 0.020934843, step = 2376800 (2.262 sec) -2022-05-01 22:14:22,810 - tensorflow - INFO - global_step/sec: 88.7867 -2022-05-01 22:14:22,810 - tensorflow - INFO - loss = 0.036132008, step = 2377000 (2.252 sec) -2022-05-01 22:14:25,056 - tensorflow - INFO - global_step/sec: 89.0351 -2022-05-01 22:14:25,057 - tensorflow - INFO - loss = 0.033706702, step = 2377200 (2.246 sec) -2022-05-01 22:14:27,310 - tensorflow - INFO - global_step/sec: 88.7539 -2022-05-01 22:14:27,310 - tensorflow - INFO - loss = 0.01699885, step = 2377400 (2.253 sec) -2022-05-01 22:14:29,563 - tensorflow - INFO - global_step/sec: 88.7562 -2022-05-01 22:14:29,563 - tensorflow - INFO - loss = 0.022424644, step = 2377600 (2.253 sec) -2022-05-01 22:14:31,821 - tensorflow - INFO - global_step/sec: 88.58 -2022-05-01 22:14:31,821 - tensorflow - INFO - loss = 0.01934461, step = 2377800 (2.258 sec) -2022-05-01 22:14:34,078 - tensorflow - INFO - global_step/sec: 88.6221 -2022-05-01 22:14:34,078 - tensorflow - INFO - loss = 0.0123040015, step = 2378000 (2.257 sec) -2022-05-01 22:14:36,333 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.01770787, step = 2379400 (2.252 sec) -2022-05-01 22:14:52,100 - tensorflow - INFO - global_step/sec: 88.835 -2022-05-01 22:14:52,100 - tensorflow - INFO - loss = 0.0119537, step = 2379600 (2.251 sec) -2022-05-01 22:14:54,354 - tensorflow - INFO - global_step/sec: 88.7293 -2022-05-01 22:14:54,354 - tensorflow - INFO - loss = 0.076885924, step = 2379800 (2.254 sec) -2022-05-01 22:14:56,608 - tensorflow - INFO - global_step/sec: 88.7097 -2022-05-01 22:14:56,609 - tensorflow - INFO - loss = 0.01063635, step = 2380000 (2.255 sec) -2022-05-01 22:14:58,868 - tensorflow - INFO - global_step/sec: 88.5057 -2022-05-01 22:14:58,868 - tensorflow - INFO - loss = 0.029029515, step = 2380200 (2.260 sec) -2022-05-01 22:15:01,113 - tensorflow - INFO - global_step/sec: 89.0694 -2022-05-01 22:15:01,114 - tensorflow - INFO - loss = 0.04142113, step = 2380400 (2.245 sec) -2022-05-01 22:15:03,371 - tensorflow - INFO - global_step/sec: 88.5906 -2022-05-01 22:15:03,371 - tensorflow - INFO - loss = 0.008125562, step = 2380600 (2.258 sec) -2022-05-01 22:15:05,625 - tensorflow - INFO - global_step/sec: 88.7348 -2022-05-01 22:15:05,625 - tensorflow - INFO - loss = 0.7975176, step = 2380800 (2.254 sec) -2022-05-01 22:15:07,883 - tensorflow - INFO - global_step/sec: 88.5644 -2022-05-01 22:15:07,884 - tensorflow - INFO - loss = 0.0044040154, step = 2381000 (2.258 sec) -2022-05-01 22:15:10,136 - tensorflow - INFO - global_step/sec: 88.784 -2022-05-01 22:15:10,136 - tensorflow - INFO - loss = 0.039620418, step = 2381200 (2.253 sec) -2022-05-01 22:15:12,395 - tensorflow - INFO - global_step/sec: 88.5206 -2022-05-01 22:15:12,396 - tensorflow - INFO - loss = 0.017591197, step = 2381400 (2.259 sec) -2022-05-01 22:15:14,647 - tensorflow - INFO - global_step/sec: 88.8092 -2022-05-01 22:15:14,648 - tensorflow - INFO - loss = 0.097436376, step = 2381600 (2.252 sec) -2022-05-01 22:15:16,901 - tensorflow - INFO - global_step/sec: 88.7243 -2022-05-01 22:15:16,902 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.7508 -2022-05-01 22:15:32,684 - tensorflow - INFO - loss = 0.02424026, step = 2383200 (2.253 sec) -2022-05-01 22:15:34,936 - tensorflow - INFO - global_step/sec: 88.8125 -2022-05-01 22:15:34,936 - tensorflow - INFO - loss = 0.04004424, step = 2383400 (2.252 sec) -2022-05-01 22:15:37,192 - tensorflow - INFO - global_step/sec: 88.6456 -2022-05-01 22:15:37,192 - tensorflow - INFO - loss = 0.06911096, step = 2383600 (2.256 sec) -2022-05-01 22:15:39,448 - tensorflow - INFO - global_step/sec: 88.6641 -2022-05-01 22:15:39,448 - tensorflow - INFO - loss = 0.0045621977, step = 2383800 (2.256 sec) -2022-05-01 22:15:41,704 - tensorflow - INFO - global_step/sec: 88.6504 -2022-05-01 22:15:41,704 - tensorflow - INFO - loss = 0.01261645, step = 2384000 (2.256 sec) -2022-05-01 22:15:43,957 - tensorflow - INFO - global_step/sec: 88.7578 -2022-05-01 22:15:43,957 - tensorflow - INFO - loss = 0.0063984967, step = 2384200 (2.253 sec) -2022-05-01 22:15:46,210 - tensorflow - INFO - global_step/sec: 88.7579 -2022-05-01 22:15:46,211 - tensorflow - INFO - loss = 0.09875474, step = 2384400 (2.253 sec) -2022-05-01 22:15:48,463 - tensorflow - INFO - global_step/sec: 88.7832 -2022-05-01 22:15:48,463 - tensorflow - INFO - loss = 0.0077966563, step = 2384600 (2.253 sec) -2022-05-01 22:15:50,723 - tensorflow - INFO - global_step/sec: 88.5068 -2022-05-01 22:15:50,723 - tensorflow - INFO - loss = 0.0064509623, step = 2384800 (2.260 sec) -2022-05-01 22:15:52,979 - tensorflow - INFO - global_step/sec: 88.6624 -2022-05-01 22:15:52,979 - tensorflow - INFO - loss = 0.016903702, step = 2385000 (2.256 sec) -2022-05-01 22:15:55,232 - tensorflow - INFO - global_step/sec: 88.748 -2022-05-01 22:15:55,232 - tensorflow - INFO - loss = 0.0073978687, step = 2385200 (2.254 sec) -2022-05-01 22:15:57,488 - tensorflow - INFO - global_step/sec: 88.656 -2022-05-01 22:15:57,488 - tensorflow - INFO - loss = 0.04489147, step = 2385400 (2.256 sec) -2022-05-01 22:15:59,742 - tensorflow - INFO - global_step/sec: 88.7456 -2022-05-01 22:15:59,742 - tensorflow - INFO - loss = 0.016343482, step = 2385600 (2.254 sec) -2022-05-01 22:16:02,001 - tensorflow - INFO - global_step/sec: 88.5057 -2022-05-01 22:16:02,002 - tensorflow - INFO - loss = 0.008009482, step = 2385800 (2.260 sec) -2022-05-01 22:16:04,257 - tensorflow - INFO - global_step/sec: 88.6761 -2022-05-01 22:16:04,257 - tensorflow - INFO - loss = 0.0510852, step = 2386000 (2.255 sec) -2022-05-01 22:16:06,510 - tensorflow - INFO - global_step/sec: 88.7768 -2022-05-01 22:16:06,510 - tensorflow - INFO - loss = 0.058420647, step = 2386200 (2.253 sec) -2022-05-01 22:16:08,761 - tensorflow - INFO - global_step/sec: 88.8516 -2022-05-01 22:16:08,761 - tensorflow - INFO - loss = 0.018244365, step = 2386400 (2.251 sec) -2022-05-01 22:16:11,013 - tensorflow - INFO - global_step/sec: 88.8016 -2022-05-01 22:16:11,013 - tensorflow - INFO - loss = 0.07714485, step = 2386600 (2.252 sec) -2022-05-01 22:16:13,266 - tensorflow - INFO - global_step/sec: 88.7739 -2022-05-01 22:16:13,266 - tensorflow - INFO - loss = 0.1699921, step = 2386800 (2.253 sec) -2022-05-01 22:16:15,529 - tensorflow - INFO - global_step/sec: 88.3722 -2022-05-01 22:16:15,529 - tensorflow - INFO - loss = 0.03827375, step = 2387000 (2.263 sec) -2022-05-01 22:16:17,789 - tensorflow - INFO - global_step/sec: 88.5068 -2022-05-01 22:16:17,789 - tensorflow - INFO - loss = 0.046373516, step = 2387200 (2.260 sec) -2022-05-01 22:16:20,055 - tensorflow - INFO - global_step/sec: 88.2586 -2022-05-01 22:16:20,055 - tensorflow - INFO - loss = 0.013266853, step = 2387400 (2.266 sec) -2022-05-01 22:16:22,317 - tensorflow - INFO - global_step/sec: 88.3962 -2022-05-01 22:16:22,318 - tensorflow - INFO - loss = 0.021442072, step = 2387600 (2.263 sec) -2022-05-01 22:16:24,578 - tensorflow - INFO - global_step/sec: 88.4747 -2022-05-01 22:16:24,578 - tensorflow - INFO - loss = 0.10181264, step = 2387800 (2.260 sec) -2022-05-01 22:16:26,841 - tensorflow - INFO - global_step/sec: 88.3802 -2022-05-01 22:16:26,841 - tensorflow - INFO - loss = 0.05081415, step = 2388000 (2.263 sec) -2022-05-01 22:16:29,106 - tensorflow - INFO - global_step/sec: 88.3035 -2022-05-01 22:16:29,106 - tensorflow - INFO - loss = 0.025215954, step = 2388200 (2.265 sec) -2022-05-01 22:16:31,376 - tensorflow - INFO - global_step/sec: 88.071 -2022-05-01 22:16:31,377 - tensorflow - INFO - loss = 0.017948493, step = 2388400 (2.271 sec) -2022-05-01 22:16:33,645 - tensorflow - INFO - global_step/sec: 88.1614 -2022-05-01 22:16:33,645 - tensorflow - INFO - loss = 0.008095983, step = 2388600 (2.269 sec) -2022-05-01 22:16:35,909 - tensorflow - INFO - global_step/sec: 88.3501 -2022-05-01 22:16:35,909 - tensorflow - INFO - loss = 0.030127618, step = 2388800 (2.264 sec) -2022-05-01 22:16:38,171 - tensorflow - INFO - global_step/sec: 88.4056 -2022-05-01 22:16:38,171 - tensorflow - INFO - loss = 0.12454489, step = 2389000 (2.262 sec) -2022-05-01 22:16:40,432 - tensorflow - INFO - global_step/sec: 88.4749 -2022-05-01 22:16:40,432 - tensorflow - INFO - loss = 0.013886469, step = 2389200 (2.260 sec) -2022-05-01 22:16:42,694 - tensorflow - INFO - global_step/sec: 88.405 -2022-05-01 22:16:42,694 - tensorflow - INFO - loss = 0.005526525, step = 2389400 (2.262 sec) -2022-05-01 22:16:44,958 - tensorflow - INFO - global_step/sec: 88.3333 -2022-05-01 22:16:44,958 - tensorflow - INFO - loss = 0.018395491, step = 2389600 (2.264 sec) -2022-05-01 22:16:47,219 - tensorflow - INFO - global_step/sec: 88.4527 -2022-05-01 22:16:47,220 - tensorflow - INFO - loss = 0.00997955, step = 2389800 (2.261 sec) -2022-05-01 22:16:49,470 - tensorflow - INFO - global_step/sec: 88.8441 -2022-05-01 22:16:49,471 - tensorflow - INFO - loss = 0.0037690697, step = 2390000 (2.251 sec) -2022-05-01 22:16:51,721 - tensorflow - INFO - global_step/sec: 88.863 -2022-05-01 22:16:51,721 - tensorflow - INFO - loss = 0.013914542, step = 2390200 (2.251 sec) -2022-05-01 22:16:53,980 - tensorflow - INFO - global_step/sec: 88.5495 -2022-05-01 22:16:53,980 - tensorflow - INFO - loss = 0.009430278, step = 2390400 (2.259 sec) -2022-05-01 22:16:56,239 - tensorflow - INFO - global_step/sec: 88.5315 -2022-05-01 22:16:56,239 - tensorflow - INFO - loss = 0.024905298, step = 2390600 (2.259 sec) -2022-05-01 22:16:58,494 - tensorflow - INFO - global_step/sec: 88.6647 -2022-05-01 22:16:58,495 - tensorflow - INFO - loss = 0.0072831106, step = 2390800 (2.256 sec) -2022-05-01 22:17:00,745 - tensorflow - INFO - global_step/sec: 88.8468 -2022-05-01 22:17:00,746 - tensorflow - INFO - loss = 0.03236274, step = 2391000 (2.251 sec) -2022-05-01 22:17:03,000 - tensorflow - INFO - global_step/sec: 88.7116 -2022-05-01 22:17:03,000 - tensorflow - INFO - loss = 0.015077673, step = 2391200 (2.255 sec) -2022-05-01 22:17:05,257 - tensorflow - INFO - global_step/sec: 88.5927 -2022-05-01 22:17:05,258 - tensorflow - INFO - loss = 0.0067395577, step = 2391400 (2.258 sec) -2022-05-01 22:17:07,509 - tensorflow - INFO - global_step/sec: 88.8246 -2022-05-01 22:17:07,509 - tensorflow - INFO - loss = 0.019622741, step = 2391600 (2.252 sec) -2022-05-01 22:17:09,765 - tensorflow - INFO - global_step/sec: 88.6531 -2022-05-01 22:17:09,765 - tensorflow - INFO - loss = 0.014148858, step = 2391800 (2.256 sec) -2022-05-01 22:17:12,020 - tensorflow - INFO - global_step/sec: 88.6967 -2022-05-01 22:17:12,020 - tensorflow - INFO - loss = 0.005203164, step = 2392000 (2.255 sec) -2022-05-01 22:17:14,276 - tensorflow - INFO - global_step/sec: 88.645 -2022-05-01 22:17:14,277 - tensorflow - INFO - loss = 0.040775083, step = 2392200 (2.256 sec) -2022-05-01 22:17:16,531 - tensorflow - INFO - global_step/sec: 88.705 -2022-05-01 22:17:16,531 - tensorflow - INFO - loss = 0.07990815, step = 2392400 (2.255 sec) -2022-05-01 22:17:18,782 - tensorflow - INFO - global_step/sec: 88.8364 -2022-05-01 22:17:18,782 - tensorflow - INFO - loss = 0.041910116, step = 2392600 (2.251 sec) -2022-05-01 22:17:21,034 - tensorflow - INFO - global_step/sec: 88.8098 -2022-05-01 22:17:21,034 - tensorflow - INFO - loss = 0.022378735, step = 2392800 (2.252 sec) -2022-05-01 22:17:23,292 - tensorflow - INFO - global_step/sec: 88.5926 -2022-05-01 22:17:23,292 - tensorflow - INFO - loss = 0.22880238, step = 2393000 (2.258 sec) -2022-05-01 22:17:25,543 - tensorflow - INFO - global_step/sec: 88.8418 -2022-05-01 22:17:25,543 - tensorflow - INFO - loss = 0.029437074, step = 2393200 (2.251 sec) -2022-05-01 22:17:27,794 - tensorflow - INFO - global_step/sec: 88.8368 -2022-05-01 22:17:27,795 - tensorflow - INFO - loss = 0.02802345, step = 2393400 (2.251 sec) -2022-05-01 22:17:30,049 - tensorflow - INFO - global_step/sec: 88.6802 -2022-05-01 22:17:30,050 - tensorflow - INFO - loss = 0.063665904, step = 2393600 (2.255 sec) -2022-05-01 22:17:32,307 - tensorflow - INFO - global_step/sec: 88.5758 -2022-05-01 22:17:32,308 - tensorflow - INFO - loss = 0.021801218, step = 2393800 (2.258 sec) -2022-05-01 22:17:34,568 - tensorflow - INFO - global_step/sec: 88.4643 -2022-05-01 22:17:34,569 - tensorflow - INFO - loss = 0.11958213, step = 2394000 (2.261 sec) -2022-05-01 22:17:36,821 - tensorflow - INFO - global_step/sec: 88.7855 -2022-05-01 22:17:36,821 - tensorflow - INFO - loss = 0.028965376, step = 2394200 (2.253 sec) -2022-05-01 22:17:39,071 - tensorflow - INFO - global_step/sec: 88.8764 -2022-05-01 22:17:39,072 - tensorflow - INFO - loss = 0.0116002485, step = 2394400 (2.250 sec) -2022-05-01 22:17:41,326 - tensorflow - INFO - global_step/sec: 88.6799 -2022-05-01 22:17:41,327 - tensorflow - INFO - loss = 0.01459889, step = 2394600 (2.255 sec) -2022-05-01 22:17:43,582 - tensorflow - INFO - global_step/sec: 88.6758 -2022-05-01 22:17:43,582 - tensorflow - INFO - loss = 0.077938415, step = 2394800 (2.255 sec) -2022-05-01 22:17:45,836 - tensorflow - INFO - global_step/sec: 88.7167 -2022-05-01 22:17:45,837 - tensorflow - INFO - loss = 0.032044075, step = 2395000 (2.254 sec) -2022-05-01 22:17:48,097 - tensorflow - INFO - global_step/sec: 88.4702 -2022-05-01 22:17:48,097 - tensorflow - INFO - loss = 0.03634771, step = 2395200 (2.261 sec) -2022-05-01 22:17:50,352 - tensorflow - INFO - global_step/sec: 88.6998 -2022-05-01 22:17:50,352 - tensorflow - INFO - loss = 0.07824456, step = 2395400 (2.255 sec) -2022-05-01 22:17:52,603 - tensorflow - INFO - global_step/sec: 88.8301 -2022-05-01 22:17:52,603 - tensorflow - INFO - loss = 0.0139762, step = 2395600 (2.251 sec) -2022-05-01 22:17:54,855 - tensorflow - INFO - global_step/sec: 88.8065 -2022-05-01 22:17:54,856 - tensorflow - INFO - loss = 0.009006003, step = 2395800 (2.252 sec) -2022-05-01 22:17:57,107 - tensorflow - INFO - global_step/sec: 88.804 -2022-05-01 22:17:57,108 - tensorflow - INFO - loss = 0.019991372, step = 2396000 (2.252 sec) -2022-05-01 22:17:59,358 - tensorflow - INFO - global_step/sec: 88.8513 -2022-05-01 22:17:59,359 - tensorflow - INFO - loss = 0.5126882, step = 2396200 (2.251 sec) -2022-05-01 22:18:01,617 - tensorflow - INFO - global_step/sec: 88.5474 -2022-05-01 22:18:01,617 - tensorflow - INFO - loss = 0.01509488, step = 2396400 (2.259 sec) -2022-05-01 22:18:03,870 - tensorflow - INFO - global_step/sec: 88.7642 -2022-05-01 22:18:03,871 - tensorflow - INFO - loss = 0.01103553, step = 2396600 (2.253 sec) -2022-05-01 22:18:06,124 - tensorflow - INFO - global_step/sec: 88.7297 -2022-05-01 22:18:06,125 - tensorflow - INFO - loss = 0.01015729, step = 2396800 (2.254 sec) -2022-05-01 22:18:08,379 - tensorflow - INFO - global_step/sec: 88.7154 -2022-05-01 22:18:08,379 - tensorflow - INFO - loss = 0.01095845, step = 2397000 (2.254 sec) -2022-05-01 22:18:10,628 - tensorflow - INFO - global_step/sec: 88.9017 -2022-05-01 22:18:10,629 - tensorflow - INFO - loss = 0.019706031, step = 2397200 (2.250 sec) -2022-05-01 22:18:12,888 - tensorflow - INFO - global_step/sec: 88.5183 -2022-05-01 22:18:12,888 - tensorflow - INFO - loss = 0.01793155, step = 2397400 (2.259 sec) -2022-05-01 22:18:15,137 - tensorflow - INFO - global_step/sec: 88.9022 -2022-05-01 22:18:15,138 - tensorflow - INFO - loss = 0.0090178745, step = 2397600 (2.250 sec) -2022-05-01 22:18:17,389 - tensorflow - INFO - global_step/sec: 88.8423 -2022-05-01 22:18:17,389 - tensorflow - INFO - loss = 0.004080846, step = 2397800 (2.251 sec) -2022-05-01 22:18:19,643 - tensorflow - INFO - global_step/sec: 88.7274 -2022-05-01 22:18:19,643 - tensorflow - INFO - loss = 0.013300663, step = 2398000 (2.254 sec) -2022-05-01 22:18:21,900 - tensorflow - INFO - global_step/sec: 88.5801 -2022-05-01 22:18:21,901 - tensorflow - INFO - loss = 0.029440576, step = 2398200 (2.258 sec) -2022-05-01 22:18:24,157 - tensorflow - INFO - global_step/sec: 88.6256 -2022-05-01 22:18:24,158 - tensorflow - INFO - loss = 0.021608679, step = 2398400 (2.257 sec) -2022-05-01 22:18:26,413 - tensorflow - INFO - global_step/sec: 88.6579 -2022-05-01 22:18:26,413 - tensorflow - INFO - loss = 0.013700175, step = 2398600 (2.256 sec) -2022-05-01 22:18:28,669 - tensorflow - INFO - global_step/sec: 88.662 -2022-05-01 22:18:28,669 - tensorflow - INFO - loss = 0.018216874, step = 2398800 (2.256 sec) -2022-05-01 22:18:30,923 - tensorflow - INFO - global_step/sec: 88.7059 -2022-05-01 22:18:30,924 - tensorflow - INFO - loss = 0.087347396, step = 2399000 (2.255 sec) -2022-05-01 22:18:33,175 - tensorflow - INFO - global_step/sec: 88.8152 -2022-05-01 22:18:33,176 - tensorflow - INFO - loss = 0.010069039, step = 2399200 (2.252 sec) -2022-05-01 22:18:35,430 - tensorflow - INFO - global_step/sec: 88.7169 -2022-05-01 22:18:35,430 - tensorflow - INFO - loss = 0.0043498473, step = 2399400 (2.254 sec) -2022-05-01 22:18:37,685 - tensorflow - INFO - global_step/sec: 88.6671 -2022-05-01 22:18:37,686 - tensorflow - INFO - loss = 0.045280192, step = 2399600 (2.256 sec) -2022-05-01 22:18:39,940 - tensorflow - INFO - global_step/sec: 88.7167 -2022-05-01 22:18:39,940 - tensorflow - INFO - loss = 0.009058259, step = 2399800 (2.254 sec) -2022-05-01 22:18:42,178 - tensorflow - INFO - Saving checkpoints for 2400000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. -2022-05-01 22:18:42,193 - tensorflow - INFO - model.ckpt-2400000 is not in all_model_checkpoint_paths. Manually adding it. -2022-05-01 22:18:42,743 - tensorflow - INFO - Calling model_fn. -2022-05-01 22:18:42,869 - tensorflow - INFO - Done calling model_fn. -2022-05-01 22:18:42,884 - tensorflow - INFO - Starting evaluation at 2022-05-01T22:18:42Z -2022-05-01 22:18:42,947 - tensorflow - INFO - Graph was finalized. -2022-05-01 22:18:42,951 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-2400000 -2022-05-01 22:18:43,230 - tensorflow - INFO - Running local_init_op. -2022-05-01 22:18:43,338 - tensorflow - INFO - Done running local_init_op. -2022-05-01 22:18:53,570 - tensorflow - INFO - Evaluation [3990/39900] -2022-05-01 22:19:03,090 - tensorflow - INFO - Evaluation [7980/39900] -2022-05-01 22:19:12,586 - tensorflow - INFO - Evaluation [11970/39900] -2022-05-01 22:19:22,093 - tensorflow - INFO - Evaluation [15960/39900] -2022-05-01 22:19:31,631 - tensorflow - INFO - Evaluation [19950/39900] -2022-05-01 22:19:41,171 - tensorflow - INFO - Evaluation [23940/39900] -2022-05-01 22:19:50,691 - tensorflow - INFO - Evaluation [27930/39900] -2022-05-01 22:20:00,276 - tensorflow - INFO - Evaluation [31920/39900] -2022-05-01 22:20:09,832 - tensorflow - INFO - Evaluation [35910/39900] -2022-05-01 22:20:19,401 - tensorflow - INFO - Evaluation [39900/39900] -2022-05-01 22:20:19,539 - tensorflow - INFO - Finished evaluation at 2022-05-01-22:20:19 -2022-05-01 22:20:19,539 - tensorflow - INFO - Saving dict for global step 2400000: global_step = 2400000, loss = 0.0163621, mean_degree_err = 3.2771058 -2022-05-01 22:20:19,540 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 2400000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-2400000 -2022-05-01 22:20:19,553 - tensorflow - INFO - global_step/sec: 2.00776 -2022-05-01 22:20:19,554 - tensorflow - INFO - loss = 0.017489435, step = 2400000 (99.614 sec) -2022-05-01 22:20:21,826 - tensorflow - INFO - global_step/sec: 87.9881 -2022-05-01 22:20:21,827 - tensorflow - INFO - loss = 0.07216262, step = 2400200 (2.273 sec) -2022-05-01 22:20:24,081 - tensorflow - INFO - global_step/sec: 88.6938 -2022-05-01 22:20:24,082 - tensorflow - INFO - loss = 0.092333764, step = 2400400 (2.255 sec) -2022-05-01 22:20:26,335 - tensorflow - INFO - global_step/sec: 88.7409 -2022-05-01 22:20:26,336 - tensorflow - INFO - loss = 0.0081663225, step = 2400600 (2.254 sec) -2022-05-01 22:20:28,588 - tensorflow - INFO - global_step/sec: 88.7691 -2022-05-01 22:20:28,589 - tensorflow - INFO - loss = 0.008921932, step = 2400800 (2.253 sec) -2022-05-01 22:20:30,851 - tensorflow - INFO - global_step/sec: 88.3872 -2022-05-01 22:20:30,851 - tensorflow - INFO - loss = 0.020071022, step = 2401000 (2.263 sec) -2022-05-01 22:20:33,106 - tensorflow - INFO - global_step/sec: 88.6718 -2022-05-01 22:20:33,107 - tensorflow - INFO - loss = 0.029509716, step = 2401200 (2.256 sec) -2022-05-01 22:20:35,366 - tensorflow - INFO - global_step/sec: 88.5227 -2022-05-01 22:20:35,366 - tensorflow - INFO - loss = 0.021414602, step = 2401400 (2.259 sec) -2022-05-01 22:20:37,615 - tensorflow - INFO - global_step/sec: 88.9051 -2022-05-01 22:20:37,616 - tensorflow - INFO - loss = 0.0034311865, step = 2401600 (2.250 sec) -2022-05-01 22:20:39,872 - tensorflow - INFO - global_step/sec: 88.6275 -2022-05-01 22:20:39,872 - tensorflow - INFO - loss = 0.045313366, step = 2401800 (2.257 sec) -2022-05-01 22:20:42,122 - tensorflow - INFO - global_step/sec: 88.9013 -2022-05-01 22:20:42,122 - tensorflow - INFO - loss = 0.018083166, step = 2402000 (2.250 sec) -2022-05-01 22:20:44,372 - tensorflow - INFO - global_step/sec: 88.8594 -2022-05-01 22:20:44,373 - tensorflow - INFO - loss = 0.011318088, step = 2402200 (2.251 sec) -2022-05-01 22:20:46,626 - tensorflow - INFO - global_step/sec: 88.7272 -2022-05-01 22:20:46,627 - tensorflow - INFO - loss = 0.011177439, step = 2402400 (2.254 sec) -2022-05-01 22:20:48,879 - tensorflow - INFO - global_step/sec: 88.7988 -2022-05-01 22:20:48,879 - tensorflow - INFO - loss = 0.036629625, step = 2402600 (2.252 sec) -2022-05-01 22:20:51,136 - tensorflow - INFO - global_step/sec: 88.5901 -2022-05-01 22:20:51,137 - tensorflow - INFO - loss = 0.09368414, step = 2402800 (2.258 sec) -2022-05-01 22:20:53,387 - tensorflow - INFO - global_step/sec: 88.8506 -2022-05-01 22:20:53,388 - tensorflow - INFO - loss = 0.07625322, step = 2403000 (2.251 sec) -2022-05-01 22:20:55,643 - tensorflow - INFO - global_step/sec: 88.6679 -2022-05-01 22:20:55,643 - tensorflow - INFO - loss = 0.0075386334, step = 2403200 (2.256 sec) -2022-05-01 22:20:57,896 - tensorflow - INFO - global_step/sec: 88.7743 -2022-05-01 22:20:57,896 - tensorflow - INFO - loss = 0.01009267, step = 2403400 (2.253 sec) -2022-05-01 22:21:00,153 - tensorflow - INFO - global_step/sec: 88.6053 -2022-05-01 22:21:00,153 - tensorflow - INFO - loss = 0.021714091, step = 2403600 (2.257 sec) -2022-05-01 22:21:02,406 - tensorflow - INFO - global_step/sec: 88.7812 -2022-05-01 22:21:02,406 - tensorflow - INFO - loss = 0.03484129, step = 2403800 (2.253 sec) -2022-05-01 22:21:04,653 - tensorflow - INFO - global_step/sec: 88.9997 -2022-05-01 22:21:04,653 - tensorflow - INFO - loss = 0.046384007, step = 2404000 (2.247 sec) -2022-05-01 22:21:06,901 - tensorflow - INFO - global_step/sec: 88.9751 -2022-05-01 22:21:06,901 - tensorflow - INFO - loss = 0.017480731, step = 2404200 (2.248 sec) -2022-05-01 22:21:09,153 - tensorflow - INFO - global_step/sec: 88.8041 -2022-05-01 22:21:09,153 - tensorflow - INFO - loss = 0.02646036, step = 2404400 (2.252 sec) -2022-05-01 22:21:11,411 - tensorflow - INFO - global_step/sec: 88.5687 -2022-05-01 22:21:11,411 - tensorflow - INFO - loss = 0.01452543, step = 2404600 (2.258 sec) -2022-05-01 22:21:13,662 - tensorflow - INFO - global_step/sec: 88.8672 -2022-05-01 22:21:13,662 - tensorflow - INFO - loss = 0.00674339, step = 2404800 (2.251 sec) -2022-05-01 22:21:15,916 - tensorflow - INFO - global_step/sec: 88.7083 -2022-05-01 22:21:15,917 - tensorflow - INFO - loss = 0.013400199, step = 2405000 (2.255 sec) -2022-05-01 22:21:18,168 - tensorflow - INFO - global_step/sec: 88.8052 -2022-05-01 22:21:18,169 - tensorflow - INFO - loss = 0.010974922, step = 2405200 (2.252 sec) -2022-05-01 22:21:20,419 - tensorflow - INFO - global_step/sec: 88.8626 -2022-05-01 22:21:20,419 - tensorflow - INFO - loss = 0.013504675, step = 2405400 (2.251 sec) -2022-05-01 22:21:22,675 - tensorflow - INFO - global_step/sec: 88.6573 -2022-05-01 22:21:22,675 - tensorflow - INFO - loss = 0.07476987, step = 2405600 (2.256 sec) -2022-05-01 22:21:24,939 - tensorflow - INFO - global_step/sec: 88.3407 -2022-05-01 22:21:24,939 - tensorflow - INFO - loss = 0.07896981, step = 2405800 (2.264 sec) -2022-05-01 22:21:27,199 - tensorflow - INFO - global_step/sec: 88.4758 -2022-05-01 22:21:27,200 - tensorflow - INFO - loss = 0.022436867, step = 2406000 (2.260 sec) -2022-05-01 22:21:29,450 - tensorflow - INFO - global_step/sec: 88.8492 -2022-05-01 22:21:29,451 - tensorflow - INFO - loss = 0.012631004, step = 2406200 (2.251 sec) -2022-05-01 22:21:31,712 - tensorflow - INFO - global_step/sec: 88.4304 -2022-05-01 22:21:31,712 - tensorflow - INFO - loss = 0.039282817, step = 2406400 (2.262 sec) -2022-05-01 22:21:33,969 - tensorflow - INFO - global_step/sec: 88.6097 -2022-05-01 22:21:33,969 - tensorflow - INFO - loss = 0.032221712, step = 2406600 (2.257 sec) -2022-05-01 22:21:36,221 - tensorflow - INFO - global_step/sec: 88.7976 -2022-05-01 22:21:36,222 - tensorflow - INFO - loss = 0.025849657, step = 2406800 (2.252 sec) -2022-05-01 22:21:38,481 - tensorflow - INFO - global_step/sec: 88.505 -2022-05-01 22:21:38,482 - tensorflow - INFO - loss = 0.0321095, step = 2407000 (2.260 sec) -2022-05-01 22:21:40,732 - tensorflow - INFO - global_step/sec: 88.8545 -2022-05-01 22:21:40,732 - tensorflow - INFO - loss = 0.024980348, step = 2407200 (2.251 sec) -2022-05-01 22:21:42,986 - tensorflow - INFO - global_step/sec: 88.7484 -2022-05-01 22:21:42,986 - tensorflow - INFO - loss = 0.28917772, step = 2407400 (2.254 sec) -2022-05-01 22:21:45,239 - tensorflow - INFO - global_step/sec: 88.7736 -2022-05-01 22:21:45,239 - tensorflow - INFO - loss = 0.005209112, step = 2407600 (2.253 sec) -2022-05-01 22:21:47,492 - tensorflow - INFO - global_step/sec: 88.7671 -2022-05-01 22:21:47,492 - tensorflow - INFO - loss = 0.020049214, step = 2407800 (2.253 sec) -2022-05-01 22:21:49,750 - tensorflow - INFO - global_step/sec: 88.5526 -2022-05-01 22:21:49,751 - tensorflow - INFO - loss = 0.032050703, step = 2408000 (2.259 sec) -2022-05-01 22:21:52,005 - tensorflow - INFO - global_step/sec: 88.7151 -2022-05-01 22:21:52,005 - tensorflow - INFO - loss = 0.011353227, step = 2408200 (2.254 sec) -2022-05-01 22:21:54,262 - tensorflow - INFO - global_step/sec: 88.6049 -2022-05-01 22:21:54,262 - tensorflow - INFO - loss = 0.00505063, step = 2408400 (2.257 sec) -2022-05-01 22:21:56,519 - tensorflow - INFO - global_step/sec: 88.614 -2022-05-01 22:21:56,519 - tensorflow - INFO - loss = 0.07129796, step = 2408600 (2.257 sec) -2022-05-01 22:21:58,773 - tensorflow - INFO - global_step/sec: 88.7389 -2022-05-01 22:21:58,773 - tensorflow - INFO - loss = 0.04968427, step = 2408800 (2.254 sec) -2022-05-01 22:22:01,032 - tensorflow - INFO - global_step/sec: 88.5163 -2022-05-01 22:22:01,032 - tensorflow - INFO - loss = 0.080510594, step = 2409000 (2.260 sec) -2022-05-01 22:22:03,290 - tensorflow - INFO - global_step/sec: 88.5805 -2022-05-01 22:22:03,290 - tensorflow - INFO - loss = 0.015240785, step = 2409200 (2.258 sec) -2022-05-01 22:22:05,541 - tensorflow - INFO - global_step/sec: 88.8291 -2022-05-01 22:22:05,542 - tensorflow - INFO - loss = 0.041671883, step = 2409400 (2.252 sec) -2022-05-01 22:22:07,792 - tensorflow - INFO - global_step/sec: 88.8674 -2022-05-01 22:22:07,792 - tensorflow - INFO - loss = 0.016763734, step = 2409600 (2.251 sec) -2022-05-01 22:22:10,045 - tensorflow - INFO - global_step/sec: 88.7713 -2022-05-01 22:22:10,045 - tensorflow - INFO - loss = 0.022382911, step = 2409800 (2.253 sec) -2022-05-01 22:22:12,304 - tensorflow - INFO - global_step/sec: 88.517 -2022-05-01 22:22:12,305 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.4631 -2022-05-01 22:22:28,084 - tensorflow - INFO - loss = 0.0067545497, step = 2411400 (2.261 sec) -2022-05-01 22:22:30,341 - tensorflow - INFO - global_step/sec: 88.5863 -2022-05-01 22:22:30,341 - tensorflow - INFO - loss = 0.0063256193, step = 2411600 (2.258 sec) -2022-05-01 22:22:32,599 - tensorflow - INFO - global_step/sec: 88.5583 -2022-05-01 22:22:32,600 - tensorflow - INFO - loss = 0.044202406, step = 2411800 (2.258 sec) -2022-05-01 22:22:34,850 - tensorflow - INFO - global_step/sec: 88.8734 -2022-05-01 22:22:34,850 - tensorflow - INFO - loss = 0.05210459, step = 2412000 (2.250 sec) -2022-05-01 22:22:37,102 - tensorflow - INFO - global_step/sec: 88.8103 -2022-05-01 22:22:37,102 - tensorflow - INFO - loss = 0.07206778, step = 2412200 (2.252 sec) -2022-05-01 22:22:39,351 - tensorflow - INFO - global_step/sec: 88.9343 -2022-05-01 22:22:39,351 - tensorflow - INFO - loss = 0.028545413, step = 2412400 (2.249 sec) -2022-05-01 22:22:41,604 - tensorflow - INFO - global_step/sec: 88.7339 -2022-05-01 22:22:41,605 - tensorflow - INFO - loss = 0.00634375, step = 2412600 (2.254 sec) -2022-05-01 22:22:43,859 - tensorflow - INFO - global_step/sec: 88.7042 -2022-05-01 22:22:43,860 - tensorflow - INFO - loss = 0.016428966, step = 2412800 (2.255 sec) -2022-05-01 22:22:46,108 - tensorflow - INFO - global_step/sec: 88.9321 -2022-05-01 22:22:46,108 - tensorflow - INFO - loss = 0.007920577, step = 2413000 (2.249 sec) -2022-05-01 22:22:48,365 - tensorflow - INFO - global_step/sec: 88.626 -2022-05-01 22:22:48,365 - tensorflow - INFO - loss = 0.04073445, step = 2413200 (2.257 sec) -2022-05-01 22:22:50,617 - tensorflow - INFO - global_step/sec: 88.7912 -2022-05-01 22:22:50,618 - tensorflow - INFO - loss = 0.013052452, step = 2413400 (2.253 sec) -2022-05-01 22:22:52,872 - tensorflow - INFO - global_step/sec: 88.6946 -2022-05-01 22:22:52,873 - tensorflow - INFO - loss = 0.01770258, step = 2413600 (2.255 sec) -2022-05-01 22:22:55,129 - tensorflow - INFO - global_step/sec: 88.6314 -2022-05-01 22:22:55,129 - tensorflow - INFO - loss = 0.051142707, step = 2413800 (2.257 sec) -2022-05-01 22:22:57,382 - tensorflow - INFO - global_step/sec: 88.7708 -2022-05-01 22:22:57,382 - tensorflow - INFO - loss = 4.831999, step = 2414000 (2.253 sec) -2022-05-01 22:22:59,637 - tensorflow - INFO - global_step/sec: 88.6601 -2022-05-01 22:22:59,638 - tensorflow - INFO - loss = 0.01260927, step = 2414200 (2.256 sec) -2022-05-01 22:23:01,888 - tensorflow - INFO - global_step/sec: 88.8853 -2022-05-01 22:23:01,888 - tensorflow - INFO - loss = 0.010449916, step = 2414400 (2.250 sec) -2022-05-01 22:23:04,140 - tensorflow - INFO - global_step/sec: 88.7775 -2022-05-01 22:23:04,141 - tensorflow - INFO - loss = 0.010623086, step = 2414600 (2.253 sec) -2022-05-01 22:23:06,398 - tensorflow - INFO - global_step/sec: 88.5814 -2022-05-01 22:23:06,399 - tensorflow - INFO - loss = 0.016910218, step = 2414800 (2.258 sec) -2022-05-01 22:23:08,657 - tensorflow - INFO - global_step/sec: 88.5485 -2022-05-01 22:23:08,657 - tensorflow - INFO - loss = 0.013798049, step = 2415000 (2.259 sec) -2022-05-01 22:23:10,910 - tensorflow - INFO - global_step/sec: 88.7463 -2022-05-01 22:23:10,911 - tensorflow - INFO - loss = 0.0064181024, step = 2415200 (2.254 sec) -2022-05-01 22:23:13,160 - tensorflow - INFO - global_step/sec: 88.9038 -2022-05-01 22:23:13,160 - tensorflow - INFO - loss = 0.007887255, step = 2415400 (2.250 sec) -2022-05-01 22:23:15,415 - tensorflow - INFO - global_step/sec: 88.6954 -2022-05-01 22:23:15,415 - tensorflow - INFO - loss = 0.010746698, step = 2415600 (2.255 sec) -2022-05-01 22:23:17,675 - tensorflow - INFO - global_step/sec: 88.4763 -2022-05-01 22:23:17,676 - tensorflow - INFO - loss = 0.033685423, step = 2415800 (2.260 sec) -2022-05-01 22:23:19,928 - tensorflow - INFO - global_step/sec: 88.7741 -2022-05-01 22:23:19,929 - tensorflow - INFO - loss = 0.010350782, step = 2416000 (2.253 sec) -2022-05-01 22:23:22,191 - tensorflow - INFO - global_step/sec: 88.4083 -2022-05-01 22:23:22,191 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.5215 -2022-05-01 22:23:38,032 - tensorflow - INFO - loss = 0.010946408, step = 2417600 (2.259 sec) -2022-05-01 22:23:40,292 - tensorflow - INFO - global_step/sec: 88.5035 -2022-05-01 22:23:40,292 - tensorflow - INFO - loss = 0.040977396, step = 2417800 (2.260 sec) -2022-05-01 22:23:42,552 - tensorflow - INFO - global_step/sec: 88.5014 -2022-05-01 22:23:42,552 - tensorflow - INFO - loss = 0.019095192, step = 2418000 (2.260 sec) -2022-05-01 22:23:44,816 - tensorflow - INFO - global_step/sec: 88.3294 -2022-05-01 22:23:44,816 - tensorflow - INFO - loss = 0.030950455, step = 2418200 (2.264 sec) -2022-05-01 22:23:47,082 - tensorflow - INFO - global_step/sec: 88.2481 -2022-05-01 22:23:47,083 - tensorflow - INFO - loss = 0.025897225, step = 2418400 (2.266 sec) -2022-05-01 22:23:49,340 - tensorflow - INFO - global_step/sec: 88.5658 -2022-05-01 22:23:49,341 - tensorflow - INFO - loss = 0.04686994, step = 2418600 (2.258 sec) -2022-05-01 22:23:51,593 - tensorflow - INFO - global_step/sec: 88.7811 -2022-05-01 22:23:51,594 - tensorflow - INFO - loss = 0.012097688, step = 2418800 (2.253 sec) -2022-05-01 22:23:53,845 - tensorflow - INFO - global_step/sec: 88.8195 -2022-05-01 22:23:53,845 - tensorflow - INFO - loss = 0.018058829, step = 2419000 (2.252 sec) -2022-05-01 22:23:56,101 - tensorflow - INFO - global_step/sec: 88.6559 -2022-05-01 22:23:56,101 - tensorflow - INFO - loss = 0.005407006, step = 2419200 (2.256 sec) -2022-05-01 22:23:58,361 - tensorflow - INFO - global_step/sec: 88.4868 -2022-05-01 22:23:58,362 - tensorflow - INFO - loss = 0.010135386, step = 2419400 (2.260 sec) -2022-05-01 22:24:00,615 - tensorflow - INFO - global_step/sec: 88.7319 -2022-05-01 22:24:00,616 - tensorflow - INFO - loss = 0.010889774, step = 2419600 (2.254 sec) -2022-05-01 22:24:02,879 - tensorflow - INFO - global_step/sec: 88.332 -2022-05-01 22:24:02,880 - tensorflow - INFO - loss = 0.014974509, step = 2419800 (2.264 sec) -2022-05-01 22:24:05,134 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.06388452, step = 2421200 (2.252 sec) -2022-05-01 22:24:20,916 - tensorflow - INFO - global_step/sec: 88.7929 -2022-05-01 22:24:20,917 - tensorflow - INFO - loss = 0.025522318, step = 2421400 (2.252 sec) -2022-05-01 22:24:23,175 - tensorflow - INFO - global_step/sec: 88.5368 -2022-05-01 22:24:23,176 - tensorflow - INFO - loss = 0.01230228, step = 2421600 (2.259 sec) -2022-05-01 22:24:25,431 - tensorflow - INFO - global_step/sec: 88.6837 -2022-05-01 22:24:25,431 - tensorflow - INFO - loss = 0.075653605, step = 2421800 (2.255 sec) -2022-05-01 22:24:27,681 - tensorflow - INFO - global_step/sec: 88.8626 -2022-05-01 22:24:27,682 - tensorflow - INFO - loss = 0.025689162, step = 2422000 (2.251 sec) -2022-05-01 22:24:29,939 - tensorflow - INFO - global_step/sec: 88.6005 -2022-05-01 22:24:29,939 - tensorflow - INFO - loss = 0.3590022, step = 2422200 (2.257 sec) -2022-05-01 22:24:32,203 - tensorflow - INFO - global_step/sec: 88.33 -2022-05-01 22:24:32,203 - tensorflow - INFO - loss = 0.04820884, step = 2422400 (2.264 sec) -2022-05-01 22:24:34,456 - tensorflow - INFO - global_step/sec: 88.743 -2022-05-01 22:24:34,457 - tensorflow - INFO - loss = 0.00656184, step = 2422600 (2.254 sec) -2022-05-01 22:24:36,711 - tensorflow - INFO - global_step/sec: 88.693 -2022-05-01 22:24:36,712 - tensorflow - INFO - loss = 0.007785233, step = 2422800 (2.255 sec) -2022-05-01 22:24:38,963 - tensorflow - INFO - global_step/sec: 88.8087 -2022-05-01 22:24:38,964 - tensorflow - INFO - loss = 0.018070709, step = 2423000 (2.252 sec) -2022-05-01 22:24:41,218 - tensorflow - INFO - global_step/sec: 88.7246 -2022-05-01 22:24:41,218 - tensorflow - INFO - loss = 0.007902175, step = 2423200 (2.254 sec) -2022-05-01 22:24:43,470 - tensorflow - INFO - global_step/sec: 88.7828 -2022-05-01 22:24:43,471 - tensorflow - INFO - loss = 0.018189617, step = 2423400 (2.253 sec) -2022-05-01 22:24:45,724 - tensorflow - INFO - global_step/sec: 88.7473 -2022-05-01 22:24:45,724 - tensorflow - INFO - loss = 0.03778987, step = 2423600 (2.254 sec) -2022-05-01 22:24:47,980 - tensorflow - INFO - global_step/sec: 88.6356 -2022-05-01 22:24:47,981 - tensorflow - INFO - loss = 0.009193819, step = 2423800 (2.256 sec) -2022-05-01 22:24:50,236 - tensorflow - INFO - global_step/sec: 88.6787 -2022-05-01 22:24:50,236 - tensorflow - INFO - loss = 0.22480245, step = 2424000 (2.255 sec) -2022-05-01 22:24:52,490 - tensorflow - INFO - global_step/sec: 88.7344 -2022-05-01 22:24:52,490 - tensorflow - INFO - loss = 0.037477303, step = 2424200 (2.254 sec) -2022-05-01 22:24:54,742 - tensorflow - INFO - global_step/sec: 88.7901 -2022-05-01 22:24:54,742 - tensorflow - INFO - loss = 0.065649144, step = 2424400 (2.253 sec) -2022-05-01 22:24:56,996 - tensorflow - INFO - global_step/sec: 88.747 -2022-05-01 22:24:56,996 - tensorflow - INFO - loss = 0.014988281, step = 2424600 (2.254 sec) -2022-05-01 22:24:59,247 - tensorflow - INFO - global_step/sec: 88.8217 -2022-05-01 22:24:59,248 - tensorflow - INFO - loss = 0.010490783, step = 2424800 (2.252 sec) -2022-05-01 22:25:01,505 - tensorflow - INFO - global_step/sec: 88.5808 -2022-05-01 22:25:01,506 - tensorflow - INFO - loss = 0.01600422, step = 2425000 (2.258 sec) -2022-05-01 22:25:03,761 - tensorflow - INFO - global_step/sec: 88.6668 -2022-05-01 22:25:03,761 - tensorflow - INFO - loss = 0.021280637, step = 2425200 (2.255 sec) -2022-05-01 22:25:06,014 - tensorflow - INFO - global_step/sec: 88.7725 -2022-05-01 22:25:06,014 - tensorflow - INFO - loss = 0.039950836, step = 2425400 (2.253 sec) -2022-05-01 22:25:08,268 - tensorflow - INFO - global_step/sec: 88.7166 -2022-05-01 22:25:08,269 - tensorflow - INFO - loss = 0.11885853, step = 2425600 (2.254 sec) -2022-05-01 22:25:10,520 - tensorflow - INFO - global_step/sec: 88.821 -2022-05-01 22:25:10,520 - tensorflow - INFO - loss = 0.0047355983, step = 2425800 (2.252 sec) -2022-05-01 22:25:12,776 - tensorflow - INFO - global_step/sec: 88.6627 -2022-05-01 22:25:12,776 - tensorflow - INFO - loss = 0.015703008, step = 2426000 (2.256 sec) -2022-05-01 22:25:15,026 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.022055803, step = 2427400 (2.258 sec) -2022-05-01 22:25:30,819 - tensorflow - INFO - global_step/sec: 88.3754 -2022-05-01 22:25:30,819 - tensorflow - INFO - loss = 0.0149949975, step = 2427600 (2.263 sec) -2022-05-01 22:25:33,079 - tensorflow - INFO - global_step/sec: 88.4682 -2022-05-01 22:25:33,080 - tensorflow - INFO - loss = 0.030170036, step = 2427800 (2.261 sec) -2022-05-01 22:25:35,335 - tensorflow - INFO - global_step/sec: 88.6683 -2022-05-01 22:25:35,335 - tensorflow - INFO - loss = 0.033823244, step = 2428000 (2.256 sec) -2022-05-01 22:25:37,592 - tensorflow - INFO - global_step/sec: 88.6136 -2022-05-01 22:25:37,592 - tensorflow - INFO - loss = 0.36012655, step = 2428200 (2.257 sec) -2022-05-01 22:25:39,849 - tensorflow - INFO - global_step/sec: 88.5972 -2022-05-01 22:25:39,850 - tensorflow - INFO - loss = 0.020032069, step = 2428400 (2.257 sec) -2022-05-01 22:25:42,103 - tensorflow - INFO - global_step/sec: 88.725 -2022-05-01 22:25:42,104 - tensorflow - INFO - loss = 0.012113841, step = 2428600 (2.254 sec) -2022-05-01 22:25:44,358 - tensorflow - INFO - global_step/sec: 88.7113 -2022-05-01 22:25:44,358 - tensorflow - INFO - loss = 0.0061306115, step = 2428800 (2.255 sec) -2022-05-01 22:25:46,609 - tensorflow - INFO - global_step/sec: 88.8417 -2022-05-01 22:25:46,610 - tensorflow - INFO - loss = 0.010476081, step = 2429000 (2.251 sec) -2022-05-01 22:25:48,860 - tensorflow - INFO - global_step/sec: 88.8504 -2022-05-01 22:25:48,861 - tensorflow - INFO - loss = 0.012106378, step = 2429200 (2.251 sec) -2022-05-01 22:25:51,122 - tensorflow - INFO - global_step/sec: 88.4123 -2022-05-01 22:25:51,123 - tensorflow - INFO - loss = 0.12133789, step = 2429400 (2.262 sec) -2022-05-01 22:25:53,375 - tensorflow - INFO - global_step/sec: 88.786 -2022-05-01 22:25:53,375 - tensorflow - INFO - loss = 0.013075585, step = 2429600 (2.253 sec) -2022-05-01 22:25:55,626 - tensorflow - INFO - global_step/sec: 88.8334 -2022-05-01 22:25:55,627 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.9791 -2022-05-01 22:26:11,413 - tensorflow - INFO - loss = 0.008713246, step = 2431200 (2.248 sec) -2022-05-01 22:26:13,670 - tensorflow - INFO - global_step/sec: 88.5915 -2022-05-01 22:26:13,670 - tensorflow - INFO - loss = 0.016446285, step = 2431400 (2.258 sec) -2022-05-01 22:26:15,931 - tensorflow - INFO - global_step/sec: 88.4519 -2022-05-01 22:26:15,931 - tensorflow - INFO - loss = 0.32633343, step = 2431600 (2.261 sec) -2022-05-01 22:26:18,183 - tensorflow - INFO - global_step/sec: 88.8223 -2022-05-01 22:26:18,183 - tensorflow - INFO - loss = 0.008611464, step = 2431800 (2.252 sec) -2022-05-01 22:26:20,434 - tensorflow - INFO - global_step/sec: 88.8315 -2022-05-01 22:26:20,435 - tensorflow - INFO - loss = 0.014535236, step = 2432000 (2.251 sec) -2022-05-01 22:26:22,691 - tensorflow - INFO - global_step/sec: 88.6179 -2022-05-01 22:26:22,692 - tensorflow - INFO - loss = 0.17081915, step = 2432200 (2.257 sec) -2022-05-01 22:26:24,948 - tensorflow - INFO - global_step/sec: 88.6224 -2022-05-01 22:26:24,948 - tensorflow - INFO - loss = 0.004440788, step = 2432400 (2.257 sec) -2022-05-01 22:26:27,203 - tensorflow - INFO - global_step/sec: 88.6937 -2022-05-01 22:26:27,203 - tensorflow - INFO - loss = 0.014876054, step = 2432600 (2.255 sec) -2022-05-01 22:26:29,463 - tensorflow - INFO - global_step/sec: 88.4734 -2022-05-01 22:26:29,464 - tensorflow - INFO - loss = 0.011966743, step = 2432800 (2.261 sec) -2022-05-01 22:26:31,726 - tensorflow - INFO - global_step/sec: 88.3975 -2022-05-01 22:26:31,726 - tensorflow - INFO - loss = 0.0031124237, step = 2433000 (2.263 sec) -2022-05-01 22:26:33,982 - tensorflow - INFO - global_step/sec: 88.6325 -2022-05-01 22:26:33,983 - tensorflow - INFO - loss = 0.24724612, step = 2433200 (2.257 sec) -2022-05-01 22:26:36,240 - tensorflow - INFO - global_step/sec: 88.5953 -2022-05-01 22:26:36,240 - tensorflow - INFO - loss = 0.07949506, step = 2433400 (2.257 sec) -2022-05-01 22:26:38,497 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.038247883, step = 2434800 (2.260 sec) -2022-05-01 22:26:54,297 - tensorflow - INFO - global_step/sec: 88.3733 -2022-05-01 22:26:54,297 - tensorflow - INFO - loss = 0.008712643, step = 2435000 (2.263 sec) -2022-05-01 22:26:56,564 - tensorflow - INFO - global_step/sec: 88.2227 -2022-05-01 22:26:56,564 - tensorflow - INFO - loss = 0.017594587, step = 2435200 (2.267 sec) -2022-05-01 22:26:58,821 - tensorflow - INFO - global_step/sec: 88.6141 -2022-05-01 22:26:58,821 - tensorflow - INFO - loss = 0.0076810466, step = 2435400 (2.257 sec) -2022-05-01 22:27:01,082 - tensorflow - INFO - global_step/sec: 88.4511 -2022-05-01 22:27:01,082 - tensorflow - INFO - loss = 0.0088895075, step = 2435600 (2.261 sec) -2022-05-01 22:27:03,342 - tensorflow - INFO - global_step/sec: 88.4914 -2022-05-01 22:27:03,342 - tensorflow - INFO - loss = 0.009010309, step = 2435800 (2.260 sec) -2022-05-01 22:27:05,605 - tensorflow - INFO - global_step/sec: 88.3706 -2022-05-01 22:27:05,605 - tensorflow - INFO - loss = 0.39461958, step = 2436000 (2.263 sec) -2022-05-01 22:27:07,864 - tensorflow - INFO - global_step/sec: 88.52 -2022-05-01 22:27:07,865 - tensorflow - INFO - loss = 0.22795394, step = 2436200 (2.259 sec) -2022-05-01 22:27:10,124 - tensorflow - INFO - global_step/sec: 88.5009 -2022-05-01 22:27:10,125 - tensorflow - INFO - loss = 0.026495546, step = 2436400 (2.260 sec) -2022-05-01 22:27:12,384 - tensorflow - INFO - global_step/sec: 88.5125 -2022-05-01 22:27:12,384 - tensorflow - INFO - loss = 0.015892439, step = 2436600 (2.260 sec) -2022-05-01 22:27:14,644 - tensorflow - INFO - global_step/sec: 88.4907 -2022-05-01 22:27:14,644 - tensorflow - INFO - loss = 0.009224594, step = 2436800 (2.260 sec) -2022-05-01 22:27:16,904 - tensorflow - INFO - global_step/sec: 88.4822 -2022-05-01 22:27:16,905 - tensorflow - INFO - loss = 0.020486783, step = 2437000 (2.260 sec) -2022-05-01 22:27:19,167 - tensorflow - INFO - global_step/sec: 88.4028 -2022-05-01 22:27:19,167 - tensorflow - INFO - loss = 0.25998735, step = 2437200 (2.262 sec) -2022-05-01 22:27:21,423 - tensorflow - INFO - global_step/sec: 88.6533 -2022-05-01 22:27:21,423 - tensorflow - INFO - loss = 0.037896115, step = 2437400 (2.256 sec) -2022-05-01 22:27:23,685 - tensorflow - INFO - global_step/sec: 88.3866 -2022-05-01 22:27:23,686 - tensorflow - INFO - loss = 0.022613788, step = 2437600 (2.263 sec) -2022-05-01 22:27:25,938 - tensorflow - INFO - global_step/sec: 88.7694 -2022-05-01 22:27:25,939 - tensorflow - INFO - loss = 0.011877377, step = 2437800 (2.253 sec) -2022-05-01 22:27:28,192 - tensorflow - INFO - global_step/sec: 88.7644 -2022-05-01 22:27:28,192 - tensorflow - INFO - loss = 0.01136734, step = 2438000 (2.253 sec) -2022-05-01 22:27:30,448 - tensorflow - INFO - global_step/sec: 88.6327 -2022-05-01 22:27:30,448 - tensorflow - INFO - loss = 0.102835014, step = 2438200 (2.257 sec) -2022-05-01 22:27:32,702 - tensorflow - INFO - global_step/sec: 88.7245 -2022-05-01 22:27:32,703 - tensorflow - INFO - loss = 0.024789814, step = 2438400 (2.254 sec) -2022-05-01 22:27:34,959 - tensorflow - INFO - global_step/sec: 88.6221 -2022-05-01 22:27:34,959 - tensorflow - INFO - loss = 0.60774803, step = 2438600 (2.257 sec) -2022-05-01 22:27:37,213 - tensorflow - INFO - global_step/sec: 88.7369 -2022-05-01 22:27:37,213 - tensorflow - INFO - loss = 0.009254277, step = 2438800 (2.254 sec) -2022-05-01 22:27:39,467 - tensorflow - INFO - global_step/sec: 88.7076 -2022-05-01 22:27:39,468 - tensorflow - INFO - loss = 0.019108947, step = 2439000 (2.255 sec) -2022-05-01 22:27:41,722 - tensorflow - INFO - global_step/sec: 88.716 -2022-05-01 22:27:41,722 - tensorflow - INFO - loss = 0.021962412, step = 2439200 (2.254 sec) -2022-05-01 22:27:43,972 - tensorflow - INFO - global_step/sec: 88.8979 -2022-05-01 22:27:43,972 - tensorflow - INFO - loss = 0.0046605812, step = 2439400 (2.250 sec) -2022-05-01 22:27:46,223 - tensorflow - INFO - global_step/sec: 88.823 -2022-05-01 22:27:46,224 - tensorflow - INFO - loss = 0.02036841, step = 2439600 (2.252 sec) -2022-05-01 22:27:48,481 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.018889189, step = 2441000 (2.259 sec) -2022-05-01 22:28:04,258 - tensorflow - INFO - global_step/sec: 88.8125 -2022-05-01 22:28:04,258 - tensorflow - INFO - loss = 0.020466503, step = 2441200 (2.252 sec) -2022-05-01 22:28:06,520 - tensorflow - INFO - global_step/sec: 88.4021 -2022-05-01 22:28:06,521 - tensorflow - INFO - loss = 0.14971241, step = 2441400 (2.262 sec) -2022-05-01 22:28:08,777 - tensorflow - INFO - global_step/sec: 88.6368 -2022-05-01 22:28:08,777 - tensorflow - INFO - loss = 0.015328262, step = 2441600 (2.256 sec) -2022-05-01 22:28:11,028 - tensorflow - INFO - global_step/sec: 88.8224 -2022-05-01 22:28:11,029 - tensorflow - INFO - loss = 0.03621527, step = 2441800 (2.252 sec) -2022-05-01 22:28:13,284 - tensorflow - INFO - global_step/sec: 88.6748 -2022-05-01 22:28:13,284 - tensorflow - INFO - loss = 0.021771828, step = 2442000 (2.255 sec) -2022-05-01 22:28:15,537 - tensorflow - INFO - global_step/sec: 88.7476 -2022-05-01 22:28:15,538 - tensorflow - INFO - loss = 0.013880587, step = 2442200 (2.254 sec) -2022-05-01 22:28:17,790 - tensorflow - INFO - global_step/sec: 88.8041 -2022-05-01 22:28:17,790 - tensorflow - INFO - loss = 0.004753161, step = 2442400 (2.252 sec) -2022-05-01 22:28:20,040 - tensorflow - INFO - global_step/sec: 88.8679 -2022-05-01 22:28:20,041 - tensorflow - INFO - loss = 0.0054917885, step = 2442600 (2.251 sec) -2022-05-01 22:28:22,294 - tensorflow - INFO - global_step/sec: 88.7443 -2022-05-01 22:28:22,294 - tensorflow - INFO - loss = 0.007898835, step = 2442800 (2.254 sec) -2022-05-01 22:28:24,546 - tensorflow - INFO - global_step/sec: 88.8188 -2022-05-01 22:28:24,546 - tensorflow - INFO - loss = 0.028259933, step = 2443000 (2.252 sec) -2022-05-01 22:28:26,802 - tensorflow - INFO - global_step/sec: 88.6237 -2022-05-01 22:28:26,803 - tensorflow - INFO - loss = 0.013659278, step = 2443200 (2.257 sec) -2022-05-01 22:28:29,057 - tensorflow - INFO - global_step/sec: 88.719 -2022-05-01 22:28:29,057 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.7621 -2022-05-01 22:28:44,837 - tensorflow - INFO - loss = 0.01237338, step = 2444800 (2.253 sec) -2022-05-01 22:28:47,088 - tensorflow - INFO - global_step/sec: 88.8243 -2022-05-01 22:28:47,088 - tensorflow - INFO - loss = 0.010459521, step = 2445000 (2.252 sec) -2022-05-01 22:28:49,344 - tensorflow - INFO - global_step/sec: 88.6343 -2022-05-01 22:28:49,345 - tensorflow - INFO - loss = 0.02367907, step = 2445200 (2.256 sec) -2022-05-01 22:28:51,604 - tensorflow - INFO - global_step/sec: 88.5057 -2022-05-01 22:28:51,604 - tensorflow - INFO - loss = 0.023524862, step = 2445400 (2.260 sec) -2022-05-01 22:28:53,855 - tensorflow - INFO - global_step/sec: 88.8334 -2022-05-01 22:28:53,856 - tensorflow - INFO - loss = 0.07011649, step = 2445600 (2.251 sec) -2022-05-01 22:28:56,108 - tensorflow - INFO - global_step/sec: 88.7745 -2022-05-01 22:28:56,109 - tensorflow - INFO - loss = 0.011394534, step = 2445800 (2.253 sec) -2022-05-01 22:28:58,360 - tensorflow - INFO - global_step/sec: 88.8141 -2022-05-01 22:28:58,361 - tensorflow - INFO - loss = 0.010070166, step = 2446000 (2.252 sec) -2022-05-01 22:29:00,611 - tensorflow - INFO - global_step/sec: 88.8679 -2022-05-01 22:29:00,611 - tensorflow - INFO - loss = 0.016251925, step = 2446200 (2.251 sec) -2022-05-01 22:29:02,868 - tensorflow - INFO - global_step/sec: 88.6177 -2022-05-01 22:29:02,868 - tensorflow - INFO - loss = 0.023029368, step = 2446400 (2.257 sec) -2022-05-01 22:29:05,119 - tensorflow - INFO - global_step/sec: 88.8504 -2022-05-01 22:29:05,119 - tensorflow - INFO - loss = 0.029826157, step = 2446600 (2.251 sec) -2022-05-01 22:29:07,378 - tensorflow - INFO - global_step/sec: 88.5366 -2022-05-01 22:29:07,378 - tensorflow - INFO - loss = 0.012460985, step = 2446800 (2.259 sec) -2022-05-01 22:29:09,629 - tensorflow - INFO - global_step/sec: 88.8393 -2022-05-01 22:29:09,629 - tensorflow - INFO - loss = 0.052463446, step = 2447000 (2.251 sec) -2022-05-01 22:29:11,884 - tensorflow - INFO - 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INFO - loss = 0.0068839383, step = 2449600 (2.251 sec) -2022-05-01 22:29:41,179 - tensorflow - INFO - global_step/sec: 88.6605 -2022-05-01 22:29:41,180 - tensorflow - INFO - loss = 0.02295091, step = 2449800 (2.256 sec) -2022-05-01 22:29:43,429 - tensorflow - INFO - global_step/sec: 88.9152 -2022-05-01 22:29:43,430 - tensorflow - INFO - loss = 0.0068595465, step = 2450000 (2.250 sec) -2022-05-01 22:29:45,680 - tensorflow - INFO - global_step/sec: 88.8375 -2022-05-01 22:29:45,681 - tensorflow - INFO - loss = 0.004242505, step = 2450200 (2.251 sec) -2022-05-01 22:29:47,933 - tensorflow - INFO - global_step/sec: 88.7797 -2022-05-01 22:29:47,933 - tensorflow - INFO - loss = 0.018911486, step = 2450400 (2.253 sec) -2022-05-01 22:29:50,188 - tensorflow - INFO - global_step/sec: 88.6806 -2022-05-01 22:29:50,189 - tensorflow - INFO - loss = 0.015513633, step = 2450600 (2.255 sec) -2022-05-01 22:29:52,439 - tensorflow - INFO - global_step/sec: 88.8485 -2022-05-01 22:29:52,440 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.561 -2022-05-01 22:30:08,230 - tensorflow - INFO - loss = 0.014634468, step = 2452200 (2.258 sec) -2022-05-01 22:30:10,483 - tensorflow - INFO - global_step/sec: 88.773 -2022-05-01 22:30:10,483 - tensorflow - INFO - loss = 0.01357374, step = 2452400 (2.253 sec) -2022-05-01 22:30:12,732 - tensorflow - INFO - global_step/sec: 88.9067 -2022-05-01 22:30:12,733 - tensorflow - INFO - loss = 0.018299773, step = 2452600 (2.250 sec) -2022-05-01 22:30:14,986 - tensorflow - INFO - global_step/sec: 88.741 -2022-05-01 22:30:14,986 - tensorflow - INFO - loss = 0.006838115, step = 2452800 (2.254 sec) -2022-05-01 22:30:17,240 - tensorflow - INFO - global_step/sec: 88.746 -2022-05-01 22:30:17,240 - tensorflow - INFO - loss = 0.12840974, step = 2453000 (2.254 sec) -2022-05-01 22:30:19,492 - tensorflow - INFO - global_step/sec: 88.7793 -2022-05-01 22:30:19,493 - tensorflow - INFO - loss = 0.011005908, step = 2453200 (2.253 sec) -2022-05-01 22:30:21,751 - tensorflow - INFO - global_step/sec: 88.5557 -2022-05-01 22:30:21,751 - tensorflow - INFO - loss = 0.054185353, step = 2453400 (2.258 sec) -2022-05-01 22:30:24,014 - tensorflow - INFO - global_step/sec: 88.3793 -2022-05-01 22:30:24,014 - tensorflow - INFO - loss = 0.050072975, step = 2453600 (2.263 sec) -2022-05-01 22:30:26,272 - tensorflow - INFO - global_step/sec: 88.5699 -2022-05-01 22:30:26,272 - tensorflow - INFO - loss = 0.017814076, step = 2453800 (2.258 sec) -2022-05-01 22:30:28,533 - tensorflow - INFO - global_step/sec: 88.4725 -2022-05-01 22:30:28,533 - tensorflow - INFO - loss = 0.0115552675, step = 2454000 (2.261 sec) -2022-05-01 22:30:30,803 - tensorflow - INFO - global_step/sec: 88.1052 -2022-05-01 22:30:30,803 - tensorflow - INFO - loss = 0.025704335, step = 2454200 (2.270 sec) -2022-05-01 22:30:33,063 - tensorflow - INFO - global_step/sec: 88.4666 -2022-05-01 22:30:33,064 - tensorflow - INFO - loss = 0.007582742, step = 2454400 (2.261 sec) -2022-05-01 22:30:35,327 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.02097673, step = 2455800 (2.260 sec) -2022-05-01 22:30:51,164 - tensorflow - INFO - global_step/sec: 88.6419 -2022-05-01 22:30:51,165 - tensorflow - INFO - loss = 0.012825205, step = 2456000 (2.256 sec) -2022-05-01 22:30:53,414 - tensorflow - INFO - global_step/sec: 88.8868 -2022-05-01 22:30:53,415 - tensorflow - INFO - loss = 0.015099917, step = 2456200 (2.250 sec) -2022-05-01 22:30:55,674 - tensorflow - INFO - global_step/sec: 88.5171 -2022-05-01 22:30:55,674 - tensorflow - INFO - loss = 0.010448134, step = 2456400 (2.260 sec) -2022-05-01 22:30:57,931 - tensorflow - INFO - global_step/sec: 88.5842 -2022-05-01 22:30:57,932 - tensorflow - INFO - loss = 0.010750689, step = 2456600 (2.258 sec) -2022-05-01 22:31:00,187 - tensorflow - INFO - global_step/sec: 88.6547 -2022-05-01 22:31:00,188 - tensorflow - INFO - loss = 0.03213253, step = 2456800 (2.256 sec) -2022-05-01 22:31:02,438 - tensorflow - INFO - global_step/sec: 88.866 -2022-05-01 22:31:02,438 - tensorflow - INFO - loss = 0.030992215, step = 2457000 (2.251 sec) -2022-05-01 22:31:04,693 - tensorflow - INFO - global_step/sec: 88.6988 -2022-05-01 22:31:04,693 - tensorflow - INFO - loss = 0.019934999, step = 2457200 (2.255 sec) -2022-05-01 22:31:06,949 - tensorflow - INFO - global_step/sec: 88.6582 -2022-05-01 22:31:06,949 - tensorflow - INFO - loss = 0.009189779, step = 2457400 (2.256 sec) -2022-05-01 22:31:09,201 - tensorflow - INFO - global_step/sec: 88.7951 -2022-05-01 22:31:09,201 - tensorflow - INFO - loss = 0.021401089, step = 2457600 (2.252 sec) -2022-05-01 22:31:11,456 - tensorflow - INFO - global_step/sec: 88.6949 -2022-05-01 22:31:11,456 - tensorflow - INFO - loss = 0.020945026, step = 2457800 (2.255 sec) -2022-05-01 22:31:13,708 - tensorflow - INFO - global_step/sec: 88.8239 -2022-05-01 22:31:13,708 - tensorflow - INFO - loss = 0.025445186, step = 2458000 (2.252 sec) -2022-05-01 22:31:15,960 - tensorflow - INFO - global_step/sec: 88.7913 -2022-05-01 22:31:15,960 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.3728 -2022-05-01 22:31:31,760 - tensorflow - INFO - loss = 0.0067091575, step = 2459600 (2.263 sec) -2022-05-01 22:31:34,010 - tensorflow - INFO - global_step/sec: 88.8492 -2022-05-01 22:31:34,011 - tensorflow - INFO - loss = 0.11180801, step = 2459800 (2.251 sec) -2022-05-01 22:31:36,270 - tensorflow - INFO - global_step/sec: 88.4965 -2022-05-01 22:31:36,271 - tensorflow - INFO - loss = 0.0076923836, step = 2460000 (2.260 sec) -2022-05-01 22:31:38,527 - tensorflow - INFO - global_step/sec: 88.6318 -2022-05-01 22:31:38,527 - tensorflow - INFO - loss = 0.003023584, step = 2460200 (2.256 sec) -2022-05-01 22:31:40,780 - tensorflow - INFO - global_step/sec: 88.7695 -2022-05-01 22:31:40,780 - tensorflow - INFO - loss = 0.016734779, step = 2460400 (2.253 sec) -2022-05-01 22:31:43,032 - tensorflow - INFO - global_step/sec: 88.8001 -2022-05-01 22:31:43,033 - tensorflow - INFO - loss = 0.009536225, step = 2460600 (2.252 sec) -2022-05-01 22:31:45,289 - tensorflow - INFO - global_step/sec: 88.6013 -2022-05-01 22:31:45,290 - tensorflow - INFO - loss = 0.00845288, step = 2460800 (2.257 sec) -2022-05-01 22:31:47,544 - tensorflow - INFO - global_step/sec: 88.717 -2022-05-01 22:31:47,544 - tensorflow - INFO - loss = 0.049344245, step = 2461000 (2.254 sec) -2022-05-01 22:31:49,802 - tensorflow - INFO - global_step/sec: 88.5711 -2022-05-01 22:31:49,802 - tensorflow - INFO - loss = 0.030603003, step = 2461200 (2.258 sec) -2022-05-01 22:31:52,060 - tensorflow - INFO - global_step/sec: 88.554 -2022-05-01 22:31:52,061 - tensorflow - INFO - loss = 0.00895907, step = 2461400 (2.258 sec) -2022-05-01 22:31:54,316 - tensorflow - INFO - global_step/sec: 88.6735 -2022-05-01 22:31:54,316 - tensorflow - INFO - loss = 0.015357697, step = 2461600 (2.256 sec) -2022-05-01 22:31:56,575 - tensorflow - INFO - global_step/sec: 88.5328 -2022-05-01 22:31:56,575 - tensorflow - INFO - loss = 0.02388647, step = 2461800 (2.259 sec) -2022-05-01 22:31:58,830 - tensorflow - INFO - global_step/sec: 88.6927 -2022-05-01 22:31:58,830 - tensorflow - INFO - loss = 0.023603702, step = 2462000 (2.255 sec) -2022-05-01 22:32:01,080 - tensorflow - INFO - global_step/sec: 88.901 -2022-05-01 22:32:01,080 - tensorflow - INFO - loss = 0.0283974, step = 2462200 (2.250 sec) -2022-05-01 22:32:03,344 - tensorflow - INFO - global_step/sec: 88.3334 -2022-05-01 22:32:03,344 - tensorflow - INFO - loss = 0.068761826, step = 2462400 (2.264 sec) -2022-05-01 22:32:05,596 - tensorflow - INFO - global_step/sec: 88.8006 -2022-05-01 22:32:05,596 - tensorflow - INFO - loss = 0.013406992, step = 2462600 (2.252 sec) -2022-05-01 22:32:07,848 - tensorflow - INFO - global_step/sec: 88.8213 -2022-05-01 22:32:07,848 - tensorflow - INFO - loss = 0.0074104825, step = 2462800 (2.252 sec) -2022-05-01 22:32:10,102 - tensorflow - INFO - global_step/sec: 88.7141 -2022-05-01 22:32:10,102 - tensorflow - INFO - loss = 0.07611355, step = 2463000 (2.254 sec) -2022-05-01 22:32:12,354 - tensorflow - INFO - global_step/sec: 88.8007 -2022-05-01 22:32:12,355 - tensorflow - INFO - loss = 0.019530449, step = 2463200 (2.252 sec) -2022-05-01 22:32:14,607 - tensorflow - INFO - global_step/sec: 88.7737 -2022-05-01 22:32:14,608 - tensorflow - INFO - loss = 0.062303066, step = 2463400 (2.253 sec) -2022-05-01 22:32:16,863 - tensorflow - INFO - global_step/sec: 88.6468 -2022-05-01 22:32:16,864 - tensorflow - INFO - loss = 0.053038966, step = 2463600 (2.256 sec) -2022-05-01 22:32:19,114 - tensorflow - INFO - global_step/sec: 88.879 -2022-05-01 22:32:19,114 - tensorflow - INFO - loss = 0.022775115, step = 2463800 (2.250 sec) -2022-05-01 22:32:21,362 - tensorflow - INFO - global_step/sec: 88.9374 -2022-05-01 22:32:21,363 - tensorflow - INFO - loss = 0.040615294, step = 2464000 (2.249 sec) -2022-05-01 22:32:23,621 - tensorflow - INFO - global_step/sec: 88.5326 -2022-05-01 22:32:23,622 - tensorflow - INFO - loss = 0.0070890756, step = 2464200 (2.259 sec) -2022-05-01 22:32:25,878 - tensorflow - INFO - global_step/sec: 88.6446 -2022-05-01 22:32:25,878 - tensorflow - INFO - loss = 0.010253248, step = 2464400 (2.256 sec) -2022-05-01 22:32:28,140 - tensorflow - INFO - global_step/sec: 88.3912 -2022-05-01 22:32:28,141 - tensorflow - INFO - loss = 0.022638936, step = 2464600 (2.263 sec) -2022-05-01 22:32:30,398 - tensorflow - INFO - global_step/sec: 88.5675 -2022-05-01 22:32:30,399 - tensorflow - INFO - loss = 0.014777252, step = 2464800 (2.258 sec) -2022-05-01 22:32:32,655 - tensorflow - INFO - global_step/sec: 88.633 -2022-05-01 22:32:32,655 - tensorflow - INFO - loss = 0.0031788982, step = 2465000 (2.256 sec) -2022-05-01 22:32:34,905 - tensorflow - INFO - global_step/sec: 88.8968 -2022-05-01 22:32:34,905 - tensorflow - INFO - loss = 0.0057559973, step = 2465200 (2.250 sec) -2022-05-01 22:32:37,157 - tensorflow - INFO - global_step/sec: 88.8177 -2022-05-01 22:32:37,157 - tensorflow - INFO - loss = 0.025105083, step = 2465400 (2.252 sec) -2022-05-01 22:32:39,413 - tensorflow - INFO - global_step/sec: 88.6544 -2022-05-01 22:32:39,413 - tensorflow - INFO - loss = 3.044093, step = 2465600 (2.256 sec) -2022-05-01 22:32:41,667 - tensorflow - INFO - global_step/sec: 88.7003 -2022-05-01 22:32:41,668 - tensorflow - INFO - loss = 0.022113722, step = 2465800 (2.255 sec) -2022-05-01 22:32:43,923 - tensorflow - INFO - global_step/sec: 88.6783 -2022-05-01 22:32:43,923 - tensorflow - INFO - loss = 0.11212695, step = 2466000 (2.255 sec) -2022-05-01 22:32:46,176 - tensorflow - INFO - global_step/sec: 88.7475 -2022-05-01 22:32:46,177 - tensorflow - INFO - loss = 0.017850898, step = 2466200 (2.254 sec) -2022-05-01 22:32:48,431 - tensorflow - INFO - global_step/sec: 88.7069 -2022-05-01 22:32:48,431 - tensorflow - INFO - loss = 0.03084237, step = 2466400 (2.255 sec) -2022-05-01 22:32:50,684 - tensorflow - INFO - global_step/sec: 88.7483 -2022-05-01 22:32:50,685 - tensorflow - INFO - loss = 0.014584415, step = 2466600 (2.254 sec) -2022-05-01 22:32:52,939 - tensorflow - INFO - global_step/sec: 88.7025 -2022-05-01 22:32:52,940 - tensorflow - INFO - loss = 0.0087754745, step = 2466800 (2.255 sec) -2022-05-01 22:32:55,194 - tensorflow - INFO - global_step/sec: 88.6887 -2022-05-01 22:32:55,195 - tensorflow - INFO - loss = 0.21998209, step = 2467000 (2.255 sec) -2022-05-01 22:32:57,446 - tensorflow - INFO - global_step/sec: 88.8013 -2022-05-01 22:32:57,447 - tensorflow - INFO - loss = 0.018761933, step = 2467200 (2.252 sec) -2022-05-01 22:32:59,700 - tensorflow - INFO - global_step/sec: 88.737 -2022-05-01 22:32:59,701 - tensorflow - INFO - loss = 0.0160272, step = 2467400 (2.254 sec) -2022-05-01 22:33:01,958 - tensorflow - INFO - global_step/sec: 88.5996 -2022-05-01 22:33:01,958 - tensorflow - INFO - loss = 0.015052554, step = 2467600 (2.257 sec) -2022-05-01 22:33:04,212 - tensorflow - INFO - global_step/sec: 88.7208 -2022-05-01 22:33:04,212 - tensorflow - INFO - loss = 0.032359116, step = 2467800 (2.254 sec) -2022-05-01 22:33:06,473 - tensorflow - INFO - global_step/sec: 88.4461 -2022-05-01 22:33:06,474 - tensorflow - INFO - loss = 0.12189176, step = 2468000 (2.261 sec) -2022-05-01 22:33:08,725 - tensorflow - INFO - global_step/sec: 88.8208 -2022-05-01 22:33:08,725 - 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INFO - global_step/sec: 88.6425 -2022-05-01 22:34:05,126 - tensorflow - INFO - loss = 0.046241548, step = 2473200 (2.256 sec) -2022-05-01 22:34:07,388 - tensorflow - INFO - global_step/sec: 88.4257 -2022-05-01 22:34:07,388 - tensorflow - INFO - loss = 0.01648511, step = 2473400 (2.262 sec) -2022-05-01 22:34:09,647 - tensorflow - INFO - global_step/sec: 88.5198 -2022-05-01 22:34:09,648 - tensorflow - INFO - loss = 0.060620237, step = 2473600 (2.259 sec) -2022-05-01 22:34:11,910 - tensorflow - INFO - global_step/sec: 88.3943 -2022-05-01 22:34:11,910 - tensorflow - INFO - loss = 0.010266771, step = 2473800 (2.263 sec) -2022-05-01 22:34:14,174 - tensorflow - INFO - global_step/sec: 88.3261 -2022-05-01 22:34:14,174 - tensorflow - INFO - loss = 0.009591879, step = 2474000 (2.264 sec) -2022-05-01 22:34:16,437 - tensorflow - INFO - global_step/sec: 88.3703 -2022-05-01 22:34:16,438 - tensorflow - INFO - loss = 0.0050626313, step = 2474200 (2.263 sec) -2022-05-01 22:34:18,699 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.008434938, step = 2475600 (2.256 sec) -2022-05-01 22:34:34,483 - tensorflow - INFO - global_step/sec: 88.8282 -2022-05-01 22:34:34,484 - tensorflow - INFO - loss = 0.0056113084, step = 2475800 (2.251 sec) -2022-05-01 22:34:36,737 - tensorflow - INFO - global_step/sec: 88.7215 -2022-05-01 22:34:36,738 - tensorflow - INFO - loss = 0.016700625, step = 2476000 (2.254 sec) -2022-05-01 22:34:38,991 - tensorflow - INFO - global_step/sec: 88.7505 -2022-05-01 22:34:38,991 - tensorflow - INFO - loss = 0.014226502, step = 2476200 (2.254 sec) -2022-05-01 22:34:41,243 - tensorflow - INFO - global_step/sec: 88.8209 -2022-05-01 22:34:41,243 - tensorflow - INFO - loss = 0.0097963195, step = 2476400 (2.252 sec) -2022-05-01 22:34:43,500 - tensorflow - INFO - global_step/sec: 88.5915 -2022-05-01 22:34:43,501 - tensorflow - INFO - loss = 0.14534214, step = 2476600 (2.258 sec) -2022-05-01 22:34:45,754 - tensorflow - INFO - global_step/sec: 88.7335 -2022-05-01 22:34:45,755 - tensorflow - INFO - loss = 0.21932498, step = 2476800 (2.254 sec) -2022-05-01 22:34:48,005 - tensorflow - INFO - global_step/sec: 88.8431 -2022-05-01 22:34:48,006 - tensorflow - INFO - loss = 0.019473905, step = 2477000 (2.251 sec) -2022-05-01 22:34:50,267 - tensorflow - INFO - global_step/sec: 88.425 -2022-05-01 22:34:50,267 - tensorflow - INFO - loss = 0.0079299025, step = 2477200 (2.262 sec) -2022-05-01 22:34:52,519 - tensorflow - INFO - global_step/sec: 88.7958 -2022-05-01 22:34:52,520 - tensorflow - INFO - loss = 0.011020434, step = 2477400 (2.252 sec) -2022-05-01 22:34:54,771 - tensorflow - INFO - global_step/sec: 88.8472 -2022-05-01 22:34:54,771 - tensorflow - INFO - loss = 0.011515933, step = 2477600 (2.251 sec) -2022-05-01 22:34:57,029 - tensorflow - INFO - global_step/sec: 88.5652 -2022-05-01 22:34:57,029 - tensorflow - INFO - loss = 0.0076202303, step = 2477800 (2.258 sec) -2022-05-01 22:34:59,281 - tensorflow - INFO - global_step/sec: 88.8007 -2022-05-01 22:34:59,281 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.6998 -2022-05-01 22:35:15,069 - tensorflow - INFO - loss = 0.5366832, step = 2479400 (2.255 sec) -2022-05-01 22:35:17,321 - tensorflow - INFO - global_step/sec: 88.7707 -2022-05-01 22:35:17,322 - tensorflow - INFO - loss = 0.02988251, step = 2479600 (2.253 sec) -2022-05-01 22:35:19,575 - tensorflow - INFO - global_step/sec: 88.755 -2022-05-01 22:35:19,575 - tensorflow - INFO - loss = 0.055242106, step = 2479800 (2.253 sec) -2022-05-01 22:35:21,828 - tensorflow - INFO - global_step/sec: 88.7484 -2022-05-01 22:35:21,829 - tensorflow - INFO - loss = 0.017433317, step = 2480000 (2.254 sec) -2022-05-01 22:35:24,087 - tensorflow - INFO - global_step/sec: 88.5587 -2022-05-01 22:35:24,087 - tensorflow - INFO - loss = 0.05514112, step = 2480200 (2.258 sec) -2022-05-01 22:35:26,337 - tensorflow - INFO - global_step/sec: 88.872 -2022-05-01 22:35:26,337 - tensorflow - INFO - loss = 0.0416425, step = 2480400 (2.250 sec) -2022-05-01 22:35:28,595 - tensorflow - INFO - global_step/sec: 88.5884 -2022-05-01 22:35:28,595 - tensorflow - INFO - loss = 0.0061695157, step = 2480600 (2.258 sec) -2022-05-01 22:35:30,856 - tensorflow - INFO - global_step/sec: 88.4619 -2022-05-01 22:35:30,856 - tensorflow - INFO - loss = 0.01364998, step = 2480800 (2.261 sec) -2022-05-01 22:35:33,112 - tensorflow - INFO - global_step/sec: 88.6304 -2022-05-01 22:35:33,113 - tensorflow - INFO - loss = 0.0075875903, step = 2481000 (2.257 sec) -2022-05-01 22:35:35,373 - tensorflow - INFO - global_step/sec: 88.4488 -2022-05-01 22:35:35,374 - tensorflow - INFO - loss = 0.009406906, step = 2481200 (2.261 sec) -2022-05-01 22:35:37,631 - tensorflow - INFO - global_step/sec: 88.5808 -2022-05-01 22:35:37,631 - tensorflow - INFO - loss = 0.013360513, step = 2481400 (2.258 sec) -2022-05-01 22:35:39,885 - tensorflow - INFO - global_step/sec: 88.7445 -2022-05-01 22:35:39,885 - tensorflow - INFO - loss = 0.016177379, step = 2481600 (2.254 sec) -2022-05-01 22:35:42,139 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.03847104, step = 2483000 (2.256 sec) -2022-05-01 22:35:57,933 - tensorflow - INFO - global_step/sec: 88.6801 -2022-05-01 22:35:57,933 - tensorflow - INFO - loss = 0.02437882, step = 2483200 (2.255 sec) -2022-05-01 22:36:00,182 - tensorflow - INFO - global_step/sec: 88.935 -2022-05-01 22:36:00,182 - tensorflow - INFO - loss = 0.021544535, step = 2483400 (2.249 sec) -2022-05-01 22:36:02,433 - tensorflow - INFO - global_step/sec: 88.8342 -2022-05-01 22:36:02,433 - tensorflow - INFO - loss = 0.010321522, step = 2483600 (2.251 sec) -2022-05-01 22:36:04,690 - tensorflow - INFO - global_step/sec: 88.6144 -2022-05-01 22:36:04,690 - tensorflow - INFO - loss = 0.07524426, step = 2483800 (2.257 sec) -2022-05-01 22:36:06,947 - tensorflow - INFO - global_step/sec: 88.6021 -2022-05-01 22:36:06,948 - tensorflow - INFO - loss = 0.01738942, step = 2484000 (2.257 sec) -2022-05-01 22:36:09,204 - tensorflow - INFO - global_step/sec: 88.6336 -2022-05-01 22:36:09,204 - tensorflow - INFO - loss = 0.015435829, step = 2484200 (2.256 sec) -2022-05-01 22:36:11,455 - tensorflow - INFO - global_step/sec: 88.8429 -2022-05-01 22:36:11,455 - tensorflow - INFO - loss = 0.13900219, step = 2484400 (2.251 sec) -2022-05-01 22:36:13,708 - tensorflow - INFO - global_step/sec: 88.7728 -2022-05-01 22:36:13,708 - tensorflow - INFO - loss = 0.022148643, step = 2484600 (2.253 sec) -2022-05-01 22:36:15,962 - tensorflow - INFO - global_step/sec: 88.7212 -2022-05-01 22:36:15,962 - tensorflow - INFO - loss = 0.009017587, step = 2484800 (2.254 sec) -2022-05-01 22:36:18,218 - tensorflow - INFO - global_step/sec: 88.6473 -2022-05-01 22:36:18,219 - tensorflow - INFO - loss = 0.030413501, step = 2485000 (2.256 sec) -2022-05-01 22:36:20,469 - tensorflow - INFO - global_step/sec: 88.8619 -2022-05-01 22:36:20,469 - tensorflow - INFO - loss = 0.008298157, step = 2485200 (2.251 sec) -2022-05-01 22:36:22,724 - tensorflow - INFO - global_step/sec: 88.6869 -2022-05-01 22:36:22,724 - tensorflow - INFO - loss = 0.0050102044, step = 2485400 (2.255 sec) -2022-05-01 22:36:24,979 - tensorflow - INFO - global_step/sec: 88.6926 -2022-05-01 22:36:24,979 - tensorflow - INFO - loss = 0.011594127, step = 2485600 (2.255 sec) -2022-05-01 22:36:27,234 - tensorflow - INFO - global_step/sec: 88.6906 -2022-05-01 22:36:27,234 - tensorflow - INFO - loss = 0.006304825, step = 2485800 (2.255 sec) -2022-05-01 22:36:29,490 - tensorflow - INFO - global_step/sec: 88.6356 -2022-05-01 22:36:29,491 - tensorflow - INFO - loss = 2.7082615, step = 2486000 (2.256 sec) -2022-05-01 22:36:31,756 - tensorflow - INFO - global_step/sec: 88.2866 -2022-05-01 22:36:31,756 - tensorflow - INFO - loss = 0.015781693, step = 2486200 (2.265 sec) -2022-05-01 22:36:34,013 - tensorflow - INFO - global_step/sec: 88.6173 -2022-05-01 22:36:34,013 - tensorflow - INFO - loss = 0.2971293, step = 2486400 (2.257 sec) -2022-05-01 22:36:36,267 - tensorflow - INFO - global_step/sec: 88.7221 -2022-05-01 22:36:36,267 - tensorflow - INFO - loss = 0.004865778, step = 2486600 (2.254 sec) -2022-05-01 22:36:38,517 - tensorflow - INFO - global_step/sec: 88.8855 -2022-05-01 22:36:38,517 - tensorflow - INFO - loss = 0.008420302, step = 2486800 (2.250 sec) -2022-05-01 22:36:40,768 - tensorflow - INFO - global_step/sec: 88.8477 -2022-05-01 22:36:40,768 - tensorflow - INFO - loss = 0.07152127, step = 2487000 (2.251 sec) -2022-05-01 22:36:43,024 - tensorflow - INFO - global_step/sec: 88.6567 -2022-05-01 22:36:43,024 - tensorflow - INFO - loss = 0.009115946, step = 2487200 (2.256 sec) -2022-05-01 22:36:45,277 - tensorflow - INFO - global_step/sec: 88.7715 -2022-05-01 22:36:45,277 - tensorflow - INFO - loss = 0.005687272, step = 2487400 (2.253 sec) -2022-05-01 22:36:47,526 - tensorflow - INFO - global_step/sec: 88.9087 -2022-05-01 22:36:47,527 - tensorflow - INFO - loss = 0.19188553, step = 2487600 (2.249 sec) -2022-05-01 22:36:49,780 - tensorflow - INFO - global_step/sec: 88.7629 -2022-05-01 22:36:49,780 - tensorflow - INFO - loss = 0.023123372, step = 2487800 (2.253 sec) -2022-05-01 22:36:52,035 - tensorflow - INFO - global_step/sec: 88.6651 -2022-05-01 22:36:52,036 - tensorflow - INFO - loss = 1.4362056, step = 2488000 (2.256 sec) -2022-05-01 22:36:54,287 - tensorflow - INFO - global_step/sec: 88.8069 -2022-05-01 22:36:54,288 - tensorflow - INFO - loss = 0.49593955, step = 2488200 (2.252 sec) -2022-05-01 22:36:56,547 - tensorflow - INFO - global_step/sec: 88.5042 -2022-05-01 22:36:56,547 - tensorflow - INFO - loss = 0.0058627683, step = 2488400 (2.260 sec) -2022-05-01 22:36:58,797 - tensorflow - INFO - global_step/sec: 88.8873 -2022-05-01 22:36:58,798 - tensorflow - INFO - loss = 0.04569395, step = 2488600 (2.250 sec) -2022-05-01 22:37:01,050 - tensorflow - INFO - global_step/sec: 88.7574 -2022-05-01 22:37:01,051 - tensorflow - INFO - loss = 0.1335107, step = 2488800 (2.253 sec) -2022-05-01 22:37:03,305 - tensorflow - INFO - global_step/sec: 88.6996 -2022-05-01 22:37:03,306 - tensorflow - INFO - loss = 0.01614601, step = 2489000 (2.255 sec) -2022-05-01 22:37:05,558 - tensorflow - INFO - global_step/sec: 88.7925 -2022-05-01 22:37:05,558 - tensorflow - INFO - loss = 0.007931199, step = 2489200 (2.252 sec) -2022-05-01 22:37:07,816 - tensorflow - INFO - global_step/sec: 88.5643 -2022-05-01 22:37:07,816 - tensorflow - INFO - loss = 0.05090235, step = 2489400 (2.258 sec) -2022-05-01 22:37:10,076 - tensorflow - INFO - global_step/sec: 88.504 -2022-05-01 22:37:10,076 - tensorflow - INFO - loss = 0.017152905, step = 2489600 (2.260 sec) -2022-05-01 22:37:12,325 - tensorflow - INFO - global_step/sec: 88.9359 -2022-05-01 22:37:12,325 - tensorflow - INFO - loss = 0.010166051, step = 2489800 (2.249 sec) -2022-05-01 22:37:14,576 - tensorflow - INFO - global_step/sec: 88.8497 -2022-05-01 22:37:14,576 - tensorflow - INFO - loss = 0.008025802, step = 2490000 (2.251 sec) -2022-05-01 22:37:16,828 - tensorflow - INFO - global_step/sec: 88.7933 -2022-05-01 22:37:16,828 - tensorflow - INFO - loss = 0.02635665, step = 2490200 (2.252 sec) -2022-05-01 22:37:19,082 - tensorflow - INFO - global_step/sec: 88.7142 -2022-05-01 22:37:19,083 - tensorflow - INFO - loss = 0.05959672, step = 2490400 (2.254 sec) -2022-05-01 22:37:21,338 - tensorflow - INFO - global_step/sec: 88.6671 -2022-05-01 22:37:21,338 - tensorflow - INFO - loss = 0.007345924, step = 2490600 (2.256 sec) -2022-05-01 22:37:23,601 - tensorflow - INFO - global_step/sec: 88.3764 -2022-05-01 22:37:23,601 - tensorflow - INFO - loss = 0.6229799, step = 2490800 (2.263 sec) -2022-05-01 22:37:25,862 - tensorflow - INFO - global_step/sec: 88.4762 -2022-05-01 22:37:25,862 - tensorflow - INFO - loss = 0.023782894, step = 2491000 (2.260 sec) -2022-05-01 22:37:28,124 - tensorflow - INFO - global_step/sec: 88.3935 -2022-05-01 22:37:28,125 - tensorflow - INFO - loss = 0.017684136, step = 2491200 (2.263 sec) -2022-05-01 22:37:30,389 - tensorflow - INFO - global_step/sec: 88.3029 -2022-05-01 22:37:30,390 - tensorflow - INFO - loss = 0.029672848, step = 2491400 (2.265 sec) -2022-05-01 22:37:32,652 - tensorflow - INFO - global_step/sec: 88.3824 -2022-05-01 22:37:32,652 - tensorflow - INFO - loss = 0.018330775, step = 2491600 (2.263 sec) -2022-05-01 22:37:34,917 - tensorflow - INFO - global_step/sec: 88.3055 -2022-05-01 22:37:34,917 - tensorflow - INFO - loss = 0.19323613, step = 2491800 (2.265 sec) -2022-05-01 22:37:37,180 - tensorflow - INFO - global_step/sec: 88.3689 -2022-05-01 22:37:37,181 - tensorflow - INFO - loss = 0.0035518394, step = 2492000 (2.263 sec) -2022-05-01 22:37:39,442 - tensorflow - INFO - global_step/sec: 88.4067 -2022-05-01 22:37:39,443 - tensorflow - INFO - loss = 0.0065744384, step = 2492200 (2.262 sec) -2022-05-01 22:37:41,703 - tensorflow - INFO - global_step/sec: 88.453 -2022-05-01 22:37:41,704 - tensorflow - INFO - loss = 0.007689911, step = 2492400 (2.261 sec) -2022-05-01 22:37:43,967 - tensorflow - INFO - global_step/sec: 88.3656 -2022-05-01 22:37:43,967 - tensorflow - INFO - loss = 0.03819196, step = 2492600 (2.263 sec) -2022-05-01 22:37:46,227 - tensorflow - INFO - global_step/sec: 88.476 -2022-05-01 22:37:46,228 - tensorflow - INFO - loss = 0.047451183, step = 2492800 (2.261 sec) -2022-05-01 22:37:48,487 - tensorflow - INFO - global_step/sec: 88.5072 -2022-05-01 22:37:48,487 - tensorflow - INFO - loss = 0.014459378, step = 2493000 (2.260 sec) -2022-05-01 22:37:50,747 - tensorflow - INFO - global_step/sec: 88.5062 -2022-05-01 22:37:50,747 - tensorflow - INFO - loss = 0.20836158, step = 2493200 (2.260 sec) -2022-05-01 22:37:53,006 - tensorflow - INFO - global_step/sec: 88.5224 -2022-05-01 22:37:53,006 - tensorflow - INFO - loss = 0.03848868, step = 2493400 (2.259 sec) -2022-05-01 22:37:55,263 - tensorflow - INFO - global_step/sec: 88.6129 -2022-05-01 22:37:55,263 - tensorflow - INFO - loss = 0.17222637, step = 2493600 (2.257 sec) -2022-05-01 22:37:57,517 - tensorflow - INFO - global_step/sec: 88.7318 -2022-05-01 22:37:57,517 - tensorflow - INFO - loss = 0.012568856, step = 2493800 (2.254 sec) -2022-05-01 22:37:59,768 - tensorflow - INFO - global_step/sec: 88.8338 -2022-05-01 22:37:59,769 - tensorflow - INFO - loss = 0.036384873, step = 2494000 (2.251 sec) -2022-05-01 22:38:02,022 - tensorflow - INFO - global_step/sec: 88.7564 -2022-05-01 22:38:02,022 - tensorflow - INFO - loss = 0.010013607, step = 2494200 (2.253 sec) -2022-05-01 22:38:04,274 - tensorflow - INFO - global_step/sec: 88.7988 -2022-05-01 22:38:04,274 - tensorflow - INFO - loss = 0.011140664, step = 2494400 (2.252 sec) -2022-05-01 22:38:06,524 - tensorflow - INFO - global_step/sec: 88.8942 -2022-05-01 22:38:06,524 - tensorflow - INFO - loss = 0.00332475, step = 2494600 (2.250 sec) -2022-05-01 22:38:08,779 - tensorflow - INFO - global_step/sec: 88.6708 -2022-05-01 22:38:08,780 - tensorflow - INFO - loss = 0.03149625, step = 2494800 (2.256 sec) -2022-05-01 22:38:11,030 - tensorflow - INFO - global_step/sec: 88.8648 -2022-05-01 22:38:11,030 - tensorflow - INFO - loss = 0.008571399, step = 2495000 (2.251 sec) -2022-05-01 22:38:13,281 - tensorflow - INFO - global_step/sec: 88.8418 -2022-05-01 22:38:13,282 - tensorflow - INFO - loss = 0.024274005, step = 2495200 (2.251 sec) -2022-05-01 22:38:15,539 - tensorflow - INFO - global_step/sec: 88.5933 -2022-05-01 22:38:15,539 - tensorflow - INFO - loss = 0.026418442, step = 2495400 (2.257 sec) -2022-05-01 22:38:17,792 - tensorflow - INFO - global_step/sec: 88.7513 -2022-05-01 22:38:17,793 - tensorflow - INFO - loss = 0.019441668, step = 2495600 (2.253 sec) -2022-05-01 22:38:20,042 - tensorflow - INFO - global_step/sec: 88.8905 -2022-05-01 22:38:20,043 - tensorflow - INFO - loss = 0.010732288, step = 2495800 (2.250 sec) -2022-05-01 22:38:22,287 - tensorflow - INFO - global_step/sec: 89.1048 -2022-05-01 22:38:22,287 - tensorflow - INFO - loss = 0.03142559, step = 2496000 (2.245 sec) -2022-05-01 22:38:24,533 - tensorflow - INFO - global_step/sec: 89.0417 -2022-05-01 22:38:24,533 - tensorflow - INFO - loss = 0.059488736, step = 2496200 (2.246 sec) -2022-05-01 22:38:26,792 - tensorflow - INFO - global_step/sec: 88.5427 -2022-05-01 22:38:26,792 - tensorflow - INFO - loss = 0.014048614, step = 2496400 (2.259 sec) -2022-05-01 22:38:29,042 - tensorflow - INFO - global_step/sec: 88.8708 -2022-05-01 22:38:29,043 - tensorflow - INFO - loss = 0.047158502, step = 2496600 (2.250 sec) -2022-05-01 22:38:31,297 - tensorflow - INFO - global_step/sec: 88.7053 -2022-05-01 22:38:31,297 - tensorflow - INFO - loss = 0.019878263, step = 2496800 (2.255 sec) -2022-05-01 22:38:33,548 - tensorflow - INFO - global_step/sec: 88.8446 -2022-05-01 22:38:33,548 - tensorflow - INFO - loss = 0.0071397563, step = 2497000 (2.251 sec) -2022-05-01 22:38:35,800 - tensorflow - INFO - global_step/sec: 88.7965 -2022-05-01 22:38:35,801 - tensorflow - INFO - loss = 0.7433055, step = 2497200 (2.252 sec) -2022-05-01 22:38:38,053 - tensorflow - INFO - global_step/sec: 88.7709 -2022-05-01 22:38:38,054 - tensorflow - INFO - loss = 0.0055767056, step = 2497400 (2.253 sec) -2022-05-01 22:38:40,309 - tensorflow - INFO - global_step/sec: 88.661 -2022-05-01 22:38:40,309 - tensorflow - INFO - loss = 0.012999417, step = 2497600 (2.256 sec) -2022-05-01 22:38:42,559 - tensorflow - INFO - global_step/sec: 88.8825 -2022-05-01 22:38:42,560 - tensorflow - INFO - loss = 0.009196865, step = 2497800 (2.250 sec) -2022-05-01 22:38:44,817 - tensorflow - INFO - global_step/sec: 88.5973 -2022-05-01 22:38:44,817 - tensorflow - INFO - loss = 0.019697893, step = 2498000 (2.257 sec) -2022-05-01 22:38:47,067 - tensorflow - INFO - global_step/sec: 88.8669 -2022-05-01 22:38:47,068 - tensorflow - INFO - loss = 0.0067554098, step = 2498200 (2.251 sec) -2022-05-01 22:38:49,321 - tensorflow - INFO - global_step/sec: 88.7454 -2022-05-01 22:38:49,321 - tensorflow - INFO - loss = 0.012799524, step = 2498400 (2.254 sec) -2022-05-01 22:38:51,579 - tensorflow - INFO - global_step/sec: 88.5476 -2022-05-01 22:38:51,580 - tensorflow - INFO - loss = 0.04408863, step = 2498600 (2.259 sec) -2022-05-01 22:38:53,828 - tensorflow - INFO - global_step/sec: 88.95 -2022-05-01 22:38:53,828 - tensorflow - INFO - loss = 0.056328095, step = 2498800 (2.248 sec) -2022-05-01 22:38:56,084 - tensorflow - INFO - global_step/sec: 88.6324 -2022-05-01 22:38:56,085 - tensorflow - INFO - loss = 0.011546372, step = 2499000 (2.256 sec) -2022-05-01 22:38:58,335 - tensorflow - INFO - global_step/sec: 88.8491 -2022-05-01 22:38:58,336 - tensorflow - INFO - loss = 0.0033892635, step = 2499200 (2.251 sec) -2022-05-01 22:39:00,593 - tensorflow - INFO - global_step/sec: 88.6082 -2022-05-01 22:39:00,593 - tensorflow - INFO - loss = 0.004750331, step = 2499400 (2.257 sec) -2022-05-01 22:39:02,853 - tensorflow - INFO - global_step/sec: 88.4876 -2022-05-01 22:39:02,853 - tensorflow - INFO - loss = 0.007829336, step = 2499600 (2.260 sec) -2022-05-01 22:39:05,113 - tensorflow - INFO - global_step/sec: 88.4993 -2022-05-01 22:39:05,113 - tensorflow - INFO - loss = 0.024423394, step = 2499800 (2.260 sec) -2022-05-01 22:39:07,351 - tensorflow - INFO - Saving checkpoints for 2500000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. -2022-05-01 22:39:07,369 - tensorflow - INFO - model.ckpt-2500000 is not in all_model_checkpoint_paths. Manually adding it. -2022-05-01 22:39:07,900 - tensorflow - INFO - Calling model_fn. -2022-05-01 22:39:08,027 - tensorflow - INFO - Done calling model_fn. -2022-05-01 22:39:08,042 - tensorflow - INFO - Starting evaluation at 2022-05-01T22:39:08Z -2022-05-01 22:39:08,106 - tensorflow - INFO - Graph was finalized. -2022-05-01 22:39:08,109 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-2500000 -2022-05-01 22:39:08,391 - tensorflow - INFO - Running local_init_op. -2022-05-01 22:39:08,499 - tensorflow - INFO - Done running local_init_op. -2022-05-01 22:39:18,635 - tensorflow - INFO - Evaluation [3990/39900] -2022-05-01 22:39:28,069 - tensorflow - INFO - Evaluation [7980/39900] -2022-05-01 22:39:37,498 - tensorflow - INFO - Evaluation [11970/39900] -2022-05-01 22:39:46,922 - tensorflow - INFO - Evaluation [15960/39900] -2022-05-01 22:39:56,366 - tensorflow - INFO - Evaluation [19950/39900] -2022-05-01 22:40:05,832 - tensorflow - INFO - Evaluation [23940/39900] -2022-05-01 22:40:15,280 - tensorflow - INFO - Evaluation [27930/39900] -2022-05-01 22:40:24,719 - tensorflow - INFO - Evaluation [31920/39900] -2022-05-01 22:40:34,151 - tensorflow - INFO - Evaluation [35910/39900] -2022-05-01 22:40:43,604 - tensorflow - INFO - Evaluation [39900/39900] -2022-05-01 22:40:43,741 - tensorflow - INFO - Finished evaluation at 2022-05-01-22:40:43 -2022-05-01 22:40:43,741 - tensorflow - INFO - Saving dict for global step 2500000: global_step = 2500000, loss = 0.01607213, mean_degree_err = 3.0870223 -2022-05-01 22:40:43,742 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 2500000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-2500000 -2022-05-01 22:40:43,755 - tensorflow - INFO - global_step/sec: 2.02752 -2022-05-01 22:40:43,756 - tensorflow - INFO - loss = 0.028217498, step = 2500000 (98.643 sec) -2022-05-01 22:40:46,023 - tensorflow - INFO - global_step/sec: 88.1845 -2022-05-01 22:40:46,024 - tensorflow - INFO - loss = 0.01755622, step = 2500200 (2.268 sec) -2022-05-01 22:40:48,279 - tensorflow - INFO - global_step/sec: 88.675 -2022-05-01 22:40:48,279 - tensorflow - INFO - loss = 0.015188168, step = 2500400 (2.255 sec) -2022-05-01 22:40:50,536 - tensorflow - INFO - global_step/sec: 88.597 -2022-05-01 22:40:50,537 - tensorflow - INFO - loss = 0.011648623, step = 2500600 (2.257 sec) -2022-05-01 22:40:52,787 - tensorflow - INFO - global_step/sec: 88.8475 -2022-05-01 22:40:52,788 - tensorflow - INFO - loss = 0.08985994, step = 2500800 (2.251 sec) -2022-05-01 22:40:55,051 - tensorflow - INFO - global_step/sec: 88.3396 -2022-05-01 22:40:55,052 - tensorflow - INFO - loss = 0.0031446307, step = 2501000 (2.264 sec) -2022-05-01 22:40:57,317 - tensorflow - INFO - global_step/sec: 88.2696 -2022-05-01 22:40:57,317 - tensorflow - INFO - loss = 0.007888079, step = 2501200 (2.266 sec) -2022-05-01 22:40:59,584 - tensorflow - INFO - global_step/sec: 88.2311 -2022-05-01 22:40:59,584 - tensorflow - INFO - loss = 0.0051378547, step = 2501400 (2.267 sec) -2022-05-01 22:41:01,844 - tensorflow - INFO - global_step/sec: 88.5079 -2022-05-01 22:41:01,844 - tensorflow - INFO - loss = 0.013122154, step = 2501600 (2.260 sec) -2022-05-01 22:41:04,105 - tensorflow - INFO - global_step/sec: 88.4465 -2022-05-01 22:41:04,105 - tensorflow - INFO - loss = 0.01367711, step = 2501800 (2.261 sec) -2022-05-01 22:41:06,365 - tensorflow - INFO - global_step/sec: 88.4751 -2022-05-01 22:41:06,366 - tensorflow - INFO - loss = 0.014673911, step = 2502000 (2.261 sec) -2022-05-01 22:41:08,624 - tensorflow - INFO - global_step/sec: 88.5279 -2022-05-01 22:41:08,625 - tensorflow - INFO - loss = 0.010844378, step = 2502200 (2.259 sec) -2022-05-01 22:41:10,879 - tensorflow - INFO - global_step/sec: 88.6954 -2022-05-01 22:41:10,880 - tensorflow - INFO - loss = 0.011447811, step = 2502400 (2.255 sec) -2022-05-01 22:41:13,145 - tensorflow - INFO - global_step/sec: 88.2901 -2022-05-01 22:41:13,145 - tensorflow - INFO - loss = 0.043297, step = 2502600 (2.265 sec) -2022-05-01 22:41:15,407 - tensorflow - INFO - global_step/sec: 88.4005 -2022-05-01 22:41:15,407 - tensorflow - INFO - loss = 0.027249252, step = 2502800 (2.262 sec) -2022-05-01 22:41:17,667 - tensorflow - INFO - global_step/sec: 88.4985 -2022-05-01 22:41:17,667 - tensorflow - INFO - loss = 0.01280329, step = 2503000 (2.260 sec) -2022-05-01 22:41:19,928 - tensorflow - INFO - global_step/sec: 88.4401 -2022-05-01 22:41:19,929 - tensorflow - INFO - loss = 0.01926428, step = 2503200 (2.261 sec) -2022-05-01 22:41:22,187 - tensorflow - INFO - global_step/sec: 88.5565 -2022-05-01 22:41:22,187 - tensorflow - INFO - loss = 0.07693437, step = 2503400 (2.258 sec) -2022-05-01 22:41:24,446 - tensorflow - INFO - global_step/sec: 88.5255 -2022-05-01 22:41:24,447 - tensorflow - INFO - loss = 0.89175195, step = 2503600 (2.259 sec) -2022-05-01 22:41:26,705 - tensorflow - INFO - global_step/sec: 88.5356 -2022-05-01 22:41:26,705 - tensorflow - INFO - loss = 0.10346782, step = 2503800 (2.259 sec) -2022-05-01 22:41:28,960 - tensorflow - INFO - global_step/sec: 88.6761 -2022-05-01 22:41:28,961 - tensorflow - INFO - loss = 0.09175624, step = 2504000 (2.255 sec) -2022-05-01 22:41:31,216 - tensorflow - INFO - global_step/sec: 88.6662 -2022-05-01 22:41:31,217 - tensorflow - INFO - loss = 0.050385676, step = 2504200 (2.256 sec) -2022-05-01 22:41:33,467 - tensorflow - INFO - global_step/sec: 88.8368 -2022-05-01 22:41:33,468 - tensorflow - INFO - loss = 0.010782375, step = 2504400 (2.251 sec) -2022-05-01 22:41:35,722 - tensorflow - INFO - global_step/sec: 88.7187 -2022-05-01 22:41:35,722 - tensorflow - INFO - loss = 0.0145096, step = 2504600 (2.254 sec) -2022-05-01 22:41:37,976 - tensorflow - INFO - global_step/sec: 88.7105 -2022-05-01 22:41:37,977 - tensorflow - INFO - loss = 0.10901194, step = 2504800 (2.254 sec) -2022-05-01 22:41:40,233 - tensorflow - INFO - global_step/sec: 88.6185 -2022-05-01 22:41:40,234 - tensorflow - INFO - loss = 0.030011967, step = 2505000 (2.257 sec) -2022-05-01 22:41:42,481 - tensorflow - INFO - global_step/sec: 88.9853 -2022-05-01 22:41:42,481 - tensorflow - INFO - loss = 0.041071665, step = 2505200 (2.248 sec) -2022-05-01 22:41:44,733 - tensorflow - INFO - global_step/sec: 88.815 -2022-05-01 22:41:44,733 - tensorflow - INFO - loss = 0.007870996, step = 2505400 (2.252 sec) -2022-05-01 22:41:46,982 - tensorflow - INFO - global_step/sec: 88.9093 -2022-05-01 22:41:46,983 - tensorflow - INFO - loss = 0.0217721, step = 2505600 (2.250 sec) -2022-05-01 22:41:49,234 - tensorflow - INFO - global_step/sec: 88.8162 -2022-05-01 22:41:49,234 - tensorflow - INFO - loss = 0.0058401357, step = 2505800 (2.252 sec) -2022-05-01 22:41:51,491 - tensorflow - INFO - global_step/sec: 88.6144 -2022-05-01 22:41:51,491 - tensorflow - INFO - loss = 0.010875938, step = 2506000 (2.257 sec) -2022-05-01 22:41:53,748 - tensorflow - INFO - global_step/sec: 88.5955 -2022-05-01 22:41:53,749 - tensorflow - INFO - loss = 0.018075192, step = 2506200 (2.257 sec) -2022-05-01 22:41:56,006 - tensorflow - INFO - 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INFO - global_step/sec: 88.8338 -2022-05-01 22:43:05,851 - tensorflow - INFO - loss = 0.032996435, step = 2512600 (2.251 sec) -2022-05-01 22:43:08,105 - tensorflow - INFO - global_step/sec: 88.7179 -2022-05-01 22:43:08,106 - tensorflow - INFO - loss = 0.011973894, step = 2512800 (2.254 sec) -2022-05-01 22:43:10,365 - tensorflow - INFO - global_step/sec: 88.5172 -2022-05-01 22:43:10,365 - tensorflow - INFO - loss = 0.021260012, step = 2513000 (2.259 sec) -2022-05-01 22:43:12,613 - tensorflow - INFO - global_step/sec: 88.97 -2022-05-01 22:43:12,613 - tensorflow - INFO - loss = 0.06239277, step = 2513200 (2.248 sec) -2022-05-01 22:43:14,858 - tensorflow - INFO - global_step/sec: 89.0595 -2022-05-01 22:43:14,859 - tensorflow - INFO - loss = 0.011447022, step = 2513400 (2.246 sec) -2022-05-01 22:43:17,114 - tensorflow - INFO - global_step/sec: 88.6814 -2022-05-01 22:43:17,114 - tensorflow - INFO - loss = 0.01663798, step = 2513600 (2.255 sec) -2022-05-01 22:43:19,366 - tensorflow - INFO - global_step/sec: 88.7862 -2022-05-01 22:43:19,367 - tensorflow - INFO - loss = 0.0050092377, step = 2513800 (2.253 sec) -2022-05-01 22:43:21,616 - tensorflow - INFO - global_step/sec: 88.9065 -2022-05-01 22:43:21,616 - tensorflow - INFO - loss = 0.026145354, step = 2514000 (2.250 sec) -2022-05-01 22:43:23,873 - tensorflow - INFO - global_step/sec: 88.6185 -2022-05-01 22:43:23,873 - tensorflow - INFO - loss = 0.021748584, step = 2514200 (2.257 sec) -2022-05-01 22:43:26,124 - tensorflow - INFO - global_step/sec: 88.8162 -2022-05-01 22:43:26,125 - tensorflow - INFO - loss = 0.011702653, step = 2514400 (2.252 sec) -2022-05-01 22:43:28,380 - tensorflow - INFO - global_step/sec: 88.6809 -2022-05-01 22:43:28,380 - tensorflow - INFO - loss = 0.022134826, step = 2514600 (2.255 sec) -2022-05-01 22:43:30,637 - tensorflow - INFO - global_step/sec: 88.587 -2022-05-01 22:43:30,638 - tensorflow - INFO - loss = 0.007043754, step = 2514800 (2.258 sec) -2022-05-01 22:43:32,891 - tensorflow - INFO - global_step/sec: 88.7408 -2022-05-01 22:43:32,892 - tensorflow - INFO - loss = 0.015608694, step = 2515000 (2.254 sec) -2022-05-01 22:43:35,148 - tensorflow - INFO - global_step/sec: 88.6108 -2022-05-01 22:43:35,149 - tensorflow - INFO - loss = 0.008972997, step = 2515200 (2.257 sec) -2022-05-01 22:43:37,399 - tensorflow - INFO - global_step/sec: 88.859 -2022-05-01 22:43:37,399 - tensorflow - INFO - loss = 0.009503961, step = 2515400 (2.251 sec) -2022-05-01 22:43:39,649 - tensorflow - INFO - global_step/sec: 88.8693 -2022-05-01 22:43:39,650 - tensorflow - INFO - loss = 0.0039321072, step = 2515600 (2.251 sec) -2022-05-01 22:43:41,901 - tensorflow - INFO - global_step/sec: 88.8226 -2022-05-01 22:43:41,902 - tensorflow - INFO - loss = 0.014092447, step = 2515800 (2.252 sec) -2022-05-01 22:43:44,151 - tensorflow - INFO - global_step/sec: 88.9042 -2022-05-01 22:43:44,151 - tensorflow - INFO - loss = 0.005770107, step = 2516000 (2.250 sec) -2022-05-01 22:43:46,408 - tensorflow - INFO - global_step/sec: 88.6123 -2022-05-01 22:43:46,408 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.8257 -2022-05-01 22:44:02,194 - tensorflow - INFO - loss = 0.1319748, step = 2517600 (2.252 sec) -2022-05-01 22:44:04,447 - tensorflow - INFO - global_step/sec: 88.7198 -2022-05-01 22:44:04,448 - tensorflow - INFO - loss = 0.0036735244, step = 2517800 (2.254 sec) -2022-05-01 22:44:06,698 - tensorflow - INFO - global_step/sec: 88.8619 -2022-05-01 22:44:06,699 - tensorflow - INFO - loss = 0.047612377, step = 2518000 (2.251 sec) -2022-05-01 22:44:08,948 - tensorflow - INFO - global_step/sec: 88.8772 -2022-05-01 22:44:08,949 - tensorflow - INFO - loss = 0.0066236276, step = 2518200 (2.250 sec) -2022-05-01 22:44:11,202 - tensorflow - INFO - global_step/sec: 88.7675 -2022-05-01 22:44:11,202 - tensorflow - INFO - loss = 0.0067285597, step = 2518400 (2.253 sec) -2022-05-01 22:44:13,457 - tensorflow - INFO - global_step/sec: 88.6561 -2022-05-01 22:44:13,458 - tensorflow - INFO - loss = 0.018304791, step = 2518600 (2.256 sec) -2022-05-01 22:44:15,709 - tensorflow - 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INFO - loss = 0.00386234, step = 2522400 (2.257 sec) -2022-05-01 22:44:58,644 - tensorflow - INFO - global_step/sec: 88.7059 -2022-05-01 22:44:58,645 - tensorflow - INFO - loss = 0.019720316, step = 2522600 (2.255 sec) -2022-05-01 22:45:00,900 - tensorflow - INFO - global_step/sec: 88.6435 -2022-05-01 22:45:00,901 - tensorflow - INFO - loss = 0.0074373996, step = 2522800 (2.256 sec) -2022-05-01 22:45:03,157 - tensorflow - INFO - global_step/sec: 88.6473 -2022-05-01 22:45:03,157 - tensorflow - INFO - loss = 0.09727685, step = 2523000 (2.256 sec) -2022-05-01 22:45:05,411 - tensorflow - INFO - global_step/sec: 88.7344 -2022-05-01 22:45:05,411 - tensorflow - INFO - loss = 0.03645045, step = 2523200 (2.254 sec) -2022-05-01 22:45:07,663 - tensorflow - INFO - global_step/sec: 88.8097 -2022-05-01 22:45:07,663 - tensorflow - INFO - loss = 0.018618433, step = 2523400 (2.252 sec) -2022-05-01 22:45:09,911 - tensorflow - INFO - global_step/sec: 88.9577 -2022-05-01 22:45:09,911 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.7759 -2022-05-01 22:45:25,690 - tensorflow - INFO - loss = 0.054611653, step = 2525000 (2.253 sec) -2022-05-01 22:45:27,944 - tensorflow - INFO - global_step/sec: 88.684 -2022-05-01 22:45:27,945 - tensorflow - INFO - loss = 0.0085484935, step = 2525200 (2.255 sec) -2022-05-01 22:45:30,203 - tensorflow - INFO - global_step/sec: 88.5647 -2022-05-01 22:45:30,203 - tensorflow - INFO - loss = 0.017433915, step = 2525400 (2.258 sec) -2022-05-01 22:45:32,466 - tensorflow - INFO - global_step/sec: 88.3641 -2022-05-01 22:45:32,466 - tensorflow - INFO - loss = 0.02791887, step = 2525600 (2.263 sec) -2022-05-01 22:45:34,720 - tensorflow - INFO - global_step/sec: 88.7419 -2022-05-01 22:45:34,720 - tensorflow - INFO - loss = 0.065674186, step = 2525800 (2.254 sec) -2022-05-01 22:45:36,968 - tensorflow - INFO - global_step/sec: 88.9489 -2022-05-01 22:45:36,968 - tensorflow - INFO - loss = 0.02218478, step = 2526000 (2.248 sec) -2022-05-01 22:45:39,216 - tensorflow - INFO - global_step/sec: 88.9606 -2022-05-01 22:45:39,217 - tensorflow - INFO - loss = 0.008726221, step = 2526200 (2.248 sec) -2022-05-01 22:45:41,469 - tensorflow - INFO - global_step/sec: 88.7926 -2022-05-01 22:45:41,469 - tensorflow - INFO - loss = 0.035256118, step = 2526400 (2.252 sec) -2022-05-01 22:45:43,729 - tensorflow - INFO - global_step/sec: 88.4914 -2022-05-01 22:45:43,729 - tensorflow - INFO - loss = 0.015490872, step = 2526600 (2.260 sec) -2022-05-01 22:45:45,984 - tensorflow - INFO - global_step/sec: 88.7003 -2022-05-01 22:45:45,984 - tensorflow - INFO - loss = 0.0068812855, step = 2526800 (2.255 sec) -2022-05-01 22:45:48,234 - tensorflow - INFO - global_step/sec: 88.8671 -2022-05-01 22:45:48,235 - tensorflow - INFO - loss = 0.040634274, step = 2527000 (2.251 sec) -2022-05-01 22:45:50,491 - tensorflow - INFO - global_step/sec: 88.6024 -2022-05-01 22:45:50,492 - tensorflow - INFO - loss = 0.08081141, step = 2527200 (2.257 sec) -2022-05-01 22:45:52,740 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.023867954, step = 2528600 (2.254 sec) -2022-05-01 22:46:08,531 - tensorflow - INFO - global_step/sec: 88.5211 -2022-05-01 22:46:08,531 - tensorflow - INFO - loss = 0.021360926, step = 2528800 (2.259 sec) -2022-05-01 22:46:10,786 - tensorflow - INFO - global_step/sec: 88.6647 -2022-05-01 22:46:10,787 - tensorflow - INFO - loss = 0.0070251855, step = 2529000 (2.256 sec) -2022-05-01 22:46:13,041 - tensorflow - INFO - global_step/sec: 88.6977 -2022-05-01 22:46:13,042 - tensorflow - INFO - loss = 0.020961937, step = 2529200 (2.255 sec) -2022-05-01 22:46:15,294 - tensorflow - INFO - global_step/sec: 88.7715 -2022-05-01 22:46:15,295 - tensorflow - INFO - loss = 0.00983898, step = 2529400 (2.253 sec) -2022-05-01 22:46:17,548 - tensorflow - INFO - global_step/sec: 88.74 -2022-05-01 22:46:17,548 - tensorflow - INFO - loss = 0.021158015, step = 2529600 (2.254 sec) -2022-05-01 22:46:19,803 - tensorflow - INFO - global_step/sec: 88.7043 -2022-05-01 22:46:19,803 - tensorflow - INFO - loss = 0.1289204, step = 2529800 (2.255 sec) -2022-05-01 22:46:22,063 - tensorflow - INFO - global_step/sec: 88.474 -2022-05-01 22:46:22,064 - tensorflow - INFO - loss = 0.00733544, step = 2530000 (2.261 sec) -2022-05-01 22:46:24,321 - tensorflow - INFO - global_step/sec: 88.5594 -2022-05-01 22:46:24,322 - tensorflow - INFO - loss = 0.006650264, step = 2530200 (2.258 sec) -2022-05-01 22:46:26,578 - tensorflow - INFO - global_step/sec: 88.619 -2022-05-01 22:46:26,579 - tensorflow - INFO - loss = 1.1148852, step = 2530400 (2.257 sec) -2022-05-01 22:46:28,839 - tensorflow - INFO - global_step/sec: 88.4881 -2022-05-01 22:46:28,839 - tensorflow - INFO - loss = 0.025489006, step = 2530600 (2.260 sec) -2022-05-01 22:46:31,099 - tensorflow - INFO - global_step/sec: 88.4711 -2022-05-01 22:46:31,100 - tensorflow - INFO - loss = 0.026309218, step = 2530800 (2.261 sec) -2022-05-01 22:46:33,362 - tensorflow - INFO - global_step/sec: 88.3953 -2022-05-01 22:46:33,362 - tensorflow - INFO - loss = 0.037741657, step = 2531000 (2.263 sec) -2022-05-01 22:46:35,616 - tensorflow - INFO - global_step/sec: 88.7372 -2022-05-01 22:46:35,616 - tensorflow - INFO - loss = 0.0073168147, step = 2531200 (2.254 sec) -2022-05-01 22:46:37,869 - tensorflow - INFO - global_step/sec: 88.7679 -2022-05-01 22:46:37,869 - tensorflow - INFO - loss = 0.07530931, step = 2531400 (2.253 sec) -2022-05-01 22:46:40,124 - tensorflow - INFO - global_step/sec: 88.6971 -2022-05-01 22:46:40,124 - tensorflow - INFO - loss = 0.004444405, step = 2531600 (2.255 sec) -2022-05-01 22:46:42,376 - tensorflow - INFO - global_step/sec: 88.8019 -2022-05-01 22:46:42,376 - tensorflow - INFO - loss = 0.033372402, step = 2531800 (2.252 sec) -2022-05-01 22:46:44,626 - tensorflow - INFO - global_step/sec: 88.8685 -2022-05-01 22:46:44,627 - tensorflow - INFO - loss = 0.005551937, step = 2532000 (2.251 sec) -2022-05-01 22:46:46,886 - tensorflow - INFO - global_step/sec: 88.5173 -2022-05-01 22:46:46,886 - tensorflow - INFO - loss = 0.013973923, step = 2532200 (2.259 sec) -2022-05-01 22:46:49,139 - tensorflow - INFO - global_step/sec: 88.7619 -2022-05-01 22:46:49,139 - tensorflow - INFO - loss = 0.007855685, step = 2532400 (2.253 sec) -2022-05-01 22:46:51,391 - tensorflow - INFO - global_step/sec: 88.7898 -2022-05-01 22:46:51,392 - tensorflow - INFO - loss = 0.013866673, step = 2532600 (2.252 sec) -2022-05-01 22:46:53,644 - tensorflow - INFO - global_step/sec: 88.7947 -2022-05-01 22:46:53,644 - tensorflow - INFO - loss = 0.010676412, step = 2532800 (2.252 sec) -2022-05-01 22:46:55,901 - tensorflow - INFO - global_step/sec: 88.5894 -2022-05-01 22:46:55,902 - tensorflow - INFO - loss = 0.01469085, step = 2533000 (2.258 sec) -2022-05-01 22:46:58,165 - tensorflow - INFO - global_step/sec: 88.3471 -2022-05-01 22:46:58,166 - tensorflow - INFO - loss = 0.013090211, step = 2533200 (2.264 sec) -2022-05-01 22:47:00,422 - tensorflow - INFO - global_step/sec: 88.6138 -2022-05-01 22:47:00,423 - tensorflow - INFO - loss = 0.009685187, step = 2533400 (2.257 sec) -2022-05-01 22:47:02,674 - tensorflow - INFO - global_step/sec: 88.8319 -2022-05-01 22:47:02,674 - tensorflow - INFO - loss = 0.016568955, step = 2533600 (2.251 sec) -2022-05-01 22:47:04,926 - tensorflow - INFO - global_step/sec: 88.7756 -2022-05-01 22:47:04,927 - tensorflow - INFO - loss = 0.087907135, step = 2533800 (2.253 sec) -2022-05-01 22:47:07,187 - tensorflow - INFO - global_step/sec: 88.4786 -2022-05-01 22:47:07,187 - tensorflow - INFO - loss = 0.0128628425, step = 2534000 (2.260 sec) -2022-05-01 22:47:09,440 - tensorflow - INFO - global_step/sec: 88.7678 -2022-05-01 22:47:09,440 - tensorflow - INFO - loss = 0.039572977, step = 2534200 (2.253 sec) -2022-05-01 22:47:11,694 - tensorflow - INFO - global_step/sec: 88.7353 -2022-05-01 22:47:11,694 - tensorflow - INFO - loss = 0.031185715, step = 2534400 (2.254 sec) -2022-05-01 22:47:13,951 - tensorflow - INFO - global_step/sec: 88.6052 -2022-05-01 22:47:13,952 - tensorflow - INFO - loss = 0.014337154, step = 2534600 (2.257 sec) -2022-05-01 22:47:16,205 - tensorflow - INFO - global_step/sec: 88.7285 -2022-05-01 22:47:16,206 - tensorflow - INFO - loss = 0.012679648, step = 2534800 (2.254 sec) -2022-05-01 22:47:18,460 - tensorflow - INFO - global_step/sec: 88.6938 -2022-05-01 22:47:18,461 - tensorflow - INFO - loss = 0.0075245304, step = 2535000 (2.255 sec) -2022-05-01 22:47:20,714 - tensorflow - INFO - global_step/sec: 88.7246 -2022-05-01 22:47:20,715 - tensorflow - INFO - loss = 0.010959899, step = 2535200 (2.254 sec) -2022-05-01 22:47:22,968 - tensorflow - INFO - global_step/sec: 88.7615 -2022-05-01 22:47:22,968 - tensorflow - INFO - loss = 0.0083835805, step = 2535400 (2.253 sec) -2022-05-01 22:47:25,221 - tensorflow - INFO - global_step/sec: 88.7635 -2022-05-01 22:47:25,221 - tensorflow - INFO - loss = 0.11738077, step = 2535600 (2.253 sec) -2022-05-01 22:47:27,473 - tensorflow - INFO - global_step/sec: 88.8011 -2022-05-01 22:47:27,473 - tensorflow - INFO - loss = 0.00811711, step = 2535800 (2.252 sec) -2022-05-01 22:47:29,723 - tensorflow - INFO - global_step/sec: 88.8746 -2022-05-01 22:47:29,724 - tensorflow - INFO - loss = 0.05884538, step = 2536000 (2.250 sec) -2022-05-01 22:47:31,980 - tensorflow - INFO - global_step/sec: 88.6219 -2022-05-01 22:47:31,980 - tensorflow - INFO - loss = 0.050516434, step = 2536200 (2.257 sec) -2022-05-01 22:47:34,234 - tensorflow - INFO - global_step/sec: 88.7353 -2022-05-01 22:47:34,234 - tensorflow - INFO - loss = 0.031141484, step = 2536400 (2.254 sec) -2022-05-01 22:47:36,495 - tensorflow - INFO - global_step/sec: 88.4556 -2022-05-01 22:47:36,495 - tensorflow - INFO - loss = 0.029849585, step = 2536600 (2.261 sec) -2022-05-01 22:47:38,746 - tensorflow - INFO - global_step/sec: 88.8618 -2022-05-01 22:47:38,746 - tensorflow - INFO - loss = 0.014284352, step = 2536800 (2.251 sec) -2022-05-01 22:47:40,995 - tensorflow - INFO - global_step/sec: 88.9144 -2022-05-01 22:47:40,995 - tensorflow - INFO - loss = 0.014337454, step = 2537000 (2.249 sec) -2022-05-01 22:47:43,249 - tensorflow - INFO - global_step/sec: 88.7188 -2022-05-01 22:47:43,250 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.3167 -2022-05-01 22:47:59,054 - tensorflow - INFO - loss = 0.06591574, step = 2538600 (2.265 sec) -2022-05-01 22:48:01,316 - tensorflow - INFO - global_step/sec: 88.3856 -2022-05-01 22:48:01,317 - tensorflow - INFO - loss = 0.010528269, step = 2538800 (2.263 sec) -2022-05-01 22:48:03,575 - tensorflow - INFO - global_step/sec: 88.5373 -2022-05-01 22:48:03,575 - tensorflow - INFO - loss = 0.0035607866, step = 2539000 (2.259 sec) -2022-05-01 22:48:05,836 - tensorflow - INFO - global_step/sec: 88.4755 -2022-05-01 22:48:05,836 - tensorflow - INFO - loss = 0.0062068263, step = 2539200 (2.261 sec) -2022-05-01 22:48:08,100 - tensorflow - INFO - global_step/sec: 88.3318 -2022-05-01 22:48:08,100 - tensorflow - INFO - loss = 0.014400102, step = 2539400 (2.264 sec) -2022-05-01 22:48:10,362 - tensorflow - INFO - global_step/sec: 88.4241 -2022-05-01 22:48:10,362 - tensorflow - INFO - loss = 0.016748605, step = 2539600 (2.262 sec) -2022-05-01 22:48:12,622 - tensorflow - INFO - global_step/sec: 88.4698 -2022-05-01 22:48:12,623 - tensorflow - INFO - loss = 0.009805948, step = 2539800 (2.261 sec) -2022-05-01 22:48:14,883 - tensorflow - INFO - global_step/sec: 88.4636 -2022-05-01 22:48:14,883 - tensorflow - INFO - loss = 0.0056213485, step = 2540000 (2.261 sec) -2022-05-01 22:48:17,149 - tensorflow - INFO - global_step/sec: 88.2496 -2022-05-01 22:48:17,150 - tensorflow - INFO - loss = 0.01510028, step = 2540200 (2.266 sec) -2022-05-01 22:48:19,412 - tensorflow - INFO - global_step/sec: 88.3843 -2022-05-01 22:48:19,413 - tensorflow - INFO - loss = 0.038076065, step = 2540400 (2.263 sec) -2022-05-01 22:48:21,679 - tensorflow - INFO - global_step/sec: 88.2475 -2022-05-01 22:48:21,679 - tensorflow - INFO - loss = 0.018373983, step = 2540600 (2.266 sec) -2022-05-01 22:48:23,936 - tensorflow - INFO - global_step/sec: 88.5829 -2022-05-01 22:48:23,937 - tensorflow - INFO - loss = 0.0557938, step = 2540800 (2.258 sec) -2022-05-01 22:48:26,198 - tensorflow - INFO - global_step/sec: 88.4091 -2022-05-01 22:48:26,199 - tensorflow - INFO - loss = 0.07594441, step = 2541000 (2.262 sec) -2022-05-01 22:48:28,450 - tensorflow - INFO - global_step/sec: 88.8371 -2022-05-01 22:48:28,450 - tensorflow - INFO - loss = 0.00945507, step = 2541200 (2.251 sec) -2022-05-01 22:48:30,705 - tensorflow - INFO - global_step/sec: 88.6965 -2022-05-01 22:48:30,705 - tensorflow - INFO - loss = 0.029454486, step = 2541400 (2.255 sec) -2022-05-01 22:48:32,953 - tensorflow - INFO - global_step/sec: 88.9577 -2022-05-01 22:48:32,953 - tensorflow - INFO - loss = 0.013326672, step = 2541600 (2.248 sec) -2022-05-01 22:48:35,197 - tensorflow - INFO - global_step/sec: 89.1064 -2022-05-01 22:48:35,198 - tensorflow - INFO - loss = 0.016604941, step = 2541800 (2.244 sec) -2022-05-01 22:48:37,451 - tensorflow - INFO - global_step/sec: 88.7576 -2022-05-01 22:48:37,451 - tensorflow - INFO - loss = 0.0717519, step = 2542000 (2.253 sec) -2022-05-01 22:48:39,707 - tensorflow - INFO - global_step/sec: 88.6594 -2022-05-01 22:48:39,707 - tensorflow - INFO - loss = 0.019117307, step = 2542200 (2.256 sec) -2022-05-01 22:48:41,966 - tensorflow - INFO - global_step/sec: 88.5254 -2022-05-01 22:48:41,966 - tensorflow - INFO - loss = 0.014296472, step = 2542400 (2.259 sec) -2022-05-01 22:48:44,217 - tensorflow - INFO - global_step/sec: 88.8361 -2022-05-01 22:48:44,218 - tensorflow - INFO - loss = 0.005062365, step = 2542600 (2.251 sec) -2022-05-01 22:48:46,468 - tensorflow - INFO - global_step/sec: 88.8366 -2022-05-01 22:48:46,469 - tensorflow - INFO - loss = 0.038442142, step = 2542800 (2.251 sec) -2022-05-01 22:48:48,720 - tensorflow - INFO - global_step/sec: 88.8246 -2022-05-01 22:48:48,721 - tensorflow - INFO - loss = 0.23086584, step = 2543000 (2.252 sec) -2022-05-01 22:48:50,974 - tensorflow - INFO - global_step/sec: 88.7239 -2022-05-01 22:48:50,975 - tensorflow - INFO - loss = 0.015884178, step = 2543200 (2.254 sec) -2022-05-01 22:48:53,235 - tensorflow - INFO - global_step/sec: 88.4688 -2022-05-01 22:48:53,235 - tensorflow - INFO - loss = 0.010811222, step = 2543400 (2.261 sec) -2022-05-01 22:48:55,485 - tensorflow - INFO - global_step/sec: 88.8903 -2022-05-01 22:48:55,485 - tensorflow - INFO - loss = 0.006758169, step = 2543600 (2.250 sec) -2022-05-01 22:48:57,747 - tensorflow - INFO - global_step/sec: 88.4124 -2022-05-01 22:48:57,748 - tensorflow - INFO - loss = 0.081859335, step = 2543800 (2.262 sec) -2022-05-01 22:48:59,996 - tensorflow - INFO - global_step/sec: 88.9153 -2022-05-01 22:48:59,997 - tensorflow - INFO - loss = 0.005912382, step = 2544000 (2.249 sec) -2022-05-01 22:49:02,255 - tensorflow - INFO - global_step/sec: 88.5666 -2022-05-01 22:49:02,255 - tensorflow - INFO - loss = 0.04825076, step = 2544200 (2.258 sec) -2022-05-01 22:49:04,510 - tensorflow - INFO - global_step/sec: 88.6872 -2022-05-01 22:49:04,510 - tensorflow - INFO - loss = 0.0073155835, step = 2544400 (2.255 sec) -2022-05-01 22:49:06,767 - tensorflow - INFO - global_step/sec: 88.6002 -2022-05-01 22:49:06,767 - tensorflow - INFO - 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INFO - global_step/sec: 88.8661 -2022-05-01 22:49:36,071 - tensorflow - INFO - loss = 0.008072868, step = 2547200 (2.251 sec) -2022-05-01 22:49:38,325 - tensorflow - INFO - global_step/sec: 88.7389 -2022-05-01 22:49:38,325 - tensorflow - INFO - loss = 0.004705961, step = 2547400 (2.254 sec) -2022-05-01 22:49:40,581 - tensorflow - INFO - global_step/sec: 88.6195 -2022-05-01 22:49:40,582 - tensorflow - INFO - loss = 0.040594444, step = 2547600 (2.257 sec) -2022-05-01 22:49:42,832 - tensorflow - INFO - global_step/sec: 88.8781 -2022-05-01 22:49:42,832 - tensorflow - INFO - loss = 0.0070745815, step = 2547800 (2.250 sec) -2022-05-01 22:49:45,092 - tensorflow - INFO - global_step/sec: 88.4629 -2022-05-01 22:49:45,093 - tensorflow - INFO - loss = 0.006131235, step = 2548000 (2.261 sec) -2022-05-01 22:49:47,343 - tensorflow - INFO - global_step/sec: 88.8685 -2022-05-01 22:49:47,343 - tensorflow - INFO - loss = 0.03124081, step = 2548200 (2.250 sec) -2022-05-01 22:49:49,593 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.6074 -2022-05-01 22:50:32,430 - tensorflow - INFO - loss = 0.83199763, step = 2552200 (2.257 sec) -2022-05-01 22:50:34,687 - tensorflow - INFO - global_step/sec: 88.5733 -2022-05-01 22:50:34,688 - tensorflow - INFO - loss = 0.021190714, step = 2552400 (2.258 sec) -2022-05-01 22:50:36,938 - tensorflow - INFO - global_step/sec: 88.8412 -2022-05-01 22:50:36,939 - tensorflow - INFO - loss = 0.0023580533, step = 2552600 (2.251 sec) -2022-05-01 22:50:39,191 - tensorflow - INFO - global_step/sec: 88.7684 -2022-05-01 22:50:39,192 - tensorflow - INFO - loss = 0.011092663, step = 2552800 (2.253 sec) -2022-05-01 22:50:41,446 - tensorflow - INFO - global_step/sec: 88.7127 -2022-05-01 22:50:41,446 - tensorflow - INFO - loss = 0.11473206, step = 2553000 (2.254 sec) -2022-05-01 22:50:43,696 - tensorflow - INFO - global_step/sec: 88.8791 -2022-05-01 22:50:43,697 - tensorflow - INFO - loss = 0.010452032, step = 2553200 (2.250 sec) -2022-05-01 22:50:45,956 - tensorflow - INFO - global_step/sec: 88.5052 -2022-05-01 22:50:45,956 - tensorflow - INFO - loss = 0.016556587, step = 2553400 (2.260 sec) -2022-05-01 22:50:48,207 - tensorflow - INFO - global_step/sec: 88.8291 -2022-05-01 22:50:48,208 - tensorflow - INFO - loss = 0.33795607, step = 2553600 (2.252 sec) -2022-05-01 22:50:50,460 - tensorflow - INFO - global_step/sec: 88.7849 -2022-05-01 22:50:50,460 - tensorflow - INFO - loss = 0.026521377, step = 2553800 (2.253 sec) -2022-05-01 22:50:52,710 - tensorflow - INFO - global_step/sec: 88.9013 -2022-05-01 22:50:52,710 - tensorflow - INFO - loss = 0.03137654, step = 2554000 (2.250 sec) -2022-05-01 22:50:54,967 - tensorflow - INFO - global_step/sec: 88.6091 -2022-05-01 22:50:54,967 - tensorflow - INFO - loss = 0.01058252, step = 2554200 (2.257 sec) -2022-05-01 22:50:57,218 - tensorflow - INFO - global_step/sec: 88.8363 -2022-05-01 22:50:57,219 - tensorflow - INFO - loss = 0.008173717, step = 2554400 (2.251 sec) -2022-05-01 22:50:59,476 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.042044383, step = 2555800 (2.262 sec) -2022-05-01 22:51:15,253 - tensorflow - INFO - global_step/sec: 88.7395 -2022-05-01 22:51:15,253 - tensorflow - INFO - loss = 0.026508592, step = 2556000 (2.254 sec) -2022-05-01 22:51:17,504 - tensorflow - INFO - global_step/sec: 88.8343 -2022-05-01 22:51:17,505 - tensorflow - INFO - loss = 0.014341069, step = 2556200 (2.251 sec) -2022-05-01 22:51:19,754 - tensorflow - INFO - global_step/sec: 88.9068 -2022-05-01 22:51:19,754 - tensorflow - INFO - loss = 0.04941809, step = 2556400 (2.250 sec) -2022-05-01 22:51:22,009 - tensorflow - INFO - global_step/sec: 88.6659 -2022-05-01 22:51:22,010 - tensorflow - INFO - loss = 0.016061569, step = 2556600 (2.256 sec) -2022-05-01 22:51:24,272 - tensorflow - INFO - global_step/sec: 88.3814 -2022-05-01 22:51:24,273 - tensorflow - INFO - loss = 0.0072139446, step = 2556800 (2.263 sec) -2022-05-01 22:51:26,531 - tensorflow - INFO - global_step/sec: 88.5473 -2022-05-01 22:51:26,531 - tensorflow - INFO - loss = 0.021163104, step = 2557000 (2.259 sec) -2022-05-01 22:51:28,788 - tensorflow - INFO - global_step/sec: 88.6157 -2022-05-01 22:51:28,788 - tensorflow - INFO - loss = 0.0047359876, step = 2557200 (2.257 sec) -2022-05-01 22:51:31,056 - tensorflow - INFO - global_step/sec: 88.185 -2022-05-01 22:51:31,056 - tensorflow - INFO - loss = 0.0071138768, step = 2557400 (2.268 sec) -2022-05-01 22:51:33,315 - tensorflow - INFO - global_step/sec: 88.5097 -2022-05-01 22:51:33,316 - tensorflow - INFO - loss = 0.014201727, step = 2557600 (2.260 sec) -2022-05-01 22:51:35,581 - tensorflow - INFO - global_step/sec: 88.2699 -2022-05-01 22:51:35,582 - tensorflow - INFO - loss = 0.03632896, step = 2557800 (2.266 sec) -2022-05-01 22:51:37,845 - tensorflow - INFO - global_step/sec: 88.3564 -2022-05-01 22:51:37,845 - tensorflow - INFO - loss = 0.043422718, step = 2558000 (2.264 sec) -2022-05-01 22:51:40,106 - tensorflow - INFO - global_step/sec: 88.4415 -2022-05-01 22:51:40,107 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.3785 -2022-05-01 22:51:55,942 - tensorflow - INFO - loss = 0.010030072, step = 2559600 (2.263 sec) -2022-05-01 22:51:58,199 - tensorflow - INFO - global_step/sec: 88.6044 -2022-05-01 22:51:58,199 - tensorflow - INFO - loss = 0.1980426, step = 2559800 (2.257 sec) -2022-05-01 22:52:00,453 - tensorflow - INFO - global_step/sec: 88.7164 -2022-05-01 22:52:00,454 - tensorflow - INFO - loss = 0.019649642, step = 2560000 (2.254 sec) -2022-05-01 22:52:02,706 - tensorflow - INFO - global_step/sec: 88.7925 -2022-05-01 22:52:02,706 - tensorflow - INFO - loss = 0.013645825, step = 2560200 (2.252 sec) -2022-05-01 22:52:04,962 - tensorflow - INFO - global_step/sec: 88.6468 -2022-05-01 22:52:04,962 - tensorflow - INFO - loss = 0.008293188, step = 2560400 (2.256 sec) -2022-05-01 22:52:07,216 - tensorflow - INFO - global_step/sec: 88.7106 -2022-05-01 22:52:07,217 - tensorflow - INFO - loss = 0.032863967, step = 2560600 (2.255 sec) -2022-05-01 22:52:09,465 - tensorflow - INFO - global_step/sec: 88.9278 -2022-05-01 22:52:09,466 - tensorflow - INFO - loss = 0.010850931, step = 2560800 (2.249 sec) -2022-05-01 22:52:11,720 - tensorflow - INFO - global_step/sec: 88.6977 -2022-05-01 22:52:11,720 - tensorflow - INFO - loss = 0.0175879, step = 2561000 (2.255 sec) -2022-05-01 22:52:13,974 - tensorflow - INFO - global_step/sec: 88.7345 -2022-05-01 22:52:13,974 - tensorflow - INFO - loss = 0.014139969, step = 2561200 (2.254 sec) -2022-05-01 22:52:16,227 - tensorflow - INFO - global_step/sec: 88.7889 -2022-05-01 22:52:16,227 - tensorflow - INFO - loss = 0.009444745, step = 2561400 (2.253 sec) -2022-05-01 22:52:18,480 - tensorflow - INFO - global_step/sec: 88.7545 -2022-05-01 22:52:18,480 - tensorflow - INFO - loss = 0.020438613, step = 2561600 (2.253 sec) -2022-05-01 22:52:20,734 - tensorflow - INFO - global_step/sec: 88.7219 -2022-05-01 22:52:20,735 - tensorflow - INFO - loss = 0.003945601, step = 2561800 (2.254 sec) -2022-05-01 22:52:22,986 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.833 -2022-05-01 22:54:42,731 - tensorflow - INFO - loss = 0.005400113, step = 2574400 (2.251 sec) -2022-05-01 22:54:44,984 - tensorflow - INFO - global_step/sec: 88.7463 -2022-05-01 22:54:44,984 - tensorflow - INFO - loss = 0.0032447493, step = 2574600 (2.254 sec) -2022-05-01 22:54:47,237 - tensorflow - INFO - global_step/sec: 88.7822 -2022-05-01 22:54:47,237 - tensorflow - INFO - loss = 0.03992021, step = 2574800 (2.253 sec) -2022-05-01 22:54:49,495 - tensorflow - INFO - global_step/sec: 88.5497 -2022-05-01 22:54:49,496 - tensorflow - INFO - loss = 0.006976843, step = 2575000 (2.259 sec) -2022-05-01 22:54:51,751 - tensorflow - INFO - global_step/sec: 88.6778 -2022-05-01 22:54:51,751 - tensorflow - INFO - loss = 0.022812184, step = 2575200 (2.255 sec) -2022-05-01 22:54:54,009 - tensorflow - INFO - global_step/sec: 88.568 -2022-05-01 22:54:54,009 - tensorflow - INFO - loss = 0.0229411, step = 2575400 (2.258 sec) -2022-05-01 22:54:56,261 - tensorflow - INFO - global_step/sec: 88.784 -2022-05-01 22:54:56,262 - tensorflow - INFO - loss = 0.037632674, step = 2575600 (2.253 sec) -2022-05-01 22:54:58,518 - tensorflow - INFO - global_step/sec: 88.6304 -2022-05-01 22:54:58,518 - tensorflow - INFO - loss = 0.055111006, step = 2575800 (2.256 sec) -2022-05-01 22:55:00,782 - tensorflow - INFO - global_step/sec: 88.3433 -2022-05-01 22:55:00,782 - tensorflow - INFO - loss = 0.05147831, step = 2576000 (2.264 sec) -2022-05-01 22:55:03,046 - tensorflow - INFO - global_step/sec: 88.3119 -2022-05-01 22:55:03,047 - tensorflow - INFO - loss = 0.048165888, step = 2576200 (2.265 sec) -2022-05-01 22:55:05,307 - tensorflow - INFO - global_step/sec: 88.4672 -2022-05-01 22:55:05,308 - tensorflow - INFO - loss = 0.010406117, step = 2576400 (2.261 sec) -2022-05-01 22:55:07,566 - tensorflow - INFO - global_step/sec: 88.5268 -2022-05-01 22:55:07,567 - tensorflow - INFO - loss = 0.021043975, step = 2576600 (2.259 sec) -2022-05-01 22:55:09,825 - tensorflow - INFO - 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INFO - loss = 0.009138454, step = 2579200 (2.252 sec) -2022-05-01 22:55:39,210 - tensorflow - INFO - global_step/sec: 88.9324 -2022-05-01 22:55:39,210 - tensorflow - INFO - loss = 0.011386326, step = 2579400 (2.249 sec) -2022-05-01 22:55:41,466 - tensorflow - INFO - global_step/sec: 88.659 -2022-05-01 22:55:41,466 - tensorflow - INFO - loss = 0.05404347, step = 2579600 (2.256 sec) -2022-05-01 22:55:43,721 - tensorflow - INFO - global_step/sec: 88.6752 -2022-05-01 22:55:43,722 - tensorflow - INFO - loss = 0.05870767, step = 2579800 (2.255 sec) -2022-05-01 22:55:45,979 - tensorflow - INFO - global_step/sec: 88.5692 -2022-05-01 22:55:45,980 - tensorflow - INFO - loss = 0.013865546, step = 2580000 (2.258 sec) -2022-05-01 22:55:48,231 - tensorflow - INFO - global_step/sec: 88.805 -2022-05-01 22:55:48,232 - tensorflow - INFO - loss = 0.020384759, step = 2580200 (2.252 sec) -2022-05-01 22:55:50,490 - tensorflow - INFO - global_step/sec: 88.5525 -2022-05-01 22:55:50,490 - tensorflow - INFO - loss = 0.03880316, step = 2580400 (2.259 sec) -2022-05-01 22:55:52,740 - tensorflow - INFO - global_step/sec: 88.8892 -2022-05-01 22:55:52,740 - tensorflow - INFO - loss = 0.18459275, step = 2580600 (2.250 sec) -2022-05-01 22:55:54,996 - tensorflow - INFO - global_step/sec: 88.6499 -2022-05-01 22:55:54,996 - tensorflow - INFO - loss = 0.022431366, step = 2580800 (2.256 sec) -2022-05-01 22:55:57,256 - tensorflow - INFO - global_step/sec: 88.4933 -2022-05-01 22:55:57,257 - tensorflow - INFO - loss = 0.010495337, step = 2581000 (2.260 sec) -2022-05-01 22:55:59,508 - tensorflow - INFO - global_step/sec: 88.7956 -2022-05-01 22:55:59,509 - tensorflow - INFO - loss = 0.009700634, step = 2581200 (2.252 sec) -2022-05-01 22:56:01,765 - tensorflow - INFO - global_step/sec: 88.6344 -2022-05-01 22:56:01,765 - tensorflow - INFO - loss = 0.00733386, step = 2581400 (2.256 sec) -2022-05-01 22:56:04,023 - tensorflow - INFO - global_step/sec: 88.5672 -2022-05-01 22:56:04,023 - tensorflow - INFO - loss = 0.01729981, step = 2581600 (2.258 sec) -2022-05-01 22:56:06,277 - tensorflow - INFO - global_step/sec: 88.7544 -2022-05-01 22:56:06,277 - tensorflow - INFO - loss = 0.0063993847, step = 2581800 (2.253 sec) -2022-05-01 22:56:08,534 - tensorflow - INFO - global_step/sec: 88.5805 -2022-05-01 22:56:08,535 - tensorflow - INFO - loss = 0.0073182443, step = 2582000 (2.258 sec) -2022-05-01 22:56:10,786 - tensorflow - INFO - global_step/sec: 88.8317 -2022-05-01 22:56:10,786 - tensorflow - INFO - loss = 0.021946015, step = 2582200 (2.251 sec) -2022-05-01 22:56:13,039 - tensorflow - INFO - global_step/sec: 88.7672 -2022-05-01 22:56:13,039 - tensorflow - INFO - loss = 0.0060194437, step = 2582400 (2.253 sec) -2022-05-01 22:56:15,292 - tensorflow - INFO - global_step/sec: 88.7481 -2022-05-01 22:56:15,293 - tensorflow - INFO - loss = 2.427406, step = 2582600 (2.254 sec) -2022-05-01 22:56:17,543 - tensorflow - INFO - global_step/sec: 88.8731 -2022-05-01 22:56:17,543 - tensorflow - INFO - loss = 0.14152369, step = 2582800 (2.250 sec) -2022-05-01 22:56:19,800 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.011723658, step = 2584200 (2.257 sec) -2022-05-01 22:56:35,606 - tensorflow - INFO - global_step/sec: 88.5769 -2022-05-01 22:56:35,607 - tensorflow - INFO - loss = 0.04543133, step = 2584400 (2.258 sec) -2022-05-01 22:56:37,854 - tensorflow - INFO - global_step/sec: 88.9811 -2022-05-01 22:56:37,854 - tensorflow - INFO - loss = 0.109359056, step = 2584600 (2.248 sec) -2022-05-01 22:56:40,106 - tensorflow - INFO - global_step/sec: 88.8278 -2022-05-01 22:56:40,106 - tensorflow - INFO - loss = 0.019882528, step = 2584800 (2.252 sec) -2022-05-01 22:56:42,356 - tensorflow - INFO - global_step/sec: 88.8675 -2022-05-01 22:56:42,357 - tensorflow - INFO - loss = 0.29308128, step = 2585000 (2.251 sec) -2022-05-01 22:56:44,607 - tensorflow - INFO - global_step/sec: 88.8432 -2022-05-01 22:56:44,608 - tensorflow - INFO - loss = 0.011806726, step = 2585200 (2.251 sec) -2022-05-01 22:56:46,862 - tensorflow - INFO - global_step/sec: 88.6902 -2022-05-01 22:56:46,863 - tensorflow - INFO - loss = 0.06566036, step = 2585400 (2.255 sec) -2022-05-01 22:56:49,111 - tensorflow - INFO - global_step/sec: 88.9266 -2022-05-01 22:56:49,112 - tensorflow - INFO - loss = 0.014203774, step = 2585600 (2.249 sec) -2022-05-01 22:56:51,365 - tensorflow - INFO - global_step/sec: 88.7547 -2022-05-01 22:56:51,365 - tensorflow - INFO - loss = 0.013998233, step = 2585800 (2.253 sec) -2022-05-01 22:56:53,618 - tensorflow - INFO - global_step/sec: 88.7695 -2022-05-01 22:56:53,618 - tensorflow - INFO - loss = 0.012911035, step = 2586000 (2.253 sec) -2022-05-01 22:56:55,873 - tensorflow - INFO - global_step/sec: 88.6755 -2022-05-01 22:56:55,874 - tensorflow - INFO - loss = 0.013721411, step = 2586200 (2.255 sec) -2022-05-01 22:56:58,133 - tensorflow - INFO - global_step/sec: 88.523 -2022-05-01 22:56:58,133 - tensorflow - INFO - loss = 0.0067030694, step = 2586400 (2.259 sec) -2022-05-01 22:57:00,384 - tensorflow - INFO - global_step/sec: 88.8318 -2022-05-01 22:57:00,384 - tensorflow - INFO - loss = 0.009887196, step = 2586600 (2.251 sec) -2022-05-01 22:57:02,643 - tensorflow - INFO - global_step/sec: 88.547 -2022-05-01 22:57:02,643 - tensorflow - INFO - loss = 0.31959456, step = 2586800 (2.259 sec) -2022-05-01 22:57:04,896 - tensorflow - INFO - global_step/sec: 88.7475 -2022-05-01 22:57:04,897 - tensorflow - INFO - loss = 0.018577524, step = 2587000 (2.254 sec) -2022-05-01 22:57:07,148 - tensorflow - INFO - global_step/sec: 88.8043 -2022-05-01 22:57:07,149 - tensorflow - INFO - loss = 0.0077270763, step = 2587200 (2.252 sec) -2022-05-01 22:57:09,406 - tensorflow - INFO - global_step/sec: 88.5889 -2022-05-01 22:57:09,406 - tensorflow - INFO - loss = 0.057520434, step = 2587400 (2.258 sec) -2022-05-01 22:57:11,659 - tensorflow - INFO - global_step/sec: 88.7908 -2022-05-01 22:57:11,659 - tensorflow - INFO - loss = 0.15892008, step = 2587600 (2.253 sec) -2022-05-01 22:57:13,909 - tensorflow - INFO - global_step/sec: 88.8624 -2022-05-01 22:57:13,910 - tensorflow - INFO - loss = 0.03677657, step = 2587800 (2.251 sec) -2022-05-01 22:57:16,159 - tensorflow - INFO - global_step/sec: 88.9004 -2022-05-01 22:57:16,159 - tensorflow - INFO - loss = 0.011227585, step = 2588000 (2.250 sec) -2022-05-01 22:57:18,409 - tensorflow - INFO - global_step/sec: 88.8677 -2022-05-01 22:57:18,410 - tensorflow - INFO - loss = 0.050311897, step = 2588200 (2.251 sec) -2022-05-01 22:57:20,664 - tensorflow - INFO - global_step/sec: 88.6989 -2022-05-01 22:57:20,665 - tensorflow - INFO - loss = 0.0051274067, step = 2588400 (2.255 sec) -2022-05-01 22:57:22,915 - tensorflow - INFO - global_step/sec: 88.8735 -2022-05-01 22:57:22,915 - tensorflow - INFO - loss = 0.031046556, step = 2588600 (2.250 sec) -2022-05-01 22:57:25,170 - tensorflow - INFO - global_step/sec: 88.6787 -2022-05-01 22:57:25,170 - tensorflow - INFO - loss = 0.008272649, step = 2588800 (2.255 sec) -2022-05-01 22:57:27,428 - tensorflow - INFO - global_step/sec: 88.5865 -2022-05-01 22:57:27,428 - tensorflow - INFO - loss = 0.0075667934, step = 2589000 (2.258 sec) -2022-05-01 22:57:29,683 - tensorflow - INFO - global_step/sec: 88.6647 -2022-05-01 22:57:29,684 - tensorflow - INFO - loss = 0.03137114, step = 2589200 (2.256 sec) -2022-05-01 22:57:31,937 - tensorflow - INFO - global_step/sec: 88.749 -2022-05-01 22:57:31,937 - tensorflow - INFO - loss = 0.0040960596, step = 2589400 (2.254 sec) -2022-05-01 22:57:34,189 - tensorflow - INFO - global_step/sec: 88.7869 -2022-05-01 22:57:34,190 - tensorflow - INFO - loss = 0.01772147, step = 2589600 (2.253 sec) -2022-05-01 22:57:36,445 - tensorflow - INFO - global_step/sec: 88.6831 -2022-05-01 22:57:36,445 - tensorflow - INFO - loss = 0.050396413, step = 2589800 (2.255 sec) -2022-05-01 22:57:38,696 - tensorflow - INFO - global_step/sec: 88.8194 -2022-05-01 22:57:38,697 - tensorflow - INFO - loss = 0.01502031, step = 2590000 (2.252 sec) -2022-05-01 22:57:40,950 - tensorflow - INFO - global_step/sec: 88.768 -2022-05-01 22:57:40,950 - tensorflow - INFO - loss = 0.009751061, step = 2590200 (2.253 sec) -2022-05-01 22:57:43,205 - tensorflow - INFO - global_step/sec: 88.6663 -2022-05-01 22:57:43,206 - tensorflow - INFO - loss = 0.021950861, step = 2590400 (2.256 sec) -2022-05-01 22:57:45,458 - tensorflow - INFO - global_step/sec: 88.7737 -2022-05-01 22:57:45,458 - tensorflow - INFO - loss = 0.022604462, step = 2590600 (2.253 sec) -2022-05-01 22:57:47,712 - tensorflow - INFO - global_step/sec: 88.7264 -2022-05-01 22:57:47,713 - tensorflow - INFO - loss = 0.0073060235, step = 2590800 (2.254 sec) -2022-05-01 22:57:49,968 - tensorflow - INFO - global_step/sec: 88.671 -2022-05-01 22:57:49,968 - tensorflow - INFO - loss = 0.013161661, step = 2591000 (2.256 sec) -2022-05-01 22:57:52,221 - tensorflow - INFO - global_step/sec: 88.76 -2022-05-01 22:57:52,221 - tensorflow - INFO - loss = 0.038956575, step = 2591200 (2.253 sec) -2022-05-01 22:57:54,471 - tensorflow - INFO - global_step/sec: 88.8912 -2022-05-01 22:57:54,471 - tensorflow - INFO - loss = 0.0062237633, step = 2591400 (2.250 sec) -2022-05-01 22:57:56,727 - tensorflow - INFO - global_step/sec: 88.6646 -2022-05-01 22:57:56,727 - tensorflow - INFO - loss = 0.013718772, step = 2591600 (2.256 sec) -2022-05-01 22:57:58,979 - tensorflow - INFO - global_step/sec: 88.7919 -2022-05-01 22:57:58,980 - tensorflow - INFO - loss = 0.7088275, step = 2591800 (2.252 sec) -2022-05-01 22:58:01,239 - tensorflow - INFO - global_step/sec: 88.5091 -2022-05-01 22:58:01,239 - tensorflow - INFO - loss = 0.006887424, step = 2592000 (2.260 sec) -2022-05-01 22:58:03,492 - tensorflow - INFO - global_step/sec: 88.7505 -2022-05-01 22:58:03,493 - tensorflow - INFO - loss = 0.11528784, step = 2592200 (2.254 sec) -2022-05-01 22:58:05,743 - tensorflow - INFO - global_step/sec: 88.8667 -2022-05-01 22:58:05,743 - tensorflow - INFO - loss = 0.009931682, step = 2592400 (2.250 sec) -2022-05-01 22:58:07,996 - tensorflow - INFO - global_step/sec: 88.7633 -2022-05-01 22:58:07,996 - tensorflow - INFO - loss = 0.013525553, step = 2592600 (2.253 sec) -2022-05-01 22:58:10,249 - tensorflow - INFO - global_step/sec: 88.7874 -2022-05-01 22:58:10,249 - tensorflow - INFO - loss = 0.007436597, step = 2592800 (2.253 sec) -2022-05-01 22:58:12,503 - tensorflow - INFO - global_step/sec: 88.7123 -2022-05-01 22:58:12,503 - tensorflow - INFO - loss = 0.0054980563, step = 2593000 (2.254 sec) -2022-05-01 22:58:14,757 - tensorflow - INFO - global_step/sec: 88.7201 -2022-05-01 22:58:14,758 - tensorflow - INFO - loss = 0.0050919726, step = 2593200 (2.254 sec) -2022-05-01 22:58:17,015 - tensorflow - INFO - global_step/sec: 88.6058 -2022-05-01 22:58:17,015 - tensorflow - INFO - loss = 0.0148495585, step = 2593400 (2.257 sec) -2022-05-01 22:58:19,265 - tensorflow - INFO - global_step/sec: 88.8656 -2022-05-01 22:58:19,266 - tensorflow - INFO - loss = 0.10807957, step = 2593600 (2.251 sec) -2022-05-01 22:58:21,516 - tensorflow - INFO - global_step/sec: 88.8721 -2022-05-01 22:58:21,516 - tensorflow - INFO - loss = 0.03904371, step = 2593800 (2.250 sec) -2022-05-01 22:58:23,771 - tensorflow - INFO - global_step/sec: 88.6903 -2022-05-01 22:58:23,771 - tensorflow - INFO - loss = 0.010118168, step = 2594000 (2.255 sec) -2022-05-01 22:58:26,028 - tensorflow - INFO - global_step/sec: 88.6155 -2022-05-01 22:58:26,028 - tensorflow - INFO - loss = 0.026852738, step = 2594200 (2.257 sec) -2022-05-01 22:58:28,280 - tensorflow - INFO - global_step/sec: 88.7723 -2022-05-01 22:58:28,281 - tensorflow - INFO - loss = 0.01683753, step = 2594400 (2.253 sec) -2022-05-01 22:58:30,540 - tensorflow - INFO - global_step/sec: 88.5013 -2022-05-01 22:58:30,541 - tensorflow - INFO - loss = 0.033845864, step = 2594600 (2.260 sec) -2022-05-01 22:58:32,804 - tensorflow - INFO - global_step/sec: 88.3342 -2022-05-01 22:58:32,805 - tensorflow - INFO - loss = 0.12980647, step = 2594800 (2.264 sec) -2022-05-01 22:58:35,062 - tensorflow - INFO - global_step/sec: 88.5774 -2022-05-01 22:58:35,063 - tensorflow - INFO - loss = 0.008408929, step = 2595000 (2.258 sec) -2022-05-01 22:58:37,324 - tensorflow - INFO - global_step/sec: 88.417 -2022-05-01 22:58:37,325 - tensorflow - INFO - loss = 0.018310972, step = 2595200 (2.262 sec) -2022-05-01 22:58:39,589 - tensorflow - INFO - global_step/sec: 88.3019 -2022-05-01 22:58:39,590 - tensorflow - INFO - loss = 0.013260505, step = 2595400 (2.265 sec) -2022-05-01 22:58:41,845 - tensorflow - INFO - global_step/sec: 88.6845 -2022-05-01 22:58:41,845 - tensorflow - INFO - loss = 0.019862723, step = 2595600 (2.255 sec) -2022-05-01 22:58:44,108 - tensorflow - INFO - global_step/sec: 88.3498 -2022-05-01 22:58:44,109 - tensorflow - INFO - loss = 0.0025866749, step = 2595800 (2.264 sec) -2022-05-01 22:58:46,372 - tensorflow - INFO - global_step/sec: 88.3395 -2022-05-01 22:58:46,373 - tensorflow - INFO - loss = 0.013068748, step = 2596000 (2.264 sec) -2022-05-01 22:58:48,631 - tensorflow - INFO - global_step/sec: 88.5417 -2022-05-01 22:58:48,631 - tensorflow - INFO - loss = 0.018583922, step = 2596200 (2.259 sec) -2022-05-01 22:58:50,891 - tensorflow - INFO - global_step/sec: 88.4857 -2022-05-01 22:58:50,892 - tensorflow - INFO - loss = 0.019659905, step = 2596400 (2.260 sec) -2022-05-01 22:58:53,154 - tensorflow - INFO - global_step/sec: 88.3723 -2022-05-01 22:58:53,155 - tensorflow - INFO - loss = 0.06279365, step = 2596600 (2.263 sec) -2022-05-01 22:58:55,418 - tensorflow - INFO - global_step/sec: 88.3445 -2022-05-01 22:58:55,419 - tensorflow - INFO - loss = 0.017705481, step = 2596800 (2.264 sec) -2022-05-01 22:58:57,682 - tensorflow - INFO - global_step/sec: 88.3529 -2022-05-01 22:58:57,682 - tensorflow - INFO - loss = 0.01881864, step = 2597000 (2.264 sec) -2022-05-01 22:58:59,948 - tensorflow - INFO - global_step/sec: 88.2484 -2022-05-01 22:58:59,949 - tensorflow - INFO - loss = 0.0077836798, step = 2597200 (2.266 sec) -2022-05-01 22:59:02,204 - tensorflow - INFO - global_step/sec: 88.6699 -2022-05-01 22:59:02,204 - tensorflow - INFO - loss = 0.16888902, step = 2597400 (2.255 sec) -2022-05-01 22:59:04,459 - tensorflow - INFO - global_step/sec: 88.6693 -2022-05-01 22:59:04,460 - tensorflow - INFO - loss = 0.023880437, step = 2597600 (2.256 sec) -2022-05-01 22:59:06,720 - tensorflow - INFO - global_step/sec: 88.4835 -2022-05-01 22:59:06,720 - tensorflow - INFO - loss = 0.0019687468, step = 2597800 (2.260 sec) -2022-05-01 22:59:08,973 - tensorflow - INFO - global_step/sec: 88.772 -2022-05-01 22:59:08,973 - tensorflow - INFO - loss = 0.009858871, step = 2598000 (2.253 sec) -2022-05-01 22:59:11,226 - tensorflow - INFO - global_step/sec: 88.751 -2022-05-01 22:59:11,227 - tensorflow - INFO - loss = 0.03571446, step = 2598200 (2.253 sec) -2022-05-01 22:59:13,477 - tensorflow - INFO - global_step/sec: 88.8674 -2022-05-01 22:59:13,477 - tensorflow - INFO - loss = 0.004305632, step = 2598400 (2.251 sec) -2022-05-01 22:59:15,728 - tensorflow - INFO - global_step/sec: 88.8317 -2022-05-01 22:59:15,729 - tensorflow - INFO - loss = 0.011081892, step = 2598600 (2.251 sec) -2022-05-01 22:59:17,983 - tensorflow - INFO - global_step/sec: 88.6949 -2022-05-01 22:59:17,984 - tensorflow - INFO - loss = 0.029049512, step = 2598800 (2.255 sec) -2022-05-01 22:59:20,237 - tensorflow - INFO - global_step/sec: 88.7534 -2022-05-01 22:59:20,237 - tensorflow - INFO - loss = 0.045791313, step = 2599000 (2.253 sec) -2022-05-01 22:59:22,487 - tensorflow - INFO - global_step/sec: 88.881 -2022-05-01 22:59:22,487 - tensorflow - INFO - loss = 0.016003875, step = 2599200 (2.250 sec) -2022-05-01 22:59:24,740 - tensorflow - INFO - global_step/sec: 88.7705 -2022-05-01 22:59:24,740 - tensorflow - INFO - loss = 0.007047058, step = 2599400 (2.253 sec) -2022-05-01 22:59:26,994 - tensorflow - INFO - global_step/sec: 88.7231 -2022-05-01 22:59:26,994 - tensorflow - INFO - loss = 0.1277457, step = 2599600 (2.254 sec) -2022-05-01 22:59:29,242 - tensorflow - INFO - global_step/sec: 88.9479 -2022-05-01 22:59:29,243 - tensorflow - INFO - loss = 0.031332817, step = 2599800 (2.249 sec) -2022-05-01 22:59:31,492 - tensorflow - INFO - Saving checkpoints for 2600000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. -2022-05-01 22:59:31,508 - tensorflow - INFO - model.ckpt-2600000 is not in all_model_checkpoint_paths. Manually adding it. -2022-05-01 22:59:32,027 - tensorflow - INFO - Calling model_fn. -2022-05-01 22:59:32,158 - tensorflow - INFO - Done calling model_fn. -2022-05-01 22:59:32,173 - tensorflow - INFO - Starting evaluation at 2022-05-01T22:59:32Z -2022-05-01 22:59:32,238 - tensorflow - INFO - Graph was finalized. -2022-05-01 22:59:32,241 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-2600000 -2022-05-01 22:59:32,519 - tensorflow - INFO - Running local_init_op. -2022-05-01 22:59:32,625 - tensorflow - INFO - Done running local_init_op. -2022-05-01 22:59:42,867 - tensorflow - INFO - Evaluation [3990/39900] -2022-05-01 22:59:52,420 - tensorflow - INFO - Evaluation [7980/39900] -2022-05-01 23:00:01,983 - tensorflow - INFO - Evaluation [11970/39900] -2022-05-01 23:00:11,571 - tensorflow - INFO - Evaluation [15960/39900] -2022-05-01 23:00:21,147 - tensorflow - INFO - Evaluation [19950/39900] -2022-05-01 23:00:30,752 - tensorflow - INFO - Evaluation [23940/39900] -2022-05-01 23:00:40,323 - tensorflow - INFO - Evaluation [27930/39900] -2022-05-01 23:00:49,929 - tensorflow - INFO - Evaluation [31920/39900] -2022-05-01 23:00:59,510 - tensorflow - INFO - Evaluation [35910/39900] -2022-05-01 23:01:09,073 - tensorflow - INFO - Evaluation [39900/39900] -2022-05-01 23:01:09,208 - tensorflow - INFO - Finished evaluation at 2022-05-01-23:01:09 -2022-05-01 23:01:09,208 - tensorflow - INFO - Saving dict for global step 2600000: global_step = 2600000, loss = 0.020267775, mean_degree_err = 3.9795845 -2022-05-01 23:01:09,209 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 2600000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-2600000 -2022-05-01 23:01:09,209 - tensorflow - DEBUG - Calling exporter with the `is_the_final_export=True`. -2022-05-01 23:01:09,230 - tensorflow - INFO - Loss for final step: 0.01898054. \ No newline at end of file -- Gitee From a545cb00d61690303fee7cdcde2dffe4ee434e7e Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Sat, 7 May 2022 14:09:13 +0000 Subject: [PATCH 29/88] =?UTF-8?q?=E5=88=A0=E9=99=A4=E6=96=87=E4=BB=B6=20Te?= =?UTF-8?q?nsorFlow/contrib/cv/SVD=5FID2019=5Ffor=5FTensorflow/GPU?= =?UTF-8?q?=E7=B2=BE=E5=BA=A6(=E4=BB=A3=E7=A0=81=E9=80=BB=E8=BE=91?= =?UTF-8?q?=E5=90=8CNPU=E7=89=88=E6=9C=AC).txt?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...5\220\214NPU\347\211\210\346\234\254).txt" | 105 ------------------ 1 file changed, 105 deletions(-) delete mode 100644 "TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU\347\262\276\345\272\246(\344\273\243\347\240\201\351\200\273\350\276\221\345\220\214NPU\347\211\210\346\234\254).txt" diff --git "a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU\347\262\276\345\272\246(\344\273\243\347\240\201\351\200\273\350\276\221\345\220\214NPU\347\211\210\346\234\254).txt" "b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU\347\262\276\345\272\246(\344\273\243\347\240\201\351\200\273\350\276\221\345\220\214NPU\347\211\210\346\234\254).txt" deleted file mode 100644 index 15a4f409e..000000000 --- "a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU\347\262\276\345\272\246(\344\273\243\347\240\201\351\200\273\350\276\221\345\220\214NPU\347\211\210\346\234\254).txt" +++ /dev/null @@ -1,105 +0,0 @@ -Mean error: %f degrees 3.9795678 -Median error: %f degrees 2.5962727 -Std: %f degrees 9.361983 - -Percentiles: - 1%: 0.615485 - 2%: 0.765661 - 3%: 0.859573 - 4%: 0.933770 - 5%: 0.991698 - 6%: 1.044177 - 7%: 1.096108 - 8%: 1.139179 - 9%: 1.178358 - 10%: 1.218525 - 11%: 1.255853 - 12%: 1.294827 - 13%: 1.331633 - 14%: 1.368879 - 15%: 1.406948 - 16%: 1.441844 - 17%: 1.474588 - 18%: 1.508179 - 19%: 1.543069 - 20%: 1.576939 - 21%: 1.608395 - 22%: 1.644729 - 23%: 1.676781 - 24%: 1.711550 - 25%: 1.743160 - 26%: 1.777402 - 27%: 1.811321 - 28%: 1.847797 - 29%: 1.880135 - 30%: 1.913359 - 31%: 1.947926 - 32%: 1.980112 - 33%: 2.014214 - 34%: 2.047557 - 35%: 2.079143 - 36%: 2.111832 - 37%: 2.144206 - 38%: 2.178525 - 39%: 2.212224 - 40%: 2.245766 - 41%: 2.279673 - 42%: 2.314099 - 43%: 2.347187 - 44%: 2.381295 - 45%: 2.418161 - 46%: 2.452399 - 47%: 2.488370 - 48%: 2.523829 - 49%: 2.562771 - 50%: 2.596235 - 51%: 2.636707 - 52%: 2.675837 - 53%: 2.713537 - 54%: 2.753353 - 55%: 2.791760 - 56%: 2.830061 - 57%: 2.869558 - 58%: 2.909864 - 59%: 2.951013 - 60%: 2.995325 - 61%: 3.037444 - 62%: 3.085022 - 63%: 3.137624 - 64%: 3.186842 - 65%: 3.234585 - 66%: 3.286463 - 67%: 3.345505 - 68%: 3.400300 - 69%: 3.454171 - 70%: 3.511901 - 71%: 3.568425 - 72%: 3.631408 - 73%: 3.698511 - 74%: 3.770657 - 75%: 3.847002 - 76%: 3.918766 - 77%: 4.001153 - 78%: 4.092133 - 79%: 4.175701 - 80%: 4.280524 - 81%: 4.383471 - 82%: 4.505587 - 83%: 4.643044 - 84%: 4.795996 - 85%: 4.951040 - 86%: 5.116305 - 87%: 5.308140 - 88%: 5.553438 - 89%: 5.796063 - 90%: 6.108622 - 91%: 6.445337 - 92%: 6.875063 - 93%: 7.384479 - 94%: 7.999909 - 95%: 8.852993 - 96%: 10.022167 - 97%: 12.029458 - 98%: 15.299019 - 99%: 22.932156 -100%: 179.803162 \ No newline at end of file -- Gitee From 9772d969fe1ef9cf8e66dd9f6ff85fda354da63f Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Sat, 7 May 2022 14:09:20 +0000 Subject: [PATCH 30/88] =?UTF-8?q?=E5=88=A0=E9=99=A4=E6=96=87=E4=BB=B6=20Te?= =?UTF-8?q?nsorFlow/contrib/cv/SVD=5FID2019=5Ffor=5FTensorflow/main=5Fpoin?= =?UTF-8?q?t=5Fcloud=5Fgpu.py?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../main_point_cloud_gpu.py | 431 ------------------ 1 file changed, 431 deletions(-) delete mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/main_point_cloud_gpu.py diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/main_point_cloud_gpu.py b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/main_point_cloud_gpu.py deleted file mode 100644 index a3cbfe37d..000000000 --- a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/main_point_cloud_gpu.py +++ /dev/null @@ -1,431 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ - -# coding=utf-8 -# Copyright 2021 The Google Research Authors. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -"""Training and evaluation for the point cloud alignment experiment.""" -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import utils_gpu -import time -from absl import flags -import numpy as np -import tensorflow as tf -import logging -FLAGS = flags.FLAGS -log = logging.getLogger('tensorflow') -log.setLevel(logging.DEBUG) - -# General flags. -flags.DEFINE_string('method', 'svd', - 'Specifies the method to use for predicting rotations. ' - 'Choices are "svd", "svd-inf", or "gs".') -flags.DEFINE_string('checkpoint_dir', '', - 'Locations for checkpoints, summaries, etc.') -flags.DEFINE_integer('train_steps', 2600000, 'Number of training iterations.') -flags.DEFINE_integer('save_checkpoints_steps', 100000, - 'How often to save checkpoints') -flags.DEFINE_integer('log_step_count', 200, 'How often to log the step count') -flags.DEFINE_integer('save_summaries_steps', 25000, - 'How often to save summaries.') -flags.DEFINE_float('learning_rate', 1e-5, 'Learning rate') -flags.DEFINE_boolean('lr_decay', False, 'Decay the learning rate if True.') -flags.DEFINE_integer('lr_decay_steps', 35000, - 'Learning rate decays steps.') -flags.DEFINE_float('lr_decay_rate', 0.95, - 'Learning rate decays rate.') -flags.DEFINE_boolean('predict_all_test', False, - 'If true, runs an eval job on latest checkpoint and ' - 'prints the error for each input.') -flags.DEFINE_integer('eval_examples', 39900, 'Number of test examples.') -flags.DEFINE_boolean('print_variable_names', False, - 'Print model variable names.') - -# Flags only used in the point cloud alignment experiment. -flags.DEFINE_integer('num_train_augmentations', 10, - 'Number of random rotations for augmenting each input ' - 'point cloud.') -flags.DEFINE_string('pt_cloud_train_files', '', - 'Expression matching all training point files, e.g. ' - '/path/to/files/pc_plane/points/*.pts') -flags.DEFINE_string('pt_cloud_test_files', '', - 'Expression matching all modified test point files, e.g. ' - '/path/to/files/pc_plane/points_test/*.pts') -flags.DEFINE_boolean('random_rotation_axang', True, - 'If true, samples random rotations using the method ' - 'from the original benchmark code. Otherwise samples ' - 'by Haar measure.') - - -def pt_features(batch_pts): - """Input shape: [B, N, 3], output shape: [B, 1024].""" - with tf.variable_scope('ptenc', reuse=tf.AUTO_REUSE): - f1 = tf.layers.conv1d(inputs=batch_pts, filters=64, kernel_size=1) - f1 = tf.nn.leaky_relu(f1) - f2 = tf.layers.conv1d(inputs=f1, filters=128, kernel_size=1) - f2 = tf.nn.leaky_relu(f2) - f3 = tf.layers.conv1d(inputs=f2, filters=1024, kernel_size=1) - - f = tf.reduce_max(f3, axis=1, keep_dims=False) - return f - - -def regress_from_features(batch_features, out_dim): - """Regress to a rotation representation from point cloud encodings. - - In Zhou et al, CVPR19, the paper describes this regression network as an MLP - mapping 2048->512->512->out_dim, but the associated code implements it with - one less layer: 2048->512->out_dim. We mimic the code. - - Args: - batch_features: [batch_size, in_dim]. - out_dim: desired output dimensionality. - - Returns: - A [batch_size, out_dim] tensor. - """ - f1 = tf.layers.dense(batch_features, 512) - f1 = tf.nn.leaky_relu(f1) - f2 = tf.layers.dense(f1, out_dim) - return f2 - - -def net_point_cloud(points1, points2, mode): - """Predict a relative rotation given two point clouds. - - Args: - points1: [batch_size, N, 3] float tensor. - points2: [batch_size, N, 3] float tensor. - mode: tf.estimator.ModeKeys. - - Returns: - [batch_size, 3, 3] matrices. - """ - f1 = pt_features(points1) - f2 = pt_features(points2) - f = tf.concat([f1, f2], axis=-1) - - if FLAGS.method == 'svd': - p = regress_from_features(f, 9) - return utils_gpu.symmetric_orthogonalization(p) - - if FLAGS.method == 'svd-inf': - p = regress_from_features(f, 9) - if mode == tf.estimator.ModeKeys.TRAIN: - return tf.reshape(p, (-1, 3, 3)) - else: - return utils_gpu.symmetric_orthogonalization(p) - - if FLAGS.method == 'gs': - p = regress_from_features(f, 6) - return utils_gpu.gs_orthogonalization(p) - - -def model_fn(features, labels, mode, params): - """The model_fn used to construct the tf.Estimator.""" - del labels, params # Unused. - if mode == tf.estimator.ModeKeys.TRAIN: - # Training data has point cloud of size [1, N, 3] and random rotations - # of size [1, FLAGS.num_train_augmentations, 3, 3] - rot = features['rot'][0] - num_rot = FLAGS.num_train_augmentations - batch_pts1 = tf.tile(features['data'], [num_rot, 1, 1]) - #batch_pts1 = tf.reshape(batch_pts1,(num_rot,-1,3)) - # In this experiment it does not matter if we pre or post-multiply the - # rotation as long as we are consistent between training and eval. - batch_pts2 = tf.matmul(batch_pts1, rot) # post-multiplying! - else: - # Test data has point cloud of size [1, N, 3] and a single random - # rotation of size [1, 3, 3] - batch_pts1 = features['data'] - rot = features['rot'] - batch_pts2 = tf.matmul(batch_pts1, rot) - rot = tf.reshape(rot, (-1, 3, 3)) - - # Predict the rotation. - r = net_point_cloud(batch_pts1, batch_pts2, mode) - - # Compute the loss. - loss = tf.nn.l2_loss(rot - r) - - # Compute the relative angle in radians. - theta = utils_gpu.relative_angle(r, rot) - - # Mean angle error over the batch. - mean_theta = tf.reduce_mean(theta) - mean_theta_deg = mean_theta * 180.0 / np.pi - - # Train, eval, or predict depending on mode. - if mode == tf.estimator.ModeKeys.TRAIN: - tf.summary.scalar('train/loss', loss) - tf.summary.scalar('train/theta', mean_theta_deg) - global_step = tf.train.get_or_create_global_step() - - if FLAGS.lr_decay: - learning_rate = tf.train.exponential_decay( - FLAGS.learning_rate, - global_step, - FLAGS.lr_decay_steps, - FLAGS.lr_decay_rate) - else: - learning_rate = FLAGS.learning_rate - - tf.summary.scalar('train/learning_rate', learning_rate) - optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate) - update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) - with tf.control_dependencies(update_ops): - train_op = optimizer.minimize(loss, global_step=global_step) - return tf.estimator.EstimatorSpec( - mode=mode, - loss=loss, - train_op=train_op) - - if mode == tf.estimator.ModeKeys.EVAL: - if FLAGS.predict_all_test: - print_error_op = tf.print('error:', mean_theta_deg) - with tf.control_dependencies([print_error_op]): - eval_metric_ops = { - 'mean_degree_err': tf.metrics.mean(mean_theta_deg), - } - else: - eval_metric_ops = { - 'mean_degree_err': tf.metrics.mean(mean_theta_deg), - } - - return tf.estimator.EstimatorSpec( - mode=mode, - loss=loss, - eval_metric_ops=eval_metric_ops) - - if mode == tf.estimator.ModeKeys.PREDICT: - pred = {'error': mean_theta_deg} - return tf.estimator.EstimatorSpec( - mode=mode, - predictions=pred) - - -def train_input_fn(): - """Generate training data iterator from the .pts files.""" - def _file_to_matrix(pts_path): - """Read Nx3 point cloud from a .pts file.""" - file_buffer = tf.read_file(pts_path) - lines = tf.string_split([file_buffer], delimiter='\n') - lines1 = tf.string_split(lines.values, delimiter='\r') - values = tf.stack(tf.decode_csv(lines1.values, - record_defaults=[[0.0], [0.0], [0.0]], - field_delim=' ')) - values = tf.transpose(values)# 3xN --> Nx3 - values = values[:(tf.shape(values)[0]// 2), :] - diff_num = 1500-tf.shape(values)[0] - repeat_pts = tf.tile(tf.reshape(values[1,:],(1,-1)),[diff_num,1]) - values = tf.concat([values,repeat_pts],axis=0) - # The experiment code in - # github.com/papagina/RotationContinuity/.../shapenet/code/train_pointnet.py - # only used the first half of the points in each file. - return values - - - - def _random_rotation(pts): - """Attach N random rotations to a point cloud.""" - if FLAGS.random_rotation_axang: - rotations = utils_gpu.random_rotation_benchmark(FLAGS.num_train_augmentations) - else: - rotations = utils_gpu.random_rotation(FLAGS.num_train_augmentations) - return pts, rotations - - pts_paths = tf.gfile.Glob(FLAGS.pt_cloud_train_files) - dataset = tf.data.Dataset.from_tensor_slices(pts_paths) - dataset = dataset.map(_file_to_matrix) - dataset = dataset.cache() # Comment out if memory cannot hold all the data. - dataset = dataset.shuffle(buffer_size=50, reshuffle_each_iteration=True) - dataset = dataset.repeat() - dataset = dataset.map(_random_rotation) - dataset = dataset.batch(1) - iterator = tf.data.make_one_shot_iterator(dataset) - batch_data, batch_rot = iterator.get_next() - features_dict = {'data': batch_data, 'rot': batch_rot} - batch_size = tf.shape(batch_data)[0] - batch_labels_dummy = tf.zeros(shape=(batch_size, 1)) - return (features_dict, batch_labels_dummy) - - - - -def eval_input_fn(): - """Generate test data from *modified* .pts files. - - See README and comments below for details on how the data is modified. - - Returns: - A tuple of features and associated labels. - """ - def _file_to_matrix(pts_path): - """Read Nx3 point cloud and 3x3 rotation matrix from a .pts file. - - The test data is a modified version of the original files. For each .pts - file we have (1) added a 3x3 rotation matrix for testing, and (2) removed - the second half of the point cloud since it is not used at all. - - Args: - pts_path: path to a .pts file. - - Returns: - A Nx3 point cloud. - A 3x3 rotation matrix. - """ - file_buffer = tf.read_file(pts_path) - lines = tf.string_split([file_buffer], delimiter='\n') - lines1 = tf.string_split(lines.values, delimiter='\r') - values = tf.stack(tf.decode_csv(lines1.values, - record_defaults=[[0.0], [0.0], [0.0]], field_delim=' ')) - values = tf.transpose(values) # 3xN --> Nx3. - diff_num = 1414-tf.shape(values)[0] - repeat_pts = tf.tile(tf.reshape(values[4,:],(1,-1)),[diff_num,1]) - - values = tf.concat([values,repeat_pts],axis=0) - # First three rows are the rotation matrix, remaining rows the point cloud. - rot = values[:3, :] - return values[4:, :], rot - - pts_paths = tf.gfile.Glob(FLAGS.pt_cloud_test_files) - dataset = tf.data.Dataset.from_tensor_slices(pts_paths) - dataset = dataset.map(_file_to_matrix) - dataset = dataset.batch(1) - iterator = tf.data.make_one_shot_iterator(dataset) - batch_data, batch_rot = iterator.get_next() - features_dict = {'data': batch_data, 'rot': batch_rot} - batch_size = tf.shape(batch_data)[0] - batch_labels_dummy = tf.zeros(shape=(batch_size, 1)) - return (features_dict, batch_labels_dummy) - - -def print_variable_names(): - """Print variable names in a model.""" - params = {'dummy': 0} - estimator = tf.estimator.Estimator( - model_fn=model_fn, - model_dir=FLAGS.checkpoint_dir, - params=params) - names = estimator.get_variable_names() - for name in names: - print(name) - - -def predict_all_test(): - """Print error statistics for the test dataset.""" - params = {'dummy': 0} - estimator = tf.estimator.Estimator( - model_fn=model_fn, - model_dir=FLAGS.checkpoint_dir, - params=params) - evals = estimator.predict(input_fn=eval_input_fn, yield_single_examples=False) - - # Print error statistics. - all_errors = [x['error'] for x in evals] - errors = np.array(all_errors) - print('Evaluated %d examples'%np.size(errors)) - print('Mean error: %f degrees', np.mean(errors)) - print('Median error: %f degrees', np.median(errors)) - print('Std: %f degrees', np.std(errors)) - sorted_errors = np.sort(errors) - n = np.size(sorted_errors) - print('\nPercentiles:') - for perc in range(1, 101): - index = np.int32(np.float32(n * perc) / 100.0) - 1 - print('%3d%%: %f'%(perc, sorted_errors[index])) - - -def train_and_eval(): - """Train and evaluate a model.""" - # create formatter and add it to the handlers - formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') - - # create file handler which logs even debug messages - fh = logging.FileHandler('loss.log') - fh.setLevel(logging.DEBUG) - fh.setFormatter(formatter) - log.addHandler(fh) - - save_summary_steps = FLAGS.save_summaries_steps - save_checkpoints_steps = FLAGS.save_checkpoints_steps - log_step_count = FLAGS.log_step_count - - config = tf.estimator.RunConfig( - save_summary_steps=save_summary_steps, - save_checkpoints_steps=save_checkpoints_steps, - log_step_count_steps=log_step_count, - keep_checkpoint_max=None) - - params = {'dummy': 0} - estimator = tf.estimator.Estimator( - model_fn=model_fn, - model_dir=FLAGS.checkpoint_dir, - config=config, - params=params) - - train_spec = tf.estimator.TrainSpec( - input_fn=train_input_fn, - max_steps=FLAGS.train_steps) - - eval_spec = tf.estimator.EvalSpec(input_fn=eval_input_fn, - start_delay_secs=60, - steps=FLAGS.eval_examples, - throttle_secs=60) - - tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec) - - -def main(argv=None): # pylint: disable=unused-argument - if FLAGS.print_variable_names: - print_variable_names() - return - - if FLAGS.predict_all_test: - predict_all_test() - else: - train_and_eval() - - -if __name__ == '__main__': - tf.app.run() -- Gitee From 98e0fcfad2cd1456227e77160fd995e6d2f26e0a Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Sat, 7 May 2022 14:09:25 +0000 Subject: [PATCH 31/88] =?UTF-8?q?=E5=88=A0=E9=99=A4=E6=96=87=E4=BB=B6=20Te?= =?UTF-8?q?nsorFlow/contrib/cv/SVD=5FID2019=5Ffor=5FTensorflow/requirement?= =?UTF-8?q?s.txt?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../requirements.txt | 69 ------------------- 1 file changed, 69 deletions(-) delete mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/requirements.txt diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/requirements.txt b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/requirements.txt deleted file mode 100644 index de8ee8edb..000000000 --- a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/requirements.txt +++ /dev/null @@ -1,69 +0,0 @@ -absl-py==0.11.0 - -astunparse==1.6.3 - -cachetools==4.1.1 - -certifi==2020.6.20 - -chardet==3.0.4 - -gast==0.3.3 - -google-auth==1.23.0 - -google-auth-oauthlib==0.4.2 - -google-pasta==0.2.0 - -grpcio==1.33.2 - -h5py==2.10.0 - -idna==2.10 - -importlib-metadata==2.0.0 - -Keras-Preprocessing==1.1.2 - -Markdown==3.3.3 - -numpy==1.18.5 - -oauthlib==3.1.0 - -opt-einsum==3.3.0 - -protobuf==3.13.0 - -pyasn1==0.4.8 - -pyasn1-modules==0.2.8 - -requests==2.24.0 - -requests-oauthlib==1.3.0 - -rsa==4.6 - -scipy==1.5.3 - -six==1.15.0 - -tensorboard==2.3.0 - -tensorboard-plugin-wit==1.7.0 - -tensorflow==2.3.1 - -tensorflow-estimator==2.3.0 - -termcolor==1.1.0 - -urllib3==1.25.11 - -Werkzeug==1.0.1 - -wrapt==1.12.1 - -zipp==3.4.0 -- Gitee From 95c3e771b8f0bce063594cbe442f848104c5b612 Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Sat, 7 May 2022 14:09:32 +0000 Subject: [PATCH 32/88] =?UTF-8?q?=E5=88=A0=E9=99=A4=E6=96=87=E4=BB=B6=20Te?= =?UTF-8?q?nsorFlow/contrib/cv/SVD=5FID2019=5Ffor=5FTensorflow/utils=5Fgpu?= =?UTF-8?q?.py?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../cv/SVD_ID2019_for_Tensorflow/utils_gpu.py | 144 ------------------ 1 file changed, 144 deletions(-) delete mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/utils_gpu.py diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/utils_gpu.py b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/utils_gpu.py deleted file mode 100644 index a08b4fa97..000000000 --- a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/utils_gpu.py +++ /dev/null @@ -1,144 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ - -# coding=utf-8 -# Copyright 2021 The Google Research Authors. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""Utility functions.""" -import numpy as np -from scipy.stats import special_ortho_group -import tensorflow as tf - - -def relative_angle(r1, r2): - """Relative angle (radians) between 3D rotation matrices.""" - rel_rot = tf.matmul(tf.transpose(r1, perm=[0, 2, 1]), r2) - trace = rel_rot[:, 0, 0] + rel_rot[:, 1, 1] + rel_rot[:, 2, 2] - cos_theta = (trace - 1.0) / 2.0 - cos_theta = tf.minimum(cos_theta, tf.ones_like(cos_theta)) - cos_theta = tf.maximum(cos_theta, (-1.0) * tf.ones_like(cos_theta)) - theta = tf.acos(cos_theta) - return theta - - -def random_rotation_benchmark_np(n): - """Sample a random 3D rotation by method used in Zhou et al, CVPR19. - - This numpy function is a copy of the PyTorch function - get_sampled_rotation_matrices_by_axisAngle() in the code made available - for Zhou et al, CVPR19, at https://github.com/papagina/RotationContinuity/. - - Args: - n: the number of rotation matrices to return. - - Returns: - [n, 3, 3] np array. - """ - theta = np.random.uniform(-1, 1, n) * np.pi - sin = np.sin(theta) - axis = np.random.randn(n, 3) - axis = axis / np.maximum(np.linalg.norm(axis, axis=-1, keepdims=True), 1e-7) - qw = np.cos(theta) - qx = axis[:, 0] * sin - qy = axis[:, 1] * sin - qz = axis[:, 2] * sin - - xx = qx*qx - yy = qy*qy - zz = qz*qz - xy = qx*qy - xz = qx*qz - yz = qy*qz - xw = qx*qw - yw = qy*qw - zw = qz*qw - - row0 = np.stack((1-2*yy-2*zz, 2*xy-2*zw, 2*xz+2*yw), axis=-1) - row1 = np.stack((2*xy+2*zw, 1-2*xx-2*zz, 2*yz-2*xw), axis=-1) - row2 = np.stack((2*xz-2*yw, 2*yz+2*xw, 1-2*xx-2*yy), axis=-1) - matrix = np.stack((row0, row1, row2), axis=1) - - return matrix - - -def random_rotation_benchmark(n): - """A TF wrapper for random_rotation_benchmark_np().""" - mat = tf.py_func( - func=lambda t: np.float32(random_rotation_benchmark_np(t)), - inp=[n], - Tout=tf.float32, - stateful=True) - return tf.reshape(mat, (n, 3, 3)) - - -def random_rotation(n): - """Sample rotations from a uniform distribution on SO(3).""" - mat = tf.py_func( - func=lambda t: np.float32(special_ortho_group.rvs(3, size=t)), - inp=[n], - Tout=tf.float32, - stateful=True) - return tf.reshape(mat, (n, 3, 3)) - - -def symmetric_orthogonalization(x): - """Maps 9D input vectors onto SO(3) via symmetric orthogonalization.""" - # Innner dimensions of the input should be 3x3 matrices. - m = tf.reshape(x, (-1, 3, 3)) - _, u, v = tf.svd(m) - det = tf.linalg.det(tf.matmul(u, v, transpose_b=True)) - r = tf.matmul( - tf.concat([u[:, :, :-1], u[:, :, -1:] * tf.reshape(det, [-1, 1, 1])], 2), - v, transpose_b=True) - return r - - -def gs_orthogonalization(p6): - """Gram-Schmidt orthogonalization from 6D input.""" - # Input should be [batch_size, 6] - x = p6[:, 0:3] - y = p6[:, 3:6] - xn = tf.math.l2_normalize(x, axis=-1) - z = tf.linalg.cross(xn, y) - zn = tf.math.l2_normalize(z, axis=-1) - y = tf.linalg.cross(zn, xn) - r = tf.stack([xn, y, zn], -1) - return r -- Gitee From e0d10cd8e070fc973166348323a55fd567245f5b Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Sat, 7 May 2022 14:10:13 +0000 Subject: [PATCH 33/88] =?UTF-8?q?=E6=96=B0=E5=BB=BA=20NPU?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU/.keep | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU/.keep diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU/.keep b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU/.keep new file mode 100644 index 000000000..e69de29bb -- Gitee From 82ce3bd4b18be31866451e94080aa21f64fcc417 Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Sat, 7 May 2022 14:13:15 +0000 Subject: [PATCH 34/88] =?UTF-8?q?NPU=E7=9B=B8=E5=85=B3=E4=BB=A3=E7=A0=81?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../NPU/genStatistical.sh | 203 +++++++++ .../NPU/genTestData.sh | 203 +++++++++ .../NPU/gen_pt_test_data.py | 72 +++ .../NPU/main_point_cloud.py | 419 ++++++++++++++++++ .../NPU/modelarts_entry_stat.py | 40 ++ .../NPU/train_full_1p.sh | 209 +++++++++ .../cv/SVD_ID2019_for_Tensorflow/NPU/utils.py | 116 +++++ 7 files changed, 1262 insertions(+) create mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU/genStatistical.sh create mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU/genTestData.sh create mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU/gen_pt_test_data.py create mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU/main_point_cloud.py create mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU/modelarts_entry_stat.py create mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU/train_full_1p.sh create mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU/utils.py diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU/genStatistical.sh b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU/genStatistical.sh new file mode 100644 index 000000000..990ca29a5 --- /dev/null +++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU/genStatistical.sh @@ -0,0 +1,203 @@ +#!/bin/bash + +########################################################## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +########################################################## +# shell脚本所在路径 +cur_path=`echo $(cd $(dirname $0);pwd)` + +# 判断当前shell是否是performance +perf_flag=`echo $0 | grep performance | wc -l` + +# 当前执行网络的名称 +Network=`echo $(cd $(dirname $0);pwd) | awk -F"/" '{print $(NF-1)}'` + +export RANK_SIZE=1 +export RANK_ID=0 +export JOB_ID=10087 + +# 路径参数初始化 +data_path="" +output_path="" + +# 帮助信息，不需要修改 +if [[ $1 == --help || $1 == -h ]];then + echo"usage:./train_performance_1P.sh " + echo " " + echo "parameter explain: + --data_path # dataset of training + --output_path # output of training + --train_steps # max_step for training + --train_epochs # max_epoch for training + --batch_size # batch size + -h/--help show help message + " + exit 1 +fi + +# 参数校验，不需要修改 +for para in $* +do + if [[ $para == --data_path* ]];then + data_path=`echo ${para#*=}` + elif [[ $para == --output_path* ]];then + output_path=`echo ${para#*=}` + elif [[ $para == --train_steps* ]];then + train_steps=`echo ${para#*=}` + elif [[ $para == --train_epochs* ]];then + train_epochs=`echo ${para#*=}` + elif [[ $para == --batch_size* ]];then + batch_size=`echo ${para#*=}` + fi +done + +# 校验是否传入data_path,不需要修改 +if [[ $data_path == "" ]];then + echo "[Error] para \"data_path\" must be config" + exit 1 +fi + +# 校验是否传入output_path,不需要修改 +if [[ $output_path == "" ]];then + output_path="./test/output/${ASCEND_DEVICE_ID}" +fi + +# 设置打屏日志文件名，请保留，文件名为${print_log} +print_log="./test/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log" +etp_flag=${etp_running_flag} +if [ x"${etp_flag}" != xtrue ]; +then + echo "running without etp..." + print_log_name=`ls /home/ma-user/modelarts/log/ | grep proc-rank` + print_log="/home/ma-user/modelarts/log/${print_log_name}" +fi +echo ${print_log} + +CaseName="" +function get_casename() +{ + if [ x"${perf_flag}" = x1 ]; + then + CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'perf' + else + CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'acc' + fi +} + +# 跳转到code目录 +cd ${cur_path}/../ +rm -rf ./test/output/${ASCEND_DEVICE_ID} +mkdir -p ./test/output/${ASCEND_DEVICE_ID} + +# 训练开始时间记录，不需要修改 +start_time=$(date +%s) +########################################################## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +########################################################## + +#========================================================= +#========================================================= +#========训练执行命令，需要根据您的网络进行修改============== +#========================================================= +#========================================================= +# 您的训练数据集在${data_path}路径下，请直接使用这个变量获取 +# 您的训练输出目录在${output_path}路径下，请直接使用这个变量获取 +# 您的其他基础参数，可以自定义增加，但是batch_size请保留，并且设置正确的值 +batch_size=1 + +echo "------------------***PRINT DATA PATH***-----------------" + +docuClass="test_points_modified/*.pts" +modelPath="output" + +testData_Path="$data_path$docuClass" +outputPath="$data_path$modelPath" + +echo "$outputPath" +echo "$testData_Path" + + +if [ x"${modelarts_flag}" != x ]; +then + python /home/ma-user/modelarts/user-job-dir/code/main_point_cloud.py \ + --method=svd \ + --checkpoint_dir=${outputPath} \ + --pt_cloud_test_files=${testData_Path} \ + --predict_all_test=True +else + python /home/ma-user/modelarts/user-job-dir/code/main_point_cloud.py \ + --method=svd \ + --checkpoint_dir=${outputPath} \ + --pt_cloud_test_files=${testData_Path} \ + --predict_all_test=True +fi + + +# 性能相关数据计算 +StepTime=`grep "sec/step :" ${print_log} | tail -n 10 | awk '{print $NF}' | awk '{sum+=$1} END {print sum/NR}'` +FPS=`awk 'BEGIN{ "%.2f\n", '${batch_size}'/'${StepTime}'}'` + +# 精度相关数据计算 +train_accuracy=`grep "Final Accuracy accuracy" ${print_log} | awk '{print $NF}'` +# 提取所有loss打印信息 +grep "loss :" ${print_log} | awk -F ":" '{print $4}' | awk -F "-" '{print $1}' > ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt + + +########################################################### +#########后面的所有内容请不要修改########################### +#########后面的所有内容请不要修改########################### +#########后面的所有内容请不要修改########################### +########################################################### + +# 判断本次执行是否正确使用Ascend NPU +use_npu_flag=`grep "The model has been compiled on the Ascend AI processor" ${print_log} | wc -l` +if [ x"${use_npu_flag}" == x0 ]; +then + echo "------------------ ERROR NOTICE START ------------------" + echo "ERROR, your task haven't used Ascend NPU, please check your npu Migration." + echo "------------------ ERROR NOTICE END------------------" +else + echo "------------------ INFO NOTICE START------------------" + echo "INFO, your task have used Ascend NPU, please check your result." + echo "------------------ INFO NOTICE END------------------" +fi + +# 获取最终的casename，请保留，case文件名为${CaseName} +get_casename + +# 重命名loss文件 +if [ -f ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ]; +then + mv ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ./test/output/${ASCEND_DEVICE_ID}/${CaseName}_loss.txt +fi + +# 训练端到端耗时 +end_time=$(date +%s) +e2e_time=$(( $end_time - $start_time )) + +echo "------------------ Final result ------------------" +# 输出性能FPS/单step耗时/端到端耗时 +echo "Final Performance images/sec : $FPS" +echo "Final Performance sec/step : $StepTime" +echo "E2E Training Duration sec : $e2e_time" + +# 输出训练精度 +echo "Final Train Accuracy : ${train_accuracy}" + +# 最后一个迭代loss值，不需要修改 +ActualLoss=(`awk 'END {print $NF}' $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}_loss.txt`) + +#关键信息打印到${CaseName}.log中，不需要修改 +echo "Network = ${Network}" > $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "RankSize = ${RANK_SIZE}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "BatchSize = ${batch_size}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "DeviceType = `uname -m`" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualFPS = ${FPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "TrainingTime = ${StepTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log \ No newline at end of file diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU/genTestData.sh b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU/genTestData.sh new file mode 100644 index 000000000..416b71b04 --- /dev/null +++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU/genTestData.sh @@ -0,0 +1,203 @@ +#!/bin/bash + +########################################################## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +########################################################## +# shell脚本所在路径 +cur_path=`echo $(cd $(dirname $0);pwd)` + +# 判断当前shell是否是performance +perf_flag=`echo $0 | grep performance | wc -l` + +# 当前执行网络的名称 +Network=`echo $(cd $(dirname $0);pwd) | awk -F"/" '{print $(NF-1)}'` + +export RANK_SIZE=1 +export RANK_ID=0 +export JOB_ID=10087 + +# 路径参数初始化 +data_path="" +output_path="" + +# 帮助信息，不需要修改 +if [[ $1 == --help || $1 == -h ]];then + echo"usage:./train_performance_1P.sh " + echo " " + echo "parameter explain: + --data_path # dataset of training + --output_path # output of training + --train_steps # max_step for training + --train_epochs # max_epoch for training + --batch_size # batch size + -h/--help show help message + " + exit 1 +fi + +# 参数校验，不需要修改 +for para in $* +do + if [[ $para == --data_path* ]];then + data_path=`echo ${para#*=}` + elif [[ $para == --output_path* ]];then + output_path=`echo ${para#*=}` + elif [[ $para == --train_steps* ]];then + train_steps=`echo ${para#*=}` + elif [[ $para == --train_epochs* ]];then + train_epochs=`echo ${para#*=}` + elif [[ $para == --batch_size* ]];then + batch_size=`echo ${para#*=}` + fi +done + +# 校验是否传入data_path,不需要修改 +if [[ $data_path == "" ]];then + echo "[Error] para \"data_path\" must be config" + exit 1 +fi + +# 校验是否传入output_path,不需要修改 +if [[ $output_path == "" ]];then + output_path="./test/output/${ASCEND_DEVICE_ID}" +fi + +# 设置打屏日志文件名，请保留，文件名为${print_log} +print_log="./test/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log" +etp_flag=${etp_running_flag} +if [ x"${etp_flag}" != xtrue ]; +then + echo "running without etp..." + print_log_name=`ls /home/ma-user/modelarts/log/ | grep proc-rank` + print_log="/home/ma-user/modelarts/log/${print_log_name}" +fi +echo ${print_log} + +CaseName="" +function get_casename() +{ + if [ x"${perf_flag}" = x1 ]; + then + CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'perf' + else + CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'acc' + fi +} + +# 跳转到code目录 +cd ${cur_path}/../ +rm -rf ./test/output/${ASCEND_DEVICE_ID} +mkdir -p ./test/output/${ASCEND_DEVICE_ID} + +# 训练开始时间记录，不需要修改 +start_time=$(date +%s) +########################################################## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +########################################################## + +#========================================================= +#========================================================= +#========训练执行命令，需要根据您的网络进行修改============== +#========================================================= +#========================================================= +# 您的训练数据集在${data_path}路径下，请直接使用这个变量获取 +# 您的训练输出目录在${output_path}路径下，请直接使用这个变量获取 +# 您的其他基础参数，可以自定义增加，但是batch_size请保留，并且设置正确的值 +batch_size=1 + +echo "------------------***PRINT DATA PATH***-----------------" + +inputDataPath="points_test/*.pts" +TestdataPath="test_points_modified" + +Data_Path="$data_path$inputDataPath" +outputPath="$output_path$TestdataPath" +AXANG_SAMPLIE="True" + + +echo "$outputPath" +echo "$testData_Path" + + +if [ x"${modelarts_flag}" != x ]; +then + python /home/ma-user/modelarts/user-job-dir/code/gen_pt_test_data.py\ + --input_test_files=${Data_Path} \ + --output_directory=${outputPath} \ + --random_rotation_axang=${AXANG_SAMPLIE} +else + python /home/ma-user/modelarts/user-job-dir/code/gen_pt_test_data.py\ + --input_test_files=${Data_Path} \ + --output_directory=${outputPath} \ + --random_rotation_axang=${AXANG_SAMPLIE} +fi + + +# 性能相关数据计算 +StepTime=`grep "sec/step :" ${print_log} | tail -n 10 | awk '{print $NF}' | awk '{sum+=$1} END {print sum/NR}'` +FPS=`awk 'BEGIN{ "%.2f\n", '${batch_size}'/'${StepTime}'}'` + +# 精度相关数据计算 +train_accuracy=`grep "Final Accuracy accuracy" ${print_log} | awk '{print $NF}'` +# 提取所有loss打印信息 +grep "loss :" ${print_log} | awk -F ":" '{print $4}' | awk -F "-" '{print $1}' > ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt + + +########################################################### +#########后面的所有内容请不要修改########################### +#########后面的所有内容请不要修改########################### +#########后面的所有内容请不要修改########################### +########################################################### + +# 判断本次执行是否正确使用Ascend NPU +use_npu_flag=`grep "The model has been compiled on the Ascend AI processor" ${print_log} | wc -l` +if [ x"${use_npu_flag}" == x0 ]; +then + echo "------------------ ERROR NOTICE START ------------------" + echo "ERROR, your task haven't used Ascend NPU, please check your npu Migration." + echo "------------------ ERROR NOTICE END------------------" +else + echo "------------------ INFO NOTICE START------------------" + echo "INFO, your task have used Ascend NPU, please check your result." + echo "------------------ INFO NOTICE END------------------" +fi + +# 获取最终的casename，请保留，case文件名为${CaseName} +get_casename + +# 重命名loss文件 +if [ -f ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ]; +then + mv ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ./test/output/${ASCEND_DEVICE_ID}/${CaseName}_loss.txt +fi + +# 训练端到端耗时 +end_time=$(date +%s) +e2e_time=$(( $end_time - $start_time )) + +echo "------------------ Final result ------------------" +# 输出性能FPS/单step耗时/端到端耗时 +echo "Final Performance images/sec : $FPS" +echo "Final Performance sec/step : $StepTime" +echo "E2E Training Duration sec : $e2e_time" + +# 输出训练精度 +echo "Final Train Accuracy : ${train_accuracy}" + +# 最后一个迭代loss值，不需要修改 +ActualLoss=(`awk 'END {print $NF}' $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}_loss.txt`) + +#关键信息打印到${CaseName}.log中，不需要修改 +echo "Network = ${Network}" > $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "RankSize = ${RANK_SIZE}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "BatchSize = ${batch_size}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "DeviceType = `uname -m`" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualFPS = ${FPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "TrainingTime = ${StepTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log \ No newline at end of file diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU/gen_pt_test_data.py b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU/gen_pt_test_data.py new file mode 100644 index 000000000..c1c299409 --- /dev/null +++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU/gen_pt_test_data.py @@ -0,0 +1,72 @@ +# coding=utf-8 +# Copyright 2021 The Google Research Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Generate modified point cloud test data with fixed random rotations.""" +import glob +import os +import pathlib + +import utils +from absl import app +from absl import flags +import numpy as np +from scipy.stats import special_ortho_group +from npu_bridge.npu_init import * + +FLAGS = flags.FLAGS +flags.DEFINE_string('input_test_files', '', + 'Regular expression for the original input test files.') +flags.DEFINE_string('output_directory', '', + 'Output directory where new test files will be stored.') +flags.DEFINE_integer('num_rotations_per_file', 100, + 'Number of random rotation augmentations per test point ' + 'cloud.') +flags.DEFINE_boolean('random_rotation_axang', True, + 'If true, samples random rotations using the method ' + 'from the original benchmark code. Otherwise samples ' + 'by Haar measure.') + + +def gen_test_data(): + """Generate the new (modified) test data.""" + # Create output directory. + os.makedirs(FLAGS.output_directory, exist_ok=True) + + # Get all test point cloud files in the original dataset. + input_test_files = glob.glob(FLAGS.input_test_files) + + for in_file in input_test_files: + out_file_prefix = pathlib.Path(in_file).stem + pts = np.loadtxt(in_file) # N x 3 + num_pts_to_keep = pts.shape[0] // 2 + pts = pts[:num_pts_to_keep, :] # N//2 x 3. + + for k in range(FLAGS.num_rotations_per_file): + if FLAGS.random_rotation_axang: + r = utils.random_rotation_benchmark_np(1) + r = r[0] + else: + r = special_ortho_group.rvs(3) + joined = np.float32(np.concatenate((r, pts), axis=0)) # (N//2+3) x 3. + out_file = os.path.join( + FLAGS.output_directory, '%s_r%03d.pts'%(out_file_prefix, k)) + np.savetxt(out_file, joined) + + +def main(unused_argv): + gen_test_data() + +if __name__ == '__main__': + app.run(main) diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU/main_point_cloud.py b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU/main_point_cloud.py new file mode 100644 index 000000000..a3b4e9a22 --- /dev/null +++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU/main_point_cloud.py @@ -0,0 +1,419 @@ +# coding=utf-8 +# Copyright 2021 The Google Research Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Training and evaluation for the point cloud alignment experiment.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import utils +from absl import flags +import numpy as np +import tensorflow as tf +import glob +import pathlib +import datetime +from npu_bridge.npu_init import * +from npu_bridge.estimator.npu.npu_estimator import NPUEstimatorSpec +from npu_bridge.estimator.npu.npu_estimator import NPUEstimator +from npu_bridge.estimator.npu.npu_config import NPURunConfig +#from npu_bridge.estimator.npu.npu_config import ProfilingConfig +import os +FLAGS = tf.app.flags.FLAGS + +# General flags. +flags.DEFINE_string('method', 'svd', + 'Specifies the method to use for predicting rotations. ' + 'Choices are "svd", "svd-inf", or "gs".') +flags.DEFINE_string('checkpoint_dir', '', + 'Locations for checkpoints, summaries, etc.') +flags.DEFINE_integer('train_steps', 2600000, 'Number of training iterations.') +flags.DEFINE_integer('save_checkpoints_steps', 10000, + 'How often to save checkpoints') +flags.DEFINE_integer('log_step_count', 500, 'How often to log the step count') +flags.DEFINE_integer('save_summaries_steps', 5000, + 'How often to save summaries.') +flags.DEFINE_float('learning_rate', 1e-5, 'Learning rate') +flags.DEFINE_boolean('lr_decay', False, 'Decay the learning rate if True.') +flags.DEFINE_integer('lr_decay_steps', 35000, + 'Learning rate decays steps.') +flags.DEFINE_float('lr_decay_rate', 0.95, + 'Learning rate decays rate.') +flags.DEFINE_boolean('predict_all_test', False, + 'If true, runs an eval job on latest checkpoint and ' + 'prints the error for each input.') +flags.DEFINE_integer('eval_examples', 39900, 'Number of test examples.') +flags.DEFINE_boolean('print_variable_names', False, + 'Print model variable names.') + +# Flags only used in the point cloud alignment experiment. +flags.DEFINE_integer('num_train_augmentations', 10, + 'Number of random rotations for augmenting each input ' + 'point cloud.') +flags.DEFINE_string('pt_cloud_train_files', '', + 'Expression matching all training point files, e.g. ' + '/path/to/files/pc_plane/points/*.pts') +flags.DEFINE_string('pt_cloud_test_files','', + 'Expression matching all modified test point files, e.g. ' + '/path/to/files/pc_plane/points_test/*.pts') +flags.DEFINE_boolean('random_rotation_axang', True, + 'If true, samples random rotations using the method ' + 'from the original benchmark code. Otherwise samples ' + 'by Haar measure.') + + +def pt_features(batch_pts): + """Input shape: [B, N, 3], output shape: [B, 1024].""" + with tf.variable_scope('ptenc', reuse=tf.AUTO_REUSE): + f1 = tf.layers.conv1d(inputs=batch_pts, filters=64, kernel_size=1) + f1 = tf.nn.leaky_relu(f1) + f2 = tf.layers.conv1d(inputs=f1, filters=128, kernel_size=1) + f2 = tf.nn.leaky_relu(f2) + f3 = tf.layers.conv1d(inputs=f2, filters=1024, kernel_size=1) + f = tf.reduce_max(f3, axis=1, keep_dims=False) + return f + + +def regress_from_features(batch_features, out_dim): + """Regress to a rotation representation from point cloud encodings. + + In Zhou et al, CVPR19, the paper describes this regression network as an MLP + mapping 2048->512->512->out_dim, but the associated code implements it with + one less layer: 2048->512->out_dim. We mimic the code. + + Args: + batch_features: [batch_size, in_dim]. + out_dim: desired output dimensionality. + + Returns: + A [batch_size, out_dim] tensor. + """ + f1 = tf.layers.dense(batch_features, 512) + f1 = tf.nn.leaky_relu(f1) + f2 = tf.layers.dense(f1, out_dim) + return f2 + + +def net_point_cloud(points1, points2, mode): + """Predict a relative rotation given two point clouds. + + Args: + points1: [batch_size, N, 3] float tensor. + points2: [batch_size, N, 3] float tensor. + mode: tf.estimator.ModeKeys. + + Returns: + [batch_size, 3, 3] matrices. + """ + f1 = pt_features(points1) + f2 = pt_features(points2) + f = tf.concat([f1, f2], axis=-1) + + if FLAGS.method == 'svd': + p = regress_from_features(f, 9) + return utils.symmetric_orthogonalization(p) + + if FLAGS.method == 'svd-inf': + p = regress_from_features(f, 9) + if mode == tf.estimator.ModeKeys.TRAIN: + return tf.reshape(p, (-1, 3, 3)) + else: + return utils.symmetric_orthogonalization(p) + + if FLAGS.method == 'gs': + p = regress_from_features(f, 6) + return utils.gs_orthogonalization(p) + + +def model_fn(features, labels, mode, params): + """The model_fn used to construct the tf.Estimator.""" + del labels, params # Unused. + if mode == tf.estimator.ModeKeys.TRAIN: + # Training data has point cloud of size [1, N, 3] and random rotations + # of size [1, FLAGS.num_train_augmentations, 3, 3] + rot = features['rot'][0] + num_rot = FLAGS.num_train_augmentations + batch_pts1 = tf.tile(features['data'], [num_rot, 1, 1]) + #batch_pts1 = tf.reshape(batch_pts1,(num_rot,-1,3)) + # In this experiment it does not matter if we pre or post-multiply the + # rotation as long as we are consistent between training and eval. + batch_pts2 = tf.matmul(batch_pts1, rot) # post-multiplying! + else: + # Test data has point cloud of size [1, N, 3] and a single random + # rotation of size [1, 3, 3] + batch_pts1 = features['data'] + rot = features['rot'] + batch_pts2 = tf.matmul(batch_pts1, rot) + rot = tf.reshape(rot, (-1, 3, 3)) + + # Predict the rotation. + r = net_point_cloud(batch_pts1, batch_pts2, mode) + + # Compute the loss. + loss = tf.nn.l2_loss(rot - r) + + # Compute the relative angle in radians. + theta = utils.relative_angle(r, rot) + + # Mean angle error over the batch. + mean_theta = tf.reduce_mean(theta) + mean_theta_deg = mean_theta * 180.0 / np.pi + + # Train, eval, or predict depending on mode. + if mode == tf.estimator.ModeKeys.TRAIN: + tf.summary.scalar('train/loss', loss) + tf.summary.scalar('train/theta', mean_theta_deg) + global_step = tf.train.get_or_create_global_step() + + if FLAGS.lr_decay: + learning_rate = tf.train.exponential_decay( + FLAGS.learning_rate, + global_step, + FLAGS.lr_decay_steps, + FLAGS.lr_decay_rate) + else: + learning_rate = FLAGS.learning_rate + + tf.summary.scalar('train/learning_rate', learning_rate) + optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate) + update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) + with tf.control_dependencies(update_ops): + train_op = optimizer.minimize(loss, global_step=global_step) + #train_op = util.set_graph_exec_config(train_op, dynamic_input=True,dynamic_graph_execute_mode="lazy_recompile") + + return NPUEstimatorSpec( + mode=mode, + loss=loss, + train_op=train_op) + + if mode == tf.estimator.ModeKeys.EVAL: + if FLAGS.predict_all_test: + print_error_op = tf.print('error:', mean_theta_deg) + with tf.control_dependencies([print_error_op]): + eval_metric_ops = { + 'mean_degree_err': tf.metrics.mean(mean_theta_deg), + } + else: + eval_metric_ops = { + 'mean_degree_err': tf.metrics.mean(mean_theta_deg), + } + + return NPUEstimatorSpec( + mode=mode, + loss=loss, + eval_metric_ops=eval_metric_ops) + + if mode == tf.estimator.ModeKeys.PREDICT: + pred = {'error': mean_theta_deg} + return NPUEstimatorSpec( + mode=mode, + predictions=pred) + + +def train_input_fn(): + """Generate training data iterator from the .pts files.""" + def _file_to_matrix(pts_path): + """Read Nx3 point cloud from a .pts file.""" + file_buffer = tf.read_file(pts_path) + lines = tf.string_split([file_buffer], delimiter='\n') + lines1 = tf.string_split(lines.values, delimiter='\r') + values = tf.stack(tf.decode_csv(lines1.values, + record_defaults=[[0.0], [0.0], [0.0]], + field_delim=' ')) + values = tf.transpose(values) # 3xN --> Nx3. + values = values[:tf.shape(values)[0] // 2, :] + + # modified by Lyoung + diff_num = 1500-tf.shape(values)[0] + repeat_pts = tf.tile(tf.reshape(values[1, :], (1, -1)), [diff_num, 1]) + values = tf.concat([values,repeat_pts], axis=0) + # The experiment code in + # github.com/papagina/RotationContinuity/.../shapenet/code/train_pointnet.py + # only used the first half of the points in each file. + return values + + def _random_rotation(pts): + """Attach N random rotations to a point cloud.""" + if FLAGS.random_rotation_axang: + rotations = utils.random_rotation_benchmark(FLAGS.num_train_augmentations) + else: + rotations = utils.random_rotation(FLAGS.num_train_augmentations) + return pts,rotations + + pts_paths = tf.gfile.Glob(FLAGS.pt_cloud_train_files) + dataset = tf.data.Dataset.from_tensor_slices(pts_paths) + dataset = dataset.map(_file_to_matrix) + dataset = dataset.cache() # Comment out if memory cannot hold all the data. + dataset = dataset.shuffle(buffer_size=50, reshuffle_each_iteration=True) + dataset = dataset.repeat() + dataset = dataset.map(_random_rotation) + dataset = dataset.batch(1) + #dataset = dataset.prefetch(buffer_size=buffer_size) + iterator = tf.data.make_one_shot_iterator(dataset) + batch_data, batch_rot = iterator.get_next() + features_dict = {'data': batch_data, 'rot': batch_rot} + batch_size = tf.shape(batch_data)[0] + batch_labels_dummy = tf.zeros(shape=(batch_size, 1)) + return (features_dict, batch_labels_dummy) + + +def eval_input_fn(): + """Generate test data from *modified* .pts files. + + See README and comments below for details on how the data is modified. + + Returns: + A tuple of features and associated labels. + """ + def _file_to_matrix(pts_path): + """Read Nx3 point cloud and 3x3 rotation matrix from a .pts file. + + The test data is a modified version of the original files. For each .pts + file we have (1) added a 3x3 rotation matrix for testing, and (2) removed + the second half of the point cloud since it is not used at all. + + Args: + pts_path: path to a .pts file. + + Returns: + A Nx3 point cloud. + A 3x3 rotation matrix. + """ + file_buffer = tf.read_file(pts_path) + lines = tf.string_split([file_buffer], delimiter='\n') + lines1 = tf.string_split(lines.values,delimiter='\r') + values = tf.stack(tf.decode_csv(lines1.values, + record_defaults=[[0.0], [0.0], [0.0]],field_delim=' ')) + values = tf.transpose(values)# 3xN --> Nx3. + + #modified by Lyoung + diff_num = 1414-tf.shape(values)[0] + repeat_pts = tf.tile(tf.reshape(values[4, :], (1, -1)), [diff_num, 1]) + values = tf.concat([values, repeat_pts], axis=0) + # First three rows are the rotation matrix, remaining rows the point cloud. + rot = values[:3, :] + + return values[4:, :], rot + + pts_paths = tf.io.gfile.glob(FLAGS.pt_cloud_test_files) + dataset = tf.data.Dataset.from_tensor_slices(pts_paths) + dataset = dataset.map(_file_to_matrix) + dataset = dataset.batch(1) + iterator = tf.data.make_one_shot_iterator(dataset) + batch_data, batch_rot = iterator.get_next() + + features_dict = {'data': batch_data, 'rot': batch_rot} + batch_size = tf.shape(batch_data)[0] + batch_labels_dummy = tf.zeros(shape=(batch_size, 1)) + return (features_dict, batch_labels_dummy) + + +def print_variable_names(): + """Print variable names in a model.""" + params = {'dummy': 0} + estimator = NPUEstimator( + model_fn=model_fn, + model_dir=FLAGS.checkpoint_dir, + params=params) + + names = estimator.get_variable_names() + for name in names: + print(name) + + +def predict_all_test(): + """Print error statistics for the test dataset.""" + + config = NPURunConfig( + save_summary_steps=None, + save_checkpoints_steps=None, + log_step_count_steps=None, + keep_checkpoint_max=None) + + params = {'dummy': 0} + estimator = NPUEstimator( + model_fn=model_fn, + model_dir=FLAGS.checkpoint_dir, + params=params, + config=config) + evals = estimator.predict(input_fn=eval_input_fn, yield_single_examples=True) + + # Print error statistics. + + all_errors = [x['error'] for x in evals] + errors = np.array(all_errors) + print('Evaluated %d examples'%np.size(errors)) + print('Mean error: %f degrees',np.mean(errors)) + print('Median error: %f degrees', np.median(errors)) + print('Std: %f degrees', np.std(errors)) + sorted_errors = np.sort(errors) + n = np.size(sorted_errors) + print('\nPercentiles:') + for perc in range(1, 101): + index = np.int32(np.float32(n * perc) / 100.0) - 1 + print('%3d%%: %f'%(perc, sorted_errors[index])) + + +def train_and_eval(): + """Train and evaluate a model.""" + save_summary_steps = FLAGS.save_summaries_steps + save_checkpoints_steps = FLAGS.save_checkpoints_steps + log_step_count = FLAGS.log_step_count + + config = NPURunConfig( + save_summary_steps=save_summary_steps, + save_checkpoints_steps=save_checkpoints_steps, + log_step_count_steps=log_step_count, + keep_checkpoint_max=None, + precision_mode = "all_mix_precision") + + params = {'dummy': 0} + estimator = NPUEstimator( + model_fn=model_fn, + model_dir=FLAGS.checkpoint_dir, + config=config, + params=params) + + + train_spec = tf.estimator.TrainSpec( + input_fn=train_input_fn, + max_steps=FLAGS.train_steps) + + + eval_spec = tf.estimator.EvalSpec(input_fn=eval_input_fn, + start_delay_secs=60, + steps=FLAGS.eval_examples, + throttle_secs=60) + + + + tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec) + + +def main(argv=None): # pylint: disable=unused-argument + + if FLAGS.print_variable_names: + print_variable_names() + return + + if FLAGS.predict_all_test: + predict_all_test() + else: + train_and_eval() + + +if __name__ == '__main__': + + tf.app.run() diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU/modelarts_entry_stat.py b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU/modelarts_entry_stat.py new file mode 100644 index 000000000..78debea21 --- /dev/null +++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU/modelarts_entry_stat.py @@ -0,0 +1,40 @@ +import os +import argparse +import sys + +# 解析输入参数data_url +parser = argparse.ArgumentParser() +parser.add_argument("--data_url", type=str, default="/home/ma-user/modelarts/inputs/data_url_0") +parser.add_argument("--train_url", type=str, default="/home/ma-user/modelarts/outputs/train_url_0/") +config = parser.parse_args() + +print("[CANN-Modelzoo] code_dir path is [%s]" % (sys.path[0])) +code_dir = sys.path[0] +os.chdir(code_dir) +print("[CANN-Modelzoo] work_dir path is [%s]" % (os.getcwd())) + +print("[CANN-Modelzoo] before train - list my run files:") +os.system("ls -al /usr/local/Ascend/ascend-toolkit/") + +print("[CANN-Modelzoo] before train - list my dataset files:") +os.system("ls -al %s" % config.data_url) + +files = os.listdir(config.data_url) +print("===>>>Files:", files) + + + +print("[CANN-Modelzoo] start run train shell") +# 设置sh文件格式为linux可执行 +os.system("dos2unix ./test/*") + +# 执行train_full_1p.sh或者train_performance_1p.sh，需要用户自己指定 +# full和performance的差异，performance只需要执行很少的step，控制在15分钟以内，主要关注性能FPS +os.system("bash /home/ma-user/modelarts/user-job-dir/code/genStatistical.sh --data_path=%s --output_path=%s " % (config.data_url, config.train_url)) + +print("[CANN-Modelzoo] finish run train shell") + +# 将当前执行目录所有文件拷贝到obs的output进行备份 +print("[CANN-Modelzoo] after train - list my output files:") +os.system("cp -r %s %s " % (code_dir, config.train_url)) +os.system("ls -al %s" % config.train_url) \ No newline at end of file diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU/train_full_1p.sh b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU/train_full_1p.sh new file mode 100644 index 000000000..edd5eed9a --- /dev/null +++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU/train_full_1p.sh @@ -0,0 +1,209 @@ +#!/bin/bash + +########################################################## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +########################################################## +# shell脚本所在路径 +cur_path=`echo $(cd $(dirname $0);pwd)` + +# 判断当前shell是否是performance +perf_flag=`echo $0 | grep performance | wc -l` + +# 当前执行网络的名称 +Network=`echo $(cd $(dirname $0);pwd) | awk -F"/" '{print $(NF-1)}'` + +export RANK_SIZE=1 +export RANK_ID=0 +export JOB_ID=10087 + +# 路径参数初始化 +data_path="" +output_path="" + +# 帮助信息，不需要修改 +if [[ $1 == --help || $1 == -h ]];then + echo"usage:./train_performance_1P.sh " + echo " " + echo "parameter explain: + --data_path # dataset of training + --output_path # output of training + --train_steps # max_step for training + --train_epochs # max_epoch for training + --batch_size # batch size + -h/--help show help message + " + exit 1 +fi + +# 参数校验，不需要修改 +for para in $* +do + if [[ $para == --data_path* ]];then + data_path=`echo ${para#*=}` + elif [[ $para == --output_path* ]];then + output_path=`echo ${para#*=}` + elif [[ $para == --train_steps* ]];then + train_steps=`echo ${para#*=}` + elif [[ $para == --train_epochs* ]];then + train_epochs=`echo ${para#*=}` + elif [[ $para == --batch_size* ]];then + batch_size=`echo ${para#*=}` + fi +done + +# 校验是否传入data_path,不需要修改 +if [[ $data_path == "" ]];then + echo "[Error] para \"data_path\" must be config" + exit 1 +fi + +# 校验是否传入output_path,不需要修改 +if [[ $output_path == "" ]];then + output_path="./test/output/${ASCEND_DEVICE_ID}" +fi + +# 设置打屏日志文件名，请保留，文件名为${print_log} +print_log="./test/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log" +etp_flag=${etp_running_flag} +if [ x"${etp_flag}" != xtrue ]; +then + echo "running without etp..." + print_log_name=`ls /home/ma-user/modelarts/log/ | grep proc-rank` + print_log="/home/ma-user/modelarts/log/${print_log_name}" +fi +echo ${print_log} + +CaseName="" +function get_casename() +{ + if [ x"${perf_flag}" = x1 ]; + then + CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'perf' + else + CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'acc' + fi +} + +# 跳转到code目录 +cd ${cur_path}/../ +rm -rf ./test/output/${ASCEND_DEVICE_ID} +mkdir -p ./test/output/${ASCEND_DEVICE_ID} + +# 训练开始时间记录，不需要修改 +start_time=$(date +%s) +########################################################## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +########################################################## + +#========================================================= +#========================================================= +#========训练执行命令，需要根据您的网络进行修改============== +#========================================================= +#========================================================= +# 您的训练数据集在${data_path}路径下，请直接使用这个变量获取 +# 您的训练输出目录在${output_path}路径下，请直接使用这个变量获取 +# 您的其他基础参数，可以自定义增加，但是batch_size请保留，并且设置正确的值 +batch_size=1 + +echo "------------------***PRINT DATA PATH***-----------------" +train_data="points/*.pts" +test_data="test_points_modified/*.pts" + +trainData_Path="$data_path$train_data" +testData_Path="$data_path$test_data" + + +if [ x"${modelarts_flag}" != x ]; +then + python3.7 /home/ma-user/modelarts/user-job-dir/code/main_point_cloud_boostPerf.py \ + --method=svd \ + --checkpoint_dir=${output_path} \ + --log_step_count=200 \ + --save_summaries_steps=25000 \ + --pt_cloud_train_files=${trainData_Path} \ + --pt_cloud_test_files=${testData_Path} \ + --train_steps=2600000 \ + --save_checkpoints_steps=100000 \ + --eval_examples=39900 +else + python3.7 /home/ma-user/modelarts/user-job-dir/code/main_point_cloud_boostPerf.py \ + --method=svd \ + --checkpoint_dir=${output_path} \ + --log_step_count=200 \ + --save_summaries_steps=25000 \ + --pt_cloud_train_files=${trainData_Path}\ + --pt_cloud_test_files=${testData_Path} \ + --train_steps=2600000 \ + --save_checkpoints_steps=100000 \ + --eval_examples=39900 > ${print_log} +fi + + +# 性能相关数据计算 +StepTime=`grep "sec/step :" ${print_log} | tail -n 10 | awk '{print $NF}' | awk '{sum+=$1} END {print sum/NR}'` +FPS=`awk 'BEGIN{ "%.2f\n", '${batch_size}'/'${StepTime}'}'` + +# 精度相关数据计算 +train_accuracy=`grep "Final Accuracy accuracy" ${print_log} | awk '{print $NF}'` +# 提取所有loss打印信息 +grep "loss :" ${print_log} | awk -F ":" '{print $4}' | awk -F "-" '{print $1}' > ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt + + +########################################################### +#########后面的所有内容请不要修改########################### +#########后面的所有内容请不要修改########################### +#########后面的所有内容请不要修改########################### +########################################################### + +# 判断本次执行是否正确使用Ascend NPU +use_npu_flag=`grep "The model has been compiled on the Ascend AI processor" ${print_log} | wc -l` +if [ x"${use_npu_flag}" == x0 ]; +then + echo "------------------ ERROR NOTICE START ------------------" + echo "ERROR, your task haven't used Ascend NPU, please check your npu Migration." + echo "------------------ ERROR NOTICE END------------------" +else + echo "------------------ INFO NOTICE START------------------" + echo "INFO, your task have used Ascend NPU, please check your result." + echo "------------------ INFO NOTICE END------------------" +fi + +# 获取最终的casename，请保留，case文件名为${CaseName} +get_casename + +# 重命名loss文件 +if [ -f ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ]; +then + mv ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ./test/output/${ASCEND_DEVICE_ID}/${CaseName}_loss.txt +fi + +# 训练端到端耗时 +end_time=$(date +%s) +e2e_time=$(( $end_time - $start_time )) + +echo "------------------ Final result ------------------" +# 输出性能FPS/单step耗时/端到端耗时 +echo "Final Performance images/sec : $FPS" +echo "Final Performance sec/step : $StepTime" +echo "E2E Training Duration sec : $e2e_time" + +# 输出训练精度 +echo "Final Train Accuracy : ${train_accuracy}" + +# 最后一个迭代loss值，不需要修改 +ActualLoss=(`awk 'END {print $NF}' $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}_loss.txt`) + +#关键信息打印到${CaseName}.log中，不需要修改 +echo "Network = ${Network}" > $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "RankSize = ${RANK_SIZE}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "BatchSize = ${batch_size}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "DeviceType = `uname -m`" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualFPS = ${FPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "TrainingTime = ${StepTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log \ No newline at end of file diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU/utils.py b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU/utils.py new file mode 100644 index 000000000..38d66a8da --- /dev/null +++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU/utils.py @@ -0,0 +1,116 @@ +# coding=utf-8 +# Copyright 2021 The Google Research Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Utility functions.""" +import numpy as np +from scipy.stats import special_ortho_group +import tensorflow as tf +from npu_bridge.npu_init import * + + +def relative_angle(r1, r2): + """Relative angle (radians) between 3D rotation matrices.""" + rel_rot = tf.matmul(tf.transpose(r1, perm=[0, 2, 1]), r2) + trace = rel_rot[:, 0, 0] + rel_rot[:, 1, 1] + rel_rot[:, 2, 2] + cos_theta = (trace - 1.0) / 2.0 + cos_theta = tf.minimum(cos_theta, tf.ones_like(cos_theta)) + cos_theta = tf.maximum(cos_theta, (-1.0) * tf.ones_like(cos_theta)) + theta = tf.acos(cos_theta) + return theta + + +def random_rotation_benchmark_np(n): + """Sample a random 3D rotation by method used in Zhou et al, CVPR19. + + This numpy function is a copy of the PyTorch function + get_sampled_rotation_matrices_by_axisAngle() in the code made available + for Zhou et al, CVPR19, at https://github.com/papagina/RotationContinuity/. + + Args: + n: the number of rotation matrices to return. + + Returns: + [n, 3, 3] np array. + """ + theta = np.random.uniform(-1, 1, n) * np.pi + sin = np.sin(theta) + axis = np.random.randn(n, 3) + axis = axis / np.maximum(np.linalg.norm(axis, axis=-1, keepdims=True), 1e-7) + qw = np.cos(theta) + qx = axis[:, 0] * sin + qy = axis[:, 1] * sin + qz = axis[:, 2] * sin + + xx = qx*qx + yy = qy*qy + zz = qz*qz + xy = qx*qy + xz = qx*qz + yz = qy*qz + xw = qx*qw + yw = qy*qw + zw = qz*qw + + row0 = np.stack((1-2*yy-2*zz, 2*xy-2*zw, 2*xz+2*yw), axis=-1) + row1 = np.stack((2*xy+2*zw, 1-2*xx-2*zz, 2*yz-2*xw), axis=-1) + row2 = np.stack((2*xz-2*yw, 2*yz+2*xw, 1-2*xx-2*yy), axis=-1) + matrix = np.stack((row0, row1, row2), axis=1) + + return matrix + + +def random_rotation_benchmark(n): + """A TF wrapper for random_rotation_benchmark_np().""" + mat = tf.py_func( + func=lambda t: np.float32(random_rotation_benchmark_np(t)), + inp=[n], + Tout=tf.float32, + stateful=True) + return tf.reshape(mat, (n, 3, 3)) + + +def random_rotation(n): + """Sample rotations from a uniform distribution on SO(3).""" + mat = tf.py_func( + func=lambda t: np.float32(special_ortho_group.rvs(3, size=t)), + inp=[n], + Tout=tf.float32, + stateful=True) + return tf.reshape(mat, (n, 3, 3)) + + +def symmetric_orthogonalization(x): + """Maps 9D input vectors onto SO(3) via symmetric orthogonalization.""" + # Innner dimensions of the input should be 3x3 matrices. + m = tf.reshape(x, (-1, 3, 3)) + _, u, v = tf.svd(m) + det = tf.linalg.det(tf.matmul(u, v, transpose_b=True)) + r = tf.matmul( + tf.concat([u[:, :, :-1], u[:, :, -1:] * tf.reshape(det, [-1, 1, 1])], 2), + v, transpose_b=True) + return r + + +def gs_orthogonalization(p6): + """Gram-Schmidt orthogonalization from 6D input.""" + # Input should be [batch_size, 6] + x = p6[:, 0:3] + y = p6[:, 3:6] + xn = tf.math.l2_normalize(x, axis=-1) + z = tf.linalg.cross(xn, y) + zn = tf.math.l2_normalize(z, axis=-1) + y = tf.linalg.cross(zn, xn) + r = tf.stack([xn, y, zn], -1) + return r -- Gitee From 903f8860d337b56bd43df2b3996bcd045d3f3047 Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Sat, 7 May 2022 14:15:48 +0000 Subject: [PATCH 35/88] =?UTF-8?q?NPU=E7=9B=B8=E5=85=B3=E4=BB=A3=E7=A0=81?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../NPU/NPU\347\262\276\345\272\246.txt" | 106 ++++++++++++++++++ 1 file changed, 106 insertions(+) create mode 100644 "TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU/NPU\347\262\276\345\272\246.txt" diff --git "a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU/NPU\347\262\276\345\272\246.txt" "b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU/NPU\347\262\276\345\272\246.txt" new file mode 100644 index 000000000..954bca432 --- /dev/null +++ "b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU/NPU\347\262\276\345\272\246.txt" @@ -0,0 +1,106 @@ +Evaluated 39900 examples +Mean error: %f degrees 2.9278567 +Median error: %f degrees 1.7031833 +Std: %f degrees 8.454575 + +Percentiles: + 1%: 0.322641 + 2%: 0.414959 + 3%: 0.483760 + 4%: 0.539592 + 5%: 0.581482 + 6%: 0.623068 + 7%: 0.659679 + 8%: 0.691538 + 9%: 0.723617 + 10%: 0.751734 + 11%: 0.778837 + 12%: 0.803569 + 13%: 0.829923 + 14%: 0.853175 + 15%: 0.875810 + 16%: 0.898746 + 17%: 0.920686 + 18%: 0.941284 + 19%: 0.963068 + 20%: 0.985562 + 21%: 1.006582 + 22%: 1.029266 + 23%: 1.050157 + 24%: 1.071371 + 25%: 1.092174 + 26%: 1.113466 + 27%: 1.135911 + 28%: 1.157751 + 29%: 1.178524 + 30%: 1.201056 + 31%: 1.223973 + 32%: 1.247724 + 33%: 1.270570 + 34%: 1.294524 + 35%: 1.317301 + 36%: 1.340275 + 37%: 1.363005 + 38%: 1.388467 + 39%: 1.411946 + 40%: 1.436132 + 41%: 1.460185 + 42%: 1.484376 + 43%: 1.511031 + 44%: 1.535951 + 45%: 1.562352 + 46%: 1.589054 + 47%: 1.618583 + 48%: 1.644610 + 49%: 1.671404 + 50%: 1.703183 + 51%: 1.733704 + 52%: 1.763252 + 53%: 1.791003 + 54%: 1.822308 + 55%: 1.854563 + 56%: 1.886785 + 57%: 1.922339 + 58%: 1.957048 + 59%: 1.992330 + 60%: 2.030376 + 61%: 2.073393 + 62%: 2.113592 + 63%: 2.152860 + 64%: 2.195707 + 65%: 2.241841 + 66%: 2.286874 + 67%: 2.330366 + 68%: 2.379650 + 69%: 2.427613 + 70%: 2.480492 + 71%: 2.534432 + 72%: 2.593293 + 73%: 2.657559 + 74%: 2.726347 + 75%: 2.795964 + 76%: 2.867306 + 77%: 2.933115 + 78%: 3.011294 + 79%: 3.097182 + 80%: 3.192182 + 81%: 3.284318 + 82%: 3.385412 + 83%: 3.501069 + 84%: 3.619581 + 85%: 3.753794 + 86%: 3.906153 + 87%: 4.080823 + 88%: 4.264343 + 89%: 4.474133 + 90%: 4.726048 + 91%: 5.013212 + 92%: 5.333844 + 93%: 5.743374 + 94%: 6.223213 + 95%: 6.846590 + 96%: 7.720470 + 97%: 9.113199 + 98%: 11.549816 + 99%: 18.274883 +100%: 179.585983 \ No newline at end of file -- Gitee From da801f374816f620abfe1d6467d46d01f9497694 Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Sat, 7 May 2022 14:22:00 +0000 Subject: [PATCH 36/88] =?UTF-8?q?=E5=88=A0=E9=99=A4=E6=96=87=E4=BB=B6=20Te?= =?UTF-8?q?nsorFlow/contrib/cv/SVD=5FID2019=5Ffor=5FTensorflow/README.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../cv/SVD_ID2019_for_Tensorflow/README.md | 229 ------------------ 1 file changed, 229 deletions(-) delete mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md deleted file mode 100644 index 5724c3409..000000000 --- a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md +++ /dev/null @@ -1,229 +0,0 @@ -

基本信息

- -**发布者（Publisher）：Huawei** - -**应用领域（Application Domain）：CV** - -**版本（Version）：** - -**修改时间（Modified） ：2022.05.05** - -**框架（Framework）：TensorFlow 1.15.0** - -**处理器（Processor）：昇腾910** - -**描述（Description）：使用训练好的SVD模型，评估对称正交化在点云对准中的应用效果。** - -

概述

- -给定两个三维点云图，利用SVD正交化过程SVDO+(M)将其投射到SO(3)上，要求网络预测最佳对齐它们的3D旋转。代码的训练逻辑是每训练10w步保存一个模型，并且在测试集上检验该模型的精度，最后比较的都是260w步的模型精度 - -- 开源代码： - - https://github.com/google-research/google-research/tree/master/special_orthogonalization。 - -- 参考论文： - - [An Analysis of SVD for Deep Rotation Estimation](https://arxiv.org/abs/2006.14616) - -- 参考实现： - - obs://cann-id2019/gpu/ - -- 相关迁移的工作： - 在进行代码迁移到NPU上时，输入的训练数据为点云数据，点云数据的shape为(N,3)，其中N并不是固定的，因此在NPU上存在动态shape的问题，导致模型训练无法正常进行。我们为此想了三个解决方法：1、找出所有点云数据中最小的N，对于大于N的点云数据，仅取前N行的数据输入训练。2、找到所有点云数据中最大的N，对于小于N的点云进行补0操作，将所有数据固定为最大的N后，输入网络进行训练。3、找到所有点云数据中最大的N，对小于N的点云数据，从原数据中选择一个点云进行填补至行数为N，再将数据输入网络进行训练。该三种方法均成功解决了NPU上的动态shape问题，但是第一种方法删除了样本点，因此导致最后训练出的模型精度很差；第二种方法虽然并没有丢失样本信息，但是向数据中填入大量的0，改变了本来的代码逻辑，导致最后训练出的模型精度也并不高。对于第三种方法，即没有丢失样本信息，对每个点云数据中的某一个点云样本点进行重复操作，没有改变原始的代码逻辑，最后也获得了不错的精度表现。 - -- 通过Git获取对应commit\_id的代码方法如下： - - ``` - git clone {repository_url} # 克隆仓库的代码 - cd {repository_name} # 切换到模型的代码仓目录 - git checkout {branch} # 切换到对应分支 - git reset --hard ｛commit_id｝ # 代码设置到对应的commit_id - cd ｛code_path｝ # 切换到模型代码所在路径，若仓库下只有该模型，则无需切换 - ``` - -## 默认配置 - -- 数据集获取：obs://cann-id2019/dataset/ - -- 训练超参 - - - log_step_count=200 - - save_summaries_steps=25000 - - train_steps=2600000 - - save_checkpoints_steps=100000 - - eval_examples=39900 - - - -

训练环境准备

- -1. 硬件环境准备请参见各硬件产品文档"[驱动和固件安装升级指南]( https://support.huawei.com/enterprise/zh/category/ai-computing-platform-pid-1557196528909)"。需要在硬件设备上安装与CANN版本配套的固件与驱动。 -2. 宿主机上需要安装Docker并登录[Ascend Hub中心](https://ascendhub.huawei.com/#/detail?name=ascend-tensorflow-arm)获取镜像。 - - 当前模型支持的镜像列表如[表1](#zh-cn_topic_0000001074498056_table1519011227314)所示。 - - **表 1** 镜像列表 - - - - - - - - - - - - -

镜像名称

-

镜像版本

-

配套CANN版本

-
-

20.2.0

-

20.2

-
- - - -## 脚本参数 - -``` -gen_pt_test_data_gpu.py 中的参数 - ---input_test_files 传入需要进行旋转的点云数据集 ---output_directory 存储旋转后点云集的路径 ---random_rotation_axang boole型，若为真将会对传入的数据集采用指定方法旋转，默认为真 ---num_rotations_per_file 每个测试点云的随机旋转增加数。默认为100 - - -main_point_cloud_gpu.py 中的参数 - ---pt_cloud_test_files 测试数据集路径 ---pt_cloud_train_files 熟练数据集路径 ---method 指定用于预测旋转的方式。选项为"svd", "svd-inf", or "gs"。默认为“svd” ---checkpoint_dir 训练模型的存放位置 ---train_steps 训练迭代的次数。默认为2600000 ---save_checkpoints_steps 保存检查点的频率。默认为10000 ---log_step_count 日志记录一次的步数。默认为200 ---save_summaries_steps 保存一次summary的步数。默认为5000 ---learning_rate 默认为1e-5 ---lr_decay 如果为真，则衰减learning rate。默认为假 ---lr_decay_steps learning rate衰减步数。默认为35000 ---lr_decay_rate learning rate衰减速率。默认为0.95 ---predict_all_test 如果为真，则在最新的检查点上运行eval作业，并打印每个输入的误差信息。默认为假 ---eval_examples 测试样本的数量。默认为0 ---print_variable_names 打印模型变量名。默认为假 ---num_train_augmentations 增加每个输入点云的随机旋转数。默认为10 - -``` - - - -## 运行 - -GPU运行命令如下： - -**生成测试数据** - -注：生成的文件points_test_modified、points已包含在dataset文件夹中。 -```bash -# 将路径设置到训练点云图文件 -IN_FILES=/shapenet/data/pc_plane/points_test/*.pts - -NEW_TEST_FILES_DIR=/shapenet/data/pc_plane/points_test_modified - -AXANG_SAMPLING=True - -# 决定旋转轴角的分布 -AXANG_SAMPLING=True - -python -m special_orthogonalization.gen_pt_test_data_gpu --input_test_files=$IN_FILES --output_directory=$NEW_TEST_FILES_DIR --random_rotation_axang=$AXANG_SAMPLING -``` - -**训练与评价** -```bash -# 将路径设置到原始训练数据 -TRAIN_FILES=/shapenet/data/pc_plane/points/*.pts - -#将路径设置到旋转后的训练数据 -TEST_FILES=$NEW_TEST_FILES_DIR/*.pts - -# 指定旋转预测方式 -METHOD=svd - -# 指定ckpt、summaries、评价结果等的存储路径 -OUT_DIR=/path/to/model - -python -m special_orthogonalization.main_point_cloud_gpu --method=$METHOD --checkpoint_dir=$OUT_DIR --log_step_count=200 --save_summaries_steps=25000 --pt_cloud_train_files=$TRAIN_FILES --pt_cloud_test_files=$TEST_FILES --train_steps=2600000 --save_checkpoints_steps=100000 --eval_examples=39900 -``` - -**从所有训练样本中生成统计数据** -```bash -# 打印均值、中位数、标准差和分位数 -python -m special_orthogonalization.main_point_cloud_gpu --method=$METHOD --checkpoint_dir=$OUT_DIR --pt_cloud_test_files=$TEST_FILES --predict_all_test=True -``` -## 运行 - -NPU运行命令方式如下: - -对于所有的三个步骤程序来说，modelarts插件obs桶中的数据路径均要写到真正包含数据的那一个路径 -如在pc_plane文件夹中含有points、points_test等包含数据的文件夹 -modelarts插件中的数据路径写为 obs://cann-id2019/data/pc_palne/ - -**生成测试数据** - -运行这一步我们需要的程序文件为gen_pt_test_data.py、modelarts_entry_Axang.py、genTestData.sh -这三个文件中的代码均不需要修改 -最后生成的旋转后的数据文件存放在obs桶当次程序文件的output路径中，文件名为test_points_modified， -为进行第二步模型训练，需要将生成旋转后的文件转移至obs桶中存放data的路径 - -注：需要确保的是存在obs桶里的data文件名为points_test - -**训练与评价** - -运行这一步我们需要的程序文件为main_point_cloud.py、modelarts_entry_acc_train.py、train_full_1p.sh -这三个文件中的代码均不需要修改 -最后生成的旋转后的数据文件存放在obs桶当次程序文件的output路径中，文件名为test_points_modified， -为进行第三步，需要将生成的output文件转移至obs桶中存放data的路径 - -注意：该次训练的模型保存在该次的obs文件夹中，进行第三步时又需要重启一次新的modelarts，因此我们需要将output文件中的 -checkpoint文件中最新模型的路径修改 -"/home/ma-user/modelarts/inputs/data_url_0/output" -这样第三步才能跑出正确的精度指标 - -**从所有训练样本中生成统计数据** - -运行这一步我们需要的程序文件为main_point_cloud.py、modelarts_entry_stat.py、genStatistical.sh -这三个文件的代码均不需要修改 -运行成功后将会在屏幕上打印出关于精度相应的统计量值 - -注意：由于加入了混合精度提高训练性能，导致该步出现未知错误，输出的误差均为0，因此建议将训练后的结果下载到本地 -修改checkpoint文件中模型的路径后，利用本地的GPU进行运行，实际上该步骤在运行时NPU成功启动但也未曾利用。 - -## 训练结果 -**精度对比：** - - - -| 测地线误差（°） | 论文发布 | GPU(初始代码未改动版本) | GPU实测|NPU实测 | -| ------------------------ | ------- | ----- | --------- |----------| -| 平均值 | 1.63 | 2.58 | 3.98 | 2.92 | -| 中值 | 0.89 | 1.68 | 2.6 | 1.7 | -| 标准差 | 6.70 | 6.93 | 9.36 | 8.45 | - -相比于论文中的精度，我们NPU迁移后实测差距依然较大，但是与我们未对代码任何改动初始的版本在GPU上跑出来的精度相差较小， -且对于相同的代码的代码改动，NPU上的精度优于GPU上的精度。需要注意的是，在NPU上运行程序时，我们采用混合精度来提升训练 -的性能，但是其中产生了未知的错误，导致代表的精度指标mean_degree_err在整个训练过程中始终为0，因此我们无法得知在NPU训练的 -260w步的过程中，精度指标是下降的过程是怎样的。值得庆幸的是通过NPU训练出的模型，能够在GPU上计算出精度，并且精度还不错。 - -**性能对比：** - -取华为v100上GPU运行的前2w步的global_step/sec平均值和NPU运行的前2w步的global_step/sec平均值进行对比，以达到性能对比的目的。 - -| 性能指标项 | 论文发布 | GPU实测 | NPU实测 | -| ------------------- | ------- | ------ | ------ | -| global_step/sec| 无 | 87.64 | 101.24 | - - -- Gitee From abdfeea585cfe682754407a8b06b190862386cc0 Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Sat, 7 May 2022 14:22:32 +0000 Subject: [PATCH 37/88] =?UTF-8?q?=E6=9B=B4=E6=96=B0=E5=90=8E=E7=9A=84readm?= =?UTF-8?q?e=EF=BC=8C=E5=8A=A0=E5=85=A5=E4=BA=86NPU=E4=B8=8A=E8=BF=90?= =?UTF-8?q?=E8=A1=8C=E7=9A=84=E6=9C=80=E6=96=B0=E7=BB=93=E6=9E=9C?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../cv/SVD_ID2019_for_Tensorflow/README.md | 241 ++++++++++++++++++ 1 file changed, 241 insertions(+) create mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md new file mode 100644 index 000000000..7e6736d65 --- /dev/null +++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md @@ -0,0 +1,241 @@ +

基本信息

+ +**发布者（Publisher）：Huawei** + +**应用领域（Application Domain）：CV** + +**版本（Version）：** + +**修改时间（Modified） ：2022.05.05** + +**框架（Framework）：TensorFlow 1.15.0** + +**处理器（Processor）：昇腾910** + +**描述（Description）：使用训练好的SVD模型，评估对称正交化在点云对准中的应用效果。** + +

概述

+ +给定两个三维点云图，利用SVD正交化过程SVDO+(M)将其投射到SO(3)上，要求网络预测最佳对齐它们的3D旋转。代码的训练逻辑是每训练10w步保存一个模型，并且在测试集上检验该模型的精度，最后比较的都是260w步的模型精度 + +- 开源代码： + + https://github.com/google-research/google-research/tree/master/special_orthogonalization。 + +- 参考论文： + + [An Analysis of SVD for Deep Rotation Estimation](https://arxiv.org/abs/2006.14616) + +- 参考实现： + gpu最后训练出来的模型在 + obs://cann-id2019/gpu + + 数据在 + obs://cann-id2019/data/pc_plane + 共有3个数据集 + 训练数据集points + 测试数据集points_test + 第一步旋转后的数据集test_points_modified + + npu训练出来的模型在 + obs://cann-id2019/data/pc_plane/output + + + 相关代码均以上传，在NPU和GPU文件夹中可以找到 +- 相关迁移的工作： + 在进行代码迁移到NPU上时，输入的训练数据为点云数据，点云数据的shape为(N,3)，其中N并不是固定的，因此在NPU上存在动态shape的问题，导致模型训练无法正常进行。我们为此想了三个解决方法：1、找出所有点云数据中最小的N，对于大于N的点云数据，仅取前N行的数据输入训练。2、找到所有点云数据中最大的N，对于小于N的点云进行补0操作，将所有数据固定为最大的N后，输入网络进行训练。3、找到所有点云数据中最大的N，对小于N的点云数据，从原数据中选择一个点云进行填补至行数为N，再将数据输入网络进行训练。该三种方法均成功解决了NPU上的动态shape问题，但是第一种方法删除了样本点，因此导致最后训练出的模型精度很差；第二种方法虽然并没有丢失样本信息，但是向数据中填入大量的0，改变了本来的代码逻辑，导致最后训练出的模型精度也并不高。对于第三种方法，即没有丢失样本信息，对每个点云数据中的某一个点云样本点进行重复操作，没有改变原始的代码逻辑，最后也获得了不错的精度表现。 + +- 通过Git获取对应commit\_id的代码方法如下： + + ``` + git clone {repository_url} # 克隆仓库的代码 + cd {repository_name} # 切换到模型的代码仓目录 + git checkout {branch} # 切换到对应分支 + git reset --hard ｛commit_id｝ # 代码设置到对应的commit_id + cd ｛code_path｝ # 切换到模型代码所在路径，若仓库下只有该模型，则无需切换 + ``` + +## 默认配置 + +- 数据集获取：obs://cann-id2019/dataset/ + +- 训练超参 + + - log_step_count=200 + - save_summaries_steps=25000 + - train_steps=2600000 + - save_checkpoints_steps=100000 + - eval_examples=39900 + + + +

训练环境准备

+ +1. 硬件环境准备请参见各硬件产品文档"[驱动和固件安装升级指南]( https://support.huawei.com/enterprise/zh/category/ai-computing-platform-pid-1557196528909)"。需要在硬件设备上安装与CANN版本配套的固件与驱动。 +2. 宿主机上需要安装Docker并登录[Ascend Hub中心](https://ascendhub.huawei.com/#/detail?name=ascend-tensorflow-arm)获取镜像。 + + 当前模型支持的镜像列表如[表1](#zh-cn_topic_0000001074498056_table1519011227314)所示。 + + **表 1** 镜像列表 + + + + + + + + + + + + +

镜像名称

+

镜像版本

+

配套CANN版本

+
+

20.2.0

+

20.2

+
+ + + +## 脚本参数 + +``` +gen_pt_test_data_gpu.py 中的参数 + +--input_test_files 传入需要进行旋转的点云数据集 +--output_directory 存储旋转后点云集的路径 +--random_rotation_axang boole型，若为真将会对传入的数据集采用指定方法旋转，默认为真 +--num_rotations_per_file 每个测试点云的随机旋转增加数。默认为100 + + +main_point_cloud_gpu.py 中的参数 + +--pt_cloud_test_files 测试数据集路径 +--pt_cloud_train_files 熟练数据集路径 +--method 指定用于预测旋转的方式。选项为"svd", "svd-inf", or "gs"。默认为“svd” +--checkpoint_dir 训练模型的存放位置 +--train_steps 训练迭代的次数。默认为2600000 +--save_checkpoints_steps 保存检查点的频率。默认为10000 +--log_step_count 日志记录一次的步数。默认为200 +--save_summaries_steps 保存一次summary的步数。默认为5000 +--learning_rate 默认为1e-5 +--lr_decay 如果为真，则衰减learning rate。默认为假 +--lr_decay_steps learning rate衰减步数。默认为35000 +--lr_decay_rate learning rate衰减速率。默认为0.95 +--predict_all_test 如果为真，则在最新的检查点上运行eval作业，并打印每个输入的误差信息。默认为假 +--eval_examples 测试样本的数量。默认为0 +--print_variable_names 打印模型变量名。默认为假 +--num_train_augmentations 增加每个输入点云的随机旋转数。默认为10 + +``` + + + +## 运行 + +GPU运行命令如下： + +**生成测试数据** + +注：生成的文件points_test_modified、points已包含在dataset文件夹中。 +```bash +# 将路径设置到训练点云图文件 +IN_FILES=/shapenet/data/pc_plane/points_test/*.pts + +NEW_TEST_FILES_DIR=/shapenet/data/pc_plane/points_test_modified + +AXANG_SAMPLING=True + +# 决定旋转轴角的分布 +AXANG_SAMPLING=True + +python -m special_orthogonalization.gen_pt_test_data_gpu --input_test_files=$IN_FILES --output_directory=$NEW_TEST_FILES_DIR --random_rotation_axang=$AXANG_SAMPLING +``` + +**训练与评价** +```bash +# 将路径设置到原始训练数据 +TRAIN_FILES=/shapenet/data/pc_plane/points/*.pts + +#将路径设置到旋转后的训练数据 +TEST_FILES=$NEW_TEST_FILES_DIR/*.pts + +# 指定旋转预测方式 +METHOD=svd + +# 指定ckpt、summaries、评价结果等的存储路径 +OUT_DIR=/path/to/model + +python -m special_orthogonalization.main_point_cloud_gpu --method=$METHOD --checkpoint_dir=$OUT_DIR --log_step_count=200 --save_summaries_steps=25000 --pt_cloud_train_files=$TRAIN_FILES --pt_cloud_test_files=$TEST_FILES --train_steps=2600000 --save_checkpoints_steps=100000 --eval_examples=39900 +``` + +**从所有训练样本中生成统计数据** +```bash +# 打印均值、中位数、标准差和分位数 +python -m special_orthogonalization.main_point_cloud_gpu --method=$METHOD --checkpoint_dir=$OUT_DIR --pt_cloud_test_files=$TEST_FILES --predict_all_test=True +``` +## 运行 + +NPU运行命令方式如下: + +对于所有的三个步骤程序来说，modelarts插件obs桶中的数据路径均要写到真正包含数据的那一个路径 +如在pc_plane文件夹中含有points、points_test等包含数据的文件夹 +modelarts插件中的数据路径写为 obs://cann-id2019/data/pc_palne/ + +**生成测试数据** + +运行这一步我们需要的程序文件为gen_pt_test_data.py、modelarts_entry_Axang.py、genTestData.sh +这三个文件中的代码均不需要修改 +最后生成的旋转后的数据文件存放在obs桶当次程序文件的output路径中，文件名为test_points_modified， +为进行第二步模型训练，需要将生成旋转后的文件转移至obs桶中存放data的路径 + +注：需要确保的是存在obs桶里的data文件名为points_test + +**训练与评价** + +运行这一步我们需要的程序文件为main_point_cloud.py、modelarts_entry_acc_train.py、train_full_1p.sh +这三个文件中的代码均不需要修改 +最后生成的旋转后的数据文件存放在obs桶当次程序文件的output路径中，文件名为test_points_modified， +为进行第三步，需要将生成的output文件转移至obs桶中存放data的路径 + +注意：该次训练的模型保存在该次的obs文件夹中，进行第三步时又需要重启一次新的modelarts，因此我们需要将output文件中的 +checkpoint文件中最新模型的路径修改 +"/home/ma-user/modelarts/inputs/data_url_0/output" +这样第三步才能跑出正确的精度指标 + +**从所有训练样本中生成统计数据** + +运行这一步我们需要的程序文件为main_point_cloud.py、modelarts_entry_stat.py、genStatistical.sh +这三个文件的代码均不需要修改 +运行成功后将会在屏幕上打印出关于精度相应的统计量值 + +注意：由于加入了混合精度提高训练性能，导致该步出现未知错误，输出的误差均为0，因此建议将训练后的结果下载到本地 +修改checkpoint文件中模型的路径后，利用本地的GPU进行运行，实际上该步骤在运行时NPU成功启动但也未曾利用。 + +## 训练结果 +**精度对比：** + + + +| 测地线误差（°） | 论文发布 | GPU(初始代码未改动版本) | GPU实测|NPU实测 | +| ------------------------ | ------- | ----- | --------- |----------| +| 平均值 | 1.63 | 2.58 | 3.98 | 2.92 | +| 中值 | 0.89 | 1.68 | 2.6 | 1.7 | +| 标准差 | 6.70 | 6.93 | 9.36 | 8.45 | + +相比于论文中的精度，我们NPU迁移后实测差距依然较大，但是与我们未对代码任何改动初始的版本在GPU上跑出来的精度相差较小， +且对于相同的代码的代码改动，NPU上的精度优于GPU上的精度。需要注意的是，在NPU上运行程序时，我们采用混合精度来提升训练 +的性能，但是其中产生了未知的错误，导致代表的精度指标mean_degree_err在整个训练过程中始终为0，因此我们无法得知在NPU训练的 +260w步的过程中，精度指标是下降的过程是怎样的。值得庆幸的是通过NPU训练出的模型，能够在GPU上计算出精度，并且精度还不错。 + +**性能对比：** + +取华为v100上GPU运行的前2w步的global_step/sec平均值和NPU运行的前2w步的global_step/sec平均值进行对比，以达到性能对比的目的。 + +| 性能指标项 | 论文发布 | GPU实测 | NPU实测 | +| ------------------- | ------- | ------ | ------ | +| global_step/sec| 无 | 87.64 | 101.24 | + + -- Gitee From ad0aee9d8bddfd84c7635c7a88fde51f488be68c Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Sat, 7 May 2022 14:23:48 +0000 Subject: [PATCH 38/88] =?UTF-8?q?=E5=88=A0=E9=99=A4=E6=96=87=E4=BB=B6=20Te?= =?UTF-8?q?nsorFlow/contrib/cv/SVD=5FID2019=5Ffor=5FTensorflow/README.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../cv/SVD_ID2019_for_Tensorflow/README.md | 241 ------------------ 1 file changed, 241 deletions(-) delete mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md deleted file mode 100644 index 7e6736d65..000000000 --- a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md +++ /dev/null @@ -1,241 +0,0 @@ -

基本信息

- -**发布者（Publisher）：Huawei** - -**应用领域（Application Domain）：CV** - -**版本（Version）：** - -**修改时间（Modified） ：2022.05.05** - -**框架（Framework）：TensorFlow 1.15.0** - -**处理器（Processor）：昇腾910** - -**描述（Description）：使用训练好的SVD模型，评估对称正交化在点云对准中的应用效果。** - -

概述

- -给定两个三维点云图，利用SVD正交化过程SVDO+(M)将其投射到SO(3)上，要求网络预测最佳对齐它们的3D旋转。代码的训练逻辑是每训练10w步保存一个模型，并且在测试集上检验该模型的精度，最后比较的都是260w步的模型精度 - -- 开源代码： - - https://github.com/google-research/google-research/tree/master/special_orthogonalization。 - -- 参考论文： - - [An Analysis of SVD for Deep Rotation Estimation](https://arxiv.org/abs/2006.14616) - -- 参考实现： - gpu最后训练出来的模型在 - obs://cann-id2019/gpu - - 数据在 - obs://cann-id2019/data/pc_plane - 共有3个数据集 - 训练数据集points - 测试数据集points_test - 第一步旋转后的数据集test_points_modified - - npu训练出来的模型在 - obs://cann-id2019/data/pc_plane/output - - - 相关代码均以上传，在NPU和GPU文件夹中可以找到 -- 相关迁移的工作： - 在进行代码迁移到NPU上时，输入的训练数据为点云数据，点云数据的shape为(N,3)，其中N并不是固定的，因此在NPU上存在动态shape的问题，导致模型训练无法正常进行。我们为此想了三个解决方法：1、找出所有点云数据中最小的N，对于大于N的点云数据，仅取前N行的数据输入训练。2、找到所有点云数据中最大的N，对于小于N的点云进行补0操作，将所有数据固定为最大的N后，输入网络进行训练。3、找到所有点云数据中最大的N，对小于N的点云数据，从原数据中选择一个点云进行填补至行数为N，再将数据输入网络进行训练。该三种方法均成功解决了NPU上的动态shape问题，但是第一种方法删除了样本点，因此导致最后训练出的模型精度很差；第二种方法虽然并没有丢失样本信息，但是向数据中填入大量的0，改变了本来的代码逻辑，导致最后训练出的模型精度也并不高。对于第三种方法，即没有丢失样本信息，对每个点云数据中的某一个点云样本点进行重复操作，没有改变原始的代码逻辑，最后也获得了不错的精度表现。 - -- 通过Git获取对应commit\_id的代码方法如下： - - ``` - git clone {repository_url} # 克隆仓库的代码 - cd {repository_name} # 切换到模型的代码仓目录 - git checkout {branch} # 切换到对应分支 - git reset --hard ｛commit_id｝ # 代码设置到对应的commit_id - cd ｛code_path｝ # 切换到模型代码所在路径，若仓库下只有该模型，则无需切换 - ``` - -## 默认配置 - -- 数据集获取：obs://cann-id2019/dataset/ - -- 训练超参 - - - log_step_count=200 - - save_summaries_steps=25000 - - train_steps=2600000 - - save_checkpoints_steps=100000 - - eval_examples=39900 - - - -

训练环境准备

- -1. 硬件环境准备请参见各硬件产品文档"[驱动和固件安装升级指南]( https://support.huawei.com/enterprise/zh/category/ai-computing-platform-pid-1557196528909)"。需要在硬件设备上安装与CANN版本配套的固件与驱动。 -2. 宿主机上需要安装Docker并登录[Ascend Hub中心](https://ascendhub.huawei.com/#/detail?name=ascend-tensorflow-arm)获取镜像。 - - 当前模型支持的镜像列表如[表1](#zh-cn_topic_0000001074498056_table1519011227314)所示。 - - **表 1** 镜像列表 - - - - - - - - - - - - -

镜像名称

-

镜像版本

-

配套CANN版本

-
-

20.2.0

-

20.2

-
- - - -## 脚本参数 - -``` -gen_pt_test_data_gpu.py 中的参数 - ---input_test_files 传入需要进行旋转的点云数据集 ---output_directory 存储旋转后点云集的路径 ---random_rotation_axang boole型，若为真将会对传入的数据集采用指定方法旋转，默认为真 ---num_rotations_per_file 每个测试点云的随机旋转增加数。默认为100 - - -main_point_cloud_gpu.py 中的参数 - ---pt_cloud_test_files 测试数据集路径 ---pt_cloud_train_files 熟练数据集路径 ---method 指定用于预测旋转的方式。选项为"svd", "svd-inf", or "gs"。默认为“svd” ---checkpoint_dir 训练模型的存放位置 ---train_steps 训练迭代的次数。默认为2600000 ---save_checkpoints_steps 保存检查点的频率。默认为10000 ---log_step_count 日志记录一次的步数。默认为200 ---save_summaries_steps 保存一次summary的步数。默认为5000 ---learning_rate 默认为1e-5 ---lr_decay 如果为真，则衰减learning rate。默认为假 ---lr_decay_steps learning rate衰减步数。默认为35000 ---lr_decay_rate learning rate衰减速率。默认为0.95 ---predict_all_test 如果为真，则在最新的检查点上运行eval作业，并打印每个输入的误差信息。默认为假 ---eval_examples 测试样本的数量。默认为0 ---print_variable_names 打印模型变量名。默认为假 ---num_train_augmentations 增加每个输入点云的随机旋转数。默认为10 - -``` - - - -## 运行 - -GPU运行命令如下： - -**生成测试数据** - -注：生成的文件points_test_modified、points已包含在dataset文件夹中。 -```bash -# 将路径设置到训练点云图文件 -IN_FILES=/shapenet/data/pc_plane/points_test/*.pts - -NEW_TEST_FILES_DIR=/shapenet/data/pc_plane/points_test_modified - -AXANG_SAMPLING=True - -# 决定旋转轴角的分布 -AXANG_SAMPLING=True - -python -m special_orthogonalization.gen_pt_test_data_gpu --input_test_files=$IN_FILES --output_directory=$NEW_TEST_FILES_DIR --random_rotation_axang=$AXANG_SAMPLING -``` - -**训练与评价** -```bash -# 将路径设置到原始训练数据 -TRAIN_FILES=/shapenet/data/pc_plane/points/*.pts - -#将路径设置到旋转后的训练数据 -TEST_FILES=$NEW_TEST_FILES_DIR/*.pts - -# 指定旋转预测方式 -METHOD=svd - -# 指定ckpt、summaries、评价结果等的存储路径 -OUT_DIR=/path/to/model - -python -m special_orthogonalization.main_point_cloud_gpu --method=$METHOD --checkpoint_dir=$OUT_DIR --log_step_count=200 --save_summaries_steps=25000 --pt_cloud_train_files=$TRAIN_FILES --pt_cloud_test_files=$TEST_FILES --train_steps=2600000 --save_checkpoints_steps=100000 --eval_examples=39900 -``` - -**从所有训练样本中生成统计数据** -```bash -# 打印均值、中位数、标准差和分位数 -python -m special_orthogonalization.main_point_cloud_gpu --method=$METHOD --checkpoint_dir=$OUT_DIR --pt_cloud_test_files=$TEST_FILES --predict_all_test=True -``` -## 运行 - -NPU运行命令方式如下: - -对于所有的三个步骤程序来说，modelarts插件obs桶中的数据路径均要写到真正包含数据的那一个路径 -如在pc_plane文件夹中含有points、points_test等包含数据的文件夹 -modelarts插件中的数据路径写为 obs://cann-id2019/data/pc_palne/ - -**生成测试数据** - -运行这一步我们需要的程序文件为gen_pt_test_data.py、modelarts_entry_Axang.py、genTestData.sh -这三个文件中的代码均不需要修改 -最后生成的旋转后的数据文件存放在obs桶当次程序文件的output路径中，文件名为test_points_modified， -为进行第二步模型训练，需要将生成旋转后的文件转移至obs桶中存放data的路径 - -注：需要确保的是存在obs桶里的data文件名为points_test - -**训练与评价** - -运行这一步我们需要的程序文件为main_point_cloud.py、modelarts_entry_acc_train.py、train_full_1p.sh -这三个文件中的代码均不需要修改 -最后生成的旋转后的数据文件存放在obs桶当次程序文件的output路径中，文件名为test_points_modified， -为进行第三步，需要将生成的output文件转移至obs桶中存放data的路径 - -注意：该次训练的模型保存在该次的obs文件夹中，进行第三步时又需要重启一次新的modelarts，因此我们需要将output文件中的 -checkpoint文件中最新模型的路径修改 -"/home/ma-user/modelarts/inputs/data_url_0/output" -这样第三步才能跑出正确的精度指标 - -**从所有训练样本中生成统计数据** - -运行这一步我们需要的程序文件为main_point_cloud.py、modelarts_entry_stat.py、genStatistical.sh -这三个文件的代码均不需要修改 -运行成功后将会在屏幕上打印出关于精度相应的统计量值 - -注意：由于加入了混合精度提高训练性能，导致该步出现未知错误，输出的误差均为0，因此建议将训练后的结果下载到本地 -修改checkpoint文件中模型的路径后，利用本地的GPU进行运行，实际上该步骤在运行时NPU成功启动但也未曾利用。 - -## 训练结果 -**精度对比：** - - - -| 测地线误差（°） | 论文发布 | GPU(初始代码未改动版本) | GPU实测|NPU实测 | -| ------------------------ | ------- | ----- | --------- |----------| -| 平均值 | 1.63 | 2.58 | 3.98 | 2.92 | -| 中值 | 0.89 | 1.68 | 2.6 | 1.7 | -| 标准差 | 6.70 | 6.93 | 9.36 | 8.45 | - -相比于论文中的精度，我们NPU迁移后实测差距依然较大，但是与我们未对代码任何改动初始的版本在GPU上跑出来的精度相差较小， -且对于相同的代码的代码改动，NPU上的精度优于GPU上的精度。需要注意的是，在NPU上运行程序时，我们采用混合精度来提升训练 -的性能，但是其中产生了未知的错误，导致代表的精度指标mean_degree_err在整个训练过程中始终为0，因此我们无法得知在NPU训练的 -260w步的过程中，精度指标是下降的过程是怎样的。值得庆幸的是通过NPU训练出的模型，能够在GPU上计算出精度，并且精度还不错。 - -**性能对比：** - -取华为v100上GPU运行的前2w步的global_step/sec平均值和NPU运行的前2w步的global_step/sec平均值进行对比，以达到性能对比的目的。 - -| 性能指标项 | 论文发布 | GPU实测 | NPU实测 | -| ------------------- | ------- | ------ | ------ | -| global_step/sec| 无 | 87.64 | 101.24 | - - -- Gitee From 0b96ee4960e5f6c787d0d0dc3b7bdebf9b859a53 Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Sat, 7 May 2022 14:24:04 +0000 Subject: [PATCH 39/88] =?UTF-8?q?=E6=9B=B4=E6=96=B0=E5=90=8E=E7=9A=84readm?= =?UTF-8?q?e=EF=BC=8C=E5=8A=A0=E5=85=A5=E4=BA=86NPU=E4=B8=8A=E8=BF=90?= =?UTF-8?q?=E8=A1=8C=E7=9A=84=E6=9C=80=E6=96=B0=E7=BB=93=E6=9E=9C?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/checkpoint | 6 ++++++ 1 file changed, 6 insertions(+) create mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/checkpoint diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/checkpoint b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/checkpoint new file mode 100644 index 000000000..8ea0b9f34 --- /dev/null +++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/checkpoint @@ -0,0 +1,6 @@ +model_checkpoint_path: "/home/ma-user/modelarts/inputs/data_url_0/output/model.ckpt-2600000" +all_model_checkpoint_paths: "/home/ma-user/modelarts/outputs/train_url_0/model.ckpt-2200000" +all_model_checkpoint_paths: "/home/ma-user/modelarts/outputs/train_url_0/model.ckpt-2300000" +all_model_checkpoint_paths: "/home/ma-user/modelarts/outputs/train_url_0/model.ckpt-2400000" +all_model_checkpoint_paths: "/home/ma-user/modelarts/outputs/train_url_0/model.ckpt-2500000" +all_model_checkpoint_paths: "/home/ma-user/modelarts/outputs/train_url_0/model.ckpt-2600000" -- Gitee From 473b3e3abed22fe779f4cc4e636e1ca84f671def Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Sat, 7 May 2022 14:24:15 +0000 Subject: [PATCH 40/88] =?UTF-8?q?=E5=88=A0=E9=99=A4=E6=96=87=E4=BB=B6=20Te?= =?UTF-8?q?nsorFlow/contrib/cv/SVD=5FID2019=5Ffor=5FTensorflow/checkpoint?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/checkpoint | 6 ------ 1 file changed, 6 deletions(-) delete mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/checkpoint diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/checkpoint b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/checkpoint deleted file mode 100644 index 8ea0b9f34..000000000 --- a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/checkpoint +++ /dev/null @@ -1,6 +0,0 @@ -model_checkpoint_path: "/home/ma-user/modelarts/inputs/data_url_0/output/model.ckpt-2600000" -all_model_checkpoint_paths: "/home/ma-user/modelarts/outputs/train_url_0/model.ckpt-2200000" -all_model_checkpoint_paths: "/home/ma-user/modelarts/outputs/train_url_0/model.ckpt-2300000" -all_model_checkpoint_paths: "/home/ma-user/modelarts/outputs/train_url_0/model.ckpt-2400000" -all_model_checkpoint_paths: "/home/ma-user/modelarts/outputs/train_url_0/model.ckpt-2500000" -all_model_checkpoint_paths: "/home/ma-user/modelarts/outputs/train_url_0/model.ckpt-2600000" -- Gitee From e0dc53f2a3db2825ef111465ee1c84575c6152f7 Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Sat, 7 May 2022 14:24:28 +0000 Subject: [PATCH 41/88] =?UTF-8?q?=E6=9B=B4=E6=96=B0=E5=90=8E=E7=9A=84readm?= =?UTF-8?q?e=EF=BC=8C=E5=8A=A0=E5=85=A5=E4=BA=86NPU=E4=B8=8A=E8=BF=90?= =?UTF-8?q?=E8=A1=8C=E7=9A=84=E6=9C=80=E6=96=B0=E7=BB=93=E6=9E=9C?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../cv/SVD_ID2019_for_Tensorflow/README.md | 248 ++++++++++++++++++ 1 file changed, 248 insertions(+) create mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md new file mode 100644 index 000000000..f2eb0d0fe --- /dev/null +++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md @@ -0,0 +1,248 @@ +

基本信息

+ +**发布者（Publisher）：Huawei** + +**应用领域（Application Domain）：CV** + +**版本（Version）：** + +**修改时间（Modified） ：2022.05.05** + +**框架（Framework）：TensorFlow 1.15.0** + +**处理器（Processor）：昇腾910** + +**描述（Description）：使用训练好的SVD模型，评估对称正交化在点云对准中的应用效果。** + +

概述

+ +给定两个三维点云图，利用SVD正交化过程SVDO+(M)将其投射到SO(3)上，要求网络预测最佳对齐它们的3D旋转。代码的训练逻辑是每训练10w步保存一个模型，并且在测试集上检验该模型的精度，最后比较的都是260w步的模型精度 + +- 开源代码： + + https://github.com/google-research/google-research/tree/master/special_orthogonalization。 + +- 参考论文： + + [An Analysis of SVD for Deep Rotation Estimation](https://arxiv.org/abs/2006.14616) + +- 参考实现： + gpu最后训练出来的模型在 + + obs://cann-id2019/gpu + + 数据在 + obs://cann-id2019/data/pc_plane + + 共有3个数据集 + + 训练数据集points + + 测试数据集 points_test + + 第一步旋转后的数据集 test_points_modified + + npu训练出来的模型在 + + obs://cann-id2019/data/pc_plane/output + + + 相关代码均以上传，在NPU和GPU文件夹中可以找到 + +- 相关迁移的工作： + 在进行代码迁移到NPU上时，输入的训练数据为点云数据，点云数据的shape为(N,3)，其中N并不是固定的，因此在NPU上存在动态shape的问题，导致模型训练无法正常进行。我们为此想了三个解决方法：1、找出所有点云数据中最小的N，对于大于N的点云数据，仅取前N行的数据输入训练。2、找到所有点云数据中最大的N，对于小于N的点云进行补0操作，将所有数据固定为最大的N后，输入网络进行训练。3、找到所有点云数据中最大的N，对小于N的点云数据，从原数据中选择一个点云进行填补至行数为N，再将数据输入网络进行训练。该三种方法均成功解决了NPU上的动态shape问题，但是第一种方法删除了样本点，因此导致最后训练出的模型精度很差；第二种方法虽然并没有丢失样本信息，但是向数据中填入大量的0，改变了本来的代码逻辑，导致最后训练出的模型精度也并不高。对于第三种方法，即没有丢失样本信息，对每个点云数据中的某一个点云样本点进行重复操作，没有改变原始的代码逻辑，最后也获得了不错的精度表现。 + +- 通过Git获取对应commit\_id的代码方法如下： + + ``` + git clone {repository_url} # 克隆仓库的代码 + cd {repository_name} # 切换到模型的代码仓目录 + git checkout {branch} # 切换到对应分支 + git reset --hard ｛commit_id｝ # 代码设置到对应的commit_id + cd ｛code_path｝ # 切换到模型代码所在路径，若仓库下只有该模型，则无需切换 + ``` + +## 默认配置 + +- 数据集获取：obs://cann-id2019/dataset/ + +- 训练超参 + + - log_step_count=200 + - save_summaries_steps=25000 + - train_steps=2600000 + - save_checkpoints_steps=100000 + - eval_examples=39900 + + + +

训练环境准备

+ +1. 硬件环境准备请参见各硬件产品文档"[驱动和固件安装升级指南]( https://support.huawei.com/enterprise/zh/category/ai-computing-platform-pid-1557196528909)"。需要在硬件设备上安装与CANN版本配套的固件与驱动。 +2. 宿主机上需要安装Docker并登录[Ascend Hub中心](https://ascendhub.huawei.com/#/detail?name=ascend-tensorflow-arm)获取镜像。 + + 当前模型支持的镜像列表如[表1](#zh-cn_topic_0000001074498056_table1519011227314)所示。 + + **表 1** 镜像列表 + + + + + + + + + + + + +

镜像名称

+

镜像版本

+

配套CANN版本

+
+

20.2.0

+

20.2

+
+ + + +## 脚本参数 + +``` +gen_pt_test_data_gpu.py 中的参数 + +--input_test_files 传入需要进行旋转的点云数据集 +--output_directory 存储旋转后点云集的路径 +--random_rotation_axang boole型，若为真将会对传入的数据集采用指定方法旋转，默认为真 +--num_rotations_per_file 每个测试点云的随机旋转增加数。默认为100 + + +main_point_cloud_gpu.py 中的参数 + +--pt_cloud_test_files 测试数据集路径 +--pt_cloud_train_files 熟练数据集路径 +--method 指定用于预测旋转的方式。选项为"svd", "svd-inf", or "gs"。默认为“svd” +--checkpoint_dir 训练模型的存放位置 +--train_steps 训练迭代的次数。默认为2600000 +--save_checkpoints_steps 保存检查点的频率。默认为10000 +--log_step_count 日志记录一次的步数。默认为200 +--save_summaries_steps 保存一次summary的步数。默认为5000 +--learning_rate 默认为1e-5 +--lr_decay 如果为真，则衰减learning rate。默认为假 +--lr_decay_steps learning rate衰减步数。默认为35000 +--lr_decay_rate learning rate衰减速率。默认为0.95 +--predict_all_test 如果为真，则在最新的检查点上运行eval作业，并打印每个输入的误差信息。默认为假 +--eval_examples 测试样本的数量。默认为0 +--print_variable_names 打印模型变量名。默认为假 +--num_train_augmentations 增加每个输入点云的随机旋转数。默认为10 + +``` + + + +## 运行 + +GPU运行命令如下： + +**生成测试数据** + +注：生成的文件points_test_modified、points已包含在dataset文件夹中。 +```bash +# 将路径设置到训练点云图文件 +IN_FILES=/shapenet/data/pc_plane/points_test/*.pts + +NEW_TEST_FILES_DIR=/shapenet/data/pc_plane/points_test_modified + +AXANG_SAMPLING=True + +# 决定旋转轴角的分布 +AXANG_SAMPLING=True + +python -m special_orthogonalization.gen_pt_test_data_gpu --input_test_files=$IN_FILES --output_directory=$NEW_TEST_FILES_DIR --random_rotation_axang=$AXANG_SAMPLING +``` + +**训练与评价** +```bash +# 将路径设置到原始训练数据 +TRAIN_FILES=/shapenet/data/pc_plane/points/*.pts + +#将路径设置到旋转后的训练数据 +TEST_FILES=$NEW_TEST_FILES_DIR/*.pts + +# 指定旋转预测方式 +METHOD=svd + +# 指定ckpt、summaries、评价结果等的存储路径 +OUT_DIR=/path/to/model + +python -m special_orthogonalization.main_point_cloud_gpu --method=$METHOD --checkpoint_dir=$OUT_DIR --log_step_count=200 --save_summaries_steps=25000 --pt_cloud_train_files=$TRAIN_FILES --pt_cloud_test_files=$TEST_FILES --train_steps=2600000 --save_checkpoints_steps=100000 --eval_examples=39900 +``` + +**从所有训练样本中生成统计数据** +```bash +# 打印均值、中位数、标准差和分位数 +python -m special_orthogonalization.main_point_cloud_gpu --method=$METHOD --checkpoint_dir=$OUT_DIR --pt_cloud_test_files=$TEST_FILES --predict_all_test=True +``` +## 运行 + +NPU运行命令方式如下: + +对于所有的三个步骤程序来说，modelarts插件obs桶中的数据路径均要写到真正包含数据的那一个路径 +如在pc_plane文件夹中含有points、points_test等包含数据的文件夹 +modelarts插件中的数据路径写为 obs://cann-id2019/data/pc_palne/ + +**生成测试数据** + +运行这一步我们需要的程序文件为gen_pt_test_data.py、modelarts_entry_Axang.py、genTestData.sh +这三个文件中的代码均不需要修改 +最后生成的旋转后的数据文件存放在obs桶当次程序文件的output路径中，文件名为test_points_modified， +为进行第二步模型训练，需要将生成旋转后的文件转移至obs桶中存放data的路径 + +注：需要确保的是存在obs桶里的data文件名为points_test + +**训练与评价** + +运行这一步我们需要的程序文件为main_point_cloud.py、modelarts_entry_acc_train.py、train_full_1p.sh +这三个文件中的代码均不需要修改 +最后生成的旋转后的数据文件存放在obs桶当次程序文件的output路径中，文件名为test_points_modified， +为进行第三步，需要将生成的output文件转移至obs桶中存放data的路径 + +注意：该次训练的模型保存在该次的obs文件夹中，进行第三步时又需要重启一次新的modelarts，因此我们需要将output文件中的 +checkpoint文件中最新模型的路径修改 +"/home/ma-user/modelarts/inputs/data_url_0/output" +这样第三步才能跑出正确的精度指标 + +**从所有训练样本中生成统计数据** + +运行这一步我们需要的程序文件为main_point_cloud.py、modelarts_entry_stat.py、genStatistical.sh +这三个文件的代码均不需要修改 +运行成功后将会在屏幕上打印出关于精度相应的统计量值 + +注意：由于加入了混合精度提高训练性能，导致该步出现未知错误，输出的误差均为0，因此建议将训练后的结果下载到本地 +修改checkpoint文件中模型的路径后，利用本地的GPU进行运行，实际上该步骤在运行时NPU成功启动但也未曾利用。 + +## 训练结果 +**精度对比：** + + + +| 测地线误差（°） | 论文发布 | GPU(初始代码未改动版本) | GPU实测|NPU实测 | +| ------------------------ | ------- | ----- | --------- |----------| +| 平均值 | 1.63 | 2.58 | 3.98 | 2.92 | +| 中值 | 0.89 | 1.68 | 2.6 | 1.7 | +| 标准差 | 6.70 | 6.93 | 9.36 | 8.45 | + +相比于论文中的精度，我们NPU迁移后实测差距依然较大，但是与我们未对代码任何改动初始的版本在GPU上跑出来的精度相差较小， +且对于相同的代码的代码改动，NPU上的精度优于GPU上的精度。需要注意的是，在NPU上运行程序时，我们采用混合精度来提升训练 +的性能，但是其中产生了未知的错误，导致代表的精度指标mean_degree_err在整个训练过程中始终为0，因此我们无法得知在NPU训练的 +260w步的过程中，精度指标是下降的过程是怎样的。值得庆幸的是通过NPU训练出的模型，能够在GPU上计算出精度，并且精度还不错。 + +**性能对比：** + +取华为v100上GPU运行的前2w步的global_step/sec平均值和NPU运行的前2w步的global_step/sec平均值进行对比，以达到性能对比的目的。 + +| 性能指标项 | 论文发布 | GPU实测 | NPU实测 | +| ------------------- | ------- | ------ | ------ | +| global_step/sec| 无 | 87.64 | 101.24 | + + -- Gitee From 596c224cb8182238b0c0b16bebdca4b3cc9ffc12 Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Sun, 8 May 2022 01:01:39 +0000 Subject: [PATCH 42/88] =?UTF-8?q?=E5=88=A0=E9=99=A4=E6=96=87=E4=BB=B6=20Te?= =?UTF-8?q?nsorFlow/contrib/cv/SVD=5FID2019=5Ffor=5FTensorflow/README.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../cv/SVD_ID2019_for_Tensorflow/README.md | 248 ------------------ 1 file changed, 248 deletions(-) delete mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md deleted file mode 100644 index f2eb0d0fe..000000000 --- a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md +++ /dev/null @@ -1,248 +0,0 @@ -

基本信息

- -**发布者（Publisher）：Huawei** - -**应用领域（Application Domain）：CV** - -**版本（Version）：** - -**修改时间（Modified） ：2022.05.05** - -**框架（Framework）：TensorFlow 1.15.0** - -**处理器（Processor）：昇腾910** - -**描述（Description）：使用训练好的SVD模型，评估对称正交化在点云对准中的应用效果。** - -

概述

- -给定两个三维点云图，利用SVD正交化过程SVDO+(M)将其投射到SO(3)上，要求网络预测最佳对齐它们的3D旋转。代码的训练逻辑是每训练10w步保存一个模型，并且在测试集上检验该模型的精度，最后比较的都是260w步的模型精度 - -- 开源代码： - - https://github.com/google-research/google-research/tree/master/special_orthogonalization。 - -- 参考论文： - - [An Analysis of SVD for Deep Rotation Estimation](https://arxiv.org/abs/2006.14616) - -- 参考实现： - gpu最后训练出来的模型在 - - obs://cann-id2019/gpu - - 数据在 - obs://cann-id2019/data/pc_plane - - 共有3个数据集 - - 训练数据集points - - 测试数据集 points_test - - 第一步旋转后的数据集 test_points_modified - - npu训练出来的模型在 - - obs://cann-id2019/data/pc_plane/output - - - 相关代码均以上传，在NPU和GPU文件夹中可以找到 - -- 相关迁移的工作： - 在进行代码迁移到NPU上时，输入的训练数据为点云数据，点云数据的shape为(N,3)，其中N并不是固定的，因此在NPU上存在动态shape的问题，导致模型训练无法正常进行。我们为此想了三个解决方法：1、找出所有点云数据中最小的N，对于大于N的点云数据，仅取前N行的数据输入训练。2、找到所有点云数据中最大的N，对于小于N的点云进行补0操作，将所有数据固定为最大的N后，输入网络进行训练。3、找到所有点云数据中最大的N，对小于N的点云数据，从原数据中选择一个点云进行填补至行数为N，再将数据输入网络进行训练。该三种方法均成功解决了NPU上的动态shape问题，但是第一种方法删除了样本点，因此导致最后训练出的模型精度很差；第二种方法虽然并没有丢失样本信息，但是向数据中填入大量的0，改变了本来的代码逻辑，导致最后训练出的模型精度也并不高。对于第三种方法，即没有丢失样本信息，对每个点云数据中的某一个点云样本点进行重复操作，没有改变原始的代码逻辑，最后也获得了不错的精度表现。 - -- 通过Git获取对应commit\_id的代码方法如下： - - ``` - git clone {repository_url} # 克隆仓库的代码 - cd {repository_name} # 切换到模型的代码仓目录 - git checkout {branch} # 切换到对应分支 - git reset --hard ｛commit_id｝ # 代码设置到对应的commit_id - cd ｛code_path｝ # 切换到模型代码所在路径，若仓库下只有该模型，则无需切换 - ``` - -## 默认配置 - -- 数据集获取：obs://cann-id2019/dataset/ - -- 训练超参 - - - log_step_count=200 - - save_summaries_steps=25000 - - train_steps=2600000 - - save_checkpoints_steps=100000 - - eval_examples=39900 - - - -

训练环境准备

- -1. 硬件环境准备请参见各硬件产品文档"[驱动和固件安装升级指南]( https://support.huawei.com/enterprise/zh/category/ai-computing-platform-pid-1557196528909)"。需要在硬件设备上安装与CANN版本配套的固件与驱动。 -2. 宿主机上需要安装Docker并登录[Ascend Hub中心](https://ascendhub.huawei.com/#/detail?name=ascend-tensorflow-arm)获取镜像。 - - 当前模型支持的镜像列表如[表1](#zh-cn_topic_0000001074498056_table1519011227314)所示。 - - **表 1** 镜像列表 - - - - - - - - - - - - -

镜像名称

-

镜像版本

-

配套CANN版本

-
-

20.2.0

-

20.2

-
- - - -## 脚本参数 - -``` -gen_pt_test_data_gpu.py 中的参数 - ---input_test_files 传入需要进行旋转的点云数据集 ---output_directory 存储旋转后点云集的路径 ---random_rotation_axang boole型，若为真将会对传入的数据集采用指定方法旋转，默认为真 ---num_rotations_per_file 每个测试点云的随机旋转增加数。默认为100 - - -main_point_cloud_gpu.py 中的参数 - ---pt_cloud_test_files 测试数据集路径 ---pt_cloud_train_files 熟练数据集路径 ---method 指定用于预测旋转的方式。选项为"svd", "svd-inf", or "gs"。默认为“svd” ---checkpoint_dir 训练模型的存放位置 ---train_steps 训练迭代的次数。默认为2600000 ---save_checkpoints_steps 保存检查点的频率。默认为10000 ---log_step_count 日志记录一次的步数。默认为200 ---save_summaries_steps 保存一次summary的步数。默认为5000 ---learning_rate 默认为1e-5 ---lr_decay 如果为真，则衰减learning rate。默认为假 ---lr_decay_steps learning rate衰减步数。默认为35000 ---lr_decay_rate learning rate衰减速率。默认为0.95 ---predict_all_test 如果为真，则在最新的检查点上运行eval作业，并打印每个输入的误差信息。默认为假 ---eval_examples 测试样本的数量。默认为0 ---print_variable_names 打印模型变量名。默认为假 ---num_train_augmentations 增加每个输入点云的随机旋转数。默认为10 - -``` - - - -## 运行 - -GPU运行命令如下： - -**生成测试数据** - -注：生成的文件points_test_modified、points已包含在dataset文件夹中。 -```bash -# 将路径设置到训练点云图文件 -IN_FILES=/shapenet/data/pc_plane/points_test/*.pts - -NEW_TEST_FILES_DIR=/shapenet/data/pc_plane/points_test_modified - -AXANG_SAMPLING=True - -# 决定旋转轴角的分布 -AXANG_SAMPLING=True - -python -m special_orthogonalization.gen_pt_test_data_gpu --input_test_files=$IN_FILES --output_directory=$NEW_TEST_FILES_DIR --random_rotation_axang=$AXANG_SAMPLING -``` - -**训练与评价** -```bash -# 将路径设置到原始训练数据 -TRAIN_FILES=/shapenet/data/pc_plane/points/*.pts - -#将路径设置到旋转后的训练数据 -TEST_FILES=$NEW_TEST_FILES_DIR/*.pts - -# 指定旋转预测方式 -METHOD=svd - -# 指定ckpt、summaries、评价结果等的存储路径 -OUT_DIR=/path/to/model - -python -m special_orthogonalization.main_point_cloud_gpu --method=$METHOD --checkpoint_dir=$OUT_DIR --log_step_count=200 --save_summaries_steps=25000 --pt_cloud_train_files=$TRAIN_FILES --pt_cloud_test_files=$TEST_FILES --train_steps=2600000 --save_checkpoints_steps=100000 --eval_examples=39900 -``` - -**从所有训练样本中生成统计数据** -```bash -# 打印均值、中位数、标准差和分位数 -python -m special_orthogonalization.main_point_cloud_gpu --method=$METHOD --checkpoint_dir=$OUT_DIR --pt_cloud_test_files=$TEST_FILES --predict_all_test=True -``` -## 运行 - -NPU运行命令方式如下: - -对于所有的三个步骤程序来说，modelarts插件obs桶中的数据路径均要写到真正包含数据的那一个路径 -如在pc_plane文件夹中含有points、points_test等包含数据的文件夹 -modelarts插件中的数据路径写为 obs://cann-id2019/data/pc_palne/ - -**生成测试数据** - -运行这一步我们需要的程序文件为gen_pt_test_data.py、modelarts_entry_Axang.py、genTestData.sh -这三个文件中的代码均不需要修改 -最后生成的旋转后的数据文件存放在obs桶当次程序文件的output路径中，文件名为test_points_modified， -为进行第二步模型训练，需要将生成旋转后的文件转移至obs桶中存放data的路径 - -注：需要确保的是存在obs桶里的data文件名为points_test - -**训练与评价** - -运行这一步我们需要的程序文件为main_point_cloud.py、modelarts_entry_acc_train.py、train_full_1p.sh -这三个文件中的代码均不需要修改 -最后生成的旋转后的数据文件存放在obs桶当次程序文件的output路径中，文件名为test_points_modified， -为进行第三步，需要将生成的output文件转移至obs桶中存放data的路径 - -注意：该次训练的模型保存在该次的obs文件夹中，进行第三步时又需要重启一次新的modelarts，因此我们需要将output文件中的 -checkpoint文件中最新模型的路径修改 -"/home/ma-user/modelarts/inputs/data_url_0/output" -这样第三步才能跑出正确的精度指标 - -**从所有训练样本中生成统计数据** - -运行这一步我们需要的程序文件为main_point_cloud.py、modelarts_entry_stat.py、genStatistical.sh -这三个文件的代码均不需要修改 -运行成功后将会在屏幕上打印出关于精度相应的统计量值 - -注意：由于加入了混合精度提高训练性能，导致该步出现未知错误，输出的误差均为0，因此建议将训练后的结果下载到本地 -修改checkpoint文件中模型的路径后，利用本地的GPU进行运行，实际上该步骤在运行时NPU成功启动但也未曾利用。 - -## 训练结果 -**精度对比：** - - - -| 测地线误差（°） | 论文发布 | GPU(初始代码未改动版本) | GPU实测|NPU实测 | -| ------------------------ | ------- | ----- | --------- |----------| -| 平均值 | 1.63 | 2.58 | 3.98 | 2.92 | -| 中值 | 0.89 | 1.68 | 2.6 | 1.7 | -| 标准差 | 6.70 | 6.93 | 9.36 | 8.45 | - -相比于论文中的精度，我们NPU迁移后实测差距依然较大，但是与我们未对代码任何改动初始的版本在GPU上跑出来的精度相差较小， -且对于相同的代码的代码改动，NPU上的精度优于GPU上的精度。需要注意的是，在NPU上运行程序时，我们采用混合精度来提升训练 -的性能，但是其中产生了未知的错误，导致代表的精度指标mean_degree_err在整个训练过程中始终为0，因此我们无法得知在NPU训练的 -260w步的过程中，精度指标是下降的过程是怎样的。值得庆幸的是通过NPU训练出的模型，能够在GPU上计算出精度，并且精度还不错。 - -**性能对比：** - -取华为v100上GPU运行的前2w步的global_step/sec平均值和NPU运行的前2w步的global_step/sec平均值进行对比，以达到性能对比的目的。 - -| 性能指标项 | 论文发布 | GPU实测 | NPU实测 | -| ------------------- | ------- | ------ | ------ | -| global_step/sec| 无 | 87.64 | 101.24 | - - -- Gitee From 2bf7e89294af378730bec3962daeaef690dfc069 Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Sun, 8 May 2022 01:01:56 +0000 Subject: [PATCH 43/88] =?UTF-8?q?=E6=9B=B4=E6=96=B0=E5=90=8E=E7=9A=84readm?= =?UTF-8?q?e=EF=BC=8C=E5=8A=A0=E5=85=A5=E4=BA=86NPU=E4=B8=8A=E8=BF=90?= =?UTF-8?q?=E8=A1=8C=E7=9A=84=E6=9C=80=E6=96=B0=E7=BB=93=E6=9E=9C?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../cv/SVD_ID2019_for_Tensorflow/README.md | 248 ++++++++++++++++++ 1 file changed, 248 insertions(+) create mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md new file mode 100644 index 000000000..0982c3178 --- /dev/null +++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md @@ -0,0 +1,248 @@ +

基本信息

+ +**发布者（Publisher）：Huawei** + +**应用领域（Application Domain）：CV** + +**版本（Version）：** + +**修改时间（Modified） ：2022.05.05** + +**框架（Framework）：TensorFlow 1.15.0** + +**处理器（Processor）：昇腾910** + +**描述（Description）：使用训练好的SVD模型，评估对称正交化在点云对准中的应用效果。** + +

概述

+ +给定两个三维点云图，利用SVD正交化过程SVDO+(M)将其投射到SO(3)上，要求网络预测最佳对齐它们的3D旋转。代码的训练逻辑是每训练10w步保存一个模型，并且在测试集上检验该模型的精度，最后比较的都是260w步的模型精度 + +- 开源代码： + + https://github.com/google-research/google-research/tree/master/special_orthogonalization。 + +- 参考论文： + + [An Analysis of SVD for Deep Rotation Estimation](https://arxiv.org/abs/2006.14616) + +- 参考实现： + gpu最后训练出来的模型在 + + obs://cann-id2019/gpu + + 数据在 + obs://cann-id2019/dataset/ + + 共有3个数据集 + + 训练数据集points + + 测试数据集 points_test + + 第一步旋转后的数据集 test_points_modified + + npu训练出来的模型在 + + obs://cann-id2019/dataset//output + + + 相关代码均以上传，在NPU和GPU文件夹中可以找到 + +- 相关迁移的工作： + 在进行代码迁移到NPU上时，输入的训练数据为点云数据，点云数据的shape为(N,3)，其中N并不是固定的，因此在NPU上存在动态shape的问题，导致模型训练无法正常进行。我们为此想了三个解决方法：1、找出所有点云数据中最小的N，对于大于N的点云数据，仅取前N行的数据输入训练。2、找到所有点云数据中最大的N，对于小于N的点云进行补0操作，将所有数据固定为最大的N后，输入网络进行训练。3、找到所有点云数据中最大的N，对小于N的点云数据，从原数据中选择一个点云进行填补至行数为N，再将数据输入网络进行训练。该三种方法均成功解决了NPU上的动态shape问题，但是第一种方法删除了样本点，因此导致最后训练出的模型精度很差；第二种方法虽然并没有丢失样本信息，但是向数据中填入大量的0，改变了本来的代码逻辑，导致最后训练出的模型精度也并不高。对于第三种方法，即没有丢失样本信息，对每个点云数据中的某一个点云样本点进行重复操作，没有改变原始的代码逻辑，最后也获得了不错的精度表现。 + +- 通过Git获取对应commit\_id的代码方法如下： + + ``` + git clone {repository_url} # 克隆仓库的代码 + cd {repository_name} # 切换到模型的代码仓目录 + git checkout {branch} # 切换到对应分支 + git reset --hard ｛commit_id｝ # 代码设置到对应的commit_id + cd ｛code_path｝ # 切换到模型代码所在路径，若仓库下只有该模型，则无需切换 + ``` + +## 默认配置 + +- 数据集获取：obs://cann-id2019/dataset/ + +- 训练超参 + + - log_step_count=200 + - save_summaries_steps=25000 + - train_steps=2600000 + - save_checkpoints_steps=100000 + - eval_examples=39900 + + + +

训练环境准备

+ +1. 硬件环境准备请参见各硬件产品文档"[驱动和固件安装升级指南]( https://support.huawei.com/enterprise/zh/category/ai-computing-platform-pid-1557196528909)"。需要在硬件设备上安装与CANN版本配套的固件与驱动。 +2. 宿主机上需要安装Docker并登录[Ascend Hub中心](https://ascendhub.huawei.com/#/detail?name=ascend-tensorflow-arm)获取镜像。 + + 当前模型支持的镜像列表如[表1](#zh-cn_topic_0000001074498056_table1519011227314)所示。 + + **表 1** 镜像列表 + + + + + + + + + + + + +

镜像名称

+

镜像版本

+

配套CANN版本

+
+

20.2.0

+

20.2

+
+ + + +## 脚本参数 + +``` +gen_pt_test_data_gpu.py 中的参数 + +--input_test_files 传入需要进行旋转的点云数据集 +--output_directory 存储旋转后点云集的路径 +--random_rotation_axang boole型，若为真将会对传入的数据集采用指定方法旋转，默认为真 +--num_rotations_per_file 每个测试点云的随机旋转增加数。默认为100 + + +main_point_cloud_gpu.py 中的参数 + +--pt_cloud_test_files 测试数据集路径 +--pt_cloud_train_files 熟练数据集路径 +--method 指定用于预测旋转的方式。选项为"svd", "svd-inf", or "gs"。默认为“svd” +--checkpoint_dir 训练模型的存放位置 +--train_steps 训练迭代的次数。默认为2600000 +--save_checkpoints_steps 保存检查点的频率。默认为10000 +--log_step_count 日志记录一次的步数。默认为200 +--save_summaries_steps 保存一次summary的步数。默认为5000 +--learning_rate 默认为1e-5 +--lr_decay 如果为真，则衰减learning rate。默认为假 +--lr_decay_steps learning rate衰减步数。默认为35000 +--lr_decay_rate learning rate衰减速率。默认为0.95 +--predict_all_test 如果为真，则在最新的检查点上运行eval作业，并打印每个输入的误差信息。默认为假 +--eval_examples 测试样本的数量。默认为0 +--print_variable_names 打印模型变量名。默认为假 +--num_train_augmentations 增加每个输入点云的随机旋转数。默认为10 + +``` + + + +## 运行 + +GPU运行命令如下： + +**生成测试数据** + +注：生成的文件test_points_modified、points已包含在dataset文件夹中。 +```bash +# 将路径设置到训练点云图文件 +IN_FILES=/points_test/*.pts + +NEW_TEST_FILES_DIR=/test_points_modified + +AXANG_SAMPLING=True + +# 决定旋转轴角的分布 +AXANG_SAMPLING=True + +python -m special_orthogonalization.gen_pt_test_data_gpu --input_test_files=$IN_FILES --output_directory=$NEW_TEST_FILES_DIR --random_rotation_axang=$AXANG_SAMPLING +``` + +**训练与评价** +```bash +# 将路径设置到原始训练数据 +TRAIN_FILES=/points/*.pts + +#将路径设置到旋转后的训练数据 +TEST_FILES=$NEW_TEST_FILES_DIR/*.pts + +# 指定旋转预测方式 +METHOD=svd + +# 指定ckpt、summaries、评价结果等的存储路径 +OUT_DIR=/path/to/model + +python -m special_orthogonalization.main_point_cloud_gpu --method=$METHOD --checkpoint_dir=$OUT_DIR --log_step_count=200 --save_summaries_steps=25000 --pt_cloud_train_files=$TRAIN_FILES --pt_cloud_test_files=$TEST_FILES --train_steps=2600000 --save_checkpoints_steps=100000 --eval_examples=39900 +``` + +**从所有训练样本中生成统计数据** +```bash +# 打印均值、中位数、标准差和分位数 +python -m special_orthogonalization.main_point_cloud_gpu --method=$METHOD --checkpoint_dir=$OUT_DIR --pt_cloud_test_files=$TEST_FILES --predict_all_test=True +``` +## 运行 + +NPU运行命令方式如下: + +对于所有的三个步骤程序来说，modelarts插件obs桶中的数据路径均要写到真正包含数据的那一个路径 +如在dataset文件夹中含有points、points_test等包含数据的文件夹 +modelarts插件中的数据路径写为 obs://cann-id2019/dataset/ + +**生成测试数据** + +运行这一步我们需要的程序文件为gen_pt_test_data.py、modelarts_entry_Axang.py、genTestData.sh +这三个文件中的代码均不需要修改 +最后生成的旋转后的数据文件存放在obs桶当次程序文件的output路径中，文件名为test_points_modified， +为进行第二步模型训练，需要将生成旋转后的文件转移至obs桶中存放data的路径 + +注：需要确保的是存在obs桶里的data文件名为points_test + +**训练与评价** + +运行这一步我们需要的程序文件为main_point_cloud.py、modelarts_entry_acc_train.py、train_full_1p.sh +这三个文件中的代码均不需要修改 +最后生成的旋转后的数据文件存放在obs桶当次程序文件的output路径中，文件名为test_points_modified， +为进行第三步，需要将生成的output文件转移至obs桶中存放data的路径 + +注意：该次训练的模型保存在该次的obs文件夹中，进行第三步时又需要重启一次新的modelarts，因此我们需要将output文件中的 +checkpoint文件中最新模型的路径修改 +"/home/ma-user/modelarts/inputs/data_url_0/output" +这样第三步才能跑出正确的精度指标 + +**从所有训练样本中生成统计数据** + +运行这一步我们需要的程序文件为main_point_cloud.py、modelarts_entry_stat.py、genStatistical.sh +这三个文件的代码均不需要修改 +运行成功后将会在屏幕上打印出关于精度相应的统计量值 + +注意：由于加入了混合精度提高训练性能，导致该步出现未知错误，输出的误差均为0，因此建议将训练后的结果下载到本地 +修改checkpoint文件中模型的路径后，利用本地的GPU进行运行，实际上该步骤在运行时NPU成功启动但也未曾利用。 + +## 训练结果 +**精度对比：** + + + +| 测地线误差（°） | 论文发布 | GPU(初始代码未改动版本) | GPU实测|NPU实测 | +| ------------------------ | ------- | ----- | --------- |----------| +| 平均值 | 1.63 | 2.58 | 3.98 | 2.92 | +| 中值 | 0.89 | 1.68 | 2.6 | 1.7 | +| 标准差 | 6.70 | 6.93 | 9.36 | 8.45 | + +相比于论文中的精度，我们NPU迁移后实测差距依然较大，但是与我们未对代码任何改动初始的版本在GPU上跑出来的精度相差较小， +且对于相同的代码的代码改动，NPU上的精度优于GPU上的精度。需要注意的是，在NPU上运行程序时，我们采用混合精度来提升训练 +的性能，但是其中产生了未知的错误，导致代表的精度指标mean_degree_err在整个训练过程中始终为0，因此我们无法得知在NPU训练的 +260w步的过程中，精度指标是下降的过程是怎样的。值得庆幸的是通过NPU训练出的模型，能够在GPU上计算出精度，并且精度还不错。 + +**性能对比：** + +取华为v100上GPU运行的前2w步的global_step/sec平均值和NPU运行的前2w步的global_step/sec平均值进行对比，以达到性能对比的目的。 + +| 性能指标项 | 论文发布 | GPU实测 | NPU实测 | +| ------------------- | ------- | ------ | ------ | +| global_step/sec| 无 | 87.64 | 101.24 | + + -- Gitee From f8962a8d709d903859ba5387aa26d0e3539c8951 Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Sun, 8 May 2022 01:08:06 +0000 Subject: [PATCH 44/88] =?UTF-8?q?=E5=88=A0=E9=99=A4=E6=96=87=E4=BB=B6=20Te?= =?UTF-8?q?nsorFlow/contrib/cv/SVD=5FID2019=5Ffor=5FTensorflow/README.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../cv/SVD_ID2019_for_Tensorflow/README.md | 248 ------------------ 1 file changed, 248 deletions(-) delete mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md deleted file mode 100644 index 0982c3178..000000000 --- a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md +++ /dev/null @@ -1,248 +0,0 @@ -

基本信息

- -**发布者（Publisher）：Huawei** - -**应用领域（Application Domain）：CV** - -**版本（Version）：** - -**修改时间（Modified） ：2022.05.05** - -**框架（Framework）：TensorFlow 1.15.0** - -**处理器（Processor）：昇腾910** - -**描述（Description）：使用训练好的SVD模型，评估对称正交化在点云对准中的应用效果。** - -

概述

- -给定两个三维点云图，利用SVD正交化过程SVDO+(M)将其投射到SO(3)上，要求网络预测最佳对齐它们的3D旋转。代码的训练逻辑是每训练10w步保存一个模型，并且在测试集上检验该模型的精度，最后比较的都是260w步的模型精度 - -- 开源代码： - - https://github.com/google-research/google-research/tree/master/special_orthogonalization。 - -- 参考论文： - - [An Analysis of SVD for Deep Rotation Estimation](https://arxiv.org/abs/2006.14616) - -- 参考实现： - gpu最后训练出来的模型在 - - obs://cann-id2019/gpu - - 数据在 - obs://cann-id2019/dataset/ - - 共有3个数据集 - - 训练数据集points - - 测试数据集 points_test - - 第一步旋转后的数据集 test_points_modified - - npu训练出来的模型在 - - obs://cann-id2019/dataset//output - - - 相关代码均以上传，在NPU和GPU文件夹中可以找到 - -- 相关迁移的工作： - 在进行代码迁移到NPU上时，输入的训练数据为点云数据，点云数据的shape为(N,3)，其中N并不是固定的，因此在NPU上存在动态shape的问题，导致模型训练无法正常进行。我们为此想了三个解决方法：1、找出所有点云数据中最小的N，对于大于N的点云数据，仅取前N行的数据输入训练。2、找到所有点云数据中最大的N，对于小于N的点云进行补0操作，将所有数据固定为最大的N后，输入网络进行训练。3、找到所有点云数据中最大的N，对小于N的点云数据，从原数据中选择一个点云进行填补至行数为N，再将数据输入网络进行训练。该三种方法均成功解决了NPU上的动态shape问题，但是第一种方法删除了样本点，因此导致最后训练出的模型精度很差；第二种方法虽然并没有丢失样本信息，但是向数据中填入大量的0，改变了本来的代码逻辑，导致最后训练出的模型精度也并不高。对于第三种方法，即没有丢失样本信息，对每个点云数据中的某一个点云样本点进行重复操作，没有改变原始的代码逻辑，最后也获得了不错的精度表现。 - -- 通过Git获取对应commit\_id的代码方法如下： - - ``` - git clone {repository_url} # 克隆仓库的代码 - cd {repository_name} # 切换到模型的代码仓目录 - git checkout {branch} # 切换到对应分支 - git reset --hard ｛commit_id｝ # 代码设置到对应的commit_id - cd ｛code_path｝ # 切换到模型代码所在路径，若仓库下只有该模型，则无需切换 - ``` - -## 默认配置 - -- 数据集获取：obs://cann-id2019/dataset/ - -- 训练超参 - - - log_step_count=200 - - save_summaries_steps=25000 - - train_steps=2600000 - - save_checkpoints_steps=100000 - - eval_examples=39900 - - - -

训练环境准备

- -1. 硬件环境准备请参见各硬件产品文档"[驱动和固件安装升级指南]( https://support.huawei.com/enterprise/zh/category/ai-computing-platform-pid-1557196528909)"。需要在硬件设备上安装与CANN版本配套的固件与驱动。 -2. 宿主机上需要安装Docker并登录[Ascend Hub中心](https://ascendhub.huawei.com/#/detail?name=ascend-tensorflow-arm)获取镜像。 - - 当前模型支持的镜像列表如[表1](#zh-cn_topic_0000001074498056_table1519011227314)所示。 - - **表 1** 镜像列表 - - - - - - - - - - - - -

镜像名称

-

镜像版本

-

配套CANN版本

-
-

20.2.0

-

20.2

-
- - - -## 脚本参数 - -``` -gen_pt_test_data_gpu.py 中的参数 - ---input_test_files 传入需要进行旋转的点云数据集 ---output_directory 存储旋转后点云集的路径 ---random_rotation_axang boole型，若为真将会对传入的数据集采用指定方法旋转，默认为真 ---num_rotations_per_file 每个测试点云的随机旋转增加数。默认为100 - - -main_point_cloud_gpu.py 中的参数 - ---pt_cloud_test_files 测试数据集路径 ---pt_cloud_train_files 熟练数据集路径 ---method 指定用于预测旋转的方式。选项为"svd", "svd-inf", or "gs"。默认为“svd” ---checkpoint_dir 训练模型的存放位置 ---train_steps 训练迭代的次数。默认为2600000 ---save_checkpoints_steps 保存检查点的频率。默认为10000 ---log_step_count 日志记录一次的步数。默认为200 ---save_summaries_steps 保存一次summary的步数。默认为5000 ---learning_rate 默认为1e-5 ---lr_decay 如果为真，则衰减learning rate。默认为假 ---lr_decay_steps learning rate衰减步数。默认为35000 ---lr_decay_rate learning rate衰减速率。默认为0.95 ---predict_all_test 如果为真，则在最新的检查点上运行eval作业，并打印每个输入的误差信息。默认为假 ---eval_examples 测试样本的数量。默认为0 ---print_variable_names 打印模型变量名。默认为假 ---num_train_augmentations 增加每个输入点云的随机旋转数。默认为10 - -``` - - - -## 运行 - -GPU运行命令如下： - -**生成测试数据** - -注：生成的文件test_points_modified、points已包含在dataset文件夹中。 -```bash -# 将路径设置到训练点云图文件 -IN_FILES=/points_test/*.pts - -NEW_TEST_FILES_DIR=/test_points_modified - -AXANG_SAMPLING=True - -# 决定旋转轴角的分布 -AXANG_SAMPLING=True - -python -m special_orthogonalization.gen_pt_test_data_gpu --input_test_files=$IN_FILES --output_directory=$NEW_TEST_FILES_DIR --random_rotation_axang=$AXANG_SAMPLING -``` - -**训练与评价** -```bash -# 将路径设置到原始训练数据 -TRAIN_FILES=/points/*.pts - -#将路径设置到旋转后的训练数据 -TEST_FILES=$NEW_TEST_FILES_DIR/*.pts - -# 指定旋转预测方式 -METHOD=svd - -# 指定ckpt、summaries、评价结果等的存储路径 -OUT_DIR=/path/to/model - -python -m special_orthogonalization.main_point_cloud_gpu --method=$METHOD --checkpoint_dir=$OUT_DIR --log_step_count=200 --save_summaries_steps=25000 --pt_cloud_train_files=$TRAIN_FILES --pt_cloud_test_files=$TEST_FILES --train_steps=2600000 --save_checkpoints_steps=100000 --eval_examples=39900 -``` - -**从所有训练样本中生成统计数据** -```bash -# 打印均值、中位数、标准差和分位数 -python -m special_orthogonalization.main_point_cloud_gpu --method=$METHOD --checkpoint_dir=$OUT_DIR --pt_cloud_test_files=$TEST_FILES --predict_all_test=True -``` -## 运行 - -NPU运行命令方式如下: - -对于所有的三个步骤程序来说，modelarts插件obs桶中的数据路径均要写到真正包含数据的那一个路径 -如在dataset文件夹中含有points、points_test等包含数据的文件夹 -modelarts插件中的数据路径写为 obs://cann-id2019/dataset/ - -**生成测试数据** - -运行这一步我们需要的程序文件为gen_pt_test_data.py、modelarts_entry_Axang.py、genTestData.sh -这三个文件中的代码均不需要修改 -最后生成的旋转后的数据文件存放在obs桶当次程序文件的output路径中，文件名为test_points_modified， -为进行第二步模型训练，需要将生成旋转后的文件转移至obs桶中存放data的路径 - -注：需要确保的是存在obs桶里的data文件名为points_test - -**训练与评价** - -运行这一步我们需要的程序文件为main_point_cloud.py、modelarts_entry_acc_train.py、train_full_1p.sh -这三个文件中的代码均不需要修改 -最后生成的旋转后的数据文件存放在obs桶当次程序文件的output路径中，文件名为test_points_modified， -为进行第三步，需要将生成的output文件转移至obs桶中存放data的路径 - -注意：该次训练的模型保存在该次的obs文件夹中，进行第三步时又需要重启一次新的modelarts，因此我们需要将output文件中的 -checkpoint文件中最新模型的路径修改 -"/home/ma-user/modelarts/inputs/data_url_0/output" -这样第三步才能跑出正确的精度指标 - -**从所有训练样本中生成统计数据** - -运行这一步我们需要的程序文件为main_point_cloud.py、modelarts_entry_stat.py、genStatistical.sh -这三个文件的代码均不需要修改 -运行成功后将会在屏幕上打印出关于精度相应的统计量值 - -注意：由于加入了混合精度提高训练性能，导致该步出现未知错误，输出的误差均为0，因此建议将训练后的结果下载到本地 -修改checkpoint文件中模型的路径后，利用本地的GPU进行运行，实际上该步骤在运行时NPU成功启动但也未曾利用。 - -## 训练结果 -**精度对比：** - - - -| 测地线误差（°） | 论文发布 | GPU(初始代码未改动版本) | GPU实测|NPU实测 | -| ------------------------ | ------- | ----- | --------- |----------| -| 平均值 | 1.63 | 2.58 | 3.98 | 2.92 | -| 中值 | 0.89 | 1.68 | 2.6 | 1.7 | -| 标准差 | 6.70 | 6.93 | 9.36 | 8.45 | - -相比于论文中的精度，我们NPU迁移后实测差距依然较大，但是与我们未对代码任何改动初始的版本在GPU上跑出来的精度相差较小， -且对于相同的代码的代码改动，NPU上的精度优于GPU上的精度。需要注意的是，在NPU上运行程序时，我们采用混合精度来提升训练 -的性能，但是其中产生了未知的错误，导致代表的精度指标mean_degree_err在整个训练过程中始终为0，因此我们无法得知在NPU训练的 -260w步的过程中，精度指标是下降的过程是怎样的。值得庆幸的是通过NPU训练出的模型，能够在GPU上计算出精度，并且精度还不错。 - -**性能对比：** - -取华为v100上GPU运行的前2w步的global_step/sec平均值和NPU运行的前2w步的global_step/sec平均值进行对比，以达到性能对比的目的。 - -| 性能指标项 | 论文发布 | GPU实测 | NPU实测 | -| ------------------- | ------- | ------ | ------ | -| global_step/sec| 无 | 87.64 | 101.24 | - - -- Gitee From b6dfe67b855bde04403dfba9dd076c8cbed1ef3b Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Sun, 8 May 2022 01:08:50 +0000 Subject: [PATCH 45/88] =?UTF-8?q?=E6=9B=B4=E6=96=B0=E5=90=8E=E7=9A=84readm?= =?UTF-8?q?e=EF=BC=8C=E5=8A=A0=E5=85=A5=E4=BA=86NPU=E4=B8=8A=E8=BF=90?= =?UTF-8?q?=E8=A1=8C=E7=9A=84=E6=9C=80=E6=96=B0=E7=BB=93=E6=9E=9C?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../cv/SVD_ID2019_for_Tensorflow/README.md | 248 ++++++++++++++++++ 1 file changed, 248 insertions(+) create mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md new file mode 100644 index 000000000..0c004e315 --- /dev/null +++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md @@ -0,0 +1,248 @@ +

基本信息

+ +**发布者（Publisher）：Huawei** + +**应用领域（Application Domain）：CV** + +**版本（Version）：** + +**修改时间（Modified） ：2022.05.05** + +**框架（Framework）：TensorFlow 1.15.0** + +**处理器（Processor）：昇腾910** + +**描述（Description）：使用训练好的SVD模型，评估对称正交化在点云对准中的应用效果。** + +

概述

+ +给定两个三维点云图，利用SVD正交化过程SVDO+(M)将其投射到SO(3)上，要求网络预测最佳对齐它们的3D旋转。代码的训练逻辑是每训练10w步保存一个模型，并且在测试集上检验该模型的精度，最后比较的都是260w步的模型精度 + +- 开源代码： + + https://github.com/google-research/google-research/tree/master/special_orthogonalization。 + +- 参考论文： + + [An Analysis of SVD for Deep Rotation Estimation](https://arxiv.org/abs/2006.14616) + +- 参考实现： + gpu最后训练出来的模型在 + + obs://cann-id2019/gpu + + 数据在 + obs://cann-id2019/dataset/ + + 共有3个数据集 + + 训练数据集points + + 测试数据集 points_test + + 第一步旋转后的数据集 test_points_modified + + npu训练出来的模型在 + + obs://cann-id2019/dataset/output + + + 相关代码均以上传，在NPU和GPU文件夹中可以找到 + +- 相关迁移的工作： + 在进行代码迁移到NPU上时，输入的训练数据为点云数据，点云数据的shape为(N,3)，其中N并不是固定的，因此在NPU上存在动态shape的问题，导致模型训练无法正常进行。我们为此想了三个解决方法：1、找出所有点云数据中最小的N，对于大于N的点云数据，仅取前N行的数据输入训练。2、找到所有点云数据中最大的N，对于小于N的点云进行补0操作，将所有数据固定为最大的N后，输入网络进行训练。3、找到所有点云数据中最大的N，对小于N的点云数据，从原数据中选择一个点云进行填补至行数为N，再将数据输入网络进行训练。该三种方法均成功解决了NPU上的动态shape问题，但是第一种方法删除了样本点，因此导致最后训练出的模型精度很差；第二种方法虽然并没有丢失样本信息，但是向数据中填入大量的0，改变了本来的代码逻辑，导致最后训练出的模型精度也并不高。对于第三种方法，即没有丢失样本信息，对每个点云数据中的某一个点云样本点进行重复操作，没有改变原始的代码逻辑，最后也获得了不错的精度表现。 + +- 通过Git获取对应commit\_id的代码方法如下： + + ``` + git clone {repository_url} # 克隆仓库的代码 + cd {repository_name} # 切换到模型的代码仓目录 + git checkout {branch} # 切换到对应分支 + git reset --hard ｛commit_id｝ # 代码设置到对应的commit_id + cd ｛code_path｝ # 切换到模型代码所在路径，若仓库下只有该模型，则无需切换 + ``` + +## 默认配置 + +- 数据集获取：obs://cann-id2019/dataset/ + +- 训练超参 + + - log_step_count=200 + - save_summaries_steps=25000 + - train_steps=2600000 + - save_checkpoints_steps=100000 + - eval_examples=39900 + + + +

训练环境准备

+ +1. 硬件环境准备请参见各硬件产品文档"[驱动和固件安装升级指南]( https://support.huawei.com/enterprise/zh/category/ai-computing-platform-pid-1557196528909)"。需要在硬件设备上安装与CANN版本配套的固件与驱动。 +2. 宿主机上需要安装Docker并登录[Ascend Hub中心](https://ascendhub.huawei.com/#/detail?name=ascend-tensorflow-arm)获取镜像。 + + 当前模型支持的镜像列表如[表1](#zh-cn_topic_0000001074498056_table1519011227314)所示。 + + **表 1** 镜像列表 + + + + + + + + + + + + +

镜像名称

+

镜像版本

+

配套CANN版本

+
+

20.2.0

+

20.2

+
+ + + +## 脚本参数 + +``` +gen_pt_test_data_gpu.py 中的参数 + +--input_test_files 传入需要进行旋转的点云数据集 +--output_directory 存储旋转后点云集的路径 +--random_rotation_axang boole型，若为真将会对传入的数据集采用指定方法旋转，默认为真 +--num_rotations_per_file 每个测试点云的随机旋转增加数。默认为100 + + +main_point_cloud_gpu.py 中的参数 + +--pt_cloud_test_files 测试数据集路径 +--pt_cloud_train_files 熟练数据集路径 +--method 指定用于预测旋转的方式。选项为"svd", "svd-inf", or "gs"。默认为“svd” +--checkpoint_dir 训练模型的存放位置 +--train_steps 训练迭代的次数。默认为2600000 +--save_checkpoints_steps 保存检查点的频率。默认为10000 +--log_step_count 日志记录一次的步数。默认为200 +--save_summaries_steps 保存一次summary的步数。默认为5000 +--learning_rate 默认为1e-5 +--lr_decay 如果为真，则衰减learning rate。默认为假 +--lr_decay_steps learning rate衰减步数。默认为35000 +--lr_decay_rate learning rate衰减速率。默认为0.95 +--predict_all_test 如果为真，则在最新的检查点上运行eval作业，并打印每个输入的误差信息。默认为假 +--eval_examples 测试样本的数量。默认为0 +--print_variable_names 打印模型变量名。默认为假 +--num_train_augmentations 增加每个输入点云的随机旋转数。默认为10 + +``` + + + +## 运行 + +GPU运行命令如下： + +**生成测试数据** + +注：生成的文件test_points_modified、points已包含在dataset文件夹中。 +```bash +# 将路径设置到训练点云图文件 +IN_FILES=/points_test/*.pts + +NEW_TEST_FILES_DIR=/test_points_modified + +AXANG_SAMPLING=True + +# 决定旋转轴角的分布 +AXANG_SAMPLING=True + +python -m special_orthogonalization.gen_pt_test_data_gpu --input_test_files=$IN_FILES --output_directory=$NEW_TEST_FILES_DIR --random_rotation_axang=$AXANG_SAMPLING +``` + +**训练与评价** +```bash +# 将路径设置到原始训练数据 +TRAIN_FILES=/points/*.pts + +#将路径设置到旋转后的训练数据 +TEST_FILES=$NEW_TEST_FILES_DIR/*.pts + +# 指定旋转预测方式 +METHOD=svd + +# 指定ckpt、summaries、评价结果等的存储路径 +OUT_DIR=/path/to/model + +python -m special_orthogonalization.main_point_cloud_gpu --method=$METHOD --checkpoint_dir=$OUT_DIR --log_step_count=200 --save_summaries_steps=25000 --pt_cloud_train_files=$TRAIN_FILES --pt_cloud_test_files=$TEST_FILES --train_steps=2600000 --save_checkpoints_steps=100000 --eval_examples=39900 +``` + +**从所有训练样本中生成统计数据** +```bash +# 打印均值、中位数、标准差和分位数 +python -m special_orthogonalization.main_point_cloud_gpu --method=$METHOD --checkpoint_dir=$OUT_DIR --pt_cloud_test_files=$TEST_FILES --predict_all_test=True +``` +## 运行 + +NPU运行命令方式如下: + +对于所有的三个步骤程序来说，modelarts插件obs桶中的数据路径均要写到真正包含数据的那一个路径 +如在dataset文件夹中含有points、points_test等包含数据的文件夹 +modelarts插件中的数据路径写为 obs://cann-id2019/dataset/ + +**生成测试数据** + +运行这一步我们需要的程序文件为gen_pt_test_data.py、modelarts_entry_Axang.py、genTestData.sh +这三个文件中的代码均不需要修改 +最后生成的旋转后的数据文件存放在obs桶当次程序文件的output路径中，文件名为test_points_modified， +为进行第二步模型训练，需要将生成旋转后的文件转移至obs桶中存放data的路径 + +注：需要确保的是存在obs桶里的data文件名为points_test + +**训练与评价** + +运行这一步我们需要的程序文件为main_point_cloud.py、modelarts_entry_acc_train.py、train_full_1p.sh +这三个文件中的代码均不需要修改 +最后生成的旋转后的数据文件存放在obs桶当次程序文件的output路径中，文件名为test_points_modified， +为进行第三步，需要将生成的output文件转移至obs桶中存放data的路径 + +注意：该次训练的模型保存在该次的obs文件夹中，进行第三步时又需要重启一次新的modelarts，因此我们需要将output文件中的 +checkpoint文件中最新模型的路径修改 +"/home/ma-user/modelarts/inputs/data_url_0/output" +这样第三步才能跑出正确的精度指标 + +**从所有训练样本中生成统计数据** + +运行这一步我们需要的程序文件为main_point_cloud.py、modelarts_entry_stat.py、genStatistical.sh +这三个文件的代码均不需要修改 +运行成功后将会在屏幕上打印出关于精度相应的统计量值 + +注意：由于加入了混合精度提高训练性能，导致该步出现未知错误，输出的误差均为0，因此建议将训练后的结果下载到本地 +修改checkpoint文件中模型的路径后，利用本地的GPU进行运行，实际上该步骤在运行时NPU成功启动但也未曾利用。 + +## 训练结果 +**精度对比：** + + + +| 测地线误差（°） | 论文发布 | GPU(初始代码未改动版本) | GPU实测|NPU实测 | +| ------------------------ | ------- | ----- | --------- |----------| +| 平均值 | 1.63 | 2.58 | 3.98 | 2.92 | +| 中值 | 0.89 | 1.68 | 2.6 | 1.7 | +| 标准差 | 6.70 | 6.93 | 9.36 | 8.45 | + +相比于论文中的精度，我们NPU迁移后实测差距依然较大，但是与我们未对代码任何改动初始的版本在GPU上跑出来的精度相差较小， +且对于相同的代码的代码改动，NPU上的精度优于GPU上的精度。需要注意的是，在NPU上运行程序时，我们采用混合精度来提升训练 +的性能，但是其中产生了未知的错误，导致代表的精度指标mean_degree_err在整个训练过程中始终为0，因此我们无法得知在NPU训练的 +260w步的过程中，精度指标是下降的过程是怎样的。值得庆幸的是通过NPU训练出的模型，能够在GPU上计算出精度，并且精度还不错。 + +**性能对比：** + +取华为v100上GPU运行的前2w步的global_step/sec平均值和NPU运行的前2w步的global_step/sec平均值进行对比，以达到性能对比的目的。 + +| 性能指标项 | 论文发布 | GPU实测 | NPU实测 | +| ------------------- | ------- | ------ | ------ | +| global_step/sec| 无 | 87.64 | 101.24 | + + -- Gitee From 8bd2de5e497b063ddf139ad1b709b9235cc4e73a Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Sun, 8 May 2022 14:35:32 +0000 Subject: [PATCH 46/88] =?UTF-8?q?=E5=88=A0=E9=99=A4=E6=96=87=E4=BB=B6=20Te?= =?UTF-8?q?nsorFlow/contrib/cv/SVD=5FID2019=5Ffor=5FTensorflow/GPU?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../cv/SVD_ID2019_for_Tensorflow/GPU/.keep | 0 ...\346\224\271\347\211\210\346\234\254).txt" | 106 - ...5\220\214NPU\347\211\210\346\234\254).txt" | 105 - .../GPU/gen_pt_test_data_gpu.py | 99 - .../SVD_ID2019_for_Tensorflow/GPU/log_gpu.log | 26651 ---------------- .../GPU/main_point_cloud_gpu.py | 431 - .../GPU/requirements.txt | 69 - .../GPU/utils_gpu.py | 144 - 8 files changed, 27605 deletions(-) delete mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/.keep delete mode 100644 "TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/GPU\347\262\276\345\272\246(github\345\216\237\344\273\243\347\240\201\346\234\252\346\233\264\346\224\271\347\211\210\346\234\254).txt" delete mode 100644 "TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/GPU\347\262\276\345\272\246(\344\273\243\347\240\201\351\200\273\350\276\221\345\220\214NPU\347\211\210\346\234\254).txt" delete mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/gen_pt_test_data_gpu.py delete mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/log_gpu.log delete mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/main_point_cloud_gpu.py delete mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/requirements.txt delete mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/utils_gpu.py diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/.keep b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/.keep deleted file mode 100644 index e69de29bb..000000000 diff --git "a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/GPU\347\262\276\345\272\246(github\345\216\237\344\273\243\347\240\201\346\234\252\346\233\264\346\224\271\347\211\210\346\234\254).txt" "b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/GPU\347\262\276\345\272\246(github\345\216\237\344\273\243\347\240\201\346\234\252\346\233\264\346\224\271\347\211\210\346\234\254).txt" deleted file mode 100644 index e8a33b3ef..000000000 --- "a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/GPU\347\262\276\345\272\246(github\345\216\237\344\273\243\347\240\201\346\234\252\346\233\264\346\224\271\347\211\210\346\234\254).txt" +++ /dev/null @@ -1,106 +0,0 @@ -Evaluated 39900 examples -Mean error: %f degrees 2.582546 -Median error: %f degrees 1.6805115 -Std: %f degrees 6.938891 - -Percentiles: - 1%: 0.300014 - 2%: 0.380522 - 3%: 0.437902 - 4%: 0.487788 - 5%: 0.526747 - 6%: 0.566482 - 7%: 0.602634 - 8%: 0.632420 - 9%: 0.662343 - 10%: 0.689838 - 11%: 0.714364 - 12%: 0.740193 - 13%: 0.766683 - 14%: 0.791300 - 15%: 0.816133 - 16%: 0.840466 - 17%: 0.863887 - 18%: 0.886470 - 19%: 0.909567 - 20%: 0.933351 - 21%: 0.955725 - 22%: 0.978388 - 23%: 1.002101 - 24%: 1.026220 - 25%: 1.049225 - 26%: 1.074472 - 27%: 1.098249 - 28%: 1.120823 - 29%: 1.143979 - 30%: 1.165668 - 31%: 1.189761 - 32%: 1.215631 - 33%: 1.238436 - 34%: 1.261760 - 35%: 1.286031 - 36%: 1.308956 - 37%: 1.333835 - 38%: 1.358547 - 39%: 1.383949 - 40%: 1.407643 - 41%: 1.433814 - 42%: 1.458442 - 43%: 1.485167 - 44%: 1.513619 - 45%: 1.540785 - 46%: 1.567729 - 47%: 1.595813 - 48%: 1.623171 - 49%: 1.651379 - 50%: 1.680511 - 51%: 1.709835 - 52%: 1.740463 - 53%: 1.769457 - 54%: 1.803091 - 55%: 1.837601 - 56%: 1.872417 - 57%: 1.909058 - 58%: 1.944507 - 59%: 1.979321 - 60%: 2.016059 - 61%: 2.052808 - 62%: 2.091812 - 63%: 2.131297 - 64%: 2.175648 - 65%: 2.219467 - 66%: 2.265031 - 67%: 2.307493 - 68%: 2.354846 - 69%: 2.401918 - 70%: 2.450324 - 71%: 2.507802 - 72%: 2.558491 - 73%: 2.610895 - 74%: 2.670931 - 75%: 2.737095 - 76%: 2.795334 - 77%: 2.862317 - 78%: 2.931846 - 79%: 3.004461 - 80%: 3.083880 - 81%: 3.159387 - 82%: 3.250103 - 83%: 3.346382 - 84%: 3.453207 - 85%: 3.562164 - 86%: 3.686474 - 87%: 3.808481 - 88%: 3.965510 - 89%: 4.142220 - 90%: 4.348930 - 91%: 4.576029 - 92%: 4.841852 - 93%: 5.135875 - 94%: 5.504097 - 95%: 5.996492 - 96%: 6.660559 - 97%: 7.634066 - 98%: 9.250069 - 99%: 12.710493 -100%: 178.697479 \ No newline at end of file diff --git "a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/GPU\347\262\276\345\272\246(\344\273\243\347\240\201\351\200\273\350\276\221\345\220\214NPU\347\211\210\346\234\254).txt" "b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/GPU\347\262\276\345\272\246(\344\273\243\347\240\201\351\200\273\350\276\221\345\220\214NPU\347\211\210\346\234\254).txt" deleted file mode 100644 index 15a4f409e..000000000 --- "a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/GPU\347\262\276\345\272\246(\344\273\243\347\240\201\351\200\273\350\276\221\345\220\214NPU\347\211\210\346\234\254).txt" +++ /dev/null @@ -1,105 +0,0 @@ -Mean error: %f degrees 3.9795678 -Median error: %f degrees 2.5962727 -Std: %f degrees 9.361983 - -Percentiles: - 1%: 0.615485 - 2%: 0.765661 - 3%: 0.859573 - 4%: 0.933770 - 5%: 0.991698 - 6%: 1.044177 - 7%: 1.096108 - 8%: 1.139179 - 9%: 1.178358 - 10%: 1.218525 - 11%: 1.255853 - 12%: 1.294827 - 13%: 1.331633 - 14%: 1.368879 - 15%: 1.406948 - 16%: 1.441844 - 17%: 1.474588 - 18%: 1.508179 - 19%: 1.543069 - 20%: 1.576939 - 21%: 1.608395 - 22%: 1.644729 - 23%: 1.676781 - 24%: 1.711550 - 25%: 1.743160 - 26%: 1.777402 - 27%: 1.811321 - 28%: 1.847797 - 29%: 1.880135 - 30%: 1.913359 - 31%: 1.947926 - 32%: 1.980112 - 33%: 2.014214 - 34%: 2.047557 - 35%: 2.079143 - 36%: 2.111832 - 37%: 2.144206 - 38%: 2.178525 - 39%: 2.212224 - 40%: 2.245766 - 41%: 2.279673 - 42%: 2.314099 - 43%: 2.347187 - 44%: 2.381295 - 45%: 2.418161 - 46%: 2.452399 - 47%: 2.488370 - 48%: 2.523829 - 49%: 2.562771 - 50%: 2.596235 - 51%: 2.636707 - 52%: 2.675837 - 53%: 2.713537 - 54%: 2.753353 - 55%: 2.791760 - 56%: 2.830061 - 57%: 2.869558 - 58%: 2.909864 - 59%: 2.951013 - 60%: 2.995325 - 61%: 3.037444 - 62%: 3.085022 - 63%: 3.137624 - 64%: 3.186842 - 65%: 3.234585 - 66%: 3.286463 - 67%: 3.345505 - 68%: 3.400300 - 69%: 3.454171 - 70%: 3.511901 - 71%: 3.568425 - 72%: 3.631408 - 73%: 3.698511 - 74%: 3.770657 - 75%: 3.847002 - 76%: 3.918766 - 77%: 4.001153 - 78%: 4.092133 - 79%: 4.175701 - 80%: 4.280524 - 81%: 4.383471 - 82%: 4.505587 - 83%: 4.643044 - 84%: 4.795996 - 85%: 4.951040 - 86%: 5.116305 - 87%: 5.308140 - 88%: 5.553438 - 89%: 5.796063 - 90%: 6.108622 - 91%: 6.445337 - 92%: 6.875063 - 93%: 7.384479 - 94%: 7.999909 - 95%: 8.852993 - 96%: 10.022167 - 97%: 12.029458 - 98%: 15.299019 - 99%: 22.932156 -100%: 179.803162 \ No newline at end of file diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/gen_pt_test_data_gpu.py b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/gen_pt_test_data_gpu.py deleted file mode 100644 index 96b2770d7..000000000 --- a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/gen_pt_test_data_gpu.py +++ /dev/null @@ -1,99 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ - -# coding=utf-8 -# Copyright 2021 The Google Research Authors. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -"""Generate modified point cloud test data with fixed random rotations.""" -import glob -import os -import pathlib - -import utils_gpu -from absl import app -from absl import flags -import numpy as np -from scipy.stats import special_ortho_group - -FLAGS = flags.FLAGS -flags.DEFINE_string('input_test_files', '/cache/data_modelarts/points_test/*.pts', - 'Regular expression for the original input test files.') -flags.DEFINE_string('output_directory', '/cache/data_modelarts/test_modified', - 'Output directory where new test files will be stored.') -flags.DEFINE_integer('num_rotations_per_file', 100, - 'Number of random rotation augmentations per test point ' - 'cloud.') -flags.DEFINE_boolean('random_rotation_axang', True, - 'If true, samples random rotations using the method ' - 'from the original benchmark code. Otherwise samples ' - 'by Haar measure.') - - -def gen_test_data(): - """Generate the new (modified) test data.""" - # Create output directory. - os.makedirs(FLAGS.output_directory, exist_ok=True) - - # Get all test point cloud files in the original dataset. - input_test_files = glob.glob(FLAGS.input_test_files) - - for in_file in input_test_files: - out_file_prefix = pathlib.Path(in_file).stem - pts = np.loadtxt(in_file) # N x 3 - num_pts_to_keep = pts.shape[0] // 2 - pts = pts[:num_pts_to_keep, :] # N//2 x 3. - - for k in range(FLAGS.num_rotations_per_file): - if FLAGS.random_rotation_axang: - r = utils_gpu.random_rotation_benchmark_np(1) - r = r[0] - else: - r = special_ortho_group.rvs(3) - joined = np.float32(np.concatenate((r, pts), axis=0)) # (N//2+3) x 3. - out_file = os.path.join( - FLAGS.output_directory, '%s_r%03d.pts'%(out_file_prefix, k)) - np.savetxt(out_file, joined) - - -def main(unused_argv): - gen_test_data() - -if __name__ == '__main__': - app.run(main) diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/log_gpu.log b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/log_gpu.log deleted file mode 100644 index 60f26f4b1..000000000 --- a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/log_gpu.log +++ /dev/null @@ -1,26651 +0,0 @@ -2022-05-01 14:07:22,858 - tensorflow - INFO - Using config: {'_model_dir': '/root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51', '_tf_random_seed': None, '_save_summary_steps': 25000, '_save_checkpoints_steps': 100000, '_save_checkpoints_secs': None, '_session_config': allow_soft_placement: true -graph_options { - rewrite_options { - meta_optimizer_iterations: ONE - } -} -, '_keep_checkpoint_max': None, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 200, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_service': None, '_cluster_spec': , '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1} -2022-05-01 14:07:22,859 - tensorflow - INFO - Not using Distribute Coordinator. -2022-05-01 14:07:22,859 - tensorflow - INFO - Running training and evaluation locally (non-distributed). -2022-05-01 14:07:22,860 - tensorflow - INFO - Start train and evaluate loop. The evaluate will happen after every checkpoint. Checkpoint frequency is determined based on RunConfig arguments: save_checkpoints_steps 100000 or save_checkpoints_secs None. -2022-05-01 14:07:22,871 - tensorflow - WARNING - From /root/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow_core/python/training/training_util.py:236: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version. -Instructions for updating: -Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts. -2022-05-01 14:07:22,878 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:249: The name tf.gfile.Glob is deprecated. Please use tf.io.gfile.glob instead. - -2022-05-01 14:07:24,110 - tensorflow - WARNING - From /root/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow_core/python/autograph/converters/directives.py:119: The name tf.read_file is deprecated. Please use tf.io.read_file instead. - -2022-05-01 14:07:24,110 - tensorflow - WARNING - From /root/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow_core/python/autograph/converters/directives.py:119: The name tf.decode_csv is deprecated. Please use tf.io.decode_csv instead. - -2022-05-01 14:07:24,230 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:224: calling string_split (from tensorflow.python.ops.ragged.ragged_string_ops) with delimiter is deprecated and will be removed in a future version. -Instructions for updating: -delimiter is deprecated, please use sep instead. -2022-05-01 14:07:24,412 - tensorflow - WARNING - From /root/special_orthogonalization2/utils_gpu.py:75: py_func (from tensorflow.python.ops.script_ops) is deprecated and will be removed in a future version. -Instructions for updating: -tf.py_func is deprecated in TF V2. Instead, there are two - options available in V2. - - tf.py_function takes a python function which manipulates tf eager - tensors instead of numpy arrays. It's easy to convert a tf eager tensor to - an ndarray (just call tensor.numpy()) but having access to eager tensors - means `tf.py_function`s can use accelerators such as GPUs as well as - being differentiable using a gradient tape. - - tf.numpy_function maintains the semantics of the deprecated tf.py_func - (it is not differentiable, and manipulates numpy arrays). It drops the - stateful argument making all functions stateful. - -2022-05-01 14:07:24,420 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:257: The name tf.data.make_one_shot_iterator is deprecated. Please use tf.compat.v1.data.make_one_shot_iterator instead. - -2022-05-01 14:07:24,443 - tensorflow - INFO - Calling model_fn. -2022-05-01 14:07:24,448 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:74: The name tf.variable_scope is deprecated. Please use tf.compat.v1.variable_scope instead. - -2022-05-01 14:07:24,448 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:74: The name tf.AUTO_REUSE is deprecated. Please use tf.compat.v1.AUTO_REUSE instead. - -2022-05-01 14:07:24,448 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:75: conv1d (from tensorflow.python.layers.convolutional) is deprecated and will be removed in a future version. -Instructions for updating: -Use `tf.keras.layers.Conv1D` instead. -2022-05-01 14:07:24,449 - tensorflow - WARNING - From /root/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow_core/python/layers/convolutional.py:218: Layer.apply (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version. -Instructions for updating: -Please use `layer.__call__` method instead. -2022-05-01 14:07:24,498 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:81: calling reduce_max_v1 (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version. -Instructions for updating: -keep_dims is deprecated, use keepdims instead -2022-05-01 14:07:24,521 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:99: dense (from tensorflow.python.layers.core) is deprecated and will be removed in a future version. -Instructions for updating: -Use keras.layers.Dense instead. -2022-05-01 14:07:24,545 - tensorflow - WARNING - From /root/special_orthogonalization2/utils_gpu.py:97: The name tf.svd is deprecated. Please use tf.linalg.svd instead. - -2022-05-01 14:07:24,570 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:172: The name tf.summary.scalar is deprecated. Please use tf.compat.v1.summary.scalar instead. - -2022-05-01 14:07:24,572 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:174: The name tf.train.get_or_create_global_step is deprecated. Please use tf.compat.v1.train.get_or_create_global_step instead. - -2022-05-01 14:07:24,573 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:186: The name tf.train.AdamOptimizer is deprecated. Please use tf.compat.v1.train.AdamOptimizer instead. - -2022-05-01 14:07:24,573 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:187: The name tf.get_collection is deprecated. Please use tf.compat.v1.get_collection instead. - -2022-05-01 14:07:24,574 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:187: The name tf.GraphKeys is deprecated. Please use tf.compat.v1.GraphKeys instead. - -2022-05-01 14:07:24,856 - tensorflow - INFO - Done calling model_fn. -2022-05-01 14:07:24,857 - tensorflow - INFO - Create CheckpointSaverHook. -2022-05-01 14:07:25,039 - tensorflow - WARNING - From /root/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow_core/python/ops/array_ops.py:1475: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version. -Instructions for updating: -Use tf.where in 2.0, which has the same broadcast rule as np.where -2022-05-01 14:07:25,108 - tensorflow - INFO - Graph was finalized. -2022-05-01 14:07:30,102 - tensorflow - INFO - Running local_init_op. -2022-05-01 14:07:30,119 - tensorflow - INFO - Done running local_init_op. -2022-05-01 14:07:30,573 - tensorflow - INFO - Saving checkpoints for 0 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. -2022-05-01 14:07:30,648 - tensorflow - INFO - model.ckpt-0 is not in all_model_checkpoint_paths. Manually adding it. -2022-05-01 14:07:35,533 - tensorflow - INFO - loss = 32.038086, step = 0 -2022-05-01 14:07:38,208 - tensorflow - INFO - global_step/sec: 74.7414 -2022-05-01 14:07:38,208 - tensorflow - INFO - loss = 13.331531, step = 200 (2.676 sec) -2022-05-01 14:07:40,484 - tensorflow - INFO - global_step/sec: 87.8576 -2022-05-01 14:07:40,485 - tensorflow - INFO - loss = 17.656946, step = 400 (2.276 sec) -2022-05-01 14:07:42,758 - tensorflow - INFO - global_step/sec: 87.9518 -2022-05-01 14:07:42,759 - tensorflow - INFO - loss = 13.776253, step = 600 (2.274 sec) -2022-05-01 14:07:45,033 - tensorflow - INFO - global_step/sec: 87.9402 -2022-05-01 14:07:45,033 - tensorflow - INFO - loss = 16.600023, step = 800 (2.274 sec) -2022-05-01 14:07:47,313 - tensorflow - INFO - global_step/sec: 87.7138 -2022-05-01 14:07:47,313 - tensorflow - INFO - loss = 11.457915, step = 1000 (2.280 sec) -2022-05-01 14:07:49,589 - tensorflow - INFO - global_step/sec: 87.8775 -2022-05-01 14:07:49,589 - tensorflow - INFO - loss = 18.556915, step = 1200 (2.276 sec) -2022-05-01 14:07:51,866 - tensorflow - INFO - global_step/sec: 87.8345 -2022-05-01 14:07:51,866 - tensorflow - INFO - loss = 17.53027, step = 1400 (2.277 sec) -2022-05-01 14:07:54,133 - tensorflow - INFO - global_step/sec: 88.2186 -2022-05-01 14:07:54,133 - tensorflow - INFO - loss = 15.867823, step = 1600 (2.267 sec) -2022-05-01 14:07:56,414 - tensorflow - INFO - global_step/sec: 87.6843 -2022-05-01 14:07:56,414 - tensorflow - INFO - loss = 14.14325, step = 1800 (2.281 sec) -2022-05-01 14:07:58,688 - tensorflow - INFO - global_step/sec: 87.9504 -2022-05-01 14:07:58,688 - tensorflow - INFO - loss = 14.731254, step = 2000 (2.274 sec) -2022-05-01 14:08:00,969 - tensorflow - INFO - global_step/sec: 87.6853 -2022-05-01 14:08:00,969 - tensorflow - INFO - loss = 7.6048627, step = 2200 (2.281 sec) -2022-05-01 14:08:03,225 - tensorflow - INFO - global_step/sec: 88.6196 -2022-05-01 14:08:03,226 - tensorflow - INFO - loss = 5.806036, step = 2400 (2.257 sec) -2022-05-01 14:08:05,477 - tensorflow - INFO - global_step/sec: 88.8202 -2022-05-01 14:08:05,478 - tensorflow - INFO - loss = 24.230932, step = 2600 (2.252 sec) -2022-05-01 14:08:07,731 - tensorflow - INFO - global_step/sec: 88.7332 -2022-05-01 14:08:07,732 - tensorflow - INFO - loss = 3.6557422, step = 2800 (2.254 sec) -2022-05-01 14:08:09,980 - tensorflow - INFO - global_step/sec: 88.9168 -2022-05-01 14:08:09,981 - tensorflow - INFO - loss = 22.55999, step = 3000 (2.249 sec) -2022-05-01 14:08:12,234 - tensorflow - INFO - global_step/sec: 88.7507 -2022-05-01 14:08:12,234 - tensorflow - INFO - loss = 5.92297, step = 3200 (2.254 sec) -2022-05-01 14:08:14,483 - tensorflow - INFO - global_step/sec: 88.9076 -2022-05-01 14:08:14,484 - tensorflow - INFO - loss = 9.746002, step = 3400 (2.250 sec) -2022-05-01 14:08:16,739 - tensorflow - INFO - global_step/sec: 88.6754 -2022-05-01 14:08:16,739 - tensorflow - INFO - loss = 6.337427, step = 3600 (2.255 sec) -2022-05-01 14:08:18,995 - tensorflow - INFO - global_step/sec: 88.6477 -2022-05-01 14:08:18,995 - tensorflow - INFO - loss = 13.799805, step = 3800 (2.256 sec) -2022-05-01 14:08:21,252 - tensorflow - INFO - global_step/sec: 88.6198 -2022-05-01 14:08:21,252 - tensorflow - INFO - loss = 23.801136, step = 4000 (2.257 sec) -2022-05-01 14:08:23,510 - tensorflow - INFO - global_step/sec: 88.5703 -2022-05-01 14:08:23,510 - tensorflow - INFO - loss = 22.836792, step = 4200 (2.258 sec) -2022-05-01 14:08:25,774 - tensorflow - INFO - global_step/sec: 88.3396 -2022-05-01 14:08:25,774 - tensorflow - INFO - loss = 4.7546825, step = 4400 (2.264 sec) -2022-05-01 14:08:28,036 - tensorflow - INFO - global_step/sec: 88.413 -2022-05-01 14:08:28,036 - tensorflow - INFO - loss = 4.444308, step = 4600 (2.262 sec) -2022-05-01 14:08:30,300 - tensorflow - INFO - global_step/sec: 88.3341 -2022-05-01 14:08:30,301 - tensorflow - INFO - loss = 22.58392, step = 4800 (2.264 sec) -2022-05-01 14:08:32,565 - tensorflow - INFO - global_step/sec: 88.3069 -2022-05-01 14:08:32,565 - tensorflow - INFO - loss = 2.2934127, step = 5000 (2.265 sec) -2022-05-01 14:08:34,824 - tensorflow - INFO - global_step/sec: 88.5425 -2022-05-01 14:08:34,824 - tensorflow - INFO - loss = 9.195248, step = 5200 (2.259 sec) -2022-05-01 14:08:37,085 - tensorflow - INFO - global_step/sec: 88.4473 -2022-05-01 14:08:37,085 - tensorflow - INFO - loss = 20.765274, step = 5400 (2.261 sec) -2022-05-01 14:08:39,343 - tensorflow - INFO - global_step/sec: 88.5905 -2022-05-01 14:08:39,343 - tensorflow - INFO - loss = 6.617299, step = 5600 (2.258 sec) -2022-05-01 14:08:41,606 - tensorflow - INFO - global_step/sec: 88.3606 -2022-05-01 14:08:41,606 - tensorflow - INFO - loss = 5.0666003, step = 5800 (2.263 sec) -2022-05-01 14:08:43,870 - tensorflow - INFO - global_step/sec: 88.335 -2022-05-01 14:08:43,871 - tensorflow - INFO - loss = 0.7100632, step = 6000 (2.264 sec) -2022-05-01 14:08:46,121 - tensorflow - INFO - global_step/sec: 88.8393 -2022-05-01 14:08:46,122 - tensorflow - INFO - loss = 5.172297, step = 6200 (2.251 sec) -2022-05-01 14:08:48,372 - tensorflow - INFO - global_step/sec: 88.8673 -2022-05-01 14:08:48,372 - tensorflow - INFO - loss = 2.6311967, step = 6400 (2.251 sec) -2022-05-01 14:08:50,626 - tensorflow - INFO - global_step/sec: 88.7158 -2022-05-01 14:08:50,627 - tensorflow - INFO - loss = 2.473042, step = 6600 (2.254 sec) -2022-05-01 14:08:52,889 - tensorflow - INFO - global_step/sec: 88.3812 -2022-05-01 14:08:52,890 - tensorflow - INFO - loss = 1.4561142, step = 6800 (2.263 sec) -2022-05-01 14:08:55,156 - tensorflow - INFO - global_step/sec: 88.243 -2022-05-01 14:08:55,156 - tensorflow - INFO - loss = 9.346189, step = 7000 (2.266 sec) -2022-05-01 14:08:57,421 - tensorflow - INFO - global_step/sec: 88.2959 -2022-05-01 14:08:57,421 - tensorflow - INFO - loss = 9.677809, step = 7200 (2.265 sec) -2022-05-01 14:08:59,683 - tensorflow - INFO - global_step/sec: 88.3994 -2022-05-01 14:08:59,684 - tensorflow - INFO - loss = 1.9972081, step = 7400 (2.262 sec) -2022-05-01 14:09:01,938 - tensorflow - INFO - global_step/sec: 88.7117 -2022-05-01 14:09:01,938 - tensorflow - INFO - loss = 1.6406038, step = 7600 (2.255 sec) -2022-05-01 14:09:04,212 - tensorflow - INFO - global_step/sec: 87.9525 -2022-05-01 14:09:04,212 - tensorflow - INFO - loss = 0.55838716, step = 7800 (2.274 sec) -2022-05-01 14:09:06,478 - tensorflow - INFO - global_step/sec: 88.266 -2022-05-01 14:09:06,478 - tensorflow - INFO - loss = 1.4037358, step = 8000 (2.266 sec) -2022-05-01 14:09:08,750 - tensorflow - INFO - global_step/sec: 88.0226 -2022-05-01 14:09:08,750 - tensorflow - INFO - loss = 2.4998298, step = 8200 (2.272 sec) -2022-05-01 14:09:11,032 - tensorflow - INFO - global_step/sec: 87.6369 -2022-05-01 14:09:11,032 - tensorflow - INFO - loss = 9.273857, step = 8400 (2.282 sec) -2022-05-01 14:09:13,318 - tensorflow - INFO - global_step/sec: 87.4763 -2022-05-01 14:09:13,319 - tensorflow - INFO - loss = 3.2947733, step = 8600 (2.286 sec) -2022-05-01 14:09:15,595 - tensorflow - INFO - global_step/sec: 87.8609 -2022-05-01 14:09:15,595 - tensorflow - INFO - loss = 8.8844185, step = 8800 (2.276 sec) -2022-05-01 14:09:17,887 - tensorflow - INFO - global_step/sec: 87.2339 -2022-05-01 14:09:17,888 - tensorflow - INFO - loss = 1.5026308, step = 9000 (2.293 sec) -2022-05-01 14:09:20,169 - tensorflow - INFO - global_step/sec: 87.6604 -2022-05-01 14:09:20,169 - tensorflow - INFO - loss = 1.2300944, step = 9200 (2.282 sec) -2022-05-01 14:09:22,442 - tensorflow - INFO - global_step/sec: 87.9892 -2022-05-01 14:09:22,442 - tensorflow - INFO - loss = 0.62815464, step = 9400 (2.273 sec) -2022-05-01 14:09:24,718 - tensorflow - INFO - global_step/sec: 87.8782 -2022-05-01 14:09:24,718 - tensorflow - INFO - loss = 0.56881064, step = 9600 (2.276 sec) -2022-05-01 14:09:27,028 - tensorflow - INFO - global_step/sec: 86.5549 -2022-05-01 14:09:27,029 - tensorflow - INFO - loss = 3.8879156, step = 9800 (2.311 sec) -2022-05-01 14:09:29,330 - tensorflow - INFO - global_step/sec: 86.894 -2022-05-01 14:09:29,330 - tensorflow - INFO - loss = 0.5907526, step = 10000 (2.302 sec) -2022-05-01 14:09:31,612 - tensorflow - INFO - global_step/sec: 87.6382 -2022-05-01 14:09:31,613 - tensorflow - INFO - loss = 1.1593596, step = 10200 (2.282 sec) -2022-05-01 14:09:33,912 - tensorflow - INFO - global_step/sec: 86.9791 -2022-05-01 14:09:33,912 - tensorflow - INFO - loss = 0.42828047, step = 10400 (2.299 sec) -2022-05-01 14:09:36,210 - tensorflow - INFO - global_step/sec: 86.9989 -2022-05-01 14:09:36,211 - tensorflow - INFO - loss = 0.5336071, step = 10600 (2.299 sec) -2022-05-01 14:09:38,509 - tensorflow - INFO - global_step/sec: 86.9931 -2022-05-01 14:09:38,510 - tensorflow - INFO - loss = 1.3948362, step = 10800 (2.299 sec) -2022-05-01 14:09:40,808 - tensorflow - INFO - global_step/sec: 87.0139 -2022-05-01 14:09:40,808 - tensorflow - INFO - loss = 0.6679225, step = 11000 (2.299 sec) -2022-05-01 14:09:43,115 - tensorflow - INFO - global_step/sec: 86.7037 -2022-05-01 14:09:43,115 - tensorflow - INFO - loss = 0.78241736, step = 11200 (2.307 sec) -2022-05-01 14:09:45,399 - tensorflow - INFO - global_step/sec: 87.5425 -2022-05-01 14:09:45,400 - tensorflow - INFO - loss = 5.9322524, step = 11400 (2.285 sec) -2022-05-01 14:09:47,688 - tensorflow - INFO - global_step/sec: 87.3706 -2022-05-01 14:09:47,689 - tensorflow - INFO - loss = 0.52881104, step = 11600 (2.289 sec) -2022-05-01 14:09:49,978 - tensorflow - INFO - global_step/sec: 87.3595 -2022-05-01 14:09:49,978 - tensorflow - INFO - loss = 0.56047755, step = 11800 (2.289 sec) -2022-05-01 14:09:52,269 - tensorflow - INFO - global_step/sec: 87.2898 -2022-05-01 14:09:52,269 - tensorflow - INFO - loss = 0.28127378, step = 12000 (2.291 sec) -2022-05-01 14:09:54,561 - tensorflow - INFO - global_step/sec: 87.2696 -2022-05-01 14:09:54,561 - tensorflow - INFO - loss = 25.888077, step = 12200 (2.292 sec) -2022-05-01 14:09:56,851 - tensorflow - INFO - global_step/sec: 87.3247 -2022-05-01 14:09:56,851 - tensorflow - INFO - loss = 9.627051, step = 12400 (2.290 sec) -2022-05-01 14:09:59,137 - tensorflow - INFO - global_step/sec: 87.4895 -2022-05-01 14:09:59,137 - tensorflow - INFO - loss = 1.9770696, step = 12600 (2.286 sec) -2022-05-01 14:10:01,428 - tensorflow - INFO - global_step/sec: 87.2868 -2022-05-01 14:10:01,429 - tensorflow - INFO - loss = 11.038982, step = 12800 (2.291 sec) -2022-05-01 14:10:03,714 - tensorflow - INFO - global_step/sec: 87.4859 -2022-05-01 14:10:03,715 - tensorflow - INFO - loss = 1.3177816, step = 13000 (2.286 sec) -2022-05-01 14:10:06,003 - tensorflow - INFO - global_step/sec: 87.3913 -2022-05-01 14:10:06,003 - tensorflow - INFO - loss = 12.677236, step = 13200 (2.289 sec) -2022-05-01 14:10:08,301 - tensorflow - INFO - global_step/sec: 87.0449 -2022-05-01 14:10:08,301 - tensorflow - INFO - loss = 2.7487645, step = 13400 (2.298 sec) -2022-05-01 14:10:10,583 - tensorflow - INFO - global_step/sec: 87.6369 -2022-05-01 14:10:10,583 - tensorflow - INFO - loss = 0.9024451, step = 13600 (2.282 sec) -2022-05-01 14:10:12,867 - tensorflow - INFO - global_step/sec: 87.5497 -2022-05-01 14:10:12,868 - tensorflow - INFO - loss = 2.074489, step = 13800 (2.284 sec) -2022-05-01 14:10:15,159 - tensorflow - INFO - global_step/sec: 87.256 -2022-05-01 14:10:15,160 - tensorflow - INFO - loss = 1.01836, step = 14000 (2.292 sec) -2022-05-01 14:10:17,448 - tensorflow - INFO - global_step/sec: 87.3997 -2022-05-01 14:10:17,448 - tensorflow - INFO - loss = 0.4934891, step = 14200 (2.288 sec) -2022-05-01 14:10:19,735 - tensorflow - INFO - global_step/sec: 87.4333 -2022-05-01 14:10:19,735 - tensorflow - INFO - loss = 1.0520558, step = 14400 (2.287 sec) -2022-05-01 14:10:22,022 - tensorflow - INFO - global_step/sec: 87.4513 -2022-05-01 14:10:22,022 - tensorflow - INFO - loss = 2.0643926, step = 14600 (2.287 sec) -2022-05-01 14:10:24,301 - tensorflow - INFO - global_step/sec: 87.7653 -2022-05-01 14:10:24,301 - tensorflow - INFO - loss = 1.3036213, step = 14800 (2.279 sec) -2022-05-01 14:10:26,586 - tensorflow - INFO - global_step/sec: 87.5088 -2022-05-01 14:10:26,587 - tensorflow - INFO - loss = 3.2743459, step = 15000 (2.285 sec) -2022-05-01 14:10:28,874 - tensorflow - INFO - global_step/sec: 87.4281 -2022-05-01 14:10:28,874 - tensorflow - INFO - loss = 5.0951295, step = 15200 (2.288 sec) -2022-05-01 14:10:31,172 - tensorflow - INFO - global_step/sec: 87.0174 -2022-05-01 14:10:31,173 - tensorflow - INFO - loss = 16.01076, step = 15400 (2.298 sec) -2022-05-01 14:10:33,467 - tensorflow - INFO - global_step/sec: 87.1391 -2022-05-01 14:10:33,468 - tensorflow - INFO - loss = 0.71901804, step = 15600 (2.295 sec) -2022-05-01 14:10:35,757 - tensorflow - INFO - global_step/sec: 87.368 -2022-05-01 14:10:35,757 - tensorflow - INFO - loss = 0.5623523, step = 15800 (2.289 sec) -2022-05-01 14:10:38,044 - tensorflow - INFO - global_step/sec: 87.4192 -2022-05-01 14:10:38,045 - tensorflow - INFO - loss = 6.976879, step = 16000 (2.288 sec) -2022-05-01 14:10:40,329 - tensorflow - INFO - global_step/sec: 87.5449 -2022-05-01 14:10:40,329 - tensorflow - INFO - loss = 0.54529786, step = 16200 (2.285 sec) -2022-05-01 14:10:42,620 - tensorflow - INFO - global_step/sec: 87.3064 -2022-05-01 14:10:42,620 - tensorflow - INFO - loss = 16.176308, step = 16400 (2.291 sec) -2022-05-01 14:10:44,919 - tensorflow - INFO - global_step/sec: 87.0021 -2022-05-01 14:10:44,919 - tensorflow - INFO - loss = 0.96182084, step = 16600 (2.299 sec) -2022-05-01 14:10:47,207 - tensorflow - INFO - global_step/sec: 87.4072 -2022-05-01 14:10:47,207 - tensorflow - INFO - loss = 0.30624458, step = 16800 (2.288 sec) -2022-05-01 14:10:49,499 - tensorflow - INFO - global_step/sec: 87.2616 -2022-05-01 14:10:49,499 - tensorflow - INFO - loss = 6.4469113, step = 17000 (2.292 sec) -2022-05-01 14:10:51,790 - tensorflow - INFO - global_step/sec: 87.283 -2022-05-01 14:10:51,791 - tensorflow - INFO - loss = 18.890911, step = 17200 (2.291 sec) -2022-05-01 14:10:54,078 - tensorflow - INFO - global_step/sec: 87.4266 -2022-05-01 14:10:54,078 - tensorflow - INFO - loss = 0.55347186, step = 17400 (2.288 sec) -2022-05-01 14:10:56,371 - tensorflow - INFO - global_step/sec: 87.2254 -2022-05-01 14:10:56,371 - tensorflow - INFO - loss = 0.9430723, step = 17600 (2.293 sec) -2022-05-01 14:10:58,657 - tensorflow - INFO - global_step/sec: 87.4742 -2022-05-01 14:10:58,657 - tensorflow - INFO - loss = 0.43198982, step = 17800 (2.286 sec) -2022-05-01 14:11:00,930 - tensorflow - INFO - global_step/sec: 88.0087 -2022-05-01 14:11:00,930 - tensorflow - INFO - loss = 12.870156, step = 18000 (2.273 sec) -2022-05-01 14:11:03,196 - 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tensorflow - INFO - loss = 17.669123, step = 24600 (2.289 sec) -2022-05-01 14:12:18,875 - tensorflow - INFO - global_step/sec: 87.2932 -2022-05-01 14:12:18,875 - tensorflow - INFO - loss = 13.332111, step = 24800 (2.291 sec) -2022-05-01 14:12:21,159 - tensorflow - INFO - global_step/sec: 87.5453 -2022-05-01 14:12:21,160 - tensorflow - INFO - loss = 0.3878757, step = 25000 (2.285 sec) -2022-05-01 14:12:23,445 - tensorflow - INFO - global_step/sec: 87.5046 -2022-05-01 14:12:23,445 - tensorflow - INFO - loss = 6.0420566, step = 25200 (2.285 sec) -2022-05-01 14:12:25,732 - tensorflow - INFO - global_step/sec: 87.4241 -2022-05-01 14:12:25,733 - tensorflow - INFO - loss = 0.2254105, step = 25400 (2.288 sec) -2022-05-01 14:12:28,012 - tensorflow - INFO - global_step/sec: 87.7177 -2022-05-01 14:12:28,013 - tensorflow - INFO - loss = 0.25320306, step = 25600 (2.280 sec) -2022-05-01 14:12:30,296 - tensorflow - INFO - global_step/sec: 87.5957 -2022-05-01 14:12:30,296 - tensorflow - INFO - loss = 0.740721, step = 25800 (2.283 sec) -2022-05-01 14:12:32,560 - 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tensorflow - INFO - loss = 18.543062, step = 27200 (2.285 sec) -2022-05-01 14:12:48,489 - tensorflow - INFO - global_step/sec: 87.3634 -2022-05-01 14:12:48,490 - tensorflow - INFO - loss = 0.2762187, step = 27400 (2.289 sec) -2022-05-01 14:12:50,782 - tensorflow - INFO - global_step/sec: 87.2428 -2022-05-01 14:12:50,782 - tensorflow - INFO - loss = 11.74934, step = 27600 (2.292 sec) -2022-05-01 14:12:53,074 - tensorflow - INFO - global_step/sec: 87.2513 -2022-05-01 14:12:53,074 - tensorflow - INFO - loss = 0.3331861, step = 27800 (2.292 sec) -2022-05-01 14:12:55,366 - tensorflow - INFO - global_step/sec: 87.2526 -2022-05-01 14:12:55,366 - tensorflow - INFO - loss = 0.36796296, step = 28000 (2.292 sec) -2022-05-01 14:12:57,655 - tensorflow - INFO - global_step/sec: 87.3956 -2022-05-01 14:12:57,655 - tensorflow - INFO - loss = 7.9382463, step = 28200 (2.288 sec) -2022-05-01 14:12:59,940 - tensorflow - INFO - global_step/sec: 87.5061 -2022-05-01 14:12:59,940 - tensorflow - INFO - loss = 1.565325, step = 28400 (2.286 sec) -2022-05-01 14:13:02,233 - 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tensorflow - INFO - loss = 0.24280512, step = 29800 (2.287 sec) -2022-05-01 14:13:18,253 - tensorflow - INFO - global_step/sec: 87.5188 -2022-05-01 14:13:18,254 - tensorflow - INFO - loss = 0.18203947, step = 30000 (2.285 sec) -2022-05-01 14:13:20,550 - tensorflow - INFO - global_step/sec: 87.082 -2022-05-01 14:13:20,550 - tensorflow - INFO - loss = 0.6151756, step = 30200 (2.297 sec) -2022-05-01 14:13:22,839 - tensorflow - INFO - global_step/sec: 87.37 -2022-05-01 14:13:22,840 - tensorflow - INFO - loss = 0.24321884, step = 30400 (2.289 sec) -2022-05-01 14:13:25,127 - tensorflow - INFO - global_step/sec: 87.4018 -2022-05-01 14:13:25,128 - tensorflow - INFO - loss = 0.48046696, step = 30600 (2.288 sec) -2022-05-01 14:13:27,411 - tensorflow - INFO - global_step/sec: 87.601 -2022-05-01 14:13:27,411 - tensorflow - INFO - loss = 0.47329658, step = 30800 (2.283 sec) -2022-05-01 14:13:29,697 - tensorflow - INFO - global_step/sec: 87.4817 -2022-05-01 14:13:29,697 - tensorflow - INFO - loss = 0.13862364, step = 31000 (2.286 sec) -2022-05-01 14:13:31,995 - 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tensorflow - INFO - loss = 0.16938096, step = 70000 (2.296 sec) -2022-05-01 14:20:58,377 - tensorflow - INFO - global_step/sec: 87.2423 -2022-05-01 14:20:58,377 - tensorflow - INFO - loss = 0.10998814, step = 70200 (2.292 sec) -2022-05-01 14:21:00,656 - tensorflow - INFO - global_step/sec: 87.7521 -2022-05-01 14:21:00,656 - tensorflow - INFO - loss = 0.27979133, step = 70400 (2.279 sec) -2022-05-01 14:21:02,940 - tensorflow - INFO - global_step/sec: 87.5467 -2022-05-01 14:21:02,941 - tensorflow - INFO - loss = 0.7517905, step = 70600 (2.284 sec) -2022-05-01 14:21:05,228 - tensorflow - INFO - global_step/sec: 87.4383 -2022-05-01 14:21:05,228 - tensorflow - INFO - loss = 0.13313249, step = 70800 (2.287 sec) -2022-05-01 14:21:07,513 - tensorflow - INFO - global_step/sec: 87.4928 -2022-05-01 14:21:07,514 - tensorflow - INFO - loss = 0.23826009, step = 71000 (2.286 sec) -2022-05-01 14:21:09,803 - tensorflow - INFO - global_step/sec: 87.3701 -2022-05-01 14:21:09,803 - tensorflow - INFO - loss = 0.46423978, step = 71200 (2.289 sec) -2022-05-01 14:21:12,084 - 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tensorflow - INFO - loss = 1.8678666, step = 85600 (2.292 sec) -2022-05-01 14:23:56,851 - tensorflow - INFO - global_step/sec: 87.2976 -2022-05-01 14:23:56,851 - tensorflow - INFO - loss = 0.19676991, step = 85800 (2.291 sec) -2022-05-01 14:23:59,138 - tensorflow - INFO - global_step/sec: 87.4451 -2022-05-01 14:23:59,138 - tensorflow - INFO - loss = 8.195176, step = 86000 (2.287 sec) -2022-05-01 14:24:01,425 - tensorflow - INFO - global_step/sec: 87.4531 -2022-05-01 14:24:01,425 - tensorflow - INFO - loss = 0.08395229, step = 86200 (2.287 sec) -2022-05-01 14:24:03,717 - tensorflow - INFO - global_step/sec: 87.2589 -2022-05-01 14:24:03,717 - tensorflow - INFO - loss = 5.7395077, step = 86400 (2.292 sec) -2022-05-01 14:24:06,002 - tensorflow - INFO - global_step/sec: 87.505 -2022-05-01 14:24:06,003 - tensorflow - INFO - loss = 0.17323343, step = 86600 (2.286 sec) -2022-05-01 14:24:08,299 - tensorflow - INFO - global_step/sec: 87.0962 -2022-05-01 14:24:08,299 - tensorflow - INFO - loss = 0.32396364, step = 86800 (2.296 sec) -2022-05-01 14:24:10,590 - tensorflow - INFO - global_step/sec: 87.3045 -2022-05-01 14:24:10,590 - tensorflow - INFO - loss = 22.962328, step = 87000 (2.291 sec) -2022-05-01 14:24:12,884 - tensorflow - INFO - global_step/sec: 87.163 -2022-05-01 14:24:12,885 - tensorflow - INFO - loss = 0.19104755, step = 87200 (2.294 sec) -2022-05-01 14:24:15,173 - tensorflow - INFO - global_step/sec: 87.3884 -2022-05-01 14:24:15,173 - tensorflow - INFO - loss = 0.05848871, step = 87400 (2.289 sec) -2022-05-01 14:24:17,461 - tensorflow - INFO - global_step/sec: 87.4179 -2022-05-01 14:24:17,461 - tensorflow - INFO - loss = 0.19390681, step = 87600 (2.288 sec) -2022-05-01 14:24:19,758 - tensorflow - INFO - global_step/sec: 87.0593 -2022-05-01 14:24:19,758 - tensorflow - INFO - loss = 0.18075919, step = 87800 (2.297 sec) -2022-05-01 14:24:22,055 - tensorflow - INFO - global_step/sec: 87.0804 -2022-05-01 14:24:22,055 - tensorflow - INFO - loss = 0.29285538, step = 88000 (2.297 sec) -2022-05-01 14:24:24,338 - tensorflow - INFO - global_step/sec: 87.5764 -2022-05-01 14:24:24,339 - tensorflow - INFO - loss = 0.1336847, step = 88200 (2.284 sec) -2022-05-01 14:24:26,637 - tensorflow - INFO - global_step/sec: 86.9889 -2022-05-01 14:24:26,638 - tensorflow - INFO - loss = 20.40362, step = 88400 (2.299 sec) -2022-05-01 14:24:28,918 - tensorflow - INFO - global_step/sec: 87.7009 -2022-05-01 14:24:28,918 - tensorflow - INFO - loss = 0.1908592, step = 88600 (2.280 sec) -2022-05-01 14:24:31,213 - tensorflow - INFO - global_step/sec: 87.1393 -2022-05-01 14:24:31,214 - tensorflow - INFO - loss = 0.40158635, step = 88800 (2.295 sec) -2022-05-01 14:24:33,511 - tensorflow - INFO - global_step/sec: 87.0228 -2022-05-01 14:24:33,512 - tensorflow - INFO - loss = 0.23553915, step = 89000 (2.298 sec) -2022-05-01 14:24:35,801 - tensorflow - INFO - global_step/sec: 87.3497 -2022-05-01 14:24:35,801 - tensorflow - INFO - loss = 1.1698062, step = 89200 (2.290 sec) -2022-05-01 14:24:38,086 - tensorflow - INFO - global_step/sec: 87.5318 -2022-05-01 14:24:38,086 - tensorflow - INFO - loss = 0.11512068, step = 89400 (2.285 sec) -2022-05-01 14:24:40,375 - tensorflow - INFO - global_step/sec: 87.3655 -2022-05-01 14:24:40,376 - tensorflow - INFO - loss = 0.17605992, step = 89600 (2.289 sec) -2022-05-01 14:24:42,661 - tensorflow - INFO - global_step/sec: 87.5102 -2022-05-01 14:24:42,661 - tensorflow - INFO - loss = 0.22128661, step = 89800 (2.285 sec) -2022-05-01 14:24:44,953 - tensorflow - INFO - global_step/sec: 87.2428 -2022-05-01 14:24:44,953 - tensorflow - INFO - loss = 0.75772536, step = 90000 (2.292 sec) -2022-05-01 14:24:47,238 - tensorflow - INFO - global_step/sec: 87.5261 -2022-05-01 14:24:47,239 - tensorflow - INFO - loss = 1.981938, step = 90200 (2.285 sec) -2022-05-01 14:24:49,523 - tensorflow - INFO - global_step/sec: 87.539 -2022-05-01 14:24:49,523 - tensorflow - INFO - loss = 3.6954958, step = 90400 (2.285 sec) -2022-05-01 14:24:51,808 - tensorflow - INFO - global_step/sec: 87.5312 -2022-05-01 14:24:51,808 - tensorflow - INFO - loss = 23.398697, step = 90600 (2.285 sec) -2022-05-01 14:24:54,089 - tensorflow - INFO - global_step/sec: 87.6596 -2022-05-01 14:24:54,090 - tensorflow - INFO - loss = 0.37941447, step = 90800 (2.282 sec) -2022-05-01 14:24:56,366 - tensorflow - INFO - global_step/sec: 87.8273 -2022-05-01 14:24:56,367 - tensorflow - INFO - loss = 0.13895091, step = 91000 (2.277 sec) -2022-05-01 14:24:58,624 - tensorflow - INFO - global_step/sec: 88.588 -2022-05-01 14:24:58,624 - tensorflow - INFO - loss = 13.276718, step = 91200 (2.258 sec) -2022-05-01 14:25:00,904 - tensorflow - INFO - global_step/sec: 87.7149 -2022-05-01 14:25:00,905 - tensorflow - INFO - loss = 0.19925176, step = 91400 (2.280 sec) -2022-05-01 14:25:03,189 - tensorflow - INFO - global_step/sec: 87.5326 -2022-05-01 14:25:03,189 - tensorflow - INFO - loss = 0.14478576, step = 91600 (2.285 sec) -2022-05-01 14:25:05,479 - tensorflow - INFO - global_step/sec: 87.3428 -2022-05-01 14:25:05,479 - tensorflow - INFO - loss = 0.113933235, step = 91800 (2.290 sec) -2022-05-01 14:25:07,769 - tensorflow - INFO - global_step/sec: 87.3376 -2022-05-01 14:25:07,769 - tensorflow - INFO - loss = 0.7221588, step = 92000 (2.290 sec) -2022-05-01 14:25:10,059 - tensorflow - INFO - global_step/sec: 87.3229 -2022-05-01 14:25:10,060 - tensorflow - INFO - loss = 2.7999012, step = 92200 (2.290 sec) -2022-05-01 14:25:12,352 - tensorflow - INFO - global_step/sec: 87.2151 -2022-05-01 14:25:12,353 - tensorflow - INFO - loss = 0.29915583, step = 92400 (2.293 sec) -2022-05-01 14:25:14,645 - tensorflow - INFO - global_step/sec: 87.2291 -2022-05-01 14:25:14,646 - tensorflow - INFO - loss = 0.057438828, step = 92600 (2.293 sec) -2022-05-01 14:25:16,938 - tensorflow - INFO - global_step/sec: 87.2267 -2022-05-01 14:25:16,939 - tensorflow - INFO - loss = 0.13389596, step = 92800 (2.293 sec) -2022-05-01 14:25:19,229 - tensorflow - INFO - global_step/sec: 87.2822 -2022-05-01 14:25:19,230 - tensorflow - INFO - loss = 0.3385266, step = 93000 (2.291 sec) -2022-05-01 14:25:21,517 - tensorflow - INFO - global_step/sec: 87.4451 -2022-05-01 14:25:21,517 - tensorflow - INFO - loss = 0.16020179, step = 93200 (2.287 sec) -2022-05-01 14:25:23,806 - tensorflow - INFO - global_step/sec: 87.3654 -2022-05-01 14:25:23,806 - tensorflow - INFO - loss = 0.1617248, step = 93400 (2.289 sec) -2022-05-01 14:25:26,097 - tensorflow - INFO - global_step/sec: 87.3069 -2022-05-01 14:25:26,097 - tensorflow - INFO - loss = 0.18052283, step = 93600 (2.291 sec) -2022-05-01 14:25:28,392 - tensorflow - INFO - global_step/sec: 87.1383 -2022-05-01 14:25:28,392 - tensorflow - INFO - loss = 0.2854377, step = 93800 (2.295 sec) -2022-05-01 14:25:30,689 - tensorflow - INFO - global_step/sec: 87.0448 -2022-05-01 14:25:30,690 - tensorflow - INFO - loss = 0.65452284, step = 94000 (2.298 sec) -2022-05-01 14:25:32,974 - tensorflow - INFO - global_step/sec: 87.5423 -2022-05-01 14:25:32,975 - tensorflow - INFO - loss = 0.48012242, step = 94200 (2.285 sec) -2022-05-01 14:25:35,265 - tensorflow - INFO - global_step/sec: 87.2856 -2022-05-01 14:25:35,266 - tensorflow - INFO - loss = 0.17184481, step = 94400 (2.291 sec) -2022-05-01 14:25:37,547 - tensorflow - INFO - global_step/sec: 87.6663 -2022-05-01 14:25:37,547 - tensorflow - INFO - loss = 0.31042734, step = 94600 (2.281 sec) -2022-05-01 14:25:39,832 - tensorflow - INFO - global_step/sec: 87.5264 -2022-05-01 14:25:39,832 - tensorflow - INFO - loss = 0.15828839, step = 94800 (2.285 sec) -2022-05-01 14:25:42,126 - tensorflow - INFO - global_step/sec: 87.182 -2022-05-01 14:25:42,126 - tensorflow - INFO - loss = 0.35003784, step = 95000 (2.294 sec) -2022-05-01 14:25:44,421 - tensorflow - INFO - global_step/sec: 87.1344 -2022-05-01 14:25:44,422 - tensorflow - INFO - loss = 0.47904184, step = 95200 (2.295 sec) -2022-05-01 14:25:46,720 - tensorflow - INFO - global_step/sec: 86.983 -2022-05-01 14:25:46,721 - tensorflow - INFO - loss = 0.21531895, step = 95400 (2.299 sec) -2022-05-01 14:25:49,022 - tensorflow - INFO - global_step/sec: 86.885 -2022-05-01 14:25:49,023 - tensorflow - INFO - loss = 0.15111923, step = 95600 (2.302 sec) -2022-05-01 14:25:51,318 - tensorflow - INFO - global_step/sec: 87.116 -2022-05-01 14:25:51,319 - tensorflow - INFO - loss = 0.9417725, step = 95800 (2.296 sec) -2022-05-01 14:25:53,611 - tensorflow - INFO - global_step/sec: 87.2297 -2022-05-01 14:25:53,611 - tensorflow - INFO - loss = 0.08343916, step = 96000 (2.293 sec) -2022-05-01 14:25:55,906 - tensorflow - INFO - global_step/sec: 87.1625 -2022-05-01 14:25:55,906 - tensorflow - INFO - loss = 0.11967882, step = 96200 (2.295 sec) -2022-05-01 14:25:58,207 - tensorflow - INFO - global_step/sec: 86.9043 -2022-05-01 14:25:58,207 - tensorflow - INFO - loss = 0.2613772, step = 96400 (2.301 sec) -2022-05-01 14:26:00,505 - tensorflow - INFO - global_step/sec: 87.0403 -2022-05-01 14:26:00,505 - tensorflow - INFO - loss = 0.3371205, step = 96600 (2.298 sec) -2022-05-01 14:26:02,802 - tensorflow - INFO - global_step/sec: 87.0751 -2022-05-01 14:26:02,802 - tensorflow - INFO - loss = 0.28187898, step = 96800 (2.297 sec) -2022-05-01 14:26:05,100 - tensorflow - INFO - global_step/sec: 87.0297 -2022-05-01 14:26:05,100 - tensorflow - INFO - loss = 0.42920962, step = 97000 (2.298 sec) -2022-05-01 14:26:07,396 - tensorflow - INFO - global_step/sec: 87.1099 -2022-05-01 14:26:07,396 - tensorflow - INFO - loss = 0.056796335, step = 97200 (2.296 sec) -2022-05-01 14:26:09,684 - tensorflow - INFO - global_step/sec: 87.3879 -2022-05-01 14:26:09,685 - tensorflow - INFO - loss = 0.09578763, step = 97400 (2.289 sec) -2022-05-01 14:26:11,974 - tensorflow - INFO - global_step/sec: 87.3522 -2022-05-01 14:26:11,974 - tensorflow - INFO - loss = 2.4198983, step = 97600 (2.290 sec) -2022-05-01 14:26:14,263 - tensorflow - INFO - global_step/sec: 87.3526 -2022-05-01 14:26:14,264 - tensorflow - INFO - loss = 0.13744435, step = 97800 (2.290 sec) -2022-05-01 14:26:16,549 - tensorflow - INFO - global_step/sec: 87.4993 -2022-05-01 14:26:16,550 - tensorflow - INFO - loss = 0.20825619, step = 98000 (2.286 sec) -2022-05-01 14:26:18,837 - tensorflow - INFO - global_step/sec: 87.4067 -2022-05-01 14:26:18,838 - tensorflow - INFO - loss = 0.19770706, step = 98200 (2.288 sec) -2022-05-01 14:26:21,124 - tensorflow - INFO - global_step/sec: 87.4774 -2022-05-01 14:26:21,124 - tensorflow - INFO - loss = 0.31616545, step = 98400 (2.286 sec) -2022-05-01 14:26:23,411 - tensorflow - INFO - global_step/sec: 87.4225 -2022-05-01 14:26:23,412 - tensorflow - INFO - loss = 24.242685, step = 98600 (2.288 sec) -2022-05-01 14:26:25,696 - tensorflow - INFO - global_step/sec: 87.5488 -2022-05-01 14:26:25,696 - tensorflow - INFO - loss = 0.5029043, step = 98800 (2.284 sec) -2022-05-01 14:26:27,967 - tensorflow - INFO - global_step/sec: 88.0532 -2022-05-01 14:26:27,968 - tensorflow - INFO - loss = 0.74712604, step = 99000 (2.271 sec) -2022-05-01 14:26:30,226 - tensorflow - INFO - global_step/sec: 88.5269 -2022-05-01 14:26:30,227 - tensorflow - INFO - loss = 0.21298502, step = 99200 (2.259 sec) -2022-05-01 14:26:32,511 - tensorflow - INFO - global_step/sec: 87.5423 -2022-05-01 14:26:32,511 - tensorflow - INFO - loss = 0.21085733, step = 99400 (2.285 sec) -2022-05-01 14:26:34,811 - tensorflow - INFO - global_step/sec: 86.9667 -2022-05-01 14:26:34,811 - tensorflow - INFO - loss = 0.32836705, step = 99600 (2.300 sec) -2022-05-01 14:26:37,093 - tensorflow - INFO - global_step/sec: 87.6294 -2022-05-01 14:26:37,093 - tensorflow - INFO - loss = 0.24103443, step = 99800 (2.282 sec) -2022-05-01 14:26:39,372 - tensorflow - INFO - Saving checkpoints for 100000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. -2022-05-01 14:26:39,390 - tensorflow - INFO - model.ckpt-100000 is not in all_model_checkpoint_paths. Manually adding it. -2022-05-01 14:26:40,405 - tensorflow - INFO - Calling model_fn. -2022-05-01 14:26:40,530 - tensorflow - WARNING - From /root/special_orthogonalization2/main_point_cloud_gpu.py:204: The name tf.metrics.mean is deprecated. Please use tf.compat.v1.metrics.mean instead. - -2022-05-01 14:26:40,541 - tensorflow - INFO - Done calling model_fn. -2022-05-01 14:26:40,556 - tensorflow - INFO - Starting evaluation at 2022-05-01T14:26:40Z -2022-05-01 14:26:40,620 - tensorflow - INFO - Graph was finalized. -2022-05-01 14:26:40,623 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-100000 -2022-05-01 14:26:40,936 - tensorflow - INFO - Running local_init_op. -2022-05-01 14:26:41,050 - tensorflow - INFO - Done running local_init_op. -2022-05-01 14:26:56,055 - tensorflow - INFO - Evaluation [3990/39900] -2022-05-01 14:27:10,591 - tensorflow - INFO - Evaluation [7980/39900] -2022-05-01 14:27:24,889 - tensorflow - INFO - Evaluation [11970/39900] -2022-05-01 14:27:39,136 - tensorflow - INFO - Evaluation [15960/39900] -2022-05-01 14:27:53,364 - tensorflow - INFO - Evaluation [19950/39900] -2022-05-01 14:28:07,812 - tensorflow - INFO - Evaluation [23940/39900] -2022-05-01 14:28:22,113 - tensorflow - INFO - Evaluation [27930/39900] -2022-05-01 14:28:36,293 - tensorflow - INFO - Evaluation [31920/39900] -2022-05-01 14:28:50,471 - tensorflow - INFO - Evaluation [35910/39900] -2022-05-01 14:29:04,661 - tensorflow - INFO - Evaluation [39900/39900] -2022-05-01 14:29:04,800 - tensorflow - INFO - Finished evaluation at 2022-05-01-14:29:04 -2022-05-01 14:29:04,800 - tensorflow - INFO - Saving dict for global step 100000: global_step = 100000, loss = 0.18664514, mean_degree_err = 15.5360365 -2022-05-01 14:29:04,973 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 100000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-100000 -2022-05-01 14:29:04,987 - tensorflow - INFO - global_step/sec: 1.35232 -2022-05-01 14:29:04,988 - tensorflow - INFO - loss = 0.09665474, step = 100000 (147.894 sec) -2022-05-01 14:29:07,296 - tensorflow - INFO - global_step/sec: 86.6181 -2022-05-01 14:29:07,296 - tensorflow - INFO - loss = 0.11935344, step = 100200 (2.309 sec) -2022-05-01 14:29:09,600 - tensorflow - INFO - global_step/sec: 86.7917 -2022-05-01 14:29:09,601 - tensorflow - INFO - loss = 0.23293598, step = 100400 (2.304 sec) -2022-05-01 14:29:11,905 - tensorflow - INFO - global_step/sec: 86.7663 -2022-05-01 14:29:11,906 - tensorflow - INFO - loss = 13.853834, step = 100600 (2.305 sec) -2022-05-01 14:29:14,200 - tensorflow - INFO - global_step/sec: 87.1497 -2022-05-01 14:29:14,200 - tensorflow - INFO - loss = 0.28267854, step = 100800 (2.295 sec) -2022-05-01 14:29:16,493 - tensorflow - INFO - global_step/sec: 87.2407 -2022-05-01 14:29:16,493 - tensorflow - INFO - loss = 0.13818274, step = 101000 (2.292 sec) -2022-05-01 14:29:18,789 - tensorflow - INFO - global_step/sec: 87.1046 -2022-05-01 14:29:18,789 - tensorflow - INFO - loss = 0.191855, step = 101200 (2.296 sec) -2022-05-01 14:29:21,092 - tensorflow - INFO - global_step/sec: 86.8156 -2022-05-01 14:29:21,093 - tensorflow - INFO - loss = 0.09758256, step = 101400 (2.304 sec) -2022-05-01 14:29:23,394 - tensorflow - INFO - global_step/sec: 86.8777 -2022-05-01 14:29:23,395 - tensorflow - INFO - loss = 0.23463662, step = 101600 (2.302 sec) -2022-05-01 14:29:25,696 - tensorflow - INFO - global_step/sec: 86.9051 -2022-05-01 14:29:25,696 - tensorflow - INFO - loss = 0.09981724, step = 101800 (2.301 sec) -2022-05-01 14:29:27,995 - tensorflow - INFO - global_step/sec: 86.9773 -2022-05-01 14:29:27,996 - tensorflow - INFO - loss = 0.3753961, step = 102000 (2.299 sec) -2022-05-01 14:29:30,294 - tensorflow - INFO - global_step/sec: 87.017 -2022-05-01 14:29:30,294 - tensorflow - INFO - loss = 0.16868381, step = 102200 (2.298 sec) -2022-05-01 14:29:32,592 - tensorflow - INFO - global_step/sec: 87.0017 -2022-05-01 14:29:32,593 - tensorflow - INFO - loss = 8.778584, step = 102400 (2.299 sec) -2022-05-01 14:29:34,886 - tensorflow - INFO - global_step/sec: 87.2183 -2022-05-01 14:29:34,886 - tensorflow - INFO - loss = 0.090705186, step = 102600 (2.293 sec) -2022-05-01 14:29:37,177 - tensorflow - INFO - global_step/sec: 87.2853 -2022-05-01 14:29:37,177 - tensorflow - INFO - loss = 0.0820912, step = 102800 (2.291 sec) -2022-05-01 14:29:39,464 - tensorflow - INFO - global_step/sec: 87.4505 -2022-05-01 14:29:39,464 - tensorflow - INFO - loss = 0.6370168, step = 103000 (2.287 sec) -2022-05-01 14:29:41,745 - tensorflow - INFO - global_step/sec: 87.6902 -2022-05-01 14:29:41,745 - tensorflow - INFO - loss = 0.043927055, step = 103200 (2.281 sec) -2022-05-01 14:29:44,025 - tensorflow - INFO - global_step/sec: 87.704 -2022-05-01 14:29:44,026 - tensorflow - INFO - loss = 0.7123804, step = 103400 (2.280 sec) -2022-05-01 14:29:46,318 - tensorflow - INFO - global_step/sec: 87.2407 -2022-05-01 14:29:46,318 - tensorflow - INFO - loss = 0.06848099, step = 103600 (2.292 sec) -2022-05-01 14:29:48,611 - tensorflow - INFO - global_step/sec: 87.2065 -2022-05-01 14:29:48,611 - tensorflow - INFO - loss = 0.16801745, step = 103800 (2.293 sec) -2022-05-01 14:29:50,907 - tensorflow - INFO - global_step/sec: 87.0944 -2022-05-01 14:29:50,908 - tensorflow - INFO - loss = 0.18619172, step = 104000 (2.296 sec) -2022-05-01 14:29:53,209 - tensorflow - INFO - global_step/sec: 86.8939 -2022-05-01 14:29:53,209 - tensorflow - INFO - loss = 4.6792035, step = 104200 (2.302 sec) -2022-05-01 14:29:55,497 - tensorflow - INFO - global_step/sec: 87.3975 -2022-05-01 14:29:55,498 - tensorflow - INFO - loss = 5.7233157, step = 104400 (2.288 sec) -2022-05-01 14:29:57,797 - tensorflow - INFO - global_step/sec: 86.9713 -2022-05-01 14:29:57,797 - tensorflow - INFO - loss = 0.21849597, step = 104600 (2.300 sec) -2022-05-01 14:30:00,091 - tensorflow - INFO - global_step/sec: 87.165 -2022-05-01 14:30:00,092 - tensorflow - INFO - loss = 4.5911975, step = 104800 (2.294 sec) -2022-05-01 14:30:02,394 - tensorflow - INFO - global_step/sec: 86.8542 -2022-05-01 14:30:02,395 - tensorflow - INFO - loss = 10.481457, step = 105000 (2.303 sec) -2022-05-01 14:30:04,688 - tensorflow - INFO - global_step/sec: 87.1941 -2022-05-01 14:30:04,688 - tensorflow - INFO - loss = 0.26965386, step = 105200 (2.294 sec) -2022-05-01 14:30:07,005 - tensorflow - INFO - global_step/sec: 86.3277 -2022-05-01 14:30:07,005 - tensorflow - INFO - loss = 0.17812374, step = 105400 (2.317 sec) -2022-05-01 14:30:09,328 - tensorflow - INFO - global_step/sec: 86.0889 -2022-05-01 14:30:09,328 - tensorflow - INFO - loss = 0.38415286, step = 105600 (2.323 sec) -2022-05-01 14:30:11,632 - tensorflow - INFO - global_step/sec: 86.8154 -2022-05-01 14:30:11,632 - tensorflow - INFO - loss = 0.18962869, step = 105800 (2.304 sec) -2022-05-01 14:30:13,928 - tensorflow - INFO - global_step/sec: 87.0754 -2022-05-01 14:30:13,929 - tensorflow - INFO - loss = 0.104067475, step = 106000 (2.297 sec) -2022-05-01 14:30:16,219 - tensorflow - INFO - global_step/sec: 87.3113 -2022-05-01 14:30:16,220 - tensorflow - INFO - loss = 0.09176713, step = 106200 (2.291 sec) -2022-05-01 14:30:18,518 - tensorflow - INFO - global_step/sec: 86.9935 -2022-05-01 14:30:18,519 - tensorflow - INFO - loss = 0.08783853, step = 106400 (2.299 sec) -2022-05-01 14:30:20,805 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.09977777, step = 107800 (2.292 sec) -2022-05-01 14:30:36,850 - tensorflow - INFO - global_step/sec: 87.1778 -2022-05-01 14:30:36,851 - tensorflow - INFO - loss = 0.26099685, step = 108000 (2.294 sec) -2022-05-01 14:30:39,144 - tensorflow - INFO - global_step/sec: 87.1819 -2022-05-01 14:30:39,145 - tensorflow - INFO - loss = 0.30809918, step = 108200 (2.294 sec) -2022-05-01 14:30:41,440 - tensorflow - INFO - global_step/sec: 87.1208 -2022-05-01 14:30:41,440 - tensorflow - INFO - loss = 0.22293222, step = 108400 (2.296 sec) -2022-05-01 14:30:43,730 - tensorflow - INFO - global_step/sec: 87.3257 -2022-05-01 14:30:43,731 - tensorflow - INFO - loss = 0.04886747, step = 108600 (2.290 sec) -2022-05-01 14:30:46,017 - tensorflow - INFO - global_step/sec: 87.4719 -2022-05-01 14:30:46,017 - tensorflow - INFO - loss = 0.23293927, step = 108800 (2.286 sec) -2022-05-01 14:30:48,304 - tensorflow - INFO - global_step/sec: 87.4261 -2022-05-01 14:30:48,305 - tensorflow - INFO - loss = 0.3128304, step = 109000 (2.288 sec) -2022-05-01 14:30:50,599 - tensorflow - INFO - global_step/sec: 87.162 -2022-05-01 14:30:50,599 - tensorflow - INFO - loss = 0.034860868, step = 109200 (2.295 sec) -2022-05-01 14:30:52,890 - tensorflow - INFO - global_step/sec: 87.2961 -2022-05-01 14:30:52,890 - tensorflow - INFO - loss = 0.2239242, step = 109400 (2.291 sec) -2022-05-01 14:30:55,186 - tensorflow - INFO - global_step/sec: 87.1116 -2022-05-01 14:30:55,186 - tensorflow - INFO - loss = 0.06228984, step = 109600 (2.296 sec) -2022-05-01 14:30:57,474 - tensorflow - INFO - global_step/sec: 87.4197 -2022-05-01 14:30:57,474 - tensorflow - INFO - loss = 0.19710249, step = 109800 (2.288 sec) -2022-05-01 14:30:59,766 - tensorflow - INFO - global_step/sec: 87.2504 -2022-05-01 14:30:59,766 - tensorflow - INFO - loss = 0.1518274, step = 110000 (2.292 sec) -2022-05-01 14:31:02,062 - tensorflow - INFO - global_step/sec: 87.0991 -2022-05-01 14:31:02,063 - tensorflow - INFO - loss = 0.13968316, step = 110200 (2.296 sec) -2022-05-01 14:31:04,365 - tensorflow - INFO - global_step/sec: 86.8329 -2022-05-01 14:31:04,366 - tensorflow - INFO - loss = 0.40964472, step = 110400 (2.303 sec) -2022-05-01 14:31:06,664 - tensorflow - INFO - global_step/sec: 87.0225 -2022-05-01 14:31:06,664 - tensorflow - INFO - loss = 1.5274282, step = 110600 (2.298 sec) -2022-05-01 14:31:08,955 - tensorflow - INFO - global_step/sec: 87.2741 -2022-05-01 14:31:08,956 - tensorflow - INFO - loss = 0.053806704, step = 110800 (2.292 sec) -2022-05-01 14:31:11,241 - tensorflow - INFO - global_step/sec: 87.4924 -2022-05-01 14:31:11,242 - tensorflow - INFO - loss = 0.24567077, step = 111000 (2.286 sec) -2022-05-01 14:31:13,523 - tensorflow - INFO - global_step/sec: 87.6389 -2022-05-01 14:31:13,524 - tensorflow - INFO - loss = 0.09340665, step = 111200 (2.282 sec) -2022-05-01 14:31:15,793 - tensorflow - INFO - global_step/sec: 88.1058 -2022-05-01 14:31:15,794 - tensorflow - INFO - loss = 0.12997474, step = 111400 (2.270 sec) -2022-05-01 14:31:18,048 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.21400702, step = 112800 (2.295 sec) -2022-05-01 14:31:34,125 - tensorflow - INFO - global_step/sec: 86.956 -2022-05-01 14:31:34,125 - tensorflow - INFO - loss = 0.80321956, step = 113000 (2.300 sec) -2022-05-01 14:31:36,417 - tensorflow - INFO - global_step/sec: 87.2408 -2022-05-01 14:31:36,418 - tensorflow - INFO - loss = 0.042050008, step = 113200 (2.292 sec) -2022-05-01 14:31:38,712 - tensorflow - INFO - global_step/sec: 87.1417 -2022-05-01 14:31:38,713 - tensorflow - INFO - loss = 0.083693594, step = 113400 (2.295 sec) -2022-05-01 14:31:41,001 - tensorflow - INFO - global_step/sec: 87.3917 -2022-05-01 14:31:41,001 - tensorflow - INFO - loss = 0.20052454, step = 113600 (2.289 sec) -2022-05-01 14:31:43,290 - tensorflow - INFO - global_step/sec: 87.3738 -2022-05-01 14:31:43,290 - tensorflow - INFO - loss = 0.17425779, step = 113800 (2.289 sec) -2022-05-01 14:31:45,585 - tensorflow - INFO - global_step/sec: 87.1585 -2022-05-01 14:31:45,585 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 87.701 -2022-05-01 14:32:01,606 - tensorflow - INFO - loss = 0.1623515, step = 115400 (2.281 sec) -2022-05-01 14:32:03,895 - tensorflow - INFO - global_step/sec: 87.3523 -2022-05-01 14:32:03,896 - tensorflow - INFO - loss = 0.05452565, step = 115600 (2.290 sec) -2022-05-01 14:32:06,181 - tensorflow - INFO - global_step/sec: 87.512 -2022-05-01 14:32:06,181 - tensorflow - INFO - loss = 0.109019935, step = 115800 (2.285 sec) -2022-05-01 14:32:08,474 - tensorflow - INFO - global_step/sec: 87.1993 -2022-05-01 14:32:08,475 - tensorflow - INFO - loss = 0.2902754, step = 116000 (2.294 sec) -2022-05-01 14:32:10,763 - tensorflow - INFO - global_step/sec: 87.4093 -2022-05-01 14:32:10,763 - tensorflow - INFO - loss = 0.19051288, step = 116200 (2.288 sec) -2022-05-01 14:32:13,051 - tensorflow - INFO - global_step/sec: 87.4118 -2022-05-01 14:32:13,051 - tensorflow - INFO - loss = 0.09656765, step = 116400 (2.288 sec) -2022-05-01 14:32:15,346 - tensorflow - INFO - global_step/sec: 87.1497 -2022-05-01 14:32:15,346 - tensorflow - INFO - loss = 0.615744, step = 116600 (2.295 sec) -2022-05-01 14:32:17,632 - tensorflow - INFO - global_step/sec: 87.4631 -2022-05-01 14:32:17,633 - tensorflow - INFO - loss = 0.22290188, step = 116800 (2.287 sec) -2022-05-01 14:32:19,921 - tensorflow - INFO - global_step/sec: 87.3943 -2022-05-01 14:32:19,921 - tensorflow - INFO - loss = 0.41463685, step = 117000 (2.288 sec) -2022-05-01 14:32:22,203 - tensorflow - INFO - global_step/sec: 87.615 -2022-05-01 14:32:22,204 - tensorflow - INFO - loss = 0.44903523, step = 117200 (2.283 sec) -2022-05-01 14:32:24,496 - tensorflow - INFO - global_step/sec: 87.2181 -2022-05-01 14:32:24,497 - tensorflow - INFO - loss = 0.12465903, step = 117400 (2.293 sec) -2022-05-01 14:32:26,786 - tensorflow - INFO - global_step/sec: 87.3363 -2022-05-01 14:32:26,787 - tensorflow - INFO - loss = 0.07329932, step = 117600 (2.290 sec) -2022-05-01 14:32:29,081 - tensorflow - INFO - global_step/sec: 87.1561 -2022-05-01 14:32:29,082 - tensorflow - INFO - loss = 0.27690414, step = 117800 (2.295 sec) -2022-05-01 14:32:31,377 - tensorflow - INFO - global_step/sec: 87.0971 -2022-05-01 14:32:31,378 - tensorflow - INFO - loss = 0.119977236, step = 118000 (2.296 sec) -2022-05-01 14:32:33,669 - tensorflow - INFO - global_step/sec: 87.288 -2022-05-01 14:32:33,669 - tensorflow - INFO - loss = 0.16365752, step = 118200 (2.291 sec) -2022-05-01 14:32:35,962 - tensorflow - INFO - global_step/sec: 87.2152 -2022-05-01 14:32:35,962 - tensorflow - INFO - loss = 0.052812696, step = 118400 (2.293 sec) -2022-05-01 14:32:38,245 - tensorflow - INFO - global_step/sec: 87.601 -2022-05-01 14:32:38,245 - tensorflow - INFO - loss = 0.27458555, step = 118600 (2.283 sec) -2022-05-01 14:32:40,540 - tensorflow - INFO - global_step/sec: 87.153 -2022-05-01 14:32:40,540 - tensorflow - INFO - loss = 0.25292706, step = 118800 (2.295 sec) -2022-05-01 14:32:42,840 - tensorflow - INFO - global_step/sec: 86.9475 -2022-05-01 14:32:42,840 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.06685576, step = 161600 (2.288 sec) -2022-05-01 14:40:53,031 - tensorflow - INFO - global_step/sec: 87.3679 -2022-05-01 14:40:53,032 - tensorflow - INFO - loss = 0.5176064, step = 161800 (2.289 sec) -2022-05-01 14:40:55,328 - tensorflow - INFO - global_step/sec: 87.0956 -2022-05-01 14:40:55,328 - tensorflow - INFO - loss = 0.058833763, step = 162000 (2.296 sec) -2022-05-01 14:40:57,623 - tensorflow - INFO - global_step/sec: 87.1308 -2022-05-01 14:40:57,623 - tensorflow - INFO - loss = 0.026311778, step = 162200 (2.295 sec) -2022-05-01 14:40:59,913 - tensorflow - INFO - global_step/sec: 87.3186 -2022-05-01 14:40:59,914 - tensorflow - INFO - loss = 0.10072592, step = 162400 (2.291 sec) -2022-05-01 14:41:02,212 - tensorflow - INFO - global_step/sec: 87.0248 -2022-05-01 14:41:02,212 - tensorflow - INFO - loss = 0.19837397, step = 162600 (2.298 sec) -2022-05-01 14:41:04,509 - tensorflow - INFO - global_step/sec: 87.0624 -2022-05-01 14:41:04,509 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 87.0488 -2022-05-01 14:41:20,544 - tensorflow - INFO - loss = 3.6271083, step = 164200 (2.298 sec) -2022-05-01 14:41:22,842 - tensorflow - INFO - global_step/sec: 87.018 -2022-05-01 14:41:22,843 - tensorflow - INFO - loss = 0.5529985, step = 164400 (2.298 sec) -2022-05-01 14:41:25,140 - tensorflow - INFO - global_step/sec: 87.0349 -2022-05-01 14:41:25,141 - tensorflow - INFO - loss = 0.05640729, step = 164600 (2.298 sec) -2022-05-01 14:41:27,438 - tensorflow - INFO - global_step/sec: 87.0245 -2022-05-01 14:41:27,439 - tensorflow - INFO - loss = 0.093827695, step = 164800 (2.298 sec) -2022-05-01 14:41:29,733 - tensorflow - INFO - global_step/sec: 87.1755 -2022-05-01 14:41:29,733 - tensorflow - INFO - loss = 0.06989246, step = 165000 (2.294 sec) -2022-05-01 14:41:32,027 - tensorflow - INFO - global_step/sec: 87.1554 -2022-05-01 14:41:32,028 - tensorflow - INFO - loss = 0.23597883, step = 165200 (2.295 sec) -2022-05-01 14:41:34,329 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.36415687, step = 166600 (2.293 sec) -2022-05-01 14:41:50,355 - tensorflow - INFO - global_step/sec: 87.6206 -2022-05-01 14:41:50,356 - tensorflow - INFO - loss = 0.06011443, step = 166800 (2.283 sec) -2022-05-01 14:41:52,651 - tensorflow - INFO - global_step/sec: 87.1117 -2022-05-01 14:41:52,652 - tensorflow - INFO - loss = 1.5290744, step = 167000 (2.296 sec) -2022-05-01 14:41:54,935 - tensorflow - INFO - global_step/sec: 87.5885 -2022-05-01 14:41:54,935 - tensorflow - INFO - loss = 0.13554856, step = 167200 (2.283 sec) -2022-05-01 14:41:57,226 - tensorflow - INFO - global_step/sec: 87.2919 -2022-05-01 14:41:57,226 - tensorflow - INFO - loss = 1.9560692, step = 167400 (2.291 sec) -2022-05-01 14:41:59,518 - tensorflow - INFO - global_step/sec: 87.2707 -2022-05-01 14:41:59,518 - tensorflow - INFO - loss = 11.647721, step = 167600 (2.292 sec) -2022-05-01 14:42:01,792 - tensorflow - INFO - global_step/sec: 87.9364 -2022-05-01 14:42:01,792 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 87.3605 -2022-05-01 14:42:17,747 - tensorflow - INFO - loss = 0.04703176, step = 169200 (2.289 sec) -2022-05-01 14:42:20,036 - tensorflow - INFO - global_step/sec: 87.3689 -2022-05-01 14:42:20,036 - tensorflow - INFO - loss = 0.079767734, step = 169400 (2.289 sec) -2022-05-01 14:42:22,320 - tensorflow - INFO - global_step/sec: 87.5432 -2022-05-01 14:42:22,321 - tensorflow - INFO - loss = 0.19493052, step = 169600 (2.285 sec) -2022-05-01 14:42:24,620 - tensorflow - INFO - global_step/sec: 86.9627 -2022-05-01 14:42:24,620 - tensorflow - INFO - loss = 0.17314672, step = 169800 (2.300 sec) -2022-05-01 14:42:26,930 - tensorflow - INFO - global_step/sec: 86.573 -2022-05-01 14:42:26,931 - tensorflow - INFO - loss = 0.19313194, step = 170000 (2.310 sec) -2022-05-01 14:42:29,235 - tensorflow - INFO - global_step/sec: 86.7809 -2022-05-01 14:42:29,235 - tensorflow - INFO - loss = 1.9932071, step = 170200 (2.305 sec) -2022-05-01 14:42:31,547 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.26205248, step = 171600 (2.307 sec) -2022-05-01 14:42:47,663 - tensorflow - INFO - global_step/sec: 87.0458 -2022-05-01 14:42:47,664 - tensorflow - INFO - loss = 0.1026774, step = 171800 (2.298 sec) -2022-05-01 14:42:49,974 - tensorflow - INFO - global_step/sec: 86.5564 -2022-05-01 14:42:49,974 - tensorflow - INFO - loss = 0.16498551, step = 172000 (2.311 sec) -2022-05-01 14:42:52,267 - tensorflow - INFO - global_step/sec: 87.2269 -2022-05-01 14:42:52,267 - tensorflow - INFO - loss = 1.1943704, step = 172200 (2.293 sec) -2022-05-01 14:42:54,563 - tensorflow - INFO - global_step/sec: 87.0851 -2022-05-01 14:42:54,564 - tensorflow - INFO - loss = 0.17324907, step = 172400 (2.297 sec) -2022-05-01 14:42:56,850 - tensorflow - INFO - global_step/sec: 87.4675 -2022-05-01 14:42:56,850 - tensorflow - INFO - loss = 0.116132826, step = 172600 (2.287 sec) -2022-05-01 14:42:59,136 - tensorflow - INFO - global_step/sec: 87.4654 -2022-05-01 14:42:59,137 - tensorflow - INFO - 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loss = 0.3858026, step = 177800 (2.309 sec) -2022-05-01 14:43:58,840 - tensorflow - INFO - global_step/sec: 86.9967 -2022-05-01 14:43:58,840 - tensorflow - INFO - loss = 9.982061, step = 178000 (2.299 sec) -2022-05-01 14:44:01,141 - tensorflow - INFO - global_step/sec: 86.9124 -2022-05-01 14:44:01,141 - tensorflow - INFO - loss = 0.08773741, step = 178200 (2.301 sec) -2022-05-01 14:44:03,445 - tensorflow - INFO - global_step/sec: 86.809 -2022-05-01 14:44:03,445 - tensorflow - INFO - loss = 0.05614555, step = 178400 (2.304 sec) -2022-05-01 14:44:05,747 - tensorflow - INFO - global_step/sec: 86.8789 -2022-05-01 14:44:05,747 - tensorflow - INFO - loss = 0.10069659, step = 178600 (2.302 sec) -2022-05-01 14:44:08,052 - tensorflow - INFO - global_step/sec: 86.7543 -2022-05-01 14:44:08,052 - tensorflow - INFO - loss = 0.051411755, step = 178800 (2.305 sec) -2022-05-01 14:44:10,359 - tensorflow - INFO - global_step/sec: 86.6948 -2022-05-01 14:44:10,359 - tensorflow - INFO - loss = 0.34316087, step = 179000 (2.307 sec) -2022-05-01 14:44:12,650 - tensorflow - INFO - global_step/sec: 87.2905 -2022-05-01 14:44:12,651 - tensorflow - INFO - loss = 2.5134597, step = 179200 (2.291 sec) -2022-05-01 14:44:14,939 - tensorflow - INFO - global_step/sec: 87.368 -2022-05-01 14:44:14,940 - tensorflow - INFO - loss = 0.03856497, step = 179400 (2.289 sec) -2022-05-01 14:44:17,229 - tensorflow - INFO - global_step/sec: 87.3668 -2022-05-01 14:44:17,229 - tensorflow - INFO - loss = 0.053672902, step = 179600 (2.289 sec) -2022-05-01 14:44:19,520 - tensorflow - INFO - global_step/sec: 87.2861 -2022-05-01 14:44:19,520 - tensorflow - INFO - loss = 0.0375527, step = 179800 (2.291 sec) -2022-05-01 14:44:21,813 - tensorflow - INFO - global_step/sec: 87.2185 -2022-05-01 14:44:21,813 - tensorflow - INFO - loss = 0.19441155, step = 180000 (2.293 sec) -2022-05-01 14:44:24,105 - tensorflow - INFO - global_step/sec: 87.2724 -2022-05-01 14:44:24,105 - tensorflow - INFO - loss = 0.08336852, step = 180200 (2.292 sec) -2022-05-01 14:44:26,397 - tensorflow - INFO - global_step/sec: 87.2413 -2022-05-01 14:44:26,398 - tensorflow - INFO - loss = 0.18960495, step = 180400 (2.293 sec) -2022-05-01 14:44:28,681 - tensorflow - INFO - global_step/sec: 87.568 -2022-05-01 14:44:28,681 - tensorflow - INFO - loss = 0.11018174, step = 180600 (2.284 sec) -2022-05-01 14:44:30,978 - tensorflow - INFO - global_step/sec: 87.0575 -2022-05-01 14:44:30,979 - tensorflow - INFO - loss = 0.065801285, step = 180800 (2.297 sec) -2022-05-01 14:44:33,269 - tensorflow - INFO - global_step/sec: 87.3313 -2022-05-01 14:44:33,269 - tensorflow - INFO - loss = 0.1354256, step = 181000 (2.290 sec) -2022-05-01 14:44:35,556 - tensorflow - INFO - global_step/sec: 87.4305 -2022-05-01 14:44:35,556 - tensorflow - INFO - loss = 0.067804396, step = 181200 (2.288 sec) -2022-05-01 14:44:37,847 - tensorflow - INFO - global_step/sec: 87.289 -2022-05-01 14:44:37,848 - tensorflow - INFO - loss = 0.86255145, step = 181400 (2.291 sec) -2022-05-01 14:44:40,137 - tensorflow - INFO - global_step/sec: 87.3531 -2022-05-01 14:44:40,137 - tensorflow - INFO - loss = 0.7926186, step = 181600 (2.290 sec) -2022-05-01 14:44:42,431 - tensorflow - INFO - global_step/sec: 87.1673 -2022-05-01 14:44:42,432 - tensorflow - INFO - loss = 0.1802915, step = 181800 (2.294 sec) -2022-05-01 14:44:44,721 - tensorflow - INFO - global_step/sec: 87.3559 -2022-05-01 14:44:44,721 - tensorflow - INFO - loss = 0.032432634, step = 182000 (2.289 sec) -2022-05-01 14:44:47,010 - tensorflow - INFO - global_step/sec: 87.3801 -2022-05-01 14:44:47,010 - tensorflow - INFO - loss = 0.06632277, step = 182200 (2.289 sec) -2022-05-01 14:44:49,299 - tensorflow - INFO - global_step/sec: 87.3673 -2022-05-01 14:44:49,299 - tensorflow - INFO - loss = 0.15085332, step = 182400 (2.289 sec) -2022-05-01 14:44:51,585 - tensorflow - INFO - global_step/sec: 87.4909 -2022-05-01 14:44:51,585 - tensorflow - INFO - loss = 0.20117849, step = 182600 (2.286 sec) -2022-05-01 14:44:53,881 - tensorflow - INFO - global_step/sec: 87.0908 -2022-05-01 14:44:53,882 - tensorflow - INFO - loss = 0.11522024, step = 182800 (2.296 sec) -2022-05-01 14:44:56,172 - tensorflow - INFO - global_step/sec: 87.324 -2022-05-01 14:44:56,172 - tensorflow - INFO - loss = 0.3229064, step = 183000 (2.290 sec) -2022-05-01 14:44:58,457 - tensorflow - INFO - global_step/sec: 87.5155 -2022-05-01 14:44:58,457 - tensorflow - INFO - loss = 0.34929866, step = 183200 (2.285 sec) -2022-05-01 14:45:00,751 - tensorflow - INFO - global_step/sec: 87.1811 -2022-05-01 14:45:00,751 - tensorflow - INFO - loss = 0.10278164, step = 183400 (2.294 sec) -2022-05-01 14:45:03,042 - tensorflow - INFO - global_step/sec: 87.2927 -2022-05-01 14:45:03,042 - tensorflow - INFO - loss = 0.105486035, step = 183600 (2.291 sec) -2022-05-01 14:45:05,327 - tensorflow - INFO - global_step/sec: 87.537 -2022-05-01 14:45:05,327 - tensorflow - INFO - loss = 16.610962, step = 183800 (2.285 sec) -2022-05-01 14:45:07,615 - tensorflow - INFO - global_step/sec: 87.4199 -2022-05-01 14:45:07,615 - tensorflow - INFO - loss = 0.04139348, step = 184000 (2.288 sec) -2022-05-01 14:45:09,894 - tensorflow - INFO - global_step/sec: 87.7259 -2022-05-01 14:45:09,895 - tensorflow - INFO - loss = 0.027574524, step = 184200 (2.280 sec) -2022-05-01 14:45:12,164 - tensorflow - INFO - global_step/sec: 88.1344 -2022-05-01 14:45:12,164 - tensorflow - INFO - loss = 0.14222334, step = 184400 (2.269 sec) -2022-05-01 14:45:14,420 - tensorflow - INFO - global_step/sec: 88.6393 -2022-05-01 14:45:14,420 - tensorflow - INFO - loss = 0.17393786, step = 184600 (2.256 sec) -2022-05-01 14:45:16,675 - tensorflow - INFO - global_step/sec: 88.6945 -2022-05-01 14:45:16,675 - tensorflow - INFO - loss = 0.06557072, step = 184800 (2.255 sec) -2022-05-01 14:45:18,953 - tensorflow - INFO - global_step/sec: 87.8073 -2022-05-01 14:45:18,953 - tensorflow - INFO - loss = 0.028205689, step = 185000 (2.278 sec) -2022-05-01 14:45:21,244 - tensorflow - INFO - global_step/sec: 87.2987 -2022-05-01 14:45:21,244 - tensorflow - INFO - loss = 0.041646503, step = 185200 (2.291 sec) -2022-05-01 14:45:23,530 - tensorflow - INFO - global_step/sec: 87.4761 -2022-05-01 14:45:23,530 - tensorflow - INFO - loss = 0.093024604, step = 185400 (2.286 sec) -2022-05-01 14:45:25,818 - tensorflow - INFO - global_step/sec: 87.4176 -2022-05-01 14:45:25,818 - tensorflow - INFO - loss = 0.11461769, step = 185600 (2.288 sec) -2022-05-01 14:45:28,110 - tensorflow - INFO - global_step/sec: 87.2538 -2022-05-01 14:45:28,110 - tensorflow - INFO - loss = 2.0448453, step = 185800 (2.292 sec) -2022-05-01 14:45:30,403 - tensorflow - INFO - global_step/sec: 87.2398 -2022-05-01 14:45:30,403 - tensorflow - INFO - loss = 0.15032089, step = 186000 (2.292 sec) -2022-05-01 14:45:32,694 - tensorflow - INFO - global_step/sec: 87.2979 -2022-05-01 14:45:32,694 - tensorflow - INFO - loss = 0.17604691, step = 186200 (2.291 sec) -2022-05-01 14:45:34,986 - tensorflow - INFO - global_step/sec: 87.2264 -2022-05-01 14:45:34,987 - tensorflow - INFO - loss = 0.044206016, step = 186400 (2.293 sec) -2022-05-01 14:45:37,277 - tensorflow - INFO - global_step/sec: 87.3331 -2022-05-01 14:45:37,277 - tensorflow - INFO - loss = 0.17563851, step = 186600 (2.290 sec) -2022-05-01 14:45:39,564 - tensorflow - INFO - global_step/sec: 87.4231 -2022-05-01 14:45:39,565 - tensorflow - INFO - loss = 0.14848632, step = 186800 (2.288 sec) -2022-05-01 14:45:41,852 - tensorflow - INFO - global_step/sec: 87.4146 -2022-05-01 14:45:41,853 - tensorflow - INFO - loss = 0.09012383, step = 187000 (2.288 sec) -2022-05-01 14:45:44,140 - tensorflow - INFO - global_step/sec: 87.4321 -2022-05-01 14:45:44,140 - tensorflow - INFO - loss = 0.18280177, step = 187200 (2.287 sec) -2022-05-01 14:45:46,423 - tensorflow - INFO - global_step/sec: 87.592 -2022-05-01 14:45:46,423 - tensorflow - INFO - loss = 0.048017383, step = 187400 (2.283 sec) -2022-05-01 14:45:48,722 - tensorflow - INFO - global_step/sec: 86.9997 -2022-05-01 14:45:48,722 - tensorflow - INFO - loss = 11.872378, step = 187600 (2.299 sec) -2022-05-01 14:45:51,011 - tensorflow - INFO - global_step/sec: 87.3555 -2022-05-01 14:45:51,012 - tensorflow - INFO - loss = 0.07027066, step = 187800 (2.289 sec) -2022-05-01 14:45:53,298 - tensorflow - INFO - global_step/sec: 87.4623 -2022-05-01 14:45:53,298 - tensorflow - INFO - loss = 0.16031665, step = 188000 (2.287 sec) -2022-05-01 14:45:55,589 - tensorflow - INFO - global_step/sec: 87.3037 -2022-05-01 14:45:55,589 - tensorflow - INFO - loss = 0.14436464, step = 188200 (2.291 sec) -2022-05-01 14:45:57,879 - tensorflow - INFO - global_step/sec: 87.3348 -2022-05-01 14:45:57,879 - tensorflow - INFO - loss = 0.34885636, step = 188400 (2.290 sec) -2022-05-01 14:46:00,171 - tensorflow - INFO - global_step/sec: 87.2641 -2022-05-01 14:46:00,171 - tensorflow - INFO - loss = 0.040362917, step = 188600 (2.292 sec) -2022-05-01 14:46:02,463 - tensorflow - INFO - global_step/sec: 87.2572 -2022-05-01 14:46:02,463 - tensorflow - INFO - loss = 0.04270378, step = 188800 (2.292 sec) -2022-05-01 14:46:04,750 - tensorflow - INFO - global_step/sec: 87.4552 -2022-05-01 14:46:04,750 - tensorflow - INFO - loss = 0.0711705, step = 189000 (2.287 sec) -2022-05-01 14:46:07,040 - tensorflow - INFO - global_step/sec: 87.3171 -2022-05-01 14:46:07,041 - tensorflow - INFO - loss = 0.1641151, step = 189200 (2.291 sec) -2022-05-01 14:46:09,329 - tensorflow - INFO - global_step/sec: 87.3957 -2022-05-01 14:46:09,329 - tensorflow - INFO - loss = 0.18184356, step = 189400 (2.288 sec) -2022-05-01 14:46:11,615 - tensorflow - INFO - global_step/sec: 87.4962 -2022-05-01 14:46:11,615 - tensorflow - INFO - loss = 0.17193224, step = 189600 (2.286 sec) -2022-05-01 14:46:13,914 - tensorflow - INFO - global_step/sec: 86.995 -2022-05-01 14:46:13,914 - tensorflow - INFO - loss = 0.19972838, step = 189800 (2.299 sec) -2022-05-01 14:46:16,204 - tensorflow - INFO - global_step/sec: 87.3129 -2022-05-01 14:46:16,205 - tensorflow - INFO - loss = 0.8434968, step = 190000 (2.291 sec) -2022-05-01 14:46:18,488 - tensorflow - INFO - global_step/sec: 87.5691 -2022-05-01 14:46:18,488 - tensorflow - INFO - loss = 0.1671878, step = 190200 (2.284 sec) -2022-05-01 14:46:20,777 - tensorflow - INFO - global_step/sec: 87.3945 -2022-05-01 14:46:20,777 - tensorflow - INFO - loss = 0.060731374, step = 190400 (2.288 sec) -2022-05-01 14:46:23,070 - tensorflow - INFO - global_step/sec: 87.2185 -2022-05-01 14:46:23,070 - tensorflow - INFO - loss = 0.08488855, step = 190600 (2.293 sec) -2022-05-01 14:46:25,367 - tensorflow - INFO - global_step/sec: 87.0444 -2022-05-01 14:46:25,368 - tensorflow - INFO - loss = 0.05223095, step = 190800 (2.298 sec) -2022-05-01 14:46:27,663 - tensorflow - INFO - global_step/sec: 87.1166 -2022-05-01 14:46:27,664 - tensorflow - INFO - loss = 0.067917466, step = 191000 (2.296 sec) -2022-05-01 14:46:29,958 - tensorflow - INFO - global_step/sec: 87.1681 -2022-05-01 14:46:29,958 - tensorflow - INFO - loss = 0.27582443, step = 191200 (2.294 sec) -2022-05-01 14:46:32,256 - tensorflow - INFO - global_step/sec: 87.0233 -2022-05-01 14:46:32,256 - tensorflow - INFO - loss = 0.42234778, step = 191400 (2.298 sec) -2022-05-01 14:46:34,547 - tensorflow - INFO - global_step/sec: 87.27 -2022-05-01 14:46:34,548 - tensorflow - INFO - loss = 0.10319117, step = 191600 (2.292 sec) -2022-05-01 14:46:36,838 - tensorflow - INFO - global_step/sec: 87.2976 -2022-05-01 14:46:36,839 - tensorflow - INFO - loss = 0.09261024, step = 191800 (2.291 sec) -2022-05-01 14:46:39,131 - tensorflow - INFO - global_step/sec: 87.2527 -2022-05-01 14:46:39,131 - tensorflow - INFO - loss = 0.040254287, step = 192000 (2.292 sec) -2022-05-01 14:46:41,416 - tensorflow - INFO - global_step/sec: 87.4974 -2022-05-01 14:46:41,417 - tensorflow - INFO - loss = 0.35942328, step = 192200 (2.286 sec) -2022-05-01 14:46:43,704 - tensorflow - INFO - global_step/sec: 87.4408 -2022-05-01 14:46:43,704 - tensorflow - INFO - loss = 0.08916014, step = 192400 (2.287 sec) -2022-05-01 14:46:45,988 - tensorflow - INFO - global_step/sec: 87.5359 -2022-05-01 14:46:45,989 - tensorflow - INFO - loss = 0.13586244, step = 192600 (2.285 sec) -2022-05-01 14:46:48,267 - tensorflow - INFO - global_step/sec: 87.7855 -2022-05-01 14:46:48,267 - tensorflow - INFO - loss = 0.09050533, step = 192800 (2.278 sec) -2022-05-01 14:46:50,529 - tensorflow - INFO - global_step/sec: 88.391 -2022-05-01 14:46:50,530 - tensorflow - INFO - loss = 0.13040312, step = 193000 (2.263 sec) -2022-05-01 14:46:52,829 - tensorflow - INFO - global_step/sec: 86.9863 -2022-05-01 14:46:52,829 - tensorflow - INFO - loss = 0.094426826, step = 193200 (2.299 sec) -2022-05-01 14:46:55,124 - tensorflow - INFO - global_step/sec: 87.1413 -2022-05-01 14:46:55,124 - tensorflow - INFO - loss = 0.102052435, step = 193400 (2.295 sec) -2022-05-01 14:46:57,425 - tensorflow - INFO - global_step/sec: 86.903 -2022-05-01 14:46:57,426 - tensorflow - INFO - loss = 0.05099783, step = 193600 (2.301 sec) -2022-05-01 14:46:59,725 - tensorflow - INFO - global_step/sec: 86.9525 -2022-05-01 14:46:59,726 - tensorflow - INFO - loss = 0.1822123, step = 193800 (2.300 sec) -2022-05-01 14:47:02,026 - tensorflow - INFO - global_step/sec: 86.9489 -2022-05-01 14:47:02,026 - tensorflow - INFO - loss = 0.059502125, step = 194000 (2.300 sec) -2022-05-01 14:47:04,322 - tensorflow - INFO - global_step/sec: 87.0813 -2022-05-01 14:47:04,323 - tensorflow - INFO - loss = 0.12372404, step = 194200 (2.297 sec) -2022-05-01 14:47:06,624 - tensorflow - INFO - global_step/sec: 86.9004 -2022-05-01 14:47:06,624 - tensorflow - INFO - loss = 4.9969735, step = 194400 (2.302 sec) -2022-05-01 14:47:08,917 - tensorflow - INFO - global_step/sec: 87.2197 -2022-05-01 14:47:08,917 - tensorflow - INFO - loss = 0.46680868, step = 194600 (2.293 sec) -2022-05-01 14:47:11,216 - tensorflow - INFO - global_step/sec: 86.9929 -2022-05-01 14:47:11,216 - tensorflow - INFO - loss = 0.21966107, step = 194800 (2.299 sec) -2022-05-01 14:47:13,512 - tensorflow - INFO - global_step/sec: 87.0903 -2022-05-01 14:47:13,513 - tensorflow - INFO - loss = 1.4392605, step = 195000 (2.296 sec) -2022-05-01 14:47:15,796 - tensorflow - INFO - global_step/sec: 87.5799 -2022-05-01 14:47:15,796 - tensorflow - INFO - loss = 2.9109988, step = 195200 (2.284 sec) -2022-05-01 14:47:18,088 - tensorflow - INFO - global_step/sec: 87.2639 -2022-05-01 14:47:18,088 - tensorflow - INFO - loss = 0.06750238, step = 195400 (2.292 sec) -2022-05-01 14:47:20,377 - tensorflow - INFO - global_step/sec: 87.3571 -2022-05-01 14:47:20,378 - tensorflow - INFO - loss = 0.13745224, step = 195600 (2.289 sec) -2022-05-01 14:47:22,658 - tensorflow - INFO - global_step/sec: 87.6826 -2022-05-01 14:47:22,659 - tensorflow - INFO - loss = 0.22367324, step = 195800 (2.281 sec) -2022-05-01 14:47:24,952 - tensorflow - INFO - global_step/sec: 87.1776 -2022-05-01 14:47:24,953 - tensorflow - INFO - loss = 0.29652923, step = 196000 (2.294 sec) -2022-05-01 14:47:27,238 - tensorflow - INFO - global_step/sec: 87.5058 -2022-05-01 14:47:27,238 - tensorflow - INFO - loss = 0.0928004, step = 196200 (2.286 sec) -2022-05-01 14:47:29,522 - tensorflow - INFO - global_step/sec: 87.5486 -2022-05-01 14:47:29,523 - tensorflow - INFO - loss = 7.4939165, step = 196400 (2.284 sec) -2022-05-01 14:47:31,823 - tensorflow - INFO - global_step/sec: 86.9363 -2022-05-01 14:47:31,823 - tensorflow - INFO - loss = 0.436599, step = 196600 (2.301 sec) -2022-05-01 14:47:34,107 - tensorflow - INFO - global_step/sec: 87.5778 -2022-05-01 14:47:34,107 - tensorflow - INFO - loss = 0.024931233, step = 196800 (2.284 sec) -2022-05-01 14:47:36,397 - tensorflow - INFO - global_step/sec: 87.3185 -2022-05-01 14:47:36,398 - tensorflow - INFO - loss = 0.07194897, step = 197000 (2.291 sec) -2022-05-01 14:47:38,684 - tensorflow - INFO - global_step/sec: 87.4662 -2022-05-01 14:47:38,684 - tensorflow - INFO - loss = 23.8871, step = 197200 (2.287 sec) -2022-05-01 14:47:40,978 - tensorflow - INFO - global_step/sec: 87.1584 -2022-05-01 14:47:40,979 - tensorflow - INFO - loss = 0.10765213, step = 197400 (2.295 sec) -2022-05-01 14:47:43,279 - tensorflow - INFO - global_step/sec: 86.9478 -2022-05-01 14:47:43,279 - tensorflow - INFO - loss = 0.07624713, step = 197600 (2.300 sec) -2022-05-01 14:47:45,570 - tensorflow - INFO - global_step/sec: 87.2936 -2022-05-01 14:47:45,570 - tensorflow - INFO - loss = 9.676068, step = 197800 (2.291 sec) -2022-05-01 14:47:47,857 - tensorflow - INFO - global_step/sec: 87.4508 -2022-05-01 14:47:47,857 - tensorflow - INFO - loss = 0.14174747, step = 198000 (2.287 sec) -2022-05-01 14:47:50,144 - tensorflow - INFO - global_step/sec: 87.4219 -2022-05-01 14:47:50,145 - tensorflow - INFO - loss = 0.090489924, step = 198200 (2.288 sec) -2022-05-01 14:47:52,427 - tensorflow - INFO - global_step/sec: 87.6189 -2022-05-01 14:47:52,427 - tensorflow - INFO - loss = 0.074647754, step = 198400 (2.283 sec) -2022-05-01 14:47:54,723 - tensorflow - INFO - global_step/sec: 87.1252 -2022-05-01 14:47:54,723 - tensorflow - INFO - loss = 0.0646258, step = 198600 (2.296 sec) -2022-05-01 14:47:57,018 - tensorflow - INFO - global_step/sec: 87.1412 -2022-05-01 14:47:57,018 - tensorflow - INFO - loss = 0.46771583, step = 198800 (2.295 sec) -2022-05-01 14:47:59,300 - tensorflow - INFO - global_step/sec: 87.6377 -2022-05-01 14:47:59,300 - tensorflow - INFO - loss = 0.3469265, step = 199000 (2.282 sec) -2022-05-01 14:48:01,586 - tensorflow - INFO - global_step/sec: 87.4839 -2022-05-01 14:48:01,586 - tensorflow - INFO - loss = 1.0086424, step = 199200 (2.286 sec) -2022-05-01 14:48:03,872 - tensorflow - INFO - global_step/sec: 87.4938 -2022-05-01 14:48:03,872 - tensorflow - INFO - loss = 1.1862116, step = 199400 (2.286 sec) -2022-05-01 14:48:06,165 - tensorflow - INFO - global_step/sec: 87.2047 -2022-05-01 14:48:06,166 - tensorflow - INFO - loss = 0.15060207, step = 199600 (2.293 sec) -2022-05-01 14:48:08,463 - tensorflow - INFO - global_step/sec: 87.0599 -2022-05-01 14:48:08,463 - tensorflow - INFO - loss = 0.055810157, step = 199800 (2.297 sec) -2022-05-01 14:48:10,743 - tensorflow - INFO - Saving checkpoints for 200000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. -2022-05-01 14:48:10,758 - tensorflow - INFO - model.ckpt-200000 is not in all_model_checkpoint_paths. Manually adding it. -2022-05-01 14:48:11,312 - tensorflow - INFO - Calling model_fn. -2022-05-01 14:48:11,443 - tensorflow - INFO - Done calling model_fn. -2022-05-01 14:48:11,458 - tensorflow - INFO - Starting evaluation at 2022-05-01T14:48:11Z -2022-05-01 14:48:11,523 - tensorflow - INFO - Graph was finalized. -2022-05-01 14:48:11,526 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-200000 -2022-05-01 14:48:11,811 - tensorflow - INFO - Running local_init_op. -2022-05-01 14:48:11,921 - tensorflow - INFO - Done running local_init_op. -2022-05-01 14:48:22,202 - tensorflow - INFO - Evaluation [3990/39900] -2022-05-01 14:48:31,752 - tensorflow - INFO - Evaluation [7980/39900] -2022-05-01 14:48:41,342 - tensorflow - INFO - Evaluation [11970/39900] -2022-05-01 14:48:50,922 - tensorflow - INFO - Evaluation [15960/39900] -2022-05-01 14:49:00,471 - tensorflow - INFO - Evaluation [19950/39900] -2022-05-01 14:49:10,047 - tensorflow - INFO - Evaluation [23940/39900] -2022-05-01 14:49:19,631 - tensorflow - INFO - Evaluation [27930/39900] -2022-05-01 14:49:29,222 - tensorflow - INFO - Evaluation [31920/39900] -2022-05-01 14:49:38,795 - tensorflow - INFO - Evaluation [35910/39900] -2022-05-01 14:49:48,375 - tensorflow - INFO - Evaluation [39900/39900] -2022-05-01 14:49:48,512 - tensorflow - INFO - Finished evaluation at 2022-05-01-14:49:48 -2022-05-01 14:49:48,512 - tensorflow - INFO - Saving dict for global step 200000: global_step = 200000, loss = 0.100737154, mean_degree_err = 10.012244 -2022-05-01 14:49:48,513 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 200000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-200000 -2022-05-01 14:49:48,526 - tensorflow - INFO - global_step/sec: 1.99873 -2022-05-01 14:49:48,527 - tensorflow - INFO - loss = 0.12662719, step = 200000 (100.064 sec) -2022-05-01 14:49:50,832 - tensorflow - INFO - global_step/sec: 86.7276 -2022-05-01 14:49:50,833 - tensorflow - INFO - loss = 0.8001151, step = 200200 (2.306 sec) -2022-05-01 14:49:53,113 - tensorflow - INFO - global_step/sec: 87.6802 -2022-05-01 14:49:53,114 - tensorflow - INFO - loss = 0.045994636, step = 200400 (2.281 sec) -2022-05-01 14:49:55,390 - tensorflow - INFO - global_step/sec: 87.8451 -2022-05-01 14:49:55,391 - tensorflow - INFO - loss = 4.106056, step = 200600 (2.277 sec) -2022-05-01 14:49:57,661 - tensorflow - INFO - global_step/sec: 88.0574 -2022-05-01 14:49:57,662 - tensorflow - INFO - loss = 0.4528215, step = 200800 (2.271 sec) -2022-05-01 14:49:59,923 - tensorflow - INFO - global_step/sec: 88.4209 -2022-05-01 14:49:59,924 - tensorflow - INFO - loss = 0.07186222, step = 201000 (2.262 sec) -2022-05-01 14:50:02,210 - tensorflow - INFO - global_step/sec: 87.4507 -2022-05-01 14:50:02,211 - tensorflow - INFO - loss = 0.20265803, step = 201200 (2.287 sec) -2022-05-01 14:50:04,500 - tensorflow - INFO - global_step/sec: 87.3595 -2022-05-01 14:50:04,500 - tensorflow - INFO - loss = 0.03260638, step = 201400 (2.289 sec) -2022-05-01 14:50:06,787 - tensorflow - INFO - global_step/sec: 87.4457 -2022-05-01 14:50:06,787 - tensorflow - INFO - loss = 0.11148474, step = 201600 (2.287 sec) -2022-05-01 14:50:09,072 - tensorflow - INFO - global_step/sec: 87.5177 -2022-05-01 14:50:09,072 - tensorflow - INFO - loss = 1.6456022, step = 201800 (2.285 sec) -2022-05-01 14:50:11,361 - tensorflow - INFO - global_step/sec: 87.3907 -2022-05-01 14:50:11,361 - tensorflow - INFO - loss = 0.17919509, step = 202000 (2.289 sec) -2022-05-01 14:50:13,661 - tensorflow - INFO - global_step/sec: 86.9405 -2022-05-01 14:50:13,662 - tensorflow - INFO - loss = 0.09502393, step = 202200 (2.300 sec) -2022-05-01 14:50:15,963 - tensorflow - INFO - global_step/sec: 86.8866 -2022-05-01 14:50:15,963 - tensorflow - INFO - loss = 0.05594068, step = 202400 (2.302 sec) -2022-05-01 14:50:18,261 - tensorflow - INFO - global_step/sec: 87.0316 -2022-05-01 14:50:18,261 - tensorflow - INFO - loss = 0.09115332, step = 202600 (2.298 sec) -2022-05-01 14:50:20,561 - tensorflow - INFO - global_step/sec: 86.9407 -2022-05-01 14:50:20,562 - tensorflow - INFO - loss = 10.594648, step = 202800 (2.300 sec) -2022-05-01 14:50:22,854 - tensorflow - INFO - global_step/sec: 87.2173 -2022-05-01 14:50:22,855 - tensorflow - INFO - loss = 0.09624572, step = 203000 (2.293 sec) -2022-05-01 14:50:25,156 - tensorflow - INFO - global_step/sec: 86.8796 -2022-05-01 14:50:25,157 - tensorflow - INFO - loss = 0.03879582, step = 203200 (2.302 sec) -2022-05-01 14:50:27,458 - tensorflow - INFO - global_step/sec: 86.9109 -2022-05-01 14:50:27,458 - tensorflow - INFO - loss = 1.6161735, step = 203400 (2.301 sec) -2022-05-01 14:50:29,753 - tensorflow - INFO - global_step/sec: 87.1401 -2022-05-01 14:50:29,753 - tensorflow - INFO - loss = 0.12163468, step = 203600 (2.295 sec) -2022-05-01 14:50:32,061 - tensorflow - INFO - global_step/sec: 86.646 -2022-05-01 14:50:32,062 - tensorflow - INFO - loss = 11.411995, step = 203800 (2.308 sec) -2022-05-01 14:50:34,362 - tensorflow - INFO - global_step/sec: 86.9162 -2022-05-01 14:50:34,363 - tensorflow - INFO - loss = 0.72658515, step = 204000 (2.301 sec) -2022-05-01 14:50:36,659 - tensorflow - INFO - global_step/sec: 87.0901 -2022-05-01 14:50:36,659 - tensorflow - INFO - loss = 0.3619779, step = 204200 (2.296 sec) -2022-05-01 14:50:38,961 - tensorflow - INFO - global_step/sec: 86.8832 -2022-05-01 14:50:38,961 - tensorflow - INFO - loss = 5.773318, step = 204400 (2.302 sec) -2022-05-01 14:50:41,262 - tensorflow - INFO - global_step/sec: 86.9034 -2022-05-01 14:50:41,262 - tensorflow - INFO - loss = 0.10278251, step = 204600 (2.301 sec) -2022-05-01 14:50:43,554 - tensorflow - INFO - global_step/sec: 87.2608 -2022-05-01 14:50:43,554 - tensorflow - INFO - loss = 0.21095583, step = 204800 (2.292 sec) -2022-05-01 14:50:45,840 - tensorflow - INFO - global_step/sec: 87.4786 -2022-05-01 14:50:45,841 - tensorflow - INFO - loss = 0.046599954, step = 205000 (2.286 sec) -2022-05-01 14:50:48,127 - tensorflow - INFO - global_step/sec: 87.466 -2022-05-01 14:50:48,127 - tensorflow - INFO - loss = 0.34111094, step = 205200 (2.287 sec) -2022-05-01 14:50:50,406 - tensorflow - INFO - global_step/sec: 87.7639 -2022-05-01 14:50:50,406 - tensorflow - INFO - loss = 0.14029244, step = 205400 (2.279 sec) -2022-05-01 14:50:52,706 - tensorflow - INFO - global_step/sec: 86.946 -2022-05-01 14:50:52,706 - tensorflow - INFO - loss = 0.115294844, step = 205600 (2.300 sec) -2022-05-01 14:50:54,996 - tensorflow - INFO - global_step/sec: 87.3392 -2022-05-01 14:50:54,996 - tensorflow - INFO - loss = 1.6688011, step = 205800 (2.290 sec) -2022-05-01 14:50:57,284 - tensorflow - INFO - global_step/sec: 87.426 -2022-05-01 14:50:57,284 - tensorflow - INFO - loss = 1.0651115, step = 206000 (2.288 sec) -2022-05-01 14:50:59,574 - tensorflow - INFO - global_step/sec: 87.3021 -2022-05-01 14:50:59,575 - tensorflow - INFO - loss = 0.05911135, step = 206200 (2.291 sec) -2022-05-01 14:51:01,863 - tensorflow - INFO - global_step/sec: 87.3795 -2022-05-01 14:51:01,864 - tensorflow - INFO - loss = 3.0845604, step = 206400 (2.289 sec) -2022-05-01 14:51:04,155 - tensorflow - INFO - global_step/sec: 87.2606 -2022-05-01 14:51:04,156 - tensorflow - INFO - loss = 1.0244889, step = 206600 (2.292 sec) -2022-05-01 14:51:06,450 - tensorflow - INFO - global_step/sec: 87.1581 -2022-05-01 14:51:06,450 - tensorflow - INFO - loss = 0.06411409, step = 206800 (2.295 sec) -2022-05-01 14:51:08,742 - tensorflow - INFO - global_step/sec: 87.2733 -2022-05-01 14:51:08,742 - tensorflow - INFO - loss = 0.093899466, step = 207000 (2.292 sec) -2022-05-01 14:51:11,032 - tensorflow - INFO - global_step/sec: 87.3387 -2022-05-01 14:51:11,032 - tensorflow - INFO - loss = 0.057339266, step = 207200 (2.290 sec) -2022-05-01 14:51:13,323 - tensorflow - INFO - global_step/sec: 87.2977 -2022-05-01 14:51:13,323 - tensorflow - INFO - loss = 0.079025224, step = 207400 (2.291 sec) -2022-05-01 14:51:15,614 - tensorflow - INFO - global_step/sec: 87.2702 -2022-05-01 14:51:15,615 - tensorflow - INFO - loss = 0.08560538, step = 207600 (2.292 sec) -2022-05-01 14:51:17,904 - tensorflow - INFO - global_step/sec: 87.3517 -2022-05-01 14:51:17,904 - tensorflow - INFO - loss = 0.61651057, step = 207800 (2.290 sec) -2022-05-01 14:51:20,189 - tensorflow - INFO - global_step/sec: 87.5134 -2022-05-01 14:51:20,190 - tensorflow - INFO - loss = 0.050484687, step = 208000 (2.285 sec) -2022-05-01 14:51:22,480 - tensorflow - INFO - global_step/sec: 87.3092 -2022-05-01 14:51:22,480 - tensorflow - INFO - loss = 0.12937605, step = 208200 (2.291 sec) -2022-05-01 14:51:24,772 - tensorflow - INFO - global_step/sec: 87.273 -2022-05-01 14:51:24,772 - tensorflow - INFO - loss = 0.10451312, step = 208400 (2.292 sec) -2022-05-01 14:51:27,042 - tensorflow - INFO - global_step/sec: 88.1056 -2022-05-01 14:51:27,042 - tensorflow - INFO - loss = 0.25230247, step = 208600 (2.270 sec) -2022-05-01 14:51:29,322 - tensorflow - INFO - global_step/sec: 87.6994 -2022-05-01 14:51:29,323 - tensorflow - INFO - loss = 0.11170903, step = 208800 (2.281 sec) -2022-05-01 14:51:31,588 - tensorflow - INFO - global_step/sec: 88.2585 -2022-05-01 14:51:31,589 - tensorflow - INFO - loss = 0.29632747, step = 209000 (2.266 sec) -2022-05-01 14:51:33,873 - tensorflow - INFO - global_step/sec: 87.5372 -2022-05-01 14:51:33,873 - tensorflow - INFO - loss = 0.06806228, step = 209200 (2.285 sec) -2022-05-01 14:51:36,165 - tensorflow - INFO - global_step/sec: 87.2485 -2022-05-01 14:51:36,166 - tensorflow - INFO - loss = 0.14363146, step = 209400 (2.292 sec) -2022-05-01 14:51:38,456 - tensorflow - INFO - global_step/sec: 87.2922 -2022-05-01 14:51:38,457 - tensorflow - INFO - loss = 0.1182349, step = 209600 (2.291 sec) -2022-05-01 14:51:40,745 - tensorflow - INFO - global_step/sec: 87.4044 -2022-05-01 14:51:40,745 - tensorflow - INFO - loss = 0.19589159, step = 209800 (2.288 sec) -2022-05-01 14:51:43,044 - tensorflow - INFO - global_step/sec: 86.9773 -2022-05-01 14:51:43,045 - tensorflow - INFO - loss = 0.19596815, step = 210000 (2.299 sec) -2022-05-01 14:51:45,334 - tensorflow - INFO - global_step/sec: 87.3312 -2022-05-01 14:51:45,335 - tensorflow - INFO - loss = 0.20116207, step = 210200 (2.290 sec) -2022-05-01 14:51:47,629 - tensorflow - INFO - global_step/sec: 87.1483 -2022-05-01 14:51:47,630 - tensorflow - INFO - loss = 0.029008688, step = 210400 (2.295 sec) -2022-05-01 14:51:49,921 - tensorflow - INFO - global_step/sec: 87.2641 -2022-05-01 14:51:49,922 - tensorflow - INFO - loss = 0.072042614, step = 210600 (2.292 sec) -2022-05-01 14:51:52,215 - tensorflow - INFO - global_step/sec: 87.1826 -2022-05-01 14:51:52,216 - tensorflow - INFO - loss = 0.05465828, step = 210800 (2.294 sec) -2022-05-01 14:51:54,507 - tensorflow - INFO - global_step/sec: 87.246 -2022-05-01 14:51:54,508 - tensorflow - INFO - loss = 0.060184408, step = 211000 (2.292 sec) -2022-05-01 14:51:56,803 - tensorflow - INFO - global_step/sec: 87.1235 -2022-05-01 14:51:56,803 - tensorflow - INFO - loss = 0.041051302, step = 211200 (2.296 sec) -2022-05-01 14:51:59,088 - tensorflow - INFO - global_step/sec: 87.5112 -2022-05-01 14:51:59,089 - tensorflow - INFO - loss = 0.13065943, step = 211400 (2.285 sec) -2022-05-01 14:52:01,382 - tensorflow - INFO - global_step/sec: 87.1999 -2022-05-01 14:52:01,382 - tensorflow - INFO - loss = 0.13711402, step = 211600 (2.294 sec) -2022-05-01 14:52:03,671 - tensorflow - INFO - global_step/sec: 87.3831 -2022-05-01 14:52:03,671 - tensorflow - INFO - loss = 2.0083685, step = 211800 (2.289 sec) -2022-05-01 14:52:05,955 - tensorflow - INFO - global_step/sec: 87.5641 -2022-05-01 14:52:05,955 - tensorflow - INFO - loss = 0.4175942, step = 212000 (2.284 sec) -2022-05-01 14:52:08,249 - tensorflow - INFO - global_step/sec: 87.1732 -2022-05-01 14:52:08,250 - tensorflow - INFO - loss = 1.8437014, step = 212200 (2.294 sec) -2022-05-01 14:52:10,542 - tensorflow - INFO - global_step/sec: 87.228 -2022-05-01 14:52:10,542 - tensorflow - INFO - loss = 0.0708262, step = 212400 (2.293 sec) -2022-05-01 14:52:12,831 - tensorflow - INFO - global_step/sec: 87.3639 -2022-05-01 14:52:12,832 - tensorflow - INFO - loss = 0.08293774, step = 212600 (2.289 sec) -2022-05-01 14:52:15,121 - tensorflow - INFO - global_step/sec: 87.3419 -2022-05-01 14:52:15,122 - tensorflow - INFO - loss = 0.06429095, step = 212800 (2.290 sec) -2022-05-01 14:52:17,408 - tensorflow - INFO - global_step/sec: 87.4582 -2022-05-01 14:52:17,408 - tensorflow - INFO - loss = 0.6658572, step = 213000 (2.287 sec) -2022-05-01 14:52:19,708 - tensorflow - INFO - global_step/sec: 86.9439 -2022-05-01 14:52:19,709 - tensorflow - INFO - loss = 0.72445893, step = 213200 (2.300 sec) -2022-05-01 14:52:21,994 - tensorflow - INFO - global_step/sec: 87.4842 -2022-05-01 14:52:21,995 - tensorflow - INFO - loss = 0.18320945, step = 213400 (2.286 sec) -2022-05-01 14:52:24,282 - tensorflow - INFO - global_step/sec: 87.4326 -2022-05-01 14:52:24,282 - tensorflow - INFO - loss = 0.86827636, step = 213600 (2.288 sec) -2022-05-01 14:52:26,571 - tensorflow - INFO - global_step/sec: 87.3856 -2022-05-01 14:52:26,571 - tensorflow - INFO - loss = 2.887658, step = 213800 (2.289 sec) -2022-05-01 14:52:28,861 - tensorflow - INFO - global_step/sec: 87.315 -2022-05-01 14:52:28,862 - tensorflow - INFO - loss = 0.083644465, step = 214000 (2.291 sec) -2022-05-01 14:52:31,156 - tensorflow - INFO - global_step/sec: 87.1385 -2022-05-01 14:52:31,157 - tensorflow - INFO - loss = 0.6637585, step = 214200 (2.295 sec) -2022-05-01 14:52:33,456 - tensorflow - INFO - global_step/sec: 86.9644 -2022-05-01 14:52:33,457 - tensorflow - INFO - loss = 0.18146542, step = 214400 (2.300 sec) -2022-05-01 14:52:35,736 - tensorflow - INFO - global_step/sec: 87.7042 -2022-05-01 14:52:35,737 - tensorflow - INFO - loss = 0.06060461, step = 214600 (2.280 sec) -2022-05-01 14:52:38,021 - tensorflow - INFO - global_step/sec: 87.5462 -2022-05-01 14:52:38,021 - tensorflow - INFO - loss = 0.093060724, step = 214800 (2.285 sec) -2022-05-01 14:52:40,307 - tensorflow - INFO - global_step/sec: 87.4917 -2022-05-01 14:52:40,307 - tensorflow - INFO - loss = 0.056459446, step = 215000 (2.286 sec) -2022-05-01 14:52:42,593 - tensorflow - INFO - global_step/sec: 87.4837 -2022-05-01 14:52:42,594 - tensorflow - INFO - loss = 0.09824064, step = 215200 (2.286 sec) -2022-05-01 14:52:44,888 - tensorflow - INFO - global_step/sec: 87.1658 -2022-05-01 14:52:44,888 - tensorflow - INFO - loss = 0.13120337, step = 215400 (2.294 sec) -2022-05-01 14:52:47,178 - tensorflow - INFO - global_step/sec: 87.3078 -2022-05-01 14:52:47,179 - tensorflow - INFO - loss = 0.29566246, step = 215600 (2.291 sec) -2022-05-01 14:52:49,466 - tensorflow - INFO - global_step/sec: 87.4319 -2022-05-01 14:52:49,466 - tensorflow - INFO - loss = 0.13072793, step = 215800 (2.287 sec) -2022-05-01 14:52:51,746 - tensorflow - INFO - global_step/sec: 87.7028 -2022-05-01 14:52:51,747 - tensorflow - INFO - loss = 0.028415624, step = 216000 (2.280 sec) -2022-05-01 14:52:54,041 - tensorflow - INFO - global_step/sec: 87.1664 -2022-05-01 14:52:54,041 - tensorflow - INFO - loss = 2.1365101, step = 216200 (2.294 sec) -2022-05-01 14:52:56,327 - tensorflow - INFO - global_step/sec: 87.4888 -2022-05-01 14:52:56,327 - tensorflow - INFO - loss = 0.3180861, step = 216400 (2.286 sec) -2022-05-01 14:52:58,600 - tensorflow - INFO - global_step/sec: 87.9768 -2022-05-01 14:52:58,600 - tensorflow - INFO - loss = 0.13694525, step = 216600 (2.273 sec) -2022-05-01 14:53:00,856 - tensorflow - INFO - global_step/sec: 88.6491 -2022-05-01 14:53:00,857 - tensorflow - INFO - loss = 0.14439583, step = 216800 (2.256 sec) -2022-05-01 14:53:03,129 - tensorflow - INFO - global_step/sec: 87.9793 -2022-05-01 14:53:03,130 - tensorflow - INFO - loss = 0.12243773, step = 217000 (2.273 sec) -2022-05-01 14:53:05,416 - tensorflow - INFO - global_step/sec: 87.4588 -2022-05-01 14:53:05,417 - tensorflow - INFO - loss = 0.8855525, step = 217200 (2.287 sec) -2022-05-01 14:53:07,701 - tensorflow - INFO - global_step/sec: 87.524 -2022-05-01 14:53:07,702 - tensorflow - INFO - loss = 0.041931666, step = 217400 (2.285 sec) -2022-05-01 14:53:09,993 - tensorflow - INFO - global_step/sec: 87.2756 -2022-05-01 14:53:09,993 - tensorflow - INFO - loss = 0.22627923, step = 217600 (2.292 sec) -2022-05-01 14:53:12,289 - tensorflow - INFO - global_step/sec: 87.0987 -2022-05-01 14:53:12,290 - tensorflow - INFO - loss = 0.06976569, step = 217800 (2.296 sec) -2022-05-01 14:53:14,585 - tensorflow - INFO - global_step/sec: 87.129 -2022-05-01 14:53:14,585 - tensorflow - INFO - loss = 0.27322647, step = 218000 (2.295 sec) -2022-05-01 14:53:16,878 - tensorflow - INFO - global_step/sec: 87.195 -2022-05-01 14:53:16,879 - tensorflow - INFO - loss = 0.056805912, step = 218200 (2.294 sec) -2022-05-01 14:53:19,170 - tensorflow - INFO - global_step/sec: 87.2698 -2022-05-01 14:53:19,170 - tensorflow - INFO - loss = 0.06335604, step = 218400 (2.292 sec) -2022-05-01 14:53:21,456 - tensorflow - INFO - global_step/sec: 87.4844 -2022-05-01 14:53:21,457 - tensorflow - INFO - loss = 0.21221682, step = 218600 (2.286 sec) -2022-05-01 14:53:23,747 - tensorflow - INFO - global_step/sec: 87.2995 -2022-05-01 14:53:23,747 - tensorflow - INFO - loss = 0.34483534, step = 218800 (2.291 sec) -2022-05-01 14:53:26,036 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.03577576, step = 280200 (2.291 sec) -2022-05-01 15:05:09,845 - tensorflow - INFO - global_step/sec: 87.4674 -2022-05-01 15:05:09,845 - tensorflow - INFO - loss = 0.036211703, step = 280400 (2.287 sec) -2022-05-01 15:05:12,149 - tensorflow - INFO - global_step/sec: 86.8219 -2022-05-01 15:05:12,149 - tensorflow - INFO - loss = 0.10441546, step = 280600 (2.303 sec) -2022-05-01 15:05:14,439 - tensorflow - INFO - global_step/sec: 87.3036 -2022-05-01 15:05:14,440 - tensorflow - INFO - loss = 0.3937174, step = 280800 (2.291 sec) -2022-05-01 15:05:16,733 - tensorflow - INFO - global_step/sec: 87.2133 -2022-05-01 15:05:16,733 - tensorflow - INFO - loss = 0.07562833, step = 281000 (2.293 sec) -2022-05-01 15:05:19,009 - tensorflow - INFO - global_step/sec: 87.8687 -2022-05-01 15:05:19,009 - tensorflow - INFO - loss = 17.1679, step = 281200 (2.276 sec) -2022-05-01 15:05:21,268 - tensorflow - INFO - global_step/sec: 88.5244 -2022-05-01 15:05:21,269 - tensorflow - INFO - loss = 0.22217152, step = 281400 (2.259 sec) -2022-05-01 15:05:23,530 - tensorflow - INFO - global_step/sec: 88.4288 -2022-05-01 15:05:23,530 - tensorflow - INFO - loss = 0.044527538, step = 281600 (2.262 sec) -2022-05-01 15:05:25,788 - tensorflow - INFO - global_step/sec: 88.5624 -2022-05-01 15:05:25,788 - tensorflow - INFO - loss = 0.047858927, step = 281800 (2.258 sec) -2022-05-01 15:05:28,084 - tensorflow - INFO - global_step/sec: 87.0944 -2022-05-01 15:05:28,085 - tensorflow - INFO - loss = 0.273233, step = 282000 (2.296 sec) -2022-05-01 15:05:30,376 - tensorflow - INFO - global_step/sec: 87.2719 -2022-05-01 15:05:30,376 - tensorflow - INFO - loss = 0.12601215, step = 282200 (2.292 sec) -2022-05-01 15:05:32,664 - tensorflow - INFO - global_step/sec: 87.3965 -2022-05-01 15:05:32,665 - tensorflow - INFO - loss = 0.070912674, step = 282400 (2.288 sec) -2022-05-01 15:05:34,959 - tensorflow - INFO - global_step/sec: 87.1493 -2022-05-01 15:05:34,960 - tensorflow - INFO - loss = 0.06730513, step = 282600 (2.295 sec) -2022-05-01 15:05:37,245 - tensorflow - INFO - global_step/sec: 87.4996 -2022-05-01 15:05:37,246 - tensorflow - INFO - loss = 0.07735733, step = 282800 (2.286 sec) -2022-05-01 15:05:39,537 - tensorflow - INFO - global_step/sec: 87.2568 -2022-05-01 15:05:39,538 - tensorflow - INFO - loss = 0.9476394, step = 283000 (2.292 sec) -2022-05-01 15:05:41,831 - tensorflow - INFO - global_step/sec: 87.1754 -2022-05-01 15:05:41,832 - tensorflow - INFO - loss = 0.034305893, step = 283200 (2.294 sec) -2022-05-01 15:05:44,117 - tensorflow - INFO - global_step/sec: 87.5036 -2022-05-01 15:05:44,118 - tensorflow - INFO - loss = 0.06655292, step = 283400 (2.286 sec) -2022-05-01 15:05:46,409 - tensorflow - INFO - global_step/sec: 87.2471 -2022-05-01 15:05:46,410 - tensorflow - INFO - loss = 0.12519856, step = 283600 (2.292 sec) -2022-05-01 15:05:48,704 - tensorflow - INFO - global_step/sec: 87.177 -2022-05-01 15:05:48,704 - tensorflow - INFO - loss = 0.037646405, step = 283800 (2.294 sec) -2022-05-01 15:05:50,993 - tensorflow - INFO - global_step/sec: 87.3466 -2022-05-01 15:05:50,994 - tensorflow - INFO - loss = 0.31579885, step = 284000 (2.290 sec) -2022-05-01 15:05:53,291 - tensorflow - INFO - global_step/sec: 87.041 -2022-05-01 15:05:53,291 - tensorflow - INFO - loss = 0.0757684, step = 284200 (2.298 sec) -2022-05-01 15:05:55,586 - tensorflow - INFO - global_step/sec: 87.1638 -2022-05-01 15:05:55,586 - tensorflow - INFO - loss = 0.06544681, step = 284400 (2.295 sec) -2022-05-01 15:05:57,874 - tensorflow - INFO - global_step/sec: 87.3884 -2022-05-01 15:05:57,875 - tensorflow - INFO - loss = 0.17665538, step = 284600 (2.289 sec) -2022-05-01 15:06:00,160 - tensorflow - INFO - global_step/sec: 87.5141 -2022-05-01 15:06:00,160 - tensorflow - INFO - loss = 0.07511485, step = 284800 (2.285 sec) -2022-05-01 15:06:02,452 - tensorflow - INFO - global_step/sec: 87.2328 -2022-05-01 15:06:02,453 - tensorflow - INFO - loss = 0.041726876, step = 285000 (2.293 sec) -2022-05-01 15:06:04,747 - tensorflow - INFO - global_step/sec: 87.149 -2022-05-01 15:06:04,748 - tensorflow - INFO - loss = 0.10438813, step = 285200 (2.295 sec) -2022-05-01 15:06:07,047 - tensorflow - INFO - global_step/sec: 86.9643 -2022-05-01 15:06:07,047 - tensorflow - INFO - loss = 0.077885404, step = 285400 (2.300 sec) -2022-05-01 15:06:09,339 - tensorflow - INFO - global_step/sec: 87.2501 -2022-05-01 15:06:09,340 - tensorflow - INFO - loss = 1.0299636, step = 285600 (2.292 sec) -2022-05-01 15:06:11,628 - tensorflow - INFO - global_step/sec: 87.3806 -2022-05-01 15:06:11,629 - tensorflow - INFO - loss = 0.14138225, step = 285800 (2.289 sec) -2022-05-01 15:06:13,916 - tensorflow - INFO - global_step/sec: 87.4141 -2022-05-01 15:06:13,917 - tensorflow - INFO - loss = 0.20049463, step = 286000 (2.288 sec) -2022-05-01 15:06:16,208 - tensorflow - INFO - global_step/sec: 87.2772 -2022-05-01 15:06:16,208 - tensorflow - INFO - loss = 0.47803906, step = 286200 (2.292 sec) -2022-05-01 15:06:18,506 - tensorflow - INFO - global_step/sec: 87.0097 -2022-05-01 15:06:18,507 - tensorflow - INFO - loss = 0.15883633, step = 286400 (2.299 sec) -2022-05-01 15:06:20,803 - tensorflow - INFO - global_step/sec: 87.0895 -2022-05-01 15:06:20,803 - tensorflow - INFO - loss = 2.694594, step = 286600 (2.297 sec) -2022-05-01 15:06:23,105 - tensorflow - INFO - global_step/sec: 86.8571 -2022-05-01 15:06:23,106 - tensorflow - INFO - loss = 0.08450651, step = 286800 (2.303 sec) -2022-05-01 15:06:25,416 - tensorflow - INFO - global_step/sec: 86.5552 -2022-05-01 15:06:25,416 - tensorflow - INFO - loss = 2.9337602, step = 287000 (2.311 sec) -2022-05-01 15:06:27,729 - tensorflow - INFO - global_step/sec: 86.4548 -2022-05-01 15:06:27,730 - tensorflow - INFO - loss = 0.10834767, step = 287200 (2.313 sec) -2022-05-01 15:06:30,034 - tensorflow - INFO - global_step/sec: 86.7786 -2022-05-01 15:06:30,034 - tensorflow - INFO - loss = 0.06139145, step = 287400 (2.305 sec) -2022-05-01 15:06:32,355 - tensorflow - INFO - global_step/sec: 86.156 -2022-05-01 15:06:32,356 - tensorflow - INFO - loss = 0.8649575, step = 287600 (2.321 sec) -2022-05-01 15:06:34,661 - tensorflow - INFO - global_step/sec: 86.7513 -2022-05-01 15:06:34,661 - tensorflow - INFO - loss = 0.038943138, step = 287800 (2.305 sec) -2022-05-01 15:06:36,951 - tensorflow - INFO - global_step/sec: 87.3428 -2022-05-01 15:06:36,951 - tensorflow - INFO - loss = 0.724728, step = 288000 (2.290 sec) -2022-05-01 15:06:39,236 - tensorflow - INFO - global_step/sec: 87.5281 -2022-05-01 15:06:39,236 - tensorflow - INFO - loss = 0.036998108, step = 288200 (2.285 sec) -2022-05-01 15:06:41,532 - tensorflow - INFO - global_step/sec: 87.1033 -2022-05-01 15:06:41,532 - tensorflow - INFO - loss = 0.04534033, step = 288400 (2.296 sec) -2022-05-01 15:06:43,829 - tensorflow - INFO - global_step/sec: 87.0777 -2022-05-01 15:06:43,829 - tensorflow - INFO - loss = 0.03104715, step = 288600 (2.297 sec) -2022-05-01 15:06:46,118 - tensorflow - INFO - global_step/sec: 87.3417 -2022-05-01 15:06:46,119 - tensorflow - INFO - loss = 0.08332451, step = 288800 (2.290 sec) -2022-05-01 15:06:48,409 - tensorflow - INFO - global_step/sec: 87.3342 -2022-05-01 15:06:48,409 - tensorflow - INFO - loss = 0.06010709, step = 289000 (2.290 sec) -2022-05-01 15:06:50,705 - tensorflow - INFO - global_step/sec: 87.0776 -2022-05-01 15:06:50,706 - tensorflow - INFO - loss = 0.113809615, step = 289200 (2.297 sec) -2022-05-01 15:06:52,976 - tensorflow - INFO - global_step/sec: 88.0683 -2022-05-01 15:06:52,977 - tensorflow - INFO - loss = 0.054298207, step = 289400 (2.271 sec) -2022-05-01 15:06:55,252 - tensorflow - INFO - global_step/sec: 87.8883 -2022-05-01 15:06:55,252 - tensorflow - INFO - loss = 0.103841156, step = 289600 (2.276 sec) -2022-05-01 15:06:57,526 - tensorflow - INFO - global_step/sec: 87.9469 -2022-05-01 15:06:57,526 - tensorflow - INFO - loss = 0.033746388, step = 289800 (2.274 sec) -2022-05-01 15:06:59,815 - tensorflow - INFO - global_step/sec: 87.3567 -2022-05-01 15:06:59,816 - tensorflow - INFO - loss = 0.06811048, step = 290000 (2.289 sec) -2022-05-01 15:07:02,111 - tensorflow - INFO - global_step/sec: 87.1256 -2022-05-01 15:07:02,111 - tensorflow - INFO - loss = 1.4815083, step = 290200 (2.296 sec) -2022-05-01 15:07:04,405 - tensorflow - INFO - global_step/sec: 87.1876 -2022-05-01 15:07:04,405 - tensorflow - INFO - loss = 0.09884867, step = 290400 (2.294 sec) -2022-05-01 15:07:06,695 - tensorflow - INFO - global_step/sec: 87.3445 -2022-05-01 15:07:06,695 - tensorflow - INFO - loss = 0.120853096, step = 290600 (2.290 sec) -2022-05-01 15:07:08,989 - tensorflow - INFO - global_step/sec: 87.1564 -2022-05-01 15:07:08,990 - tensorflow - INFO - loss = 0.10058601, step = 290800 (2.295 sec) -2022-05-01 15:07:11,282 - tensorflow - INFO - global_step/sec: 87.2368 -2022-05-01 15:07:11,282 - tensorflow - INFO - loss = 0.019281892, step = 291000 (2.293 sec) -2022-05-01 15:07:13,572 - tensorflow - INFO - global_step/sec: 87.3316 -2022-05-01 15:07:13,573 - tensorflow - INFO - loss = 0.099681005, step = 291200 (2.290 sec) -2022-05-01 15:07:15,865 - tensorflow - INFO - global_step/sec: 87.2468 -2022-05-01 15:07:15,865 - tensorflow - INFO - loss = 0.29632992, step = 291400 (2.292 sec) -2022-05-01 15:07:18,156 - tensorflow - INFO - global_step/sec: 87.2636 -2022-05-01 15:07:18,157 - tensorflow - INFO - loss = 0.13116612, step = 291600 (2.292 sec) -2022-05-01 15:07:20,446 - tensorflow - INFO - global_step/sec: 87.3528 -2022-05-01 15:07:20,446 - tensorflow - INFO - loss = 0.04381878, step = 291800 (2.290 sec) -2022-05-01 15:07:22,734 - tensorflow - INFO - global_step/sec: 87.4134 -2022-05-01 15:07:22,734 - tensorflow - INFO - loss = 0.7160268, step = 292000 (2.288 sec) -2022-05-01 15:07:25,026 - tensorflow - INFO - global_step/sec: 87.2645 -2022-05-01 15:07:25,026 - tensorflow - INFO - loss = 0.20757207, step = 292200 (2.292 sec) -2022-05-01 15:07:27,320 - tensorflow - INFO - global_step/sec: 87.1772 -2022-05-01 15:07:27,320 - tensorflow - INFO - loss = 0.04452872, step = 292400 (2.294 sec) -2022-05-01 15:07:29,606 - tensorflow - INFO - global_step/sec: 87.4936 -2022-05-01 15:07:29,606 - tensorflow - INFO - loss = 0.09249899, step = 292600 (2.286 sec) -2022-05-01 15:07:31,906 - tensorflow - INFO - global_step/sec: 86.9628 -2022-05-01 15:07:31,906 - tensorflow - INFO - loss = 0.43281564, step = 292800 (2.300 sec) -2022-05-01 15:07:34,199 - tensorflow - INFO - global_step/sec: 87.2053 -2022-05-01 15:07:34,200 - tensorflow - INFO - loss = 0.1831511, step = 293000 (2.293 sec) -2022-05-01 15:07:36,491 - tensorflow - INFO - global_step/sec: 87.2675 -2022-05-01 15:07:36,491 - tensorflow - INFO - loss = 0.04634072, step = 293200 (2.292 sec) -2022-05-01 15:07:38,798 - tensorflow - INFO - global_step/sec: 86.7001 -2022-05-01 15:07:38,798 - tensorflow - INFO - loss = 0.04415472, step = 293400 (2.307 sec) -2022-05-01 15:07:41,097 - tensorflow - INFO - global_step/sec: 86.9977 -2022-05-01 15:07:41,097 - tensorflow - INFO - loss = 0.16743091, step = 293600 (2.299 sec) -2022-05-01 15:07:43,401 - tensorflow - INFO - global_step/sec: 86.7761 -2022-05-01 15:07:43,402 - tensorflow - INFO - loss = 0.45817947, step = 293800 (2.305 sec) -2022-05-01 15:07:45,696 - tensorflow - INFO - global_step/sec: 87.165 -2022-05-01 15:07:45,696 - tensorflow - INFO - loss = 0.07792872, step = 294000 (2.295 sec) -2022-05-01 15:07:47,998 - tensorflow - INFO - global_step/sec: 86.8976 -2022-05-01 15:07:47,998 - tensorflow - INFO - loss = 0.022944633, step = 294200 (2.302 sec) -2022-05-01 15:07:50,299 - tensorflow - INFO - global_step/sec: 86.8999 -2022-05-01 15:07:50,299 - tensorflow - INFO - loss = 0.08060293, step = 294400 (2.302 sec) -2022-05-01 15:07:52,601 - tensorflow - INFO - global_step/sec: 86.8839 -2022-05-01 15:07:52,601 - tensorflow - INFO - loss = 0.18352179, step = 294600 (2.302 sec) -2022-05-01 15:07:54,902 - tensorflow - INFO - global_step/sec: 86.9161 -2022-05-01 15:07:54,902 - tensorflow - INFO - loss = 4.4136095, step = 294800 (2.301 sec) -2022-05-01 15:07:57,200 - tensorflow - INFO - global_step/sec: 87.0263 -2022-05-01 15:07:57,201 - tensorflow - INFO - loss = 0.062210653, step = 295000 (2.298 sec) -2022-05-01 15:07:59,502 - tensorflow - INFO - global_step/sec: 86.8744 -2022-05-01 15:07:59,503 - tensorflow - INFO - loss = 1.1744771, step = 295200 (2.302 sec) -2022-05-01 15:08:01,806 - tensorflow - INFO - global_step/sec: 86.8037 -2022-05-01 15:08:01,807 - tensorflow - INFO - loss = 0.07121699, step = 295400 (2.304 sec) -2022-05-01 15:08:04,117 - tensorflow - INFO - global_step/sec: 86.5692 -2022-05-01 15:08:04,117 - tensorflow - INFO - loss = 0.027087681, step = 295600 (2.310 sec) -2022-05-01 15:08:06,425 - tensorflow - INFO - global_step/sec: 86.6403 -2022-05-01 15:08:06,426 - tensorflow - INFO - loss = 0.6950201, step = 295800 (2.308 sec) -2022-05-01 15:08:08,710 - tensorflow - INFO - global_step/sec: 87.5187 -2022-05-01 15:08:08,711 - tensorflow - INFO - loss = 0.18592377, step = 296000 (2.285 sec) -2022-05-01 15:08:11,004 - tensorflow - INFO - global_step/sec: 87.2033 -2022-05-01 15:08:11,004 - tensorflow - INFO - loss = 0.05259351, step = 296200 (2.294 sec) -2022-05-01 15:08:13,291 - tensorflow - INFO - global_step/sec: 87.4529 -2022-05-01 15:08:13,291 - tensorflow - INFO - loss = 0.026855329, step = 296400 (2.287 sec) -2022-05-01 15:08:15,593 - tensorflow - INFO - global_step/sec: 86.8636 -2022-05-01 15:08:15,594 - tensorflow - INFO - loss = 0.038337093, step = 296600 (2.302 sec) -2022-05-01 15:08:17,881 - tensorflow - INFO - global_step/sec: 87.4079 -2022-05-01 15:08:17,882 - tensorflow - INFO - loss = 16.317427, step = 296800 (2.288 sec) -2022-05-01 15:08:20,174 - tensorflow - INFO - global_step/sec: 87.2457 -2022-05-01 15:08:20,174 - tensorflow - INFO - loss = 0.073704466, step = 297000 (2.292 sec) -2022-05-01 15:08:22,468 - tensorflow - INFO - global_step/sec: 87.1762 -2022-05-01 15:08:22,468 - tensorflow - INFO - loss = 0.03904153, step = 297200 (2.294 sec) -2022-05-01 15:08:24,754 - tensorflow - INFO - global_step/sec: 87.4825 -2022-05-01 15:08:24,754 - tensorflow - INFO - loss = 0.027742313, step = 297400 (2.286 sec) -2022-05-01 15:08:27,032 - tensorflow - INFO - global_step/sec: 87.7926 -2022-05-01 15:08:27,033 - tensorflow - INFO - loss = 0.20203239, step = 297600 (2.278 sec) -2022-05-01 15:08:29,305 - tensorflow - INFO - global_step/sec: 88.0037 -2022-05-01 15:08:29,305 - tensorflow - INFO - loss = 2.553576, step = 297800 (2.273 sec) -2022-05-01 15:08:31,566 - tensorflow - INFO - global_step/sec: 88.4544 -2022-05-01 15:08:31,566 - tensorflow - INFO - loss = 0.0866318, step = 298000 (2.261 sec) -2022-05-01 15:08:33,855 - tensorflow - INFO - global_step/sec: 87.3581 -2022-05-01 15:08:33,856 - tensorflow - INFO - loss = 0.067000255, step = 298200 (2.289 sec) -2022-05-01 15:08:36,143 - tensorflow - INFO - global_step/sec: 87.4357 -2022-05-01 15:08:36,143 - tensorflow - INFO - loss = 0.102808684, step = 298400 (2.287 sec) -2022-05-01 15:08:38,430 - tensorflow - INFO - global_step/sec: 87.4537 -2022-05-01 15:08:38,430 - tensorflow - INFO - loss = 0.031817265, step = 298600 (2.287 sec) -2022-05-01 15:08:40,730 - tensorflow - INFO - global_step/sec: 86.9441 -2022-05-01 15:08:40,730 - tensorflow - INFO - loss = 0.14091843, step = 298800 (2.300 sec) -2022-05-01 15:08:43,027 - tensorflow - INFO - global_step/sec: 87.0573 -2022-05-01 15:08:43,028 - tensorflow - INFO - loss = 0.042238776, step = 299000 (2.297 sec) -2022-05-01 15:08:45,310 - tensorflow - INFO - global_step/sec: 87.6187 -2022-05-01 15:08:45,310 - tensorflow - INFO - loss = 0.03812917, step = 299200 (2.283 sec) -2022-05-01 15:08:47,597 - tensorflow - INFO - global_step/sec: 87.4589 -2022-05-01 15:08:47,597 - tensorflow - INFO - loss = 0.11857946, step = 299400 (2.287 sec) -2022-05-01 15:08:49,888 - tensorflow - INFO - global_step/sec: 87.2812 -2022-05-01 15:08:49,889 - tensorflow - INFO - loss = 0.037918597, step = 299600 (2.291 sec) -2022-05-01 15:08:52,184 - tensorflow - INFO - global_step/sec: 87.0992 -2022-05-01 15:08:52,185 - tensorflow - INFO - loss = 0.12851797, step = 299800 (2.296 sec) -2022-05-01 15:08:54,471 - tensorflow - INFO - Saving checkpoints for 300000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. -2022-05-01 15:08:54,488 - tensorflow - INFO - model.ckpt-300000 is not in all_model_checkpoint_paths. Manually adding it. -2022-05-01 15:08:55,028 - tensorflow - INFO - Calling model_fn. -2022-05-01 15:08:55,159 - tensorflow - INFO - Done calling model_fn. -2022-05-01 15:08:55,174 - tensorflow - INFO - Starting evaluation at 2022-05-01T15:08:55Z -2022-05-01 15:08:55,239 - tensorflow - INFO - Graph was finalized. -2022-05-01 15:08:55,242 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-300000 -2022-05-01 15:08:55,530 - tensorflow - INFO - Running local_init_op. -2022-05-01 15:08:55,640 - tensorflow - INFO - Done running local_init_op. -2022-05-01 15:09:05,911 - tensorflow - INFO - Evaluation [3990/39900] -2022-05-01 15:09:15,477 - tensorflow - INFO - Evaluation [7980/39900] -2022-05-01 15:09:25,029 - tensorflow - INFO - Evaluation [11970/39900] -2022-05-01 15:09:34,594 - tensorflow - INFO - Evaluation [15960/39900] -2022-05-01 15:09:44,157 - tensorflow - INFO - Evaluation [19950/39900] -2022-05-01 15:09:53,715 - tensorflow - INFO - Evaluation [23940/39900] -2022-05-01 15:10:03,243 - tensorflow - INFO - Evaluation [27930/39900] -2022-05-01 15:10:12,772 - tensorflow - INFO - Evaluation [31920/39900] -2022-05-01 15:10:22,332 - tensorflow - INFO - Evaluation [35910/39900] -2022-05-01 15:10:31,903 - tensorflow - INFO - Evaluation [39900/39900] -2022-05-01 15:10:32,041 - tensorflow - INFO - Finished evaluation at 2022-05-01-15:10:32 -2022-05-01 15:10:32,042 - tensorflow - INFO - Saving dict for global step 300000: global_step = 300000, loss = 0.05468036, mean_degree_err = 7.492244 -2022-05-01 15:10:32,042 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 300000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-300000 -2022-05-01 15:10:32,056 - tensorflow - INFO - global_step/sec: 2.00257 -2022-05-01 15:10:32,057 - tensorflow - INFO - loss = 0.40683693, step = 300000 (99.872 sec) -2022-05-01 15:10:34,365 - tensorflow - INFO - global_step/sec: 86.6192 -2022-05-01 15:10:34,365 - tensorflow - INFO - loss = 0.07048734, step = 300200 (2.308 sec) -2022-05-01 15:10:36,657 - tensorflow - INFO - global_step/sec: 87.2609 -2022-05-01 15:10:36,657 - tensorflow - INFO - loss = 0.06908274, step = 300400 (2.292 sec) -2022-05-01 15:10:38,956 - tensorflow - INFO - global_step/sec: 86.9731 -2022-05-01 15:10:38,957 - tensorflow - INFO - loss = 0.031588264, step = 300600 (2.300 sec) -2022-05-01 15:10:41,244 - tensorflow - INFO - global_step/sec: 87.4289 -2022-05-01 15:10:41,244 - tensorflow - INFO - loss = 0.059713908, step = 300800 (2.288 sec) -2022-05-01 15:10:43,538 - tensorflow - INFO - global_step/sec: 87.1703 -2022-05-01 15:10:43,539 - tensorflow - INFO - loss = 0.06908436, step = 301000 (2.294 sec) -2022-05-01 15:10:45,839 - tensorflow - INFO - global_step/sec: 86.9113 -2022-05-01 15:10:45,840 - tensorflow - INFO - loss = 0.058627427, step = 301200 (2.301 sec) -2022-05-01 15:10:48,130 - tensorflow - INFO - global_step/sec: 87.3322 -2022-05-01 15:10:48,130 - tensorflow - INFO - loss = 0.22693896, step = 301400 (2.290 sec) -2022-05-01 15:10:50,416 - tensorflow - INFO - global_step/sec: 87.459 -2022-05-01 15:10:50,417 - tensorflow - INFO - loss = 0.033071175, step = 301600 (2.287 sec) -2022-05-01 15:10:52,714 - tensorflow - INFO - global_step/sec: 87.0478 -2022-05-01 15:10:52,714 - tensorflow - INFO - loss = 0.20088597, step = 301800 (2.298 sec) -2022-05-01 15:10:55,012 - tensorflow - INFO - global_step/sec: 87.0374 -2022-05-01 15:10:55,012 - tensorflow - INFO - loss = 0.11038767, step = 302000 (2.298 sec) -2022-05-01 15:10:57,312 - tensorflow - INFO - global_step/sec: 86.9659 -2022-05-01 15:10:57,312 - tensorflow - INFO - loss = 0.024178479, step = 302200 (2.300 sec) -2022-05-01 15:10:59,607 - tensorflow - INFO - global_step/sec: 87.1475 -2022-05-01 15:10:59,607 - tensorflow - INFO - loss = 0.051052075, step = 302400 (2.295 sec) -2022-05-01 15:11:01,904 - tensorflow - INFO - global_step/sec: 87.058 -2022-05-01 15:11:01,904 - tensorflow - INFO - loss = 0.12262677, step = 302600 (2.297 sec) -2022-05-01 15:11:04,191 - tensorflow - INFO - global_step/sec: 87.4306 -2022-05-01 15:11:04,192 - tensorflow - INFO - loss = 0.051911946, step = 302800 (2.288 sec) -2022-05-01 15:11:06,482 - tensorflow - INFO - global_step/sec: 87.3025 -2022-05-01 15:11:06,483 - tensorflow - INFO - loss = 0.017697304, step = 303000 (2.291 sec) -2022-05-01 15:11:08,782 - tensorflow - INFO - global_step/sec: 86.971 -2022-05-01 15:11:08,782 - tensorflow - INFO - loss = 0.07305166, step = 303200 (2.300 sec) -2022-05-01 15:11:11,086 - tensorflow - INFO - global_step/sec: 86.7852 -2022-05-01 15:11:11,087 - tensorflow - INFO - loss = 0.03269926, step = 303400 (2.305 sec) -2022-05-01 15:11:13,393 - tensorflow - INFO - global_step/sec: 86.7138 -2022-05-01 15:11:13,393 - tensorflow - INFO - loss = 0.046545926, step = 303600 (2.306 sec) -2022-05-01 15:11:15,698 - tensorflow - INFO - global_step/sec: 86.7779 -2022-05-01 15:11:15,698 - tensorflow - INFO - loss = 0.06280299, step = 303800 (2.305 sec) -2022-05-01 15:11:17,991 - tensorflow - INFO - global_step/sec: 87.2206 -2022-05-01 15:11:17,991 - tensorflow - INFO - loss = 0.04077121, step = 304000 (2.293 sec) -2022-05-01 15:11:20,283 - tensorflow - INFO - global_step/sec: 87.2438 -2022-05-01 15:11:20,283 - tensorflow - INFO - loss = 0.10791534, step = 304200 (2.292 sec) -2022-05-01 15:11:22,591 - tensorflow - INFO - global_step/sec: 86.6598 -2022-05-01 15:11:22,591 - tensorflow - INFO - loss = 0.7236721, step = 304400 (2.308 sec) -2022-05-01 15:11:24,899 - tensorflow - INFO - global_step/sec: 86.6611 -2022-05-01 15:11:24,899 - tensorflow - INFO - loss = 0.13847843, step = 304600 (2.308 sec) -2022-05-01 15:11:27,201 - tensorflow - INFO - global_step/sec: 86.8704 -2022-05-01 15:11:27,202 - tensorflow - INFO - loss = 0.107650064, step = 304800 (2.302 sec) -2022-05-01 15:11:29,502 - tensorflow - INFO - global_step/sec: 86.9391 -2022-05-01 15:11:29,502 - tensorflow - INFO - loss = 1.1622391, step = 305000 (2.300 sec) -2022-05-01 15:11:31,809 - tensorflow - INFO - global_step/sec: 86.6717 -2022-05-01 15:11:31,810 - tensorflow - INFO - loss = 0.07405165, step = 305200 (2.308 sec) -2022-05-01 15:11:34,110 - tensorflow - INFO - global_step/sec: 86.9039 -2022-05-01 15:11:34,111 - tensorflow - INFO - loss = 0.39629376, step = 305400 (2.301 sec) -2022-05-01 15:11:36,404 - tensorflow - INFO - global_step/sec: 87.2114 -2022-05-01 15:11:36,404 - tensorflow - INFO - loss = 0.03248347, step = 305600 (2.293 sec) -2022-05-01 15:11:38,697 - tensorflow - INFO - global_step/sec: 87.2072 -2022-05-01 15:11:38,698 - tensorflow - INFO - loss = 0.19235176, step = 305800 (2.293 sec) -2022-05-01 15:11:40,976 - tensorflow - INFO - global_step/sec: 87.7727 -2022-05-01 15:11:40,976 - tensorflow - INFO - loss = 0.23082805, step = 306000 (2.279 sec) -2022-05-01 15:11:43,245 - tensorflow - INFO - global_step/sec: 88.1357 -2022-05-01 15:11:43,245 - tensorflow - INFO - loss = 0.09423758, step = 306200 (2.269 sec) -2022-05-01 15:11:45,528 - tensorflow - INFO - global_step/sec: 87.5988 -2022-05-01 15:11:45,529 - tensorflow - INFO - loss = 0.09137006, step = 306400 (2.283 sec) -2022-05-01 15:11:47,824 - tensorflow - INFO - global_step/sec: 87.1219 -2022-05-01 15:11:47,824 - tensorflow - INFO - loss = 0.03408081, step = 306600 (2.296 sec) -2022-05-01 15:11:50,124 - tensorflow - INFO - global_step/sec: 86.9651 -2022-05-01 15:11:50,124 - tensorflow - INFO - loss = 0.20070125, step = 306800 (2.300 sec) -2022-05-01 15:11:52,421 - tensorflow - INFO - global_step/sec: 87.0634 -2022-05-01 15:11:52,421 - tensorflow - INFO - loss = 0.44823924, step = 307000 (2.297 sec) -2022-05-01 15:11:54,716 - tensorflow - INFO - global_step/sec: 87.1318 -2022-05-01 15:11:54,716 - tensorflow - INFO - loss = 0.090255864, step = 307200 (2.295 sec) -2022-05-01 15:11:57,013 - tensorflow - INFO - global_step/sec: 87.0697 -2022-05-01 15:11:57,014 - tensorflow - INFO - loss = 0.6353988, step = 307400 (2.297 sec) -2022-05-01 15:11:59,302 - tensorflow - INFO - global_step/sec: 87.3928 -2022-05-01 15:11:59,302 - tensorflow - INFO - loss = 0.0418145, step = 307600 (2.288 sec) -2022-05-01 15:12:01,594 - tensorflow - INFO - global_step/sec: 87.2546 -2022-05-01 15:12:01,594 - tensorflow - INFO - loss = 0.4108369, step = 307800 (2.292 sec) -2022-05-01 15:12:03,899 - tensorflow - INFO - global_step/sec: 86.7711 -2022-05-01 15:12:03,899 - tensorflow - INFO - loss = 0.15063372, step = 308000 (2.305 sec) -2022-05-01 15:12:06,199 - tensorflow - INFO - global_step/sec: 86.9332 -2022-05-01 15:12:06,200 - tensorflow - INFO - loss = 0.2462475, step = 308200 (2.301 sec) -2022-05-01 15:12:08,500 - tensorflow - INFO - global_step/sec: 86.9282 -2022-05-01 15:12:08,500 - tensorflow - INFO - loss = 0.040768966, step = 308400 (2.301 sec) -2022-05-01 15:12:10,794 - tensorflow - INFO - global_step/sec: 87.1863 -2022-05-01 15:12:10,794 - tensorflow - INFO - loss = 0.2742311, step = 308600 (2.294 sec) -2022-05-01 15:12:13,092 - tensorflow - INFO - global_step/sec: 87.039 -2022-05-01 15:12:13,092 - tensorflow - INFO - loss = 0.1378581, step = 308800 (2.298 sec) -2022-05-01 15:12:15,382 - tensorflow - INFO - global_step/sec: 87.3243 -2022-05-01 15:12:15,383 - tensorflow - INFO - loss = 10.739632, step = 309000 (2.290 sec) -2022-05-01 15:12:17,679 - tensorflow - INFO - global_step/sec: 87.0808 -2022-05-01 15:12:17,679 - tensorflow - INFO - loss = 0.054495342, step = 309200 (2.297 sec) -2022-05-01 15:12:19,972 - tensorflow - INFO - global_step/sec: 87.2328 -2022-05-01 15:12:19,972 - tensorflow - INFO - loss = 0.085411735, step = 309400 (2.293 sec) -2022-05-01 15:12:22,263 - tensorflow - INFO - global_step/sec: 87.2891 -2022-05-01 15:12:22,263 - tensorflow - INFO - loss = 0.14313415, step = 309600 (2.291 sec) -2022-05-01 15:12:24,548 - tensorflow - INFO - global_step/sec: 87.5073 -2022-05-01 15:12:24,549 - tensorflow - INFO - loss = 0.032904863, step = 309800 (2.285 sec) -2022-05-01 15:12:26,839 - tensorflow - INFO - global_step/sec: 87.3072 -2022-05-01 15:12:26,840 - tensorflow - INFO - loss = 0.033875167, step = 310000 (2.291 sec) -2022-05-01 15:12:29,139 - tensorflow - INFO - global_step/sec: 86.9747 -2022-05-01 15:12:29,139 - tensorflow - INFO - loss = 0.038221262, step = 310200 (2.299 sec) -2022-05-01 15:12:31,440 - tensorflow - INFO - global_step/sec: 86.8985 -2022-05-01 15:12:31,441 - tensorflow - INFO - loss = 0.14979117, step = 310400 (2.302 sec) -2022-05-01 15:12:33,734 - tensorflow - INFO - global_step/sec: 87.1854 -2022-05-01 15:12:33,735 - tensorflow - INFO - loss = 0.08342664, step = 310600 (2.294 sec) -2022-05-01 15:12:36,025 - tensorflow - INFO - global_step/sec: 87.3146 -2022-05-01 15:12:36,025 - tensorflow - INFO - loss = 0.07314127, step = 310800 (2.290 sec) -2022-05-01 15:12:38,319 - tensorflow - INFO - global_step/sec: 87.175 -2022-05-01 15:12:38,319 - tensorflow - INFO - loss = 0.04351961, step = 311000 (2.294 sec) -2022-05-01 15:12:40,620 - tensorflow - INFO - global_step/sec: 86.9052 -2022-05-01 15:12:40,621 - tensorflow - INFO - loss = 0.030328657, step = 311200 (2.301 sec) -2022-05-01 15:12:42,907 - tensorflow - INFO - global_step/sec: 87.4603 -2022-05-01 15:12:42,907 - tensorflow - INFO - loss = 0.18517587, step = 311400 (2.287 sec) -2022-05-01 15:12:45,201 - tensorflow - INFO - global_step/sec: 87.1943 -2022-05-01 15:12:45,201 - tensorflow - INFO - loss = 0.1633918, step = 311600 (2.294 sec) -2022-05-01 15:12:47,491 - tensorflow - INFO - global_step/sec: 87.3401 -2022-05-01 15:12:47,491 - tensorflow - INFO - loss = 0.072592765, step = 311800 (2.290 sec) -2022-05-01 15:12:49,783 - tensorflow - INFO - global_step/sec: 87.2394 -2022-05-01 15:12:49,784 - tensorflow - INFO - loss = 0.06983033, step = 312000 (2.293 sec) -2022-05-01 15:12:52,074 - tensorflow - INFO - global_step/sec: 87.2979 -2022-05-01 15:12:52,075 - tensorflow - INFO - loss = 0.08322585, step = 312200 (2.291 sec) -2022-05-01 15:12:54,368 - tensorflow - INFO - global_step/sec: 87.1976 -2022-05-01 15:12:54,368 - tensorflow - INFO - loss = 0.1724514, step = 312400 (2.294 sec) -2022-05-01 15:12:56,662 - tensorflow - INFO - global_step/sec: 87.1684 -2022-05-01 15:12:56,663 - tensorflow - INFO - loss = 0.043952804, step = 312600 (2.294 sec) -2022-05-01 15:12:58,956 - tensorflow - INFO - global_step/sec: 87.1994 -2022-05-01 15:12:58,956 - tensorflow - INFO - loss = 0.04208758, step = 312800 (2.294 sec) -2022-05-01 15:13:01,249 - tensorflow - INFO - global_step/sec: 87.2164 -2022-05-01 15:13:01,249 - tensorflow - INFO - loss = 0.040456578, step = 313000 (2.293 sec) -2022-05-01 15:13:03,545 - tensorflow - INFO - global_step/sec: 87.1212 -2022-05-01 15:13:03,545 - tensorflow - INFO - loss = 0.49297708, step = 313200 (2.296 sec) -2022-05-01 15:13:05,844 - tensorflow - INFO - global_step/sec: 86.9924 -2022-05-01 15:13:05,844 - tensorflow - INFO - loss = 0.049867652, step = 313400 (2.299 sec) -2022-05-01 15:13:08,132 - tensorflow - INFO - global_step/sec: 87.3925 -2022-05-01 15:13:08,133 - tensorflow - INFO - loss = 0.03449381, step = 313600 (2.289 sec) -2022-05-01 15:13:10,422 - tensorflow - INFO - global_step/sec: 87.3331 -2022-05-01 15:13:10,423 - tensorflow - INFO - loss = 21.256264, step = 313800 (2.290 sec) -2022-05-01 15:13:12,707 - tensorflow - INFO - global_step/sec: 87.5467 -2022-05-01 15:13:12,707 - tensorflow - INFO - loss = 1.3560358, step = 314000 (2.285 sec) -2022-05-01 15:13:14,973 - tensorflow - INFO - global_step/sec: 88.2519 -2022-05-01 15:13:14,973 - tensorflow - INFO - loss = 0.88814807, step = 314200 (2.266 sec) -2022-05-01 15:13:17,225 - tensorflow - INFO - global_step/sec: 88.8229 -2022-05-01 15:13:17,225 - tensorflow - INFO - loss = 0.1875411, step = 314400 (2.252 sec) -2022-05-01 15:13:19,511 - tensorflow - INFO - global_step/sec: 87.4897 -2022-05-01 15:13:19,511 - tensorflow - INFO - loss = 0.017372161, step = 314600 (2.286 sec) -2022-05-01 15:13:21,797 - tensorflow - INFO - global_step/sec: 87.4889 -2022-05-01 15:13:21,797 - tensorflow - INFO - loss = 0.054762032, step = 314800 (2.286 sec) -2022-05-01 15:13:24,088 - tensorflow - INFO - global_step/sec: 87.2815 -2022-05-01 15:13:24,089 - tensorflow - INFO - loss = 0.04733616, step = 315000 (2.291 sec) -2022-05-01 15:13:26,378 - tensorflow - INFO - global_step/sec: 87.3319 -2022-05-01 15:13:26,379 - tensorflow - INFO - loss = 0.031029467, step = 315200 (2.290 sec) -2022-05-01 15:13:28,669 - tensorflow - INFO - global_step/sec: 87.3215 -2022-05-01 15:13:28,669 - tensorflow - INFO - loss = 0.019960608, step = 315400 (2.290 sec) -2022-05-01 15:13:30,974 - tensorflow - INFO - global_step/sec: 86.7648 -2022-05-01 15:13:30,974 - tensorflow - INFO - loss = 0.13338916, step = 315600 (2.305 sec) -2022-05-01 15:13:33,272 - tensorflow - INFO - global_step/sec: 87.0171 -2022-05-01 15:13:33,273 - tensorflow - INFO - loss = 0.052738898, step = 315800 (2.298 sec) -2022-05-01 15:13:35,563 - tensorflow - INFO - global_step/sec: 87.3144 -2022-05-01 15:13:35,563 - tensorflow - INFO - loss = 0.2678072, step = 316000 (2.291 sec) -2022-05-01 15:13:37,860 - tensorflow - INFO - global_step/sec: 87.0492 -2022-05-01 15:13:37,861 - tensorflow - INFO - loss = 0.02358115, step = 316200 (2.298 sec) -2022-05-01 15:13:40,153 - tensorflow - INFO - global_step/sec: 87.2418 -2022-05-01 15:13:40,153 - tensorflow - INFO - loss = 0.06098066, step = 316400 (2.292 sec) -2022-05-01 15:13:42,444 - tensorflow - INFO - global_step/sec: 87.2914 -2022-05-01 15:13:42,444 - tensorflow - INFO - loss = 0.6916264, step = 316600 (2.291 sec) -2022-05-01 15:13:44,747 - tensorflow - INFO - global_step/sec: 86.83 -2022-05-01 15:13:44,748 - tensorflow - INFO - loss = 0.059347045, step = 316800 (2.303 sec) -2022-05-01 15:13:47,043 - tensorflow - INFO - global_step/sec: 87.1244 -2022-05-01 15:13:47,043 - tensorflow - INFO - loss = 0.14817724, step = 317000 (2.296 sec) -2022-05-01 15:13:49,336 - tensorflow - INFO - global_step/sec: 87.2152 -2022-05-01 15:13:49,336 - tensorflow - INFO - loss = 1.5249491, step = 317200 (2.293 sec) -2022-05-01 15:13:51,626 - tensorflow - INFO - global_step/sec: 87.337 -2022-05-01 15:13:51,626 - tensorflow - INFO - loss = 0.022557339, step = 317400 (2.290 sec) -2022-05-01 15:13:53,920 - tensorflow - INFO - global_step/sec: 87.1734 -2022-05-01 15:13:53,921 - tensorflow - INFO - loss = 0.040357865, step = 317600 (2.294 sec) -2022-05-01 15:13:56,220 - tensorflow - INFO - global_step/sec: 86.9794 -2022-05-01 15:13:56,220 - tensorflow - INFO - loss = 0.109935924, step = 317800 (2.299 sec) -2022-05-01 15:13:58,505 - tensorflow - INFO - global_step/sec: 87.496 -2022-05-01 15:13:58,506 - tensorflow - INFO - loss = 0.0337951, step = 318000 (2.286 sec) -2022-05-01 15:14:00,790 - tensorflow - INFO - global_step/sec: 87.5273 -2022-05-01 15:14:00,791 - tensorflow - INFO - loss = 21.680937, step = 318200 (2.285 sec) -2022-05-01 15:14:03,085 - tensorflow - INFO - global_step/sec: 87.1453 -2022-05-01 15:14:03,086 - tensorflow - INFO - loss = 0.045138318, step = 318400 (2.295 sec) -2022-05-01 15:14:05,373 - tensorflow - INFO - global_step/sec: 87.4451 -2022-05-01 15:14:05,373 - tensorflow - INFO - loss = 0.10982559, step = 318600 (2.287 sec) -2022-05-01 15:14:07,670 - tensorflow - INFO - global_step/sec: 87.0412 -2022-05-01 15:14:07,671 - tensorflow - INFO - loss = 1.3777144, step = 318800 (2.298 sec) -2022-05-01 15:14:09,960 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.36955315, step = 320200 (2.286 sec) -2022-05-01 15:14:25,999 - tensorflow - INFO - global_step/sec: 87.1287 -2022-05-01 15:14:25,999 - tensorflow - INFO - loss = 0.07062389, step = 320400 (2.296 sec) -2022-05-01 15:14:28,285 - tensorflow - INFO - global_step/sec: 87.4835 -2022-05-01 15:14:28,285 - tensorflow - INFO - loss = 0.33431238, step = 320600 (2.286 sec) -2022-05-01 15:14:30,580 - tensorflow - INFO - global_step/sec: 87.1468 -2022-05-01 15:14:30,580 - tensorflow - INFO - loss = 0.12153618, step = 320800 (2.295 sec) -2022-05-01 15:14:32,877 - tensorflow - INFO - global_step/sec: 87.0562 -2022-05-01 15:14:32,878 - tensorflow - INFO - loss = 0.12804434, step = 321000 (2.297 sec) -2022-05-01 15:14:35,166 - tensorflow - INFO - global_step/sec: 87.369 -2022-05-01 15:14:35,167 - tensorflow - INFO - loss = 0.07932933, step = 321200 (2.289 sec) -2022-05-01 15:14:37,457 - tensorflow - INFO - global_step/sec: 87.318 -2022-05-01 15:14:37,457 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.090596594, step = 380200 (2.303 sec) -2022-05-01 15:25:53,819 - tensorflow - INFO - global_step/sec: 87.3408 -2022-05-01 15:25:53,819 - tensorflow - INFO - loss = 0.66013145, step = 380400 (2.290 sec) -2022-05-01 15:25:56,117 - tensorflow - INFO - global_step/sec: 87.0237 -2022-05-01 15:25:56,118 - tensorflow - INFO - loss = 0.07896091, step = 380600 (2.298 sec) -2022-05-01 15:25:58,408 - tensorflow - INFO - global_step/sec: 87.3062 -2022-05-01 15:25:58,408 - tensorflow - INFO - loss = 0.30870202, step = 380800 (2.291 sec) -2022-05-01 15:26:00,706 - tensorflow - INFO - global_step/sec: 87.0512 -2022-05-01 15:26:00,706 - tensorflow - INFO - loss = 0.048311736, step = 381000 (2.298 sec) -2022-05-01 15:26:03,002 - tensorflow - INFO - global_step/sec: 87.1025 -2022-05-01 15:26:03,002 - tensorflow - INFO - loss = 0.11387083, step = 381200 (2.296 sec) -2022-05-01 15:26:05,298 - tensorflow - INFO - global_step/sec: 87.0793 -2022-05-01 15:26:05,299 - tensorflow - INFO - loss = 0.09907612, step = 381400 (2.297 sec) -2022-05-01 15:26:07,593 - tensorflow - INFO - global_step/sec: 87.1748 -2022-05-01 15:26:07,593 - tensorflow - INFO - loss = 0.06651655, step = 381600 (2.294 sec) -2022-05-01 15:26:09,889 - tensorflow - INFO - global_step/sec: 87.0882 -2022-05-01 15:26:09,890 - tensorflow - INFO - loss = 0.9802106, step = 381800 (2.297 sec) -2022-05-01 15:26:12,182 - tensorflow - INFO - global_step/sec: 87.2343 -2022-05-01 15:26:12,182 - tensorflow - INFO - loss = 0.06472394, step = 382000 (2.293 sec) -2022-05-01 15:26:14,475 - tensorflow - INFO - global_step/sec: 87.2089 -2022-05-01 15:26:14,476 - tensorflow - INFO - loss = 0.023764964, step = 382200 (2.293 sec) -2022-05-01 15:26:16,769 - tensorflow - INFO - global_step/sec: 87.1933 -2022-05-01 15:26:16,769 - tensorflow - INFO - loss = 0.105399184, step = 382400 (2.294 sec) -2022-05-01 15:26:19,070 - tensorflow - INFO - global_step/sec: 86.9331 -2022-05-01 15:26:19,070 - tensorflow - INFO - loss = 0.07112752, step = 382600 (2.301 sec) -2022-05-01 15:26:21,363 - tensorflow - INFO - global_step/sec: 87.188 -2022-05-01 15:26:21,364 - tensorflow - INFO - loss = 0.21068093, step = 382800 (2.294 sec) -2022-05-01 15:26:23,660 - tensorflow - INFO - global_step/sec: 87.0763 -2022-05-01 15:26:23,661 - tensorflow - INFO - loss = 0.024292879, step = 383000 (2.297 sec) -2022-05-01 15:26:25,958 - tensorflow - INFO - global_step/sec: 87.0598 -2022-05-01 15:26:25,958 - tensorflow - INFO - loss = 0.21530095, step = 383200 (2.297 sec) -2022-05-01 15:26:28,268 - tensorflow - INFO - global_step/sec: 86.5787 -2022-05-01 15:26:28,268 - tensorflow - INFO - loss = 0.19381899, step = 383400 (2.310 sec) -2022-05-01 15:26:30,574 - tensorflow - INFO - global_step/sec: 86.7005 -2022-05-01 15:26:30,575 - tensorflow - INFO - loss = 0.09407639, step = 383600 (2.307 sec) -2022-05-01 15:26:32,876 - tensorflow - INFO - global_step/sec: 86.9126 -2022-05-01 15:26:32,876 - tensorflow - INFO - loss = 0.21737504, step = 383800 (2.301 sec) -2022-05-01 15:26:35,180 - tensorflow - INFO - global_step/sec: 86.8002 -2022-05-01 15:26:35,180 - tensorflow - INFO - loss = 0.019173391, step = 384000 (2.304 sec) -2022-05-01 15:26:37,474 - tensorflow - INFO - global_step/sec: 87.1888 -2022-05-01 15:26:37,474 - tensorflow - INFO - loss = 0.05019468, step = 384200 (2.294 sec) -2022-05-01 15:26:39,761 - tensorflow - INFO - global_step/sec: 87.4405 -2022-05-01 15:26:39,761 - tensorflow - INFO - loss = 1.2204313, step = 384400 (2.287 sec) -2022-05-01 15:26:42,055 - tensorflow - INFO - global_step/sec: 87.1834 -2022-05-01 15:26:42,055 - tensorflow - INFO - loss = 0.026852375, step = 384600 (2.294 sec) -2022-05-01 15:26:44,346 - tensorflow - INFO - global_step/sec: 87.3054 -2022-05-01 15:26:44,346 - tensorflow - INFO - loss = 0.05077, step = 384800 (2.291 sec) -2022-05-01 15:26:46,633 - tensorflow - INFO - global_step/sec: 87.439 -2022-05-01 15:26:46,633 - tensorflow - INFO - loss = 0.3412217, step = 385000 (2.287 sec) -2022-05-01 15:26:48,919 - tensorflow - INFO - global_step/sec: 87.4908 -2022-05-01 15:26:48,919 - tensorflow - INFO - loss = 0.05305391, step = 385200 (2.286 sec) -2022-05-01 15:26:51,210 - tensorflow - INFO - global_step/sec: 87.3046 -2022-05-01 15:26:51,210 - tensorflow - INFO - loss = 0.15249148, step = 385400 (2.291 sec) -2022-05-01 15:26:53,491 - tensorflow - INFO - global_step/sec: 87.6665 -2022-05-01 15:26:53,492 - tensorflow - INFO - loss = 0.10122527, step = 385600 (2.281 sec) -2022-05-01 15:26:55,777 - tensorflow - INFO - global_step/sec: 87.4855 -2022-05-01 15:26:55,778 - tensorflow - INFO - loss = 0.041996934, step = 385800 (2.286 sec) -2022-05-01 15:26:58,054 - tensorflow - INFO - global_step/sec: 87.8417 -2022-05-01 15:26:58,054 - tensorflow - INFO - loss = 0.086239435, step = 386000 (2.277 sec) -2022-05-01 15:27:00,322 - tensorflow - INFO - global_step/sec: 88.1912 -2022-05-01 15:27:00,322 - tensorflow - INFO - loss = 0.13029584, step = 386200 (2.268 sec) -2022-05-01 15:27:02,588 - tensorflow - INFO - global_step/sec: 88.2611 -2022-05-01 15:27:02,588 - tensorflow - INFO - loss = 0.020974861, step = 386400 (2.266 sec) -2022-05-01 15:27:04,849 - tensorflow - INFO - global_step/sec: 88.4635 -2022-05-01 15:27:04,849 - tensorflow - INFO - loss = 0.20385835, step = 386600 (2.261 sec) -2022-05-01 15:27:07,115 - tensorflow - INFO - global_step/sec: 88.2618 -2022-05-01 15:27:07,115 - tensorflow - INFO - loss = 0.05887437, step = 386800 (2.266 sec) -2022-05-01 15:27:09,406 - tensorflow - INFO - global_step/sec: 87.3043 -2022-05-01 15:27:09,406 - tensorflow - INFO - loss = 0.3467544, step = 387000 (2.291 sec) -2022-05-01 15:27:11,690 - tensorflow - INFO - global_step/sec: 87.5366 -2022-05-01 15:27:11,691 - tensorflow - INFO - loss = 0.22042203, step = 387200 (2.285 sec) -2022-05-01 15:27:13,973 - tensorflow - INFO - global_step/sec: 87.622 -2022-05-01 15:27:13,973 - tensorflow - INFO - loss = 0.063067436, step = 387400 (2.283 sec) -2022-05-01 15:27:16,263 - tensorflow - INFO - global_step/sec: 87.3299 -2022-05-01 15:27:16,263 - tensorflow - INFO - loss = 0.0660839, step = 387600 (2.290 sec) -2022-05-01 15:27:18,559 - tensorflow - INFO - global_step/sec: 87.1199 -2022-05-01 15:27:18,559 - tensorflow - INFO - loss = 0.16306858, step = 387800 (2.296 sec) -2022-05-01 15:27:20,850 - tensorflow - INFO - global_step/sec: 87.2961 -2022-05-01 15:27:20,850 - tensorflow - INFO - loss = 0.12461989, step = 388000 (2.291 sec) -2022-05-01 15:27:23,139 - tensorflow - INFO - global_step/sec: 87.3612 -2022-05-01 15:27:23,140 - tensorflow - INFO - loss = 0.033755578, step = 388200 (2.289 sec) -2022-05-01 15:27:25,421 - tensorflow - INFO - global_step/sec: 87.6285 -2022-05-01 15:27:25,422 - tensorflow - INFO - loss = 0.4511457, step = 388400 (2.282 sec) -2022-05-01 15:27:27,713 - tensorflow - INFO - global_step/sec: 87.274 -2022-05-01 15:27:27,714 - tensorflow - INFO - loss = 0.1348494, step = 388600 (2.292 sec) -2022-05-01 15:27:29,998 - tensorflow - INFO - global_step/sec: 87.5477 -2022-05-01 15:27:29,998 - tensorflow - INFO - loss = 0.06808847, step = 388800 (2.284 sec) -2022-05-01 15:27:32,297 - tensorflow - INFO - global_step/sec: 86.9584 -2022-05-01 15:27:32,298 - tensorflow - INFO - loss = 0.11560288, step = 389000 (2.300 sec) -2022-05-01 15:27:34,591 - tensorflow - INFO - global_step/sec: 87.1913 -2022-05-01 15:27:34,592 - tensorflow - INFO - loss = 0.9794904, step = 389200 (2.294 sec) -2022-05-01 15:27:36,880 - tensorflow - INFO - global_step/sec: 87.3863 -2022-05-01 15:27:36,880 - tensorflow - INFO - loss = 0.26934433, step = 389400 (2.289 sec) -2022-05-01 15:27:39,166 - tensorflow - INFO - global_step/sec: 87.4754 -2022-05-01 15:27:39,167 - tensorflow - INFO - loss = 0.08934729, step = 389600 (2.286 sec) -2022-05-01 15:27:41,458 - tensorflow - INFO - global_step/sec: 87.2697 -2022-05-01 15:27:41,458 - tensorflow - INFO - loss = 0.021905141, step = 389800 (2.292 sec) -2022-05-01 15:27:43,758 - tensorflow - INFO - global_step/sec: 86.9738 -2022-05-01 15:27:43,758 - tensorflow - INFO - loss = 0.049776305, step = 390000 (2.300 sec) -2022-05-01 15:27:46,051 - tensorflow - INFO - global_step/sec: 87.1939 -2022-05-01 15:27:46,052 - tensorflow - INFO - loss = 0.040118024, step = 390200 (2.294 sec) -2022-05-01 15:27:48,335 - tensorflow - INFO - global_step/sec: 87.5897 -2022-05-01 15:27:48,335 - tensorflow - INFO - loss = 0.02597103, step = 390400 (2.283 sec) -2022-05-01 15:27:50,624 - tensorflow - INFO - global_step/sec: 87.3757 -2022-05-01 15:27:50,624 - tensorflow - INFO - loss = 0.042590633, step = 390600 (2.289 sec) -2022-05-01 15:27:52,920 - tensorflow - INFO - global_step/sec: 87.0951 -2022-05-01 15:27:52,920 - tensorflow - INFO - loss = 0.039586984, step = 390800 (2.296 sec) -2022-05-01 15:27:55,212 - tensorflow - INFO - global_step/sec: 87.2792 -2022-05-01 15:27:55,212 - tensorflow - INFO - loss = 0.04140979, step = 391000 (2.291 sec) -2022-05-01 15:27:57,504 - tensorflow - INFO - global_step/sec: 87.2236 -2022-05-01 15:27:57,505 - tensorflow - INFO - loss = 0.029703354, step = 391200 (2.293 sec) -2022-05-01 15:27:59,793 - tensorflow - INFO - global_step/sec: 87.4091 -2022-05-01 15:27:59,793 - tensorflow - INFO - loss = 0.08359525, step = 391400 (2.288 sec) -2022-05-01 15:28:02,081 - tensorflow - INFO - global_step/sec: 87.4016 -2022-05-01 15:28:02,081 - tensorflow - INFO - loss = 0.08424415, step = 391600 (2.288 sec) -2022-05-01 15:28:04,377 - tensorflow - INFO - global_step/sec: 87.0941 -2022-05-01 15:28:04,378 - tensorflow - INFO - loss = 0.02261557, step = 391800 (2.296 sec) -2022-05-01 15:28:06,668 - tensorflow - INFO - global_step/sec: 87.3149 -2022-05-01 15:28:06,668 - tensorflow - INFO - loss = 0.37749457, step = 392000 (2.291 sec) -2022-05-01 15:28:08,967 - tensorflow - INFO - global_step/sec: 86.9919 -2022-05-01 15:28:08,967 - tensorflow - INFO - loss = 0.049463645, step = 392200 (2.299 sec) -2022-05-01 15:28:11,260 - tensorflow - INFO - global_step/sec: 87.2103 -2022-05-01 15:28:11,261 - tensorflow - INFO - loss = 0.1873938, step = 392400 (2.293 sec) -2022-05-01 15:28:13,550 - tensorflow - INFO - global_step/sec: 87.3377 -2022-05-01 15:28:13,551 - tensorflow - INFO - loss = 0.02534483, step = 392600 (2.290 sec) -2022-05-01 15:28:15,844 - tensorflow - INFO - global_step/sec: 87.1993 -2022-05-01 15:28:15,844 - tensorflow - INFO - loss = 0.1180383, step = 392800 (2.294 sec) -2022-05-01 15:28:18,132 - tensorflow - INFO - global_step/sec: 87.4058 -2022-05-01 15:28:18,132 - tensorflow - INFO - loss = 0.020066151, step = 393000 (2.288 sec) -2022-05-01 15:28:20,414 - tensorflow - INFO - global_step/sec: 87.6439 -2022-05-01 15:28:20,414 - tensorflow - INFO - loss = 0.04308885, step = 393200 (2.282 sec) -2022-05-01 15:28:22,713 - tensorflow - INFO - global_step/sec: 86.9714 -2022-05-01 15:28:22,714 - tensorflow - INFO - loss = 0.08658816, step = 393400 (2.300 sec) -2022-05-01 15:28:25,006 - tensorflow - INFO - global_step/sec: 87.2448 -2022-05-01 15:28:25,006 - tensorflow - INFO - loss = 0.07704064, step = 393600 (2.292 sec) -2022-05-01 15:28:27,300 - tensorflow - INFO - global_step/sec: 87.1794 -2022-05-01 15:28:27,300 - tensorflow - INFO - loss = 0.09365944, step = 393800 (2.294 sec) -2022-05-01 15:28:29,581 - tensorflow - INFO - global_step/sec: 87.6698 -2022-05-01 15:28:29,582 - tensorflow - INFO - loss = 0.120907575, step = 394000 (2.281 sec) -2022-05-01 15:28:31,857 - tensorflow - INFO - global_step/sec: 87.9 -2022-05-01 15:28:31,857 - tensorflow - INFO - loss = 0.2553339, step = 394200 (2.275 sec) -2022-05-01 15:28:34,119 - tensorflow - INFO - global_step/sec: 88.3854 -2022-05-01 15:28:34,120 - tensorflow - INFO - loss = 0.20861246, step = 394400 (2.263 sec) -2022-05-01 15:28:36,389 - tensorflow - INFO - global_step/sec: 88.1201 -2022-05-01 15:28:36,389 - tensorflow - INFO - loss = 0.024497438, step = 394600 (2.270 sec) -2022-05-01 15:28:38,690 - tensorflow - INFO - global_step/sec: 86.9248 -2022-05-01 15:28:38,690 - tensorflow - INFO - loss = 0.2303257, step = 394800 (2.301 sec) -2022-05-01 15:28:40,987 - tensorflow - INFO - global_step/sec: 87.0582 -2022-05-01 15:28:40,988 - tensorflow - INFO - loss = 6.556267, step = 395000 (2.297 sec) -2022-05-01 15:28:43,289 - tensorflow - INFO - global_step/sec: 86.8876 -2022-05-01 15:28:43,289 - tensorflow - INFO - loss = 0.1679577, step = 395200 (2.302 sec) -2022-05-01 15:28:45,593 - tensorflow - INFO - global_step/sec: 86.8084 -2022-05-01 15:28:45,593 - tensorflow - INFO - loss = 0.072121605, step = 395400 (2.304 sec) -2022-05-01 15:28:47,892 - tensorflow - INFO - global_step/sec: 86.9844 -2022-05-01 15:28:47,893 - tensorflow - INFO - loss = 0.27504706, step = 395600 (2.299 sec) -2022-05-01 15:28:50,194 - tensorflow - INFO - global_step/sec: 86.8971 -2022-05-01 15:28:50,194 - tensorflow - INFO - loss = 0.27844322, step = 395800 (2.302 sec) -2022-05-01 15:28:52,501 - tensorflow - INFO - global_step/sec: 86.693 -2022-05-01 15:28:52,501 - tensorflow - INFO - loss = 1.3566101, step = 396000 (2.307 sec) -2022-05-01 15:28:54,801 - tensorflow - INFO - global_step/sec: 86.9296 -2022-05-01 15:28:54,802 - tensorflow - INFO - loss = 0.06718867, step = 396200 (2.301 sec) -2022-05-01 15:28:57,104 - tensorflow - INFO - global_step/sec: 86.8481 -2022-05-01 15:28:57,105 - tensorflow - INFO - loss = 0.0668054, step = 396400 (2.303 sec) -2022-05-01 15:28:59,400 - tensorflow - INFO - global_step/sec: 87.113 -2022-05-01 15:28:59,401 - tensorflow - INFO - loss = 0.12444751, step = 396600 (2.296 sec) -2022-05-01 15:29:01,701 - tensorflow - INFO - global_step/sec: 86.9377 -2022-05-01 15:29:01,701 - tensorflow - INFO - loss = 0.03913559, step = 396800 (2.301 sec) -2022-05-01 15:29:04,007 - tensorflow - INFO - global_step/sec: 86.7135 -2022-05-01 15:29:04,008 - tensorflow - INFO - loss = 0.05213821, step = 397000 (2.306 sec) -2022-05-01 15:29:06,306 - tensorflow - INFO - global_step/sec: 87.0015 -2022-05-01 15:29:06,306 - tensorflow - INFO - loss = 0.05093122, step = 397200 (2.299 sec) -2022-05-01 15:29:08,597 - tensorflow - INFO - global_step/sec: 87.2929 -2022-05-01 15:29:08,598 - tensorflow - INFO - loss = 0.023556273, step = 397400 (2.291 sec) -2022-05-01 15:29:10,882 - tensorflow - INFO - global_step/sec: 87.5211 -2022-05-01 15:29:10,883 - tensorflow - INFO - loss = 0.04940243, step = 397600 (2.285 sec) -2022-05-01 15:29:13,174 - tensorflow - INFO - global_step/sec: 87.2662 -2022-05-01 15:29:13,175 - tensorflow - INFO - loss = 0.44391492, step = 397800 (2.292 sec) -2022-05-01 15:29:15,469 - tensorflow - INFO - global_step/sec: 87.1639 -2022-05-01 15:29:15,469 - tensorflow - INFO - loss = 0.05440904, step = 398000 (2.295 sec) -2022-05-01 15:29:17,755 - tensorflow - INFO - global_step/sec: 87.4721 -2022-05-01 15:29:17,756 - tensorflow - INFO - loss = 0.021781359, step = 398200 (2.286 sec) -2022-05-01 15:29:20,050 - tensorflow - INFO - global_step/sec: 87.1435 -2022-05-01 15:29:20,051 - tensorflow - INFO - loss = 0.1761256, step = 398400 (2.295 sec) -2022-05-01 15:29:22,337 - tensorflow - INFO - global_step/sec: 87.459 -2022-05-01 15:29:22,337 - tensorflow - INFO - loss = 0.031269975, step = 398600 (2.287 sec) -2022-05-01 15:29:24,636 - tensorflow - INFO - global_step/sec: 86.9899 -2022-05-01 15:29:24,636 - tensorflow - INFO - loss = 0.029436901, step = 398800 (2.299 sec) -2022-05-01 15:29:26,921 - tensorflow - INFO - global_step/sec: 87.5231 -2022-05-01 15:29:26,922 - tensorflow - INFO - loss = 0.036546864, step = 399000 (2.285 sec) -2022-05-01 15:29:29,216 - tensorflow - INFO - global_step/sec: 87.1351 -2022-05-01 15:29:29,217 - tensorflow - INFO - loss = 0.073827825, step = 399200 (2.295 sec) -2022-05-01 15:29:31,514 - tensorflow - INFO - global_step/sec: 87.0353 -2022-05-01 15:29:31,515 - tensorflow - INFO - loss = 0.04035163, step = 399400 (2.298 sec) -2022-05-01 15:29:33,802 - tensorflow - INFO - global_step/sec: 87.4241 -2022-05-01 15:29:33,803 - tensorflow - INFO - loss = 0.01856226, step = 399600 (2.288 sec) -2022-05-01 15:29:36,094 - tensorflow - INFO - global_step/sec: 87.2769 -2022-05-01 15:29:36,094 - tensorflow - INFO - loss = 0.58974075, step = 399800 (2.292 sec) -2022-05-01 15:29:38,371 - tensorflow - INFO - Saving checkpoints for 400000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. -2022-05-01 15:29:38,389 - tensorflow - INFO - model.ckpt-400000 is not in all_model_checkpoint_paths. Manually adding it. -2022-05-01 15:29:38,933 - tensorflow - INFO - Calling model_fn. -2022-05-01 15:29:39,066 - tensorflow - INFO - Done calling model_fn. -2022-05-01 15:29:39,082 - tensorflow - INFO - Starting evaluation at 2022-05-01T15:29:39Z -2022-05-01 15:29:39,147 - tensorflow - INFO - Graph was finalized. -2022-05-01 15:29:39,149 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-400000 -2022-05-01 15:29:39,438 - tensorflow - INFO - Running local_init_op. -2022-05-01 15:29:39,547 - tensorflow - INFO - Done running local_init_op. -2022-05-01 15:29:49,774 - tensorflow - INFO - Evaluation [3990/39900] -2022-05-01 15:29:59,312 - tensorflow - INFO - Evaluation [7980/39900] -2022-05-01 15:30:08,770 - tensorflow - INFO - Evaluation [11970/39900] -2022-05-01 15:30:18,321 - tensorflow - INFO - Evaluation [15960/39900] -2022-05-01 15:30:27,839 - tensorflow - INFO - Evaluation [19950/39900] -2022-05-01 15:30:37,374 - tensorflow - INFO - Evaluation [23940/39900] -2022-05-01 15:30:46,920 - tensorflow - INFO - Evaluation [27930/39900] -2022-05-01 15:30:56,464 - tensorflow - INFO - Evaluation [31920/39900] -2022-05-01 15:31:06,005 - tensorflow - INFO - Evaluation [35910/39900] -2022-05-01 15:31:15,544 - tensorflow - INFO - Evaluation [39900/39900] -2022-05-01 15:31:15,682 - tensorflow - INFO - Finished evaluation at 2022-05-01-15:31:15 -2022-05-01 15:31:15,682 - tensorflow - INFO - Saving dict for global step 400000: global_step = 400000, loss = 0.04516113, mean_degree_err = 6.516394 -2022-05-01 15:31:15,682 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 400000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-400000 -2022-05-01 15:31:15,696 - tensorflow - INFO - global_step/sec: 2.00798 -2022-05-01 15:31:15,697 - tensorflow - INFO - loss = 0.02225634, step = 400000 (99.603 sec) -2022-05-01 15:31:18,011 - tensorflow - INFO - global_step/sec: 86.4114 -2022-05-01 15:31:18,011 - tensorflow - INFO - loss = 0.31792247, step = 400200 (2.314 sec) -2022-05-01 15:31:20,306 - tensorflow - INFO - global_step/sec: 87.1549 -2022-05-01 15:31:20,306 - tensorflow - INFO - loss = 0.10387549, step = 400400 (2.295 sec) -2022-05-01 15:31:22,607 - tensorflow - INFO - global_step/sec: 86.9092 -2022-05-01 15:31:22,607 - tensorflow - INFO - loss = 0.12722068, step = 400600 (2.301 sec) -2022-05-01 15:31:24,903 - tensorflow - INFO - global_step/sec: 87.0888 -2022-05-01 15:31:24,904 - tensorflow - INFO - loss = 0.22603582, step = 400800 (2.297 sec) -2022-05-01 15:31:27,198 - tensorflow - INFO - global_step/sec: 87.1598 -2022-05-01 15:31:27,199 - tensorflow - INFO - loss = 0.15252268, step = 401000 (2.295 sec) -2022-05-01 15:31:29,491 - tensorflow - INFO - global_step/sec: 87.2261 -2022-05-01 15:31:29,491 - tensorflow - INFO - loss = 0.24389297, step = 401200 (2.293 sec) -2022-05-01 15:31:31,795 - tensorflow - INFO - global_step/sec: 86.8182 -2022-05-01 15:31:31,795 - tensorflow - INFO - loss = 0.015286315, step = 401400 (2.304 sec) -2022-05-01 15:31:34,096 - tensorflow - INFO - global_step/sec: 86.9031 -2022-05-01 15:31:34,096 - tensorflow - INFO - loss = 0.09249173, step = 401600 (2.301 sec) -2022-05-01 15:31:36,388 - tensorflow - INFO - global_step/sec: 87.2577 -2022-05-01 15:31:36,389 - tensorflow - INFO - loss = 0.097399205, step = 401800 (2.292 sec) -2022-05-01 15:31:38,645 - tensorflow - INFO - global_step/sec: 88.6012 -2022-05-01 15:31:38,646 - tensorflow - INFO - loss = 0.030494688, step = 402000 (2.257 sec) -2022-05-01 15:31:40,923 - tensorflow - INFO - global_step/sec: 87.8078 -2022-05-01 15:31:40,924 - tensorflow - INFO - loss = 0.111601226, step = 402200 (2.278 sec) -2022-05-01 15:31:43,212 - tensorflow - INFO - global_step/sec: 87.3939 -2022-05-01 15:31:43,212 - tensorflow - INFO - loss = 0.028065145, step = 402400 (2.288 sec) -2022-05-01 15:31:45,507 - tensorflow - INFO - global_step/sec: 87.1462 -2022-05-01 15:31:45,507 - tensorflow - INFO - loss = 0.024193406, step = 402600 (2.295 sec) -2022-05-01 15:31:47,798 - tensorflow - INFO - global_step/sec: 87.2884 -2022-05-01 15:31:47,798 - tensorflow - INFO - loss = 20.300358, step = 402800 (2.291 sec) -2022-05-01 15:31:50,098 - tensorflow - INFO - global_step/sec: 86.9538 -2022-05-01 15:31:50,098 - tensorflow - INFO - loss = 17.936117, step = 403000 (2.300 sec) -2022-05-01 15:31:52,388 - tensorflow - INFO - global_step/sec: 87.3362 -2022-05-01 15:31:52,388 - tensorflow - INFO - loss = 0.04836905, step = 403200 (2.290 sec) -2022-05-01 15:31:54,683 - tensorflow - INFO - global_step/sec: 87.148 -2022-05-01 15:31:54,683 - tensorflow - INFO - loss = 11.243692, step = 403400 (2.295 sec) -2022-05-01 15:31:56,975 - tensorflow - INFO - global_step/sec: 87.2586 -2022-05-01 15:31:56,975 - tensorflow - INFO - loss = 0.024591817, step = 403600 (2.292 sec) -2022-05-01 15:31:59,270 - tensorflow - INFO - global_step/sec: 87.1365 -2022-05-01 15:31:59,271 - tensorflow - INFO - loss = 0.10885215, step = 403800 (2.295 sec) -2022-05-01 15:32:01,569 - tensorflow - INFO - global_step/sec: 87.0194 -2022-05-01 15:32:01,569 - tensorflow - INFO - loss = 0.09319634, step = 404000 (2.298 sec) -2022-05-01 15:32:03,855 - tensorflow - INFO - global_step/sec: 87.4566 -2022-05-01 15:32:03,856 - tensorflow - INFO - loss = 0.053963237, step = 404200 (2.287 sec) -2022-05-01 15:32:06,151 - tensorflow - INFO - global_step/sec: 87.1172 -2022-05-01 15:32:06,152 - tensorflow - INFO - loss = 0.053012453, step = 404400 (2.296 sec) -2022-05-01 15:32:08,451 - tensorflow - INFO - global_step/sec: 86.9517 -2022-05-01 15:32:08,452 - tensorflow - INFO - loss = 0.12956715, step = 404600 (2.300 sec) -2022-05-01 15:32:10,755 - tensorflow - INFO - global_step/sec: 86.8279 -2022-05-01 15:32:10,755 - tensorflow - INFO - loss = 0.09968604, step = 404800 (2.303 sec) -2022-05-01 15:32:13,065 - tensorflow - INFO - global_step/sec: 86.551 -2022-05-01 15:32:13,066 - tensorflow - INFO - loss = 0.026279986, step = 405000 (2.311 sec) -2022-05-01 15:32:15,371 - tensorflow - INFO - global_step/sec: 86.7337 -2022-05-01 15:32:15,372 - tensorflow - INFO - loss = 0.09798093, step = 405200 (2.306 sec) -2022-05-01 15:32:17,675 - tensorflow - INFO - global_step/sec: 86.8323 -2022-05-01 15:32:17,675 - tensorflow - INFO - loss = 0.044201896, step = 405400 (2.303 sec) -2022-05-01 15:32:19,974 - tensorflow - INFO - global_step/sec: 86.981 -2022-05-01 15:32:19,974 - tensorflow - INFO - loss = 0.05312757, step = 405600 (2.299 sec) -2022-05-01 15:32:22,279 - tensorflow - INFO - global_step/sec: 86.7527 -2022-05-01 15:32:22,280 - tensorflow - INFO - loss = 0.10622845, step = 405800 (2.305 sec) -2022-05-01 15:32:24,581 - tensorflow - INFO - global_step/sec: 86.9053 -2022-05-01 15:32:24,581 - tensorflow - INFO - loss = 0.10170749, step = 406000 (2.301 sec) -2022-05-01 15:32:26,884 - tensorflow - INFO - global_step/sec: 86.8522 -2022-05-01 15:32:26,884 - tensorflow - INFO - loss = 0.048127078, step = 406200 (2.303 sec) -2022-05-01 15:32:29,187 - tensorflow - INFO - global_step/sec: 86.8426 -2022-05-01 15:32:29,187 - tensorflow - INFO - loss = 0.052297786, step = 406400 (2.303 sec) -2022-05-01 15:32:31,494 - tensorflow - INFO - global_step/sec: 86.6579 -2022-05-01 15:32:31,495 - tensorflow - INFO - loss = 0.08015016, step = 406600 (2.308 sec) -2022-05-01 15:32:33,796 - tensorflow - INFO - global_step/sec: 86.898 -2022-05-01 15:32:33,796 - tensorflow - INFO - loss = 0.16766617, step = 406800 (2.301 sec) -2022-05-01 15:32:36,098 - tensorflow - INFO - global_step/sec: 86.898 -2022-05-01 15:32:36,098 - tensorflow - INFO - loss = 0.8134452, step = 407000 (2.302 sec) -2022-05-01 15:32:38,395 - tensorflow - INFO - global_step/sec: 87.0599 -2022-05-01 15:32:38,395 - tensorflow - INFO - loss = 0.03777675, step = 407200 (2.297 sec) -2022-05-01 15:32:40,695 - tensorflow - INFO - global_step/sec: 86.9366 -2022-05-01 15:32:40,696 - tensorflow - INFO - loss = 0.13193761, step = 407400 (2.300 sec) -2022-05-01 15:32:42,981 - tensorflow - INFO - global_step/sec: 87.5104 -2022-05-01 15:32:42,981 - tensorflow - INFO - loss = 0.11008027, step = 407600 (2.285 sec) -2022-05-01 15:32:45,272 - tensorflow - INFO - global_step/sec: 87.3061 -2022-05-01 15:32:45,272 - tensorflow - INFO - loss = 0.0595809, step = 407800 (2.291 sec) -2022-05-01 15:32:47,562 - tensorflow - INFO - global_step/sec: 87.3225 -2022-05-01 15:32:47,562 - tensorflow - INFO - loss = 0.018416446, step = 408000 (2.290 sec) -2022-05-01 15:32:49,854 - tensorflow - INFO - global_step/sec: 87.245 -2022-05-01 15:32:49,855 - tensorflow - INFO - loss = 0.22051051, step = 408200 (2.292 sec) -2022-05-01 15:32:52,157 - tensorflow - INFO - global_step/sec: 86.8691 -2022-05-01 15:32:52,157 - tensorflow - INFO - loss = 0.5370528, step = 408400 (2.302 sec) -2022-05-01 15:32:54,452 - tensorflow - INFO - global_step/sec: 87.1151 -2022-05-01 15:32:54,453 - tensorflow - INFO - loss = 0.031772103, step = 408600 (2.296 sec) -2022-05-01 15:32:56,746 - tensorflow - INFO - global_step/sec: 87.21 -2022-05-01 15:32:56,746 - tensorflow - INFO - loss = 0.2617071, step = 408800 (2.293 sec) -2022-05-01 15:32:59,045 - tensorflow - INFO - global_step/sec: 86.9771 -2022-05-01 15:32:59,046 - tensorflow - INFO - loss = 0.093198895, step = 409000 (2.300 sec) -2022-05-01 15:33:01,340 - tensorflow - INFO - global_step/sec: 87.1726 -2022-05-01 15:33:01,340 - tensorflow - INFO - loss = 0.016394991, step = 409200 (2.294 sec) -2022-05-01 15:33:03,635 - tensorflow - INFO - global_step/sec: 87.1468 -2022-05-01 15:33:03,635 - tensorflow - INFO - loss = 0.071981266, step = 409400 (2.295 sec) -2022-05-01 15:33:05,920 - tensorflow - INFO - global_step/sec: 87.4897 -2022-05-01 15:33:05,921 - tensorflow - INFO - loss = 0.045541182, step = 409600 (2.286 sec) -2022-05-01 15:33:08,189 - tensorflow - INFO - global_step/sec: 88.1814 -2022-05-01 15:33:08,189 - tensorflow - INFO - loss = 0.08847006, step = 409800 (2.268 sec) -2022-05-01 15:33:10,443 - tensorflow - INFO - global_step/sec: 88.6944 -2022-05-01 15:33:10,444 - tensorflow - INFO - loss = 0.06049387, step = 410000 (2.255 sec) -2022-05-01 15:33:12,720 - tensorflow - INFO - global_step/sec: 87.8406 -2022-05-01 15:33:12,721 - tensorflow - INFO - loss = 0.022273019, step = 410200 (2.277 sec) -2022-05-01 15:33:15,012 - tensorflow - INFO - global_step/sec: 87.2749 -2022-05-01 15:33:15,012 - tensorflow - INFO - loss = 0.025294263, step = 410400 (2.292 sec) -2022-05-01 15:33:17,304 - tensorflow - INFO - global_step/sec: 87.2735 -2022-05-01 15:33:17,304 - tensorflow - INFO - loss = 0.020777583, step = 410600 (2.292 sec) -2022-05-01 15:33:19,597 - tensorflow - INFO - global_step/sec: 87.2056 -2022-05-01 15:33:19,597 - tensorflow - INFO - loss = 0.04804317, step = 410800 (2.293 sec) -2022-05-01 15:33:21,886 - tensorflow - INFO - global_step/sec: 87.3783 -2022-05-01 15:33:21,886 - tensorflow - INFO - loss = 0.020127455, step = 411000 (2.289 sec) -2022-05-01 15:33:24,174 - tensorflow - INFO - global_step/sec: 87.397 -2022-05-01 15:33:24,175 - tensorflow - INFO - loss = 0.11351947, step = 411200 (2.288 sec) -2022-05-01 15:33:26,461 - tensorflow - INFO - global_step/sec: 87.456 -2022-05-01 15:33:26,462 - tensorflow - INFO - loss = 0.06593898, step = 411400 (2.287 sec) -2022-05-01 15:33:28,748 - tensorflow - INFO - global_step/sec: 87.4636 -2022-05-01 15:33:28,748 - tensorflow - INFO - loss = 0.073699504, step = 411600 (2.287 sec) -2022-05-01 15:33:31,048 - tensorflow - INFO - global_step/sec: 86.9668 -2022-05-01 15:33:31,048 - tensorflow - INFO - loss = 0.1304144, step = 411800 (2.300 sec) -2022-05-01 15:33:33,340 - tensorflow - INFO - global_step/sec: 87.2568 -2022-05-01 15:33:33,340 - tensorflow - INFO - loss = 0.03475838, step = 412000 (2.292 sec) -2022-05-01 15:33:35,623 - tensorflow - INFO - global_step/sec: 87.608 -2022-05-01 15:33:35,623 - tensorflow - INFO - loss = 0.025602646, step = 412200 (2.283 sec) -2022-05-01 15:33:37,907 - tensorflow - INFO - global_step/sec: 87.5365 -2022-05-01 15:33:37,908 - tensorflow - INFO - loss = 0.4589123, step = 412400 (2.285 sec) -2022-05-01 15:33:40,196 - tensorflow - INFO - global_step/sec: 87.3716 -2022-05-01 15:33:40,197 - tensorflow - INFO - loss = 0.060308713, step = 412600 (2.289 sec) -2022-05-01 15:33:42,495 - tensorflow - INFO - global_step/sec: 86.9984 -2022-05-01 15:33:42,496 - tensorflow - INFO - loss = 0.039986394, step = 412800 (2.299 sec) -2022-05-01 15:33:44,789 - tensorflow - INFO - global_step/sec: 87.2141 -2022-05-01 15:33:44,789 - tensorflow - INFO - loss = 0.036025546, step = 413000 (2.293 sec) -2022-05-01 15:33:47,078 - tensorflow - INFO - global_step/sec: 87.3417 -2022-05-01 15:33:47,079 - tensorflow - INFO - loss = 12.220699, step = 413200 (2.290 sec) -2022-05-01 15:33:49,378 - tensorflow - INFO - global_step/sec: 86.9718 -2022-05-01 15:33:49,378 - tensorflow - INFO - loss = 0.026839843, step = 413400 (2.300 sec) -2022-05-01 15:33:51,680 - tensorflow - INFO - global_step/sec: 86.8678 -2022-05-01 15:33:51,681 - tensorflow - INFO - loss = 6.3637915, step = 413600 (2.302 sec) -2022-05-01 15:33:53,977 - tensorflow - INFO - global_step/sec: 87.0721 -2022-05-01 15:33:53,978 - tensorflow - INFO - loss = 0.050607696, step = 413800 (2.297 sec) -2022-05-01 15:33:56,275 - tensorflow - INFO - global_step/sec: 87.0414 -2022-05-01 15:33:56,275 - tensorflow - INFO - loss = 0.18939602, step = 414000 (2.298 sec) -2022-05-01 15:33:58,569 - tensorflow - INFO - global_step/sec: 87.1782 -2022-05-01 15:33:58,570 - tensorflow - INFO - loss = 0.094642766, step = 414200 (2.294 sec) -2022-05-01 15:34:00,848 - tensorflow - INFO - global_step/sec: 87.7448 -2022-05-01 15:34:00,849 - tensorflow - INFO - loss = 0.036533773, step = 414400 (2.279 sec) -2022-05-01 15:34:03,138 - tensorflow - INFO - global_step/sec: 87.3375 -2022-05-01 15:34:03,139 - tensorflow - INFO - loss = 1.6116326, step = 414600 (2.290 sec) -2022-05-01 15:34:05,426 - tensorflow - INFO - global_step/sec: 87.4417 -2022-05-01 15:34:05,426 - tensorflow - INFO - loss = 0.047151618, step = 414800 (2.287 sec) -2022-05-01 15:34:07,732 - tensorflow - INFO - global_step/sec: 86.7379 -2022-05-01 15:34:07,732 - tensorflow - INFO - loss = 0.034820914, step = 415000 (2.306 sec) -2022-05-01 15:34:10,029 - tensorflow - INFO - global_step/sec: 87.0342 -2022-05-01 15:34:10,030 - tensorflow - INFO - loss = 0.25218165, step = 415200 (2.298 sec) -2022-05-01 15:34:12,326 - tensorflow - INFO - global_step/sec: 87.069 -2022-05-01 15:34:12,327 - tensorflow - INFO - loss = 0.05424107, step = 415400 (2.297 sec) -2022-05-01 15:34:14,618 - tensorflow - INFO - global_step/sec: 87.292 -2022-05-01 15:34:14,618 - tensorflow - INFO - loss = 0.1665936, step = 415600 (2.291 sec) -2022-05-01 15:34:16,904 - tensorflow - INFO - global_step/sec: 87.4635 -2022-05-01 15:34:16,905 - tensorflow - INFO - loss = 0.15828733, step = 415800 (2.287 sec) -2022-05-01 15:34:19,201 - tensorflow - INFO - global_step/sec: 87.0896 -2022-05-01 15:34:19,201 - tensorflow - INFO - loss = 0.1803932, step = 416000 (2.297 sec) -2022-05-01 15:34:21,495 - tensorflow - INFO - global_step/sec: 87.1763 -2022-05-01 15:34:21,495 - tensorflow - INFO - loss = 0.054921888, step = 416200 (2.294 sec) -2022-05-01 15:34:23,799 - tensorflow - INFO - global_step/sec: 86.8128 -2022-05-01 15:34:23,799 - tensorflow - INFO - loss = 0.048329223, step = 416400 (2.304 sec) -2022-05-01 15:34:26,096 - tensorflow - INFO - global_step/sec: 87.0795 -2022-05-01 15:34:26,096 - tensorflow - INFO - loss = 0.05945764, step = 416600 (2.297 sec) -2022-05-01 15:34:28,390 - tensorflow - INFO - global_step/sec: 87.1598 -2022-05-01 15:34:28,391 - tensorflow - INFO - loss = 0.0472399, step = 416800 (2.295 sec) -2022-05-01 15:34:30,688 - tensorflow - INFO - global_step/sec: 87.0552 -2022-05-01 15:34:30,688 - tensorflow - INFO - loss = 0.12121238, step = 417000 (2.297 sec) -2022-05-01 15:34:32,987 - tensorflow - INFO - global_step/sec: 86.9954 -2022-05-01 15:34:32,987 - tensorflow - INFO - loss = 0.03905348, step = 417200 (2.299 sec) -2022-05-01 15:34:35,280 - tensorflow - INFO - global_step/sec: 87.198 -2022-05-01 15:34:35,281 - tensorflow - INFO - loss = 0.021892853, step = 417400 (2.294 sec) -2022-05-01 15:34:37,566 - tensorflow - INFO - global_step/sec: 87.4964 -2022-05-01 15:34:37,566 - tensorflow - INFO - loss = 0.27863014, step = 417600 (2.286 sec) -2022-05-01 15:34:39,842 - tensorflow - INFO - global_step/sec: 87.8584 -2022-05-01 15:34:39,843 - tensorflow - INFO - loss = 0.05170039, step = 417800 (2.276 sec) -2022-05-01 15:34:42,106 - tensorflow - INFO - global_step/sec: 88.3724 -2022-05-01 15:34:42,106 - tensorflow - INFO - loss = 0.03168484, step = 418000 (2.263 sec) -2022-05-01 15:34:44,371 - tensorflow - INFO - global_step/sec: 88.2907 -2022-05-01 15:34:44,371 - tensorflow - INFO - loss = 8.134713, step = 418200 (2.265 sec) -2022-05-01 15:34:46,660 - tensorflow - INFO - global_step/sec: 87.3752 -2022-05-01 15:34:46,660 - tensorflow - INFO - loss = 0.024514042, step = 418400 (2.289 sec) -2022-05-01 15:34:48,951 - tensorflow - INFO - global_step/sec: 87.2795 -2022-05-01 15:34:48,952 - tensorflow - INFO - loss = 0.05076325, step = 418600 (2.291 sec) -2022-05-01 15:34:51,239 - tensorflow - INFO - global_step/sec: 87.4347 -2022-05-01 15:34:51,239 - tensorflow - INFO - loss = 0.098169036, step = 418800 (2.287 sec) -2022-05-01 15:34:53,531 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.18919368, step = 420200 (2.293 sec) -2022-05-01 15:35:09,593 - tensorflow - INFO - global_step/sec: 87.053 -2022-05-01 15:35:09,594 - tensorflow - INFO - loss = 0.05681526, step = 420400 (2.298 sec) -2022-05-01 15:35:11,893 - tensorflow - INFO - global_step/sec: 86.9691 -2022-05-01 15:35:11,893 - tensorflow - INFO - loss = 0.04830998, step = 420600 (2.300 sec) -2022-05-01 15:35:14,185 - tensorflow - INFO - global_step/sec: 87.252 -2022-05-01 15:35:14,186 - tensorflow - INFO - loss = 0.06415865, step = 420800 (2.292 sec) -2022-05-01 15:35:16,476 - tensorflow - INFO - global_step/sec: 87.2867 -2022-05-01 15:35:16,477 - tensorflow - INFO - loss = 0.06145827, step = 421000 (2.291 sec) -2022-05-01 15:35:18,765 - tensorflow - INFO - global_step/sec: 87.4079 -2022-05-01 15:35:18,765 - tensorflow - INFO - loss = 0.21703048, step = 421200 (2.288 sec) -2022-05-01 15:35:21,055 - tensorflow - INFO - global_step/sec: 87.3182 -2022-05-01 15:35:21,055 - tensorflow - INFO - 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tensorflow - INFO - loss = 1.0554403, step = 480200 (2.298 sec) -2022-05-01 15:46:37,436 - tensorflow - INFO - global_step/sec: 86.8611 -2022-05-01 15:46:37,437 - tensorflow - INFO - loss = 0.07198148, step = 480400 (2.303 sec) -2022-05-01 15:46:39,745 - tensorflow - INFO - global_step/sec: 86.6427 -2022-05-01 15:46:39,745 - tensorflow - INFO - loss = 0.017255176, step = 480600 (2.308 sec) -2022-05-01 15:46:42,049 - tensorflow - INFO - global_step/sec: 86.7825 -2022-05-01 15:46:42,050 - tensorflow - INFO - loss = 0.30203983, step = 480800 (2.305 sec) -2022-05-01 15:46:44,350 - tensorflow - INFO - global_step/sec: 86.911 -2022-05-01 15:46:44,351 - tensorflow - INFO - loss = 0.04165505, step = 481000 (2.301 sec) -2022-05-01 15:46:46,654 - tensorflow - INFO - global_step/sec: 86.8274 -2022-05-01 15:46:46,654 - tensorflow - INFO - loss = 0.7825517, step = 481200 (2.303 sec) -2022-05-01 15:46:48,955 - tensorflow - INFO - global_step/sec: 86.9151 -2022-05-01 15:46:48,955 - tensorflow - INFO - loss = 0.9384113, step = 481400 (2.301 sec) -2022-05-01 15:46:51,248 - tensorflow - INFO - global_step/sec: 87.2187 -2022-05-01 15:46:51,248 - tensorflow - INFO - loss = 0.1261738, step = 481600 (2.293 sec) -2022-05-01 15:46:53,550 - tensorflow - INFO - global_step/sec: 86.8965 -2022-05-01 15:46:53,550 - tensorflow - INFO - loss = 0.07043812, step = 481800 (2.302 sec) -2022-05-01 15:46:55,849 - tensorflow - INFO - global_step/sec: 86.972 -2022-05-01 15:46:55,850 - tensorflow - INFO - loss = 0.12807128, step = 482000 (2.300 sec) -2022-05-01 15:46:58,142 - tensorflow - INFO - global_step/sec: 87.2354 -2022-05-01 15:46:58,142 - tensorflow - INFO - loss = 0.06415939, step = 482200 (2.293 sec) -2022-05-01 15:47:00,428 - tensorflow - INFO - global_step/sec: 87.4626 -2022-05-01 15:47:00,429 - tensorflow - INFO - loss = 0.019527791, step = 482400 (2.287 sec) -2022-05-01 15:47:02,724 - tensorflow - INFO - global_step/sec: 87.1072 -2022-05-01 15:47:02,725 - tensorflow - INFO - loss = 0.08750579, step = 482600 (2.296 sec) -2022-05-01 15:47:05,009 - tensorflow - INFO - global_step/sec: 87.5461 -2022-05-01 15:47:05,009 - tensorflow - INFO - loss = 0.45276707, step = 482800 (2.285 sec) -2022-05-01 15:47:07,290 - tensorflow - INFO - global_step/sec: 87.678 -2022-05-01 15:47:07,290 - tensorflow - INFO - loss = 1.5098001, step = 483000 (2.281 sec) -2022-05-01 15:47:09,557 - tensorflow - INFO - global_step/sec: 88.2376 -2022-05-01 15:47:09,557 - tensorflow - INFO - loss = 0.07556595, step = 483200 (2.267 sec) -2022-05-01 15:47:11,813 - tensorflow - INFO - global_step/sec: 88.6504 -2022-05-01 15:47:11,813 - tensorflow - INFO - loss = 0.038961302, step = 483400 (2.256 sec) -2022-05-01 15:47:14,105 - tensorflow - INFO - global_step/sec: 87.2409 -2022-05-01 15:47:14,106 - tensorflow - INFO - loss = 0.06715776, step = 483600 (2.293 sec) -2022-05-01 15:47:16,401 - tensorflow - INFO - global_step/sec: 87.1298 -2022-05-01 15:47:16,401 - tensorflow - INFO - loss = 0.022315247, step = 483800 (2.295 sec) -2022-05-01 15:47:18,699 - tensorflow - INFO - global_step/sec: 87.0005 -2022-05-01 15:47:18,700 - tensorflow - INFO - loss = 0.08670243, step = 484000 (2.299 sec) -2022-05-01 15:47:21,000 - tensorflow - INFO - global_step/sec: 86.9297 -2022-05-01 15:47:21,001 - tensorflow - INFO - loss = 0.048578028, step = 484200 (2.301 sec) -2022-05-01 15:47:23,292 - tensorflow - INFO - global_step/sec: 87.2653 -2022-05-01 15:47:23,292 - tensorflow - INFO - loss = 0.57092, step = 484400 (2.292 sec) -2022-05-01 15:47:25,584 - tensorflow - INFO - global_step/sec: 87.2686 -2022-05-01 15:47:25,584 - tensorflow - INFO - loss = 0.055992827, step = 484600 (2.292 sec) -2022-05-01 15:47:27,876 - tensorflow - INFO - global_step/sec: 87.2445 -2022-05-01 15:47:27,877 - tensorflow - INFO - loss = 0.04843991, step = 484800 (2.292 sec) -2022-05-01 15:47:30,168 - tensorflow - INFO - global_step/sec: 87.2737 -2022-05-01 15:47:30,168 - tensorflow - INFO - loss = 0.04905627, step = 485000 (2.292 sec) -2022-05-01 15:47:32,466 - tensorflow - INFO - global_step/sec: 87.0441 -2022-05-01 15:47:32,466 - tensorflow - INFO - loss = 0.06376162, step = 485200 (2.298 sec) -2022-05-01 15:47:34,761 - tensorflow - INFO - global_step/sec: 87.1136 -2022-05-01 15:47:34,762 - tensorflow - INFO - loss = 0.0819434, step = 485400 (2.296 sec) -2022-05-01 15:47:37,049 - tensorflow - INFO - global_step/sec: 87.423 -2022-05-01 15:47:37,050 - tensorflow - INFO - loss = 0.021668667, step = 485600 (2.288 sec) -2022-05-01 15:47:39,341 - tensorflow - INFO - global_step/sec: 87.2717 -2022-05-01 15:47:39,341 - tensorflow - INFO - loss = 0.053483568, step = 485800 (2.292 sec) -2022-05-01 15:47:41,633 - tensorflow - INFO - global_step/sec: 87.2724 -2022-05-01 15:47:41,633 - tensorflow - INFO - loss = 0.04222194, step = 486000 (2.292 sec) -2022-05-01 15:47:43,928 - tensorflow - INFO - global_step/sec: 87.1425 -2022-05-01 15:47:43,928 - tensorflow - INFO - loss = 0.043749157, step = 486200 (2.295 sec) -2022-05-01 15:47:46,223 - tensorflow - INFO - global_step/sec: 87.1329 -2022-05-01 15:47:46,223 - tensorflow - INFO - loss = 0.15084764, step = 486400 (2.295 sec) -2022-05-01 15:47:48,516 - tensorflow - INFO - global_step/sec: 87.2074 -2022-05-01 15:47:48,517 - tensorflow - INFO - loss = 0.07187637, step = 486600 (2.293 sec) -2022-05-01 15:47:50,806 - tensorflow - INFO - global_step/sec: 87.3643 -2022-05-01 15:47:50,806 - tensorflow - INFO - loss = 0.053894468, step = 486800 (2.289 sec) -2022-05-01 15:47:53,102 - tensorflow - INFO - global_step/sec: 87.0776 -2022-05-01 15:47:53,103 - tensorflow - INFO - loss = 0.1304493, step = 487000 (2.297 sec) -2022-05-01 15:47:55,398 - tensorflow - INFO - global_step/sec: 87.1353 -2022-05-01 15:47:55,398 - tensorflow - INFO - loss = 11.150118, step = 487200 (2.295 sec) -2022-05-01 15:47:57,695 - tensorflow - INFO - global_step/sec: 87.0469 -2022-05-01 15:47:57,696 - tensorflow - INFO - loss = 0.039552987, step = 487400 (2.298 sec) -2022-05-01 15:47:59,985 - tensorflow - INFO - global_step/sec: 87.3537 -2022-05-01 15:47:59,985 - tensorflow - INFO - loss = 4.6886973, step = 487600 (2.289 sec) -2022-05-01 15:48:02,276 - tensorflow - INFO - global_step/sec: 87.2922 -2022-05-01 15:48:02,276 - tensorflow - INFO - loss = 0.066290066, step = 487800 (2.291 sec) -2022-05-01 15:48:04,572 - tensorflow - INFO - global_step/sec: 87.0961 -2022-05-01 15:48:04,573 - tensorflow - INFO - loss = 14.458428, step = 488000 (2.296 sec) -2022-05-01 15:48:06,865 - tensorflow - INFO - global_step/sec: 87.2209 -2022-05-01 15:48:06,866 - tensorflow - INFO - loss = 0.04509601, step = 488200 (2.293 sec) -2022-05-01 15:48:09,163 - tensorflow - INFO - global_step/sec: 87.0534 -2022-05-01 15:48:09,163 - tensorflow - INFO - loss = 0.11672298, step = 488400 (2.297 sec) -2022-05-01 15:48:11,454 - tensorflow - INFO - global_step/sec: 87.2862 -2022-05-01 15:48:11,454 - tensorflow - INFO - loss = 0.051707402, step = 488600 (2.291 sec) -2022-05-01 15:48:13,742 - tensorflow - INFO - global_step/sec: 87.4171 -2022-05-01 15:48:13,742 - tensorflow - INFO - loss = 0.061385017, step = 488800 (2.288 sec) -2022-05-01 15:48:16,037 - tensorflow - INFO - global_step/sec: 87.1478 -2022-05-01 15:48:16,037 - tensorflow - INFO - loss = 0.042890646, step = 489000 (2.295 sec) -2022-05-01 15:48:18,326 - tensorflow - INFO - global_step/sec: 87.3552 -2022-05-01 15:48:18,327 - tensorflow - INFO - loss = 1.4140427, step = 489200 (2.289 sec) -2022-05-01 15:48:20,624 - tensorflow - INFO - global_step/sec: 87.0491 -2022-05-01 15:48:20,624 - tensorflow - INFO - loss = 0.0454567, step = 489400 (2.298 sec) -2022-05-01 15:48:22,920 - tensorflow - INFO - global_step/sec: 87.1186 -2022-05-01 15:48:22,920 - tensorflow - INFO - loss = 0.23944998, step = 489600 (2.296 sec) -2022-05-01 15:48:25,209 - tensorflow - INFO - global_step/sec: 87.347 -2022-05-01 15:48:25,210 - tensorflow - INFO - loss = 0.048230134, step = 489800 (2.290 sec) -2022-05-01 15:48:27,503 - tensorflow - INFO - global_step/sec: 87.2112 -2022-05-01 15:48:27,503 - tensorflow - INFO - loss = 0.17226861, step = 490000 (2.293 sec) -2022-05-01 15:48:29,793 - tensorflow - INFO - global_step/sec: 87.3317 -2022-05-01 15:48:29,793 - tensorflow - INFO - loss = 0.063290715, step = 490200 (2.290 sec) -2022-05-01 15:48:32,082 - tensorflow - INFO - global_step/sec: 87.3637 -2022-05-01 15:48:32,082 - tensorflow - INFO - loss = 0.15427542, step = 490400 (2.289 sec) -2022-05-01 15:48:34,377 - tensorflow - INFO - global_step/sec: 87.1478 -2022-05-01 15:48:34,377 - tensorflow - INFO - loss = 0.0866156, step = 490600 (2.295 sec) -2022-05-01 15:48:36,653 - tensorflow - INFO - global_step/sec: 87.8803 -2022-05-01 15:48:36,653 - tensorflow - INFO - loss = 0.11607466, step = 490800 (2.276 sec) -2022-05-01 15:48:38,915 - tensorflow - INFO - global_step/sec: 88.393 -2022-05-01 15:48:38,916 - tensorflow - INFO - loss = 0.030506007, step = 491000 (2.263 sec) -2022-05-01 15:48:41,176 - tensorflow - INFO - global_step/sec: 88.4874 -2022-05-01 15:48:41,176 - tensorflow - INFO - loss = 0.16931124, step = 491200 (2.260 sec) -2022-05-01 15:48:43,436 - tensorflow - INFO - global_step/sec: 88.4809 -2022-05-01 15:48:43,436 - tensorflow - INFO - loss = 0.07888447, step = 491400 (2.260 sec) -2022-05-01 15:48:45,734 - tensorflow - INFO - global_step/sec: 87.0183 -2022-05-01 15:48:45,735 - tensorflow - INFO - loss = 0.013879217, step = 491600 (2.298 sec) -2022-05-01 15:48:48,028 - tensorflow - INFO - global_step/sec: 87.1904 -2022-05-01 15:48:48,029 - tensorflow - INFO - loss = 0.19325846, step = 491800 (2.294 sec) -2022-05-01 15:48:50,324 - tensorflow - INFO - global_step/sec: 87.1239 -2022-05-01 15:48:50,324 - tensorflow - INFO - loss = 0.05001726, step = 492000 (2.296 sec) -2022-05-01 15:48:52,622 - tensorflow - INFO - global_step/sec: 87.034 -2022-05-01 15:48:52,622 - tensorflow - INFO - loss = 0.08362567, step = 492200 (2.298 sec) -2022-05-01 15:48:54,918 - tensorflow - INFO - global_step/sec: 87.1066 -2022-05-01 15:48:54,918 - tensorflow - INFO - loss = 0.045164224, step = 492400 (2.296 sec) -2022-05-01 15:48:57,213 - tensorflow - INFO - global_step/sec: 87.1404 -2022-05-01 15:48:57,213 - tensorflow - INFO - loss = 0.12752767, step = 492600 (2.295 sec) -2022-05-01 15:48:59,510 - tensorflow - INFO - global_step/sec: 87.0822 -2022-05-01 15:48:59,510 - tensorflow - INFO - loss = 0.060145747, step = 492800 (2.297 sec) -2022-05-01 15:49:01,809 - tensorflow - INFO - global_step/sec: 86.9762 -2022-05-01 15:49:01,810 - tensorflow - INFO - loss = 0.503282, step = 493000 (2.299 sec) -2022-05-01 15:49:04,097 - tensorflow - INFO - global_step/sec: 87.4125 -2022-05-01 15:49:04,098 - tensorflow - INFO - loss = 0.08907508, step = 493200 (2.288 sec) -2022-05-01 15:49:06,400 - tensorflow - INFO - global_step/sec: 86.839 -2022-05-01 15:49:06,401 - tensorflow - INFO - loss = 0.036446743, step = 493400 (2.303 sec) -2022-05-01 15:49:08,695 - tensorflow - INFO - global_step/sec: 87.1736 -2022-05-01 15:49:08,695 - tensorflow - INFO - loss = 3.1655476, step = 493600 (2.294 sec) -2022-05-01 15:49:10,991 - tensorflow - INFO - global_step/sec: 87.0857 -2022-05-01 15:49:10,992 - tensorflow - INFO - loss = 0.02401543, step = 493800 (2.297 sec) -2022-05-01 15:49:13,281 - tensorflow - INFO - global_step/sec: 87.3311 -2022-05-01 15:49:13,282 - tensorflow - INFO - loss = 0.02471529, step = 494000 (2.290 sec) -2022-05-01 15:49:15,573 - tensorflow - INFO - global_step/sec: 87.2857 -2022-05-01 15:49:15,573 - tensorflow - INFO - loss = 0.048128817, step = 494200 (2.291 sec) -2022-05-01 15:49:17,856 - tensorflow - INFO - global_step/sec: 87.5903 -2022-05-01 15:49:17,856 - tensorflow - INFO - loss = 0.3277765, step = 494400 (2.283 sec) -2022-05-01 15:49:20,153 - tensorflow - INFO - global_step/sec: 87.0611 -2022-05-01 15:49:20,154 - tensorflow - INFO - loss = 0.015861625, step = 494600 (2.297 sec) -2022-05-01 15:49:22,448 - tensorflow - INFO - global_step/sec: 87.1354 -2022-05-01 15:49:22,449 - tensorflow - INFO - loss = 0.028060673, step = 494800 (2.295 sec) -2022-05-01 15:49:24,744 - tensorflow - INFO - global_step/sec: 87.1415 -2022-05-01 15:49:24,744 - tensorflow - INFO - loss = 0.03264568, step = 495000 (2.295 sec) -2022-05-01 15:49:27,031 - tensorflow - INFO - global_step/sec: 87.4192 -2022-05-01 15:49:27,032 - tensorflow - INFO - loss = 0.45592192, step = 495200 (2.288 sec) -2022-05-01 15:49:29,313 - tensorflow - INFO - global_step/sec: 87.6582 -2022-05-01 15:49:29,313 - tensorflow - INFO - loss = 0.074402526, step = 495400 (2.282 sec) -2022-05-01 15:49:31,604 - tensorflow - INFO - global_step/sec: 87.2896 -2022-05-01 15:49:31,605 - tensorflow - INFO - loss = 0.34785342, step = 495600 (2.291 sec) -2022-05-01 15:49:33,894 - tensorflow - INFO - global_step/sec: 87.3332 -2022-05-01 15:49:33,895 - tensorflow - INFO - loss = 0.014895788, step = 495800 (2.290 sec) -2022-05-01 15:49:36,192 - tensorflow - INFO - global_step/sec: 87.0249 -2022-05-01 15:49:36,193 - tensorflow - INFO - loss = 0.039041396, step = 496000 (2.298 sec) -2022-05-01 15:49:38,480 - tensorflow - INFO - global_step/sec: 87.4465 -2022-05-01 15:49:38,480 - tensorflow - INFO - loss = 0.023041882, step = 496200 (2.287 sec) -2022-05-01 15:49:40,778 - tensorflow - INFO - global_step/sec: 87.01 -2022-05-01 15:49:40,779 - tensorflow - INFO - loss = 7.3349943, step = 496400 (2.299 sec) -2022-05-01 15:49:43,075 - tensorflow - INFO - global_step/sec: 87.0711 -2022-05-01 15:49:43,076 - tensorflow - INFO - loss = 0.690408, step = 496600 (2.297 sec) -2022-05-01 15:49:45,364 - tensorflow - INFO - global_step/sec: 87.3821 -2022-05-01 15:49:45,364 - tensorflow - INFO - loss = 0.042593334, step = 496800 (2.289 sec) -2022-05-01 15:49:47,666 - tensorflow - INFO - global_step/sec: 86.8956 -2022-05-01 15:49:47,666 - tensorflow - INFO - loss = 0.0668559, step = 497000 (2.302 sec) -2022-05-01 15:49:49,973 - tensorflow - INFO - global_step/sec: 86.6792 -2022-05-01 15:49:49,973 - tensorflow - INFO - loss = 0.011198775, step = 497200 (2.307 sec) -2022-05-01 15:49:52,277 - tensorflow - INFO - global_step/sec: 86.8116 -2022-05-01 15:49:52,277 - tensorflow - INFO - loss = 0.06476675, step = 497400 (2.304 sec) -2022-05-01 15:49:54,581 - tensorflow - INFO - global_step/sec: 86.8063 -2022-05-01 15:49:54,581 - tensorflow - INFO - loss = 0.043987837, step = 497600 (2.304 sec) -2022-05-01 15:49:56,880 - tensorflow - INFO - global_step/sec: 86.9838 -2022-05-01 15:49:56,880 - tensorflow - INFO - loss = 0.04835829, step = 497800 (2.299 sec) -2022-05-01 15:49:59,180 - tensorflow - INFO - global_step/sec: 86.9417 -2022-05-01 15:49:59,181 - tensorflow - INFO - loss = 0.044502974, step = 498000 (2.300 sec) -2022-05-01 15:50:01,478 - tensorflow - INFO - global_step/sec: 87.0622 -2022-05-01 15:50:01,478 - tensorflow - INFO - loss = 0.060919967, step = 498200 (2.297 sec) -2022-05-01 15:50:03,783 - tensorflow - INFO - global_step/sec: 86.7672 -2022-05-01 15:50:03,783 - tensorflow - INFO - loss = 0.12766704, step = 498400 (2.305 sec) -2022-05-01 15:50:06,076 - tensorflow - INFO - global_step/sec: 87.2122 -2022-05-01 15:50:06,076 - tensorflow - INFO - loss = 0.026480768, step = 498600 (2.293 sec) -2022-05-01 15:50:08,366 - tensorflow - INFO - global_step/sec: 87.3191 -2022-05-01 15:50:08,367 - tensorflow - INFO - loss = 1.5395248, step = 498800 (2.290 sec) -2022-05-01 15:50:10,651 - tensorflow - INFO - global_step/sec: 87.5458 -2022-05-01 15:50:10,651 - tensorflow - INFO - loss = 0.05769307, step = 499000 (2.285 sec) -2022-05-01 15:50:12,914 - tensorflow - INFO - global_step/sec: 88.3691 -2022-05-01 15:50:12,915 - tensorflow - INFO - loss = 0.06950209, step = 499200 (2.263 sec) -2022-05-01 15:50:15,180 - tensorflow - INFO - global_step/sec: 88.2645 -2022-05-01 15:50:15,180 - tensorflow - INFO - loss = 0.41808796, step = 499400 (2.266 sec) -2022-05-01 15:50:17,438 - tensorflow - INFO - global_step/sec: 88.5739 -2022-05-01 15:50:17,438 - tensorflow - INFO - loss = 0.16664134, step = 499600 (2.258 sec) -2022-05-01 15:50:19,734 - tensorflow - INFO - global_step/sec: 87.1192 -2022-05-01 15:50:19,734 - tensorflow - INFO - loss = 20.43417, step = 499800 (2.296 sec) -2022-05-01 15:50:22,016 - tensorflow - INFO - Saving checkpoints for 500000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. -2022-05-01 15:50:22,032 - tensorflow - INFO - model.ckpt-500000 is not in all_model_checkpoint_paths. Manually adding it. -2022-05-01 15:50:22,559 - tensorflow - INFO - Calling model_fn. -2022-05-01 15:50:22,692 - tensorflow - INFO - Done calling model_fn. -2022-05-01 15:50:22,707 - tensorflow - INFO - Starting evaluation at 2022-05-01T15:50:22Z -2022-05-01 15:50:22,772 - tensorflow - INFO - Graph was finalized. -2022-05-01 15:50:22,775 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-500000 -2022-05-01 15:50:23,062 - tensorflow - INFO - Running local_init_op. -2022-05-01 15:50:23,174 - tensorflow - INFO - Done running local_init_op. -2022-05-01 15:50:33,530 - tensorflow - INFO - Evaluation [3990/39900] -2022-05-01 15:50:43,155 - tensorflow - INFO - Evaluation [7980/39900] -2022-05-01 15:50:52,766 - tensorflow - INFO - Evaluation [11970/39900] -2022-05-01 15:51:02,371 - tensorflow - INFO - Evaluation [15960/39900] -2022-05-01 15:51:11,960 - tensorflow - INFO - Evaluation [19950/39900] -2022-05-01 15:51:21,578 - tensorflow - INFO - Evaluation [23940/39900] -2022-05-01 15:51:31,204 - tensorflow - INFO - Evaluation [27930/39900] -2022-05-01 15:51:40,848 - tensorflow - INFO - Evaluation [31920/39900] -2022-05-01 15:51:50,412 - tensorflow - INFO - Evaluation [35910/39900] -2022-05-01 15:52:00,044 - tensorflow - INFO - Evaluation [39900/39900] -2022-05-01 15:52:00,185 - tensorflow - INFO - Finished evaluation at 2022-05-01-15:52:00 -2022-05-01 15:52:00,185 - tensorflow - INFO - Saving dict for global step 500000: global_step = 500000, loss = 0.046809282, mean_degree_err = 6.3430543 -2022-05-01 15:52:00,186 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 500000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-500000 -2022-05-01 15:52:00,200 - tensorflow - INFO - global_step/sec: 1.99072 -2022-05-01 15:52:00,201 - tensorflow - INFO - loss = 0.063635156, step = 500000 (100.467 sec) -2022-05-01 15:52:02,510 - tensorflow - INFO - global_step/sec: 86.5758 -2022-05-01 15:52:02,510 - tensorflow - INFO - loss = 0.07470157, step = 500200 (2.310 sec) -2022-05-01 15:52:04,794 - tensorflow - INFO - global_step/sec: 87.5492 -2022-05-01 15:52:04,795 - tensorflow - INFO - loss = 0.31317902, step = 500400 (2.284 sec) -2022-05-01 15:52:07,086 - tensorflow - INFO - global_step/sec: 87.2645 -2022-05-01 15:52:07,087 - tensorflow - INFO - loss = 0.18935311, step = 500600 (2.292 sec) -2022-05-01 15:52:09,376 - tensorflow - INFO - global_step/sec: 87.3425 -2022-05-01 15:52:09,376 - tensorflow - INFO - loss = 0.020323459, step = 500800 (2.290 sec) -2022-05-01 15:52:11,673 - tensorflow - INFO - global_step/sec: 87.067 -2022-05-01 15:52:11,673 - tensorflow - INFO - loss = 0.024707984, step = 501000 (2.297 sec) -2022-05-01 15:52:13,966 - tensorflow - INFO - global_step/sec: 87.224 -2022-05-01 15:52:13,966 - tensorflow - INFO - loss = 0.02308048, step = 501200 (2.293 sec) -2022-05-01 15:52:16,256 - tensorflow - INFO - global_step/sec: 87.3316 -2022-05-01 15:52:16,257 - tensorflow - INFO - loss = 0.084123634, step = 501400 (2.290 sec) -2022-05-01 15:52:18,545 - tensorflow - INFO - global_step/sec: 87.3829 -2022-05-01 15:52:18,545 - tensorflow - INFO - loss = 0.9689673, step = 501600 (2.289 sec) -2022-05-01 15:52:20,830 - tensorflow - INFO - global_step/sec: 87.508 -2022-05-01 15:52:20,831 - tensorflow - INFO - loss = 0.04496465, step = 501800 (2.285 sec) -2022-05-01 15:52:23,122 - tensorflow - INFO - global_step/sec: 87.2854 -2022-05-01 15:52:23,122 - tensorflow - INFO - loss = 0.026626464, step = 502000 (2.291 sec) -2022-05-01 15:52:25,412 - tensorflow - INFO - global_step/sec: 87.3072 -2022-05-01 15:52:25,413 - tensorflow - INFO - loss = 0.086509205, step = 502200 (2.291 sec) -2022-05-01 15:52:27,707 - tensorflow - INFO - global_step/sec: 87.1582 -2022-05-01 15:52:27,708 - tensorflow - INFO - loss = 0.06746422, step = 502400 (2.295 sec) -2022-05-01 15:52:29,998 - tensorflow - INFO - global_step/sec: 87.3076 -2022-05-01 15:52:29,998 - tensorflow - INFO - loss = 0.0978373, step = 502600 (2.291 sec) -2022-05-01 15:52:32,295 - tensorflow - INFO - global_step/sec: 87.078 -2022-05-01 15:52:32,295 - tensorflow - INFO - loss = 0.104280345, step = 502800 (2.297 sec) -2022-05-01 15:52:34,591 - tensorflow - INFO - global_step/sec: 87.1093 -2022-05-01 15:52:34,591 - tensorflow - INFO - loss = 0.091107875, step = 503000 (2.296 sec) -2022-05-01 15:52:36,888 - tensorflow - INFO - global_step/sec: 87.0597 -2022-05-01 15:52:36,888 - tensorflow - INFO - loss = 0.033102345, step = 503200 (2.297 sec) -2022-05-01 15:52:39,175 - tensorflow - INFO - global_step/sec: 87.4474 -2022-05-01 15:52:39,175 - tensorflow - INFO - loss = 0.09425828, step = 503400 (2.287 sec) -2022-05-01 15:52:41,466 - tensorflow - INFO - global_step/sec: 87.2883 -2022-05-01 15:52:41,467 - tensorflow - INFO - loss = 0.061163433, step = 503600 (2.291 sec) -2022-05-01 15:52:43,758 - tensorflow - INFO - global_step/sec: 87.265 -2022-05-01 15:52:43,759 - tensorflow - INFO - loss = 0.13534966, step = 503800 (2.292 sec) -2022-05-01 15:52:46,054 - tensorflow - INFO - global_step/sec: 87.108 -2022-05-01 15:52:46,055 - tensorflow - INFO - loss = 0.06398092, step = 504000 (2.296 sec) -2022-05-01 15:52:48,353 - tensorflow - INFO - global_step/sec: 86.9983 -2022-05-01 15:52:48,354 - tensorflow - INFO - loss = 0.014031668, step = 504200 (2.299 sec) -2022-05-01 15:52:50,640 - tensorflow - INFO - global_step/sec: 87.4405 -2022-05-01 15:52:50,641 - tensorflow - INFO - loss = 0.049638722, step = 504400 (2.287 sec) -2022-05-01 15:52:52,947 - tensorflow - INFO - global_step/sec: 86.7174 -2022-05-01 15:52:52,947 - tensorflow - INFO - loss = 0.03826969, step = 504600 (2.306 sec) -2022-05-01 15:52:55,241 - tensorflow - INFO - global_step/sec: 87.1834 -2022-05-01 15:52:55,241 - tensorflow - INFO - loss = 0.08917885, step = 504800 (2.294 sec) -2022-05-01 15:52:57,543 - tensorflow - INFO - global_step/sec: 86.8621 -2022-05-01 15:52:57,544 - tensorflow - INFO - loss = 0.1652728, step = 505000 (2.303 sec) -2022-05-01 15:52:59,836 - tensorflow - INFO - global_step/sec: 87.2339 -2022-05-01 15:52:59,836 - tensorflow - INFO - loss = 0.17992705, step = 505200 (2.293 sec) -2022-05-01 15:53:02,139 - tensorflow - INFO - global_step/sec: 86.8256 -2022-05-01 15:53:02,140 - tensorflow - INFO - loss = 0.037294824, step = 505400 (2.303 sec) -2022-05-01 15:53:04,432 - tensorflow - INFO - global_step/sec: 87.2285 -2022-05-01 15:53:04,433 - tensorflow - INFO - loss = 0.15038131, step = 505600 (2.293 sec) -2022-05-01 15:53:06,724 - tensorflow - INFO - global_step/sec: 87.28 -2022-05-01 15:53:06,724 - tensorflow - INFO - loss = 0.065310985, step = 505800 (2.292 sec) -2022-05-01 15:53:09,012 - tensorflow - INFO - global_step/sec: 87.396 -2022-05-01 15:53:09,013 - tensorflow - INFO - loss = 0.034203827, step = 506000 (2.288 sec) -2022-05-01 15:53:11,312 - tensorflow - INFO - global_step/sec: 86.96 -2022-05-01 15:53:11,312 - tensorflow - INFO - loss = 0.16935429, step = 506200 (2.300 sec) -2022-05-01 15:53:13,611 - tensorflow - INFO - global_step/sec: 86.985 -2022-05-01 15:53:13,612 - tensorflow - INFO - loss = 0.035027437, step = 506400 (2.299 sec) -2022-05-01 15:53:15,902 - tensorflow - INFO - global_step/sec: 87.3028 -2022-05-01 15:53:15,903 - tensorflow - INFO - loss = 0.057028934, step = 506600 (2.291 sec) -2022-05-01 15:53:18,182 - tensorflow - INFO - global_step/sec: 87.7217 -2022-05-01 15:53:18,183 - tensorflow - INFO - loss = 0.0799098, step = 506800 (2.280 sec) -2022-05-01 15:53:20,471 - tensorflow - INFO - global_step/sec: 87.379 -2022-05-01 15:53:20,471 - tensorflow - INFO - loss = 0.03132668, step = 507000 (2.289 sec) -2022-05-01 15:53:22,772 - tensorflow - INFO - global_step/sec: 86.9129 -2022-05-01 15:53:22,773 - tensorflow - INFO - loss = 0.049004238, step = 507200 (2.301 sec) -2022-05-01 15:53:25,074 - tensorflow - INFO - global_step/sec: 86.9042 -2022-05-01 15:53:25,074 - tensorflow - INFO - loss = 0.05456598, step = 507400 (2.301 sec) -2022-05-01 15:53:27,378 - tensorflow - INFO - global_step/sec: 86.7786 -2022-05-01 15:53:27,379 - tensorflow - INFO - loss = 0.03548259, step = 507600 (2.305 sec) -2022-05-01 15:53:29,677 - tensorflow - INFO - global_step/sec: 86.9937 -2022-05-01 15:53:29,678 - tensorflow - INFO - loss = 0.22562142, step = 507800 (2.299 sec) -2022-05-01 15:53:31,982 - tensorflow - INFO - global_step/sec: 86.7697 -2022-05-01 15:53:31,983 - tensorflow - INFO - loss = 0.2586946, step = 508000 (2.305 sec) -2022-05-01 15:53:34,283 - tensorflow - INFO - global_step/sec: 86.9118 -2022-05-01 15:53:34,284 - tensorflow - INFO - loss = 0.07775393, step = 508200 (2.301 sec) -2022-05-01 15:53:36,583 - tensorflow - INFO - global_step/sec: 86.9646 -2022-05-01 15:53:36,584 - tensorflow - INFO - loss = 0.079266146, step = 508400 (2.300 sec) -2022-05-01 15:53:38,878 - tensorflow - INFO - global_step/sec: 87.1614 -2022-05-01 15:53:38,878 - tensorflow - INFO - loss = 0.06857913, step = 508600 (2.295 sec) -2022-05-01 15:53:41,169 - tensorflow - INFO - global_step/sec: 87.274 -2022-05-01 15:53:41,170 - tensorflow - INFO - loss = 0.028996538, step = 508800 (2.292 sec) -2022-05-01 15:53:43,466 - tensorflow - INFO - global_step/sec: 87.09 -2022-05-01 15:53:43,466 - tensorflow - INFO - loss = 0.03027647, step = 509000 (2.296 sec) -2022-05-01 15:53:45,746 - tensorflow - INFO - global_step/sec: 87.7088 -2022-05-01 15:53:45,746 - tensorflow - INFO - loss = 0.042125713, step = 509200 (2.280 sec) -2022-05-01 15:53:48,034 - tensorflow - INFO - global_step/sec: 87.4106 -2022-05-01 15:53:48,035 - tensorflow - INFO - loss = 0.019495798, step = 509400 (2.288 sec) -2022-05-01 15:53:50,322 - tensorflow - INFO - global_step/sec: 87.4146 -2022-05-01 15:53:50,323 - tensorflow - INFO - loss = 0.070728526, step = 509600 (2.288 sec) -2022-05-01 15:53:52,614 - tensorflow - INFO - global_step/sec: 87.2603 -2022-05-01 15:53:52,615 - tensorflow - INFO - loss = 0.40481895, step = 509800 (2.292 sec) -2022-05-01 15:53:54,910 - tensorflow - INFO - global_step/sec: 87.1076 -2022-05-01 15:53:54,911 - tensorflow - INFO - loss = 0.2444518, step = 510000 (2.296 sec) -2022-05-01 15:53:57,201 - tensorflow - INFO - global_step/sec: 87.3191 -2022-05-01 15:53:57,201 - tensorflow - INFO - loss = 0.16828924, step = 510200 (2.290 sec) -2022-05-01 15:53:59,487 - tensorflow - INFO - global_step/sec: 87.4854 -2022-05-01 15:53:59,487 - tensorflow - INFO - loss = 0.051806744, step = 510400 (2.286 sec) -2022-05-01 15:54:01,770 - tensorflow - INFO - global_step/sec: 87.5859 -2022-05-01 15:54:01,771 - tensorflow - INFO - loss = 0.06130576, step = 510600 (2.284 sec) -2022-05-01 15:54:04,060 - tensorflow - INFO - global_step/sec: 87.3608 -2022-05-01 15:54:04,060 - tensorflow - INFO - loss = 0.086674064, step = 510800 (2.289 sec) -2022-05-01 15:54:06,347 - tensorflow - INFO - global_step/sec: 87.4472 -2022-05-01 15:54:06,347 - tensorflow - INFO - loss = 0.15934047, step = 511000 (2.287 sec) -2022-05-01 15:54:08,632 - tensorflow - INFO - global_step/sec: 87.5184 -2022-05-01 15:54:08,632 - tensorflow - INFO - loss = 0.03121565, step = 511200 (2.285 sec) -2022-05-01 15:54:10,926 - tensorflow - INFO - global_step/sec: 87.1944 -2022-05-01 15:54:10,926 - tensorflow - INFO - loss = 0.103663206, step = 511400 (2.294 sec) -2022-05-01 15:54:13,211 - tensorflow - INFO - global_step/sec: 87.4968 -2022-05-01 15:54:13,212 - tensorflow - INFO - loss = 0.099881515, step = 511600 (2.286 sec) -2022-05-01 15:54:15,497 - tensorflow - INFO - global_step/sec: 87.5185 -2022-05-01 15:54:15,497 - tensorflow - INFO - loss = 0.038970098, step = 511800 (2.285 sec) -2022-05-01 15:54:17,785 - tensorflow - INFO - global_step/sec: 87.4104 -2022-05-01 15:54:17,785 - tensorflow - INFO - loss = 0.029831307, step = 512000 (2.288 sec) -2022-05-01 15:54:20,073 - tensorflow - INFO - global_step/sec: 87.4173 -2022-05-01 15:54:20,073 - tensorflow - INFO - loss = 0.15872079, step = 512200 (2.288 sec) -2022-05-01 15:54:22,358 - tensorflow - INFO - global_step/sec: 87.4893 -2022-05-01 15:54:22,359 - tensorflow - INFO - loss = 0.13896562, step = 512400 (2.286 sec) -2022-05-01 15:54:24,641 - tensorflow - INFO - global_step/sec: 87.633 -2022-05-01 15:54:24,641 - tensorflow - INFO - loss = 0.08064212, step = 512600 (2.282 sec) -2022-05-01 15:54:26,925 - tensorflow - INFO - global_step/sec: 87.5531 -2022-05-01 15:54:26,926 - tensorflow - INFO - loss = 0.045587465, step = 512800 (2.284 sec) -2022-05-01 15:54:29,211 - tensorflow - INFO - global_step/sec: 87.478 -2022-05-01 15:54:29,212 - tensorflow - INFO - loss = 0.02319471, step = 513000 (2.286 sec) -2022-05-01 15:54:31,510 - tensorflow - INFO - global_step/sec: 87.0126 -2022-05-01 15:54:31,510 - tensorflow - INFO - loss = 0.06806821, step = 513200 (2.299 sec) -2022-05-01 15:54:33,808 - tensorflow - INFO - global_step/sec: 87.0461 -2022-05-01 15:54:33,808 - tensorflow - INFO - loss = 0.0633014, step = 513400 (2.298 sec) -2022-05-01 15:54:36,093 - tensorflow - INFO - global_step/sec: 87.4934 -2022-05-01 15:54:36,094 - tensorflow - INFO - loss = 0.06634851, step = 513600 (2.286 sec) -2022-05-01 15:54:38,378 - tensorflow - INFO - global_step/sec: 87.5331 -2022-05-01 15:54:38,379 - tensorflow - INFO - loss = 0.030684706, step = 513800 (2.285 sec) -2022-05-01 15:54:40,657 - tensorflow - INFO - global_step/sec: 87.7827 -2022-05-01 15:54:40,657 - tensorflow - INFO - loss = 0.04401341, step = 514000 (2.278 sec) -2022-05-01 15:54:42,921 - tensorflow - INFO - global_step/sec: 88.306 -2022-05-01 15:54:42,922 - tensorflow - INFO - loss = 0.026993984, step = 514200 (2.265 sec) -2022-05-01 15:54:45,189 - tensorflow - INFO - global_step/sec: 88.218 -2022-05-01 15:54:45,189 - tensorflow - INFO - loss = 0.034656197, step = 514400 (2.267 sec) -2022-05-01 15:54:47,444 - tensorflow - INFO - global_step/sec: 88.6682 -2022-05-01 15:54:47,445 - tensorflow - INFO - loss = 0.020687716, step = 514600 (2.256 sec) -2022-05-01 15:54:49,721 - tensorflow - INFO - global_step/sec: 87.8501 -2022-05-01 15:54:49,721 - tensorflow - INFO - loss = 9.952393, step = 514800 (2.277 sec) -2022-05-01 15:54:52,016 - tensorflow - INFO - global_step/sec: 87.129 -2022-05-01 15:54:52,017 - tensorflow - INFO - loss = 0.080806784, step = 515000 (2.295 sec) -2022-05-01 15:54:54,308 - tensorflow - INFO - global_step/sec: 87.2597 -2022-05-01 15:54:54,309 - tensorflow - INFO - loss = 0.26253784, step = 515200 (2.292 sec) -2022-05-01 15:54:56,602 - tensorflow - INFO - global_step/sec: 87.1934 -2022-05-01 15:54:56,602 - tensorflow - INFO - loss = 0.035267, step = 515400 (2.294 sec) -2022-05-01 15:54:58,896 - tensorflow - INFO - global_step/sec: 87.1762 -2022-05-01 15:54:58,897 - tensorflow - INFO - loss = 0.024436895, step = 515600 (2.294 sec) -2022-05-01 15:55:01,188 - tensorflow - INFO - global_step/sec: 87.2803 -2022-05-01 15:55:01,188 - tensorflow - INFO - loss = 0.08493805, step = 515800 (2.291 sec) -2022-05-01 15:55:03,485 - tensorflow - INFO - global_step/sec: 87.069 -2022-05-01 15:55:03,485 - tensorflow - INFO - loss = 0.022904579, step = 516000 (2.297 sec) -2022-05-01 15:55:05,775 - tensorflow - INFO - global_step/sec: 87.342 -2022-05-01 15:55:05,775 - tensorflow - INFO - loss = 0.06921731, step = 516200 (2.290 sec) -2022-05-01 15:55:08,071 - tensorflow - INFO - global_step/sec: 87.0845 -2022-05-01 15:55:08,072 - tensorflow - INFO - loss = 0.01737911, step = 516400 (2.297 sec) -2022-05-01 15:55:10,364 - tensorflow - INFO - global_step/sec: 87.2378 -2022-05-01 15:55:10,364 - tensorflow - INFO - loss = 0.2211785, step = 516600 (2.293 sec) -2022-05-01 15:55:12,651 - tensorflow - INFO - global_step/sec: 87.4553 -2022-05-01 15:55:12,651 - tensorflow - INFO - loss = 0.041149385, step = 516800 (2.287 sec) -2022-05-01 15:55:14,943 - tensorflow - INFO - global_step/sec: 87.2573 -2022-05-01 15:55:14,943 - tensorflow - INFO - loss = 0.38384295, step = 517000 (2.292 sec) -2022-05-01 15:55:17,234 - tensorflow - INFO - global_step/sec: 87.2799 -2022-05-01 15:55:17,235 - tensorflow - INFO - loss = 0.029199786, step = 517200 (2.291 sec) -2022-05-01 15:55:19,527 - tensorflow - INFO - global_step/sec: 87.2295 -2022-05-01 15:55:19,527 - tensorflow - INFO - loss = 0.052903645, step = 517400 (2.293 sec) -2022-05-01 15:55:21,818 - tensorflow - INFO - global_step/sec: 87.2992 -2022-05-01 15:55:21,818 - tensorflow - INFO - loss = 0.052237667, step = 517600 (2.291 sec) -2022-05-01 15:55:24,114 - tensorflow - INFO - global_step/sec: 87.0902 -2022-05-01 15:55:24,115 - tensorflow - INFO - loss = 0.06584891, step = 517800 (2.296 sec) -2022-05-01 15:55:26,406 - tensorflow - INFO - global_step/sec: 87.2613 -2022-05-01 15:55:26,407 - tensorflow - INFO - loss = 0.06383498, step = 518000 (2.292 sec) -2022-05-01 15:55:28,691 - tensorflow - INFO - global_step/sec: 87.5229 -2022-05-01 15:55:28,692 - tensorflow - INFO - loss = 0.29162747, step = 518200 (2.285 sec) -2022-05-01 15:55:30,992 - tensorflow - INFO - global_step/sec: 86.9451 -2022-05-01 15:55:30,992 - tensorflow - INFO - loss = 0.03455841, step = 518400 (2.300 sec) -2022-05-01 15:55:33,281 - tensorflow - INFO - global_step/sec: 87.3585 -2022-05-01 15:55:33,282 - tensorflow - INFO - loss = 0.053478647, step = 518600 (2.289 sec) -2022-05-01 15:55:35,582 - tensorflow - INFO - global_step/sec: 86.9209 -2022-05-01 15:55:35,583 - tensorflow - INFO - loss = 0.11700239, step = 518800 (2.301 sec) -2022-05-01 15:55:37,877 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.023734644, step = 520200 (2.287 sec) -2022-05-01 15:55:53,899 - tensorflow - INFO - global_step/sec: 87.228 -2022-05-01 15:55:53,899 - tensorflow - INFO - loss = 3.021659, step = 520400 (2.293 sec) -2022-05-01 15:55:56,190 - tensorflow - INFO - global_step/sec: 87.2736 -2022-05-01 15:55:56,191 - tensorflow - INFO - loss = 0.033053983, step = 520600 (2.292 sec) -2022-05-01 15:55:58,479 - tensorflow - INFO - global_step/sec: 87.3732 -2022-05-01 15:55:58,480 - tensorflow - INFO - loss = 0.11531508, step = 520800 (2.289 sec) -2022-05-01 15:56:00,779 - tensorflow - INFO - global_step/sec: 86.9634 -2022-05-01 15:56:00,780 - tensorflow - INFO - loss = 0.19145986, step = 521000 (2.300 sec) -2022-05-01 15:56:03,064 - tensorflow - INFO - global_step/sec: 87.5375 -2022-05-01 15:56:03,064 - tensorflow - INFO - loss = 0.081368074, step = 521200 (2.285 sec) -2022-05-01 15:56:05,353 - tensorflow - INFO - global_step/sec: 87.3869 -2022-05-01 15:56:05,353 - tensorflow - INFO - 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INFO - global_step/sec: 87.4126 -2022-05-01 15:58:29,737 - tensorflow - INFO - loss = 0.048247125, step = 534000 (2.288 sec) -2022-05-01 15:58:32,023 - tensorflow - INFO - global_step/sec: 87.4691 -2022-05-01 15:58:32,023 - tensorflow - INFO - loss = 0.2548982, step = 534200 (2.287 sec) -2022-05-01 15:58:34,312 - tensorflow - INFO - global_step/sec: 87.342 -2022-05-01 15:58:34,313 - tensorflow - INFO - loss = 0.112261005, step = 534400 (2.290 sec) -2022-05-01 15:58:36,599 - tensorflow - INFO - global_step/sec: 87.4758 -2022-05-01 15:58:36,599 - tensorflow - INFO - loss = 0.06416681, step = 534600 (2.286 sec) -2022-05-01 15:58:38,884 - tensorflow - INFO - global_step/sec: 87.5155 -2022-05-01 15:58:38,885 - tensorflow - INFO - loss = 0.045276202, step = 534800 (2.285 sec) -2022-05-01 15:58:41,177 - tensorflow - INFO - global_step/sec: 87.2243 -2022-05-01 15:58:41,177 - tensorflow - INFO - loss = 0.05972786, step = 535000 (2.293 sec) -2022-05-01 15:58:43,471 - tensorflow - INFO - global_step/sec: 87.1875 -2022-05-01 15:58:43,471 - 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INFO - global_step/sec: 87.722 -2022-05-01 16:02:18,929 - tensorflow - INFO - loss = 0.05830501, step = 554000 (2.280 sec) -2022-05-01 16:02:21,186 - tensorflow - INFO - global_step/sec: 88.5852 -2022-05-01 16:02:21,187 - tensorflow - INFO - loss = 0.10870462, step = 554200 (2.258 sec) -2022-05-01 16:02:23,457 - tensorflow - INFO - global_step/sec: 88.0677 -2022-05-01 16:02:23,458 - tensorflow - INFO - loss = 0.13495924, step = 554400 (2.271 sec) -2022-05-01 16:02:25,754 - tensorflow - INFO - global_step/sec: 87.0592 -2022-05-01 16:02:25,755 - tensorflow - INFO - loss = 0.22699371, step = 554600 (2.297 sec) -2022-05-01 16:02:28,041 - tensorflow - INFO - global_step/sec: 87.4849 -2022-05-01 16:02:28,041 - tensorflow - INFO - loss = 0.026929671, step = 554800 (2.286 sec) -2022-05-01 16:02:30,335 - tensorflow - INFO - global_step/sec: 87.1779 -2022-05-01 16:02:30,335 - tensorflow - INFO - loss = 0.2152446, step = 555000 (2.294 sec) -2022-05-01 16:02:32,630 - tensorflow - INFO - global_step/sec: 87.1413 -2022-05-01 16:02:32,630 - 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INFO - loss = 0.10707433, step = 556400 (2.287 sec) -2022-05-01 16:02:48,669 - tensorflow - INFO - global_step/sec: 87.2667 -2022-05-01 16:02:48,670 - tensorflow - INFO - loss = 0.044041008, step = 556600 (2.292 sec) -2022-05-01 16:02:50,962 - tensorflow - INFO - global_step/sec: 87.2451 -2022-05-01 16:02:50,962 - tensorflow - INFO - loss = 0.021362655, step = 556800 (2.292 sec) -2022-05-01 16:02:53,258 - tensorflow - INFO - global_step/sec: 87.0994 -2022-05-01 16:02:53,258 - tensorflow - INFO - loss = 0.02036528, step = 557000 (2.296 sec) -2022-05-01 16:02:55,551 - tensorflow - INFO - global_step/sec: 87.2252 -2022-05-01 16:02:55,551 - tensorflow - INFO - loss = 0.03787473, step = 557200 (2.293 sec) -2022-05-01 16:02:57,844 - tensorflow - INFO - global_step/sec: 87.2374 -2022-05-01 16:02:57,844 - tensorflow - INFO - loss = 0.054995943, step = 557400 (2.293 sec) -2022-05-01 16:03:00,138 - tensorflow - INFO - global_step/sec: 87.1789 -2022-05-01 16:03:00,138 - tensorflow - INFO - loss = 0.087319426, step = 557600 (2.294 sec) -2022-05-01 16:03:02,439 - 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tensorflow - INFO - loss = 0.027606444, step = 580200 (2.305 sec) -2022-05-01 16:07:21,337 - tensorflow - INFO - global_step/sec: 86.973 -2022-05-01 16:07:21,338 - tensorflow - INFO - loss = 0.08346665, step = 580400 (2.300 sec) -2022-05-01 16:07:23,642 - tensorflow - INFO - global_step/sec: 86.7654 -2022-05-01 16:07:23,643 - tensorflow - INFO - loss = 0.22735195, step = 580600 (2.305 sec) -2022-05-01 16:07:25,943 - tensorflow - INFO - global_step/sec: 86.9335 -2022-05-01 16:07:25,943 - tensorflow - INFO - loss = 0.056020238, step = 580800 (2.301 sec) -2022-05-01 16:07:28,249 - tensorflow - INFO - global_step/sec: 86.7091 -2022-05-01 16:07:28,250 - tensorflow - INFO - loss = 0.044762574, step = 581000 (2.307 sec) -2022-05-01 16:07:30,554 - tensorflow - INFO - global_step/sec: 86.7724 -2022-05-01 16:07:30,555 - tensorflow - INFO - loss = 0.15219203, step = 581200 (2.305 sec) -2022-05-01 16:07:32,863 - tensorflow - INFO - global_step/sec: 86.6233 -2022-05-01 16:07:32,864 - tensorflow - INFO - loss = 0.008169208, step = 581400 (2.309 sec) -2022-05-01 16:07:35,169 - tensorflow - INFO - global_step/sec: 86.7241 -2022-05-01 16:07:35,170 - tensorflow - INFO - loss = 0.058656745, step = 581600 (2.306 sec) -2022-05-01 16:07:37,469 - tensorflow - INFO - global_step/sec: 86.9728 -2022-05-01 16:07:37,469 - tensorflow - INFO - loss = 0.031593032, step = 581800 (2.300 sec) -2022-05-01 16:07:39,764 - tensorflow - INFO - global_step/sec: 87.1322 -2022-05-01 16:07:39,765 - tensorflow - INFO - loss = 0.058234237, step = 582000 (2.295 sec) -2022-05-01 16:07:42,055 - tensorflow - INFO - global_step/sec: 87.3064 -2022-05-01 16:07:42,056 - tensorflow - INFO - loss = 0.04581158, step = 582200 (2.291 sec) -2022-05-01 16:07:44,343 - tensorflow - INFO - global_step/sec: 87.4162 -2022-05-01 16:07:44,343 - tensorflow - INFO - loss = 0.03358003, step = 582400 (2.288 sec) -2022-05-01 16:07:46,645 - tensorflow - INFO - global_step/sec: 86.8834 -2022-05-01 16:07:46,645 - tensorflow - INFO - loss = 0.013812849, step = 582600 (2.302 sec) -2022-05-01 16:07:48,933 - tensorflow - INFO - global_step/sec: 87.4129 -2022-05-01 16:07:48,933 - tensorflow - INFO - loss = 0.03210587, step = 582800 (2.288 sec) -2022-05-01 16:07:51,219 - tensorflow - INFO - global_step/sec: 87.5012 -2022-05-01 16:07:51,219 - tensorflow - INFO - loss = 0.025355924, step = 583000 (2.286 sec) -2022-05-01 16:07:53,513 - tensorflow - INFO - global_step/sec: 87.1829 -2022-05-01 16:07:53,513 - tensorflow - INFO - loss = 0.024808213, step = 583200 (2.294 sec) -2022-05-01 16:07:55,804 - tensorflow - INFO - global_step/sec: 87.3027 -2022-05-01 16:07:55,804 - tensorflow - INFO - loss = 0.04379979, step = 583400 (2.291 sec) -2022-05-01 16:07:58,099 - tensorflow - INFO - global_step/sec: 87.1296 -2022-05-01 16:07:58,099 - tensorflow - INFO - loss = 0.026920808, step = 583600 (2.295 sec) -2022-05-01 16:08:00,391 - tensorflow - INFO - global_step/sec: 87.2648 -2022-05-01 16:08:00,391 - tensorflow - INFO - loss = 0.09960953, step = 583800 (2.292 sec) -2022-05-01 16:08:02,683 - tensorflow - INFO - global_step/sec: 87.2438 -2022-05-01 16:08:02,684 - tensorflow - INFO - loss = 12.821983, step = 584000 (2.293 sec) -2022-05-01 16:08:04,973 - tensorflow - INFO - global_step/sec: 87.3472 -2022-05-01 16:08:04,973 - tensorflow - INFO - loss = 0.018572694, step = 584200 (2.290 sec) -2022-05-01 16:08:07,267 - tensorflow - INFO - global_step/sec: 87.196 -2022-05-01 16:08:07,267 - tensorflow - INFO - loss = 0.2426543, step = 584400 (2.294 sec) -2022-05-01 16:08:09,552 - tensorflow - INFO - global_step/sec: 87.5324 -2022-05-01 16:08:09,552 - tensorflow - INFO - loss = 0.012297162, step = 584600 (2.285 sec) -2022-05-01 16:08:11,842 - tensorflow - INFO - global_step/sec: 87.3172 -2022-05-01 16:08:11,843 - tensorflow - INFO - loss = 0.02709882, step = 584800 (2.291 sec) -2022-05-01 16:08:14,135 - tensorflow - INFO - global_step/sec: 87.2379 -2022-05-01 16:08:14,135 - tensorflow - INFO - loss = 0.026977034, step = 585000 (2.292 sec) -2022-05-01 16:08:16,428 - tensorflow - INFO - global_step/sec: 87.2062 -2022-05-01 16:08:16,428 - tensorflow - INFO - loss = 0.06308341, step = 585200 (2.293 sec) -2022-05-01 16:08:18,716 - tensorflow - INFO - global_step/sec: 87.4137 -2022-05-01 16:08:18,716 - tensorflow - INFO - loss = 0.036703456, step = 585400 (2.288 sec) -2022-05-01 16:08:21,007 - tensorflow - INFO - global_step/sec: 87.3122 -2022-05-01 16:08:21,007 - tensorflow - INFO - loss = 0.017322699, step = 585600 (2.291 sec) -2022-05-01 16:08:23,302 - tensorflow - INFO - global_step/sec: 87.1363 -2022-05-01 16:08:23,302 - tensorflow - INFO - loss = 0.20249094, step = 585800 (2.295 sec) -2022-05-01 16:08:25,597 - tensorflow - INFO - global_step/sec: 87.123 -2022-05-01 16:08:25,598 - tensorflow - INFO - loss = 0.0323515, step = 586000 (2.296 sec) -2022-05-01 16:08:27,885 - tensorflow - INFO - global_step/sec: 87.4358 -2022-05-01 16:08:27,885 - tensorflow - INFO - loss = 0.044530425, step = 586200 (2.287 sec) -2022-05-01 16:08:30,182 - tensorflow - INFO - global_step/sec: 87.0825 -2022-05-01 16:08:30,182 - tensorflow - INFO - loss = 1.0501606, step = 586400 (2.297 sec) -2022-05-01 16:08:32,463 - tensorflow - INFO - global_step/sec: 87.6476 -2022-05-01 16:08:32,464 - tensorflow - INFO - loss = 0.03570166, step = 586600 (2.282 sec) -2022-05-01 16:08:34,734 - tensorflow - INFO - global_step/sec: 88.0702 -2022-05-01 16:08:34,735 - tensorflow - INFO - loss = 0.024391422, step = 586800 (2.271 sec) -2022-05-01 16:08:37,011 - tensorflow - INFO - global_step/sec: 87.8485 -2022-05-01 16:08:37,011 - tensorflow - INFO - loss = 0.020901958, step = 587000 (2.277 sec) -2022-05-01 16:08:39,281 - tensorflow - INFO - global_step/sec: 88.0869 -2022-05-01 16:08:39,282 - tensorflow - INFO - loss = 11.28486, step = 587200 (2.270 sec) -2022-05-01 16:08:41,552 - tensorflow - INFO - global_step/sec: 88.0718 -2022-05-01 16:08:41,553 - tensorflow - INFO - loss = 0.07651273, step = 587400 (2.271 sec) -2022-05-01 16:08:43,817 - tensorflow - INFO - global_step/sec: 88.3173 -2022-05-01 16:08:43,817 - tensorflow - INFO - loss = 0.048306994, step = 587600 (2.265 sec) -2022-05-01 16:08:46,088 - tensorflow - INFO - global_step/sec: 88.0657 -2022-05-01 16:08:46,088 - tensorflow - INFO - loss = 0.042515934, step = 587800 (2.271 sec) -2022-05-01 16:08:48,366 - tensorflow - INFO - global_step/sec: 87.7988 -2022-05-01 16:08:48,366 - tensorflow - INFO - loss = 0.31607643, step = 588000 (2.278 sec) -2022-05-01 16:08:50,632 - tensorflow - INFO - global_step/sec: 88.2473 -2022-05-01 16:08:50,633 - tensorflow - INFO - loss = 0.051129453, step = 588200 (2.266 sec) -2022-05-01 16:08:52,891 - tensorflow - INFO - global_step/sec: 88.5609 -2022-05-01 16:08:52,891 - tensorflow - INFO - loss = 0.060575508, step = 588400 (2.258 sec) -2022-05-01 16:08:55,143 - tensorflow - INFO - global_step/sec: 88.7751 -2022-05-01 16:08:55,144 - tensorflow - INFO - loss = 0.06876105, step = 588600 (2.253 sec) -2022-05-01 16:08:57,400 - tensorflow - INFO - global_step/sec: 88.6417 -2022-05-01 16:08:57,400 - tensorflow - INFO - loss = 0.01773398, step = 588800 (2.256 sec) -2022-05-01 16:08:59,655 - tensorflow - INFO - global_step/sec: 88.6811 -2022-05-01 16:08:59,655 - tensorflow - INFO - loss = 0.30537874, step = 589000 (2.255 sec) -2022-05-01 16:09:01,915 - tensorflow - INFO - global_step/sec: 88.4917 -2022-05-01 16:09:01,915 - tensorflow - INFO - loss = 0.19603918, step = 589200 (2.260 sec) -2022-05-01 16:09:04,165 - tensorflow - INFO - global_step/sec: 88.899 -2022-05-01 16:09:04,165 - tensorflow - INFO - loss = 0.103383504, step = 589400 (2.250 sec) -2022-05-01 16:09:06,418 - tensorflow - INFO - global_step/sec: 88.7487 -2022-05-01 16:09:06,419 - tensorflow - INFO - loss = 0.0767152, step = 589600 (2.254 sec) -2022-05-01 16:09:08,671 - tensorflow - INFO - global_step/sec: 88.7892 -2022-05-01 16:09:08,671 - tensorflow - INFO - loss = 12.67634, step = 589800 (2.253 sec) -2022-05-01 16:09:10,925 - tensorflow - INFO - global_step/sec: 88.7123 -2022-05-01 16:09:10,926 - tensorflow - INFO - loss = 0.1362737, step = 590000 (2.254 sec) -2022-05-01 16:09:13,183 - tensorflow - INFO - global_step/sec: 88.586 -2022-05-01 16:09:13,183 - tensorflow - INFO - loss = 0.059808686, step = 590200 (2.258 sec) -2022-05-01 16:09:15,441 - tensorflow - INFO - global_step/sec: 88.5846 -2022-05-01 16:09:15,441 - tensorflow - INFO - loss = 0.029569477, step = 590400 (2.258 sec) -2022-05-01 16:09:17,690 - tensorflow - INFO - global_step/sec: 88.9035 -2022-05-01 16:09:17,691 - tensorflow - INFO - loss = 0.04373379, step = 590600 (2.250 sec) -2022-05-01 16:09:19,940 - tensorflow - INFO - global_step/sec: 88.9028 -2022-05-01 16:09:19,940 - tensorflow - INFO - loss = 0.04323511, step = 590800 (2.250 sec) -2022-05-01 16:09:22,192 - tensorflow - INFO - global_step/sec: 88.8029 -2022-05-01 16:09:22,193 - tensorflow - INFO - loss = 0.039181337, step = 591000 (2.252 sec) -2022-05-01 16:09:24,451 - tensorflow - INFO - global_step/sec: 88.5384 -2022-05-01 16:09:24,452 - tensorflow - INFO - loss = 0.016992427, step = 591200 (2.259 sec) -2022-05-01 16:09:26,700 - tensorflow - INFO - global_step/sec: 88.9199 -2022-05-01 16:09:26,701 - tensorflow - INFO - loss = 0.012185383, step = 591400 (2.249 sec) -2022-05-01 16:09:28,952 - tensorflow - INFO - global_step/sec: 88.8107 -2022-05-01 16:09:28,953 - tensorflow - INFO - loss = 0.009116696, step = 591600 (2.252 sec) -2022-05-01 16:09:31,214 - tensorflow - INFO - global_step/sec: 88.4188 -2022-05-01 16:09:31,215 - tensorflow - INFO - loss = 0.017143156, step = 591800 (2.262 sec) -2022-05-01 16:09:33,469 - tensorflow - INFO - global_step/sec: 88.7175 -2022-05-01 16:09:33,469 - tensorflow - INFO - loss = 0.014566356, step = 592000 (2.254 sec) -2022-05-01 16:09:35,720 - tensorflow - INFO - global_step/sec: 88.8272 -2022-05-01 16:09:35,721 - tensorflow - INFO - loss = 0.13501108, step = 592200 (2.252 sec) -2022-05-01 16:09:37,975 - tensorflow - INFO - global_step/sec: 88.7008 -2022-05-01 16:09:37,975 - tensorflow - INFO - loss = 0.026087768, step = 592400 (2.255 sec) -2022-05-01 16:09:40,226 - tensorflow - INFO - global_step/sec: 88.8584 -2022-05-01 16:09:40,226 - tensorflow - INFO - loss = 0.03718528, step = 592600 (2.251 sec) -2022-05-01 16:09:42,472 - tensorflow - INFO - global_step/sec: 89.0448 -2022-05-01 16:09:42,472 - tensorflow - INFO - loss = 0.07650729, step = 592800 (2.246 sec) -2022-05-01 16:09:44,722 - tensorflow - INFO - global_step/sec: 88.8853 -2022-05-01 16:09:44,722 - tensorflow - INFO - loss = 3.1181715, step = 593000 (2.250 sec) -2022-05-01 16:09:46,975 - tensorflow - INFO - global_step/sec: 88.7733 -2022-05-01 16:09:46,975 - tensorflow - INFO - loss = 0.051136553, step = 593200 (2.253 sec) -2022-05-01 16:09:49,235 - tensorflow - INFO - global_step/sec: 88.4955 -2022-05-01 16:09:49,235 - tensorflow - INFO - loss = 0.32656357, step = 593400 (2.260 sec) -2022-05-01 16:09:51,488 - tensorflow - INFO - global_step/sec: 88.7762 -2022-05-01 16:09:51,488 - tensorflow - INFO - loss = 0.051417626, step = 593600 (2.253 sec) -2022-05-01 16:09:53,744 - tensorflow - INFO - global_step/sec: 88.6572 -2022-05-01 16:09:53,744 - tensorflow - INFO - loss = 0.0070130154, step = 593800 (2.256 sec) -2022-05-01 16:09:55,998 - tensorflow - INFO - global_step/sec: 88.7031 -2022-05-01 16:09:55,999 - tensorflow - INFO - loss = 0.012833834, step = 594000 (2.255 sec) -2022-05-01 16:09:58,251 - tensorflow - INFO - global_step/sec: 88.7779 -2022-05-01 16:09:58,252 - tensorflow - INFO - loss = 0.014024916, step = 594200 (2.253 sec) -2022-05-01 16:10:00,502 - tensorflow - INFO - global_step/sec: 88.8557 -2022-05-01 16:10:00,502 - tensorflow - INFO - loss = 0.07648599, step = 594400 (2.251 sec) -2022-05-01 16:10:02,763 - tensorflow - INFO - global_step/sec: 88.4516 -2022-05-01 16:10:02,764 - tensorflow - INFO - loss = 0.08582879, step = 594600 (2.261 sec) -2022-05-01 16:10:05,018 - tensorflow - INFO - global_step/sec: 88.6866 -2022-05-01 16:10:05,019 - tensorflow - INFO - loss = 0.04442747, step = 594800 (2.255 sec) -2022-05-01 16:10:07,268 - tensorflow - INFO - global_step/sec: 88.9089 -2022-05-01 16:10:07,268 - tensorflow - INFO - loss = 0.6102391, step = 595000 (2.249 sec) -2022-05-01 16:10:09,519 - tensorflow - INFO - global_step/sec: 88.8406 -2022-05-01 16:10:09,519 - tensorflow - INFO - loss = 0.00982367, step = 595200 (2.251 sec) -2022-05-01 16:10:11,772 - tensorflow - INFO - global_step/sec: 88.7721 -2022-05-01 16:10:11,772 - tensorflow - INFO - loss = 0.03102067, step = 595400 (2.253 sec) -2022-05-01 16:10:14,023 - tensorflow - INFO - global_step/sec: 88.8381 -2022-05-01 16:10:14,024 - tensorflow - INFO - loss = 0.020223914, step = 595600 (2.251 sec) -2022-05-01 16:10:16,277 - tensorflow - INFO - global_step/sec: 88.7384 -2022-05-01 16:10:16,277 - tensorflow - INFO - loss = 0.021211697, step = 595800 (2.254 sec) -2022-05-01 16:10:18,532 - tensorflow - INFO - global_step/sec: 88.6797 -2022-05-01 16:10:18,533 - tensorflow - INFO - loss = 0.3812176, step = 596000 (2.255 sec) -2022-05-01 16:10:20,787 - tensorflow - INFO - global_step/sec: 88.6834 -2022-05-01 16:10:20,788 - tensorflow - INFO - loss = 0.28526512, step = 596200 (2.255 sec) -2022-05-01 16:10:23,039 - tensorflow - INFO - global_step/sec: 88.8428 -2022-05-01 16:10:23,039 - tensorflow - INFO - loss = 0.016735926, step = 596400 (2.251 sec) -2022-05-01 16:10:25,288 - tensorflow - INFO - global_step/sec: 88.9144 -2022-05-01 16:10:25,288 - tensorflow - INFO - loss = 0.082976624, step = 596600 (2.249 sec) -2022-05-01 16:10:27,547 - tensorflow - INFO - global_step/sec: 88.5296 -2022-05-01 16:10:27,548 - tensorflow - INFO - loss = 0.045340955, step = 596800 (2.259 sec) -2022-05-01 16:10:29,798 - tensorflow - INFO - global_step/sec: 88.8455 -2022-05-01 16:10:29,799 - tensorflow - INFO - loss = 0.05019103, step = 597000 (2.251 sec) -2022-05-01 16:10:32,056 - tensorflow - INFO - global_step/sec: 88.5854 -2022-05-01 16:10:32,056 - tensorflow - INFO - loss = 0.2732757, step = 597200 (2.258 sec) -2022-05-01 16:10:34,310 - tensorflow - INFO - global_step/sec: 88.7338 -2022-05-01 16:10:34,310 - tensorflow - INFO - loss = 0.044302374, step = 597400 (2.254 sec) -2022-05-01 16:10:36,559 - tensorflow - INFO - global_step/sec: 88.9157 -2022-05-01 16:10:36,560 - tensorflow - INFO - loss = 0.033330336, step = 597600 (2.249 sec) -2022-05-01 16:10:38,815 - tensorflow - INFO - global_step/sec: 88.6414 -2022-05-01 16:10:38,816 - tensorflow - INFO - loss = 0.071330816, step = 597800 (2.256 sec) -2022-05-01 16:10:41,071 - tensorflow - INFO - global_step/sec: 88.6738 -2022-05-01 16:10:41,071 - tensorflow - INFO - loss = 0.03155005, step = 598000 (2.255 sec) -2022-05-01 16:10:43,331 - tensorflow - INFO - global_step/sec: 88.4949 -2022-05-01 16:10:43,331 - tensorflow - INFO - loss = 0.036724314, step = 598200 (2.260 sec) -2022-05-01 16:10:45,590 - tensorflow - INFO - global_step/sec: 88.5148 -2022-05-01 16:10:45,591 - tensorflow - INFO - loss = 0.058887675, step = 598400 (2.260 sec) -2022-05-01 16:10:47,850 - tensorflow - INFO - global_step/sec: 88.5321 -2022-05-01 16:10:47,850 - tensorflow - INFO - loss = 0.04405027, step = 598600 (2.259 sec) -2022-05-01 16:10:50,113 - tensorflow - INFO - global_step/sec: 88.3546 -2022-05-01 16:10:50,114 - tensorflow - INFO - loss = 0.026605247, step = 598800 (2.264 sec) -2022-05-01 16:10:52,375 - tensorflow - INFO - global_step/sec: 88.4125 -2022-05-01 16:10:52,376 - tensorflow - INFO - loss = 0.09087759, step = 599000 (2.262 sec) -2022-05-01 16:10:54,638 - tensorflow - INFO - global_step/sec: 88.3708 -2022-05-01 16:10:54,639 - tensorflow - INFO - loss = 0.050264105, step = 599200 (2.263 sec) -2022-05-01 16:10:56,895 - tensorflow - INFO - global_step/sec: 88.6399 -2022-05-01 16:10:56,895 - tensorflow - INFO - loss = 0.5043671, step = 599400 (2.256 sec) -2022-05-01 16:10:59,157 - tensorflow - INFO - global_step/sec: 88.41 -2022-05-01 16:10:59,157 - tensorflow - INFO - loss = 0.057621866, step = 599600 (2.262 sec) -2022-05-01 16:11:01,418 - tensorflow - INFO - global_step/sec: 88.4709 -2022-05-01 16:11:01,418 - tensorflow - INFO - loss = 0.04510246, step = 599800 (2.261 sec) -2022-05-01 16:11:03,669 - tensorflow - INFO - Saving checkpoints for 600000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. -2022-05-01 16:11:03,687 - tensorflow - INFO - model.ckpt-600000 is not in all_model_checkpoint_paths. Manually adding it. -2022-05-01 16:11:04,248 - tensorflow - INFO - Calling model_fn. -2022-05-01 16:11:04,379 - tensorflow - INFO - Done calling model_fn. -2022-05-01 16:11:04,394 - tensorflow - INFO - Starting evaluation at 2022-05-01T16:11:04Z -2022-05-01 16:11:04,458 - tensorflow - INFO - Graph was finalized. -2022-05-01 16:11:04,461 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-600000 -2022-05-01 16:11:04,742 - tensorflow - INFO - Running local_init_op. -2022-05-01 16:11:04,850 - tensorflow - INFO - Done running local_init_op. -2022-05-01 16:11:14,952 - tensorflow - INFO - Evaluation [3990/39900] -2022-05-01 16:11:24,367 - tensorflow - INFO - Evaluation [7980/39900] -2022-05-01 16:11:33,791 - tensorflow - INFO - Evaluation [11970/39900] -2022-05-01 16:11:43,194 - tensorflow - INFO - Evaluation [15960/39900] -2022-05-01 16:11:52,560 - tensorflow - INFO - Evaluation [19950/39900] -2022-05-01 16:12:01,964 - tensorflow - INFO - Evaluation [23940/39900] -2022-05-01 16:12:11,336 - tensorflow - INFO - Evaluation [27930/39900] -2022-05-01 16:12:20,741 - tensorflow - INFO - Evaluation [31920/39900] -2022-05-01 16:12:30,115 - tensorflow - INFO - Evaluation [35910/39900] -2022-05-01 16:12:39,506 - tensorflow - INFO - Evaluation [39900/39900] -2022-05-01 16:12:39,644 - tensorflow - INFO - Finished evaluation at 2022-05-01-16:12:39 -2022-05-01 16:12:39,644 - tensorflow - INFO - Saving dict for global step 600000: global_step = 600000, loss = 0.05211654, mean_degree_err = 7.7307415 -2022-05-01 16:12:39,645 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 600000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-600000 -2022-05-01 16:12:39,658 - tensorflow - INFO - global_step/sec: 2.03582 -2022-05-01 16:12:39,659 - tensorflow - INFO - loss = 1.0458663, step = 600000 (98.241 sec) -2022-05-01 16:12:41,927 - tensorflow - INFO - global_step/sec: 88.1445 -2022-05-01 16:12:41,928 - tensorflow - INFO - loss = 0.025115207, step = 600200 (2.269 sec) -2022-05-01 16:12:44,180 - tensorflow - INFO - global_step/sec: 88.7807 -2022-05-01 16:12:44,180 - tensorflow - INFO - loss = 0.103602424, step = 600400 (2.253 sec) -2022-05-01 16:12:46,441 - tensorflow - INFO - global_step/sec: 88.4632 -2022-05-01 16:12:46,441 - tensorflow - INFO - loss = 0.026233017, step = 600600 (2.261 sec) -2022-05-01 16:12:48,692 - tensorflow - INFO - global_step/sec: 88.8496 -2022-05-01 16:12:48,692 - tensorflow - INFO - loss = 0.9358847, step = 600800 (2.251 sec) -2022-05-01 16:12:50,948 - tensorflow - INFO - global_step/sec: 88.6401 -2022-05-01 16:12:50,949 - tensorflow - INFO - loss = 0.03394095, step = 601000 (2.256 sec) -2022-05-01 16:12:53,204 - tensorflow - INFO - global_step/sec: 88.6696 -2022-05-01 16:12:53,204 - tensorflow - INFO - loss = 0.020482913, step = 601200 (2.256 sec) -2022-05-01 16:12:55,460 - tensorflow - INFO - global_step/sec: 88.6249 -2022-05-01 16:12:55,461 - tensorflow - INFO - loss = 1.1575468, step = 601400 (2.257 sec) -2022-05-01 16:12:57,713 - tensorflow - INFO - global_step/sec: 88.7857 -2022-05-01 16:12:57,714 - tensorflow - INFO - loss = 0.05647299, step = 601600 (2.253 sec) -2022-05-01 16:12:59,964 - tensorflow - INFO - global_step/sec: 88.8566 -2022-05-01 16:12:59,964 - tensorflow - INFO - loss = 0.7979704, step = 601800 (2.251 sec) -2022-05-01 16:13:02,221 - tensorflow - INFO - global_step/sec: 88.6268 -2022-05-01 16:13:02,221 - tensorflow - INFO - loss = 0.042243555, step = 602000 (2.257 sec) -2022-05-01 16:13:04,476 - tensorflow - INFO - global_step/sec: 88.6789 -2022-05-01 16:13:04,476 - tensorflow - INFO - loss = 0.059275866, step = 602200 (2.255 sec) -2022-05-01 16:13:06,732 - tensorflow - INFO - global_step/sec: 88.6556 -2022-05-01 16:13:06,732 - tensorflow - INFO - loss = 0.6342204, step = 602400 (2.256 sec) -2022-05-01 16:13:08,989 - tensorflow - INFO - global_step/sec: 88.624 -2022-05-01 16:13:08,989 - tensorflow - INFO - loss = 0.03493088, step = 602600 (2.257 sec) -2022-05-01 16:13:11,246 - tensorflow - INFO - global_step/sec: 88.6143 -2022-05-01 16:13:11,246 - tensorflow - INFO - loss = 0.36002302, step = 602800 (2.257 sec) -2022-05-01 16:13:13,497 - tensorflow - INFO - global_step/sec: 88.818 -2022-05-01 16:13:13,498 - tensorflow - INFO - loss = 0.040594425, step = 603000 (2.252 sec) -2022-05-01 16:13:15,754 - tensorflow - INFO - global_step/sec: 88.6284 -2022-05-01 16:13:15,754 - tensorflow - INFO - loss = 0.10270108, step = 603200 (2.257 sec) -2022-05-01 16:13:18,007 - tensorflow - INFO - global_step/sec: 88.7484 -2022-05-01 16:13:18,008 - tensorflow - INFO - loss = 0.016271198, step = 603400 (2.254 sec) -2022-05-01 16:13:20,256 - tensorflow - INFO - global_step/sec: 88.9565 -2022-05-01 16:13:20,256 - tensorflow - INFO - loss = 0.071318746, step = 603600 (2.248 sec) -2022-05-01 16:13:22,507 - tensorflow - INFO - global_step/sec: 88.8344 -2022-05-01 16:13:22,508 - tensorflow - INFO - loss = 0.018505175, step = 603800 (2.251 sec) -2022-05-01 16:13:24,760 - tensorflow - INFO - global_step/sec: 88.7743 -2022-05-01 16:13:24,760 - tensorflow - INFO - loss = 0.015764019, step = 604000 (2.253 sec) -2022-05-01 16:13:27,018 - tensorflow - INFO - global_step/sec: 88.5653 -2022-05-01 16:13:27,019 - tensorflow - INFO - loss = 0.38297236, step = 604200 (2.258 sec) -2022-05-01 16:13:29,280 - tensorflow - INFO - global_step/sec: 88.4164 -2022-05-01 16:13:29,281 - tensorflow - INFO - loss = 0.022897664, step = 604400 (2.262 sec) -2022-05-01 16:13:31,540 - tensorflow - INFO - global_step/sec: 88.5055 -2022-05-01 16:13:31,541 - tensorflow - INFO - loss = 0.036261506, step = 604600 (2.260 sec) -2022-05-01 16:13:33,789 - tensorflow - INFO - global_step/sec: 88.9331 -2022-05-01 16:13:33,789 - tensorflow - INFO - loss = 0.026952295, step = 604800 (2.249 sec) -2022-05-01 16:13:36,043 - tensorflow - INFO - global_step/sec: 88.7405 -2022-05-01 16:13:36,043 - tensorflow - INFO - loss = 0.017694829, step = 605000 (2.254 sec) -2022-05-01 16:13:38,296 - tensorflow - INFO - global_step/sec: 88.7579 -2022-05-01 16:13:38,296 - tensorflow - INFO - loss = 0.017842809, step = 605200 (2.253 sec) -2022-05-01 16:13:40,550 - tensorflow - INFO - global_step/sec: 88.7298 -2022-05-01 16:13:40,550 - tensorflow - INFO - loss = 0.013539692, step = 605400 (2.254 sec) -2022-05-01 16:13:42,802 - tensorflow - INFO - global_step/sec: 88.7996 -2022-05-01 16:13:42,803 - tensorflow - INFO - loss = 1.015567, step = 605600 (2.252 sec) -2022-05-01 16:13:45,055 - tensorflow - INFO - global_step/sec: 88.78 -2022-05-01 16:13:45,055 - tensorflow - INFO - loss = 0.039732587, step = 605800 (2.253 sec) -2022-05-01 16:13:47,308 - tensorflow - INFO - global_step/sec: 88.7719 -2022-05-01 16:13:47,308 - tensorflow - INFO - loss = 0.052872486, step = 606000 (2.253 sec) -2022-05-01 16:13:49,564 - tensorflow - INFO - global_step/sec: 88.6697 -2022-05-01 16:13:49,564 - tensorflow - INFO - loss = 0.01597492, step = 606200 (2.256 sec) -2022-05-01 16:13:51,816 - tensorflow - INFO - global_step/sec: 88.781 -2022-05-01 16:13:51,817 - tensorflow - INFO - loss = 0.11118606, step = 606400 (2.253 sec) -2022-05-01 16:13:54,069 - tensorflow - INFO - global_step/sec: 88.7908 -2022-05-01 16:13:54,069 - tensorflow - INFO - loss = 8.458463, step = 606600 (2.253 sec) -2022-05-01 16:13:56,324 - tensorflow - INFO - global_step/sec: 88.6696 -2022-05-01 16:13:56,325 - tensorflow - INFO - loss = 0.6168818, step = 606800 (2.256 sec) -2022-05-01 16:13:58,577 - tensorflow - INFO - global_step/sec: 88.7971 -2022-05-01 16:13:58,577 - tensorflow - INFO - loss = 0.016155917, step = 607000 (2.252 sec) -2022-05-01 16:14:00,830 - tensorflow - INFO - global_step/sec: 88.7463 -2022-05-01 16:14:00,831 - tensorflow - INFO - loss = 0.025292601, step = 607200 (2.254 sec) -2022-05-01 16:14:03,089 - tensorflow - INFO - global_step/sec: 88.5512 -2022-05-01 16:14:03,089 - tensorflow - INFO - loss = 2.134423, step = 607400 (2.259 sec) -2022-05-01 16:14:05,340 - tensorflow - INFO - global_step/sec: 88.8408 -2022-05-01 16:14:05,341 - tensorflow - INFO - loss = 0.20809147, step = 607600 (2.251 sec) -2022-05-01 16:14:07,601 - tensorflow - INFO - global_step/sec: 88.47 -2022-05-01 16:14:07,601 - tensorflow - INFO - loss = 0.05131338, step = 607800 (2.261 sec) -2022-05-01 16:14:09,855 - tensorflow - INFO - global_step/sec: 88.7402 -2022-05-01 16:14:09,855 - tensorflow - INFO - loss = 0.039553158, step = 608000 (2.254 sec) -2022-05-01 16:14:12,113 - tensorflow - INFO - global_step/sec: 88.5686 -2022-05-01 16:14:12,113 - tensorflow - INFO - loss = 0.67503667, step = 608200 (2.258 sec) -2022-05-01 16:14:14,372 - tensorflow - INFO - global_step/sec: 88.5175 -2022-05-01 16:14:14,373 - tensorflow - INFO - loss = 1.8657655, step = 608400 (2.259 sec) -2022-05-01 16:14:16,635 - tensorflow - INFO - global_step/sec: 88.3683 -2022-05-01 16:14:16,636 - tensorflow - INFO - loss = 0.102950454, step = 608600 (2.263 sec) -2022-05-01 16:14:18,902 - tensorflow - INFO - global_step/sec: 88.255 -2022-05-01 16:14:18,902 - tensorflow - INFO - loss = 0.03498585, step = 608800 (2.266 sec) -2022-05-01 16:14:21,170 - tensorflow - INFO - global_step/sec: 88.1685 -2022-05-01 16:14:21,170 - tensorflow - INFO - loss = 2.6146653, step = 609000 (2.268 sec) -2022-05-01 16:14:23,433 - tensorflow - INFO - global_step/sec: 88.3812 -2022-05-01 16:14:23,433 - tensorflow - INFO - loss = 0.05584207, step = 609200 (2.263 sec) -2022-05-01 16:14:25,698 - tensorflow - INFO - global_step/sec: 88.2988 -2022-05-01 16:14:25,698 - tensorflow - INFO - loss = 0.061443545, step = 609400 (2.265 sec) -2022-05-01 16:14:27,960 - tensorflow - INFO - global_step/sec: 88.4167 -2022-05-01 16:14:27,960 - tensorflow - INFO - loss = 0.24088061, step = 609600 (2.262 sec) -2022-05-01 16:14:30,229 - tensorflow - INFO - global_step/sec: 88.1576 -2022-05-01 16:14:30,229 - tensorflow - INFO - loss = 0.03882327, step = 609800 (2.269 sec) -2022-05-01 16:14:32,502 - tensorflow - INFO - global_step/sec: 87.9705 -2022-05-01 16:14:32,502 - tensorflow - INFO - loss = 0.0358029, step = 610000 (2.273 sec) -2022-05-01 16:14:34,767 - tensorflow - INFO - global_step/sec: 88.2974 -2022-05-01 16:14:34,768 - tensorflow - INFO - loss = 0.02986932, step = 610200 (2.265 sec) -2022-05-01 16:14:37,032 - tensorflow - INFO - global_step/sec: 88.2992 -2022-05-01 16:14:37,033 - tensorflow - INFO - loss = 0.04424672, step = 610400 (2.265 sec) -2022-05-01 16:14:39,288 - tensorflow - INFO - global_step/sec: 88.6686 -2022-05-01 16:14:39,288 - tensorflow - INFO - loss = 0.0313861, step = 610600 (2.256 sec) -2022-05-01 16:14:41,541 - tensorflow - INFO - global_step/sec: 88.7708 -2022-05-01 16:14:41,541 - tensorflow - INFO - loss = 0.025802461, step = 610800 (2.253 sec) -2022-05-01 16:14:43,795 - tensorflow - INFO - global_step/sec: 88.7102 -2022-05-01 16:14:43,796 - tensorflow - INFO - loss = 0.050362512, step = 611000 (2.254 sec) -2022-05-01 16:14:46,052 - tensorflow - INFO - global_step/sec: 88.6139 -2022-05-01 16:14:46,053 - tensorflow - INFO - loss = 0.036653407, step = 611200 (2.257 sec) -2022-05-01 16:14:48,307 - tensorflow - INFO - global_step/sec: 88.7076 -2022-05-01 16:14:48,307 - tensorflow - INFO - loss = 0.0365748, step = 611400 (2.255 sec) -2022-05-01 16:14:50,558 - tensorflow - INFO - global_step/sec: 88.8249 -2022-05-01 16:14:50,559 - tensorflow - INFO - loss = 1.1359053, step = 611600 (2.252 sec) -2022-05-01 16:14:52,816 - tensorflow - INFO - global_step/sec: 88.6074 -2022-05-01 16:14:52,816 - tensorflow - INFO - loss = 0.1671949, step = 611800 (2.257 sec) -2022-05-01 16:14:55,071 - tensorflow - INFO - global_step/sec: 88.6709 -2022-05-01 16:14:55,072 - tensorflow - INFO - loss = 0.06732585, step = 612000 (2.256 sec) -2022-05-01 16:14:57,326 - tensorflow - INFO - global_step/sec: 88.6823 -2022-05-01 16:14:57,327 - tensorflow - INFO - loss = 0.030628437, step = 612200 (2.255 sec) -2022-05-01 16:14:59,578 - tensorflow - INFO - global_step/sec: 88.8077 -2022-05-01 16:14:59,579 - tensorflow - INFO - loss = 0.12056379, step = 612400 (2.252 sec) -2022-05-01 16:15:01,830 - tensorflow - INFO - global_step/sec: 88.8466 -2022-05-01 16:15:01,830 - tensorflow - INFO - loss = 0.044604473, step = 612600 (2.251 sec) -2022-05-01 16:15:04,081 - tensorflow - INFO - global_step/sec: 88.8407 -2022-05-01 16:15:04,081 - tensorflow - INFO - loss = 0.28650892, step = 612800 (2.251 sec) -2022-05-01 16:15:06,336 - tensorflow - INFO - global_step/sec: 88.6904 -2022-05-01 16:15:06,336 - tensorflow - INFO - loss = 0.014561885, step = 613000 (2.255 sec) -2022-05-01 16:15:08,594 - tensorflow - INFO - global_step/sec: 88.5642 -2022-05-01 16:15:08,594 - tensorflow - INFO - loss = 0.0681196, step = 613200 (2.258 sec) -2022-05-01 16:15:10,850 - tensorflow - INFO - global_step/sec: 88.656 -2022-05-01 16:15:10,850 - tensorflow - INFO - loss = 0.11148818, step = 613400 (2.256 sec) -2022-05-01 16:15:13,105 - tensorflow - INFO - global_step/sec: 88.7006 -2022-05-01 16:15:13,105 - tensorflow - INFO - loss = 0.078977704, step = 613600 (2.255 sec) -2022-05-01 16:15:15,359 - tensorflow - INFO - global_step/sec: 88.7006 -2022-05-01 16:15:15,360 - tensorflow - INFO - loss = 0.0747398, step = 613800 (2.255 sec) -2022-05-01 16:15:17,615 - tensorflow - INFO - global_step/sec: 88.6759 -2022-05-01 16:15:17,615 - tensorflow - INFO - loss = 0.032532763, step = 614000 (2.255 sec) -2022-05-01 16:15:19,866 - tensorflow - INFO - global_step/sec: 88.8421 -2022-05-01 16:15:19,866 - tensorflow - INFO - loss = 0.022528261, step = 614200 (2.251 sec) -2022-05-01 16:15:22,120 - tensorflow - INFO - global_step/sec: 88.7334 -2022-05-01 16:15:22,120 - tensorflow - INFO - loss = 0.032586053, step = 614400 (2.254 sec) -2022-05-01 16:15:24,375 - tensorflow - INFO - global_step/sec: 88.688 -2022-05-01 16:15:24,376 - tensorflow - INFO - loss = 0.006227595, step = 614600 (2.255 sec) -2022-05-01 16:15:26,626 - tensorflow - INFO - global_step/sec: 88.8601 -2022-05-01 16:15:26,626 - tensorflow - INFO - loss = 0.0127908895, step = 614800 (2.251 sec) -2022-05-01 16:15:28,881 - tensorflow - INFO - global_step/sec: 88.6947 -2022-05-01 16:15:28,881 - tensorflow - INFO - loss = 0.018400937, step = 615000 (2.255 sec) -2022-05-01 16:15:31,139 - tensorflow - INFO - global_step/sec: 88.5648 -2022-05-01 16:15:31,139 - tensorflow - INFO - loss = 0.015544417, step = 615200 (2.258 sec) -2022-05-01 16:15:33,391 - tensorflow - INFO - global_step/sec: 88.8067 -2022-05-01 16:15:33,391 - tensorflow - INFO - loss = 0.03676469, step = 615400 (2.252 sec) -2022-05-01 16:15:35,650 - tensorflow - INFO - global_step/sec: 88.5496 -2022-05-01 16:15:35,650 - tensorflow - INFO - loss = 0.024532415, step = 615600 (2.259 sec) -2022-05-01 16:15:37,904 - tensorflow - INFO - global_step/sec: 88.722 -2022-05-01 16:15:37,904 - tensorflow - INFO - loss = 0.03382394, step = 615800 (2.254 sec) -2022-05-01 16:15:40,158 - tensorflow - INFO - global_step/sec: 88.7179 -2022-05-01 16:15:40,159 - tensorflow - INFO - loss = 0.3950181, step = 616000 (2.254 sec) -2022-05-01 16:15:42,403 - tensorflow - INFO - global_step/sec: 89.0807 -2022-05-01 16:15:42,404 - tensorflow - INFO - loss = 0.038583353, step = 616200 (2.245 sec) -2022-05-01 16:15:44,659 - tensorflow - INFO - global_step/sec: 88.672 -2022-05-01 16:15:44,659 - tensorflow - INFO - loss = 0.033826016, step = 616400 (2.255 sec) -2022-05-01 16:15:46,917 - tensorflow - INFO - global_step/sec: 88.5798 -2022-05-01 16:15:46,917 - tensorflow - INFO - loss = 0.018127516, step = 616600 (2.258 sec) -2022-05-01 16:15:49,162 - tensorflow - INFO - global_step/sec: 89.089 -2022-05-01 16:15:49,162 - tensorflow - INFO - loss = 0.01999943, step = 616800 (2.245 sec) -2022-05-01 16:15:51,414 - tensorflow - INFO - global_step/sec: 88.8002 -2022-05-01 16:15:51,414 - tensorflow - INFO - loss = 0.037188075, step = 617000 (2.252 sec) -2022-05-01 16:15:53,670 - tensorflow - INFO - global_step/sec: 88.6327 -2022-05-01 16:15:53,671 - tensorflow - INFO - loss = 0.038181555, step = 617200 (2.257 sec) -2022-05-01 16:15:55,928 - tensorflow - INFO - global_step/sec: 88.586 -2022-05-01 16:15:55,929 - tensorflow - INFO - loss = 0.17563345, step = 617400 (2.258 sec) -2022-05-01 16:15:58,177 - tensorflow - INFO - global_step/sec: 88.954 -2022-05-01 16:15:58,177 - tensorflow - INFO - loss = 0.014711383, step = 617600 (2.248 sec) -2022-05-01 16:16:00,432 - tensorflow - INFO - global_step/sec: 88.6819 -2022-05-01 16:16:00,432 - tensorflow - INFO - loss = 0.02903166, step = 617800 (2.255 sec) -2022-05-01 16:16:02,685 - tensorflow - INFO - global_step/sec: 88.7609 -2022-05-01 16:16:02,685 - tensorflow - INFO - loss = 0.29104948, step = 618000 (2.253 sec) -2022-05-01 16:16:04,940 - tensorflow - INFO - global_step/sec: 88.6852 -2022-05-01 16:16:04,941 - tensorflow - INFO - loss = 0.926473, step = 618200 (2.255 sec) -2022-05-01 16:16:07,193 - tensorflow - INFO - global_step/sec: 88.7836 -2022-05-01 16:16:07,193 - tensorflow - INFO - loss = 0.06550895, step = 618400 (2.253 sec) -2022-05-01 16:16:09,449 - tensorflow - INFO - global_step/sec: 88.6348 -2022-05-01 16:16:09,450 - tensorflow - INFO - loss = 0.029829977, step = 618600 (2.256 sec) -2022-05-01 16:16:11,706 - tensorflow - INFO - global_step/sec: 88.6411 -2022-05-01 16:16:11,706 - tensorflow - INFO - loss = 0.045433816, step = 618800 (2.256 sec) -2022-05-01 16:16:13,959 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.026506381, step = 620200 (2.257 sec) -2022-05-01 16:16:29,752 - tensorflow - INFO - global_step/sec: 88.7358 -2022-05-01 16:16:29,753 - tensorflow - INFO - loss = 0.028152559, step = 620400 (2.254 sec) -2022-05-01 16:16:32,010 - tensorflow - INFO - global_step/sec: 88.564 -2022-05-01 16:16:32,011 - tensorflow - INFO - loss = 0.03125519, step = 620600 (2.258 sec) -2022-05-01 16:16:34,270 - tensorflow - INFO - global_step/sec: 88.5308 -2022-05-01 16:16:34,270 - tensorflow - INFO - loss = 0.020148389, step = 620800 (2.259 sec) -2022-05-01 16:16:36,529 - tensorflow - INFO - global_step/sec: 88.5157 -2022-05-01 16:16:36,530 - tensorflow - INFO - loss = 0.3082172, step = 621000 (2.260 sec) -2022-05-01 16:16:38,791 - tensorflow - INFO - global_step/sec: 88.4366 -2022-05-01 16:16:38,791 - tensorflow - INFO - loss = 0.06370544, step = 621200 (2.262 sec) -2022-05-01 16:16:41,042 - tensorflow - INFO - global_step/sec: 88.8395 -2022-05-01 16:16:41,042 - tensorflow - INFO - 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INFO - global_step/sec: 88.6099 -2022-05-01 16:17:10,362 - tensorflow - INFO - loss = 1.8016558, step = 624000 (2.257 sec) -2022-05-01 16:17:12,615 - tensorflow - INFO - global_step/sec: 88.7634 -2022-05-01 16:17:12,615 - tensorflow - INFO - loss = 0.045792885, step = 624200 (2.253 sec) -2022-05-01 16:17:14,874 - tensorflow - INFO - global_step/sec: 88.5117 -2022-05-01 16:17:14,875 - tensorflow - INFO - loss = 0.10698426, step = 624400 (2.260 sec) -2022-05-01 16:17:17,129 - tensorflow - INFO - global_step/sec: 88.7203 -2022-05-01 16:17:17,129 - tensorflow - INFO - loss = 0.020351615, step = 624600 (2.254 sec) -2022-05-01 16:17:19,380 - tensorflow - INFO - global_step/sec: 88.8468 -2022-05-01 16:17:19,380 - tensorflow - INFO - loss = 0.054590203, step = 624800 (2.251 sec) -2022-05-01 16:17:21,629 - tensorflow - INFO - global_step/sec: 88.9023 -2022-05-01 16:17:21,630 - tensorflow - INFO - loss = 0.051437117, step = 625000 (2.250 sec) -2022-05-01 16:17:23,881 - tensorflow - INFO - global_step/sec: 88.8118 -2022-05-01 16:17:23,882 - 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tensorflow - INFO - global_step/sec: 88.3127 -2022-05-01 16:17:53,248 - tensorflow - INFO - loss = 0.013710188, step = 627800 (2.265 sec) -2022-05-01 16:17:55,511 - tensorflow - INFO - global_step/sec: 88.3642 -2022-05-01 16:17:55,511 - tensorflow - INFO - loss = 0.034602355, step = 628000 (2.263 sec) -2022-05-01 16:17:57,779 - tensorflow - INFO - global_step/sec: 88.1545 -2022-05-01 16:17:57,780 - tensorflow - INFO - loss = 0.03215067, step = 628200 (2.269 sec) -2022-05-01 16:18:00,041 - tensorflow - INFO - global_step/sec: 88.4334 -2022-05-01 16:18:00,041 - tensorflow - INFO - loss = 0.046506256, step = 628400 (2.262 sec) -2022-05-01 16:18:02,302 - tensorflow - INFO - global_step/sec: 88.4437 -2022-05-01 16:18:02,303 - tensorflow - INFO - loss = 0.015580835, step = 628600 (2.261 sec) -2022-05-01 16:18:04,567 - tensorflow - INFO - global_step/sec: 88.3299 -2022-05-01 16:18:04,567 - tensorflow - INFO - loss = 0.064582884, step = 628800 (2.264 sec) -2022-05-01 16:18:06,819 - tensorflow - INFO - global_step/sec: 88.7805 -2022-05-01 16:18:06,820 - tensorflow - INFO - loss = 0.026806299, step = 629000 (2.253 sec) -2022-05-01 16:18:09,083 - tensorflow - INFO - global_step/sec: 88.3463 -2022-05-01 16:18:09,084 - tensorflow - INFO - loss = 0.09207547, step = 629200 (2.264 sec) -2022-05-01 16:18:11,333 - tensorflow - INFO - global_step/sec: 88.8901 -2022-05-01 16:18:11,334 - tensorflow - INFO - loss = 0.06292535, step = 629400 (2.250 sec) -2022-05-01 16:18:13,587 - tensorflow - INFO - global_step/sec: 88.7517 -2022-05-01 16:18:13,587 - tensorflow - INFO - loss = 0.036125876, step = 629600 (2.253 sec) -2022-05-01 16:18:15,839 - tensorflow - INFO - global_step/sec: 88.8127 -2022-05-01 16:18:15,839 - tensorflow - INFO - loss = 0.013562234, step = 629800 (2.252 sec) -2022-05-01 16:18:18,095 - tensorflow - INFO - global_step/sec: 88.6485 -2022-05-01 16:18:18,095 - tensorflow - INFO - loss = 0.13024516, step = 630000 (2.256 sec) -2022-05-01 16:18:20,352 - tensorflow - INFO - global_step/sec: 88.5861 -2022-05-01 16:18:20,353 - tensorflow - INFO - loss = 0.048751697, step = 630200 (2.258 sec) -2022-05-01 16:18:22,610 - tensorflow - INFO - global_step/sec: 88.5673 -2022-05-01 16:18:22,611 - tensorflow - INFO - loss = 0.02033265, step = 630400 (2.258 sec) -2022-05-01 16:18:24,865 - tensorflow - INFO - global_step/sec: 88.692 -2022-05-01 16:18:24,866 - tensorflow - INFO - loss = 0.05609814, step = 630600 (2.255 sec) -2022-05-01 16:18:27,118 - tensorflow - INFO - global_step/sec: 88.7745 -2022-05-01 16:18:27,119 - tensorflow - INFO - loss = 0.06963114, step = 630800 (2.253 sec) -2022-05-01 16:18:29,376 - tensorflow - INFO - global_step/sec: 88.5837 -2022-05-01 16:18:29,377 - tensorflow - INFO - loss = 0.022002216, step = 631000 (2.258 sec) -2022-05-01 16:18:31,637 - tensorflow - INFO - global_step/sec: 88.4497 -2022-05-01 16:18:31,638 - tensorflow - INFO - loss = 0.06723067, step = 631200 (2.261 sec) -2022-05-01 16:18:33,896 - tensorflow - INFO - global_step/sec: 88.5521 -2022-05-01 16:18:33,896 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.6306 -2022-05-01 16:18:49,692 - tensorflow - INFO - loss = 0.022168528, step = 632800 (2.257 sec) -2022-05-01 16:18:51,950 - tensorflow - INFO - global_step/sec: 88.5786 -2022-05-01 16:18:51,950 - tensorflow - INFO - loss = 16.258793, step = 633000 (2.258 sec) -2022-05-01 16:18:54,203 - tensorflow - INFO - global_step/sec: 88.7603 -2022-05-01 16:18:54,203 - tensorflow - INFO - loss = 0.033347547, step = 633200 (2.253 sec) -2022-05-01 16:18:56,458 - tensorflow - INFO - global_step/sec: 88.6695 -2022-05-01 16:18:56,459 - tensorflow - INFO - loss = 0.02900518, step = 633400 (2.256 sec) -2022-05-01 16:18:58,718 - tensorflow - INFO - global_step/sec: 88.5048 -2022-05-01 16:18:58,719 - tensorflow - INFO - loss = 5.884887, step = 633600 (2.260 sec) -2022-05-01 16:19:00,968 - tensorflow - INFO - global_step/sec: 88.8969 -2022-05-01 16:19:00,968 - tensorflow - INFO - loss = 17.619848, step = 633800 (2.250 sec) -2022-05-01 16:19:03,220 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.7667 -2022-05-01 16:19:46,061 - tensorflow - INFO - loss = 0.12344111, step = 637800 (2.253 sec) -2022-05-01 16:19:48,313 - tensorflow - INFO - global_step/sec: 88.7675 -2022-05-01 16:19:48,314 - tensorflow - INFO - loss = 0.090517946, step = 638000 (2.253 sec) -2022-05-01 16:19:50,569 - tensorflow - INFO - global_step/sec: 88.6549 -2022-05-01 16:19:50,570 - tensorflow - INFO - loss = 0.036494423, step = 638200 (2.256 sec) -2022-05-01 16:19:52,826 - tensorflow - INFO - global_step/sec: 88.6316 -2022-05-01 16:19:52,826 - tensorflow - INFO - loss = 0.107932344, step = 638400 (2.257 sec) -2022-05-01 16:19:55,079 - tensorflow - INFO - global_step/sec: 88.7781 -2022-05-01 16:19:55,079 - tensorflow - INFO - loss = 0.027406814, step = 638600 (2.253 sec) -2022-05-01 16:19:57,332 - tensorflow - INFO - global_step/sec: 88.7408 -2022-05-01 16:19:57,333 - tensorflow - INFO - loss = 0.040648628, step = 638800 (2.254 sec) -2022-05-01 16:19:59,592 - tensorflow - INFO - global_step/sec: 88.5269 -2022-05-01 16:19:59,592 - tensorflow - INFO - loss = 0.05482356, step = 639000 (2.259 sec) -2022-05-01 16:20:01,853 - tensorflow - INFO - global_step/sec: 88.4537 -2022-05-01 16:20:01,853 - tensorflow - INFO - loss = 0.16037363, step = 639200 (2.261 sec) -2022-05-01 16:20:04,107 - tensorflow - INFO - global_step/sec: 88.7335 -2022-05-01 16:20:04,107 - tensorflow - INFO - loss = 0.015529158, step = 639400 (2.254 sec) -2022-05-01 16:20:06,359 - tensorflow - INFO - global_step/sec: 88.8011 -2022-05-01 16:20:06,359 - tensorflow - INFO - loss = 0.05927797, step = 639600 (2.252 sec) -2022-05-01 16:20:08,611 - tensorflow - INFO - global_step/sec: 88.797 -2022-05-01 16:20:08,612 - tensorflow - INFO - loss = 0.12955801, step = 639800 (2.252 sec) -2022-05-01 16:20:10,865 - tensorflow - INFO - global_step/sec: 88.7532 -2022-05-01 16:20:10,865 - tensorflow - INFO - loss = 0.026449252, step = 640000 (2.253 sec) -2022-05-01 16:20:13,123 - tensorflow - INFO - global_step/sec: 88.5512 -2022-05-01 16:20:13,124 - 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tensorflow - INFO - loss = 0.03987413, step = 645200 (2.258 sec) -2022-05-01 16:21:11,730 - tensorflow - INFO - global_step/sec: 88.4371 -2022-05-01 16:21:11,730 - tensorflow - INFO - loss = 0.05389357, step = 645400 (2.262 sec) -2022-05-01 16:21:13,989 - tensorflow - INFO - global_step/sec: 88.517 -2022-05-01 16:21:13,990 - tensorflow - INFO - loss = 0.05849466, step = 645600 (2.259 sec) -2022-05-01 16:21:16,253 - tensorflow - INFO - global_step/sec: 88.3277 -2022-05-01 16:21:16,254 - tensorflow - INFO - loss = 0.03608981, step = 645800 (2.264 sec) -2022-05-01 16:21:18,514 - tensorflow - INFO - global_step/sec: 88.4795 -2022-05-01 16:21:18,514 - tensorflow - INFO - loss = 0.039586894, step = 646000 (2.260 sec) -2022-05-01 16:21:20,778 - tensorflow - INFO - global_step/sec: 88.3225 -2022-05-01 16:21:20,779 - tensorflow - INFO - loss = 0.08338522, step = 646200 (2.264 sec) -2022-05-01 16:21:23,041 - tensorflow - INFO - global_step/sec: 88.3985 -2022-05-01 16:21:23,041 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.3025 -2022-05-01 16:21:38,895 - tensorflow - INFO - loss = 0.07743402, step = 647800 (2.265 sec) -2022-05-01 16:21:41,156 - tensorflow - INFO - global_step/sec: 88.4322 -2022-05-01 16:21:41,156 - tensorflow - INFO - loss = 0.017677356, step = 648000 (2.262 sec) -2022-05-01 16:21:43,417 - tensorflow - INFO - global_step/sec: 88.4554 -2022-05-01 16:21:43,418 - tensorflow - INFO - loss = 0.019578267, step = 648200 (2.261 sec) -2022-05-01 16:21:45,677 - tensorflow - INFO - global_step/sec: 88.509 -2022-05-01 16:21:45,677 - tensorflow - INFO - loss = 0.06198241, step = 648400 (2.260 sec) -2022-05-01 16:21:47,928 - tensorflow - INFO - global_step/sec: 88.8393 -2022-05-01 16:21:47,928 - tensorflow - INFO - loss = 0.17423952, step = 648600 (2.251 sec) -2022-05-01 16:21:50,181 - tensorflow - INFO - global_step/sec: 88.7596 -2022-05-01 16:21:50,182 - tensorflow - INFO - loss = 0.026366603, step = 648800 (2.253 sec) -2022-05-01 16:21:52,429 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.06208293, step = 650200 (2.258 sec) -2022-05-01 16:22:08,223 - tensorflow - INFO - global_step/sec: 88.7065 -2022-05-01 16:22:08,224 - tensorflow - INFO - loss = 0.08045256, step = 650400 (2.255 sec) -2022-05-01 16:22:10,479 - tensorflow - INFO - global_step/sec: 88.6741 -2022-05-01 16:22:10,479 - tensorflow - INFO - loss = 0.013809038, step = 650600 (2.256 sec) -2022-05-01 16:22:12,726 - tensorflow - INFO - global_step/sec: 89.011 -2022-05-01 16:22:12,726 - tensorflow - INFO - loss = 0.06669504, step = 650800 (2.247 sec) -2022-05-01 16:22:14,977 - tensorflow - INFO - global_step/sec: 88.8457 -2022-05-01 16:22:14,977 - tensorflow - INFO - loss = 0.050167628, step = 651000 (2.251 sec) -2022-05-01 16:22:17,232 - tensorflow - INFO - global_step/sec: 88.6608 -2022-05-01 16:22:17,233 - tensorflow - INFO - loss = 0.2818935, step = 651200 (2.256 sec) -2022-05-01 16:22:19,485 - tensorflow - INFO - global_step/sec: 88.778 -2022-05-01 16:22:19,486 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.5094 -2022-05-01 16:22:35,274 - tensorflow - INFO - loss = 0.041744143, step = 652800 (2.260 sec) -2022-05-01 16:22:37,521 - tensorflow - INFO - global_step/sec: 89.0115 -2022-05-01 16:22:37,521 - tensorflow - INFO - loss = 0.64099216, step = 653000 (2.247 sec) -2022-05-01 16:22:39,779 - tensorflow - INFO - global_step/sec: 88.577 -2022-05-01 16:22:39,779 - tensorflow - INFO - loss = 0.20521967, step = 653200 (2.258 sec) -2022-05-01 16:22:42,031 - tensorflow - INFO - global_step/sec: 88.7804 -2022-05-01 16:22:42,032 - tensorflow - INFO - loss = 0.0182476, step = 653400 (2.253 sec) -2022-05-01 16:22:44,283 - tensorflow - INFO - global_step/sec: 88.8102 -2022-05-01 16:22:44,284 - tensorflow - INFO - loss = 0.04948442, step = 653600 (2.252 sec) -2022-05-01 16:22:46,542 - tensorflow - INFO - global_step/sec: 88.5635 -2022-05-01 16:22:46,542 - tensorflow - INFO - loss = 0.070475, step = 653800 (2.258 sec) -2022-05-01 16:22:48,800 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.016314765, step = 655200 (2.254 sec) -2022-05-01 16:23:04,586 - tensorflow - INFO - global_step/sec: 88.747 -2022-05-01 16:23:04,587 - tensorflow - INFO - loss = 0.06032333, step = 655400 (2.254 sec) -2022-05-01 16:23:06,841 - tensorflow - INFO - global_step/sec: 88.695 -2022-05-01 16:23:06,842 - tensorflow - INFO - loss = 0.053679515, step = 655600 (2.255 sec) -2022-05-01 16:23:09,095 - tensorflow - INFO - global_step/sec: 88.7328 -2022-05-01 16:23:09,096 - tensorflow - INFO - loss = 0.022284854, step = 655800 (2.254 sec) -2022-05-01 16:23:11,354 - tensorflow - INFO - global_step/sec: 88.531 -2022-05-01 16:23:11,355 - tensorflow - INFO - loss = 0.032551527, step = 656000 (2.259 sec) -2022-05-01 16:23:13,605 - tensorflow - INFO - global_step/sec: 88.8541 -2022-05-01 16:23:13,606 - tensorflow - INFO - loss = 0.015460408, step = 656200 (2.251 sec) -2022-05-01 16:23:15,859 - tensorflow - INFO - global_step/sec: 88.7214 -2022-05-01 16:23:15,860 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.1183 -2022-05-01 16:23:31,654 - tensorflow - INFO - loss = 1.6347156, step = 657800 (2.270 sec) -2022-05-01 16:23:33,910 - tensorflow - INFO - global_step/sec: 88.6339 -2022-05-01 16:23:33,910 - tensorflow - INFO - loss = 0.07399118, step = 658000 (2.256 sec) -2022-05-01 16:23:36,170 - tensorflow - INFO - global_step/sec: 88.4972 -2022-05-01 16:23:36,170 - tensorflow - INFO - loss = 0.017538741, step = 658200 (2.260 sec) -2022-05-01 16:23:38,427 - tensorflow - INFO - global_step/sec: 88.6295 -2022-05-01 16:23:38,427 - tensorflow - INFO - loss = 0.114778236, step = 658400 (2.257 sec) -2022-05-01 16:23:40,679 - tensorflow - INFO - global_step/sec: 88.7745 -2022-05-01 16:23:40,680 - tensorflow - INFO - loss = 0.03924323, step = 658600 (2.253 sec) -2022-05-01 16:23:42,931 - tensorflow - INFO - global_step/sec: 88.831 -2022-05-01 16:23:42,931 - tensorflow - INFO - loss = 0.016615653, step = 658800 (2.251 sec) -2022-05-01 16:23:45,185 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.021737969, step = 660200 (2.253 sec) -2022-05-01 16:24:00,973 - tensorflow - INFO - global_step/sec: 88.537 -2022-05-01 16:24:00,974 - tensorflow - INFO - loss = 0.06891701, step = 660400 (2.259 sec) -2022-05-01 16:24:03,232 - tensorflow - INFO - global_step/sec: 88.5562 -2022-05-01 16:24:03,232 - tensorflow - INFO - loss = 0.04322044, step = 660600 (2.258 sec) -2022-05-01 16:24:05,484 - tensorflow - INFO - global_step/sec: 88.7816 -2022-05-01 16:24:05,485 - tensorflow - INFO - loss = 0.11884213, step = 660800 (2.253 sec) -2022-05-01 16:24:07,738 - tensorflow - INFO - global_step/sec: 88.7323 -2022-05-01 16:24:07,739 - tensorflow - INFO - loss = 0.058298614, step = 661000 (2.254 sec) -2022-05-01 16:24:09,994 - tensorflow - INFO - global_step/sec: 88.6667 -2022-05-01 16:24:09,994 - tensorflow - INFO - loss = 0.012113831, step = 661200 (2.256 sec) -2022-05-01 16:24:12,251 - tensorflow - INFO - global_step/sec: 88.6203 -2022-05-01 16:24:12,251 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.7383 -2022-05-01 16:24:28,039 - tensorflow - INFO - loss = 0.029402863, step = 662800 (2.254 sec) -2022-05-01 16:24:30,300 - tensorflow - INFO - global_step/sec: 88.4568 -2022-05-01 16:24:30,300 - tensorflow - INFO - loss = 0.041033566, step = 663000 (2.261 sec) -2022-05-01 16:24:32,560 - tensorflow - INFO - global_step/sec: 88.4877 -2022-05-01 16:24:32,560 - tensorflow - INFO - loss = 10.794538, step = 663200 (2.260 sec) -2022-05-01 16:24:34,813 - tensorflow - INFO - global_step/sec: 88.758 -2022-05-01 16:24:34,814 - tensorflow - INFO - loss = 0.020068303, step = 663400 (2.253 sec) -2022-05-01 16:24:37,070 - tensorflow - INFO - global_step/sec: 88.6263 -2022-05-01 16:24:37,070 - tensorflow - INFO - loss = 0.025392253, step = 663600 (2.257 sec) -2022-05-01 16:24:39,328 - tensorflow - INFO - global_step/sec: 88.5509 -2022-05-01 16:24:39,329 - tensorflow - INFO - loss = 0.027187895, step = 663800 (2.259 sec) -2022-05-01 16:24:41,582 - tensorflow - INFO - global_step/sec: 88.761 -2022-05-01 16:24:41,582 - tensorflow - INFO - loss = 0.05288632, step = 664000 (2.253 sec) -2022-05-01 16:24:43,845 - tensorflow - INFO - global_step/sec: 88.3699 -2022-05-01 16:24:43,845 - tensorflow - INFO - loss = 0.02741351, step = 664200 (2.263 sec) -2022-05-01 16:24:46,105 - tensorflow - INFO - global_step/sec: 88.4847 -2022-05-01 16:24:46,106 - tensorflow - INFO - loss = 0.020498091, step = 664400 (2.260 sec) -2022-05-01 16:24:48,366 - tensorflow - INFO - global_step/sec: 88.4642 -2022-05-01 16:24:48,366 - tensorflow - INFO - loss = 0.03579387, step = 664600 (2.261 sec) -2022-05-01 16:24:50,629 - tensorflow - INFO - global_step/sec: 88.3919 -2022-05-01 16:24:50,629 - tensorflow - INFO - loss = 0.03373597, step = 664800 (2.263 sec) -2022-05-01 16:24:52,892 - tensorflow - INFO - global_step/sec: 88.3467 -2022-05-01 16:24:52,893 - tensorflow - INFO - loss = 0.032407492, step = 665000 (2.264 sec) -2022-05-01 16:24:55,157 - tensorflow - INFO - global_step/sec: 88.3251 -2022-05-01 16:24:55,157 - tensorflow - INFO - loss = 0.12163025, step = 665200 (2.264 sec) -2022-05-01 16:24:57,419 - tensorflow - INFO - global_step/sec: 88.4285 -2022-05-01 16:24:57,419 - tensorflow - INFO - loss = 0.16891573, step = 665400 (2.262 sec) -2022-05-01 16:24:59,682 - tensorflow - INFO - global_step/sec: 88.3761 -2022-05-01 16:24:59,682 - tensorflow - INFO - loss = 0.42661345, step = 665600 (2.263 sec) -2022-05-01 16:25:01,945 - tensorflow - INFO - global_step/sec: 88.381 -2022-05-01 16:25:01,945 - tensorflow - INFO - loss = 0.030454159, step = 665800 (2.263 sec) -2022-05-01 16:25:04,208 - tensorflow - INFO - global_step/sec: 88.3589 -2022-05-01 16:25:04,208 - tensorflow - INFO - loss = 0.026694577, step = 666000 (2.263 sec) -2022-05-01 16:25:06,470 - tensorflow - INFO - global_step/sec: 88.4072 -2022-05-01 16:25:06,471 - tensorflow - INFO - loss = 0.16553432, step = 666200 (2.262 sec) -2022-05-01 16:25:08,731 - tensorflow - INFO - global_step/sec: 88.4817 -2022-05-01 16:25:08,731 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.7381 -2022-05-01 16:25:24,529 - tensorflow - INFO - loss = 0.079008505, step = 667800 (2.254 sec) -2022-05-01 16:25:26,777 - tensorflow - INFO - global_step/sec: 88.927 -2022-05-01 16:25:26,778 - tensorflow - INFO - loss = 0.025905298, step = 668000 (2.249 sec) -2022-05-01 16:25:29,034 - tensorflow - INFO - global_step/sec: 88.6283 -2022-05-01 16:25:29,034 - tensorflow - INFO - loss = 0.74142873, step = 668200 (2.257 sec) -2022-05-01 16:25:31,295 - tensorflow - INFO - global_step/sec: 88.4364 -2022-05-01 16:25:31,296 - tensorflow - INFO - loss = 0.019601302, step = 668400 (2.262 sec) -2022-05-01 16:25:33,550 - tensorflow - INFO - global_step/sec: 88.6887 -2022-05-01 16:25:33,551 - tensorflow - INFO - loss = 0.24653198, step = 668600 (2.255 sec) -2022-05-01 16:25:35,802 - tensorflow - INFO - global_step/sec: 88.8237 -2022-05-01 16:25:35,802 - tensorflow - INFO - loss = 0.030292703, step = 668800 (2.252 sec) -2022-05-01 16:25:38,055 - tensorflow - INFO - global_step/sec: 88.7672 -2022-05-01 16:25:38,056 - tensorflow - INFO - loss = 0.029847289, step = 669000 (2.253 sec) -2022-05-01 16:25:40,312 - tensorflow - INFO - global_step/sec: 88.5988 -2022-05-01 16:25:40,313 - tensorflow - INFO - loss = 0.014756154, step = 669200 (2.257 sec) -2022-05-01 16:25:42,568 - tensorflow - INFO - global_step/sec: 88.6584 -2022-05-01 16:25:42,569 - tensorflow - INFO - loss = 0.014029396, step = 669400 (2.256 sec) -2022-05-01 16:25:44,829 - tensorflow - INFO - global_step/sec: 88.4862 -2022-05-01 16:25:44,829 - tensorflow - INFO - loss = 0.24055506, step = 669600 (2.260 sec) -2022-05-01 16:25:47,081 - tensorflow - INFO - global_step/sec: 88.7959 -2022-05-01 16:25:47,081 - tensorflow - INFO - loss = 0.06759047, step = 669800 (2.252 sec) -2022-05-01 16:25:49,334 - tensorflow - INFO - global_step/sec: 88.7823 -2022-05-01 16:25:49,334 - tensorflow - INFO - loss = 0.17116967, step = 670000 (2.253 sec) -2022-05-01 16:25:51,589 - tensorflow - INFO - global_step/sec: 88.6613 -2022-05-01 16:25:51,590 - tensorflow - INFO - loss = 0.029213611, step = 670200 (2.256 sec) -2022-05-01 16:25:53,841 - tensorflow - INFO - global_step/sec: 88.8295 -2022-05-01 16:25:53,841 - tensorflow - INFO - loss = 0.19025126, step = 670400 (2.252 sec) -2022-05-01 16:25:56,108 - tensorflow - INFO - global_step/sec: 88.2132 -2022-05-01 16:25:56,109 - tensorflow - INFO - loss = 0.057157777, step = 670600 (2.267 sec) -2022-05-01 16:25:58,367 - tensorflow - INFO - global_step/sec: 88.5263 -2022-05-01 16:25:58,368 - tensorflow - INFO - loss = 0.031075079, step = 670800 (2.259 sec) -2022-05-01 16:26:00,622 - tensorflow - INFO - global_step/sec: 88.6948 -2022-05-01 16:26:00,623 - tensorflow - INFO - loss = 1.3746629, step = 671000 (2.255 sec) -2022-05-01 16:26:02,876 - tensorflow - INFO - global_step/sec: 88.7547 -2022-05-01 16:26:02,876 - tensorflow - INFO - loss = 0.06964992, step = 671200 (2.253 sec) -2022-05-01 16:26:05,132 - tensorflow - INFO - global_step/sec: 88.6228 -2022-05-01 16:26:05,133 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.4567 -2022-05-01 16:26:20,922 - tensorflow - INFO - loss = 0.5688815, step = 672800 (2.261 sec) -2022-05-01 16:26:23,183 - tensorflow - INFO - global_step/sec: 88.4171 -2022-05-01 16:26:23,184 - tensorflow - INFO - loss = 0.03952047, step = 673000 (2.262 sec) -2022-05-01 16:26:25,439 - tensorflow - INFO - global_step/sec: 88.6751 -2022-05-01 16:26:25,439 - tensorflow - INFO - loss = 0.017576523, step = 673200 (2.255 sec) -2022-05-01 16:26:27,693 - tensorflow - INFO - global_step/sec: 88.7138 -2022-05-01 16:26:27,694 - tensorflow - INFO - loss = 0.050946683, step = 673400 (2.254 sec) -2022-05-01 16:26:29,954 - tensorflow - INFO - global_step/sec: 88.4774 -2022-05-01 16:26:29,954 - tensorflow - INFO - loss = 0.05332943, step = 673600 (2.260 sec) -2022-05-01 16:26:32,220 - tensorflow - INFO - global_step/sec: 88.2588 -2022-05-01 16:26:32,220 - tensorflow - INFO - loss = 0.051412985, step = 673800 (2.266 sec) -2022-05-01 16:26:34,480 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.60917056, step = 675200 (2.259 sec) -2022-05-01 16:26:50,276 - tensorflow - INFO - global_step/sec: 88.6203 -2022-05-01 16:26:50,277 - tensorflow - INFO - loss = 0.38935432, step = 675400 (2.257 sec) -2022-05-01 16:26:52,524 - tensorflow - INFO - global_step/sec: 88.9838 -2022-05-01 16:26:52,524 - tensorflow - INFO - loss = 0.07833657, step = 675600 (2.248 sec) -2022-05-01 16:26:54,775 - tensorflow - INFO - global_step/sec: 88.8475 -2022-05-01 16:26:54,775 - tensorflow - INFO - loss = 0.050954167, step = 675800 (2.251 sec) -2022-05-01 16:26:57,036 - tensorflow - INFO - global_step/sec: 88.4687 -2022-05-01 16:26:57,036 - tensorflow - INFO - loss = 0.06862058, step = 676000 (2.261 sec) -2022-05-01 16:26:59,291 - tensorflow - INFO - global_step/sec: 88.6875 -2022-05-01 16:26:59,291 - tensorflow - INFO - loss = 0.03530959, step = 676200 (2.255 sec) -2022-05-01 16:27:01,554 - tensorflow - INFO - global_step/sec: 88.3878 -2022-05-01 16:27:01,554 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.866 -2022-05-01 16:27:17,347 - tensorflow - INFO - loss = 0.034631237, step = 677800 (2.251 sec) -2022-05-01 16:27:19,598 - tensorflow - INFO - global_step/sec: 88.8338 -2022-05-01 16:27:19,598 - tensorflow - INFO - loss = 0.019333193, step = 678000 (2.251 sec) -2022-05-01 16:27:21,854 - tensorflow - INFO - global_step/sec: 88.6438 -2022-05-01 16:27:21,854 - tensorflow - INFO - loss = 0.05202195, step = 678200 (2.256 sec) -2022-05-01 16:27:24,110 - tensorflow - INFO - global_step/sec: 88.6356 -2022-05-01 16:27:24,111 - tensorflow - INFO - loss = 0.010659963, step = 678400 (2.256 sec) -2022-05-01 16:27:26,364 - tensorflow - INFO - global_step/sec: 88.7312 -2022-05-01 16:27:26,365 - tensorflow - INFO - loss = 0.05443508, step = 678600 (2.254 sec) -2022-05-01 16:27:28,616 - tensorflow - INFO - global_step/sec: 88.8137 -2022-05-01 16:27:28,616 - tensorflow - INFO - loss = 0.041451752, step = 678800 (2.252 sec) -2022-05-01 16:27:30,876 - tensorflow - INFO - global_step/sec: 88.485 -2022-05-01 16:27:30,877 - tensorflow - INFO - loss = 0.043228835, step = 679000 (2.260 sec) -2022-05-01 16:27:33,128 - tensorflow - INFO - global_step/sec: 88.8059 -2022-05-01 16:27:33,129 - tensorflow - INFO - loss = 0.058606185, step = 679200 (2.252 sec) -2022-05-01 16:27:35,380 - tensorflow - INFO - global_step/sec: 88.823 -2022-05-01 16:27:35,381 - tensorflow - INFO - loss = 0.4918099, step = 679400 (2.252 sec) -2022-05-01 16:27:37,642 - tensorflow - INFO - global_step/sec: 88.4342 -2022-05-01 16:27:37,642 - tensorflow - INFO - loss = 0.46750993, step = 679600 (2.262 sec) -2022-05-01 16:27:39,897 - tensorflow - INFO - global_step/sec: 88.6828 -2022-05-01 16:27:39,897 - tensorflow - INFO - loss = 0.046240468, step = 679800 (2.255 sec) -2022-05-01 16:27:42,150 - tensorflow - INFO - global_step/sec: 88.7543 -2022-05-01 16:27:42,151 - tensorflow - INFO - loss = 0.22815502, step = 680000 (2.253 sec) -2022-05-01 16:27:44,405 - tensorflow - INFO - global_step/sec: 88.7155 -2022-05-01 16:27:44,405 - tensorflow - INFO - loss = 0.05310607, step = 680200 (2.254 sec) -2022-05-01 16:27:46,658 - tensorflow - INFO - global_step/sec: 88.7544 -2022-05-01 16:27:46,659 - tensorflow - INFO - loss = 0.023877185, step = 680400 (2.253 sec) -2022-05-01 16:27:48,919 - tensorflow - INFO - global_step/sec: 88.4575 -2022-05-01 16:27:48,920 - tensorflow - INFO - loss = 0.4059246, step = 680600 (2.261 sec) -2022-05-01 16:27:51,175 - tensorflow - INFO - global_step/sec: 88.6596 -2022-05-01 16:27:51,175 - tensorflow - INFO - loss = 0.027581796, step = 680800 (2.256 sec) -2022-05-01 16:27:53,428 - tensorflow - INFO - global_step/sec: 88.7847 -2022-05-01 16:27:53,428 - tensorflow - INFO - loss = 0.014304858, step = 681000 (2.253 sec) -2022-05-01 16:27:55,687 - tensorflow - INFO - global_step/sec: 88.5016 -2022-05-01 16:27:55,688 - tensorflow - INFO - loss = 0.14080632, step = 681200 (2.260 sec) -2022-05-01 16:27:57,942 - tensorflow - INFO - global_step/sec: 88.7175 -2022-05-01 16:27:57,942 - tensorflow - INFO - loss = 0.013051263, step = 681400 (2.254 sec) -2022-05-01 16:28:00,194 - tensorflow - INFO - global_step/sec: 88.7813 -2022-05-01 16:28:00,195 - tensorflow - INFO - loss = 0.023506679, step = 681600 (2.253 sec) -2022-05-01 16:28:02,457 - tensorflow - INFO - global_step/sec: 88.3871 -2022-05-01 16:28:02,458 - tensorflow - INFO - loss = 0.026280262, step = 681800 (2.263 sec) -2022-05-01 16:28:04,711 - tensorflow - INFO - global_step/sec: 88.7497 -2022-05-01 16:28:04,711 - tensorflow - INFO - loss = 0.019026008, step = 682000 (2.254 sec) -2022-05-01 16:28:06,968 - tensorflow - INFO - global_step/sec: 88.6207 -2022-05-01 16:28:06,968 - tensorflow - INFO - loss = 0.10160596, step = 682200 (2.257 sec) -2022-05-01 16:28:09,220 - tensorflow - INFO - global_step/sec: 88.7838 -2022-05-01 16:28:09,221 - tensorflow - INFO - loss = 0.48572725, step = 682400 (2.253 sec) -2022-05-01 16:28:11,476 - tensorflow - INFO - global_step/sec: 88.6661 -2022-05-01 16:28:11,476 - tensorflow - INFO - loss = 0.030404504, step = 682600 (2.256 sec) -2022-05-01 16:28:13,733 - tensorflow - INFO - global_step/sec: 88.5948 -2022-05-01 16:28:13,734 - tensorflow - INFO - loss = 0.1416041, step = 682800 (2.258 sec) -2022-05-01 16:28:16,002 - tensorflow - INFO - global_step/sec: 88.1773 -2022-05-01 16:28:16,002 - tensorflow - INFO - loss = 0.3864634, step = 683000 (2.268 sec) -2022-05-01 16:28:18,263 - tensorflow - INFO - global_step/sec: 88.4224 -2022-05-01 16:28:18,264 - tensorflow - INFO - loss = 0.041742954, step = 683200 (2.262 sec) -2022-05-01 16:28:20,529 - tensorflow - INFO - global_step/sec: 88.2897 -2022-05-01 16:28:20,529 - tensorflow - INFO - loss = 2.4299667, step = 683400 (2.265 sec) -2022-05-01 16:28:22,790 - tensorflow - INFO - global_step/sec: 88.4459 -2022-05-01 16:28:22,790 - tensorflow - INFO - loss = 0.02620551, step = 683600 (2.261 sec) -2022-05-01 16:28:25,051 - tensorflow - INFO - global_step/sec: 88.453 -2022-05-01 16:28:25,051 - tensorflow - INFO - loss = 0.028443713, step = 683800 (2.261 sec) -2022-05-01 16:28:27,321 - tensorflow - INFO - global_step/sec: 88.1258 -2022-05-01 16:28:27,321 - tensorflow - INFO - loss = 0.04064524, step = 684000 (2.269 sec) -2022-05-01 16:28:29,584 - tensorflow - INFO - global_step/sec: 88.35 -2022-05-01 16:28:29,585 - tensorflow - INFO - loss = 0.13384426, step = 684200 (2.264 sec) -2022-05-01 16:28:31,855 - tensorflow - INFO - global_step/sec: 88.0722 -2022-05-01 16:28:31,856 - tensorflow - INFO - loss = 0.22158429, step = 684400 (2.271 sec) -2022-05-01 16:28:34,117 - tensorflow - INFO - global_step/sec: 88.4196 -2022-05-01 16:28:34,117 - tensorflow - INFO - loss = 0.049566347, step = 684600 (2.262 sec) -2022-05-01 16:28:36,384 - tensorflow - INFO - global_step/sec: 88.2218 -2022-05-01 16:28:36,384 - tensorflow - INFO - loss = 0.027135855, step = 684800 (2.267 sec) -2022-05-01 16:28:38,649 - tensorflow - INFO - global_step/sec: 88.3124 -2022-05-01 16:28:38,649 - tensorflow - INFO - loss = 0.022400133, step = 685000 (2.265 sec) -2022-05-01 16:28:40,914 - tensorflow - INFO - global_step/sec: 88.2866 -2022-05-01 16:28:40,915 - tensorflow - INFO - loss = 0.03185318, step = 685200 (2.265 sec) -2022-05-01 16:28:43,177 - tensorflow - INFO - global_step/sec: 88.3747 -2022-05-01 16:28:43,178 - tensorflow - INFO - loss = 0.26845255, step = 685400 (2.263 sec) -2022-05-01 16:28:45,436 - tensorflow - INFO - global_step/sec: 88.5482 -2022-05-01 16:28:45,436 - tensorflow - INFO - loss = 0.025080893, step = 685600 (2.259 sec) -2022-05-01 16:28:47,697 - tensorflow - INFO - global_step/sec: 88.4514 -2022-05-01 16:28:47,697 - tensorflow - INFO - loss = 0.07365398, step = 685800 (2.261 sec) -2022-05-01 16:28:49,949 - tensorflow - INFO - global_step/sec: 88.8204 -2022-05-01 16:28:49,949 - tensorflow - INFO - loss = 0.0758942, step = 686000 (2.252 sec) -2022-05-01 16:28:52,208 - tensorflow - INFO - global_step/sec: 88.5143 -2022-05-01 16:28:52,209 - tensorflow - INFO - loss = 0.034178488, step = 686200 (2.260 sec) -2022-05-01 16:28:54,475 - tensorflow - INFO - global_step/sec: 88.2336 -2022-05-01 16:28:54,475 - tensorflow - INFO - loss = 0.043272763, step = 686400 (2.267 sec) -2022-05-01 16:28:56,747 - tensorflow - INFO - global_step/sec: 88.0416 -2022-05-01 16:28:56,747 - tensorflow - INFO - loss = 0.033819206, step = 686600 (2.272 sec) -2022-05-01 16:28:59,028 - tensorflow - INFO - global_step/sec: 87.6627 -2022-05-01 16:28:59,029 - tensorflow - INFO - loss = 0.045066155, step = 686800 (2.282 sec) -2022-05-01 16:29:01,329 - tensorflow - INFO - global_step/sec: 86.9087 -2022-05-01 16:29:01,330 - tensorflow - INFO - loss = 0.14871101, step = 687000 (2.301 sec) -2022-05-01 16:29:03,605 - tensorflow - INFO - global_step/sec: 87.8715 -2022-05-01 16:29:03,606 - tensorflow - INFO - loss = 0.22603975, step = 687200 (2.276 sec) -2022-05-01 16:29:05,879 - tensorflow - INFO - global_step/sec: 87.9516 -2022-05-01 16:29:05,880 - tensorflow - INFO - loss = 0.03430932, step = 687400 (2.274 sec) -2022-05-01 16:29:08,156 - tensorflow - INFO - global_step/sec: 87.8438 -2022-05-01 16:29:08,157 - tensorflow - INFO - loss = 0.0072114863, step = 687600 (2.277 sec) -2022-05-01 16:29:10,426 - tensorflow - INFO - global_step/sec: 88.1105 -2022-05-01 16:29:10,426 - tensorflow - INFO - loss = 0.0153994765, step = 687800 (2.270 sec) -2022-05-01 16:29:12,701 - tensorflow - INFO - global_step/sec: 87.9208 -2022-05-01 16:29:12,701 - tensorflow - INFO - loss = 0.008310544, step = 688000 (2.275 sec) -2022-05-01 16:29:14,968 - tensorflow - INFO - global_step/sec: 88.201 -2022-05-01 16:29:14,969 - tensorflow - INFO - loss = 0.026683938, step = 688200 (2.268 sec) -2022-05-01 16:29:17,223 - tensorflow - INFO - global_step/sec: 88.6993 -2022-05-01 16:29:17,224 - tensorflow - INFO - loss = 0.00728536, step = 688400 (2.255 sec) -2022-05-01 16:29:19,482 - tensorflow - INFO - global_step/sec: 88.5408 -2022-05-01 16:29:19,482 - tensorflow - INFO - loss = 0.032275274, step = 688600 (2.259 sec) -2022-05-01 16:29:21,741 - tensorflow - INFO - global_step/sec: 88.5333 -2022-05-01 16:29:21,741 - tensorflow - INFO - loss = 0.51012313, step = 688800 (2.259 sec) -2022-05-01 16:29:23,990 - tensorflow - INFO - global_step/sec: 88.919 -2022-05-01 16:29:23,991 - tensorflow - INFO - loss = 0.014863342, step = 689000 (2.249 sec) -2022-05-01 16:29:26,246 - tensorflow - INFO - global_step/sec: 88.6489 -2022-05-01 16:29:26,247 - tensorflow - INFO - loss = 0.023834182, step = 689200 (2.256 sec) -2022-05-01 16:29:28,499 - tensorflow - INFO - global_step/sec: 88.7774 -2022-05-01 16:29:28,500 - tensorflow - INFO - loss = 0.03596233, step = 689400 (2.253 sec) -2022-05-01 16:29:30,760 - tensorflow - INFO - global_step/sec: 88.4604 -2022-05-01 16:29:30,761 - tensorflow - INFO - loss = 0.02390868, step = 689600 (2.261 sec) -2022-05-01 16:29:33,035 - tensorflow - INFO - global_step/sec: 87.9258 -2022-05-01 16:29:33,035 - tensorflow - INFO - loss = 0.021484703, step = 689800 (2.275 sec) -2022-05-01 16:29:35,300 - tensorflow - INFO - global_step/sec: 88.2833 -2022-05-01 16:29:35,301 - tensorflow - INFO - loss = 0.3240009, step = 690000 (2.265 sec) -2022-05-01 16:29:37,566 - tensorflow - INFO - global_step/sec: 88.264 -2022-05-01 16:29:37,567 - tensorflow - INFO - loss = 0.36914372, step = 690200 (2.266 sec) -2022-05-01 16:29:39,870 - tensorflow - INFO - global_step/sec: 86.8138 -2022-05-01 16:29:39,870 - tensorflow - INFO - loss = 0.34035963, step = 690400 (2.304 sec) -2022-05-01 16:29:42,161 - tensorflow - INFO - global_step/sec: 87.2855 -2022-05-01 16:29:42,162 - tensorflow - INFO - loss = 0.051499836, step = 690600 (2.291 sec) -2022-05-01 16:29:44,441 - tensorflow - INFO - global_step/sec: 87.7261 -2022-05-01 16:29:44,442 - tensorflow - INFO - loss = 0.063221216, step = 690800 (2.280 sec) -2022-05-01 16:29:46,714 - tensorflow - INFO - global_step/sec: 88.0058 -2022-05-01 16:29:46,714 - tensorflow - INFO - loss = 0.064412564, step = 691000 (2.273 sec) -2022-05-01 16:29:48,986 - tensorflow - INFO - global_step/sec: 88.0249 -2022-05-01 16:29:48,986 - tensorflow - INFO - loss = 0.03744691, step = 691200 (2.272 sec) -2022-05-01 16:29:51,261 - tensorflow - INFO - global_step/sec: 87.8832 -2022-05-01 16:29:51,262 - tensorflow - INFO - loss = 0.12669168, step = 691400 (2.276 sec) -2022-05-01 16:29:53,539 - tensorflow - INFO - global_step/sec: 87.8175 -2022-05-01 16:29:53,539 - tensorflow - INFO - loss = 0.40596506, step = 691600 (2.278 sec) -2022-05-01 16:29:55,806 - tensorflow - INFO - global_step/sec: 88.2063 -2022-05-01 16:29:55,807 - tensorflow - INFO - loss = 0.010353914, step = 691800 (2.267 sec) -2022-05-01 16:29:58,072 - tensorflow - INFO - global_step/sec: 88.2865 -2022-05-01 16:29:58,072 - tensorflow - INFO - loss = 0.040336475, step = 692000 (2.265 sec) -2022-05-01 16:30:00,327 - tensorflow - INFO - global_step/sec: 88.669 -2022-05-01 16:30:00,328 - tensorflow - INFO - loss = 0.020247448, step = 692200 (2.256 sec) -2022-05-01 16:30:02,586 - tensorflow - INFO - global_step/sec: 88.5495 -2022-05-01 16:30:02,586 - tensorflow - INFO - loss = 0.022701023, step = 692400 (2.259 sec) -2022-05-01 16:30:04,839 - tensorflow - INFO - global_step/sec: 88.7533 -2022-05-01 16:30:04,840 - tensorflow - INFO - loss = 0.012202943, step = 692600 (2.253 sec) -2022-05-01 16:30:07,093 - tensorflow - INFO - global_step/sec: 88.7234 -2022-05-01 16:30:07,094 - tensorflow - INFO - loss = 0.05705206, step = 692800 (2.254 sec) -2022-05-01 16:30:09,352 - tensorflow - INFO - global_step/sec: 88.5445 -2022-05-01 16:30:09,353 - tensorflow - INFO - loss = 0.063872375, step = 693000 (2.259 sec) -2022-05-01 16:30:11,612 - tensorflow - INFO - global_step/sec: 88.4898 -2022-05-01 16:30:11,613 - tensorflow - INFO - loss = 0.20911783, step = 693200 (2.260 sec) -2022-05-01 16:30:13,868 - tensorflow - INFO - global_step/sec: 88.6538 -2022-05-01 16:30:13,869 - tensorflow - INFO - loss = 0.17761394, step = 693400 (2.256 sec) -2022-05-01 16:30:16,123 - tensorflow - INFO - global_step/sec: 88.7222 -2022-05-01 16:30:16,123 - tensorflow - INFO - loss = 2.785748, step = 693600 (2.254 sec) -2022-05-01 16:30:18,375 - tensorflow - INFO - global_step/sec: 88.7818 -2022-05-01 16:30:18,376 - tensorflow - INFO - loss = 0.047160655, step = 693800 (2.253 sec) -2022-05-01 16:30:20,628 - tensorflow - INFO - global_step/sec: 88.7787 -2022-05-01 16:30:20,629 - tensorflow - INFO - loss = 0.14111653, step = 694000 (2.253 sec) -2022-05-01 16:30:22,884 - tensorflow - INFO - global_step/sec: 88.645 -2022-05-01 16:30:22,885 - tensorflow - INFO - loss = 0.061342344, step = 694200 (2.256 sec) -2022-05-01 16:30:25,140 - tensorflow - INFO - global_step/sec: 88.6744 -2022-05-01 16:30:25,140 - tensorflow - INFO - loss = 0.03867026, step = 694400 (2.255 sec) -2022-05-01 16:30:27,397 - tensorflow - INFO - global_step/sec: 88.6029 -2022-05-01 16:30:27,397 - tensorflow - INFO - loss = 0.034364447, step = 694600 (2.257 sec) -2022-05-01 16:30:29,651 - tensorflow - INFO - global_step/sec: 88.7231 -2022-05-01 16:30:29,652 - tensorflow - INFO - loss = 0.022616444, step = 694800 (2.254 sec) -2022-05-01 16:30:31,917 - tensorflow - INFO - global_step/sec: 88.2733 -2022-05-01 16:30:31,917 - tensorflow - INFO - loss = 0.078567564, step = 695000 (2.266 sec) -2022-05-01 16:30:34,172 - tensorflow - INFO - global_step/sec: 88.6967 -2022-05-01 16:30:34,172 - tensorflow - INFO - loss = 0.02870323, step = 695200 (2.255 sec) -2022-05-01 16:30:36,429 - tensorflow - INFO - global_step/sec: 88.6052 -2022-05-01 16:30:36,429 - tensorflow - INFO - loss = 0.027117027, step = 695400 (2.257 sec) -2022-05-01 16:30:38,675 - tensorflow - INFO - global_step/sec: 89.0403 -2022-05-01 16:30:38,676 - tensorflow - INFO - loss = 9.146388, step = 695600 (2.246 sec) -2022-05-01 16:30:40,926 - tensorflow - INFO - global_step/sec: 88.8497 -2022-05-01 16:30:40,927 - tensorflow - INFO - loss = 0.14333989, step = 695800 (2.251 sec) -2022-05-01 16:30:43,185 - tensorflow - INFO - global_step/sec: 88.5331 -2022-05-01 16:30:43,186 - tensorflow - INFO - loss = 0.3762799, step = 696000 (2.259 sec) -2022-05-01 16:30:45,458 - tensorflow - INFO - global_step/sec: 88.0085 -2022-05-01 16:30:45,458 - tensorflow - INFO - loss = 0.025378358, step = 696200 (2.272 sec) -2022-05-01 16:30:47,735 - tensorflow - INFO - global_step/sec: 87.8242 -2022-05-01 16:30:47,735 - tensorflow - INFO - loss = 0.028789608, step = 696400 (2.277 sec) -2022-05-01 16:30:50,009 - tensorflow - INFO - global_step/sec: 87.9507 -2022-05-01 16:30:50,009 - tensorflow - INFO - loss = 0.013650784, step = 696600 (2.274 sec) -2022-05-01 16:30:52,313 - tensorflow - INFO - global_step/sec: 86.8005 -2022-05-01 16:30:52,314 - tensorflow - INFO - loss = 0.029505478, step = 696800 (2.304 sec) -2022-05-01 16:30:54,587 - tensorflow - INFO - global_step/sec: 87.9393 -2022-05-01 16:30:54,588 - tensorflow - INFO - loss = 1.0471745, step = 697000 (2.274 sec) -2022-05-01 16:30:56,858 - tensorflow - INFO - global_step/sec: 88.0994 -2022-05-01 16:30:56,858 - tensorflow - INFO - loss = 0.037719212, step = 697200 (2.270 sec) -2022-05-01 16:30:59,134 - tensorflow - INFO - global_step/sec: 87.8514 -2022-05-01 16:30:59,135 - tensorflow - INFO - loss = 0.030568294, step = 697400 (2.277 sec) -2022-05-01 16:31:01,409 - tensorflow - INFO - global_step/sec: 87.9152 -2022-05-01 16:31:01,410 - tensorflow - INFO - loss = 0.13457468, step = 697600 (2.275 sec) -2022-05-01 16:31:03,682 - tensorflow - INFO - global_step/sec: 87.9968 -2022-05-01 16:31:03,682 - tensorflow - INFO - loss = 0.08395249, step = 697800 (2.273 sec) -2022-05-01 16:31:05,955 - tensorflow - INFO - global_step/sec: 87.9794 -2022-05-01 16:31:05,956 - tensorflow - INFO - loss = 0.06530699, step = 698000 (2.273 sec) -2022-05-01 16:31:08,212 - tensorflow - INFO - global_step/sec: 88.6067 -2022-05-01 16:31:08,213 - tensorflow - INFO - loss = 0.0882684, step = 698200 (2.257 sec) -2022-05-01 16:31:10,465 - tensorflow - INFO - global_step/sec: 88.7685 -2022-05-01 16:31:10,466 - tensorflow - INFO - loss = 0.060899876, step = 698400 (2.253 sec) -2022-05-01 16:31:12,720 - tensorflow - INFO - global_step/sec: 88.7142 -2022-05-01 16:31:12,720 - tensorflow - INFO - loss = 0.18980502, step = 698600 (2.254 sec) -2022-05-01 16:31:14,972 - tensorflow - INFO - global_step/sec: 88.7923 -2022-05-01 16:31:14,973 - tensorflow - INFO - loss = 0.28167945, step = 698800 (2.252 sec) -2022-05-01 16:31:17,229 - tensorflow - INFO - global_step/sec: 88.6269 -2022-05-01 16:31:17,229 - tensorflow - INFO - loss = 0.015430516, step = 699000 (2.257 sec) -2022-05-01 16:31:19,482 - tensorflow - INFO - global_step/sec: 88.7613 -2022-05-01 16:31:19,483 - tensorflow - INFO - loss = 0.020742904, step = 699200 (2.253 sec) -2022-05-01 16:31:21,733 - tensorflow - INFO - global_step/sec: 88.8653 -2022-05-01 16:31:21,733 - tensorflow - INFO - loss = 0.020765623, step = 699400 (2.251 sec) -2022-05-01 16:31:23,993 - tensorflow - INFO - global_step/sec: 88.4763 -2022-05-01 16:31:23,994 - tensorflow - INFO - loss = 0.018228173, step = 699600 (2.261 sec) -2022-05-01 16:31:26,250 - tensorflow - INFO - global_step/sec: 88.6307 -2022-05-01 16:31:26,250 - tensorflow - INFO - loss = 0.022793582, step = 699800 (2.257 sec) -2022-05-01 16:31:28,499 - tensorflow - INFO - Saving checkpoints for 700000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. -2022-05-01 16:31:28,517 - tensorflow - INFO - model.ckpt-700000 is not in all_model_checkpoint_paths. Manually adding it. -2022-05-01 16:31:29,045 - tensorflow - INFO - Calling model_fn. -2022-05-01 16:31:29,177 - tensorflow - INFO - Done calling model_fn. -2022-05-01 16:31:29,192 - tensorflow - INFO - Starting evaluation at 2022-05-01T16:31:29Z -2022-05-01 16:31:29,256 - tensorflow - INFO - Graph was finalized. -2022-05-01 16:31:29,259 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-700000 -2022-05-01 16:31:29,540 - tensorflow - INFO - Running local_init_op. -2022-05-01 16:31:29,646 - tensorflow - INFO - Done running local_init_op. -2022-05-01 16:31:39,785 - tensorflow - INFO - Evaluation [3990/39900] -2022-05-01 16:31:49,238 - tensorflow - INFO - Evaluation [7980/39900] -2022-05-01 16:31:58,714 - tensorflow - INFO - Evaluation [11970/39900] -2022-05-01 16:32:08,192 - tensorflow - INFO - Evaluation [15960/39900] -2022-05-01 16:32:17,622 - tensorflow - INFO - Evaluation [19950/39900] -2022-05-01 16:32:27,039 - tensorflow - INFO - Evaluation [23940/39900] -2022-05-01 16:32:36,492 - tensorflow - INFO - Evaluation [27930/39900] -2022-05-01 16:32:45,931 - tensorflow - INFO - Evaluation [31920/39900] -2022-05-01 16:32:55,356 - tensorflow - INFO - Evaluation [35910/39900] -2022-05-01 16:33:04,797 - tensorflow - INFO - Evaluation [39900/39900] -2022-05-01 16:33:04,934 - tensorflow - INFO - Finished evaluation at 2022-05-01-16:33:04 -2022-05-01 16:33:04,934 - tensorflow - INFO - Saving dict for global step 700000: global_step = 700000, loss = 0.03090773, mean_degree_err = 4.9194665 -2022-05-01 16:33:04,934 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 700000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-700000 -2022-05-01 16:33:04,948 - tensorflow - INFO - global_step/sec: 2.02638 -2022-05-01 16:33:04,949 - tensorflow - INFO - loss = 0.45383355, step = 700000 (98.699 sec) -2022-05-01 16:33:07,219 - tensorflow - INFO - global_step/sec: 88.0497 -2022-05-01 16:33:07,220 - tensorflow - INFO - loss = 0.02814351, step = 700200 (2.271 sec) -2022-05-01 16:33:09,476 - tensorflow - INFO - global_step/sec: 88.6366 -2022-05-01 16:33:09,476 - tensorflow - INFO - loss = 0.031599652, step = 700400 (2.256 sec) -2022-05-01 16:33:11,728 - tensorflow - INFO - global_step/sec: 88.8244 -2022-05-01 16:33:11,728 - tensorflow - INFO - loss = 0.06685158, step = 700600 (2.252 sec) -2022-05-01 16:33:13,984 - tensorflow - INFO - global_step/sec: 88.6234 -2022-05-01 16:33:13,985 - tensorflow - INFO - loss = 0.05536495, step = 700800 (2.257 sec) -2022-05-01 16:33:16,239 - tensorflow - INFO - global_step/sec: 88.7024 -2022-05-01 16:33:16,239 - tensorflow - INFO - loss = 0.009829298, step = 701000 (2.255 sec) -2022-05-01 16:33:18,494 - tensorflow - INFO - global_step/sec: 88.679 -2022-05-01 16:33:18,495 - tensorflow - INFO - loss = 0.02428996, step = 701200 (2.255 sec) -2022-05-01 16:33:20,752 - tensorflow - INFO - global_step/sec: 88.6027 -2022-05-01 16:33:20,752 - tensorflow - INFO - loss = 0.018015191, step = 701400 (2.257 sec) -2022-05-01 16:33:23,006 - tensorflow - INFO - global_step/sec: 88.7342 -2022-05-01 16:33:23,006 - tensorflow - INFO - loss = 0.07055589, step = 701600 (2.254 sec) -2022-05-01 16:33:25,255 - tensorflow - INFO - global_step/sec: 88.92 -2022-05-01 16:33:25,255 - tensorflow - INFO - loss = 0.6384876, step = 701800 (2.249 sec) -2022-05-01 16:33:27,511 - tensorflow - INFO - global_step/sec: 88.6497 -2022-05-01 16:33:27,511 - tensorflow - INFO - loss = 0.014400528, step = 702000 (2.256 sec) -2022-05-01 16:33:29,762 - tensorflow - INFO - global_step/sec: 88.8495 -2022-05-01 16:33:29,762 - tensorflow - INFO - loss = 0.030938236, step = 702200 (2.251 sec) -2022-05-01 16:33:32,022 - tensorflow - INFO - global_step/sec: 88.5049 -2022-05-01 16:33:32,022 - tensorflow - INFO - loss = 0.05836013, step = 702400 (2.260 sec) -2022-05-01 16:33:34,280 - tensorflow - INFO - global_step/sec: 88.5689 -2022-05-01 16:33:34,280 - tensorflow - INFO - loss = 0.014873834, step = 702600 (2.258 sec) -2022-05-01 16:33:36,535 - tensorflow - INFO - global_step/sec: 88.6665 -2022-05-01 16:33:36,536 - tensorflow - INFO - loss = 0.05012783, step = 702800 (2.256 sec) -2022-05-01 16:33:38,792 - tensorflow - INFO - global_step/sec: 88.6365 -2022-05-01 16:33:38,792 - tensorflow - INFO - loss = 0.04358377, step = 703000 (2.256 sec) -2022-05-01 16:33:41,051 - tensorflow - INFO - global_step/sec: 88.5095 -2022-05-01 16:33:41,052 - tensorflow - INFO - loss = 0.028191432, step = 703200 (2.260 sec) -2022-05-01 16:33:43,301 - tensorflow - INFO - global_step/sec: 88.914 -2022-05-01 16:33:43,301 - tensorflow - INFO - loss = 0.047327407, step = 703400 (2.249 sec) -2022-05-01 16:33:45,567 - tensorflow - INFO - global_step/sec: 88.2587 -2022-05-01 16:33:45,567 - tensorflow - INFO - loss = 0.06812384, step = 703600 (2.266 sec) -2022-05-01 16:33:47,818 - tensorflow - INFO - global_step/sec: 88.8492 -2022-05-01 16:33:47,818 - tensorflow - INFO - loss = 0.033104226, step = 703800 (2.251 sec) -2022-05-01 16:33:50,072 - tensorflow - INFO - global_step/sec: 88.7064 -2022-05-01 16:33:50,073 - tensorflow - INFO - loss = 0.06181826, step = 704000 (2.255 sec) -2022-05-01 16:33:52,331 - tensorflow - INFO - global_step/sec: 88.5635 -2022-05-01 16:33:52,331 - tensorflow - INFO - loss = 2.1382723, step = 704200 (2.258 sec) -2022-05-01 16:33:54,584 - tensorflow - INFO - global_step/sec: 88.7633 -2022-05-01 16:33:54,584 - tensorflow - INFO - loss = 0.01735505, step = 704400 (2.253 sec) -2022-05-01 16:33:56,845 - tensorflow - INFO - global_step/sec: 88.4379 -2022-05-01 16:33:56,846 - tensorflow - INFO - loss = 0.030577263, step = 704600 (2.261 sec) -2022-05-01 16:33:59,103 - tensorflow - INFO - global_step/sec: 88.5835 -2022-05-01 16:33:59,104 - tensorflow - INFO - loss = 0.053564556, step = 704800 (2.258 sec) -2022-05-01 16:34:01,356 - tensorflow - INFO - global_step/sec: 88.7922 -2022-05-01 16:34:01,356 - tensorflow - INFO - loss = 0.03156396, step = 705000 (2.252 sec) -2022-05-01 16:34:03,613 - tensorflow - INFO - global_step/sec: 88.6046 -2022-05-01 16:34:03,613 - tensorflow - INFO - loss = 0.023791179, step = 705200 (2.257 sec) -2022-05-01 16:34:05,863 - tensorflow - INFO - global_step/sec: 88.8913 -2022-05-01 16:34:05,863 - tensorflow - INFO - loss = 0.2363714, step = 705400 (2.250 sec) -2022-05-01 16:34:08,119 - tensorflow - INFO - global_step/sec: 88.6522 -2022-05-01 16:34:08,119 - tensorflow - INFO - loss = 0.22751844, step = 705600 (2.256 sec) -2022-05-01 16:34:10,378 - tensorflow - INFO - global_step/sec: 88.5083 -2022-05-01 16:34:10,379 - tensorflow - INFO - loss = 0.02583364, step = 705800 (2.260 sec) -2022-05-01 16:34:12,631 - tensorflow - INFO - global_step/sec: 88.7984 -2022-05-01 16:34:12,631 - tensorflow - INFO - loss = 0.24674636, step = 706000 (2.252 sec) -2022-05-01 16:34:14,882 - tensorflow - INFO - global_step/sec: 88.851 -2022-05-01 16:34:14,882 - tensorflow - INFO - loss = 0.05409362, step = 706200 (2.251 sec) -2022-05-01 16:34:17,134 - tensorflow - INFO - global_step/sec: 88.795 -2022-05-01 16:34:17,134 - tensorflow - INFO - loss = 0.018007115, step = 706400 (2.252 sec) -2022-05-01 16:34:19,391 - tensorflow - INFO - global_step/sec: 88.6251 -2022-05-01 16:34:19,391 - tensorflow - INFO - loss = 0.0582543, step = 706600 (2.257 sec) -2022-05-01 16:34:21,647 - tensorflow - INFO - global_step/sec: 88.6337 -2022-05-01 16:34:21,648 - tensorflow - INFO - loss = 0.027885325, step = 706800 (2.256 sec) -2022-05-01 16:34:23,910 - tensorflow - INFO - global_step/sec: 88.397 -2022-05-01 16:34:23,910 - tensorflow - INFO - loss = 0.13973656, step = 707000 (2.262 sec) -2022-05-01 16:34:26,168 - tensorflow - INFO - global_step/sec: 88.5628 -2022-05-01 16:34:26,168 - tensorflow - INFO - loss = 0.2939297, step = 707200 (2.258 sec) -2022-05-01 16:34:28,423 - tensorflow - INFO - global_step/sec: 88.7049 -2022-05-01 16:34:28,423 - tensorflow - INFO - loss = 0.033999607, step = 707400 (2.255 sec) -2022-05-01 16:34:30,681 - tensorflow - INFO - global_step/sec: 88.5625 -2022-05-01 16:34:30,681 - tensorflow - INFO - loss = 0.048705816, step = 707600 (2.258 sec) -2022-05-01 16:34:32,944 - tensorflow - INFO - global_step/sec: 88.3819 -2022-05-01 16:34:32,944 - tensorflow - INFO - loss = 0.22880709, step = 707800 (2.263 sec) -2022-05-01 16:34:35,210 - tensorflow - INFO - global_step/sec: 88.2509 -2022-05-01 16:34:35,211 - tensorflow - INFO - loss = 0.029884279, step = 708000 (2.266 sec) -2022-05-01 16:34:37,465 - tensorflow - INFO - global_step/sec: 88.7019 -2022-05-01 16:34:37,465 - tensorflow - INFO - loss = 0.043589737, step = 708200 (2.255 sec) -2022-05-01 16:34:39,720 - tensorflow - INFO - global_step/sec: 88.6972 -2022-05-01 16:34:39,720 - tensorflow - INFO - loss = 0.14944857, step = 708400 (2.255 sec) -2022-05-01 16:34:41,978 - tensorflow - INFO - global_step/sec: 88.5747 -2022-05-01 16:34:41,978 - tensorflow - INFO - loss = 0.038527794, step = 708600 (2.258 sec) -2022-05-01 16:34:44,235 - tensorflow - INFO - global_step/sec: 88.6068 -2022-05-01 16:34:44,235 - tensorflow - INFO - loss = 0.023336954, step = 708800 (2.257 sec) -2022-05-01 16:34:46,492 - tensorflow - INFO - global_step/sec: 88.5937 -2022-05-01 16:34:46,493 - tensorflow - INFO - loss = 0.1388255, step = 709000 (2.258 sec) -2022-05-01 16:34:48,754 - tensorflow - INFO - global_step/sec: 88.4207 -2022-05-01 16:34:48,755 - tensorflow - INFO - loss = 0.087768376, step = 709200 (2.262 sec) -2022-05-01 16:34:51,009 - tensorflow - INFO - global_step/sec: 88.7091 -2022-05-01 16:34:51,009 - tensorflow - INFO - loss = 0.58065426, step = 709400 (2.255 sec) -2022-05-01 16:34:53,268 - tensorflow - INFO - global_step/sec: 88.5182 -2022-05-01 16:34:53,269 - tensorflow - INFO - loss = 0.0979935, step = 709600 (2.259 sec) -2022-05-01 16:34:55,522 - tensorflow - INFO - global_step/sec: 88.7484 -2022-05-01 16:34:55,522 - tensorflow - INFO - loss = 0.064526305, step = 709800 (2.254 sec) -2022-05-01 16:34:57,782 - tensorflow - INFO - global_step/sec: 88.5071 -2022-05-01 16:34:57,782 - tensorflow - INFO - loss = 0.073237136, step = 710000 (2.260 sec) -2022-05-01 16:35:00,037 - tensorflow - INFO - global_step/sec: 88.6766 -2022-05-01 16:35:00,037 - tensorflow - INFO - loss = 0.16119538, step = 710200 (2.255 sec) -2022-05-01 16:35:02,300 - tensorflow - INFO - global_step/sec: 88.3645 -2022-05-01 16:35:02,301 - tensorflow - INFO - loss = 0.020417998, step = 710400 (2.263 sec) -2022-05-01 16:35:04,554 - tensorflow - INFO - global_step/sec: 88.7478 -2022-05-01 16:35:04,554 - tensorflow - INFO - loss = 0.12026501, step = 710600 (2.254 sec) -2022-05-01 16:35:06,808 - tensorflow - INFO - global_step/sec: 88.7098 -2022-05-01 16:35:06,809 - tensorflow - INFO - loss = 0.048558436, step = 710800 (2.255 sec) -2022-05-01 16:35:09,059 - tensorflow - INFO - global_step/sec: 88.8573 -2022-05-01 16:35:09,060 - tensorflow - INFO - loss = 0.059880316, step = 711000 (2.251 sec) -2022-05-01 16:35:11,314 - tensorflow - INFO - global_step/sec: 88.6877 -2022-05-01 16:35:11,315 - tensorflow - INFO - loss = 0.06387286, step = 711200 (2.255 sec) -2022-05-01 16:35:13,576 - tensorflow - INFO - global_step/sec: 88.4353 -2022-05-01 16:35:13,576 - tensorflow - INFO - loss = 0.047975764, step = 711400 (2.262 sec) -2022-05-01 16:35:15,839 - tensorflow - INFO - global_step/sec: 88.3751 -2022-05-01 16:35:15,839 - tensorflow - INFO - loss = 0.33253038, step = 711600 (2.263 sec) -2022-05-01 16:35:18,101 - tensorflow - INFO - global_step/sec: 88.4015 -2022-05-01 16:35:18,102 - tensorflow - INFO - loss = 0.06969159, step = 711800 (2.262 sec) -2022-05-01 16:35:20,366 - tensorflow - INFO - global_step/sec: 88.3211 -2022-05-01 16:35:20,366 - tensorflow - INFO - loss = 0.10640546, step = 712000 (2.264 sec) -2022-05-01 16:35:22,627 - tensorflow - INFO - global_step/sec: 88.4421 -2022-05-01 16:35:22,628 - tensorflow - INFO - loss = 0.0625714, step = 712200 (2.261 sec) -2022-05-01 16:35:24,893 - tensorflow - INFO - global_step/sec: 88.2579 -2022-05-01 16:35:24,894 - tensorflow - INFO - loss = 0.03881578, step = 712400 (2.266 sec) -2022-05-01 16:35:27,159 - tensorflow - INFO - global_step/sec: 88.2578 -2022-05-01 16:35:27,160 - tensorflow - INFO - loss = 0.075513996, step = 712600 (2.266 sec) -2022-05-01 16:35:29,422 - tensorflow - INFO - global_step/sec: 88.373 -2022-05-01 16:35:29,423 - tensorflow - INFO - loss = 0.02635355, step = 712800 (2.263 sec) -2022-05-01 16:35:31,691 - tensorflow - INFO - global_step/sec: 88.1749 -2022-05-01 16:35:31,691 - tensorflow - INFO - loss = 0.04076782, step = 713000 (2.268 sec) -2022-05-01 16:35:33,955 - tensorflow - INFO - global_step/sec: 88.3223 -2022-05-01 16:35:33,956 - tensorflow - INFO - loss = 0.053160693, step = 713200 (2.264 sec) -2022-05-01 16:35:36,221 - tensorflow - INFO - global_step/sec: 88.2492 -2022-05-01 16:35:36,222 - tensorflow - INFO - loss = 0.013308646, step = 713400 (2.266 sec) -2022-05-01 16:35:38,482 - tensorflow - INFO - global_step/sec: 88.4628 -2022-05-01 16:35:38,483 - tensorflow - INFO - loss = 0.050666675, step = 713600 (2.261 sec) -2022-05-01 16:35:40,745 - tensorflow - INFO - global_step/sec: 88.4056 -2022-05-01 16:35:40,745 - tensorflow - INFO - loss = 0.0086106695, step = 713800 (2.262 sec) -2022-05-01 16:35:42,998 - tensorflow - INFO - global_step/sec: 88.7668 -2022-05-01 16:35:42,998 - tensorflow - INFO - loss = 0.058115117, step = 714000 (2.253 sec) -2022-05-01 16:35:45,251 - tensorflow - INFO - global_step/sec: 88.7722 -2022-05-01 16:35:45,251 - tensorflow - INFO - loss = 0.6640632, step = 714200 (2.253 sec) -2022-05-01 16:35:47,504 - tensorflow - INFO - global_step/sec: 88.7755 -2022-05-01 16:35:47,504 - tensorflow - INFO - loss = 1.7317725, step = 714400 (2.253 sec) -2022-05-01 16:35:49,760 - tensorflow - INFO - global_step/sec: 88.6234 -2022-05-01 16:35:49,761 - tensorflow - INFO - loss = 0.03569136, step = 714600 (2.257 sec) -2022-05-01 16:35:52,017 - tensorflow - INFO - global_step/sec: 88.6171 -2022-05-01 16:35:52,018 - tensorflow - INFO - loss = 0.27631834, step = 714800 (2.257 sec) -2022-05-01 16:35:54,277 - tensorflow - INFO - global_step/sec: 88.4927 -2022-05-01 16:35:54,278 - tensorflow - INFO - loss = 0.008753429, step = 715000 (2.260 sec) -2022-05-01 16:35:56,533 - tensorflow - INFO - global_step/sec: 88.6488 -2022-05-01 16:35:56,534 - tensorflow - INFO - loss = 0.035814345, step = 715200 (2.256 sec) -2022-05-01 16:35:58,784 - tensorflow - INFO - global_step/sec: 88.8695 -2022-05-01 16:35:58,784 - tensorflow - INFO - loss = 0.0340343, step = 715400 (2.250 sec) -2022-05-01 16:36:01,036 - tensorflow - INFO - global_step/sec: 88.8126 -2022-05-01 16:36:01,036 - tensorflow - INFO - loss = 0.017041814, step = 715600 (2.252 sec) -2022-05-01 16:36:03,293 - tensorflow - INFO - global_step/sec: 88.5987 -2022-05-01 16:36:03,294 - tensorflow - INFO - loss = 0.013692578, step = 715800 (2.257 sec) -2022-05-01 16:36:05,554 - tensorflow - INFO - global_step/sec: 88.4608 -2022-05-01 16:36:05,554 - tensorflow - INFO - loss = 0.070862934, step = 716000 (2.261 sec) -2022-05-01 16:36:07,811 - tensorflow - INFO - global_step/sec: 88.629 -2022-05-01 16:36:07,811 - tensorflow - INFO - loss = 0.16747123, step = 716200 (2.257 sec) -2022-05-01 16:36:10,063 - tensorflow - INFO - global_step/sec: 88.8031 -2022-05-01 16:36:10,063 - tensorflow - INFO - loss = 0.035815883, step = 716400 (2.252 sec) -2022-05-01 16:36:12,311 - tensorflow - INFO - global_step/sec: 88.9743 -2022-05-01 16:36:12,311 - tensorflow - INFO - loss = 0.033828985, step = 716600 (2.248 sec) -2022-05-01 16:36:14,566 - tensorflow - INFO - global_step/sec: 88.6695 -2022-05-01 16:36:14,567 - tensorflow - INFO - loss = 0.020342097, step = 716800 (2.256 sec) -2022-05-01 16:36:16,819 - tensorflow - INFO - global_step/sec: 88.7713 -2022-05-01 16:36:16,820 - tensorflow - INFO - loss = 0.29343623, step = 717000 (2.253 sec) -2022-05-01 16:36:19,081 - tensorflow - INFO - global_step/sec: 88.4343 -2022-05-01 16:36:19,081 - tensorflow - INFO - loss = 0.07206562, step = 717200 (2.262 sec) -2022-05-01 16:36:21,332 - tensorflow - INFO - global_step/sec: 88.8478 -2022-05-01 16:36:21,332 - tensorflow - INFO - loss = 0.13875133, step = 717400 (2.251 sec) -2022-05-01 16:36:23,591 - tensorflow - INFO - global_step/sec: 88.5388 -2022-05-01 16:36:23,591 - tensorflow - INFO - loss = 0.075220086, step = 717600 (2.259 sec) -2022-05-01 16:36:25,847 - tensorflow - INFO - global_step/sec: 88.6257 -2022-05-01 16:36:25,848 - tensorflow - INFO - loss = 0.019268338, step = 717800 (2.257 sec) -2022-05-01 16:36:28,108 - tensorflow - INFO - global_step/sec: 88.4847 -2022-05-01 16:36:28,108 - tensorflow - INFO - loss = 0.017318504, step = 718000 (2.260 sec) -2022-05-01 16:36:30,372 - tensorflow - INFO - global_step/sec: 88.3151 -2022-05-01 16:36:30,373 - tensorflow - INFO - loss = 0.02075294, step = 718200 (2.265 sec) -2022-05-01 16:36:32,637 - tensorflow - INFO - global_step/sec: 88.3165 -2022-05-01 16:36:32,637 - tensorflow - INFO - loss = 0.013050616, step = 718400 (2.265 sec) -2022-05-01 16:36:34,895 - tensorflow - INFO - global_step/sec: 88.5666 -2022-05-01 16:36:34,895 - tensorflow - INFO - loss = 0.053279806, step = 718600 (2.258 sec) -2022-05-01 16:36:37,151 - tensorflow - INFO - global_step/sec: 88.6638 -2022-05-01 16:36:37,151 - tensorflow - INFO - loss = 0.06466445, step = 718800 (2.256 sec) -2022-05-01 16:36:39,406 - tensorflow - INFO - global_step/sec: 88.6843 -2022-05-01 16:36:39,406 - tensorflow - INFO - loss = 0.06200307, step = 719000 (2.255 sec) -2022-05-01 16:36:41,657 - tensorflow - INFO - global_step/sec: 88.8374 -2022-05-01 16:36:41,658 - tensorflow - INFO - loss = 0.123050734, step = 719200 (2.251 sec) -2022-05-01 16:36:43,917 - tensorflow - INFO - global_step/sec: 88.5099 -2022-05-01 16:36:43,917 - tensorflow - INFO - loss = 0.017426074, step = 719400 (2.260 sec) -2022-05-01 16:36:46,170 - tensorflow - INFO - global_step/sec: 88.7647 -2022-05-01 16:36:46,170 - tensorflow - INFO - loss = 0.01955615, step = 719600 (2.253 sec) -2022-05-01 16:36:48,424 - tensorflow - INFO - global_step/sec: 88.7369 -2022-05-01 16:36:48,424 - tensorflow - INFO - loss = 0.020969622, step = 719800 (2.254 sec) -2022-05-01 16:36:50,677 - tensorflow - INFO - global_step/sec: 88.7524 -2022-05-01 16:36:50,678 - tensorflow - INFO - loss = 0.012453387, step = 720000 (2.253 sec) -2022-05-01 16:36:52,936 - tensorflow - INFO - global_step/sec: 88.5565 -2022-05-01 16:36:52,936 - tensorflow - INFO - loss = 1.1794183, step = 720200 (2.258 sec) -2022-05-01 16:36:55,188 - tensorflow - INFO - global_step/sec: 88.7919 -2022-05-01 16:36:55,189 - tensorflow - INFO - loss = 0.03199632, step = 720400 (2.252 sec) -2022-05-01 16:36:57,452 - tensorflow - INFO - global_step/sec: 88.3383 -2022-05-01 16:36:57,453 - tensorflow - INFO - loss = 0.015764184, step = 720600 (2.264 sec) -2022-05-01 16:36:59,708 - tensorflow - INFO - global_step/sec: 88.6761 -2022-05-01 16:36:59,708 - tensorflow - INFO - loss = 0.045435835, step = 720800 (2.255 sec) -2022-05-01 16:37:01,959 - tensorflow - INFO - global_step/sec: 88.8219 -2022-05-01 16:37:01,960 - tensorflow - INFO - loss = 0.029593222, step = 721000 (2.252 sec) -2022-05-01 16:37:04,213 - tensorflow - INFO - global_step/sec: 88.7433 -2022-05-01 16:37:04,213 - tensorflow - INFO - loss = 0.03411385, step = 721200 (2.254 sec) -2022-05-01 16:37:06,463 - tensorflow - INFO - global_step/sec: 88.8707 -2022-05-01 16:37:06,464 - tensorflow - INFO - loss = 0.07939213, step = 721400 (2.250 sec) -2022-05-01 16:37:08,723 - tensorflow - INFO - global_step/sec: 88.5307 -2022-05-01 16:37:08,723 - tensorflow - INFO - loss = 0.026088836, step = 721600 (2.259 sec) -2022-05-01 16:37:10,975 - tensorflow - INFO - global_step/sec: 88.7871 -2022-05-01 16:37:10,976 - tensorflow - INFO - loss = 0.06628123, step = 721800 (2.253 sec) -2022-05-01 16:37:13,226 - tensorflow - INFO - global_step/sec: 88.8584 -2022-05-01 16:37:13,226 - tensorflow - INFO - loss = 0.033783603, step = 722000 (2.251 sec) -2022-05-01 16:37:15,485 - tensorflow - INFO - global_step/sec: 88.5277 -2022-05-01 16:37:15,486 - tensorflow - INFO - loss = 0.008120764, step = 722200 (2.259 sec) -2022-05-01 16:37:17,739 - tensorflow - INFO - global_step/sec: 88.7371 -2022-05-01 16:37:17,739 - tensorflow - INFO - loss = 0.064204775, step = 722400 (2.254 sec) -2022-05-01 16:37:19,994 - tensorflow - INFO - global_step/sec: 88.6942 -2022-05-01 16:37:19,994 - tensorflow - INFO - loss = 0.024690893, step = 722600 (2.255 sec) -2022-05-01 16:37:22,252 - tensorflow - INFO - global_step/sec: 88.5544 -2022-05-01 16:37:22,253 - tensorflow - INFO - loss = 0.027418625, step = 722800 (2.259 sec) -2022-05-01 16:37:24,504 - tensorflow - INFO - global_step/sec: 88.8227 -2022-05-01 16:37:24,504 - tensorflow - INFO - loss = 0.020150892, step = 723000 (2.252 sec) -2022-05-01 16:37:26,756 - tensorflow - INFO - global_step/sec: 88.8016 -2022-05-01 16:37:26,757 - tensorflow - INFO - loss = 0.04431514, step = 723200 (2.252 sec) -2022-05-01 16:37:29,015 - tensorflow - INFO - global_step/sec: 88.5523 -2022-05-01 16:37:29,015 - tensorflow - INFO - loss = 0.073784016, step = 723400 (2.259 sec) -2022-05-01 16:37:31,281 - tensorflow - INFO - global_step/sec: 88.2482 -2022-05-01 16:37:31,282 - tensorflow - INFO - loss = 0.05834811, step = 723600 (2.266 sec) -2022-05-01 16:37:33,546 - tensorflow - INFO - global_step/sec: 88.3134 -2022-05-01 16:37:33,546 - tensorflow - INFO - loss = 0.018725693, step = 723800 (2.265 sec) -2022-05-01 16:37:35,798 - tensorflow - INFO - global_step/sec: 88.8227 -2022-05-01 16:37:35,798 - tensorflow - INFO - loss = 0.014359465, step = 724000 (2.252 sec) -2022-05-01 16:37:38,054 - tensorflow - INFO - global_step/sec: 88.6434 -2022-05-01 16:37:38,054 - tensorflow - INFO - loss = 0.23278178, step = 724200 (2.256 sec) -2022-05-01 16:37:40,302 - tensorflow - INFO - global_step/sec: 88.9386 -2022-05-01 16:37:40,303 - tensorflow - INFO - loss = 0.092707746, step = 724400 (2.249 sec) -2022-05-01 16:37:42,553 - tensorflow - INFO - global_step/sec: 88.8561 -2022-05-01 16:37:42,554 - tensorflow - INFO - loss = 0.01683535, step = 724600 (2.251 sec) -2022-05-01 16:37:44,808 - tensorflow - INFO - global_step/sec: 88.6938 -2022-05-01 16:37:44,809 - tensorflow - INFO - loss = 0.29092342, step = 724800 (2.255 sec) -2022-05-01 16:37:47,066 - tensorflow - INFO - global_step/sec: 88.5925 -2022-05-01 16:37:47,067 - tensorflow - INFO - loss = 16.705872, step = 725000 (2.258 sec) -2022-05-01 16:37:49,320 - tensorflow - INFO - global_step/sec: 88.7268 -2022-05-01 16:37:49,320 - tensorflow - INFO - loss = 0.10117188, step = 725200 (2.254 sec) -2022-05-01 16:37:51,576 - tensorflow - INFO - global_step/sec: 88.6574 -2022-05-01 16:37:51,576 - tensorflow - INFO - loss = 0.482465, step = 725400 (2.256 sec) -2022-05-01 16:37:53,832 - tensorflow - INFO - global_step/sec: 88.6507 -2022-05-01 16:37:53,832 - tensorflow - INFO - loss = 0.037618928, step = 725600 (2.256 sec) -2022-05-01 16:37:56,088 - tensorflow - INFO - global_step/sec: 88.6363 -2022-05-01 16:37:56,089 - tensorflow - INFO - loss = 0.04519862, step = 725800 (2.256 sec) -2022-05-01 16:37:58,346 - tensorflow - INFO - global_step/sec: 88.589 -2022-05-01 16:37:58,346 - tensorflow - INFO - loss = 0.03897709, step = 726000 (2.258 sec) -2022-05-01 16:38:00,599 - tensorflow - INFO - global_step/sec: 88.7544 -2022-05-01 16:38:00,600 - tensorflow - INFO - loss = 0.05559309, step = 726200 (2.253 sec) -2022-05-01 16:38:02,854 - tensorflow - INFO - global_step/sec: 88.6839 -2022-05-01 16:38:02,855 - tensorflow - INFO - loss = 0.16982447, step = 726400 (2.255 sec) -2022-05-01 16:38:05,108 - tensorflow - INFO - global_step/sec: 88.7426 -2022-05-01 16:38:05,109 - tensorflow - INFO - loss = 0.09494547, step = 726600 (2.254 sec) -2022-05-01 16:38:07,365 - tensorflow - INFO - global_step/sec: 88.6271 -2022-05-01 16:38:07,365 - tensorflow - INFO - loss = 0.09298262, step = 726800 (2.257 sec) -2022-05-01 16:38:09,618 - tensorflow - INFO - global_step/sec: 88.7821 -2022-05-01 16:38:09,618 - tensorflow - INFO - loss = 0.044713005, step = 727000 (2.253 sec) -2022-05-01 16:38:11,875 - tensorflow - INFO - global_step/sec: 88.6112 -2022-05-01 16:38:11,875 - tensorflow - INFO - loss = 0.027567424, step = 727200 (2.257 sec) -2022-05-01 16:38:14,128 - tensorflow - INFO - global_step/sec: 88.7501 -2022-05-01 16:38:14,129 - tensorflow - INFO - loss = 0.027084675, step = 727400 (2.254 sec) -2022-05-01 16:38:16,380 - tensorflow - INFO - global_step/sec: 88.8139 -2022-05-01 16:38:16,380 - tensorflow - INFO - loss = 0.025534812, step = 727600 (2.252 sec) -2022-05-01 16:38:18,628 - tensorflow - INFO - global_step/sec: 88.959 -2022-05-01 16:38:18,629 - tensorflow - INFO - loss = 9.381817, step = 727800 (2.248 sec) -2022-05-01 16:38:20,882 - tensorflow - INFO - global_step/sec: 88.7315 -2022-05-01 16:38:20,883 - tensorflow - INFO - loss = 0.07206005, step = 728000 (2.254 sec) -2022-05-01 16:38:23,140 - tensorflow - INFO - global_step/sec: 88.5869 -2022-05-01 16:38:23,140 - tensorflow - INFO - loss = 0.038026977, step = 728200 (2.258 sec) -2022-05-01 16:38:25,395 - tensorflow - INFO - global_step/sec: 88.6972 -2022-05-01 16:38:25,395 - tensorflow - INFO - loss = 0.057235397, step = 728400 (2.255 sec) -2022-05-01 16:38:27,647 - tensorflow - INFO - global_step/sec: 88.7843 -2022-05-01 16:38:27,648 - tensorflow - INFO - loss = 0.03770804, step = 728600 (2.253 sec) -2022-05-01 16:38:29,900 - tensorflow - INFO - global_step/sec: 88.803 -2022-05-01 16:38:29,900 - tensorflow - INFO - loss = 18.11336, step = 728800 (2.252 sec) -2022-05-01 16:38:32,163 - tensorflow - INFO - global_step/sec: 88.3691 -2022-05-01 16:38:32,163 - tensorflow - INFO - loss = 0.01627003, step = 729000 (2.263 sec) -2022-05-01 16:38:34,418 - tensorflow - INFO - global_step/sec: 88.6737 -2022-05-01 16:38:34,419 - tensorflow - INFO - loss = 0.020768674, step = 729200 (2.256 sec) -2022-05-01 16:38:36,676 - tensorflow - INFO - global_step/sec: 88.596 -2022-05-01 16:38:36,676 - tensorflow - INFO - loss = 0.013888532, step = 729400 (2.257 sec) -2022-05-01 16:38:38,930 - tensorflow - INFO - global_step/sec: 88.7166 -2022-05-01 16:38:38,930 - tensorflow - INFO - loss = 0.12845708, step = 729600 (2.254 sec) -2022-05-01 16:38:41,190 - tensorflow - INFO - global_step/sec: 88.5067 -2022-05-01 16:38:41,190 - tensorflow - INFO - loss = 0.12044362, step = 729800 (2.260 sec) -2022-05-01 16:38:43,455 - tensorflow - INFO - global_step/sec: 88.2746 -2022-05-01 16:38:43,456 - tensorflow - INFO - loss = 0.026952935, step = 730000 (2.266 sec) -2022-05-01 16:38:45,721 - tensorflow - INFO - global_step/sec: 88.2832 -2022-05-01 16:38:45,721 - tensorflow - INFO - loss = 0.022035994, step = 730200 (2.265 sec) -2022-05-01 16:38:47,986 - tensorflow - INFO - global_step/sec: 88.2769 -2022-05-01 16:38:47,987 - tensorflow - INFO - loss = 0.020752344, step = 730400 (2.266 sec) -2022-05-01 16:38:50,249 - tensorflow - INFO - global_step/sec: 88.4036 -2022-05-01 16:38:50,249 - tensorflow - INFO - loss = 0.2895414, step = 730600 (2.262 sec) -2022-05-01 16:38:52,512 - tensorflow - INFO - global_step/sec: 88.3743 -2022-05-01 16:38:52,512 - tensorflow - INFO - loss = 0.024152901, step = 730800 (2.263 sec) -2022-05-01 16:38:54,774 - tensorflow - INFO - global_step/sec: 88.4154 -2022-05-01 16:38:54,774 - tensorflow - INFO - loss = 0.09352809, step = 731000 (2.262 sec) -2022-05-01 16:38:57,039 - tensorflow - INFO - global_step/sec: 88.285 -2022-05-01 16:38:57,040 - tensorflow - INFO - loss = 0.03484805, step = 731200 (2.265 sec) -2022-05-01 16:38:59,301 - tensorflow - INFO - global_step/sec: 88.417 -2022-05-01 16:38:59,302 - tensorflow - INFO - loss = 0.07325423, step = 731400 (2.262 sec) -2022-05-01 16:39:01,563 - tensorflow - INFO - global_step/sec: 88.4446 -2022-05-01 16:39:01,563 - tensorflow - INFO - loss = 0.130778, step = 731600 (2.261 sec) -2022-05-01 16:39:03,826 - tensorflow - INFO - global_step/sec: 88.371 -2022-05-01 16:39:03,826 - tensorflow - INFO - loss = 0.004887106, step = 731800 (2.263 sec) -2022-05-01 16:39:06,090 - tensorflow - INFO - global_step/sec: 88.319 -2022-05-01 16:39:06,091 - tensorflow - INFO - loss = 0.012417823, step = 732000 (2.265 sec) -2022-05-01 16:39:08,350 - tensorflow - INFO - global_step/sec: 88.5021 -2022-05-01 16:39:08,351 - tensorflow - INFO - loss = 0.014312791, step = 732200 (2.260 sec) -2022-05-01 16:39:10,606 - tensorflow - INFO - global_step/sec: 88.676 -2022-05-01 16:39:10,606 - tensorflow - INFO - loss = 0.029261068, step = 732400 (2.255 sec) -2022-05-01 16:39:12,860 - tensorflow - INFO - global_step/sec: 88.7093 -2022-05-01 16:39:12,861 - tensorflow - INFO - loss = 0.18301973, step = 732600 (2.255 sec) -2022-05-01 16:39:15,119 - tensorflow - INFO - global_step/sec: 88.5591 -2022-05-01 16:39:15,119 - tensorflow - INFO - loss = 0.081953034, step = 732800 (2.258 sec) -2022-05-01 16:39:17,373 - tensorflow - INFO - global_step/sec: 88.6982 -2022-05-01 16:39:17,374 - tensorflow - INFO - loss = 0.014483898, step = 733000 (2.255 sec) -2022-05-01 16:39:19,627 - tensorflow - INFO - global_step/sec: 88.7559 -2022-05-01 16:39:19,627 - tensorflow - INFO - loss = 0.021055708, step = 733200 (2.253 sec) -2022-05-01 16:39:21,878 - tensorflow - INFO - global_step/sec: 88.8538 -2022-05-01 16:39:21,878 - tensorflow - INFO - loss = 0.044311155, step = 733400 (2.251 sec) -2022-05-01 16:39:24,133 - tensorflow - INFO - global_step/sec: 88.6963 -2022-05-01 16:39:24,133 - tensorflow - INFO - loss = 0.019267552, step = 733600 (2.255 sec) -2022-05-01 16:39:26,387 - tensorflow - INFO - global_step/sec: 88.7302 -2022-05-01 16:39:26,387 - tensorflow - INFO - loss = 0.052062158, step = 733800 (2.254 sec) -2022-05-01 16:39:28,645 - tensorflow - INFO - global_step/sec: 88.5681 -2022-05-01 16:39:28,645 - tensorflow - INFO - loss = 0.027188748, step = 734000 (2.258 sec) -2022-05-01 16:39:30,905 - tensorflow - INFO - global_step/sec: 88.5038 -2022-05-01 16:39:30,905 - tensorflow - INFO - loss = 0.036001444, step = 734200 (2.260 sec) -2022-05-01 16:39:33,167 - tensorflow - INFO - global_step/sec: 88.4102 -2022-05-01 16:39:33,167 - tensorflow - INFO - loss = 0.042178534, step = 734400 (2.262 sec) -2022-05-01 16:39:35,423 - tensorflow - INFO - global_step/sec: 88.6443 -2022-05-01 16:39:35,423 - tensorflow - INFO - loss = 0.05207739, step = 734600 (2.256 sec) -2022-05-01 16:39:37,677 - tensorflow - INFO - global_step/sec: 88.7151 -2022-05-01 16:39:37,678 - tensorflow - INFO - loss = 9.82839, step = 734800 (2.254 sec) -2022-05-01 16:39:39,932 - tensorflow - INFO - global_step/sec: 88.6921 -2022-05-01 16:39:39,933 - tensorflow - INFO - loss = 0.01811117, step = 735000 (2.255 sec) -2022-05-01 16:39:42,186 - tensorflow - INFO - global_step/sec: 88.73 -2022-05-01 16:39:42,187 - tensorflow - INFO - loss = 0.15514675, step = 735200 (2.254 sec) -2022-05-01 16:39:44,438 - tensorflow - INFO - global_step/sec: 88.8431 -2022-05-01 16:39:44,438 - tensorflow - INFO - loss = 0.02380142, step = 735400 (2.251 sec) -2022-05-01 16:39:46,691 - tensorflow - INFO - global_step/sec: 88.7591 -2022-05-01 16:39:46,691 - tensorflow - INFO - loss = 0.062118415, step = 735600 (2.253 sec) -2022-05-01 16:39:48,944 - tensorflow - INFO - global_step/sec: 88.7548 -2022-05-01 16:39:48,945 - tensorflow - INFO - loss = 0.13109383, step = 735800 (2.253 sec) -2022-05-01 16:39:51,200 - tensorflow - INFO - global_step/sec: 88.6642 -2022-05-01 16:39:51,200 - tensorflow - INFO - loss = 0.017995136, step = 736000 (2.256 sec) -2022-05-01 16:39:53,459 - tensorflow - INFO - global_step/sec: 88.5465 -2022-05-01 16:39:53,459 - tensorflow - INFO - loss = 0.09423657, step = 736200 (2.259 sec) -2022-05-01 16:39:55,713 - tensorflow - INFO - global_step/sec: 88.7268 -2022-05-01 16:39:55,713 - tensorflow - INFO - loss = 0.06658428, step = 736400 (2.254 sec) -2022-05-01 16:39:57,965 - tensorflow - INFO - global_step/sec: 88.8128 -2022-05-01 16:39:57,965 - tensorflow - INFO - loss = 0.030928845, step = 736600 (2.252 sec) -2022-05-01 16:40:00,216 - tensorflow - INFO - global_step/sec: 88.8191 -2022-05-01 16:40:00,217 - tensorflow - INFO - loss = 0.019541731, step = 736800 (2.252 sec) -2022-05-01 16:40:02,477 - tensorflow - INFO - global_step/sec: 88.4903 -2022-05-01 16:40:02,477 - tensorflow - INFO - loss = 0.20681232, step = 737000 (2.260 sec) -2022-05-01 16:40:04,731 - tensorflow - INFO - global_step/sec: 88.705 -2022-05-01 16:40:04,732 - tensorflow - INFO - loss = 0.04668199, step = 737200 (2.255 sec) -2022-05-01 16:40:06,992 - tensorflow - INFO - global_step/sec: 88.4479 -2022-05-01 16:40:06,993 - tensorflow - INFO - loss = 0.04145692, step = 737400 (2.261 sec) -2022-05-01 16:40:09,245 - tensorflow - INFO - global_step/sec: 88.7793 -2022-05-01 16:40:09,246 - tensorflow - INFO - loss = 0.028742775, step = 737600 (2.253 sec) -2022-05-01 16:40:11,498 - tensorflow - INFO - global_step/sec: 88.7839 -2022-05-01 16:40:11,498 - tensorflow - INFO - loss = 0.2889277, step = 737800 (2.253 sec) -2022-05-01 16:40:13,752 - tensorflow - INFO - global_step/sec: 88.7379 -2022-05-01 16:40:13,752 - tensorflow - INFO - loss = 0.048202626, step = 738000 (2.254 sec) -2022-05-01 16:40:16,003 - tensorflow - INFO - global_step/sec: 88.8509 -2022-05-01 16:40:16,003 - tensorflow - INFO - loss = 0.036064286, step = 738200 (2.251 sec) -2022-05-01 16:40:18,258 - tensorflow - INFO - global_step/sec: 88.692 -2022-05-01 16:40:18,258 - tensorflow - INFO - loss = 0.08762132, step = 738400 (2.255 sec) -2022-05-01 16:40:20,509 - tensorflow - INFO - global_step/sec: 88.8336 -2022-05-01 16:40:20,509 - tensorflow - INFO - loss = 0.015344279, step = 738600 (2.251 sec) -2022-05-01 16:40:22,763 - tensorflow - INFO - global_step/sec: 88.7503 -2022-05-01 16:40:22,763 - tensorflow - INFO - loss = 0.065214105, step = 738800 (2.253 sec) -2022-05-01 16:40:25,013 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.12965834, step = 740200 (2.256 sec) -2022-05-01 16:40:40,798 - tensorflow - INFO - global_step/sec: 88.5863 -2022-05-01 16:40:40,799 - tensorflow - INFO - loss = 0.012902705, step = 740400 (2.258 sec) -2022-05-01 16:40:43,051 - tensorflow - INFO - global_step/sec: 88.7989 -2022-05-01 16:40:43,051 - tensorflow - INFO - loss = 0.014853411, step = 740600 (2.252 sec) -2022-05-01 16:40:45,306 - tensorflow - INFO - global_step/sec: 88.6847 -2022-05-01 16:40:45,306 - tensorflow - INFO - loss = 0.15699339, step = 740800 (2.255 sec) -2022-05-01 16:40:47,553 - tensorflow - INFO - global_step/sec: 89.0141 -2022-05-01 16:40:47,553 - tensorflow - INFO - loss = 0.046699658, step = 741000 (2.247 sec) -2022-05-01 16:40:49,804 - tensorflow - INFO - global_step/sec: 88.8405 -2022-05-01 16:40:49,804 - tensorflow - INFO - loss = 0.048899673, step = 741200 (2.251 sec) -2022-05-01 16:40:52,056 - tensorflow - INFO - global_step/sec: 88.8089 -2022-05-01 16:40:52,056 - tensorflow - INFO - loss = 0.0370473, step = 741400 (2.252 sec) -2022-05-01 16:40:54,311 - tensorflow - INFO - global_step/sec: 88.6901 -2022-05-01 16:40:54,311 - tensorflow - INFO - loss = 0.03407522, step = 741600 (2.255 sec) -2022-05-01 16:40:56,573 - tensorflow - INFO - global_step/sec: 88.3954 -2022-05-01 16:40:56,574 - tensorflow - INFO - loss = 0.60129654, step = 741800 (2.263 sec) -2022-05-01 16:40:58,831 - tensorflow - INFO - global_step/sec: 88.5793 -2022-05-01 16:40:58,832 - tensorflow - INFO - loss = 0.07023975, step = 742000 (2.258 sec) -2022-05-01 16:41:01,086 - tensorflow - INFO - global_step/sec: 88.7148 -2022-05-01 16:41:01,086 - tensorflow - INFO - loss = 0.2038961, step = 742200 (2.254 sec) -2022-05-01 16:41:03,341 - tensorflow - INFO - global_step/sec: 88.6691 -2022-05-01 16:41:03,342 - tensorflow - INFO - loss = 0.023192236, step = 742400 (2.256 sec) -2022-05-01 16:41:05,598 - tensorflow - INFO - global_step/sec: 88.6189 -2022-05-01 16:41:05,599 - tensorflow - INFO - loss = 0.023544712, step = 742600 (2.257 sec) -2022-05-01 16:41:07,856 - tensorflow - INFO - global_step/sec: 88.5655 -2022-05-01 16:41:07,857 - tensorflow - INFO - loss = 0.036484316, step = 742800 (2.258 sec) -2022-05-01 16:41:10,113 - tensorflow - INFO - global_step/sec: 88.6428 -2022-05-01 16:41:10,113 - tensorflow - INFO - loss = 0.014574307, step = 743000 (2.256 sec) -2022-05-01 16:41:12,364 - tensorflow - INFO - global_step/sec: 88.8311 -2022-05-01 16:41:12,364 - tensorflow - INFO - loss = 1.627424, step = 743200 (2.252 sec) -2022-05-01 16:41:14,622 - tensorflow - INFO - global_step/sec: 88.5812 -2022-05-01 16:41:14,622 - tensorflow - INFO - loss = 0.028682247, step = 743400 (2.258 sec) -2022-05-01 16:41:16,878 - tensorflow - INFO - global_step/sec: 88.6651 -2022-05-01 16:41:16,878 - tensorflow - INFO - loss = 0.009794436, step = 743600 (2.256 sec) -2022-05-01 16:41:19,129 - tensorflow - INFO - global_step/sec: 88.8281 -2022-05-01 16:41:19,129 - tensorflow - INFO - loss = 0.04523033, step = 743800 (2.252 sec) -2022-05-01 16:41:21,391 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.07969646, step = 745200 (2.256 sec) -2022-05-01 16:41:37,203 - tensorflow - INFO - global_step/sec: 88.5365 -2022-05-01 16:41:37,203 - tensorflow - INFO - loss = 0.013269105, step = 745400 (2.259 sec) -2022-05-01 16:41:39,456 - tensorflow - INFO - global_step/sec: 88.7705 -2022-05-01 16:41:39,456 - tensorflow - INFO - loss = 0.094168976, step = 745600 (2.253 sec) -2022-05-01 16:41:41,709 - tensorflow - INFO - global_step/sec: 88.7424 -2022-05-01 16:41:41,710 - tensorflow - INFO - loss = 2.7729783, step = 745800 (2.254 sec) -2022-05-01 16:41:43,959 - tensorflow - INFO - global_step/sec: 88.9043 -2022-05-01 16:41:43,959 - tensorflow - INFO - loss = 0.05216115, step = 746000 (2.250 sec) -2022-05-01 16:41:46,215 - tensorflow - INFO - global_step/sec: 88.628 -2022-05-01 16:41:46,216 - tensorflow - INFO - loss = 0.06271626, step = 746200 (2.257 sec) -2022-05-01 16:41:48,471 - tensorflow - INFO - global_step/sec: 88.6778 -2022-05-01 16:41:48,471 - tensorflow - INFO - loss = 2.589942, step = 746400 (2.255 sec) -2022-05-01 16:41:50,724 - tensorflow - INFO - global_step/sec: 88.7647 -2022-05-01 16:41:50,724 - tensorflow - INFO - loss = 0.07205838, step = 746600 (2.253 sec) -2022-05-01 16:41:52,977 - tensorflow - INFO - global_step/sec: 88.7602 -2022-05-01 16:41:52,978 - tensorflow - INFO - loss = 0.013799798, step = 746800 (2.253 sec) -2022-05-01 16:41:55,234 - tensorflow - INFO - global_step/sec: 88.6087 -2022-05-01 16:41:55,235 - tensorflow - INFO - loss = 0.0339761, step = 747000 (2.257 sec) -2022-05-01 16:41:57,487 - tensorflow - INFO - global_step/sec: 88.7706 -2022-05-01 16:41:57,488 - tensorflow - INFO - loss = 0.08367941, step = 747200 (2.253 sec) -2022-05-01 16:41:59,748 - tensorflow - INFO - global_step/sec: 88.4724 -2022-05-01 16:41:59,748 - tensorflow - INFO - loss = 0.011904222, step = 747400 (2.261 sec) -2022-05-01 16:42:01,999 - tensorflow - INFO - global_step/sec: 88.8351 -2022-05-01 16:42:02,000 - tensorflow - INFO - loss = 0.035004426, step = 747600 (2.251 sec) -2022-05-01 16:42:04,258 - tensorflow - INFO - global_step/sec: 88.5354 -2022-05-01 16:42:04,259 - tensorflow - INFO - loss = 0.01963403, step = 747800 (2.259 sec) -2022-05-01 16:42:06,514 - tensorflow - INFO - global_step/sec: 88.6559 -2022-05-01 16:42:06,515 - tensorflow - INFO - loss = 0.020152962, step = 748000 (2.256 sec) -2022-05-01 16:42:08,766 - tensorflow - INFO - global_step/sec: 88.8282 -2022-05-01 16:42:08,766 - tensorflow - INFO - loss = 0.01514635, step = 748200 (2.252 sec) -2022-05-01 16:42:11,025 - tensorflow - INFO - global_step/sec: 88.5125 -2022-05-01 16:42:11,026 - tensorflow - INFO - loss = 0.025370374, step = 748400 (2.260 sec) -2022-05-01 16:42:13,296 - tensorflow - INFO - global_step/sec: 88.0874 -2022-05-01 16:42:13,296 - tensorflow - INFO - loss = 8.897505, step = 748600 (2.270 sec) -2022-05-01 16:42:15,558 - tensorflow - INFO - global_step/sec: 88.4005 -2022-05-01 16:42:15,559 - tensorflow - INFO - loss = 0.14021921, step = 748800 (2.262 sec) -2022-05-01 16:42:17,823 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.037792195, step = 750200 (2.268 sec) -2022-05-01 16:42:33,665 - tensorflow - INFO - global_step/sec: 88.358 -2022-05-01 16:42:33,665 - tensorflow - INFO - loss = 0.019121954, step = 750400 (2.264 sec) -2022-05-01 16:42:35,929 - tensorflow - INFO - global_step/sec: 88.3224 -2022-05-01 16:42:35,929 - tensorflow - INFO - loss = 0.071963236, step = 750600 (2.264 sec) -2022-05-01 16:42:38,189 - tensorflow - INFO - global_step/sec: 88.5129 -2022-05-01 16:42:38,189 - tensorflow - INFO - loss = 0.041974064, step = 750800 (2.260 sec) -2022-05-01 16:42:40,446 - tensorflow - INFO - global_step/sec: 88.5983 -2022-05-01 16:42:40,446 - tensorflow - INFO - loss = 0.024198234, step = 751000 (2.257 sec) -2022-05-01 16:42:42,699 - tensorflow - INFO - global_step/sec: 88.7831 -2022-05-01 16:42:42,699 - tensorflow - INFO - loss = 0.071568094, step = 751200 (2.253 sec) -2022-05-01 16:42:44,950 - tensorflow - INFO - global_step/sec: 88.8488 -2022-05-01 16:42:44,950 - tensorflow - INFO - loss = 0.030673733, step = 751400 (2.251 sec) -2022-05-01 16:42:47,203 - tensorflow - INFO - global_step/sec: 88.7587 -2022-05-01 16:42:47,203 - tensorflow - INFO - loss = 0.034062106, step = 751600 (2.253 sec) -2022-05-01 16:42:49,457 - tensorflow - INFO - global_step/sec: 88.7155 -2022-05-01 16:42:49,458 - tensorflow - INFO - loss = 0.020252142, step = 751800 (2.254 sec) -2022-05-01 16:42:51,714 - tensorflow - INFO - global_step/sec: 88.6465 -2022-05-01 16:42:51,714 - tensorflow - INFO - loss = 0.027839506, step = 752000 (2.256 sec) -2022-05-01 16:42:53,967 - tensorflow - INFO - global_step/sec: 88.7497 -2022-05-01 16:42:53,967 - tensorflow - INFO - loss = 0.118473224, step = 752200 (2.254 sec) -2022-05-01 16:42:56,228 - tensorflow - INFO - global_step/sec: 88.473 -2022-05-01 16:42:56,228 - tensorflow - INFO - loss = 0.03127812, step = 752400 (2.261 sec) -2022-05-01 16:42:58,484 - tensorflow - INFO - global_step/sec: 88.6423 -2022-05-01 16:42:58,484 - tensorflow - INFO - loss = 0.24062337, step = 752600 (2.256 sec) -2022-05-01 16:43:00,737 - tensorflow - INFO - global_step/sec: 88.7791 -2022-05-01 16:43:00,737 - tensorflow - INFO - loss = 0.02481427, step = 752800 (2.253 sec) -2022-05-01 16:43:02,997 - tensorflow - INFO - global_step/sec: 88.4808 -2022-05-01 16:43:02,997 - tensorflow - INFO - loss = 0.02572039, step = 753000 (2.260 sec) -2022-05-01 16:43:05,252 - tensorflow - INFO - global_step/sec: 88.679 -2022-05-01 16:43:05,253 - tensorflow - INFO - loss = 0.102261215, step = 753200 (2.255 sec) -2022-05-01 16:43:07,509 - tensorflow - INFO - global_step/sec: 88.6181 -2022-05-01 16:43:07,510 - tensorflow - INFO - loss = 0.032337368, step = 753400 (2.257 sec) -2022-05-01 16:43:09,768 - tensorflow - INFO - global_step/sec: 88.5433 -2022-05-01 16:43:09,768 - tensorflow - INFO - loss = 0.014850834, step = 753600 (2.259 sec) -2022-05-01 16:43:12,025 - tensorflow - INFO - global_step/sec: 88.6328 -2022-05-01 16:43:12,025 - tensorflow - INFO - loss = 0.02281077, step = 753800 (2.256 sec) -2022-05-01 16:43:14,278 - tensorflow - INFO - global_step/sec: 88.7447 -2022-05-01 16:43:14,279 - tensorflow - INFO - loss = 0.14529234, step = 754000 (2.254 sec) -2022-05-01 16:43:16,535 - tensorflow - INFO - global_step/sec: 88.6228 -2022-05-01 16:43:16,535 - tensorflow - INFO - loss = 0.07362701, step = 754200 (2.257 sec) -2022-05-01 16:43:18,792 - tensorflow - INFO - global_step/sec: 88.6072 -2022-05-01 16:43:18,793 - tensorflow - INFO - loss = 0.010719903, step = 754400 (2.257 sec) -2022-05-01 16:43:21,041 - tensorflow - INFO - global_step/sec: 88.9448 -2022-05-01 16:43:21,041 - tensorflow - INFO - loss = 8.220845, step = 754600 (2.249 sec) -2022-05-01 16:43:23,290 - tensorflow - INFO - global_step/sec: 88.9039 -2022-05-01 16:43:23,291 - tensorflow - INFO - loss = 0.07111853, step = 754800 (2.250 sec) -2022-05-01 16:43:25,541 - tensorflow - INFO - global_step/sec: 88.8543 -2022-05-01 16:43:25,542 - tensorflow - INFO - loss = 0.013378707, step = 755000 (2.251 sec) -2022-05-01 16:43:27,797 - tensorflow - INFO - global_step/sec: 88.6771 -2022-05-01 16:43:27,797 - tensorflow - INFO - loss = 1.4978168, step = 755200 (2.255 sec) -2022-05-01 16:43:30,059 - tensorflow - INFO - global_step/sec: 88.3996 -2022-05-01 16:43:30,059 - tensorflow - INFO - loss = 8.718706, step = 755400 (2.262 sec) -2022-05-01 16:43:32,316 - tensorflow - INFO - global_step/sec: 88.6187 -2022-05-01 16:43:32,316 - tensorflow - INFO - loss = 0.044593006, step = 755600 (2.257 sec) -2022-05-01 16:43:34,564 - tensorflow - INFO - global_step/sec: 88.9641 -2022-05-01 16:43:34,564 - tensorflow - INFO - loss = 0.0884004, step = 755800 (2.248 sec) -2022-05-01 16:43:36,813 - tensorflow - INFO - global_step/sec: 88.929 -2022-05-01 16:43:36,813 - tensorflow - INFO - loss = 0.083780736, step = 756000 (2.249 sec) -2022-05-01 16:43:39,067 - tensorflow - INFO - global_step/sec: 88.7169 -2022-05-01 16:43:39,068 - tensorflow - INFO - loss = 0.024215344, step = 756200 (2.254 sec) -2022-05-01 16:43:41,330 - tensorflow - INFO - global_step/sec: 88.4013 -2022-05-01 16:43:41,330 - tensorflow - INFO - loss = 0.036362167, step = 756400 (2.262 sec) -2022-05-01 16:43:43,585 - tensorflow - INFO - global_step/sec: 88.6992 -2022-05-01 16:43:43,585 - tensorflow - INFO - loss = 0.017386153, step = 756600 (2.255 sec) -2022-05-01 16:43:45,841 - tensorflow - INFO - global_step/sec: 88.6415 -2022-05-01 16:43:45,841 - tensorflow - INFO - loss = 0.036402073, step = 756800 (2.256 sec) -2022-05-01 16:43:48,095 - tensorflow - INFO - global_step/sec: 88.7388 -2022-05-01 16:43:48,095 - tensorflow - INFO - loss = 0.023358872, step = 757000 (2.254 sec) -2022-05-01 16:43:50,350 - tensorflow - INFO - global_step/sec: 88.686 -2022-05-01 16:43:50,350 - tensorflow - INFO - loss = 0.02112079, step = 757200 (2.255 sec) -2022-05-01 16:43:52,603 - tensorflow - INFO - global_step/sec: 88.7611 -2022-05-01 16:43:52,603 - tensorflow - INFO - loss = 0.07236029, step = 757400 (2.253 sec) -2022-05-01 16:43:54,863 - tensorflow - INFO - global_step/sec: 88.5102 -2022-05-01 16:43:54,863 - tensorflow - INFO - loss = 0.3196033, step = 757600 (2.260 sec) -2022-05-01 16:43:57,116 - tensorflow - INFO - global_step/sec: 88.7567 -2022-05-01 16:43:57,116 - tensorflow - INFO - loss = 0.09343986, step = 757800 (2.253 sec) -2022-05-01 16:43:59,372 - tensorflow - INFO - global_step/sec: 88.6528 -2022-05-01 16:43:59,372 - tensorflow - INFO - loss = 0.06432448, step = 758000 (2.256 sec) -2022-05-01 16:44:01,623 - tensorflow - INFO - global_step/sec: 88.8473 -2022-05-01 16:44:01,623 - tensorflow - INFO - loss = 0.038612396, step = 758200 (2.251 sec) -2022-05-01 16:44:03,877 - tensorflow - INFO - global_step/sec: 88.7272 -2022-05-01 16:44:03,878 - tensorflow - INFO - loss = 0.3299265, step = 758400 (2.254 sec) -2022-05-01 16:44:06,132 - tensorflow - INFO - global_step/sec: 88.7053 -2022-05-01 16:44:06,132 - tensorflow - INFO - loss = 0.034532495, step = 758600 (2.255 sec) -2022-05-01 16:44:08,386 - tensorflow - INFO - global_step/sec: 88.7216 -2022-05-01 16:44:08,386 - tensorflow - INFO - loss = 0.49787322, step = 758800 (2.254 sec) -2022-05-01 16:44:10,637 - tensorflow - INFO - global_step/sec: 88.8405 -2022-05-01 16:44:10,638 - tensorflow - INFO - loss = 0.0216678, step = 759000 (2.251 sec) -2022-05-01 16:44:12,894 - tensorflow - INFO - global_step/sec: 88.6138 -2022-05-01 16:44:12,895 - tensorflow - INFO - loss = 0.10780929, step = 759200 (2.257 sec) -2022-05-01 16:44:15,148 - tensorflow - INFO - global_step/sec: 88.7475 -2022-05-01 16:44:15,148 - tensorflow - INFO - loss = 0.03311348, step = 759400 (2.254 sec) -2022-05-01 16:44:17,400 - tensorflow - INFO - global_step/sec: 88.7849 -2022-05-01 16:44:17,401 - tensorflow - INFO - loss = 0.013074419, step = 759600 (2.253 sec) -2022-05-01 16:44:19,657 - tensorflow - INFO - global_step/sec: 88.6375 -2022-05-01 16:44:19,657 - tensorflow - INFO - loss = 0.054565508, step = 759800 (2.256 sec) -2022-05-01 16:44:21,907 - tensorflow - INFO - global_step/sec: 88.8788 -2022-05-01 16:44:21,907 - tensorflow - INFO - loss = 0.05146771, step = 760000 (2.250 sec) -2022-05-01 16:44:24,162 - tensorflow - INFO - global_step/sec: 88.6991 -2022-05-01 16:44:24,162 - tensorflow - INFO - loss = 0.024711419, step = 760200 (2.255 sec) -2022-05-01 16:44:26,413 - tensorflow - INFO - global_step/sec: 88.8594 -2022-05-01 16:44:26,413 - tensorflow - INFO - loss = 0.020534966, step = 760400 (2.251 sec) -2022-05-01 16:44:28,666 - tensorflow - INFO - global_step/sec: 88.7528 -2022-05-01 16:44:28,666 - tensorflow - INFO - loss = 0.01117647, step = 760600 (2.253 sec) -2022-05-01 16:44:30,926 - tensorflow - INFO - global_step/sec: 88.4921 -2022-05-01 16:44:30,927 - tensorflow - INFO - loss = 0.20856713, step = 760800 (2.260 sec) -2022-05-01 16:44:33,187 - tensorflow - INFO - global_step/sec: 88.4512 -2022-05-01 16:44:33,188 - tensorflow - INFO - loss = 0.027001064, step = 761000 (2.261 sec) -2022-05-01 16:44:35,441 - tensorflow - INFO - global_step/sec: 88.7496 -2022-05-01 16:44:35,441 - tensorflow - INFO - loss = 0.028856246, step = 761200 (2.254 sec) -2022-05-01 16:44:37,690 - tensorflow - INFO - global_step/sec: 88.9181 -2022-05-01 16:44:37,691 - tensorflow - INFO - loss = 0.016628332, step = 761400 (2.249 sec) -2022-05-01 16:44:39,942 - tensorflow - INFO - global_step/sec: 88.8153 -2022-05-01 16:44:39,942 - tensorflow - INFO - loss = 0.015344487, step = 761600 (2.252 sec) -2022-05-01 16:44:42,189 - tensorflow - INFO - global_step/sec: 88.9995 -2022-05-01 16:44:42,190 - tensorflow - INFO - loss = 0.06295501, step = 761800 (2.247 sec) -2022-05-01 16:44:44,450 - tensorflow - INFO - global_step/sec: 88.4536 -2022-05-01 16:44:44,451 - tensorflow - INFO - loss = 0.03487993, step = 762000 (2.261 sec) -2022-05-01 16:44:46,704 - tensorflow - INFO - global_step/sec: 88.7216 -2022-05-01 16:44:46,705 - tensorflow - INFO - loss = 8.466547, step = 762200 (2.254 sec) -2022-05-01 16:44:48,956 - tensorflow - INFO - global_step/sec: 88.8244 -2022-05-01 16:44:48,957 - tensorflow - INFO - loss = 0.09064051, step = 762400 (2.252 sec) -2022-05-01 16:44:51,215 - tensorflow - INFO - global_step/sec: 88.5358 -2022-05-01 16:44:51,216 - tensorflow - INFO - loss = 0.022429889, step = 762600 (2.259 sec) -2022-05-01 16:44:53,472 - tensorflow - INFO - global_step/sec: 88.6019 -2022-05-01 16:44:53,473 - tensorflow - INFO - loss = 0.010964585, step = 762800 (2.257 sec) -2022-05-01 16:44:55,728 - tensorflow - INFO - global_step/sec: 88.6826 -2022-05-01 16:44:55,728 - tensorflow - INFO - loss = 0.0625984, step = 763000 (2.255 sec) -2022-05-01 16:44:57,982 - tensorflow - INFO - global_step/sec: 88.712 -2022-05-01 16:44:57,983 - tensorflow - INFO - loss = 0.054418862, step = 763200 (2.255 sec) -2022-05-01 16:45:00,236 - tensorflow - INFO - global_step/sec: 88.7534 -2022-05-01 16:45:00,236 - tensorflow - INFO - loss = 0.03869965, step = 763400 (2.253 sec) -2022-05-01 16:45:02,489 - tensorflow - INFO - global_step/sec: 88.754 -2022-05-01 16:45:02,489 - tensorflow - INFO - loss = 0.012486335, step = 763600 (2.253 sec) -2022-05-01 16:45:04,745 - tensorflow - INFO - global_step/sec: 88.6332 -2022-05-01 16:45:04,746 - tensorflow - INFO - loss = 0.009464521, step = 763800 (2.256 sec) -2022-05-01 16:45:07,000 - tensorflow - INFO - global_step/sec: 88.7272 -2022-05-01 16:45:07,000 - tensorflow - INFO - loss = 1.9164569, step = 764000 (2.254 sec) -2022-05-01 16:45:09,255 - tensorflow - INFO - global_step/sec: 88.6732 -2022-05-01 16:45:09,255 - tensorflow - INFO - loss = 0.01771454, step = 764200 (2.256 sec) -2022-05-01 16:45:11,511 - tensorflow - INFO - global_step/sec: 88.6512 -2022-05-01 16:45:11,511 - tensorflow - INFO - loss = 0.049625978, step = 764400 (2.256 sec) -2022-05-01 16:45:13,766 - tensorflow - INFO - global_step/sec: 88.6851 -2022-05-01 16:45:13,767 - tensorflow - INFO - loss = 0.059604965, step = 764600 (2.255 sec) -2022-05-01 16:45:16,018 - tensorflow - INFO - global_step/sec: 88.818 -2022-05-01 16:45:16,018 - tensorflow - INFO - loss = 0.0255786, step = 764800 (2.252 sec) -2022-05-01 16:45:18,269 - tensorflow - INFO - global_step/sec: 88.8647 -2022-05-01 16:45:18,269 - tensorflow - INFO - loss = 0.030189814, step = 765000 (2.251 sec) -2022-05-01 16:45:20,520 - tensorflow - INFO - global_step/sec: 88.8312 -2022-05-01 16:45:20,520 - tensorflow - INFO - loss = 0.092219666, step = 765200 (2.251 sec) -2022-05-01 16:45:22,776 - tensorflow - INFO - global_step/sec: 88.6416 -2022-05-01 16:45:22,777 - tensorflow - INFO - loss = 0.090820804, step = 765400 (2.256 sec) -2022-05-01 16:45:25,031 - tensorflow - INFO - global_step/sec: 88.72 -2022-05-01 16:45:25,031 - tensorflow - INFO - loss = 0.055353288, step = 765600 (2.254 sec) -2022-05-01 16:45:27,289 - tensorflow - INFO - global_step/sec: 88.575 -2022-05-01 16:45:27,289 - tensorflow - INFO - loss = 0.11388934, step = 765800 (2.258 sec) -2022-05-01 16:45:29,538 - tensorflow - INFO - global_step/sec: 88.9327 -2022-05-01 16:45:29,538 - tensorflow - INFO - loss = 0.13925542, step = 766000 (2.249 sec) -2022-05-01 16:45:31,795 - tensorflow - INFO - global_step/sec: 88.6053 -2022-05-01 16:45:31,795 - tensorflow - INFO - loss = 0.052290387, step = 766200 (2.257 sec) -2022-05-01 16:45:34,051 - tensorflow - INFO - global_step/sec: 88.6556 -2022-05-01 16:45:34,051 - tensorflow - INFO - loss = 0.026844043, step = 766400 (2.256 sec) -2022-05-01 16:45:36,308 - tensorflow - INFO - global_step/sec: 88.5844 -2022-05-01 16:45:36,309 - tensorflow - INFO - loss = 0.104311466, step = 766600 (2.258 sec) -2022-05-01 16:45:38,568 - tensorflow - INFO - global_step/sec: 88.4912 -2022-05-01 16:45:38,569 - tensorflow - INFO - loss = 0.09069614, step = 766800 (2.260 sec) -2022-05-01 16:45:40,825 - tensorflow - INFO - global_step/sec: 88.6413 -2022-05-01 16:45:40,825 - tensorflow - INFO - loss = 0.07376866, step = 767000 (2.256 sec) -2022-05-01 16:45:43,085 - tensorflow - INFO - global_step/sec: 88.4942 -2022-05-01 16:45:43,085 - tensorflow - INFO - loss = 0.014613312, step = 767200 (2.260 sec) -2022-05-01 16:45:45,350 - tensorflow - INFO - global_step/sec: 88.2976 -2022-05-01 16:45:45,350 - tensorflow - INFO - loss = 0.03660327, step = 767400 (2.265 sec) -2022-05-01 16:45:47,611 - tensorflow - INFO - global_step/sec: 88.4373 -2022-05-01 16:45:47,612 - tensorflow - INFO - loss = 0.016163658, step = 767600 (2.261 sec) -2022-05-01 16:45:49,874 - tensorflow - INFO - global_step/sec: 88.397 -2022-05-01 16:45:49,874 - tensorflow - INFO - loss = 0.03184437, step = 767800 (2.263 sec) -2022-05-01 16:45:52,138 - tensorflow - INFO - global_step/sec: 88.318 -2022-05-01 16:45:52,139 - tensorflow - INFO - loss = 0.05155728, step = 768000 (2.265 sec) -2022-05-01 16:45:54,401 - tensorflow - INFO - global_step/sec: 88.3757 -2022-05-01 16:45:54,402 - tensorflow - INFO - loss = 0.16172525, step = 768200 (2.263 sec) -2022-05-01 16:45:56,664 - tensorflow - INFO - global_step/sec: 88.3868 -2022-05-01 16:45:56,665 - tensorflow - INFO - loss = 0.037523683, step = 768400 (2.263 sec) -2022-05-01 16:45:58,926 - tensorflow - INFO - global_step/sec: 88.434 -2022-05-01 16:45:58,926 - tensorflow - INFO - loss = 0.017480196, step = 768600 (2.262 sec) -2022-05-01 16:46:01,189 - tensorflow - INFO - global_step/sec: 88.3785 -2022-05-01 16:46:01,189 - tensorflow - INFO - loss = 0.054639865, step = 768800 (2.263 sec) -2022-05-01 16:46:03,452 - tensorflow - INFO - global_step/sec: 88.3836 -2022-05-01 16:46:03,452 - tensorflow - INFO - loss = 0.06187892, step = 769000 (2.263 sec) -2022-05-01 16:46:05,712 - tensorflow - INFO - global_step/sec: 88.4726 -2022-05-01 16:46:05,713 - tensorflow - INFO - loss = 0.040791094, step = 769200 (2.261 sec) -2022-05-01 16:46:07,962 - tensorflow - INFO - global_step/sec: 88.8823 -2022-05-01 16:46:07,963 - tensorflow - INFO - loss = 0.056501552, step = 769400 (2.250 sec) -2022-05-01 16:46:10,215 - tensorflow - INFO - global_step/sec: 88.7794 -2022-05-01 16:46:10,216 - tensorflow - INFO - loss = 0.07831244, step = 769600 (2.253 sec) -2022-05-01 16:46:12,467 - tensorflow - INFO - global_step/sec: 88.8335 -2022-05-01 16:46:12,467 - tensorflow - INFO - loss = 0.016525311, step = 769800 (2.251 sec) -2022-05-01 16:46:14,723 - tensorflow - INFO - global_step/sec: 88.6328 -2022-05-01 16:46:14,724 - tensorflow - INFO - loss = 16.363789, step = 770000 (2.256 sec) -2022-05-01 16:46:16,982 - tensorflow - INFO - global_step/sec: 88.5457 -2022-05-01 16:46:16,982 - tensorflow - INFO - loss = 0.023497995, step = 770200 (2.259 sec) -2022-05-01 16:46:19,239 - tensorflow - INFO - global_step/sec: 88.6167 -2022-05-01 16:46:19,239 - tensorflow - INFO - loss = 0.029995466, step = 770400 (2.257 sec) -2022-05-01 16:46:21,493 - tensorflow - INFO - global_step/sec: 88.7198 -2022-05-01 16:46:21,494 - tensorflow - INFO - loss = 0.09299415, step = 770600 (2.254 sec) -2022-05-01 16:46:23,745 - tensorflow - INFO - global_step/sec: 88.8217 -2022-05-01 16:46:23,745 - tensorflow - INFO - loss = 0.052417703, step = 770800 (2.252 sec) -2022-05-01 16:46:26,007 - tensorflow - INFO - global_step/sec: 88.4075 -2022-05-01 16:46:26,007 - tensorflow - INFO - loss = 0.057726912, step = 771000 (2.262 sec) -2022-05-01 16:46:28,261 - tensorflow - INFO - global_step/sec: 88.7314 -2022-05-01 16:46:28,261 - tensorflow - INFO - loss = 0.028460063, step = 771200 (2.254 sec) -2022-05-01 16:46:30,528 - tensorflow - INFO - global_step/sec: 88.2163 -2022-05-01 16:46:30,529 - tensorflow - INFO - loss = 5.1369634, step = 771400 (2.267 sec) -2022-05-01 16:46:32,783 - tensorflow - INFO - global_step/sec: 88.6818 -2022-05-01 16:46:32,784 - tensorflow - INFO - loss = 0.041760676, step = 771600 (2.255 sec) -2022-05-01 16:46:35,041 - tensorflow - INFO - global_step/sec: 88.582 -2022-05-01 16:46:35,042 - tensorflow - INFO - loss = 0.017105188, step = 771800 (2.258 sec) -2022-05-01 16:46:37,301 - tensorflow - INFO - global_step/sec: 88.5145 -2022-05-01 16:46:37,301 - tensorflow - INFO - loss = 0.035858568, step = 772000 (2.260 sec) -2022-05-01 16:46:39,556 - tensorflow - INFO - global_step/sec: 88.6948 -2022-05-01 16:46:39,556 - tensorflow - INFO - loss = 0.014652055, step = 772200 (2.255 sec) -2022-05-01 16:46:41,809 - tensorflow - INFO - global_step/sec: 88.7612 -2022-05-01 16:46:41,809 - tensorflow - INFO - loss = 0.5293211, step = 772400 (2.253 sec) -2022-05-01 16:46:44,062 - tensorflow - INFO - global_step/sec: 88.7716 -2022-05-01 16:46:44,062 - tensorflow - INFO - loss = 0.022106223, step = 772600 (2.253 sec) -2022-05-01 16:46:46,315 - tensorflow - INFO - global_step/sec: 88.7586 -2022-05-01 16:46:46,316 - tensorflow - INFO - loss = 0.07572379, step = 772800 (2.253 sec) -2022-05-01 16:46:48,572 - tensorflow - INFO - global_step/sec: 88.6369 -2022-05-01 16:46:48,572 - tensorflow - INFO - loss = 0.07716698, step = 773000 (2.256 sec) -2022-05-01 16:46:50,825 - tensorflow - INFO - global_step/sec: 88.7564 -2022-05-01 16:46:50,825 - tensorflow - INFO - loss = 0.14256883, step = 773200 (2.253 sec) -2022-05-01 16:46:53,084 - tensorflow - INFO - global_step/sec: 88.5203 -2022-05-01 16:46:53,085 - tensorflow - INFO - loss = 0.23689112, step = 773400 (2.259 sec) -2022-05-01 16:46:55,345 - tensorflow - INFO - global_step/sec: 88.4794 -2022-05-01 16:46:55,345 - tensorflow - INFO - loss = 0.0662338, step = 773600 (2.260 sec) -2022-05-01 16:46:57,603 - tensorflow - INFO - global_step/sec: 88.5429 -2022-05-01 16:46:57,604 - tensorflow - INFO - loss = 0.09745243, step = 773800 (2.259 sec) -2022-05-01 16:46:59,861 - tensorflow - INFO - global_step/sec: 88.6121 -2022-05-01 16:46:59,861 - tensorflow - INFO - loss = 0.021856584, step = 774000 (2.257 sec) -2022-05-01 16:47:02,116 - tensorflow - INFO - global_step/sec: 88.6761 -2022-05-01 16:47:02,116 - tensorflow - INFO - loss = 0.030670226, step = 774200 (2.255 sec) -2022-05-01 16:47:04,374 - tensorflow - INFO - global_step/sec: 88.5727 -2022-05-01 16:47:04,374 - tensorflow - INFO - loss = 0.05376959, step = 774400 (2.258 sec) -2022-05-01 16:47:06,625 - tensorflow - INFO - global_step/sec: 88.834 -2022-05-01 16:47:06,626 - tensorflow - INFO - loss = 0.029265922, step = 774600 (2.251 sec) -2022-05-01 16:47:08,882 - tensorflow - INFO - global_step/sec: 88.6314 -2022-05-01 16:47:08,882 - tensorflow - INFO - loss = 0.058595054, step = 774800 (2.257 sec) -2022-05-01 16:47:11,136 - tensorflow - INFO - global_step/sec: 88.7321 -2022-05-01 16:47:11,137 - tensorflow - INFO - loss = 0.0823068, step = 775000 (2.254 sec) -2022-05-01 16:47:13,390 - tensorflow - INFO - global_step/sec: 88.7379 -2022-05-01 16:47:13,390 - tensorflow - INFO - loss = 0.022349877, step = 775200 (2.253 sec) -2022-05-01 16:47:15,644 - tensorflow - INFO - global_step/sec: 88.7231 -2022-05-01 16:47:15,644 - tensorflow - INFO - loss = 0.03451605, step = 775400 (2.254 sec) -2022-05-01 16:47:17,895 - tensorflow - INFO - global_step/sec: 88.831 -2022-05-01 16:47:17,896 - tensorflow - INFO - loss = 0.032310568, step = 775600 (2.252 sec) -2022-05-01 16:47:20,154 - tensorflow - INFO - global_step/sec: 88.5512 -2022-05-01 16:47:20,154 - tensorflow - INFO - loss = 0.016294904, step = 775800 (2.259 sec) -2022-05-01 16:47:22,411 - tensorflow - INFO - global_step/sec: 88.6172 -2022-05-01 16:47:22,411 - tensorflow - INFO - loss = 0.027242772, step = 776000 (2.257 sec) -2022-05-01 16:47:24,666 - tensorflow - INFO - global_step/sec: 88.6885 -2022-05-01 16:47:24,666 - tensorflow - INFO - loss = 0.025471672, step = 776200 (2.255 sec) -2022-05-01 16:47:26,924 - tensorflow - INFO - global_step/sec: 88.5835 -2022-05-01 16:47:26,924 - tensorflow - INFO - loss = 0.025382172, step = 776400 (2.258 sec) -2022-05-01 16:47:29,179 - tensorflow - INFO - global_step/sec: 88.6749 -2022-05-01 16:47:29,180 - tensorflow - INFO - loss = 0.09946121, step = 776600 (2.255 sec) -2022-05-01 16:47:31,437 - tensorflow - INFO - global_step/sec: 88.5769 -2022-05-01 16:47:31,437 - tensorflow - INFO - loss = 0.036002457, step = 776800 (2.258 sec) -2022-05-01 16:47:33,692 - tensorflow - INFO - global_step/sec: 88.7097 -2022-05-01 16:47:33,692 - tensorflow - INFO - loss = 0.035741895, step = 777000 (2.255 sec) -2022-05-01 16:47:35,945 - tensorflow - INFO - global_step/sec: 88.7654 -2022-05-01 16:47:35,945 - tensorflow - INFO - loss = 0.0351523, step = 777200 (2.253 sec) -2022-05-01 16:47:38,199 - tensorflow - INFO - global_step/sec: 88.7048 -2022-05-01 16:47:38,200 - tensorflow - INFO - loss = 0.4478139, step = 777400 (2.255 sec) -2022-05-01 16:47:40,450 - tensorflow - INFO - global_step/sec: 88.8447 -2022-05-01 16:47:40,451 - tensorflow - INFO - loss = 0.031508293, step = 777600 (2.251 sec) -2022-05-01 16:47:42,705 - tensorflow - INFO - global_step/sec: 88.6946 -2022-05-01 16:47:42,706 - tensorflow - INFO - loss = 0.20118828, step = 777800 (2.255 sec) -2022-05-01 16:47:44,966 - tensorflow - INFO - global_step/sec: 88.4684 -2022-05-01 16:47:44,967 - tensorflow - INFO - loss = 0.021901006, step = 778000 (2.261 sec) -2022-05-01 16:47:47,221 - tensorflow - INFO - global_step/sec: 88.7071 -2022-05-01 16:47:47,221 - tensorflow - INFO - loss = 0.37587863, step = 778200 (2.255 sec) -2022-05-01 16:47:49,478 - tensorflow - INFO - global_step/sec: 88.6095 -2022-05-01 16:47:49,478 - tensorflow - INFO - loss = 8.8121195, step = 778400 (2.257 sec) -2022-05-01 16:47:51,731 - tensorflow - INFO - global_step/sec: 88.7569 -2022-05-01 16:47:51,732 - tensorflow - INFO - loss = 0.04143539, step = 778600 (2.253 sec) -2022-05-01 16:47:53,985 - tensorflow - INFO - global_step/sec: 88.7304 -2022-05-01 16:47:53,986 - tensorflow - INFO - loss = 0.018876223, step = 778800 (2.254 sec) -2022-05-01 16:47:56,245 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.7066 -2022-05-01 16:48:39,092 - tensorflow - INFO - loss = 0.0494501, step = 782800 (2.255 sec) -2022-05-01 16:48:41,343 - tensorflow - INFO - global_step/sec: 88.8366 -2022-05-01 16:48:41,344 - tensorflow - INFO - loss = 0.02963003, step = 783000 (2.251 sec) -2022-05-01 16:48:43,598 - tensorflow - INFO - global_step/sec: 88.6945 -2022-05-01 16:48:43,598 - tensorflow - INFO - loss = 0.12955765, step = 783200 (2.255 sec) -2022-05-01 16:48:45,855 - tensorflow - INFO - global_step/sec: 88.6205 -2022-05-01 16:48:45,855 - tensorflow - INFO - loss = 0.059161827, step = 783400 (2.257 sec) -2022-05-01 16:48:48,112 - tensorflow - INFO - global_step/sec: 88.6158 -2022-05-01 16:48:48,112 - tensorflow - INFO - loss = 0.050310932, step = 783600 (2.257 sec) -2022-05-01 16:48:50,368 - tensorflow - INFO - global_step/sec: 88.6584 -2022-05-01 16:48:50,368 - tensorflow - INFO - loss = 0.019565593, step = 783800 (2.256 sec) -2022-05-01 16:48:52,629 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.031977743, step = 785200 (2.254 sec) -2022-05-01 16:49:08,423 - tensorflow - INFO - global_step/sec: 88.5657 -2022-05-01 16:49:08,424 - tensorflow - INFO - loss = 0.11330609, step = 785400 (2.258 sec) -2022-05-01 16:49:10,687 - tensorflow - INFO - global_step/sec: 88.3329 -2022-05-01 16:49:10,688 - tensorflow - INFO - loss = 0.027367208, step = 785600 (2.264 sec) -2022-05-01 16:49:12,952 - tensorflow - INFO - global_step/sec: 88.2983 -2022-05-01 16:49:12,953 - tensorflow - INFO - loss = 0.18189652, step = 785800 (2.265 sec) -2022-05-01 16:49:15,212 - tensorflow - INFO - global_step/sec: 88.5153 -2022-05-01 16:49:15,212 - tensorflow - INFO - loss = 0.078452155, step = 786000 (2.259 sec) -2022-05-01 16:49:17,476 - tensorflow - INFO - global_step/sec: 88.319 -2022-05-01 16:49:17,477 - tensorflow - INFO - loss = 0.0397557, step = 786200 (2.265 sec) -2022-05-01 16:49:19,737 - tensorflow - INFO - global_step/sec: 88.4577 -2022-05-01 16:49:19,738 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.8113 -2022-05-01 16:49:35,574 - tensorflow - INFO - loss = 0.038253732, step = 787800 (2.252 sec) -2022-05-01 16:49:37,825 - tensorflow - INFO - global_step/sec: 88.8166 -2022-05-01 16:49:37,826 - tensorflow - INFO - loss = 0.09698951, step = 788000 (2.252 sec) -2022-05-01 16:49:40,084 - tensorflow - INFO - global_step/sec: 88.5559 -2022-05-01 16:49:40,084 - tensorflow - INFO - loss = 0.4601246, step = 788200 (2.258 sec) -2022-05-01 16:49:42,340 - tensorflow - INFO - global_step/sec: 88.625 -2022-05-01 16:49:42,341 - tensorflow - INFO - loss = 9.054163, step = 788400 (2.257 sec) -2022-05-01 16:49:44,591 - tensorflow - INFO - global_step/sec: 88.8459 -2022-05-01 16:49:44,592 - tensorflow - INFO - loss = 0.031948823, step = 788600 (2.251 sec) -2022-05-01 16:49:46,845 - tensorflow - INFO - global_step/sec: 88.7617 -2022-05-01 16:49:46,845 - tensorflow - INFO - loss = 0.083035044, step = 788800 (2.253 sec) -2022-05-01 16:49:49,096 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.015727483, step = 790200 (2.255 sec) -2022-05-01 16:50:04,897 - tensorflow - INFO - global_step/sec: 88.3484 -2022-05-01 16:50:04,897 - tensorflow - INFO - loss = 0.019167326, step = 790400 (2.264 sec) -2022-05-01 16:50:07,149 - tensorflow - INFO - global_step/sec: 88.7865 -2022-05-01 16:50:07,150 - tensorflow - INFO - loss = 0.11715488, step = 790600 (2.253 sec) -2022-05-01 16:50:09,403 - tensorflow - INFO - global_step/sec: 88.757 -2022-05-01 16:50:09,403 - tensorflow - INFO - loss = 0.01616585, step = 790800 (2.253 sec) -2022-05-01 16:50:11,659 - tensorflow - INFO - global_step/sec: 88.6453 -2022-05-01 16:50:11,659 - tensorflow - INFO - loss = 0.021459151, step = 791000 (2.256 sec) -2022-05-01 16:50:13,914 - tensorflow - INFO - global_step/sec: 88.6707 -2022-05-01 16:50:13,915 - tensorflow - INFO - loss = 0.0305481, step = 791200 (2.256 sec) -2022-05-01 16:50:16,170 - tensorflow - INFO - global_step/sec: 88.6709 -2022-05-01 16:50:16,170 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.5379 -2022-05-01 16:50:31,958 - tensorflow - INFO - loss = 0.01844433, step = 792800 (2.259 sec) -2022-05-01 16:50:34,210 - tensorflow - INFO - global_step/sec: 88.7669 -2022-05-01 16:50:34,211 - tensorflow - INFO - loss = 0.017058585, step = 793000 (2.253 sec) -2022-05-01 16:50:36,459 - tensorflow - INFO - global_step/sec: 88.9281 -2022-05-01 16:50:36,460 - tensorflow - INFO - loss = 0.055735964, step = 793200 (2.249 sec) -2022-05-01 16:50:38,712 - tensorflow - INFO - global_step/sec: 88.7662 -2022-05-01 16:50:38,713 - tensorflow - INFO - loss = 0.016033482, step = 793400 (2.253 sec) -2022-05-01 16:50:40,966 - tensorflow - INFO - global_step/sec: 88.7285 -2022-05-01 16:50:40,967 - tensorflow - INFO - loss = 0.2633359, step = 793600 (2.254 sec) -2022-05-01 16:50:43,223 - tensorflow - INFO - global_step/sec: 88.6167 -2022-05-01 16:50:43,224 - tensorflow - INFO - loss = 0.04098193, step = 793800 (2.257 sec) -2022-05-01 16:50:45,478 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.008105198, step = 795200 (2.258 sec) -2022-05-01 16:51:01,276 - tensorflow - INFO - global_step/sec: 88.8152 -2022-05-01 16:51:01,276 - tensorflow - INFO - loss = 0.09291974, step = 795400 (2.252 sec) -2022-05-01 16:51:03,537 - tensorflow - INFO - global_step/sec: 88.4481 -2022-05-01 16:51:03,537 - tensorflow - INFO - loss = 0.058347143, step = 795600 (2.261 sec) -2022-05-01 16:51:05,793 - tensorflow - INFO - global_step/sec: 88.6529 -2022-05-01 16:51:05,793 - tensorflow - INFO - loss = 0.030016731, step = 795800 (2.256 sec) -2022-05-01 16:51:08,052 - tensorflow - INFO - global_step/sec: 88.5311 -2022-05-01 16:51:08,052 - tensorflow - INFO - loss = 0.46277308, step = 796000 (2.259 sec) -2022-05-01 16:51:10,309 - tensorflow - INFO - global_step/sec: 88.6285 -2022-05-01 16:51:10,309 - tensorflow - INFO - loss = 0.10902999, step = 796200 (2.257 sec) -2022-05-01 16:51:12,566 - tensorflow - INFO - global_step/sec: 88.5918 -2022-05-01 16:51:12,567 - tensorflow - INFO - loss = 0.034445357, step = 796400 (2.258 sec) -2022-05-01 16:51:14,823 - tensorflow - INFO - global_step/sec: 88.6104 -2022-05-01 16:51:14,824 - tensorflow - INFO - loss = 0.19711475, step = 796600 (2.257 sec) -2022-05-01 16:51:17,077 - tensorflow - INFO - global_step/sec: 88.7387 -2022-05-01 16:51:17,078 - tensorflow - INFO - loss = 0.04598197, step = 796800 (2.254 sec) -2022-05-01 16:51:19,333 - tensorflow - INFO - global_step/sec: 88.6389 -2022-05-01 16:51:19,334 - tensorflow - INFO - loss = 0.02298907, step = 797000 (2.256 sec) -2022-05-01 16:51:21,592 - tensorflow - INFO - global_step/sec: 88.5418 -2022-05-01 16:51:21,593 - tensorflow - INFO - loss = 0.015141054, step = 797200 (2.259 sec) -2022-05-01 16:51:23,849 - tensorflow - INFO - global_step/sec: 88.625 -2022-05-01 16:51:23,849 - tensorflow - INFO - loss = 0.021146398, step = 797400 (2.257 sec) -2022-05-01 16:51:26,106 - tensorflow - INFO - global_step/sec: 88.6213 -2022-05-01 16:51:26,106 - tensorflow - INFO - loss = 0.084369704, step = 797600 (2.257 sec) -2022-05-01 16:51:28,365 - tensorflow - INFO - global_step/sec: 88.5264 -2022-05-01 16:51:28,365 - tensorflow - INFO - loss = 0.028809236, step = 797800 (2.259 sec) -2022-05-01 16:51:30,625 - tensorflow - INFO - global_step/sec: 88.5081 -2022-05-01 16:51:30,625 - tensorflow - INFO - loss = 0.016825045, step = 798000 (2.260 sec) -2022-05-01 16:51:32,883 - tensorflow - INFO - global_step/sec: 88.5496 -2022-05-01 16:51:32,884 - tensorflow - INFO - loss = 0.03811729, step = 798200 (2.259 sec) -2022-05-01 16:51:35,144 - tensorflow - INFO - global_step/sec: 88.453 -2022-05-01 16:51:35,145 - tensorflow - INFO - loss = 0.012519284, step = 798400 (2.261 sec) -2022-05-01 16:51:37,398 - tensorflow - INFO - global_step/sec: 88.753 -2022-05-01 16:51:37,398 - tensorflow - INFO - loss = 0.38044575, step = 798600 (2.253 sec) -2022-05-01 16:51:39,652 - tensorflow - INFO - global_step/sec: 88.7151 -2022-05-01 16:51:39,653 - tensorflow - INFO - loss = 1.1314454, step = 798800 (2.254 sec) -2022-05-01 16:51:41,902 - tensorflow - INFO - global_step/sec: 88.8894 -2022-05-01 16:51:41,903 - tensorflow - INFO - loss = 0.017083233, step = 799000 (2.250 sec) -2022-05-01 16:51:44,159 - tensorflow - INFO - global_step/sec: 88.6192 -2022-05-01 16:51:44,159 - tensorflow - INFO - loss = 0.042783007, step = 799200 (2.257 sec) -2022-05-01 16:51:46,421 - tensorflow - INFO - global_step/sec: 88.4185 -2022-05-01 16:51:46,421 - tensorflow - INFO - loss = 0.030692004, step = 799400 (2.262 sec) -2022-05-01 16:51:48,680 - tensorflow - INFO - global_step/sec: 88.5367 -2022-05-01 16:51:48,680 - tensorflow - INFO - loss = 0.041929305, step = 799600 (2.259 sec) -2022-05-01 16:51:50,932 - tensorflow - INFO - global_step/sec: 88.7945 -2022-05-01 16:51:50,933 - tensorflow - INFO - loss = 0.018519888, step = 799800 (2.252 sec) -2022-05-01 16:51:53,175 - tensorflow - INFO - Saving checkpoints for 800000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. -2022-05-01 16:51:53,191 - tensorflow - INFO - model.ckpt-800000 is not in all_model_checkpoint_paths. Manually adding it. -2022-05-01 16:51:53,709 - tensorflow - INFO - Calling model_fn. -2022-05-01 16:51:53,839 - tensorflow - INFO - Done calling model_fn. -2022-05-01 16:51:53,855 - tensorflow - INFO - Starting evaluation at 2022-05-01T16:51:53Z -2022-05-01 16:51:54,134 - tensorflow - INFO - Graph was finalized. -2022-05-01 16:51:54,137 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-800000 -2022-05-01 16:51:54,419 - tensorflow - INFO - Running local_init_op. -2022-05-01 16:51:54,527 - tensorflow - INFO - Done running local_init_op. -2022-05-01 16:52:04,691 - tensorflow - INFO - Evaluation [3990/39900] -2022-05-01 16:52:14,173 - tensorflow - INFO - Evaluation [7980/39900] -2022-05-01 16:52:23,645 - tensorflow - INFO - Evaluation [11970/39900] -2022-05-01 16:52:33,137 - tensorflow - INFO - Evaluation [15960/39900] -2022-05-01 16:52:42,626 - tensorflow - INFO - Evaluation [19950/39900] -2022-05-01 16:52:52,112 - tensorflow - INFO - Evaluation [23940/39900] -2022-05-01 16:53:01,615 - tensorflow - INFO - Evaluation [27930/39900] -2022-05-01 16:53:11,104 - tensorflow - INFO - Evaluation [31920/39900] -2022-05-01 16:53:20,576 - tensorflow - INFO - Evaluation [35910/39900] -2022-05-01 16:53:30,075 - tensorflow - INFO - Evaluation [39900/39900] -2022-05-01 16:53:30,215 - tensorflow - INFO - Finished evaluation at 2022-05-01-16:53:30 -2022-05-01 16:53:30,215 - tensorflow - INFO - Saving dict for global step 800000: global_step = 800000, loss = 0.026910517, mean_degree_err = 4.5198975 -2022-05-01 16:53:30,215 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 800000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-800000 -2022-05-01 16:53:30,229 - tensorflow - INFO - global_step/sec: 2.01416 -2022-05-01 16:53:30,230 - tensorflow - INFO - loss = 0.0899144, step = 800000 (99.297 sec) -2022-05-01 16:53:32,509 - tensorflow - INFO - global_step/sec: 87.7145 -2022-05-01 16:53:32,510 - tensorflow - INFO - loss = 0.010599776, step = 800200 (2.280 sec) -2022-05-01 16:53:34,761 - tensorflow - INFO - global_step/sec: 88.8073 -2022-05-01 16:53:34,762 - tensorflow - INFO - loss = 0.010874215, step = 800400 (2.252 sec) -2022-05-01 16:53:37,020 - tensorflow - INFO - global_step/sec: 88.5681 -2022-05-01 16:53:37,020 - tensorflow - INFO - loss = 0.041985117, step = 800600 (2.258 sec) -2022-05-01 16:53:39,271 - tensorflow - INFO - global_step/sec: 88.8123 -2022-05-01 16:53:39,272 - tensorflow - INFO - loss = 0.052029114, step = 800800 (2.252 sec) -2022-05-01 16:53:41,528 - tensorflow - INFO - global_step/sec: 88.6158 -2022-05-01 16:53:41,529 - tensorflow - INFO - loss = 0.103633836, step = 801000 (2.257 sec) -2022-05-01 16:53:43,789 - tensorflow - INFO - global_step/sec: 88.4821 -2022-05-01 16:53:43,789 - tensorflow - INFO - loss = 0.024310533, step = 801200 (2.260 sec) -2022-05-01 16:53:46,046 - tensorflow - INFO - global_step/sec: 88.6088 -2022-05-01 16:53:46,046 - tensorflow - INFO - loss = 0.07490441, step = 801400 (2.257 sec) -2022-05-01 16:53:48,299 - tensorflow - INFO - global_step/sec: 88.7689 -2022-05-01 16:53:48,299 - tensorflow - INFO - loss = 0.019317463, step = 801600 (2.253 sec) -2022-05-01 16:53:50,555 - tensorflow - INFO - global_step/sec: 88.636 -2022-05-01 16:53:50,556 - tensorflow - INFO - loss = 0.05756301, step = 801800 (2.256 sec) -2022-05-01 16:53:52,814 - tensorflow - INFO - global_step/sec: 88.5411 -2022-05-01 16:53:52,815 - tensorflow - INFO - loss = 2.24759, step = 802000 (2.259 sec) -2022-05-01 16:53:55,075 - tensorflow - INFO - global_step/sec: 88.4525 -2022-05-01 16:53:55,076 - tensorflow - INFO - loss = 0.10740603, step = 802200 (2.261 sec) -2022-05-01 16:53:57,332 - tensorflow - INFO - global_step/sec: 88.6125 -2022-05-01 16:53:57,333 - tensorflow - INFO - loss = 0.020427408, step = 802400 (2.257 sec) -2022-05-01 16:53:59,589 - tensorflow - INFO - global_step/sec: 88.6375 -2022-05-01 16:53:59,589 - tensorflow - INFO - loss = 0.019146318, step = 802600 (2.256 sec) -2022-05-01 16:54:01,845 - tensorflow - INFO - global_step/sec: 88.6373 -2022-05-01 16:54:01,845 - tensorflow - INFO - loss = 0.02906144, step = 802800 (2.256 sec) -2022-05-01 16:54:04,106 - tensorflow - INFO - global_step/sec: 88.4622 -2022-05-01 16:54:04,106 - tensorflow - INFO - loss = 0.036592133, step = 803000 (2.261 sec) -2022-05-01 16:54:06,359 - tensorflow - INFO - global_step/sec: 88.7492 -2022-05-01 16:54:06,360 - tensorflow - INFO - loss = 0.42497355, step = 803200 (2.254 sec) -2022-05-01 16:54:08,617 - tensorflow - INFO - global_step/sec: 88.5852 -2022-05-01 16:54:08,618 - tensorflow - INFO - loss = 0.1766534, step = 803400 (2.258 sec) -2022-05-01 16:54:10,872 - tensorflow - INFO - global_step/sec: 88.7036 -2022-05-01 16:54:10,872 - tensorflow - INFO - loss = 0.024735363, step = 803600 (2.255 sec) -2022-05-01 16:54:13,128 - tensorflow - INFO - global_step/sec: 88.6317 -2022-05-01 16:54:13,129 - tensorflow - INFO - loss = 0.030707162, step = 803800 (2.257 sec) -2022-05-01 16:54:15,383 - tensorflow - INFO - global_step/sec: 88.7 -2022-05-01 16:54:15,384 - tensorflow - INFO - loss = 0.016483106, step = 804000 (2.255 sec) -2022-05-01 16:54:17,638 - tensorflow - INFO - global_step/sec: 88.6855 -2022-05-01 16:54:17,639 - tensorflow - INFO - loss = 0.022009581, step = 804200 (2.255 sec) -2022-05-01 16:54:19,892 - tensorflow - INFO - global_step/sec: 88.7563 -2022-05-01 16:54:19,892 - tensorflow - INFO - loss = 0.029243818, step = 804400 (2.253 sec) -2022-05-01 16:54:22,151 - tensorflow - INFO - global_step/sec: 88.5227 -2022-05-01 16:54:22,151 - tensorflow - INFO - loss = 0.058547143, step = 804600 (2.259 sec) -2022-05-01 16:54:24,407 - tensorflow - INFO - global_step/sec: 88.6612 -2022-05-01 16:54:24,407 - tensorflow - INFO - loss = 0.031076716, step = 804800 (2.256 sec) -2022-05-01 16:54:26,662 - tensorflow - INFO - global_step/sec: 88.684 -2022-05-01 16:54:26,662 - tensorflow - INFO - loss = 0.031477463, step = 805000 (2.255 sec) -2022-05-01 16:54:28,920 - tensorflow - INFO - global_step/sec: 88.5908 -2022-05-01 16:54:28,920 - tensorflow - INFO - loss = 0.018799651, step = 805200 (2.258 sec) -2022-05-01 16:54:31,182 - tensorflow - INFO - global_step/sec: 88.3974 -2022-05-01 16:54:31,182 - tensorflow - INFO - loss = 0.028745413, step = 805400 (2.263 sec) -2022-05-01 16:54:33,443 - tensorflow - INFO - global_step/sec: 88.4767 -2022-05-01 16:54:33,443 - tensorflow - INFO - loss = 0.5449137, step = 805600 (2.261 sec) -2022-05-01 16:54:35,698 - tensorflow - INFO - global_step/sec: 88.6692 -2022-05-01 16:54:35,699 - tensorflow - INFO - loss = 0.24509883, step = 805800 (2.256 sec) -2022-05-01 16:54:37,954 - tensorflow - INFO - global_step/sec: 88.6504 -2022-05-01 16:54:37,955 - tensorflow - INFO - loss = 0.025008408, step = 806000 (2.256 sec) -2022-05-01 16:54:40,206 - tensorflow - INFO - global_step/sec: 88.8094 -2022-05-01 16:54:40,207 - tensorflow - INFO - loss = 0.03249572, step = 806200 (2.252 sec) -2022-05-01 16:54:42,461 - tensorflow - INFO - global_step/sec: 88.6961 -2022-05-01 16:54:42,462 - tensorflow - INFO - loss = 0.030577507, step = 806400 (2.255 sec) -2022-05-01 16:54:44,720 - tensorflow - INFO - global_step/sec: 88.5433 -2022-05-01 16:54:44,720 - tensorflow - INFO - loss = 0.020079143, step = 806600 (2.259 sec) -2022-05-01 16:54:46,975 - tensorflow - INFO - global_step/sec: 88.6837 -2022-05-01 16:54:46,975 - tensorflow - INFO - loss = 0.03394632, step = 806800 (2.255 sec) -2022-05-01 16:54:49,234 - tensorflow - INFO - global_step/sec: 88.5236 -2022-05-01 16:54:49,235 - tensorflow - INFO - loss = 0.7879622, step = 807000 (2.259 sec) -2022-05-01 16:54:51,489 - tensorflow - INFO - global_step/sec: 88.72 -2022-05-01 16:54:51,489 - tensorflow - INFO - loss = 0.049458347, step = 807200 (2.254 sec) -2022-05-01 16:54:53,747 - tensorflow - INFO - global_step/sec: 88.5466 -2022-05-01 16:54:53,748 - tensorflow - INFO - loss = 0.049946, step = 807400 (2.259 sec) -2022-05-01 16:54:56,006 - tensorflow - INFO - global_step/sec: 88.5631 -2022-05-01 16:54:56,006 - tensorflow - INFO - loss = 0.25674763, step = 807600 (2.258 sec) -2022-05-01 16:54:58,269 - tensorflow - INFO - global_step/sec: 88.3516 -2022-05-01 16:54:58,270 - tensorflow - INFO - loss = 0.063438654, step = 807800 (2.264 sec) -2022-05-01 16:55:00,527 - tensorflow - INFO - global_step/sec: 88.5901 -2022-05-01 16:55:00,527 - tensorflow - INFO - loss = 0.033711236, step = 808000 (2.258 sec) -2022-05-01 16:55:02,783 - tensorflow - INFO - global_step/sec: 88.628 -2022-05-01 16:55:02,784 - tensorflow - INFO - loss = 0.030713592, step = 808200 (2.257 sec) -2022-05-01 16:55:05,043 - tensorflow - INFO - global_step/sec: 88.5287 -2022-05-01 16:55:05,043 - tensorflow - INFO - loss = 0.3922224, step = 808400 (2.259 sec) -2022-05-01 16:55:07,305 - tensorflow - INFO - global_step/sec: 88.3993 -2022-05-01 16:55:07,306 - tensorflow - INFO - loss = 0.014256238, step = 808600 (2.262 sec) -2022-05-01 16:55:09,559 - tensorflow - INFO - global_step/sec: 88.7537 -2022-05-01 16:55:09,559 - tensorflow - INFO - loss = 0.048423983, step = 808800 (2.253 sec) -2022-05-01 16:55:11,810 - tensorflow - INFO - global_step/sec: 88.8171 -2022-05-01 16:55:11,811 - tensorflow - INFO - loss = 0.6235335, step = 809000 (2.252 sec) -2022-05-01 16:55:14,066 - tensorflow - INFO - global_step/sec: 88.6688 -2022-05-01 16:55:14,066 - tensorflow - INFO - loss = 0.040774103, step = 809200 (2.256 sec) -2022-05-01 16:55:16,325 - tensorflow - INFO - global_step/sec: 88.5454 -2022-05-01 16:55:16,325 - tensorflow - INFO - loss = 0.008105355, step = 809400 (2.259 sec) -2022-05-01 16:55:18,583 - tensorflow - INFO - global_step/sec: 88.5712 -2022-05-01 16:55:18,583 - tensorflow - INFO - loss = 0.21221718, step = 809600 (2.258 sec) -2022-05-01 16:55:20,838 - tensorflow - INFO - global_step/sec: 88.6648 -2022-05-01 16:55:20,839 - tensorflow - INFO - loss = 0.032146968, step = 809800 (2.256 sec) -2022-05-01 16:55:23,091 - tensorflow - INFO - global_step/sec: 88.7708 -2022-05-01 16:55:23,092 - tensorflow - INFO - loss = 0.034307744, step = 810000 (2.253 sec) -2022-05-01 16:55:25,342 - tensorflow - INFO - global_step/sec: 88.8554 -2022-05-01 16:55:25,343 - tensorflow - INFO - loss = 0.3609604, step = 810200 (2.251 sec) -2022-05-01 16:55:27,590 - tensorflow - INFO - global_step/sec: 88.9818 -2022-05-01 16:55:27,590 - tensorflow - INFO - loss = 0.09175968, step = 810400 (2.248 sec) -2022-05-01 16:55:29,840 - tensorflow - INFO - global_step/sec: 88.882 -2022-05-01 16:55:29,840 - tensorflow - INFO - loss = 0.026782908, step = 810600 (2.250 sec) -2022-05-01 16:55:32,097 - tensorflow - INFO - global_step/sec: 88.6085 -2022-05-01 16:55:32,098 - tensorflow - INFO - loss = 0.017560637, step = 810800 (2.257 sec) -2022-05-01 16:55:34,350 - tensorflow - INFO - global_step/sec: 88.7788 -2022-05-01 16:55:34,350 - tensorflow - INFO - loss = 0.03503574, step = 811000 (2.253 sec) -2022-05-01 16:55:36,609 - tensorflow - INFO - global_step/sec: 88.5262 -2022-05-01 16:55:36,610 - tensorflow - INFO - loss = 11.913107, step = 811200 (2.259 sec) -2022-05-01 16:55:38,864 - tensorflow - INFO - global_step/sec: 88.7198 -2022-05-01 16:55:38,864 - tensorflow - INFO - loss = 0.034631055, step = 811400 (2.254 sec) -2022-05-01 16:55:41,112 - tensorflow - INFO - global_step/sec: 88.9584 -2022-05-01 16:55:41,112 - tensorflow - INFO - loss = 0.019718375, step = 811600 (2.248 sec) -2022-05-01 16:55:43,364 - tensorflow - INFO - global_step/sec: 88.7959 -2022-05-01 16:55:43,365 - tensorflow - INFO - loss = 0.0121886395, step = 811800 (2.252 sec) -2022-05-01 16:55:45,619 - tensorflow - INFO - global_step/sec: 88.7142 -2022-05-01 16:55:45,619 - tensorflow - INFO - loss = 0.052457172, step = 812000 (2.254 sec) -2022-05-01 16:55:47,875 - tensorflow - INFO - global_step/sec: 88.6286 -2022-05-01 16:55:47,876 - tensorflow - INFO - loss = 0.039111532, step = 812200 (2.257 sec) -2022-05-01 16:55:50,129 - tensorflow - INFO - global_step/sec: 88.746 -2022-05-01 16:55:50,129 - tensorflow - INFO - loss = 0.1315316, step = 812400 (2.254 sec) -2022-05-01 16:55:52,388 - tensorflow - INFO - global_step/sec: 88.5362 -2022-05-01 16:55:52,388 - tensorflow - INFO - loss = 0.065420076, step = 812600 (2.259 sec) -2022-05-01 16:55:54,645 - tensorflow - INFO - global_step/sec: 88.5937 -2022-05-01 16:55:54,646 - tensorflow - INFO - loss = 0.22496642, step = 812800 (2.258 sec) -2022-05-01 16:55:56,898 - tensorflow - INFO - global_step/sec: 88.7743 -2022-05-01 16:55:56,899 - tensorflow - INFO - loss = 0.057431646, step = 813000 (2.253 sec) -2022-05-01 16:55:59,154 - tensorflow - INFO - global_step/sec: 88.672 -2022-05-01 16:55:59,154 - tensorflow - INFO - loss = 0.10027319, step = 813200 (2.255 sec) -2022-05-01 16:56:01,416 - tensorflow - INFO - global_step/sec: 88.4213 -2022-05-01 16:56:01,416 - tensorflow - INFO - loss = 0.017701805, step = 813400 (2.262 sec) -2022-05-01 16:56:03,668 - tensorflow - INFO - global_step/sec: 88.8079 -2022-05-01 16:56:03,668 - tensorflow - INFO - loss = 0.045442797, step = 813600 (2.252 sec) -2022-05-01 16:56:05,920 - tensorflow - INFO - global_step/sec: 88.7759 -2022-05-01 16:56:05,921 - tensorflow - INFO - loss = 0.059727803, step = 813800 (2.253 sec) -2022-05-01 16:56:08,179 - tensorflow - INFO - global_step/sec: 88.5478 -2022-05-01 16:56:08,180 - tensorflow - INFO - loss = 0.02758157, step = 814000 (2.259 sec) -2022-05-01 16:56:10,440 - tensorflow - INFO - global_step/sec: 88.4731 -2022-05-01 16:56:10,440 - tensorflow - INFO - loss = 0.17523694, step = 814200 (2.261 sec) -2022-05-01 16:56:12,701 - tensorflow - INFO - global_step/sec: 88.4638 -2022-05-01 16:56:12,701 - tensorflow - INFO - loss = 0.03142998, step = 814400 (2.261 sec) -2022-05-01 16:56:14,962 - tensorflow - INFO - global_step/sec: 88.4498 -2022-05-01 16:56:14,962 - tensorflow - INFO - loss = 0.25013742, step = 814600 (2.261 sec) -2022-05-01 16:56:17,222 - tensorflow - INFO - global_step/sec: 88.483 -2022-05-01 16:56:17,222 - tensorflow - INFO - loss = 0.24534044, step = 814800 (2.260 sec) -2022-05-01 16:56:19,482 - tensorflow - INFO - global_step/sec: 88.4985 -2022-05-01 16:56:19,482 - tensorflow - INFO - loss = 0.028317003, step = 815000 (2.260 sec) -2022-05-01 16:56:21,743 - tensorflow - INFO - global_step/sec: 88.4711 -2022-05-01 16:56:21,743 - tensorflow - INFO - loss = 0.035891015, step = 815200 (2.261 sec) -2022-05-01 16:56:24,004 - tensorflow - INFO - global_step/sec: 88.4282 -2022-05-01 16:56:24,005 - tensorflow - INFO - loss = 0.010115391, step = 815400 (2.262 sec) -2022-05-01 16:56:26,267 - tensorflow - INFO - global_step/sec: 88.3786 -2022-05-01 16:56:26,268 - tensorflow - INFO - loss = 0.07750321, step = 815600 (2.263 sec) -2022-05-01 16:56:28,526 - tensorflow - INFO - global_step/sec: 88.5364 -2022-05-01 16:56:28,527 - tensorflow - INFO - loss = 0.03354431, step = 815800 (2.259 sec) -2022-05-01 16:56:30,797 - tensorflow - INFO - global_step/sec: 88.0603 -2022-05-01 16:56:30,798 - tensorflow - INFO - loss = 0.025497619, step = 816000 (2.271 sec) -2022-05-01 16:56:33,058 - tensorflow - INFO - global_step/sec: 88.4668 -2022-05-01 16:56:33,059 - tensorflow - INFO - loss = 0.012306116, step = 816200 (2.261 sec) -2022-05-01 16:56:35,324 - tensorflow - INFO - global_step/sec: 88.2618 -2022-05-01 16:56:35,325 - tensorflow - 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tensorflow - INFO - loss = 0.044448182, step = 820200 (2.255 sec) -2022-05-01 16:57:20,448 - tensorflow - INFO - global_step/sec: 88.5737 -2022-05-01 16:57:20,448 - tensorflow - INFO - loss = 0.036466215, step = 820400 (2.258 sec) -2022-05-01 16:57:22,699 - tensorflow - INFO - global_step/sec: 88.8727 -2022-05-01 16:57:22,699 - tensorflow - INFO - loss = 0.016094217, step = 820600 (2.250 sec) -2022-05-01 16:57:24,947 - tensorflow - INFO - global_step/sec: 88.9303 -2022-05-01 16:57:24,948 - tensorflow - INFO - loss = 0.027211662, step = 820800 (2.249 sec) -2022-05-01 16:57:27,204 - tensorflow - INFO - global_step/sec: 88.6355 -2022-05-01 16:57:27,204 - tensorflow - INFO - loss = 0.020300074, step = 821000 (2.256 sec) -2022-05-01 16:57:29,457 - tensorflow - INFO - global_step/sec: 88.7849 -2022-05-01 16:57:29,457 - tensorflow - INFO - loss = 0.0277876, step = 821200 (2.253 sec) -2022-05-01 16:57:31,721 - tensorflow - INFO - global_step/sec: 88.3328 -2022-05-01 16:57:31,721 - tensorflow - INFO - loss = 0.019403651, step = 821400 (2.264 sec) -2022-05-01 16:57:33,976 - tensorflow - INFO - global_step/sec: 88.6777 -2022-05-01 16:57:33,977 - tensorflow - INFO - loss = 0.085541576, step = 821600 (2.255 sec) -2022-05-01 16:57:36,228 - tensorflow - INFO - global_step/sec: 88.8209 -2022-05-01 16:57:36,228 - tensorflow - INFO - loss = 0.16410998, step = 821800 (2.252 sec) -2022-05-01 16:57:38,483 - tensorflow - INFO - global_step/sec: 88.6798 -2022-05-01 16:57:38,483 - tensorflow - INFO - loss = 0.067582645, step = 822000 (2.255 sec) -2022-05-01 16:57:40,735 - tensorflow - INFO - global_step/sec: 88.8003 -2022-05-01 16:57:40,736 - tensorflow - INFO - loss = 0.025372028, step = 822200 (2.252 sec) -2022-05-01 16:57:42,989 - tensorflow - INFO - global_step/sec: 88.7311 -2022-05-01 16:57:42,990 - tensorflow - INFO - loss = 0.016275853, step = 822400 (2.254 sec) -2022-05-01 16:57:45,245 - tensorflow - INFO - global_step/sec: 88.6722 -2022-05-01 16:57:45,245 - tensorflow - INFO - loss = 0.37046713, step = 822600 (2.256 sec) -2022-05-01 16:57:47,494 - tensorflow - INFO - global_step/sec: 88.9097 -2022-05-01 16:57:47,495 - tensorflow - INFO - loss = 0.017730419, step = 822800 (2.249 sec) -2022-05-01 16:57:49,747 - tensorflow - INFO - global_step/sec: 88.7978 -2022-05-01 16:57:49,747 - tensorflow - INFO - loss = 0.093039796, step = 823000 (2.252 sec) -2022-05-01 16:57:52,002 - tensorflow - INFO - global_step/sec: 88.6594 -2022-05-01 16:57:52,003 - tensorflow - INFO - loss = 0.040588602, step = 823200 (2.256 sec) -2022-05-01 16:57:54,260 - tensorflow - INFO - global_step/sec: 88.5799 -2022-05-01 16:57:54,261 - tensorflow - INFO - loss = 0.39238918, step = 823400 (2.258 sec) -2022-05-01 16:57:56,517 - tensorflow - INFO - global_step/sec: 88.643 -2022-05-01 16:57:56,517 - tensorflow - INFO - loss = 0.01678184, step = 823600 (2.256 sec) -2022-05-01 16:57:58,779 - tensorflow - INFO - global_step/sec: 88.3877 -2022-05-01 16:57:58,780 - tensorflow - INFO - loss = 0.022689862, step = 823800 (2.263 sec) -2022-05-01 16:58:01,031 - tensorflow - INFO - global_step/sec: 88.8167 -2022-05-01 16:58:01,032 - tensorflow - INFO - loss = 0.11005012, step = 824000 (2.252 sec) -2022-05-01 16:58:03,290 - tensorflow - INFO - global_step/sec: 88.5512 -2022-05-01 16:58:03,290 - tensorflow - INFO - loss = 0.19351901, step = 824200 (2.259 sec) -2022-05-01 16:58:05,542 - tensorflow - INFO - global_step/sec: 88.7824 -2022-05-01 16:58:05,543 - tensorflow - INFO - loss = 0.026002258, step = 824400 (2.253 sec) -2022-05-01 16:58:07,794 - tensorflow - INFO - global_step/sec: 88.8161 -2022-05-01 16:58:07,795 - tensorflow - INFO - loss = 0.03154104, step = 824600 (2.252 sec) -2022-05-01 16:58:10,056 - tensorflow - INFO - global_step/sec: 88.4304 -2022-05-01 16:58:10,056 - tensorflow - INFO - loss = 0.048960663, step = 824800 (2.262 sec) -2022-05-01 16:58:12,312 - tensorflow - INFO - global_step/sec: 88.6479 -2022-05-01 16:58:12,313 - tensorflow - INFO - loss = 0.014210501, step = 825000 (2.256 sec) -2022-05-01 16:58:14,565 - tensorflow - INFO - global_step/sec: 88.7528 -2022-05-01 16:58:14,566 - tensorflow - INFO - loss = 0.034409862, step = 825200 (2.253 sec) -2022-05-01 16:58:16,814 - tensorflow - INFO - global_step/sec: 88.9362 -2022-05-01 16:58:16,815 - tensorflow - INFO - loss = 0.02997354, step = 825400 (2.249 sec) -2022-05-01 16:58:19,068 - tensorflow - INFO - global_step/sec: 88.7621 -2022-05-01 16:58:19,068 - tensorflow - INFO - loss = 14.25892, step = 825600 (2.253 sec) -2022-05-01 16:58:21,317 - tensorflow - INFO - global_step/sec: 88.9072 -2022-05-01 16:58:21,317 - tensorflow - INFO - loss = 0.23673823, step = 825800 (2.250 sec) -2022-05-01 16:58:23,574 - tensorflow - INFO - global_step/sec: 88.6337 -2022-05-01 16:58:23,574 - tensorflow - INFO - loss = 0.15329827, step = 826000 (2.256 sec) -2022-05-01 16:58:25,827 - tensorflow - INFO - global_step/sec: 88.7621 -2022-05-01 16:58:25,827 - tensorflow - INFO - loss = 0.29284167, step = 826200 (2.253 sec) -2022-05-01 16:58:28,080 - tensorflow - INFO - global_step/sec: 88.7688 -2022-05-01 16:58:28,080 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.7041 -2022-05-01 16:58:43,852 - tensorflow - INFO - loss = 0.030137088, step = 827800 (2.255 sec) -2022-05-01 16:58:46,108 - tensorflow - INFO - global_step/sec: 88.6432 -2022-05-01 16:58:46,108 - tensorflow - INFO - loss = 0.013250478, step = 828000 (2.256 sec) -2022-05-01 16:58:48,364 - tensorflow - INFO - global_step/sec: 88.6335 -2022-05-01 16:58:48,365 - tensorflow - INFO - loss = 0.08264479, step = 828200 (2.256 sec) -2022-05-01 16:58:50,616 - tensorflow - INFO - global_step/sec: 88.8343 -2022-05-01 16:58:50,616 - tensorflow - INFO - loss = 0.17820087, step = 828400 (2.251 sec) -2022-05-01 16:58:52,871 - tensorflow - INFO - global_step/sec: 88.6773 -2022-05-01 16:58:52,871 - tensorflow - INFO - loss = 0.41365913, step = 828600 (2.255 sec) -2022-05-01 16:58:55,127 - tensorflow - INFO - global_step/sec: 88.6339 -2022-05-01 16:58:55,128 - tensorflow - INFO - loss = 0.03269793, step = 828800 (2.257 sec) -2022-05-01 16:58:57,380 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.24388763, step = 830200 (2.252 sec) -2022-05-01 16:59:13,159 - tensorflow - INFO - global_step/sec: 88.6404 -2022-05-01 16:59:13,159 - tensorflow - INFO - loss = 0.010640362, step = 830400 (2.256 sec) -2022-05-01 16:59:15,410 - tensorflow - INFO - global_step/sec: 88.8544 -2022-05-01 16:59:15,410 - tensorflow - INFO - loss = 0.010333479, step = 830600 (2.251 sec) -2022-05-01 16:59:17,667 - tensorflow - INFO - global_step/sec: 88.6087 -2022-05-01 16:59:17,667 - tensorflow - INFO - loss = 0.12576565, step = 830800 (2.257 sec) -2022-05-01 16:59:19,921 - tensorflow - INFO - global_step/sec: 88.7294 -2022-05-01 16:59:19,921 - tensorflow - INFO - loss = 0.045042016, step = 831000 (2.254 sec) -2022-05-01 16:59:22,172 - tensorflow - INFO - global_step/sec: 88.8491 -2022-05-01 16:59:22,172 - tensorflow - INFO - loss = 0.037481226, step = 831200 (2.251 sec) -2022-05-01 16:59:24,427 - tensorflow - INFO - global_step/sec: 88.695 -2022-05-01 16:59:24,427 - tensorflow - INFO - loss = 0.04068721, step = 831400 (2.255 sec) -2022-05-01 16:59:26,681 - tensorflow - INFO - global_step/sec: 88.7158 -2022-05-01 16:59:26,682 - tensorflow - INFO - loss = 0.011278633, step = 831600 (2.254 sec) -2022-05-01 16:59:28,931 - tensorflow - INFO - global_step/sec: 88.8847 -2022-05-01 16:59:28,932 - tensorflow - INFO - loss = 0.026288154, step = 831800 (2.250 sec) -2022-05-01 16:59:31,189 - tensorflow - INFO - global_step/sec: 88.58 -2022-05-01 16:59:31,190 - tensorflow - INFO - loss = 0.026115807, step = 832000 (2.258 sec) -2022-05-01 16:59:33,440 - tensorflow - INFO - global_step/sec: 88.8708 -2022-05-01 16:59:33,440 - tensorflow - INFO - loss = 0.008827671, step = 832200 (2.250 sec) -2022-05-01 16:59:35,690 - tensorflow - INFO - global_step/sec: 88.893 -2022-05-01 16:59:35,690 - tensorflow - INFO - loss = 0.075771615, step = 832400 (2.250 sec) -2022-05-01 16:59:37,950 - tensorflow - INFO - global_step/sec: 88.5013 -2022-05-01 16:59:37,950 - tensorflow - INFO - loss = 0.030688344, step = 832600 (2.260 sec) -2022-05-01 16:59:40,211 - tensorflow - INFO - global_step/sec: 88.4329 -2022-05-01 16:59:40,212 - tensorflow - INFO - loss = 0.1068082, step = 832800 (2.262 sec) -2022-05-01 16:59:42,468 - tensorflow - INFO - global_step/sec: 88.622 -2022-05-01 16:59:42,468 - tensorflow - INFO - loss = 0.05456999, step = 833000 (2.257 sec) -2022-05-01 16:59:44,731 - tensorflow - INFO - global_step/sec: 88.3917 -2022-05-01 16:59:44,731 - tensorflow - INFO - loss = 0.029156476, step = 833200 (2.263 sec) -2022-05-01 16:59:46,990 - tensorflow - INFO - global_step/sec: 88.5054 -2022-05-01 16:59:46,991 - tensorflow - INFO - loss = 0.02217762, step = 833400 (2.260 sec) -2022-05-01 16:59:49,252 - tensorflow - INFO - global_step/sec: 88.422 -2022-05-01 16:59:49,253 - tensorflow - INFO - loss = 0.026112422, step = 833600 (2.262 sec) -2022-05-01 16:59:51,520 - tensorflow - INFO - global_step/sec: 88.1743 -2022-05-01 16:59:51,521 - tensorflow - INFO - loss = 0.025735682, step = 833800 (2.268 sec) -2022-05-01 16:59:53,783 - tensorflow - INFO - 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INFO - loss = 0.07826371, step = 856400 (2.254 sec) -2022-05-01 17:04:08,666 - tensorflow - INFO - global_step/sec: 88.5035 -2022-05-01 17:04:08,666 - tensorflow - INFO - loss = 0.027022364, step = 856600 (2.260 sec) -2022-05-01 17:04:10,922 - tensorflow - INFO - global_step/sec: 88.6372 -2022-05-01 17:04:10,923 - tensorflow - INFO - loss = 0.0835408, step = 856800 (2.256 sec) -2022-05-01 17:04:13,175 - tensorflow - INFO - global_step/sec: 88.766 -2022-05-01 17:04:13,176 - tensorflow - INFO - loss = 0.037885983, step = 857000 (2.253 sec) -2022-05-01 17:04:15,429 - tensorflow - INFO - global_step/sec: 88.7521 -2022-05-01 17:04:15,429 - tensorflow - INFO - loss = 0.023721894, step = 857200 (2.253 sec) -2022-05-01 17:04:17,681 - tensorflow - INFO - global_step/sec: 88.807 -2022-05-01 17:04:17,681 - tensorflow - INFO - loss = 0.017154926, step = 857400 (2.252 sec) -2022-05-01 17:04:19,932 - tensorflow - INFO - global_step/sec: 88.852 -2022-05-01 17:04:19,932 - tensorflow - INFO - loss = 0.26575536, step = 857600 (2.251 sec) -2022-05-01 17:04:22,186 - tensorflow - INFO - global_step/sec: 88.7241 -2022-05-01 17:04:22,186 - tensorflow - INFO - loss = 0.08691725, step = 857800 (2.254 sec) -2022-05-01 17:04:24,440 - tensorflow - INFO - global_step/sec: 88.7346 -2022-05-01 17:04:24,440 - tensorflow - INFO - loss = 0.020663533, step = 858000 (2.254 sec) -2022-05-01 17:04:26,695 - tensorflow - INFO - global_step/sec: 88.7001 -2022-05-01 17:04:26,695 - tensorflow - INFO - loss = 0.05000124, step = 858200 (2.255 sec) -2022-05-01 17:04:28,943 - tensorflow - INFO - global_step/sec: 88.9374 -2022-05-01 17:04:28,944 - tensorflow - INFO - loss = 0.20630419, step = 858400 (2.249 sec) -2022-05-01 17:04:31,200 - tensorflow - INFO - global_step/sec: 88.6236 -2022-05-01 17:04:31,201 - tensorflow - INFO - loss = 0.033283014, step = 858600 (2.257 sec) -2022-05-01 17:04:33,457 - tensorflow - INFO - global_step/sec: 88.6326 -2022-05-01 17:04:33,457 - tensorflow - INFO - loss = 0.041254193, step = 858800 (2.256 sec) -2022-05-01 17:04:35,711 - tensorflow - INFO - global_step/sec: 88.7054 -2022-05-01 17:04:35,712 - tensorflow - INFO - loss = 0.046876565, step = 859000 (2.255 sec) -2022-05-01 17:04:37,963 - tensorflow - INFO - global_step/sec: 88.8284 -2022-05-01 17:04:37,963 - tensorflow - INFO - loss = 0.02186305, step = 859200 (2.252 sec) -2022-05-01 17:04:40,214 - tensorflow - INFO - global_step/sec: 88.8545 -2022-05-01 17:04:40,214 - tensorflow - INFO - loss = 0.018246038, step = 859400 (2.251 sec) -2022-05-01 17:04:42,467 - tensorflow - INFO - global_step/sec: 88.7486 -2022-05-01 17:04:42,468 - tensorflow - INFO - loss = 0.019937154, step = 859600 (2.254 sec) -2022-05-01 17:04:44,729 - tensorflow - INFO - global_step/sec: 88.4255 -2022-05-01 17:04:44,730 - tensorflow - INFO - loss = 0.023076858, step = 859800 (2.262 sec) -2022-05-01 17:04:46,981 - tensorflow - INFO - global_step/sec: 88.8118 -2022-05-01 17:04:46,982 - tensorflow - INFO - loss = 0.026521694, step = 860000 (2.252 sec) -2022-05-01 17:04:49,231 - tensorflow - INFO - global_step/sec: 88.8829 -2022-05-01 17:04:49,232 - tensorflow - INFO - loss = 15.845024, step = 860200 (2.250 sec) -2022-05-01 17:04:51,482 - tensorflow - INFO - global_step/sec: 88.8534 -2022-05-01 17:04:51,483 - tensorflow - INFO - loss = 0.011186894, step = 860400 (2.251 sec) -2022-05-01 17:04:53,738 - tensorflow - INFO - global_step/sec: 88.6468 -2022-05-01 17:04:53,739 - tensorflow - INFO - loss = 0.06533711, step = 860600 (2.256 sec) -2022-05-01 17:04:55,995 - tensorflow - INFO - global_step/sec: 88.6324 -2022-05-01 17:04:55,995 - tensorflow - INFO - loss = 0.4109085, step = 860800 (2.256 sec) -2022-05-01 17:04:58,257 - tensorflow - INFO - global_step/sec: 88.3963 -2022-05-01 17:04:58,258 - tensorflow - INFO - loss = 0.034719594, step = 861000 (2.263 sec) -2022-05-01 17:05:00,515 - tensorflow - INFO - global_step/sec: 88.5983 -2022-05-01 17:05:00,515 - tensorflow - INFO - loss = 0.054027583, step = 861200 (2.257 sec) -2022-05-01 17:05:02,772 - tensorflow - INFO - global_step/sec: 88.5963 -2022-05-01 17:05:02,773 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.7862 -2022-05-01 17:05:18,554 - tensorflow - INFO - loss = 0.14712748, step = 862800 (2.253 sec) -2022-05-01 17:05:20,803 - tensorflow - INFO - global_step/sec: 88.8768 -2022-05-01 17:05:20,804 - tensorflow - INFO - loss = 0.031885386, step = 863000 (2.250 sec) -2022-05-01 17:05:23,058 - tensorflow - INFO - global_step/sec: 88.7211 -2022-05-01 17:05:23,058 - tensorflow - INFO - loss = 0.058298945, step = 863200 (2.254 sec) -2022-05-01 17:05:25,313 - tensorflow - INFO - global_step/sec: 88.6924 -2022-05-01 17:05:25,313 - tensorflow - INFO - loss = 0.060620643, step = 863400 (2.255 sec) -2022-05-01 17:05:27,567 - tensorflow - INFO - global_step/sec: 88.7219 -2022-05-01 17:05:27,567 - tensorflow - INFO - loss = 0.040557064, step = 863600 (2.254 sec) -2022-05-01 17:05:29,824 - tensorflow - INFO - global_step/sec: 88.6033 -2022-05-01 17:05:29,825 - tensorflow - INFO - loss = 0.070168525, step = 863800 (2.257 sec) -2022-05-01 17:05:32,088 - tensorflow - INFO - global_step/sec: 88.3626 -2022-05-01 17:05:32,088 - tensorflow - INFO - loss = 0.014815092, step = 864000 (2.263 sec) -2022-05-01 17:05:34,349 - tensorflow - INFO - global_step/sec: 88.4244 -2022-05-01 17:05:34,350 - tensorflow - INFO - loss = 0.020924546, step = 864200 (2.262 sec) -2022-05-01 17:05:36,600 - tensorflow - INFO - global_step/sec: 88.8727 -2022-05-01 17:05:36,600 - tensorflow - INFO - loss = 0.027764486, step = 864400 (2.250 sec) -2022-05-01 17:05:38,849 - tensorflow - INFO - global_step/sec: 88.9039 -2022-05-01 17:05:38,850 - tensorflow - INFO - loss = 0.65319145, step = 864600 (2.250 sec) -2022-05-01 17:05:41,100 - tensorflow - INFO - global_step/sec: 88.8741 -2022-05-01 17:05:41,100 - tensorflow - INFO - loss = 0.015839405, step = 864800 (2.250 sec) -2022-05-01 17:05:43,354 - tensorflow - INFO - global_step/sec: 88.7387 -2022-05-01 17:05:43,354 - tensorflow - INFO - loss = 0.017803952, step = 865000 (2.254 sec) -2022-05-01 17:05:45,605 - tensorflow - INFO - global_step/sec: 88.8128 -2022-05-01 17:05:45,606 - tensorflow - INFO - loss = 0.17650893, step = 865200 (2.252 sec) -2022-05-01 17:05:47,860 - tensorflow - INFO - global_step/sec: 88.7213 -2022-05-01 17:05:47,860 - tensorflow - INFO - loss = 0.034872018, step = 865400 (2.254 sec) -2022-05-01 17:05:50,117 - tensorflow - INFO - global_step/sec: 88.6071 -2022-05-01 17:05:50,117 - tensorflow - INFO - loss = 0.030002914, step = 865600 (2.257 sec) -2022-05-01 17:05:52,373 - tensorflow - INFO - global_step/sec: 88.6476 -2022-05-01 17:05:52,373 - tensorflow - INFO - loss = 0.0352073, step = 865800 (2.256 sec) -2022-05-01 17:05:54,623 - tensorflow - INFO - global_step/sec: 88.8698 -2022-05-01 17:05:54,624 - tensorflow - INFO - loss = 0.20045105, step = 866000 (2.250 sec) -2022-05-01 17:05:56,882 - tensorflow - INFO - global_step/sec: 88.5466 -2022-05-01 17:05:56,883 - tensorflow - INFO - loss = 0.085450634, step = 866200 (2.259 sec) -2022-05-01 17:05:59,141 - tensorflow - INFO - global_step/sec: 88.5423 -2022-05-01 17:05:59,141 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.8529 -2022-05-01 17:06:14,925 - tensorflow - INFO - loss = 0.0117491465, step = 867800 (2.251 sec) -2022-05-01 17:06:17,173 - tensorflow - INFO - global_step/sec: 88.9653 -2022-05-01 17:06:17,173 - tensorflow - INFO - loss = 0.079364665, step = 868000 (2.248 sec) -2022-05-01 17:06:19,427 - tensorflow - INFO - global_step/sec: 88.7452 -2022-05-01 17:06:19,427 - tensorflow - INFO - loss = 0.17102456, step = 868200 (2.254 sec) -2022-05-01 17:06:21,679 - tensorflow - INFO - global_step/sec: 88.7789 -2022-05-01 17:06:21,680 - tensorflow - INFO - loss = 0.1309104, step = 868400 (2.253 sec) -2022-05-01 17:06:23,942 - tensorflow - INFO - global_step/sec: 88.4089 -2022-05-01 17:06:23,942 - tensorflow - INFO - loss = 0.011843708, step = 868600 (2.262 sec) -2022-05-01 17:06:26,201 - tensorflow - INFO - global_step/sec: 88.5083 -2022-05-01 17:06:26,202 - tensorflow - INFO - loss = 0.076262355, step = 868800 (2.260 sec) -2022-05-01 17:06:28,459 - tensorflow - INFO - global_step/sec: 88.6012 -2022-05-01 17:06:28,459 - tensorflow - INFO - loss = 0.052073367, step = 869000 (2.257 sec) -2022-05-01 17:06:30,712 - tensorflow - INFO - global_step/sec: 88.7546 -2022-05-01 17:06:30,712 - tensorflow - INFO - loss = 0.036755126, step = 869200 (2.253 sec) -2022-05-01 17:06:32,968 - tensorflow - INFO - global_step/sec: 88.6492 -2022-05-01 17:06:32,968 - tensorflow - INFO - loss = 0.011701852, step = 869400 (2.256 sec) -2022-05-01 17:06:35,229 - tensorflow - INFO - global_step/sec: 88.4539 -2022-05-01 17:06:35,230 - tensorflow - INFO - loss = 0.022537433, step = 869600 (2.261 sec) -2022-05-01 17:06:37,494 - tensorflow - INFO - global_step/sec: 88.2918 -2022-05-01 17:06:37,495 - tensorflow - INFO - loss = 0.094644085, step = 869800 (2.265 sec) -2022-05-01 17:06:39,757 - tensorflow - INFO - global_step/sec: 88.4093 -2022-05-01 17:06:39,757 - tensorflow - INFO - loss = 0.2601124, step = 870000 (2.262 sec) -2022-05-01 17:06:42,014 - tensorflow - INFO - global_step/sec: 88.5748 -2022-05-01 17:06:42,015 - tensorflow - INFO - loss = 0.01299309, step = 870200 (2.258 sec) -2022-05-01 17:06:44,277 - tensorflow - INFO - global_step/sec: 88.3834 -2022-05-01 17:06:44,278 - tensorflow - INFO - loss = 0.01612484, step = 870400 (2.263 sec) -2022-05-01 17:06:46,539 - tensorflow - INFO - global_step/sec: 88.4181 -2022-05-01 17:06:46,540 - tensorflow - INFO - loss = 21.621862, step = 870600 (2.262 sec) -2022-05-01 17:06:48,801 - tensorflow - INFO - global_step/sec: 88.4474 -2022-05-01 17:06:48,801 - tensorflow - INFO - loss = 0.019373389, step = 870800 (2.261 sec) -2022-05-01 17:06:51,067 - tensorflow - INFO - global_step/sec: 88.2587 -2022-05-01 17:06:51,067 - tensorflow - INFO - loss = 0.010516878, step = 871000 (2.266 sec) -2022-05-01 17:06:53,330 - tensorflow - INFO - global_step/sec: 88.3772 -2022-05-01 17:06:53,330 - tensorflow - INFO - loss = 0.10557799, step = 871200 (2.263 sec) -2022-05-01 17:06:55,596 - tensorflow - INFO - global_step/sec: 88.2539 -2022-05-01 17:06:55,596 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.9656 -2022-05-01 17:07:11,413 - tensorflow - INFO - loss = 9.742434, step = 872800 (2.248 sec) -2022-05-01 17:07:13,663 - tensorflow - INFO - global_step/sec: 88.8998 -2022-05-01 17:07:13,663 - tensorflow - INFO - loss = 0.03104175, step = 873000 (2.250 sec) -2022-05-01 17:07:15,916 - tensorflow - INFO - global_step/sec: 88.7461 -2022-05-01 17:07:15,917 - tensorflow - INFO - loss = 0.021317285, step = 873200 (2.254 sec) -2022-05-01 17:07:18,171 - tensorflow - INFO - global_step/sec: 88.7069 -2022-05-01 17:07:18,171 - tensorflow - INFO - loss = 0.0664188, step = 873400 (2.255 sec) -2022-05-01 17:07:20,424 - tensorflow - INFO - global_step/sec: 88.7515 -2022-05-01 17:07:20,425 - tensorflow - INFO - loss = 0.83080256, step = 873600 (2.254 sec) -2022-05-01 17:07:22,676 - tensorflow - INFO - global_step/sec: 88.8352 -2022-05-01 17:07:22,676 - tensorflow - INFO - loss = 0.12817049, step = 873800 (2.251 sec) -2022-05-01 17:07:24,929 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.044056162, step = 875200 (2.253 sec) -2022-05-01 17:07:40,712 - tensorflow - INFO - global_step/sec: 88.8565 -2022-05-01 17:07:40,712 - tensorflow - INFO - loss = 0.021596733, step = 875400 (2.251 sec) -2022-05-01 17:07:42,968 - tensorflow - INFO - global_step/sec: 88.646 -2022-05-01 17:07:42,969 - tensorflow - INFO - loss = 0.06474429, step = 875600 (2.256 sec) -2022-05-01 17:07:45,224 - tensorflow - INFO - global_step/sec: 88.6436 -2022-05-01 17:07:45,225 - tensorflow - INFO - loss = 0.038283028, step = 875800 (2.256 sec) -2022-05-01 17:07:47,475 - tensorflow - INFO - global_step/sec: 88.8624 -2022-05-01 17:07:47,475 - tensorflow - INFO - loss = 0.03411206, step = 876000 (2.251 sec) -2022-05-01 17:07:49,727 - tensorflow - INFO - global_step/sec: 88.8071 -2022-05-01 17:07:49,728 - tensorflow - INFO - loss = 0.04376263, step = 876200 (2.252 sec) -2022-05-01 17:07:51,985 - tensorflow - INFO - global_step/sec: 88.5961 -2022-05-01 17:07:51,985 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.4732 -2022-05-01 17:08:07,773 - tensorflow - INFO - loss = 0.024417002, step = 877800 (2.261 sec) -2022-05-01 17:08:10,029 - tensorflow - INFO - global_step/sec: 88.6399 -2022-05-01 17:08:10,029 - tensorflow - INFO - loss = 0.016935084, step = 878000 (2.256 sec) -2022-05-01 17:08:12,281 - tensorflow - INFO - global_step/sec: 88.8034 -2022-05-01 17:08:12,281 - tensorflow - INFO - loss = 0.78828967, step = 878200 (2.252 sec) -2022-05-01 17:08:14,535 - tensorflow - INFO - global_step/sec: 88.7308 -2022-05-01 17:08:14,535 - tensorflow - INFO - loss = 0.037419386, step = 878400 (2.254 sec) -2022-05-01 17:08:16,787 - tensorflow - INFO - global_step/sec: 88.7924 -2022-05-01 17:08:16,788 - tensorflow - INFO - loss = 0.021398695, step = 878600 (2.252 sec) -2022-05-01 17:08:19,038 - tensorflow - INFO - global_step/sec: 88.8631 -2022-05-01 17:08:19,038 - tensorflow - INFO - loss = 0.021772686, step = 878800 (2.251 sec) -2022-05-01 17:08:21,294 - tensorflow - INFO - global_step/sec: 88.646 -2022-05-01 17:08:21,295 - tensorflow - INFO - loss = 0.12339576, step = 879000 (2.256 sec) -2022-05-01 17:08:23,549 - tensorflow - INFO - global_step/sec: 88.6844 -2022-05-01 17:08:23,550 - tensorflow - INFO - loss = 0.11540138, step = 879200 (2.255 sec) -2022-05-01 17:08:25,802 - tensorflow - INFO - global_step/sec: 88.8013 -2022-05-01 17:08:25,802 - tensorflow - INFO - loss = 0.04531049, step = 879400 (2.252 sec) -2022-05-01 17:08:28,054 - tensorflow - INFO - global_step/sec: 88.7858 -2022-05-01 17:08:28,055 - tensorflow - INFO - loss = 0.03590607, step = 879600 (2.253 sec) -2022-05-01 17:08:30,311 - tensorflow - INFO - global_step/sec: 88.6385 -2022-05-01 17:08:30,311 - tensorflow - INFO - loss = 0.015862115, step = 879800 (2.256 sec) -2022-05-01 17:08:32,572 - tensorflow - INFO - global_step/sec: 88.4284 -2022-05-01 17:08:32,573 - tensorflow - INFO - loss = 0.03887237, step = 880000 (2.262 sec) -2022-05-01 17:08:34,823 - tensorflow - INFO - global_step/sec: 88.8708 -2022-05-01 17:08:34,823 - 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INFO - loss = 0.0777564, step = 881400 (2.256 sec) -2022-05-01 17:08:50,609 - tensorflow - INFO - global_step/sec: 88.8476 -2022-05-01 17:08:50,610 - tensorflow - INFO - loss = 0.038037412, step = 881600 (2.251 sec) -2022-05-01 17:08:52,863 - tensorflow - INFO - global_step/sec: 88.7315 -2022-05-01 17:08:52,864 - tensorflow - INFO - loss = 0.08663983, step = 881800 (2.254 sec) -2022-05-01 17:08:55,118 - tensorflow - INFO - global_step/sec: 88.6907 -2022-05-01 17:08:55,119 - tensorflow - INFO - loss = 0.04738753, step = 882000 (2.255 sec) -2022-05-01 17:08:57,378 - tensorflow - INFO - global_step/sec: 88.5168 -2022-05-01 17:08:57,378 - tensorflow - INFO - loss = 0.034706645, step = 882200 (2.259 sec) -2022-05-01 17:08:59,632 - tensorflow - INFO - global_step/sec: 88.7143 -2022-05-01 17:08:59,633 - tensorflow - INFO - loss = 0.08204596, step = 882400 (2.254 sec) -2022-05-01 17:09:01,886 - tensorflow - INFO - global_step/sec: 88.7198 -2022-05-01 17:09:01,887 - tensorflow - INFO - loss = 0.017074298, step = 882600 (2.254 sec) -2022-05-01 17:09:04,147 - tensorflow - INFO - global_step/sec: 88.4616 -2022-05-01 17:09:04,148 - tensorflow - INFO - loss = 0.020894779, step = 882800 (2.261 sec) -2022-05-01 17:09:06,403 - tensorflow - INFO - global_step/sec: 88.6612 -2022-05-01 17:09:06,403 - tensorflow - INFO - loss = 0.25742978, step = 883000 (2.256 sec) -2022-05-01 17:09:08,660 - tensorflow - INFO - global_step/sec: 88.6066 -2022-05-01 17:09:08,661 - tensorflow - INFO - loss = 0.33435446, step = 883200 (2.257 sec) -2022-05-01 17:09:10,916 - tensorflow - INFO - global_step/sec: 88.6616 -2022-05-01 17:09:10,916 - tensorflow - INFO - loss = 0.030990891, step = 883400 (2.256 sec) -2022-05-01 17:09:13,168 - tensorflow - INFO - global_step/sec: 88.8028 -2022-05-01 17:09:13,169 - tensorflow - INFO - loss = 0.09165773, step = 883600 (2.252 sec) -2022-05-01 17:09:15,425 - tensorflow - INFO - global_step/sec: 88.6206 -2022-05-01 17:09:15,425 - tensorflow - INFO - loss = 0.043104094, step = 883800 (2.257 sec) -2022-05-01 17:09:17,684 - tensorflow - INFO - global_step/sec: 88.5475 -2022-05-01 17:09:17,684 - tensorflow - INFO - loss = 0.04267798, step = 884000 (2.259 sec) -2022-05-01 17:09:19,937 - tensorflow - INFO - global_step/sec: 88.7699 -2022-05-01 17:09:19,937 - tensorflow - INFO - loss = 0.011365008, step = 884200 (2.253 sec) -2022-05-01 17:09:22,191 - tensorflow - INFO - global_step/sec: 88.7002 -2022-05-01 17:09:22,192 - tensorflow - INFO - loss = 0.017686948, step = 884400 (2.255 sec) -2022-05-01 17:09:24,447 - tensorflow - INFO - global_step/sec: 88.6606 -2022-05-01 17:09:24,448 - tensorflow - INFO - loss = 0.01443425, step = 884600 (2.256 sec) -2022-05-01 17:09:26,701 - tensorflow - INFO - global_step/sec: 88.7299 -2022-05-01 17:09:26,702 - tensorflow - INFO - loss = 0.028009282, step = 884800 (2.254 sec) -2022-05-01 17:09:28,954 - tensorflow - INFO - global_step/sec: 88.7849 -2022-05-01 17:09:28,954 - tensorflow - INFO - loss = 0.03606085, step = 885000 (2.253 sec) -2022-05-01 17:09:31,215 - tensorflow - INFO - global_step/sec: 88.4532 -2022-05-01 17:09:31,215 - tensorflow - INFO - loss = 0.05631309, step = 885200 (2.261 sec) -2022-05-01 17:09:33,467 - tensorflow - INFO - global_step/sec: 88.8258 -2022-05-01 17:09:33,467 - tensorflow - INFO - loss = 0.018005019, step = 885400 (2.252 sec) -2022-05-01 17:09:35,726 - tensorflow - INFO - global_step/sec: 88.5009 -2022-05-01 17:09:35,727 - tensorflow - INFO - loss = 0.038840145, step = 885600 (2.260 sec) -2022-05-01 17:09:37,977 - tensorflow - INFO - global_step/sec: 88.8839 -2022-05-01 17:09:37,977 - tensorflow - INFO - loss = 0.013953901, step = 885800 (2.250 sec) -2022-05-01 17:09:40,231 - tensorflow - INFO - global_step/sec: 88.7118 -2022-05-01 17:09:40,231 - tensorflow - INFO - loss = 0.023950927, step = 886000 (2.255 sec) -2022-05-01 17:09:42,485 - tensorflow - INFO - global_step/sec: 88.7519 -2022-05-01 17:09:42,485 - tensorflow - INFO - loss = 0.022658564, step = 886200 (2.253 sec) -2022-05-01 17:09:44,738 - tensorflow - INFO - global_step/sec: 88.7628 -2022-05-01 17:09:44,738 - tensorflow - INFO - loss = 0.02205094, step = 886400 (2.253 sec) -2022-05-01 17:09:46,996 - tensorflow - INFO - global_step/sec: 88.5699 -2022-05-01 17:09:46,996 - tensorflow - INFO - loss = 0.012074009, step = 886600 (2.258 sec) -2022-05-01 17:09:49,256 - tensorflow - INFO - global_step/sec: 88.4804 -2022-05-01 17:09:49,257 - tensorflow - INFO - loss = 0.05748287, step = 886800 (2.260 sec) -2022-05-01 17:09:51,510 - tensorflow - INFO - global_step/sec: 88.7343 -2022-05-01 17:09:51,511 - tensorflow - INFO - loss = 0.0186936, step = 887000 (2.254 sec) -2022-05-01 17:09:53,766 - tensorflow - INFO - global_step/sec: 88.6744 -2022-05-01 17:09:53,766 - tensorflow - INFO - loss = 0.06944361, step = 887200 (2.255 sec) -2022-05-01 17:09:56,023 - tensorflow - INFO - global_step/sec: 88.6072 -2022-05-01 17:09:56,023 - tensorflow - INFO - loss = 0.031998947, step = 887400 (2.257 sec) -2022-05-01 17:09:58,275 - tensorflow - INFO - global_step/sec: 88.7897 -2022-05-01 17:09:58,276 - tensorflow - INFO - loss = 0.032064967, step = 887600 (2.253 sec) -2022-05-01 17:10:00,532 - tensorflow - INFO - global_step/sec: 88.6205 -2022-05-01 17:10:00,532 - tensorflow - INFO - loss = 0.05730361, step = 887800 (2.257 sec) -2022-05-01 17:10:02,792 - tensorflow - INFO - global_step/sec: 88.5096 -2022-05-01 17:10:02,792 - tensorflow - INFO - loss = 0.11221716, step = 888000 (2.260 sec) -2022-05-01 17:10:05,051 - tensorflow - INFO - global_step/sec: 88.5407 -2022-05-01 17:10:05,051 - tensorflow - INFO - loss = 0.010961418, step = 888200 (2.259 sec) -2022-05-01 17:10:07,310 - tensorflow - INFO - global_step/sec: 88.5327 -2022-05-01 17:10:07,310 - tensorflow - INFO - loss = 0.019681413, step = 888400 (2.259 sec) -2022-05-01 17:10:09,572 - tensorflow - INFO - global_step/sec: 88.4162 -2022-05-01 17:10:09,572 - tensorflow - INFO - loss = 0.023460643, step = 888600 (2.262 sec) -2022-05-01 17:10:11,833 - tensorflow - INFO - global_step/sec: 88.4609 -2022-05-01 17:10:11,833 - tensorflow - INFO - loss = 0.024240648, step = 888800 (2.261 sec) -2022-05-01 17:10:14,097 - tensorflow - INFO - global_step/sec: 88.3333 -2022-05-01 17:10:14,097 - tensorflow - INFO - loss = 0.05807086, step = 889000 (2.264 sec) -2022-05-01 17:10:16,359 - tensorflow - INFO - global_step/sec: 88.4001 -2022-05-01 17:10:16,360 - tensorflow - INFO - loss = 2.2288222, step = 889200 (2.262 sec) -2022-05-01 17:10:18,621 - tensorflow - INFO - global_step/sec: 88.4196 -2022-05-01 17:10:18,622 - tensorflow - INFO - loss = 0.01446765, step = 889400 (2.262 sec) -2022-05-01 17:10:20,886 - tensorflow - INFO - global_step/sec: 88.3086 -2022-05-01 17:10:20,886 - tensorflow - INFO - loss = 0.08652735, step = 889600 (2.265 sec) -2022-05-01 17:10:23,147 - tensorflow - INFO - global_step/sec: 88.451 -2022-05-01 17:10:23,147 - tensorflow - INFO - loss = 0.034366205, step = 889800 (2.261 sec) -2022-05-01 17:10:25,409 - tensorflow - INFO - global_step/sec: 88.4101 -2022-05-01 17:10:25,410 - tensorflow - INFO - loss = 0.025828442, step = 890000 (2.262 sec) -2022-05-01 17:10:27,672 - tensorflow - INFO - global_step/sec: 88.3771 -2022-05-01 17:10:27,673 - tensorflow - INFO - loss = 0.035792287, step = 890200 (2.263 sec) -2022-05-01 17:10:29,933 - tensorflow - INFO - global_step/sec: 88.4806 -2022-05-01 17:10:29,933 - tensorflow - INFO - loss = 0.07184704, step = 890400 (2.260 sec) -2022-05-01 17:10:32,204 - tensorflow - INFO - global_step/sec: 88.0624 -2022-05-01 17:10:32,204 - tensorflow - INFO - loss = 0.020343488, step = 890600 (2.271 sec) -2022-05-01 17:10:34,462 - tensorflow - INFO - global_step/sec: 88.5795 -2022-05-01 17:10:34,462 - tensorflow - INFO - loss = 0.14220776, step = 890800 (2.258 sec) -2022-05-01 17:10:36,715 - tensorflow - INFO - global_step/sec: 88.7423 -2022-05-01 17:10:36,716 - tensorflow - INFO - loss = 0.031225761, step = 891000 (2.254 sec) -2022-05-01 17:10:38,968 - tensorflow - INFO - global_step/sec: 88.7761 -2022-05-01 17:10:38,969 - tensorflow - INFO - loss = 0.06711307, step = 891200 (2.253 sec) -2022-05-01 17:10:41,229 - tensorflow - INFO - global_step/sec: 88.4802 -2022-05-01 17:10:41,229 - tensorflow - INFO - loss = 0.052210785, step = 891400 (2.260 sec) -2022-05-01 17:10:43,482 - tensorflow - INFO - global_step/sec: 88.7556 -2022-05-01 17:10:43,482 - tensorflow - INFO - loss = 0.017388288, step = 891600 (2.253 sec) -2022-05-01 17:10:45,740 - tensorflow - INFO - global_step/sec: 88.5706 -2022-05-01 17:10:45,740 - tensorflow - INFO - loss = 0.028012885, step = 891800 (2.258 sec) -2022-05-01 17:10:47,995 - tensorflow - INFO - global_step/sec: 88.708 -2022-05-01 17:10:47,995 - tensorflow - INFO - loss = 0.03236787, step = 892000 (2.255 sec) -2022-05-01 17:10:50,248 - tensorflow - INFO - global_step/sec: 88.7693 -2022-05-01 17:10:50,248 - tensorflow - INFO - loss = 0.07721909, step = 892200 (2.253 sec) -2022-05-01 17:10:52,506 - tensorflow - INFO - global_step/sec: 88.5659 -2022-05-01 17:10:52,506 - tensorflow - INFO - loss = 0.02358967, step = 892400 (2.258 sec) -2022-05-01 17:10:54,761 - tensorflow - INFO - global_step/sec: 88.6786 -2022-05-01 17:10:54,762 - tensorflow - INFO - loss = 0.016704123, step = 892600 (2.255 sec) -2022-05-01 17:10:57,017 - tensorflow - INFO - global_step/sec: 88.6653 -2022-05-01 17:10:57,017 - tensorflow - INFO - loss = 0.02639231, step = 892800 (2.256 sec) -2022-05-01 17:10:59,272 - tensorflow - INFO - global_step/sec: 88.7048 -2022-05-01 17:10:59,272 - tensorflow - INFO - loss = 0.58438075, step = 893000 (2.255 sec) -2022-05-01 17:11:01,525 - tensorflow - INFO - global_step/sec: 88.7504 -2022-05-01 17:11:01,525 - tensorflow - INFO - loss = 0.062124535, step = 893200 (2.254 sec) -2022-05-01 17:11:03,785 - tensorflow - INFO - global_step/sec: 88.5042 -2022-05-01 17:11:03,785 - tensorflow - INFO - loss = 0.120855875, step = 893400 (2.260 sec) -2022-05-01 17:11:06,043 - tensorflow - INFO - global_step/sec: 88.5804 -2022-05-01 17:11:06,043 - tensorflow - INFO - loss = 0.037611075, step = 893600 (2.258 sec) -2022-05-01 17:11:08,298 - tensorflow - INFO - global_step/sec: 88.6667 -2022-05-01 17:11:08,299 - tensorflow - INFO - loss = 0.025271785, step = 893800 (2.256 sec) -2022-05-01 17:11:10,548 - tensorflow - INFO - global_step/sec: 88.8843 -2022-05-01 17:11:10,549 - tensorflow - INFO - loss = 0.052211516, step = 894000 (2.250 sec) -2022-05-01 17:11:12,804 - tensorflow - INFO - global_step/sec: 88.6806 -2022-05-01 17:11:12,804 - tensorflow - INFO - loss = 0.023117388, step = 894200 (2.255 sec) -2022-05-01 17:11:15,063 - tensorflow - INFO - global_step/sec: 88.5046 -2022-05-01 17:11:15,064 - tensorflow - INFO - loss = 0.0851819, step = 894400 (2.260 sec) -2022-05-01 17:11:17,316 - tensorflow - INFO - global_step/sec: 88.7882 -2022-05-01 17:11:17,316 - tensorflow - INFO - loss = 0.03454585, step = 894600 (2.253 sec) -2022-05-01 17:11:19,576 - tensorflow - INFO - global_step/sec: 88.4988 -2022-05-01 17:11:19,576 - tensorflow - INFO - loss = 0.029106721, step = 894800 (2.260 sec) -2022-05-01 17:11:21,830 - tensorflow - INFO - global_step/sec: 88.7318 -2022-05-01 17:11:21,830 - tensorflow - INFO - loss = 0.02112896, step = 895000 (2.254 sec) -2022-05-01 17:11:24,089 - tensorflow - INFO - global_step/sec: 88.5157 -2022-05-01 17:11:24,090 - tensorflow - INFO - loss = 0.031981267, step = 895200 (2.260 sec) -2022-05-01 17:11:26,349 - tensorflow - INFO - global_step/sec: 88.495 -2022-05-01 17:11:26,350 - tensorflow - INFO - loss = 0.10848446, step = 895400 (2.260 sec) -2022-05-01 17:11:28,608 - tensorflow - INFO - global_step/sec: 88.5451 -2022-05-01 17:11:28,609 - tensorflow - INFO - loss = 0.48689383, step = 895600 (2.259 sec) -2022-05-01 17:11:30,874 - tensorflow - INFO - global_step/sec: 88.2549 -2022-05-01 17:11:30,875 - tensorflow - INFO - loss = 0.0074283807, step = 895800 (2.266 sec) -2022-05-01 17:11:33,139 - tensorflow - INFO - global_step/sec: 88.3263 -2022-05-01 17:11:33,139 - tensorflow - INFO - loss = 0.26092327, step = 896000 (2.264 sec) -2022-05-01 17:11:35,401 - tensorflow - INFO - global_step/sec: 88.4119 -2022-05-01 17:11:35,401 - tensorflow - INFO - loss = 0.026314523, step = 896200 (2.262 sec) -2022-05-01 17:11:37,650 - tensorflow - INFO - global_step/sec: 88.9025 -2022-05-01 17:11:37,651 - tensorflow - INFO - loss = 2.9744234, step = 896400 (2.250 sec) -2022-05-01 17:11:39,908 - tensorflow - INFO - global_step/sec: 88.6083 -2022-05-01 17:11:39,908 - tensorflow - INFO - loss = 0.064552605, step = 896600 (2.257 sec) -2022-05-01 17:11:42,161 - tensorflow - INFO - global_step/sec: 88.7661 -2022-05-01 17:11:42,161 - tensorflow - INFO - loss = 0.012820228, step = 896800 (2.253 sec) -2022-05-01 17:11:44,418 - tensorflow - INFO - global_step/sec: 88.6105 -2022-05-01 17:11:44,418 - tensorflow - INFO - loss = 0.19172603, step = 897000 (2.257 sec) -2022-05-01 17:11:46,673 - tensorflow - INFO - global_step/sec: 88.6802 -2022-05-01 17:11:46,674 - tensorflow - INFO - loss = 0.36441582, step = 897200 (2.255 sec) -2022-05-01 17:11:48,928 - tensorflow - INFO - global_step/sec: 88.6997 -2022-05-01 17:11:48,928 - tensorflow - INFO - loss = 0.22654104, step = 897400 (2.255 sec) -2022-05-01 17:11:51,184 - tensorflow - INFO - global_step/sec: 88.656 -2022-05-01 17:11:51,184 - tensorflow - INFO - loss = 0.014491557, step = 897600 (2.256 sec) -2022-05-01 17:11:53,435 - tensorflow - INFO - global_step/sec: 88.8386 -2022-05-01 17:11:53,435 - tensorflow - INFO - loss = 0.015789127, step = 897800 (2.251 sec) -2022-05-01 17:11:55,699 - tensorflow - INFO - global_step/sec: 88.3486 -2022-05-01 17:11:55,699 - tensorflow - INFO - loss = 0.028927404, step = 898000 (2.264 sec) -2022-05-01 17:11:57,953 - tensorflow - INFO - global_step/sec: 88.7079 -2022-05-01 17:11:57,954 - tensorflow - INFO - loss = 0.3525368, step = 898200 (2.255 sec) -2022-05-01 17:12:00,206 - tensorflow - INFO - global_step/sec: 88.8036 -2022-05-01 17:12:00,206 - tensorflow - INFO - loss = 0.094420716, step = 898400 (2.252 sec) -2022-05-01 17:12:02,463 - tensorflow - INFO - global_step/sec: 88.5853 -2022-05-01 17:12:02,464 - tensorflow - INFO - loss = 0.018449508, step = 898600 (2.258 sec) -2022-05-01 17:12:04,723 - tensorflow - INFO - global_step/sec: 88.5187 -2022-05-01 17:12:04,723 - tensorflow - INFO - loss = 0.027755884, step = 898800 (2.259 sec) -2022-05-01 17:12:06,977 - tensorflow - INFO - global_step/sec: 88.7219 -2022-05-01 17:12:06,977 - tensorflow - INFO - loss = 0.2651413, step = 899000 (2.254 sec) -2022-05-01 17:12:09,238 - tensorflow - INFO - global_step/sec: 88.4568 -2022-05-01 17:12:09,238 - tensorflow - INFO - loss = 0.113350786, step = 899200 (2.261 sec) -2022-05-01 17:12:11,490 - tensorflow - INFO - global_step/sec: 88.8042 -2022-05-01 17:12:11,490 - tensorflow - INFO - loss = 0.022238614, step = 899400 (2.252 sec) -2022-05-01 17:12:13,741 - tensorflow - INFO - global_step/sec: 88.8416 -2022-05-01 17:12:13,742 - tensorflow - INFO - loss = 0.06388148, step = 899600 (2.251 sec) -2022-05-01 17:12:15,993 - tensorflow - INFO - global_step/sec: 88.8187 -2022-05-01 17:12:15,993 - tensorflow - INFO - loss = 0.16148487, step = 899800 (2.252 sec) -2022-05-01 17:12:18,237 - tensorflow - INFO - Saving checkpoints for 900000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. -2022-05-01 17:12:18,255 - tensorflow - INFO - model.ckpt-900000 is not in all_model_checkpoint_paths. Manually adding it. -2022-05-01 17:12:18,789 - tensorflow - INFO - Calling model_fn. -2022-05-01 17:12:18,919 - tensorflow - INFO - Done calling model_fn. -2022-05-01 17:12:18,934 - tensorflow - INFO - Starting evaluation at 2022-05-01T17:12:18Z -2022-05-01 17:12:18,999 - tensorflow - INFO - Graph was finalized. -2022-05-01 17:12:19,002 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-900000 -2022-05-01 17:12:19,283 - tensorflow - INFO - Running local_init_op. -2022-05-01 17:12:19,392 - tensorflow - INFO - Done running local_init_op. -2022-05-01 17:12:29,492 - tensorflow - INFO - Evaluation [3990/39900] -2022-05-01 17:12:38,917 - tensorflow - INFO - Evaluation [7980/39900] -2022-05-01 17:12:48,325 - tensorflow - INFO - Evaluation [11970/39900] -2022-05-01 17:12:57,723 - tensorflow - INFO - Evaluation [15960/39900] -2022-05-01 17:13:07,119 - tensorflow - INFO - Evaluation [19950/39900] -2022-05-01 17:13:16,541 - tensorflow - INFO - Evaluation [23940/39900] -2022-05-01 17:13:25,933 - tensorflow - INFO - Evaluation [27930/39900] -2022-05-01 17:13:35,327 - tensorflow - INFO - Evaluation [31920/39900] -2022-05-01 17:13:44,745 - tensorflow - INFO - Evaluation [35910/39900] -2022-05-01 17:13:54,197 - tensorflow - INFO - Evaluation [39900/39900] -2022-05-01 17:13:54,334 - tensorflow - INFO - Finished evaluation at 2022-05-01-17:13:54 -2022-05-01 17:13:54,334 - tensorflow - INFO - Saving dict for global step 900000: global_step = 900000, loss = 0.035565972, mean_degree_err = 6.131773 -2022-05-01 17:13:54,336 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 900000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-900000 -2022-05-01 17:13:54,350 - tensorflow - INFO - global_step/sec: 2.03342 -2022-05-01 17:13:54,350 - tensorflow - INFO - loss = 0.013065057, step = 900000 (98.357 sec) -2022-05-01 17:13:56,636 - tensorflow - INFO - global_step/sec: 87.487 -2022-05-01 17:13:56,636 - tensorflow - INFO - loss = 0.012146402, step = 900200 (2.286 sec) -2022-05-01 17:13:58,898 - tensorflow - INFO - global_step/sec: 88.4111 -2022-05-01 17:13:58,898 - tensorflow - INFO - loss = 0.016029745, step = 900400 (2.262 sec) -2022-05-01 17:14:01,161 - tensorflow - INFO - global_step/sec: 88.3742 -2022-05-01 17:14:01,161 - tensorflow - INFO - loss = 0.031824853, step = 900600 (2.263 sec) -2022-05-01 17:14:03,421 - tensorflow - INFO - global_step/sec: 88.4699 -2022-05-01 17:14:03,422 - tensorflow - INFO - loss = 0.018616183, step = 900800 (2.261 sec) -2022-05-01 17:14:05,686 - tensorflow - INFO - global_step/sec: 88.3096 -2022-05-01 17:14:05,687 - tensorflow - INFO - loss = 0.027795551, step = 901000 (2.265 sec) -2022-05-01 17:14:07,942 - tensorflow - INFO - global_step/sec: 88.6682 -2022-05-01 17:14:07,942 - tensorflow - INFO - loss = 0.014406723, step = 901200 (2.256 sec) -2022-05-01 17:14:10,203 - tensorflow - INFO - global_step/sec: 88.4331 -2022-05-01 17:14:10,204 - tensorflow - INFO - loss = 0.017534908, step = 901400 (2.262 sec) -2022-05-01 17:14:12,455 - tensorflow - INFO - global_step/sec: 88.8078 -2022-05-01 17:14:12,456 - tensorflow - INFO - loss = 0.58057, step = 901600 (2.252 sec) -2022-05-01 17:14:14,714 - tensorflow - INFO - global_step/sec: 88.5464 -2022-05-01 17:14:14,715 - tensorflow - INFO - loss = 0.038886838, step = 901800 (2.259 sec) -2022-05-01 17:14:16,967 - tensorflow - INFO - global_step/sec: 88.7601 -2022-05-01 17:14:16,968 - tensorflow - INFO - loss = 0.14230952, step = 902000 (2.253 sec) -2022-05-01 17:14:19,222 - tensorflow - INFO - global_step/sec: 88.6934 -2022-05-01 17:14:19,223 - tensorflow - INFO - loss = 0.11496422, step = 902200 (2.255 sec) -2022-05-01 17:14:21,479 - tensorflow - INFO - global_step/sec: 88.6156 -2022-05-01 17:14:21,480 - tensorflow - INFO - loss = 0.027795611, step = 902400 (2.257 sec) -2022-05-01 17:14:23,735 - tensorflow - INFO - global_step/sec: 88.6611 -2022-05-01 17:14:23,736 - tensorflow - INFO - loss = 0.27380404, step = 902600 (2.256 sec) -2022-05-01 17:14:25,991 - tensorflow - INFO - global_step/sec: 88.6636 -2022-05-01 17:14:25,991 - tensorflow - INFO - loss = 0.018897396, step = 902800 (2.256 sec) -2022-05-01 17:14:28,242 - tensorflow - INFO - global_step/sec: 88.8552 -2022-05-01 17:14:28,242 - tensorflow - INFO - loss = 0.055229053, step = 903000 (2.251 sec) -2022-05-01 17:14:30,506 - tensorflow - INFO - global_step/sec: 88.3257 -2022-05-01 17:14:30,507 - tensorflow - INFO - loss = 0.03179595, step = 903200 (2.264 sec) -2022-05-01 17:14:32,758 - tensorflow - INFO - global_step/sec: 88.796 -2022-05-01 17:14:32,759 - tensorflow - INFO - loss = 0.036151156, step = 903400 (2.252 sec) -2022-05-01 17:14:35,012 - tensorflow - INFO - global_step/sec: 88.7354 -2022-05-01 17:14:35,013 - tensorflow - INFO - loss = 0.06902796, step = 903600 (2.254 sec) -2022-05-01 17:14:37,265 - tensorflow - INFO - global_step/sec: 88.7679 -2022-05-01 17:14:37,266 - tensorflow - INFO - loss = 0.04816742, step = 903800 (2.253 sec) -2022-05-01 17:14:39,520 - tensorflow - INFO - global_step/sec: 88.722 -2022-05-01 17:14:39,520 - tensorflow - INFO - loss = 0.020497266, step = 904000 (2.254 sec) -2022-05-01 17:14:41,776 - tensorflow - INFO - global_step/sec: 88.637 -2022-05-01 17:14:41,776 - tensorflow - INFO - loss = 0.57189643, step = 904200 (2.256 sec) -2022-05-01 17:14:44,029 - tensorflow - INFO - global_step/sec: 88.756 -2022-05-01 17:14:44,030 - tensorflow - INFO - loss = 0.0389892, step = 904400 (2.253 sec) -2022-05-01 17:14:46,287 - tensorflow - INFO - global_step/sec: 88.5882 -2022-05-01 17:14:46,287 - tensorflow - INFO - loss = 0.11031467, step = 904600 (2.258 sec) -2022-05-01 17:14:48,541 - tensorflow - INFO - global_step/sec: 88.7401 -2022-05-01 17:14:48,541 - tensorflow - INFO - loss = 0.07635092, step = 904800 (2.254 sec) -2022-05-01 17:14:50,792 - tensorflow - INFO - global_step/sec: 88.8347 -2022-05-01 17:14:50,793 - tensorflow - INFO - loss = 0.015127299, step = 905000 (2.251 sec) -2022-05-01 17:14:53,051 - tensorflow - INFO - global_step/sec: 88.5252 -2022-05-01 17:14:53,052 - tensorflow - INFO - loss = 0.21705827, step = 905200 (2.259 sec) -2022-05-01 17:14:55,312 - tensorflow - INFO - global_step/sec: 88.469 -2022-05-01 17:14:55,313 - tensorflow - INFO - loss = 0.3189776, step = 905400 (2.261 sec) -2022-05-01 17:14:57,565 - tensorflow - INFO - global_step/sec: 88.7576 -2022-05-01 17:14:57,566 - tensorflow - INFO - loss = 0.010876756, step = 905600 (2.253 sec) -2022-05-01 17:14:59,816 - tensorflow - INFO - global_step/sec: 88.8569 -2022-05-01 17:14:59,817 - tensorflow - INFO - loss = 0.5480637, step = 905800 (2.251 sec) -2022-05-01 17:15:02,075 - tensorflow - INFO - global_step/sec: 88.5619 -2022-05-01 17:15:02,075 - tensorflow - INFO - loss = 0.028712355, step = 906000 (2.258 sec) -2022-05-01 17:15:04,331 - tensorflow - INFO - global_step/sec: 88.6158 -2022-05-01 17:15:04,332 - tensorflow - INFO - loss = 0.03353673, step = 906200 (2.257 sec) -2022-05-01 17:15:06,589 - tensorflow - INFO - global_step/sec: 88.6025 -2022-05-01 17:15:06,589 - tensorflow - INFO - loss = 0.048440434, step = 906400 (2.257 sec) -2022-05-01 17:15:08,848 - tensorflow - INFO - global_step/sec: 88.5289 -2022-05-01 17:15:08,848 - tensorflow - INFO - loss = 0.11641862, step = 906600 (2.259 sec) -2022-05-01 17:15:11,106 - tensorflow - INFO - global_step/sec: 88.582 -2022-05-01 17:15:11,106 - tensorflow - INFO - loss = 0.04855759, step = 906800 (2.258 sec) -2022-05-01 17:15:13,361 - tensorflow - INFO - global_step/sec: 88.6868 -2022-05-01 17:15:13,361 - tensorflow - INFO - loss = 0.020095387, step = 907000 (2.255 sec) -2022-05-01 17:15:15,617 - tensorflow - INFO - global_step/sec: 88.6428 -2022-05-01 17:15:15,617 - tensorflow - INFO - loss = 0.021196015, step = 907200 (2.256 sec) -2022-05-01 17:15:17,879 - tensorflow - INFO - global_step/sec: 88.4285 -2022-05-01 17:15:17,879 - tensorflow - INFO - loss = 0.031916, step = 907400 (2.262 sec) -2022-05-01 17:15:20,145 - tensorflow - INFO - global_step/sec: 88.2537 -2022-05-01 17:15:20,145 - tensorflow - INFO - loss = 0.056354966, step = 907600 (2.266 sec) -2022-05-01 17:15:22,402 - tensorflow - INFO - global_step/sec: 88.6152 -2022-05-01 17:15:22,402 - tensorflow - INFO - loss = 0.05101075, step = 907800 (2.257 sec) -2022-05-01 17:15:24,656 - tensorflow - INFO - global_step/sec: 88.7127 -2022-05-01 17:15:24,657 - tensorflow - INFO - loss = 0.014117379, step = 908000 (2.254 sec) -2022-05-01 17:15:26,917 - tensorflow - INFO - global_step/sec: 88.4876 -2022-05-01 17:15:26,917 - tensorflow - INFO - loss = 0.026169278, step = 908200 (2.260 sec) -2022-05-01 17:15:29,171 - tensorflow - INFO - global_step/sec: 88.7315 -2022-05-01 17:15:29,171 - tensorflow - INFO - loss = 0.4392848, step = 908400 (2.254 sec) -2022-05-01 17:15:31,436 - tensorflow - INFO - global_step/sec: 88.2919 -2022-05-01 17:15:31,436 - tensorflow - INFO - loss = 0.010062726, step = 908600 (2.265 sec) -2022-05-01 17:15:33,691 - tensorflow - INFO - global_step/sec: 88.6653 -2022-05-01 17:15:33,692 - tensorflow - INFO - loss = 0.24155402, step = 908800 (2.256 sec) -2022-05-01 17:15:35,942 - tensorflow - INFO - global_step/sec: 88.8539 -2022-05-01 17:15:35,943 - tensorflow - INFO - loss = 2.5512338, step = 909000 (2.251 sec) -2022-05-01 17:15:38,199 - tensorflow - INFO - global_step/sec: 88.617 -2022-05-01 17:15:38,200 - tensorflow - INFO - loss = 0.021701274, step = 909200 (2.257 sec) -2022-05-01 17:15:40,454 - tensorflow - INFO - global_step/sec: 88.7107 -2022-05-01 17:15:40,454 - tensorflow - INFO - loss = 0.04103797, step = 909400 (2.254 sec) -2022-05-01 17:15:42,707 - tensorflow - INFO - global_step/sec: 88.7639 -2022-05-01 17:15:42,707 - tensorflow - INFO - loss = 0.011487696, step = 909600 (2.253 sec) -2022-05-01 17:15:44,972 - tensorflow - INFO - global_step/sec: 88.2904 -2022-05-01 17:15:44,973 - tensorflow - INFO - loss = 0.017504783, step = 909800 (2.265 sec) -2022-05-01 17:15:47,229 - tensorflow - INFO - global_step/sec: 88.6267 -2022-05-01 17:15:47,229 - tensorflow - INFO - loss = 0.057098113, step = 910000 (2.257 sec) -2022-05-01 17:15:49,484 - tensorflow - INFO - global_step/sec: 88.7044 -2022-05-01 17:15:49,484 - tensorflow - INFO - loss = 0.020508293, step = 910200 (2.255 sec) -2022-05-01 17:15:51,735 - tensorflow - INFO - global_step/sec: 88.8193 -2022-05-01 17:15:51,736 - tensorflow - INFO - loss = 0.008839138, step = 910400 (2.252 sec) -2022-05-01 17:15:53,990 - tensorflow - INFO - global_step/sec: 88.6945 -2022-05-01 17:15:53,991 - tensorflow - INFO - loss = 0.028127495, step = 910600 (2.255 sec) -2022-05-01 17:15:56,254 - tensorflow - INFO - global_step/sec: 88.3461 -2022-05-01 17:15:56,255 - tensorflow - INFO - loss = 0.049872562, step = 910800 (2.264 sec) -2022-05-01 17:15:58,513 - tensorflow - INFO - global_step/sec: 88.5308 -2022-05-01 17:15:58,514 - tensorflow - INFO - loss = 0.03336969, step = 911000 (2.259 sec) -2022-05-01 17:16:00,769 - tensorflow - INFO - global_step/sec: 88.6562 -2022-05-01 17:16:00,769 - tensorflow - INFO - loss = 0.028285317, step = 911200 (2.256 sec) -2022-05-01 17:16:03,030 - tensorflow - INFO - global_step/sec: 88.4384 -2022-05-01 17:16:03,031 - tensorflow - INFO - loss = 0.12840421, step = 911400 (2.261 sec) -2022-05-01 17:16:05,290 - tensorflow - INFO - global_step/sec: 88.5284 -2022-05-01 17:16:05,290 - tensorflow - INFO - loss = 0.016192503, step = 911600 (2.259 sec) -2022-05-01 17:16:07,550 - tensorflow - INFO - global_step/sec: 88.4985 -2022-05-01 17:16:07,550 - tensorflow - INFO - loss = 0.013111943, step = 911800 (2.260 sec) -2022-05-01 17:16:09,809 - tensorflow - INFO - global_step/sec: 88.506 -2022-05-01 17:16:09,810 - tensorflow - INFO - loss = 0.04875886, step = 912000 (2.260 sec) -2022-05-01 17:16:12,064 - tensorflow - INFO - global_step/sec: 88.7177 -2022-05-01 17:16:12,064 - tensorflow - INFO - loss = 0.0111401025, step = 912200 (2.254 sec) -2022-05-01 17:16:14,315 - tensorflow - INFO - global_step/sec: 88.8319 -2022-05-01 17:16:14,316 - tensorflow - INFO - loss = 0.04116901, step = 912400 (2.251 sec) -2022-05-01 17:16:16,572 - tensorflow - INFO - global_step/sec: 88.6078 -2022-05-01 17:16:16,573 - tensorflow - INFO - loss = 0.021799387, step = 912600 (2.257 sec) -2022-05-01 17:16:18,833 - tensorflow - INFO - global_step/sec: 88.4828 -2022-05-01 17:16:18,833 - tensorflow - INFO - loss = 0.02074817, step = 912800 (2.260 sec) -2022-05-01 17:16:21,089 - tensorflow - INFO - global_step/sec: 88.6219 -2022-05-01 17:16:21,090 - tensorflow - INFO - loss = 0.009668754, step = 913000 (2.257 sec) -2022-05-01 17:16:23,353 - tensorflow - INFO - global_step/sec: 88.3617 -2022-05-01 17:16:23,353 - tensorflow - INFO - loss = 0.15812874, step = 913200 (2.263 sec) -2022-05-01 17:16:25,610 - tensorflow - INFO - global_step/sec: 88.5979 -2022-05-01 17:16:25,611 - tensorflow - INFO - loss = 0.04642421, step = 913400 (2.257 sec) -2022-05-01 17:16:27,871 - tensorflow - INFO - global_step/sec: 88.4448 -2022-05-01 17:16:27,872 - tensorflow - INFO - loss = 0.01026053, step = 913600 (2.261 sec) -2022-05-01 17:16:30,134 - tensorflow - INFO - global_step/sec: 88.3798 -2022-05-01 17:16:30,135 - tensorflow - INFO - loss = 0.15093714, step = 913800 (2.263 sec) -2022-05-01 17:16:32,399 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.4686 -2022-05-01 17:21:01,122 - tensorflow - INFO - loss = 0.018456774, step = 937800 (2.261 sec) -2022-05-01 17:21:03,382 - tensorflow - INFO - global_step/sec: 88.4825 -2022-05-01 17:21:03,382 - tensorflow - INFO - loss = 0.025731664, step = 938000 (2.260 sec) -2022-05-01 17:21:05,641 - tensorflow - INFO - global_step/sec: 88.5555 -2022-05-01 17:21:05,641 - tensorflow - INFO - loss = 0.07282418, step = 938200 (2.258 sec) -2022-05-01 17:21:07,902 - tensorflow - INFO - global_step/sec: 88.4433 -2022-05-01 17:21:07,902 - tensorflow - INFO - loss = 0.07319632, step = 938400 (2.261 sec) -2022-05-01 17:21:10,159 - tensorflow - INFO - global_step/sec: 88.5926 -2022-05-01 17:21:10,160 - tensorflow - INFO - loss = 0.085002154, step = 938600 (2.258 sec) -2022-05-01 17:21:12,415 - tensorflow - INFO - global_step/sec: 88.6844 -2022-05-01 17:21:12,415 - tensorflow - INFO - loss = 0.05168, step = 938800 (2.255 sec) -2022-05-01 17:21:14,674 - tensorflow - INFO - 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INFO - global_step/sec: 88.5988 -2022-05-01 17:22:11,111 - tensorflow - INFO - loss = 0.01842107, step = 944000 (2.257 sec) -2022-05-01 17:22:13,367 - tensorflow - INFO - global_step/sec: 88.6394 -2022-05-01 17:22:13,368 - tensorflow - INFO - loss = 0.032064628, step = 944200 (2.256 sec) -2022-05-01 17:22:15,625 - tensorflow - INFO - global_step/sec: 88.5845 -2022-05-01 17:22:15,625 - tensorflow - INFO - loss = 0.02634815, step = 944400 (2.258 sec) -2022-05-01 17:22:17,880 - tensorflow - INFO - global_step/sec: 88.6875 -2022-05-01 17:22:17,881 - tensorflow - INFO - loss = 0.052733615, step = 944600 (2.255 sec) -2022-05-01 17:22:20,139 - tensorflow - INFO - global_step/sec: 88.5233 -2022-05-01 17:22:20,140 - tensorflow - INFO - loss = 0.15861678, step = 944800 (2.259 sec) -2022-05-01 17:22:22,390 - tensorflow - INFO - global_step/sec: 88.8645 -2022-05-01 17:22:22,390 - tensorflow - INFO - loss = 0.16137886, step = 945000 (2.251 sec) -2022-05-01 17:22:24,646 - tensorflow - INFO - global_step/sec: 88.6569 -2022-05-01 17:22:24,646 - 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INFO - global_step/sec: 88.5638 -2022-05-01 17:23:07,521 - tensorflow - INFO - loss = 0.029653884, step = 949000 (2.258 sec) -2022-05-01 17:23:09,778 - tensorflow - INFO - global_step/sec: 88.5779 -2022-05-01 17:23:09,779 - tensorflow - INFO - loss = 0.018251628, step = 949200 (2.258 sec) -2022-05-01 17:23:12,032 - tensorflow - INFO - global_step/sec: 88.7311 -2022-05-01 17:23:12,033 - tensorflow - INFO - loss = 0.022625616, step = 949400 (2.254 sec) -2022-05-01 17:23:14,286 - tensorflow - INFO - global_step/sec: 88.7528 -2022-05-01 17:23:14,286 - tensorflow - INFO - loss = 1.4423913, step = 949600 (2.253 sec) -2022-05-01 17:23:16,541 - tensorflow - INFO - global_step/sec: 88.6742 -2022-05-01 17:23:16,542 - tensorflow - INFO - loss = 0.064597204, step = 949800 (2.255 sec) -2022-05-01 17:23:18,795 - tensorflow - INFO - global_step/sec: 88.7497 -2022-05-01 17:23:18,796 - tensorflow - INFO - loss = 0.019017642, step = 950000 (2.254 sec) -2022-05-01 17:23:21,052 - tensorflow - INFO - global_step/sec: 88.6168 -2022-05-01 17:23:21,052 - 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tensorflow - INFO - global_step/sec: 88.6822 -2022-05-01 17:23:50,369 - tensorflow - INFO - loss = 0.04063668, step = 952800 (2.255 sec) -2022-05-01 17:23:52,629 - tensorflow - INFO - global_step/sec: 88.4835 -2022-05-01 17:23:52,629 - tensorflow - INFO - loss = 0.024519823, step = 953000 (2.260 sec) -2022-05-01 17:23:54,881 - tensorflow - INFO - global_step/sec: 88.7976 -2022-05-01 17:23:54,882 - tensorflow - INFO - loss = 0.056098625, step = 953200 (2.252 sec) -2022-05-01 17:23:57,137 - tensorflow - INFO - global_step/sec: 88.6485 -2022-05-01 17:23:57,138 - tensorflow - INFO - loss = 0.09465159, step = 953400 (2.256 sec) -2022-05-01 17:23:59,398 - tensorflow - INFO - global_step/sec: 88.4774 -2022-05-01 17:23:59,398 - tensorflow - INFO - loss = 0.027529754, step = 953600 (2.260 sec) -2022-05-01 17:24:01,658 - tensorflow - INFO - global_step/sec: 88.4694 -2022-05-01 17:24:01,659 - tensorflow - INFO - loss = 0.13809925, step = 953800 (2.261 sec) -2022-05-01 17:24:03,923 - tensorflow - INFO - global_step/sec: 88.3192 -2022-05-01 17:24:03,923 - tensorflow - INFO - loss = 0.033749137, step = 954000 (2.265 sec) -2022-05-01 17:24:06,187 - tensorflow - INFO - global_step/sec: 88.3423 -2022-05-01 17:24:06,187 - tensorflow - INFO - loss = 0.060991913, step = 954200 (2.264 sec) -2022-05-01 17:24:08,451 - tensorflow - INFO - global_step/sec: 88.3467 -2022-05-01 17:24:08,451 - tensorflow - INFO - loss = 17.136433, step = 954400 (2.264 sec) -2022-05-01 17:24:10,711 - tensorflow - INFO - global_step/sec: 88.4849 -2022-05-01 17:24:10,711 - tensorflow - INFO - loss = 0.30171964, step = 954600 (2.260 sec) -2022-05-01 17:24:12,976 - tensorflow - INFO - global_step/sec: 88.3141 -2022-05-01 17:24:12,976 - tensorflow - INFO - loss = 0.013845048, step = 954800 (2.265 sec) -2022-05-01 17:24:15,240 - tensorflow - INFO - global_step/sec: 88.3123 -2022-05-01 17:24:15,241 - tensorflow - INFO - loss = 0.023153916, step = 955000 (2.265 sec) -2022-05-01 17:24:17,502 - tensorflow - INFO - global_step/sec: 88.4209 -2022-05-01 17:24:17,503 - 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tensorflow - INFO - global_step/sec: 88.7297 -2022-05-01 17:24:46,884 - tensorflow - INFO - loss = 0.0199404, step = 957800 (2.254 sec) -2022-05-01 17:24:49,141 - tensorflow - INFO - global_step/sec: 88.6028 -2022-05-01 17:24:49,142 - tensorflow - INFO - loss = 0.03067346, step = 958000 (2.257 sec) -2022-05-01 17:24:51,398 - tensorflow - INFO - global_step/sec: 88.6148 -2022-05-01 17:24:51,399 - tensorflow - INFO - loss = 0.030765407, step = 958200 (2.257 sec) -2022-05-01 17:24:53,655 - tensorflow - INFO - global_step/sec: 88.6222 -2022-05-01 17:24:53,655 - tensorflow - INFO - loss = 0.00927158, step = 958400 (2.257 sec) -2022-05-01 17:24:55,909 - tensorflow - INFO - global_step/sec: 88.7448 -2022-05-01 17:24:55,909 - tensorflow - INFO - loss = 0.13562378, step = 958600 (2.254 sec) -2022-05-01 17:24:58,160 - tensorflow - INFO - global_step/sec: 88.8414 -2022-05-01 17:24:58,160 - tensorflow - INFO - loss = 0.032666013, step = 958800 (2.251 sec) -2022-05-01 17:25:00,416 - tensorflow - INFO - 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INFO - loss = 0.054105025, step = 961400 (2.257 sec) -2022-05-01 17:25:29,752 - tensorflow - INFO - global_step/sec: 88.5433 -2022-05-01 17:25:29,752 - tensorflow - INFO - loss = 0.0301522, step = 961600 (2.259 sec) -2022-05-01 17:25:32,005 - tensorflow - INFO - global_step/sec: 88.7831 -2022-05-01 17:25:32,005 - tensorflow - INFO - loss = 0.07410993, step = 961800 (2.253 sec) -2022-05-01 17:25:34,261 - tensorflow - INFO - global_step/sec: 88.6545 -2022-05-01 17:25:34,261 - tensorflow - INFO - loss = 1.5347927, step = 962000 (2.256 sec) -2022-05-01 17:25:36,513 - tensorflow - INFO - global_step/sec: 88.7754 -2022-05-01 17:25:36,514 - tensorflow - INFO - loss = 0.059033245, step = 962200 (2.253 sec) -2022-05-01 17:25:38,774 - tensorflow - INFO - global_step/sec: 88.4838 -2022-05-01 17:25:38,774 - tensorflow - INFO - loss = 0.015997827, step = 962400 (2.260 sec) -2022-05-01 17:25:41,030 - tensorflow - INFO - global_step/sec: 88.6449 -2022-05-01 17:25:41,030 - tensorflow - INFO - loss = 0.015084545, step = 962600 (2.256 sec) -2022-05-01 17:25:43,283 - tensorflow - INFO - global_step/sec: 88.7834 -2022-05-01 17:25:43,283 - tensorflow - INFO - loss = 0.03374805, step = 962800 (2.253 sec) -2022-05-01 17:25:45,534 - tensorflow - INFO - global_step/sec: 88.8375 -2022-05-01 17:25:45,534 - tensorflow - INFO - loss = 0.04450837, step = 963000 (2.251 sec) -2022-05-01 17:25:47,788 - tensorflow - INFO - global_step/sec: 88.7443 -2022-05-01 17:25:47,788 - tensorflow - INFO - loss = 0.027296763, step = 963200 (2.254 sec) -2022-05-01 17:25:50,042 - tensorflow - INFO - global_step/sec: 88.7116 -2022-05-01 17:25:50,043 - tensorflow - INFO - loss = 0.009122146, step = 963400 (2.255 sec) -2022-05-01 17:25:52,304 - tensorflow - INFO - global_step/sec: 88.436 -2022-05-01 17:25:52,304 - tensorflow - INFO - loss = 0.13918698, step = 963600 (2.261 sec) -2022-05-01 17:25:54,559 - tensorflow - INFO - global_step/sec: 88.6729 -2022-05-01 17:25:54,560 - tensorflow - INFO - loss = 0.04125909, step = 963800 (2.256 sec) -2022-05-01 17:25:56,819 - tensorflow - INFO - 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INFO - loss = 0.079419926, step = 966400 (2.260 sec) -2022-05-01 17:26:26,153 - tensorflow - INFO - global_step/sec: 88.4771 -2022-05-01 17:26:26,153 - tensorflow - INFO - loss = 0.015647521, step = 966600 (2.260 sec) -2022-05-01 17:26:28,412 - tensorflow - INFO - global_step/sec: 88.526 -2022-05-01 17:26:28,413 - tensorflow - INFO - loss = 0.08310799, step = 966800 (2.259 sec) -2022-05-01 17:26:30,686 - tensorflow - INFO - global_step/sec: 87.9628 -2022-05-01 17:26:30,686 - tensorflow - INFO - loss = 0.09947473, step = 967000 (2.274 sec) -2022-05-01 17:26:32,951 - tensorflow - INFO - global_step/sec: 88.3029 -2022-05-01 17:26:32,951 - tensorflow - INFO - loss = 0.050885316, step = 967200 (2.265 sec) -2022-05-01 17:26:35,208 - tensorflow - INFO - global_step/sec: 88.6192 -2022-05-01 17:26:35,208 - tensorflow - INFO - loss = 0.014569244, step = 967400 (2.257 sec) -2022-05-01 17:26:37,456 - tensorflow - INFO - global_step/sec: 88.9568 -2022-05-01 17:26:37,456 - tensorflow - INFO - loss = 0.0064915894, step = 967600 (2.248 sec) -2022-05-01 17:26:39,712 - 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tensorflow - INFO - global_step/sec: 88.2469 -2022-05-01 17:27:36,155 - tensorflow - INFO - loss = 1.0652466, step = 972800 (2.266 sec) -2022-05-01 17:27:38,420 - tensorflow - INFO - global_step/sec: 88.3081 -2022-05-01 17:27:38,420 - tensorflow - INFO - loss = 0.013959445, step = 973000 (2.265 sec) -2022-05-01 17:27:40,680 - tensorflow - INFO - global_step/sec: 88.4964 -2022-05-01 17:27:40,680 - tensorflow - INFO - loss = 0.037181213, step = 973200 (2.260 sec) -2022-05-01 17:27:42,945 - tensorflow - INFO - global_step/sec: 88.3081 -2022-05-01 17:27:42,945 - tensorflow - INFO - loss = 0.07719441, step = 973400 (2.265 sec) -2022-05-01 17:27:45,205 - tensorflow - INFO - global_step/sec: 88.4847 -2022-05-01 17:27:45,205 - tensorflow - INFO - loss = 0.08151187, step = 973600 (2.260 sec) -2022-05-01 17:27:47,468 - tensorflow - INFO - global_step/sec: 88.3531 -2022-05-01 17:27:47,469 - tensorflow - INFO - loss = 0.08388565, step = 973800 (2.264 sec) -2022-05-01 17:27:49,731 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.2051 -2022-05-01 17:28:32,858 - tensorflow - INFO - loss = 0.012920011, step = 977800 (2.267 sec) -2022-05-01 17:28:35,125 - tensorflow - INFO - global_step/sec: 88.202 -2022-05-01 17:28:35,125 - tensorflow - INFO - loss = 0.016317671, step = 978000 (2.268 sec) -2022-05-01 17:28:37,391 - tensorflow - INFO - global_step/sec: 88.2428 -2022-05-01 17:28:37,392 - tensorflow - INFO - loss = 0.07118274, step = 978200 (2.266 sec) -2022-05-01 17:28:39,661 - tensorflow - INFO - global_step/sec: 88.1176 -2022-05-01 17:28:39,661 - tensorflow - INFO - loss = 0.015032679, step = 978400 (2.270 sec) -2022-05-01 17:28:41,926 - tensorflow - INFO - global_step/sec: 88.2807 -2022-05-01 17:28:41,927 - tensorflow - INFO - loss = 0.012356233, step = 978600 (2.266 sec) -2022-05-01 17:28:44,198 - tensorflow - INFO - global_step/sec: 88.0509 -2022-05-01 17:28:44,198 - tensorflow - INFO - loss = 0.010050238, step = 978800 (2.271 sec) -2022-05-01 17:28:46,466 - tensorflow - INFO - global_step/sec: 88.1573 -2022-05-01 17:28:46,467 - tensorflow - INFO - loss = 0.013371667, step = 979000 (2.269 sec) -2022-05-01 17:28:48,733 - tensorflow - INFO - global_step/sec: 88.2271 -2022-05-01 17:28:48,734 - tensorflow - INFO - loss = 0.20692414, step = 979200 (2.267 sec) -2022-05-01 17:28:51,004 - tensorflow - INFO - global_step/sec: 88.0974 -2022-05-01 17:28:51,004 - tensorflow - INFO - loss = 0.014547777, step = 979400 (2.270 sec) -2022-05-01 17:28:53,276 - tensorflow - INFO - global_step/sec: 88.0222 -2022-05-01 17:28:53,276 - tensorflow - INFO - loss = 0.12706436, step = 979600 (2.272 sec) -2022-05-01 17:28:55,540 - tensorflow - INFO - global_step/sec: 88.344 -2022-05-01 17:28:55,540 - tensorflow - INFO - loss = 0.05867211, step = 979800 (2.264 sec) -2022-05-01 17:28:57,807 - tensorflow - INFO - global_step/sec: 88.1949 -2022-05-01 17:28:57,808 - tensorflow - INFO - loss = 0.054758377, step = 980000 (2.268 sec) -2022-05-01 17:29:00,074 - tensorflow - INFO - global_step/sec: 88.2393 -2022-05-01 17:29:00,074 - tensorflow - INFO - loss = 0.013110551, step = 980200 (2.267 sec) -2022-05-01 17:29:02,339 - tensorflow - INFO - global_step/sec: 88.2893 -2022-05-01 17:29:02,340 - tensorflow - INFO - loss = 0.015686195, step = 980400 (2.265 sec) -2022-05-01 17:29:04,614 - tensorflow - INFO - global_step/sec: 87.9188 -2022-05-01 17:29:04,614 - tensorflow - INFO - loss = 0.7616093, step = 980600 (2.275 sec) -2022-05-01 17:29:06,886 - tensorflow - INFO - global_step/sec: 88.04 -2022-05-01 17:29:06,886 - tensorflow - INFO - loss = 0.07527063, step = 980800 (2.272 sec) -2022-05-01 17:29:09,151 - tensorflow - INFO - global_step/sec: 88.2956 -2022-05-01 17:29:09,151 - tensorflow - INFO - loss = 0.01953073, step = 981000 (2.265 sec) -2022-05-01 17:29:11,418 - tensorflow - INFO - global_step/sec: 88.1979 -2022-05-01 17:29:11,419 - tensorflow - INFO - loss = 0.023897674, step = 981200 (2.268 sec) -2022-05-01 17:29:13,689 - tensorflow - INFO - global_step/sec: 88.1014 -2022-05-01 17:29:13,689 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.1484 -2022-05-01 17:29:29,567 - tensorflow - INFO - loss = 0.021682613, step = 982800 (2.269 sec) -2022-05-01 17:29:31,841 - tensorflow - INFO - global_step/sec: 87.9505 -2022-05-01 17:29:31,841 - tensorflow - INFO - loss = 0.065413214, step = 983000 (2.274 sec) -2022-05-01 17:29:34,107 - tensorflow - INFO - global_step/sec: 88.2625 -2022-05-01 17:29:34,107 - tensorflow - INFO - loss = 0.02960616, step = 983200 (2.266 sec) -2022-05-01 17:29:36,374 - tensorflow - INFO - global_step/sec: 88.2038 -2022-05-01 17:29:36,374 - tensorflow - INFO - loss = 0.009819341, step = 983400 (2.268 sec) -2022-05-01 17:29:38,643 - tensorflow - INFO - global_step/sec: 88.1329 -2022-05-01 17:29:38,644 - tensorflow - INFO - loss = 0.010089403, step = 983600 (2.269 sec) -2022-05-01 17:29:40,908 - tensorflow - INFO - global_step/sec: 88.3044 -2022-05-01 17:29:40,909 - tensorflow - INFO - loss = 0.080353625, step = 983800 (2.265 sec) -2022-05-01 17:29:43,180 - tensorflow - INFO - global_step/sec: 88.0331 -2022-05-01 17:29:43,181 - tensorflow - INFO - loss = 0.0091108745, step = 984000 (2.272 sec) -2022-05-01 17:29:45,447 - tensorflow - INFO - global_step/sec: 88.2278 -2022-05-01 17:29:45,447 - tensorflow - INFO - loss = 0.015508783, step = 984200 (2.267 sec) -2022-05-01 17:29:47,719 - tensorflow - INFO - global_step/sec: 88.0386 -2022-05-01 17:29:47,719 - tensorflow - INFO - loss = 0.036307346, step = 984400 (2.272 sec) -2022-05-01 17:29:49,983 - tensorflow - INFO - global_step/sec: 88.3255 -2022-05-01 17:29:49,983 - tensorflow - INFO - loss = 0.07458148, step = 984600 (2.264 sec) -2022-05-01 17:29:52,249 - tensorflow - INFO - global_step/sec: 88.2767 -2022-05-01 17:29:52,249 - tensorflow - INFO - loss = 0.025107179, step = 984800 (2.266 sec) -2022-05-01 17:29:54,520 - tensorflow - INFO - global_step/sec: 88.0421 -2022-05-01 17:29:54,521 - tensorflow - INFO - loss = 0.03554614, step = 985000 (2.272 sec) -2022-05-01 17:29:56,795 - tensorflow - INFO - global_step/sec: 87.9401 -2022-05-01 17:29:56,795 - 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tensorflow - INFO - global_step/sec: 88.2279 -2022-05-01 17:30:26,300 - tensorflow - INFO - loss = 0.04953441, step = 987800 (2.267 sec) -2022-05-01 17:30:28,574 - tensorflow - INFO - global_step/sec: 87.9615 -2022-05-01 17:30:28,574 - tensorflow - INFO - loss = 0.021212831, step = 988000 (2.274 sec) -2022-05-01 17:30:30,846 - tensorflow - INFO - global_step/sec: 88.0065 -2022-05-01 17:30:30,847 - tensorflow - INFO - loss = 0.0115836, step = 988200 (2.273 sec) -2022-05-01 17:30:33,117 - tensorflow - INFO - global_step/sec: 88.0964 -2022-05-01 17:30:33,117 - tensorflow - INFO - loss = 0.03893371, step = 988400 (2.270 sec) -2022-05-01 17:30:35,382 - tensorflow - INFO - global_step/sec: 88.2646 -2022-05-01 17:30:35,383 - tensorflow - INFO - loss = 0.011640495, step = 988600 (2.266 sec) -2022-05-01 17:30:37,643 - tensorflow - INFO - global_step/sec: 88.491 -2022-05-01 17:30:37,643 - tensorflow - INFO - loss = 0.07432926, step = 988800 (2.260 sec) -2022-05-01 17:30:39,908 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.021077337, step = 990200 (2.265 sec) -2022-05-01 17:30:55,789 - tensorflow - INFO - global_step/sec: 88.131 -2022-05-01 17:30:55,789 - tensorflow - INFO - loss = 0.046095267, step = 990400 (2.269 sec) -2022-05-01 17:30:58,058 - tensorflow - INFO - global_step/sec: 88.1344 -2022-05-01 17:30:58,058 - tensorflow - INFO - loss = 0.013050471, step = 990600 (2.269 sec) -2022-05-01 17:31:00,333 - tensorflow - INFO - global_step/sec: 87.9212 -2022-05-01 17:31:00,333 - tensorflow - INFO - loss = 0.04462536, step = 990800 (2.275 sec) -2022-05-01 17:31:02,605 - tensorflow - INFO - global_step/sec: 88.0144 -2022-05-01 17:31:02,605 - tensorflow - INFO - loss = 0.13494569, step = 991000 (2.272 sec) -2022-05-01 17:31:04,877 - tensorflow - INFO - global_step/sec: 88.022 -2022-05-01 17:31:04,878 - tensorflow - INFO - loss = 0.016361812, step = 991200 (2.272 sec) -2022-05-01 17:31:07,153 - tensorflow - INFO - global_step/sec: 87.8648 -2022-05-01 17:31:07,154 - tensorflow - INFO - loss = 0.14117768, step = 991400 (2.276 sec) -2022-05-01 17:31:09,430 - tensorflow - INFO - global_step/sec: 87.8558 -2022-05-01 17:31:09,430 - tensorflow - INFO - loss = 0.010469894, step = 991600 (2.276 sec) -2022-05-01 17:31:11,706 - tensorflow - INFO - global_step/sec: 87.8807 -2022-05-01 17:31:11,706 - tensorflow - INFO - loss = 0.11327028, step = 991800 (2.276 sec) -2022-05-01 17:31:13,984 - tensorflow - INFO - global_step/sec: 87.7696 -2022-05-01 17:31:13,985 - tensorflow - INFO - loss = 0.23409489, step = 992000 (2.279 sec) -2022-05-01 17:31:16,261 - tensorflow - INFO - global_step/sec: 87.862 -2022-05-01 17:31:16,261 - tensorflow - INFO - loss = 5.709214, step = 992200 (2.276 sec) -2022-05-01 17:31:18,536 - tensorflow - INFO - global_step/sec: 87.891 -2022-05-01 17:31:18,537 - tensorflow - INFO - loss = 0.02604986, step = 992400 (2.276 sec) -2022-05-01 17:31:20,809 - tensorflow - INFO - global_step/sec: 87.995 -2022-05-01 17:31:20,809 - tensorflow - INFO - loss = 0.020889275, step = 992600 (2.273 sec) -2022-05-01 17:31:23,086 - tensorflow - INFO - global_step/sec: 87.822 -2022-05-01 17:31:23,087 - tensorflow - INFO - loss = 0.041441254, step = 992800 (2.277 sec) -2022-05-01 17:31:25,366 - tensorflow - INFO - global_step/sec: 87.7491 -2022-05-01 17:31:25,366 - tensorflow - INFO - loss = 0.0638247, step = 993000 (2.279 sec) -2022-05-01 17:31:27,642 - tensorflow - INFO - global_step/sec: 87.8521 -2022-05-01 17:31:27,643 - tensorflow - INFO - loss = 0.09642565, step = 993200 (2.277 sec) -2022-05-01 17:31:29,918 - tensorflow - INFO - global_step/sec: 87.8951 -2022-05-01 17:31:29,918 - tensorflow - INFO - loss = 0.053129744, step = 993400 (2.275 sec) -2022-05-01 17:31:32,199 - tensorflow - INFO - global_step/sec: 87.6458 -2022-05-01 17:31:32,200 - tensorflow - INFO - loss = 0.06550279, step = 993600 (2.282 sec) -2022-05-01 17:31:34,469 - tensorflow - INFO - global_step/sec: 88.107 -2022-05-01 17:31:34,470 - tensorflow - INFO - loss = 0.01763028, step = 993800 (2.270 sec) -2022-05-01 17:31:36,738 - tensorflow - INFO - global_step/sec: 88.1738 -2022-05-01 17:31:36,738 - tensorflow - INFO - loss = 0.36183137, step = 994000 (2.268 sec) -2022-05-01 17:31:39,012 - tensorflow - INFO - global_step/sec: 87.9508 -2022-05-01 17:31:39,012 - tensorflow - INFO - loss = 0.048842005, step = 994200 (2.274 sec) -2022-05-01 17:31:41,284 - tensorflow - INFO - global_step/sec: 88.0298 -2022-05-01 17:31:41,284 - tensorflow - INFO - loss = 31.629017, step = 994400 (2.272 sec) -2022-05-01 17:31:43,550 - tensorflow - INFO - global_step/sec: 88.2431 -2022-05-01 17:31:43,551 - tensorflow - INFO - loss = 0.017132096, step = 994600 (2.267 sec) -2022-05-01 17:31:45,821 - tensorflow - INFO - global_step/sec: 88.0834 -2022-05-01 17:31:45,821 - tensorflow - INFO - loss = 0.04079227, step = 994800 (2.271 sec) -2022-05-01 17:31:48,087 - tensorflow - INFO - global_step/sec: 88.2552 -2022-05-01 17:31:48,087 - tensorflow - INFO - loss = 0.825424, step = 995000 (2.266 sec) -2022-05-01 17:31:50,356 - tensorflow - INFO - global_step/sec: 88.1421 -2022-05-01 17:31:50,356 - tensorflow - INFO - loss = 0.01457743, step = 995200 (2.269 sec) -2022-05-01 17:31:52,627 - tensorflow - INFO - global_step/sec: 88.0675 -2022-05-01 17:31:52,627 - tensorflow - INFO - loss = 0.08510444, step = 995400 (2.271 sec) -2022-05-01 17:31:54,894 - tensorflow - INFO - global_step/sec: 88.2252 -2022-05-01 17:31:54,894 - tensorflow - INFO - loss = 0.10075992, step = 995600 (2.267 sec) -2022-05-01 17:31:57,164 - tensorflow - INFO - global_step/sec: 88.1038 -2022-05-01 17:31:57,164 - tensorflow - INFO - loss = 0.5939552, step = 995800 (2.270 sec) -2022-05-01 17:31:59,431 - tensorflow - INFO - global_step/sec: 88.237 -2022-05-01 17:31:59,431 - tensorflow - INFO - loss = 0.023191817, step = 996000 (2.267 sec) -2022-05-01 17:32:01,698 - tensorflow - INFO - global_step/sec: 88.2096 -2022-05-01 17:32:01,698 - tensorflow - INFO - loss = 0.03370051, step = 996200 (2.267 sec) -2022-05-01 17:32:03,972 - tensorflow - INFO - global_step/sec: 87.941 -2022-05-01 17:32:03,972 - tensorflow - INFO - loss = 0.045667723, step = 996400 (2.274 sec) -2022-05-01 17:32:06,245 - tensorflow - INFO - global_step/sec: 88.0017 -2022-05-01 17:32:06,245 - tensorflow - INFO - loss = 5.847514, step = 996600 (2.273 sec) -2022-05-01 17:32:08,515 - tensorflow - INFO - global_step/sec: 88.1051 -2022-05-01 17:32:08,515 - tensorflow - INFO - loss = 0.0076303408, step = 996800 (2.270 sec) -2022-05-01 17:32:10,780 - tensorflow - INFO - global_step/sec: 88.3048 -2022-05-01 17:32:10,780 - tensorflow - INFO - loss = 0.014137331, step = 997000 (2.265 sec) -2022-05-01 17:32:13,053 - tensorflow - INFO - global_step/sec: 87.9674 -2022-05-01 17:32:13,054 - tensorflow - INFO - loss = 0.012826626, step = 997200 (2.274 sec) -2022-05-01 17:32:15,317 - tensorflow - INFO - global_step/sec: 88.3681 -2022-05-01 17:32:15,317 - tensorflow - INFO - loss = 0.027969304, step = 997400 (2.263 sec) -2022-05-01 17:32:17,589 - tensorflow - INFO - global_step/sec: 88.0105 -2022-05-01 17:32:17,589 - tensorflow - INFO - loss = 0.05405557, step = 997600 (2.272 sec) -2022-05-01 17:32:19,864 - tensorflow - INFO - global_step/sec: 87.9009 -2022-05-01 17:32:19,865 - tensorflow - INFO - loss = 0.095805936, step = 997800 (2.275 sec) -2022-05-01 17:32:22,134 - tensorflow - INFO - global_step/sec: 88.1227 -2022-05-01 17:32:22,134 - tensorflow - INFO - loss = 0.10985251, step = 998000 (2.270 sec) -2022-05-01 17:32:24,401 - tensorflow - INFO - global_step/sec: 88.229 -2022-05-01 17:32:24,401 - tensorflow - INFO - loss = 0.031816743, step = 998200 (2.267 sec) -2022-05-01 17:32:26,668 - tensorflow - INFO - global_step/sec: 88.2149 -2022-05-01 17:32:26,668 - tensorflow - INFO - loss = 0.28096992, step = 998400 (2.267 sec) -2022-05-01 17:32:28,937 - tensorflow - INFO - global_step/sec: 88.1398 -2022-05-01 17:32:28,937 - tensorflow - INFO - loss = 0.03208027, step = 998600 (2.269 sec) -2022-05-01 17:32:31,209 - tensorflow - INFO - global_step/sec: 88.0357 -2022-05-01 17:32:31,209 - tensorflow - INFO - loss = 0.11195407, step = 998800 (2.272 sec) -2022-05-01 17:32:33,473 - tensorflow - INFO - global_step/sec: 88.3346 -2022-05-01 17:32:33,473 - tensorflow - INFO - loss = 0.11846994, step = 999000 (2.264 sec) -2022-05-01 17:32:35,741 - tensorflow - INFO - global_step/sec: 88.1636 -2022-05-01 17:32:35,742 - tensorflow - INFO - loss = 0.01635918, step = 999200 (2.268 sec) -2022-05-01 17:32:38,008 - tensorflow - INFO - global_step/sec: 88.2422 -2022-05-01 17:32:38,008 - tensorflow - INFO - loss = 0.086311236, step = 999400 (2.267 sec) -2022-05-01 17:32:40,277 - tensorflow - INFO - global_step/sec: 88.1249 -2022-05-01 17:32:40,278 - tensorflow - INFO - loss = 0.028363196, step = 999600 (2.269 sec) -2022-05-01 17:32:42,543 - tensorflow - INFO - global_step/sec: 88.2577 -2022-05-01 17:32:42,544 - tensorflow - INFO - loss = 0.02159482, step = 999800 (2.266 sec) -2022-05-01 17:32:44,799 - tensorflow - INFO - Saving checkpoints for 1000000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. -2022-05-01 17:32:44,814 - tensorflow - INFO - model.ckpt-1000000 is not in all_model_checkpoint_paths. Manually adding it. -2022-05-01 17:32:45,367 - tensorflow - INFO - Calling model_fn. -2022-05-01 17:32:45,495 - tensorflow - INFO - Done calling model_fn. -2022-05-01 17:32:45,510 - tensorflow - INFO - Starting evaluation at 2022-05-01T17:32:45Z -2022-05-01 17:32:45,575 - tensorflow - INFO - Graph was finalized. -2022-05-01 17:32:45,578 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1000000 -2022-05-01 17:32:45,861 - tensorflow - INFO - Running local_init_op. -2022-05-01 17:32:45,969 - tensorflow - INFO - Done running local_init_op. -2022-05-01 17:32:56,249 - tensorflow - INFO - Evaluation [3990/39900] -2022-05-01 17:33:05,789 - tensorflow - INFO - Evaluation [7980/39900] -2022-05-01 17:33:15,364 - tensorflow - INFO - Evaluation [11970/39900] -2022-05-01 17:33:24,953 - tensorflow - INFO - Evaluation [15960/39900] -2022-05-01 17:33:34,512 - tensorflow - INFO - Evaluation [19950/39900] -2022-05-01 17:33:44,109 - tensorflow - INFO - Evaluation [23940/39900] -2022-05-01 17:33:53,662 - tensorflow - INFO - Evaluation [27930/39900] -2022-05-01 17:34:03,220 - tensorflow - INFO - Evaluation [31920/39900] -2022-05-01 17:34:12,787 - tensorflow - INFO - Evaluation [35910/39900] -2022-05-01 17:34:22,350 - tensorflow - INFO - Evaluation [39900/39900] -2022-05-01 17:34:22,488 - tensorflow - INFO - Finished evaluation at 2022-05-01-17:34:22 -2022-05-01 17:34:22,489 - tensorflow - INFO - Saving dict for global step 1000000: global_step = 1000000, loss = 0.024924062, mean_degree_err = 4.3142204 -2022-05-01 17:34:22,489 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 1000000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1000000 -2022-05-01 17:34:22,503 - tensorflow - INFO - global_step/sec: 2.00081 -2022-05-01 17:34:22,504 - tensorflow - INFO - loss = 0.026846617, step = 1000000 (99.960 sec) -2022-05-01 17:34:24,791 - tensorflow - INFO - global_step/sec: 87.4221 -2022-05-01 17:34:24,791 - tensorflow - INFO - loss = 0.062070694, step = 1000200 (2.287 sec) -2022-05-01 17:34:27,060 - tensorflow - INFO - global_step/sec: 88.1238 -2022-05-01 17:34:27,061 - tensorflow - INFO - loss = 0.008369792, step = 1000400 (2.270 sec) -2022-05-01 17:34:29,329 - tensorflow - INFO - global_step/sec: 88.1393 -2022-05-01 17:34:29,330 - tensorflow - INFO - loss = 0.012222083, step = 1000600 (2.269 sec) -2022-05-01 17:34:31,595 - tensorflow - INFO - global_step/sec: 88.2946 -2022-05-01 17:34:31,595 - tensorflow - INFO - loss = 0.17804483, step = 1000800 (2.265 sec) -2022-05-01 17:34:33,869 - tensorflow - INFO - global_step/sec: 87.9165 -2022-05-01 17:34:33,870 - tensorflow - INFO - loss = 0.070887625, step = 1001000 (2.275 sec) -2022-05-01 17:34:36,147 - tensorflow - INFO - global_step/sec: 87.8316 -2022-05-01 17:34:36,147 - tensorflow - INFO - loss = 0.099069044, step = 1001200 (2.277 sec) -2022-05-01 17:34:38,417 - tensorflow - INFO - global_step/sec: 88.087 -2022-05-01 17:34:38,417 - tensorflow - INFO - loss = 0.24533297, step = 1001400 (2.271 sec) -2022-05-01 17:34:40,688 - tensorflow - INFO - global_step/sec: 88.0499 -2022-05-01 17:34:40,689 - tensorflow - INFO - loss = 0.025633255, step = 1001600 (2.271 sec) -2022-05-01 17:34:42,965 - tensorflow - INFO - global_step/sec: 87.8484 -2022-05-01 17:34:42,966 - tensorflow - INFO - loss = 1.1007679, step = 1001800 (2.277 sec) -2022-05-01 17:34:45,238 - tensorflow - INFO - global_step/sec: 87.9866 -2022-05-01 17:34:45,239 - tensorflow - INFO - loss = 0.12545982, step = 1002000 (2.273 sec) -2022-05-01 17:34:47,510 - tensorflow - INFO - global_step/sec: 88.0195 -2022-05-01 17:34:47,511 - tensorflow - INFO - loss = 0.060006622, step = 1002200 (2.272 sec) -2022-05-01 17:34:49,789 - tensorflow - INFO - global_step/sec: 87.7842 -2022-05-01 17:34:49,789 - tensorflow - INFO - loss = 0.058975074, step = 1002400 (2.278 sec) -2022-05-01 17:34:52,063 - tensorflow - INFO - global_step/sec: 87.9484 -2022-05-01 17:34:52,063 - tensorflow - INFO - loss = 0.3131972, step = 1002600 (2.274 sec) -2022-05-01 17:34:54,336 - tensorflow - INFO - global_step/sec: 87.9905 -2022-05-01 17:34:54,336 - tensorflow - INFO - loss = 0.08788369, step = 1002800 (2.273 sec) -2022-05-01 17:34:56,611 - tensorflow - INFO - global_step/sec: 87.9106 -2022-05-01 17:34:56,611 - tensorflow - INFO - loss = 0.22353008, step = 1003000 (2.275 sec) -2022-05-01 17:34:58,886 - tensorflow - INFO - global_step/sec: 87.9241 -2022-05-01 17:34:58,886 - tensorflow - INFO - loss = 0.022893198, step = 1003200 (2.275 sec) -2022-05-01 17:35:01,154 - tensorflow - INFO - global_step/sec: 88.1526 -2022-05-01 17:35:01,155 - tensorflow - INFO - loss = 0.029260218, step = 1003400 (2.269 sec) -2022-05-01 17:35:03,426 - tensorflow - INFO - global_step/sec: 88.0377 -2022-05-01 17:35:03,426 - tensorflow - INFO - loss = 0.03587124, step = 1003600 (2.272 sec) -2022-05-01 17:35:05,693 - tensorflow - INFO - global_step/sec: 88.2093 -2022-05-01 17:35:05,694 - tensorflow - INFO - loss = 0.027963502, step = 1003800 (2.267 sec) -2022-05-01 17:35:07,958 - tensorflow - INFO - global_step/sec: 88.3015 -2022-05-01 17:35:07,959 - tensorflow - INFO - loss = 0.21320803, step = 1004000 (2.265 sec) -2022-05-01 17:35:10,224 - tensorflow - INFO - global_step/sec: 88.269 -2022-05-01 17:35:10,225 - tensorflow - INFO - loss = 0.010730086, step = 1004200 (2.266 sec) -2022-05-01 17:35:12,488 - tensorflow - INFO - global_step/sec: 88.3264 -2022-05-01 17:35:12,489 - tensorflow - INFO - loss = 0.020996207, step = 1004400 (2.264 sec) -2022-05-01 17:35:14,760 - tensorflow - INFO - global_step/sec: 88.0305 -2022-05-01 17:35:14,761 - tensorflow - INFO - loss = 0.014935322, step = 1004600 (2.272 sec) -2022-05-01 17:35:17,027 - tensorflow - INFO - global_step/sec: 88.238 -2022-05-01 17:35:17,027 - tensorflow - INFO - loss = 0.020521518, step = 1004800 (2.267 sec) -2022-05-01 17:35:19,299 - tensorflow - INFO - global_step/sec: 88.0149 -2022-05-01 17:35:19,300 - tensorflow - INFO - loss = 0.027829817, step = 1005000 (2.272 sec) -2022-05-01 17:35:21,569 - tensorflow - INFO - global_step/sec: 88.1069 -2022-05-01 17:35:21,570 - tensorflow - INFO - loss = 0.01738238, step = 1005200 (2.270 sec) -2022-05-01 17:35:23,837 - tensorflow - INFO - global_step/sec: 88.1844 -2022-05-01 17:35:23,838 - tensorflow - INFO - loss = 0.02370587, step = 1005400 (2.268 sec) -2022-05-01 17:35:26,101 - tensorflow - INFO - global_step/sec: 88.3633 -2022-05-01 17:35:26,101 - tensorflow - INFO - loss = 0.027154919, step = 1005600 (2.263 sec) -2022-05-01 17:35:28,371 - tensorflow - INFO - global_step/sec: 88.0921 -2022-05-01 17:35:28,371 - tensorflow - INFO - loss = 0.08494438, step = 1005800 (2.270 sec) -2022-05-01 17:35:30,638 - tensorflow - INFO - global_step/sec: 88.2119 -2022-05-01 17:35:30,639 - tensorflow - INFO - loss = 0.01766038, step = 1006000 (2.267 sec) -2022-05-01 17:35:32,904 - tensorflow - INFO - global_step/sec: 88.2707 -2022-05-01 17:35:32,904 - tensorflow - INFO - loss = 0.0381309, step = 1006200 (2.266 sec) -2022-05-01 17:35:35,171 - tensorflow - INFO - global_step/sec: 88.2355 -2022-05-01 17:35:35,171 - tensorflow - INFO - loss = 0.3142389, step = 1006400 (2.267 sec) -2022-05-01 17:35:37,437 - tensorflow - INFO - global_step/sec: 88.2609 -2022-05-01 17:35:37,437 - tensorflow - INFO - loss = 0.011612243, step = 1006600 (2.266 sec) -2022-05-01 17:35:39,705 - tensorflow - INFO - global_step/sec: 88.1593 -2022-05-01 17:35:39,706 - tensorflow - INFO - loss = 0.010148924, step = 1006800 (2.269 sec) -2022-05-01 17:35:41,969 - tensorflow - INFO - global_step/sec: 88.3549 -2022-05-01 17:35:41,969 - tensorflow - INFO - loss = 0.048177227, step = 1007000 (2.264 sec) -2022-05-01 17:35:44,237 - tensorflow - INFO - global_step/sec: 88.1994 -2022-05-01 17:35:44,237 - tensorflow - INFO - loss = 0.033830248, step = 1007200 (2.268 sec) -2022-05-01 17:35:46,503 - tensorflow - INFO - global_step/sec: 88.2257 -2022-05-01 17:35:46,504 - tensorflow - INFO - loss = 0.02257288, step = 1007400 (2.267 sec) -2022-05-01 17:35:48,772 - tensorflow - INFO - global_step/sec: 88.1676 -2022-05-01 17:35:48,772 - tensorflow - INFO - loss = 0.014890956, step = 1007600 (2.268 sec) -2022-05-01 17:35:51,043 - tensorflow - INFO - global_step/sec: 88.0437 -2022-05-01 17:35:51,044 - tensorflow - INFO - loss = 0.020477772, step = 1007800 (2.272 sec) -2022-05-01 17:35:53,317 - tensorflow - INFO - global_step/sec: 87.9613 -2022-05-01 17:35:53,318 - tensorflow - INFO - loss = 0.09701069, step = 1008000 (2.274 sec) -2022-05-01 17:35:55,584 - tensorflow - INFO - global_step/sec: 88.2138 -2022-05-01 17:35:55,585 - tensorflow - INFO - loss = 0.06696297, step = 1008200 (2.267 sec) -2022-05-01 17:35:57,854 - tensorflow - INFO - global_step/sec: 88.1281 -2022-05-01 17:35:57,854 - tensorflow - INFO - loss = 0.03343594, step = 1008400 (2.269 sec) -2022-05-01 17:36:00,118 - tensorflow - INFO - global_step/sec: 88.3367 -2022-05-01 17:36:00,118 - tensorflow - INFO - loss = 0.019149898, step = 1008600 (2.264 sec) -2022-05-01 17:36:02,386 - tensorflow - INFO - global_step/sec: 88.1685 -2022-05-01 17:36:02,387 - tensorflow - INFO - loss = 0.06799473, step = 1008800 (2.268 sec) -2022-05-01 17:36:04,654 - tensorflow - INFO - global_step/sec: 88.1859 -2022-05-01 17:36:04,655 - tensorflow - INFO - loss = 0.0144699365, step = 1009000 (2.268 sec) -2022-05-01 17:36:06,921 - tensorflow - INFO - global_step/sec: 88.231 -2022-05-01 17:36:06,921 - tensorflow - INFO - loss = 0.013778967, step = 1009200 (2.267 sec) -2022-05-01 17:36:09,190 - tensorflow - INFO - global_step/sec: 88.1347 -2022-05-01 17:36:09,191 - tensorflow - INFO - loss = 0.6359856, step = 1009400 (2.269 sec) -2022-05-01 17:36:11,455 - tensorflow - INFO - global_step/sec: 88.2936 -2022-05-01 17:36:11,456 - tensorflow - INFO - loss = 0.04642318, step = 1009600 (2.265 sec) -2022-05-01 17:36:13,720 - tensorflow - INFO - global_step/sec: 88.3184 -2022-05-01 17:36:13,720 - tensorflow - INFO - loss = 0.02470339, step = 1009800 (2.265 sec) -2022-05-01 17:36:15,986 - tensorflow - INFO - global_step/sec: 88.2754 -2022-05-01 17:36:15,986 - tensorflow - INFO - loss = 0.020680029, step = 1010000 (2.266 sec) -2022-05-01 17:36:18,257 - tensorflow - INFO - global_step/sec: 88.067 -2022-05-01 17:36:18,257 - tensorflow - INFO - loss = 0.3155625, step = 1010200 (2.271 sec) -2022-05-01 17:36:20,524 - tensorflow - INFO - global_step/sec: 88.211 -2022-05-01 17:36:20,524 - tensorflow - INFO - loss = 0.02587337, step = 1010400 (2.267 sec) -2022-05-01 17:36:22,793 - tensorflow - INFO - global_step/sec: 88.1232 -2022-05-01 17:36:22,794 - tensorflow - INFO - loss = 0.0048405537, step = 1010600 (2.270 sec) -2022-05-01 17:36:25,065 - tensorflow - INFO - global_step/sec: 88.043 -2022-05-01 17:36:25,066 - tensorflow - INFO - loss = 0.16252938, step = 1010800 (2.272 sec) -2022-05-01 17:36:27,337 - tensorflow - INFO - global_step/sec: 88.0333 -2022-05-01 17:36:27,337 - tensorflow - INFO - loss = 0.07259081, step = 1011000 (2.272 sec) -2022-05-01 17:36:29,613 - tensorflow - INFO - global_step/sec: 87.8746 -2022-05-01 17:36:29,613 - tensorflow - INFO - loss = 0.079295605, step = 1011200 (2.276 sec) -2022-05-01 17:36:31,889 - tensorflow - INFO - global_step/sec: 87.8681 -2022-05-01 17:36:31,889 - tensorflow - INFO - loss = 0.13707778, step = 1011400 (2.276 sec) -2022-05-01 17:36:34,155 - tensorflow - INFO - global_step/sec: 88.2678 -2022-05-01 17:36:34,155 - tensorflow - INFO - loss = 0.15011756, step = 1011600 (2.266 sec) -2022-05-01 17:36:36,418 - tensorflow - INFO - global_step/sec: 88.3756 -2022-05-01 17:36:36,418 - tensorflow - INFO - loss = 0.026561353, step = 1011800 (2.263 sec) -2022-05-01 17:36:38,686 - tensorflow - INFO - global_step/sec: 88.1862 -2022-05-01 17:36:38,686 - tensorflow - INFO - loss = 0.01753925, step = 1012000 (2.268 sec) -2022-05-01 17:36:40,950 - tensorflow - INFO - global_step/sec: 88.334 -2022-05-01 17:36:40,950 - tensorflow - INFO - loss = 0.06748611, step = 1012200 (2.264 sec) -2022-05-01 17:36:43,215 - tensorflow - INFO - global_step/sec: 88.2923 -2022-05-01 17:36:43,216 - tensorflow - INFO - loss = 0.04561261, step = 1012400 (2.265 sec) -2022-05-01 17:36:45,484 - tensorflow - INFO - global_step/sec: 88.1397 -2022-05-01 17:36:45,485 - 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INFO - loss = 0.42224547, step = 1068400 (2.267 sec) -2022-05-01 17:47:20,884 - tensorflow - INFO - global_step/sec: 88.2765 -2022-05-01 17:47:20,884 - tensorflow - INFO - loss = 0.034962434, step = 1068600 (2.266 sec) -2022-05-01 17:47:23,150 - tensorflow - INFO - global_step/sec: 88.2516 -2022-05-01 17:47:23,150 - tensorflow - INFO - loss = 0.015111672, step = 1068800 (2.266 sec) -2022-05-01 17:47:25,415 - tensorflow - INFO - global_step/sec: 88.2953 -2022-05-01 17:47:25,416 - tensorflow - INFO - loss = 1.1196761, step = 1069000 (2.265 sec) -2022-05-01 17:47:27,684 - tensorflow - INFO - global_step/sec: 88.152 -2022-05-01 17:47:27,684 - tensorflow - INFO - loss = 0.052162483, step = 1069200 (2.269 sec) -2022-05-01 17:47:29,951 - tensorflow - INFO - global_step/sec: 88.2073 -2022-05-01 17:47:29,952 - tensorflow - INFO - loss = 0.017754044, step = 1069400 (2.267 sec) -2022-05-01 17:47:32,229 - tensorflow - INFO - global_step/sec: 87.7978 -2022-05-01 17:47:32,230 - tensorflow - INFO - loss = 0.0082748365, step = 1069600 (2.278 sec) -2022-05-01 17:47:34,493 - 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INFO - loss = 0.080452554, step = 1089600 (2.271 sec) -2022-05-01 17:51:21,410 - tensorflow - INFO - global_step/sec: 88.0926 -2022-05-01 17:51:21,411 - tensorflow - INFO - loss = 0.03880716, step = 1089800 (2.270 sec) -2022-05-01 17:51:23,679 - tensorflow - INFO - global_step/sec: 88.1435 -2022-05-01 17:51:23,680 - tensorflow - INFO - loss = 0.008254628, step = 1090000 (2.269 sec) -2022-05-01 17:51:25,951 - tensorflow - INFO - global_step/sec: 88.0392 -2022-05-01 17:51:25,951 - tensorflow - INFO - loss = 0.00797873, step = 1090200 (2.272 sec) -2022-05-01 17:51:28,220 - tensorflow - INFO - global_step/sec: 88.1287 -2022-05-01 17:51:28,221 - tensorflow - INFO - loss = 0.20207301, step = 1090400 (2.269 sec) -2022-05-01 17:51:30,497 - tensorflow - INFO - global_step/sec: 87.8379 -2022-05-01 17:51:30,498 - tensorflow - INFO - loss = 0.05177628, step = 1090600 (2.277 sec) -2022-05-01 17:51:32,779 - tensorflow - INFO - global_step/sec: 87.675 -2022-05-01 17:51:32,779 - tensorflow - INFO - loss = 0.14082907, step = 1090800 (2.281 sec) -2022-05-01 17:51:35,050 - tensorflow - INFO - global_step/sec: 88.0333 -2022-05-01 17:51:35,051 - tensorflow - INFO - loss = 0.38474733, step = 1091000 (2.272 sec) -2022-05-01 17:51:37,317 - tensorflow - INFO - global_step/sec: 88.2365 -2022-05-01 17:51:37,317 - tensorflow - INFO - loss = 0.025618026, step = 1091200 (2.267 sec) -2022-05-01 17:51:39,581 - tensorflow - INFO - global_step/sec: 88.3333 -2022-05-01 17:51:39,582 - tensorflow - INFO - loss = 0.035239328, step = 1091400 (2.264 sec) -2022-05-01 17:51:41,845 - tensorflow - INFO - global_step/sec: 88.339 -2022-05-01 17:51:41,846 - tensorflow - INFO - loss = 0.026621893, step = 1091600 (2.264 sec) -2022-05-01 17:51:44,119 - tensorflow - INFO - global_step/sec: 87.9404 -2022-05-01 17:51:44,120 - tensorflow - INFO - loss = 0.012498661, step = 1091800 (2.274 sec) -2022-05-01 17:51:46,388 - tensorflow - INFO - global_step/sec: 88.1492 -2022-05-01 17:51:46,389 - tensorflow - INFO - loss = 0.07283859, step = 1092000 (2.269 sec) -2022-05-01 17:51:48,657 - tensorflow - INFO - global_step/sec: 88.1626 -2022-05-01 17:51:48,657 - tensorflow - INFO - loss = 0.023764364, step = 1092200 (2.269 sec) -2022-05-01 17:51:50,922 - tensorflow - INFO - global_step/sec: 88.2985 -2022-05-01 17:51:50,922 - tensorflow - INFO - loss = 0.013630393, step = 1092400 (2.265 sec) -2022-05-01 17:51:53,192 - tensorflow - INFO - global_step/sec: 88.1159 -2022-05-01 17:51:53,192 - tensorflow - INFO - loss = 0.05786172, step = 1092600 (2.270 sec) -2022-05-01 17:51:55,455 - tensorflow - INFO - global_step/sec: 88.3758 -2022-05-01 17:51:55,455 - tensorflow - INFO - loss = 0.019399472, step = 1092800 (2.263 sec) -2022-05-01 17:51:57,732 - tensorflow - INFO - global_step/sec: 87.8392 -2022-05-01 17:51:57,732 - tensorflow - INFO - loss = 0.008102739, step = 1093000 (2.277 sec) -2022-05-01 17:51:59,999 - tensorflow - INFO - global_step/sec: 88.2145 -2022-05-01 17:51:59,999 - tensorflow - INFO - loss = 0.014448153, step = 1093200 (2.267 sec) -2022-05-01 17:52:02,269 - tensorflow - INFO - global_step/sec: 88.1059 -2022-05-01 17:52:02,269 - tensorflow - INFO - loss = 0.07463558, step = 1093400 (2.270 sec) -2022-05-01 17:52:04,539 - tensorflow - INFO - global_step/sec: 88.094 -2022-05-01 17:52:04,540 - tensorflow - INFO - loss = 0.029204894, step = 1093600 (2.270 sec) -2022-05-01 17:52:06,810 - tensorflow - INFO - global_step/sec: 88.0569 -2022-05-01 17:52:06,811 - tensorflow - INFO - loss = 0.095928356, step = 1093800 (2.271 sec) -2022-05-01 17:52:09,082 - tensorflow - INFO - global_step/sec: 88.0486 -2022-05-01 17:52:09,082 - tensorflow - INFO - loss = 0.008705968, step = 1094000 (2.272 sec) -2022-05-01 17:52:11,356 - tensorflow - INFO - global_step/sec: 87.9548 -2022-05-01 17:52:11,356 - tensorflow - INFO - loss = 0.02741694, step = 1094200 (2.274 sec) -2022-05-01 17:52:13,631 - tensorflow - INFO - global_step/sec: 87.8973 -2022-05-01 17:52:13,632 - tensorflow - INFO - loss = 0.026683709, step = 1094400 (2.275 sec) -2022-05-01 17:52:15,904 - tensorflow - INFO - global_step/sec: 87.9759 -2022-05-01 17:52:15,905 - tensorflow - INFO - loss = 0.039245937, step = 1094600 (2.273 sec) -2022-05-01 17:52:18,182 - tensorflow - INFO - global_step/sec: 87.8312 -2022-05-01 17:52:18,182 - tensorflow - INFO - loss = 0.03418677, step = 1094800 (2.277 sec) -2022-05-01 17:52:20,458 - tensorflow - INFO - global_step/sec: 87.8518 -2022-05-01 17:52:20,459 - tensorflow - INFO - loss = 0.018989494, step = 1095000 (2.277 sec) -2022-05-01 17:52:22,733 - tensorflow - INFO - global_step/sec: 87.904 -2022-05-01 17:52:22,734 - tensorflow - INFO - loss = 0.010458937, step = 1095200 (2.275 sec) -2022-05-01 17:52:25,010 - tensorflow - INFO - global_step/sec: 87.8573 -2022-05-01 17:52:25,010 - tensorflow - INFO - loss = 0.96352756, step = 1095400 (2.276 sec) -2022-05-01 17:52:27,283 - tensorflow - INFO - global_step/sec: 87.9611 -2022-05-01 17:52:27,284 - tensorflow - INFO - loss = 0.06753484, step = 1095600 (2.274 sec) -2022-05-01 17:52:29,557 - tensorflow - INFO - global_step/sec: 87.9567 -2022-05-01 17:52:29,558 - tensorflow - INFO - loss = 0.015580781, step = 1095800 (2.274 sec) -2022-05-01 17:52:31,831 - tensorflow - INFO - global_step/sec: 87.962 -2022-05-01 17:52:31,831 - tensorflow - INFO - loss = 0.15739396, step = 1096000 (2.274 sec) -2022-05-01 17:52:34,108 - tensorflow - INFO - global_step/sec: 87.8395 -2022-05-01 17:52:34,108 - tensorflow - INFO - loss = 0.05162877, step = 1096200 (2.277 sec) -2022-05-01 17:52:36,376 - tensorflow - INFO - global_step/sec: 88.1794 -2022-05-01 17:52:36,376 - tensorflow - INFO - loss = 0.011576347, step = 1096400 (2.268 sec) -2022-05-01 17:52:38,645 - tensorflow - INFO - global_step/sec: 88.1411 -2022-05-01 17:52:38,646 - tensorflow - INFO - loss = 0.010396266, step = 1096600 (2.269 sec) -2022-05-01 17:52:40,912 - tensorflow - INFO - global_step/sec: 88.2174 -2022-05-01 17:52:40,913 - tensorflow - INFO - loss = 0.022482887, step = 1096800 (2.267 sec) -2022-05-01 17:52:43,179 - tensorflow - INFO - global_step/sec: 88.2341 -2022-05-01 17:52:43,179 - tensorflow - INFO - loss = 0.010558477, step = 1097000 (2.267 sec) -2022-05-01 17:52:45,446 - tensorflow - INFO - global_step/sec: 88.2151 -2022-05-01 17:52:45,447 - tensorflow - INFO - loss = 0.036490947, step = 1097200 (2.267 sec) -2022-05-01 17:52:47,709 - tensorflow - INFO - global_step/sec: 88.3911 -2022-05-01 17:52:47,709 - tensorflow - INFO - loss = 0.15203127, step = 1097400 (2.263 sec) -2022-05-01 17:52:49,982 - tensorflow - INFO - global_step/sec: 87.9806 -2022-05-01 17:52:49,982 - tensorflow - INFO - loss = 0.05722553, step = 1097600 (2.273 sec) -2022-05-01 17:52:52,254 - tensorflow - INFO - global_step/sec: 88.0291 -2022-05-01 17:52:52,254 - tensorflow - INFO - loss = 0.051573113, step = 1097800 (2.272 sec) -2022-05-01 17:52:54,519 - tensorflow - INFO - global_step/sec: 88.3146 -2022-05-01 17:52:54,519 - tensorflow - INFO - loss = 0.042725034, step = 1098000 (2.265 sec) -2022-05-01 17:52:56,791 - tensorflow - INFO - global_step/sec: 88.0275 -2022-05-01 17:52:56,791 - tensorflow - INFO - loss = 0.2444915, step = 1098200 (2.272 sec) -2022-05-01 17:52:59,057 - tensorflow - INFO - global_step/sec: 88.2664 -2022-05-01 17:52:59,057 - tensorflow - INFO - loss = 0.01457686, step = 1098400 (2.266 sec) -2022-05-01 17:53:01,327 - tensorflow - INFO - global_step/sec: 88.0678 -2022-05-01 17:53:01,328 - tensorflow - INFO - loss = 0.008405423, step = 1098600 (2.271 sec) -2022-05-01 17:53:03,599 - tensorflow - INFO - global_step/sec: 88.0387 -2022-05-01 17:53:03,600 - tensorflow - INFO - loss = 0.023712724, step = 1098800 (2.272 sec) -2022-05-01 17:53:05,868 - tensorflow - INFO - global_step/sec: 88.1347 -2022-05-01 17:53:05,869 - tensorflow - INFO - loss = 0.017779306, step = 1099000 (2.269 sec) -2022-05-01 17:53:08,139 - tensorflow - INFO - global_step/sec: 88.0815 -2022-05-01 17:53:08,139 - tensorflow - INFO - loss = 0.03620185, step = 1099200 (2.271 sec) -2022-05-01 17:53:10,414 - tensorflow - INFO - global_step/sec: 87.921 -2022-05-01 17:53:10,414 - tensorflow - INFO - loss = 0.00687285, step = 1099400 (2.275 sec) -2022-05-01 17:53:12,683 - tensorflow - INFO - global_step/sec: 88.141 -2022-05-01 17:53:12,683 - tensorflow - INFO - loss = 0.015671916, step = 1099600 (2.269 sec) -2022-05-01 17:53:14,957 - tensorflow - INFO - global_step/sec: 87.9438 -2022-05-01 17:53:14,958 - tensorflow - INFO - loss = 0.021432642, step = 1099800 (2.274 sec) -2022-05-01 17:53:17,213 - tensorflow - INFO - Saving checkpoints for 1100000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. -2022-05-01 17:53:17,228 - tensorflow - INFO - model.ckpt-1100000 is not in all_model_checkpoint_paths. Manually adding it. -2022-05-01 17:53:17,753 - tensorflow - INFO - Calling model_fn. -2022-05-01 17:53:17,880 - tensorflow - INFO - Done calling model_fn. -2022-05-01 17:53:17,895 - tensorflow - INFO - Starting evaluation at 2022-05-01T17:53:17Z -2022-05-01 17:53:17,959 - tensorflow - INFO - Graph was finalized. -2022-05-01 17:53:17,962 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1100000 -2022-05-01 17:53:18,242 - tensorflow - INFO - Running local_init_op. -2022-05-01 17:53:18,350 - tensorflow - INFO - Done running local_init_op. -2022-05-01 17:53:28,495 - tensorflow - INFO - Evaluation [3990/39900] -2022-05-01 17:53:37,950 - tensorflow - INFO - Evaluation [7980/39900] -2022-05-01 17:53:47,393 - tensorflow - INFO - Evaluation [11970/39900] -2022-05-01 17:53:56,838 - tensorflow - INFO - Evaluation [15960/39900] -2022-05-01 17:54:06,279 - tensorflow - INFO - Evaluation [19950/39900] -2022-05-01 17:54:15,733 - tensorflow - INFO - Evaluation [23940/39900] -2022-05-01 17:54:25,160 - tensorflow - INFO - Evaluation [27930/39900] -2022-05-01 17:54:34,614 - tensorflow - INFO - Evaluation [31920/39900] -2022-05-01 17:54:44,055 - tensorflow - INFO - Evaluation [35910/39900] -2022-05-01 17:54:53,494 - tensorflow - INFO - Evaluation [39900/39900] -2022-05-01 17:54:53,631 - tensorflow - INFO - Finished evaluation at 2022-05-01-17:54:53 -2022-05-01 17:54:53,632 - tensorflow - INFO - Saving dict for global step 1100000: global_step = 1100000, loss = 0.025317876, mean_degree_err = 4.4118333 -2022-05-01 17:54:53,632 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 1100000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1100000 -2022-05-01 17:54:53,646 - tensorflow - INFO - global_step/sec: 2.02658 -2022-05-01 17:54:53,647 - tensorflow - INFO - loss = 5.8303533, step = 1100000 (98.689 sec) -2022-05-01 17:54:55,928 - tensorflow - INFO - global_step/sec: 87.6514 -2022-05-01 17:54:55,928 - tensorflow - INFO - loss = 0.017083686, step = 1100200 (2.281 sec) -2022-05-01 17:54:58,192 - tensorflow - INFO - global_step/sec: 88.3137 -2022-05-01 17:54:58,193 - tensorflow - INFO - loss = 0.029804125, step = 1100400 (2.265 sec) -2022-05-01 17:55:00,461 - tensorflow - INFO - global_step/sec: 88.1546 -2022-05-01 17:55:00,461 - tensorflow - INFO - loss = 0.16030154, step = 1100600 (2.269 sec) -2022-05-01 17:55:02,731 - tensorflow - INFO - global_step/sec: 88.1041 -2022-05-01 17:55:02,731 - tensorflow - INFO - loss = 0.014746798, step = 1100800 (2.270 sec) -2022-05-01 17:55:05,001 - tensorflow - INFO - global_step/sec: 88.1035 -2022-05-01 17:55:05,001 - tensorflow - INFO - loss = 0.018318102, step = 1101000 (2.270 sec) -2022-05-01 17:55:07,275 - tensorflow - INFO - global_step/sec: 87.959 -2022-05-01 17:55:07,275 - tensorflow - INFO - loss = 0.026991412, step = 1101200 (2.274 sec) -2022-05-01 17:55:09,546 - tensorflow - INFO - global_step/sec: 88.0721 -2022-05-01 17:55:09,546 - tensorflow - INFO - loss = 0.03456292, step = 1101400 (2.271 sec) -2022-05-01 17:55:11,813 - tensorflow - INFO - global_step/sec: 88.2073 -2022-05-01 17:55:11,813 - tensorflow - INFO - loss = 0.007666119, step = 1101600 (2.267 sec) -2022-05-01 17:55:14,081 - tensorflow - INFO - global_step/sec: 88.1868 -2022-05-01 17:55:14,081 - tensorflow - INFO - loss = 0.019554604, step = 1101800 (2.268 sec) -2022-05-01 17:55:16,350 - tensorflow - INFO - global_step/sec: 88.1564 -2022-05-01 17:55:16,350 - tensorflow - INFO - loss = 0.013642142, step = 1102000 (2.269 sec) -2022-05-01 17:55:18,618 - tensorflow - INFO - global_step/sec: 88.17 -2022-05-01 17:55:18,618 - tensorflow - INFO - loss = 0.01439528, step = 1102200 (2.268 sec) -2022-05-01 17:55:20,890 - tensorflow - INFO - global_step/sec: 88.0217 -2022-05-01 17:55:20,891 - tensorflow - INFO - loss = 0.015880037, step = 1102400 (2.272 sec) -2022-05-01 17:55:23,160 - tensorflow - INFO - global_step/sec: 88.1192 -2022-05-01 17:55:23,160 - tensorflow - INFO - loss = 0.048466884, step = 1102600 (2.270 sec) -2022-05-01 17:55:25,429 - tensorflow - INFO - global_step/sec: 88.1244 -2022-05-01 17:55:25,430 - tensorflow - INFO - loss = 0.049483653, step = 1102800 (2.270 sec) -2022-05-01 17:55:27,694 - tensorflow - INFO - global_step/sec: 88.3191 -2022-05-01 17:55:27,694 - tensorflow - INFO - loss = 0.3012721, step = 1103000 (2.265 sec) -2022-05-01 17:55:29,964 - tensorflow - INFO - global_step/sec: 88.0985 -2022-05-01 17:55:29,964 - tensorflow - INFO - loss = 0.019714927, step = 1103200 (2.270 sec) -2022-05-01 17:55:32,245 - tensorflow - INFO - global_step/sec: 87.6843 -2022-05-01 17:55:32,245 - tensorflow - INFO - loss = 0.022536471, step = 1103400 (2.281 sec) -2022-05-01 17:55:34,522 - tensorflow - INFO - global_step/sec: 87.8411 -2022-05-01 17:55:34,522 - tensorflow - INFO - loss = 0.04687898, step = 1103600 (2.277 sec) -2022-05-01 17:55:36,798 - tensorflow - INFO - global_step/sec: 87.8777 -2022-05-01 17:55:36,798 - tensorflow - INFO - loss = 0.058065794, step = 1103800 (2.276 sec) -2022-05-01 17:55:39,072 - tensorflow - INFO - global_step/sec: 87.9228 -2022-05-01 17:55:39,073 - tensorflow - INFO - loss = 0.45963365, step = 1104000 (2.275 sec) -2022-05-01 17:55:41,350 - tensorflow - INFO - global_step/sec: 87.8175 -2022-05-01 17:55:41,350 - tensorflow - INFO - loss = 0.6161173, step = 1104200 (2.277 sec) -2022-05-01 17:55:43,623 - tensorflow - INFO - global_step/sec: 87.9808 -2022-05-01 17:55:43,623 - tensorflow - INFO - loss = 0.025974642, step = 1104400 (2.273 sec) -2022-05-01 17:55:45,900 - tensorflow - INFO - global_step/sec: 87.8351 -2022-05-01 17:55:45,901 - tensorflow - INFO - loss = 0.04511058, step = 1104600 (2.277 sec) -2022-05-01 17:55:48,179 - tensorflow - INFO - global_step/sec: 87.7429 -2022-05-01 17:55:48,180 - tensorflow - INFO - loss = 0.14503525, step = 1104800 (2.279 sec) -2022-05-01 17:55:50,453 - tensorflow - INFO - global_step/sec: 87.965 -2022-05-01 17:55:50,454 - tensorflow - INFO - loss = 0.030037086, step = 1105000 (2.274 sec) -2022-05-01 17:55:52,733 - tensorflow - INFO - global_step/sec: 87.7381 -2022-05-01 17:55:52,733 - tensorflow - INFO - loss = 0.044755362, step = 1105200 (2.279 sec) -2022-05-01 17:55:55,006 - tensorflow - INFO - global_step/sec: 87.9747 -2022-05-01 17:55:55,006 - tensorflow - INFO - loss = 0.01589223, step = 1105400 (2.273 sec) -2022-05-01 17:55:57,288 - tensorflow - INFO - global_step/sec: 87.658 -2022-05-01 17:55:57,288 - tensorflow - INFO - loss = 1.0562906, step = 1105600 (2.282 sec) -2022-05-01 17:55:59,564 - tensorflow - INFO - global_step/sec: 87.867 -2022-05-01 17:55:59,564 - tensorflow - INFO - loss = 0.12573439, step = 1105800 (2.276 sec) -2022-05-01 17:56:01,843 - tensorflow - INFO - global_step/sec: 87.747 -2022-05-01 17:56:01,843 - tensorflow - INFO - loss = 0.018375054, step = 1106000 (2.279 sec) -2022-05-01 17:56:04,112 - tensorflow - INFO - global_step/sec: 88.1355 -2022-05-01 17:56:04,113 - tensorflow - INFO - loss = 0.018151755, step = 1106200 (2.269 sec) -2022-05-01 17:56:06,383 - tensorflow - INFO - global_step/sec: 88.0759 -2022-05-01 17:56:06,383 - tensorflow - INFO - loss = 0.021065243, step = 1106400 (2.271 sec) -2022-05-01 17:56:08,650 - tensorflow - INFO - global_step/sec: 88.2391 -2022-05-01 17:56:08,650 - tensorflow - INFO - loss = 0.3877352, step = 1106600 (2.267 sec) -2022-05-01 17:56:10,919 - tensorflow - INFO - global_step/sec: 88.138 -2022-05-01 17:56:10,919 - tensorflow - INFO - loss = 0.005250844, step = 1106800 (2.269 sec) -2022-05-01 17:56:13,192 - tensorflow - INFO - global_step/sec: 87.9927 -2022-05-01 17:56:13,192 - tensorflow - INFO - loss = 0.01313768, step = 1107000 (2.273 sec) -2022-05-01 17:56:15,463 - tensorflow - INFO - global_step/sec: 88.0475 -2022-05-01 17:56:15,464 - tensorflow - INFO - loss = 0.11005046, step = 1107200 (2.271 sec) -2022-05-01 17:56:17,733 - tensorflow - INFO - global_step/sec: 88.1181 -2022-05-01 17:56:17,733 - tensorflow - INFO - loss = 0.060840447, step = 1107400 (2.270 sec) -2022-05-01 17:56:20,001 - tensorflow - INFO - global_step/sec: 88.1591 -2022-05-01 17:56:20,002 - tensorflow - INFO - loss = 0.008830101, step = 1107600 (2.269 sec) -2022-05-01 17:56:22,270 - tensorflow - INFO - global_step/sec: 88.167 -2022-05-01 17:56:22,270 - tensorflow - INFO - loss = 0.044792574, step = 1107800 (2.268 sec) -2022-05-01 17:56:24,542 - tensorflow - INFO - global_step/sec: 88.0255 -2022-05-01 17:56:24,542 - tensorflow - INFO - loss = 0.024858685, step = 1108000 (2.272 sec) -2022-05-01 17:56:26,810 - tensorflow - INFO - global_step/sec: 88.1793 -2022-05-01 17:56:26,810 - tensorflow - INFO - loss = 0.010348566, step = 1108200 (2.268 sec) -2022-05-01 17:56:29,085 - tensorflow - INFO - global_step/sec: 87.9299 -2022-05-01 17:56:29,085 - tensorflow - INFO - loss = 0.031716585, step = 1108400 (2.275 sec) -2022-05-01 17:56:31,361 - tensorflow - INFO - global_step/sec: 87.8529 -2022-05-01 17:56:31,362 - tensorflow - INFO - loss = 0.013905285, step = 1108600 (2.277 sec) -2022-05-01 17:56:33,628 - tensorflow - INFO - global_step/sec: 88.23 -2022-05-01 17:56:33,628 - tensorflow - INFO - loss = 0.020705432, step = 1108800 (2.267 sec) -2022-05-01 17:56:35,895 - tensorflow - INFO - global_step/sec: 88.2283 -2022-05-01 17:56:35,895 - tensorflow - INFO - loss = 0.1813573, step = 1109000 (2.267 sec) -2022-05-01 17:56:38,160 - tensorflow - INFO - global_step/sec: 88.3103 -2022-05-01 17:56:38,160 - tensorflow - INFO - loss = 0.016116481, step = 1109200 (2.265 sec) -2022-05-01 17:56:40,431 - tensorflow - INFO - global_step/sec: 88.034 -2022-05-01 17:56:40,432 - tensorflow - INFO - loss = 0.11028355, step = 1109400 (2.272 sec) -2022-05-01 17:56:42,700 - tensorflow - INFO - global_step/sec: 88.1522 -2022-05-01 17:56:42,701 - tensorflow - INFO - loss = 0.0069679697, step = 1109600 (2.269 sec) -2022-05-01 17:56:44,962 - tensorflow - INFO - global_step/sec: 88.4366 -2022-05-01 17:56:44,962 - tensorflow - INFO - loss = 0.054119132, step = 1109800 (2.262 sec) -2022-05-01 17:56:47,230 - tensorflow - INFO - global_step/sec: 88.1574 -2022-05-01 17:56:47,231 - tensorflow - INFO - loss = 0.05626864, step = 1110000 (2.269 sec) -2022-05-01 17:56:49,496 - tensorflow - INFO - global_step/sec: 88.2617 -2022-05-01 17:56:49,497 - 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INFO - global_step/sec: 88.1881 -2022-05-01 17:57:46,183 - tensorflow - INFO - loss = 0.4434392, step = 1115200 (2.268 sec) -2022-05-01 17:57:48,444 - tensorflow - INFO - global_step/sec: 88.446 -2022-05-01 17:57:48,444 - tensorflow - INFO - loss = 0.015171867, step = 1115400 (2.261 sec) -2022-05-01 17:57:50,717 - tensorflow - INFO - global_step/sec: 87.9781 -2022-05-01 17:57:50,717 - tensorflow - INFO - loss = 1.0668705, step = 1115600 (2.273 sec) -2022-05-01 17:57:52,985 - tensorflow - INFO - global_step/sec: 88.2044 -2022-05-01 17:57:52,985 - tensorflow - INFO - loss = 0.009059138, step = 1115800 (2.267 sec) -2022-05-01 17:57:55,255 - tensorflow - INFO - global_step/sec: 88.1046 -2022-05-01 17:57:55,255 - tensorflow - INFO - loss = 0.011908996, step = 1116000 (2.270 sec) -2022-05-01 17:57:57,523 - tensorflow - INFO - global_step/sec: 88.1727 -2022-05-01 17:57:57,523 - tensorflow - INFO - loss = 0.046205234, step = 1116200 (2.268 sec) -2022-05-01 17:57:59,792 - tensorflow - INFO - global_step/sec: 88.1376 -2022-05-01 17:57:59,792 - 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tensorflow - INFO - global_step/sec: 88.2979 -2022-05-01 17:58:29,267 - tensorflow - INFO - loss = 0.02196571, step = 1119000 (2.265 sec) -2022-05-01 17:58:31,536 - tensorflow - INFO - global_step/sec: 88.1376 -2022-05-01 17:58:31,536 - tensorflow - INFO - loss = 0.006170545, step = 1119200 (2.269 sec) -2022-05-01 17:58:33,805 - tensorflow - INFO - global_step/sec: 88.157 -2022-05-01 17:58:33,805 - tensorflow - INFO - loss = 0.017200092, step = 1119400 (2.269 sec) -2022-05-01 17:58:36,073 - tensorflow - INFO - global_step/sec: 88.1544 -2022-05-01 17:58:36,074 - tensorflow - INFO - loss = 0.017230386, step = 1119600 (2.269 sec) -2022-05-01 17:58:38,336 - tensorflow - INFO - global_step/sec: 88.4133 -2022-05-01 17:58:38,336 - tensorflow - INFO - loss = 0.027107479, step = 1119800 (2.262 sec) -2022-05-01 17:58:40,599 - tensorflow - INFO - global_step/sec: 88.3789 -2022-05-01 17:58:40,599 - tensorflow - INFO - loss = 0.030391566, step = 1120000 (2.263 sec) -2022-05-01 17:58:42,865 - tensorflow - INFO - global_step/sec: 88.2343 -2022-05-01 17:58:42,866 - tensorflow - INFO - loss = 0.15300664, step = 1120200 (2.267 sec) -2022-05-01 17:58:45,135 - tensorflow - INFO - global_step/sec: 88.1265 -2022-05-01 17:58:45,135 - tensorflow - INFO - loss = 0.03331274, step = 1120400 (2.269 sec) -2022-05-01 17:58:47,405 - tensorflow - INFO - global_step/sec: 88.0974 -2022-05-01 17:58:47,405 - tensorflow - INFO - loss = 0.027307924, step = 1120600 (2.270 sec) -2022-05-01 17:58:49,676 - tensorflow - INFO - global_step/sec: 88.0676 -2022-05-01 17:58:49,676 - tensorflow - INFO - loss = 0.017618783, step = 1120800 (2.271 sec) -2022-05-01 17:58:51,939 - tensorflow - INFO - global_step/sec: 88.3901 -2022-05-01 17:58:51,939 - tensorflow - INFO - loss = 0.007736385, step = 1121000 (2.263 sec) -2022-05-01 17:58:54,205 - tensorflow - INFO - global_step/sec: 88.2279 -2022-05-01 17:58:54,206 - tensorflow - INFO - loss = 0.06589486, step = 1121200 (2.267 sec) -2022-05-01 17:58:56,473 - tensorflow - INFO - 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INFO - loss = 0.0068932925, step = 1123800 (2.270 sec) -2022-05-01 17:59:26,014 - tensorflow - INFO - global_step/sec: 87.9387 -2022-05-01 17:59:26,014 - tensorflow - INFO - loss = 0.019140247, step = 1124000 (2.274 sec) -2022-05-01 17:59:28,289 - tensorflow - INFO - global_step/sec: 87.9109 -2022-05-01 17:59:28,289 - tensorflow - INFO - loss = 0.039606776, step = 1124200 (2.275 sec) -2022-05-01 17:59:30,566 - tensorflow - INFO - global_step/sec: 87.8217 -2022-05-01 17:59:30,566 - tensorflow - INFO - loss = 0.11960248, step = 1124400 (2.277 sec) -2022-05-01 17:59:32,825 - tensorflow - INFO - global_step/sec: 88.515 -2022-05-01 17:59:32,826 - tensorflow - INFO - loss = 0.019091856, step = 1124600 (2.259 sec) -2022-05-01 17:59:35,091 - tensorflow - INFO - global_step/sec: 88.2854 -2022-05-01 17:59:35,091 - tensorflow - INFO - loss = 0.033167087, step = 1124800 (2.265 sec) -2022-05-01 17:59:37,351 - tensorflow - INFO - global_step/sec: 88.4847 -2022-05-01 17:59:37,352 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.0752 -2022-05-01 17:59:53,230 - tensorflow - INFO - loss = 0.046166666, step = 1126400 (2.271 sec) -2022-05-01 17:59:55,494 - tensorflow - INFO - global_step/sec: 88.3034 -2022-05-01 17:59:55,494 - tensorflow - INFO - loss = 0.11403679, step = 1126600 (2.265 sec) -2022-05-01 17:59:57,761 - tensorflow - INFO - global_step/sec: 88.2225 -2022-05-01 17:59:57,761 - tensorflow - INFO - loss = 0.020823315, step = 1126800 (2.267 sec) -2022-05-01 18:00:00,027 - tensorflow - INFO - global_step/sec: 88.2785 -2022-05-01 18:00:00,027 - tensorflow - INFO - loss = 0.023803, step = 1127000 (2.266 sec) -2022-05-01 18:00:02,297 - tensorflow - INFO - global_step/sec: 88.0914 -2022-05-01 18:00:02,297 - tensorflow - INFO - loss = 0.012753614, step = 1127200 (2.270 sec) -2022-05-01 18:00:04,571 - tensorflow - INFO - global_step/sec: 87.9508 -2022-05-01 18:00:04,571 - tensorflow - INFO - loss = 0.16387165, step = 1127400 (2.274 sec) -2022-05-01 18:00:06,838 - tensorflow - 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INFO - loss = 0.019315578, step = 1158600 (2.262 sec) -2022-05-01 18:05:59,900 - tensorflow - INFO - global_step/sec: 88.3495 -2022-05-01 18:05:59,901 - tensorflow - INFO - loss = 0.025300844, step = 1158800 (2.264 sec) -2022-05-01 18:06:02,164 - tensorflow - INFO - global_step/sec: 88.3524 -2022-05-01 18:06:02,165 - tensorflow - INFO - loss = 0.032918695, step = 1159000 (2.264 sec) -2022-05-01 18:06:04,427 - tensorflow - INFO - global_step/sec: 88.3916 -2022-05-01 18:06:04,427 - tensorflow - INFO - loss = 0.03392154, step = 1159200 (2.263 sec) -2022-05-01 18:06:06,690 - tensorflow - INFO - global_step/sec: 88.3814 -2022-05-01 18:06:06,690 - tensorflow - INFO - loss = 0.017178094, step = 1159400 (2.263 sec) -2022-05-01 18:06:08,952 - tensorflow - INFO - global_step/sec: 88.4104 -2022-05-01 18:06:08,952 - tensorflow - INFO - loss = 0.014831856, step = 1159600 (2.262 sec) -2022-05-01 18:06:11,216 - tensorflow - INFO - global_step/sec: 88.3349 -2022-05-01 18:06:11,216 - tensorflow - INFO - 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INFO - loss = 0.052462846, step = 1166000 (2.259 sec) -2022-05-01 18:07:23,478 - tensorflow - INFO - global_step/sec: 88.6444 -2022-05-01 18:07:23,479 - tensorflow - INFO - loss = 0.046224374, step = 1166200 (2.256 sec) -2022-05-01 18:07:25,731 - tensorflow - INFO - global_step/sec: 88.7964 -2022-05-01 18:07:25,731 - tensorflow - INFO - loss = 0.024157666, step = 1166400 (2.252 sec) -2022-05-01 18:07:27,984 - tensorflow - INFO - global_step/sec: 88.741 -2022-05-01 18:07:27,985 - tensorflow - INFO - loss = 0.028098095, step = 1166600 (2.254 sec) -2022-05-01 18:07:30,243 - tensorflow - INFO - global_step/sec: 88.5541 -2022-05-01 18:07:30,243 - tensorflow - INFO - loss = 0.012415642, step = 1166800 (2.259 sec) -2022-05-01 18:07:32,505 - tensorflow - INFO - global_step/sec: 88.4272 -2022-05-01 18:07:32,505 - tensorflow - INFO - loss = 0.04531084, step = 1167000 (2.262 sec) -2022-05-01 18:07:34,764 - tensorflow - INFO - global_step/sec: 88.5168 -2022-05-01 18:07:34,764 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.8064 -2022-05-01 18:07:50,538 - tensorflow - INFO - loss = 0.013101386, step = 1168600 (2.252 sec) -2022-05-01 18:07:52,793 - tensorflow - INFO - global_step/sec: 88.6936 -2022-05-01 18:07:52,793 - tensorflow - INFO - loss = 0.008198092, step = 1168800 (2.255 sec) -2022-05-01 18:07:55,051 - tensorflow - INFO - global_step/sec: 88.5533 -2022-05-01 18:07:55,052 - tensorflow - INFO - loss = 0.013200732, step = 1169000 (2.259 sec) -2022-05-01 18:07:57,311 - tensorflow - INFO - global_step/sec: 88.4882 -2022-05-01 18:07:57,312 - tensorflow - INFO - loss = 0.0046111504, step = 1169200 (2.260 sec) -2022-05-01 18:07:59,574 - tensorflow - INFO - global_step/sec: 88.4093 -2022-05-01 18:07:59,574 - tensorflow - INFO - loss = 0.11613696, step = 1169400 (2.262 sec) -2022-05-01 18:08:01,827 - tensorflow - INFO - global_step/sec: 88.7733 -2022-05-01 18:08:01,827 - tensorflow - INFO - loss = 0.043632142, step = 1169600 (2.253 sec) -2022-05-01 18:08:04,087 - tensorflow - INFO - global_step/sec: 88.4688 -2022-05-01 18:08:04,088 - tensorflow - INFO - loss = 0.51394105, step = 1169800 (2.261 sec) -2022-05-01 18:08:06,343 - tensorflow - INFO - global_step/sec: 88.6518 -2022-05-01 18:08:06,344 - tensorflow - INFO - loss = 0.015525489, step = 1170000 (2.256 sec) -2022-05-01 18:08:08,597 - tensorflow - INFO - global_step/sec: 88.7342 -2022-05-01 18:08:08,598 - tensorflow - INFO - loss = 0.12603165, step = 1170200 (2.254 sec) -2022-05-01 18:08:10,859 - tensorflow - INFO - global_step/sec: 88.4158 -2022-05-01 18:08:10,860 - tensorflow - INFO - loss = 0.04159105, step = 1170400 (2.262 sec) -2022-05-01 18:08:13,115 - tensorflow - INFO - global_step/sec: 88.6477 -2022-05-01 18:08:13,116 - tensorflow - INFO - loss = 0.080631554, step = 1170600 (2.256 sec) -2022-05-01 18:08:15,376 - tensorflow - INFO - global_step/sec: 88.4528 -2022-05-01 18:08:15,377 - tensorflow - INFO - loss = 0.013049016, step = 1170800 (2.261 sec) -2022-05-01 18:08:17,627 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.014415514, step = 1172200 (2.259 sec) -2022-05-01 18:08:33,417 - tensorflow - INFO - global_step/sec: 88.5383 -2022-05-01 18:08:33,417 - tensorflow - INFO - loss = 0.13393927, step = 1172400 (2.259 sec) -2022-05-01 18:08:35,674 - tensorflow - INFO - global_step/sec: 88.6092 -2022-05-01 18:08:35,674 - tensorflow - INFO - loss = 0.017891834, step = 1172600 (2.257 sec) -2022-05-01 18:08:37,931 - tensorflow - INFO - global_step/sec: 88.5828 -2022-05-01 18:08:37,932 - tensorflow - INFO - loss = 0.053806677, step = 1172800 (2.258 sec) -2022-05-01 18:08:40,185 - tensorflow - INFO - global_step/sec: 88.7598 -2022-05-01 18:08:40,185 - tensorflow - INFO - loss = 0.018386256, step = 1173000 (2.253 sec) -2022-05-01 18:08:42,440 - tensorflow - INFO - global_step/sec: 88.6645 -2022-05-01 18:08:42,441 - tensorflow - INFO - loss = 0.012489137, step = 1173200 (2.256 sec) -2022-05-01 18:08:44,696 - tensorflow - INFO - global_step/sec: 88.6586 -2022-05-01 18:08:44,697 - tensorflow - INFO - loss = 0.0048879846, step = 1173400 (2.256 sec) -2022-05-01 18:08:46,949 - tensorflow - INFO - global_step/sec: 88.7866 -2022-05-01 18:08:46,949 - tensorflow - INFO - loss = 0.021216586, step = 1173600 (2.253 sec) -2022-05-01 18:08:49,210 - tensorflow - INFO - global_step/sec: 88.4403 -2022-05-01 18:08:49,211 - tensorflow - INFO - loss = 0.03790966, step = 1173800 (2.261 sec) -2022-05-01 18:08:51,464 - tensorflow - INFO - global_step/sec: 88.7269 -2022-05-01 18:08:51,465 - tensorflow - INFO - loss = 0.015610474, step = 1174000 (2.254 sec) -2022-05-01 18:08:53,717 - tensorflow - INFO - global_step/sec: 88.7694 -2022-05-01 18:08:53,718 - tensorflow - INFO - loss = 0.0103667835, step = 1174200 (2.253 sec) -2022-05-01 18:08:55,978 - tensorflow - INFO - global_step/sec: 88.4637 -2022-05-01 18:08:55,979 - tensorflow - INFO - loss = 0.01396005, step = 1174400 (2.261 sec) -2022-05-01 18:08:58,233 - tensorflow - INFO - global_step/sec: 88.7032 -2022-05-01 18:08:58,233 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.4805 -2022-05-01 18:09:14,035 - tensorflow - INFO - loss = 0.025212659, step = 1176000 (2.260 sec) -2022-05-01 18:09:16,291 - tensorflow - INFO - global_step/sec: 88.6625 -2022-05-01 18:09:16,291 - tensorflow - INFO - loss = 0.02352048, step = 1176200 (2.256 sec) -2022-05-01 18:09:18,546 - tensorflow - INFO - global_step/sec: 88.6881 -2022-05-01 18:09:18,546 - tensorflow - INFO - loss = 0.07429921, step = 1176400 (2.255 sec) -2022-05-01 18:09:20,799 - tensorflow - INFO - global_step/sec: 88.7814 -2022-05-01 18:09:20,799 - tensorflow - INFO - loss = 0.09907514, step = 1176600 (2.253 sec) -2022-05-01 18:09:23,055 - tensorflow - INFO - global_step/sec: 88.649 -2022-05-01 18:09:23,055 - tensorflow - INFO - loss = 0.017612314, step = 1176800 (2.256 sec) -2022-05-01 18:09:25,314 - tensorflow - INFO - global_step/sec: 88.5069 -2022-05-01 18:09:25,315 - tensorflow - INFO - loss = 0.036762282, step = 1177000 (2.260 sec) -2022-05-01 18:09:27,573 - tensorflow - INFO - global_step/sec: 88.5352 -2022-05-01 18:09:27,574 - tensorflow - INFO - loss = 0.11830039, step = 1177200 (2.259 sec) -2022-05-01 18:09:29,828 - tensorflow - INFO - global_step/sec: 88.712 -2022-05-01 18:09:29,828 - tensorflow - INFO - loss = 0.023711583, step = 1177400 (2.254 sec) -2022-05-01 18:09:32,089 - tensorflow - INFO - global_step/sec: 88.4599 -2022-05-01 18:09:32,089 - tensorflow - INFO - loss = 0.013508679, step = 1177600 (2.261 sec) -2022-05-01 18:09:34,355 - tensorflow - INFO - global_step/sec: 88.2524 -2022-05-01 18:09:34,355 - tensorflow - INFO - loss = 0.011801957, step = 1177800 (2.266 sec) -2022-05-01 18:09:36,620 - tensorflow - INFO - global_step/sec: 88.3141 -2022-05-01 18:09:36,620 - tensorflow - INFO - loss = 0.011798587, step = 1178000 (2.265 sec) -2022-05-01 18:09:38,887 - tensorflow - INFO - global_step/sec: 88.2272 -2022-05-01 18:09:38,887 - tensorflow - INFO - loss = 0.08090085, step = 1178200 (2.267 sec) -2022-05-01 18:09:41,149 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.024037143, step = 1179600 (2.266 sec) -2022-05-01 18:09:57,006 - tensorflow - INFO - global_step/sec: 88.178 -2022-05-01 18:09:57,007 - tensorflow - INFO - loss = 0.100833595, step = 1179800 (2.268 sec) -2022-05-01 18:09:59,272 - tensorflow - INFO - global_step/sec: 88.2797 -2022-05-01 18:09:59,272 - tensorflow - INFO - loss = 0.13181756, step = 1180000 (2.265 sec) -2022-05-01 18:10:01,529 - tensorflow - INFO - global_step/sec: 88.6203 -2022-05-01 18:10:01,529 - tensorflow - INFO - loss = 0.04202525, step = 1180200 (2.257 sec) -2022-05-01 18:10:03,783 - tensorflow - INFO - global_step/sec: 88.7326 -2022-05-01 18:10:03,783 - tensorflow - INFO - loss = 0.022234075, step = 1180400 (2.254 sec) -2022-05-01 18:10:06,041 - tensorflow - INFO - global_step/sec: 88.5698 -2022-05-01 18:10:06,041 - tensorflow - INFO - loss = 0.01398593, step = 1180600 (2.258 sec) -2022-05-01 18:10:08,302 - tensorflow - INFO - global_step/sec: 88.4474 -2022-05-01 18:10:08,302 - tensorflow - INFO - loss = 0.08817243, step = 1180800 (2.261 sec) -2022-05-01 18:10:10,560 - tensorflow - INFO - global_step/sec: 88.58 -2022-05-01 18:10:10,560 - tensorflow - INFO - loss = 0.012736602, step = 1181000 (2.258 sec) -2022-05-01 18:10:12,815 - tensorflow - INFO - global_step/sec: 88.6696 -2022-05-01 18:10:12,816 - tensorflow - INFO - loss = 0.07885161, step = 1181200 (2.256 sec) -2022-05-01 18:10:15,066 - tensorflow - INFO - global_step/sec: 88.8567 -2022-05-01 18:10:15,067 - tensorflow - INFO - loss = 0.0152314175, step = 1181400 (2.251 sec) -2022-05-01 18:10:17,317 - tensorflow - INFO - global_step/sec: 88.8601 -2022-05-01 18:10:17,317 - tensorflow - INFO - loss = 1.0100908, step = 1181600 (2.251 sec) -2022-05-01 18:10:19,572 - tensorflow - INFO - global_step/sec: 88.6878 -2022-05-01 18:10:19,572 - tensorflow - INFO - loss = 0.0416034, step = 1181800 (2.255 sec) -2022-05-01 18:10:21,835 - tensorflow - INFO - global_step/sec: 88.364 -2022-05-01 18:10:21,836 - tensorflow - INFO - loss = 0.020663407, step = 1182000 (2.263 sec) -2022-05-01 18:10:24,087 - tensorflow - INFO - global_step/sec: 88.8179 -2022-05-01 18:10:24,088 - tensorflow - INFO - loss = 0.013663379, step = 1182200 (2.252 sec) -2022-05-01 18:10:26,340 - tensorflow - INFO - global_step/sec: 88.7946 -2022-05-01 18:10:26,340 - tensorflow - INFO - loss = 0.010723071, step = 1182400 (2.252 sec) -2022-05-01 18:10:28,594 - tensorflow - INFO - global_step/sec: 88.6936 -2022-05-01 18:10:28,595 - tensorflow - INFO - loss = 0.020838728, step = 1182600 (2.255 sec) -2022-05-01 18:10:30,855 - tensorflow - INFO - global_step/sec: 88.4827 -2022-05-01 18:10:30,855 - tensorflow - INFO - loss = 0.010693238, step = 1182800 (2.260 sec) -2022-05-01 18:10:33,112 - tensorflow - INFO - global_step/sec: 88.6114 -2022-05-01 18:10:33,112 - tensorflow - INFO - loss = 0.005051244, step = 1183000 (2.257 sec) -2022-05-01 18:10:35,368 - tensorflow - INFO - global_step/sec: 88.6634 -2022-05-01 18:10:35,368 - tensorflow - INFO - loss = 0.34863693, step = 1183200 (2.256 sec) -2022-05-01 18:10:37,622 - tensorflow - INFO - global_step/sec: 88.7199 -2022-05-01 18:10:37,622 - tensorflow - INFO - loss = 7.8540344, step = 1183400 (2.254 sec) -2022-05-01 18:10:39,878 - tensorflow - INFO - global_step/sec: 88.6312 -2022-05-01 18:10:39,879 - tensorflow - INFO - loss = 0.02740361, step = 1183600 (2.257 sec) -2022-05-01 18:10:42,136 - tensorflow - INFO - global_step/sec: 88.5754 -2022-05-01 18:10:42,137 - tensorflow - INFO - loss = 0.034917794, step = 1183800 (2.258 sec) -2022-05-01 18:10:44,385 - tensorflow - INFO - global_step/sec: 88.933 -2022-05-01 18:10:44,386 - tensorflow - INFO - loss = 0.038255285, step = 1184000 (2.249 sec) -2022-05-01 18:10:46,643 - tensorflow - INFO - global_step/sec: 88.5861 -2022-05-01 18:10:46,643 - tensorflow - INFO - loss = 0.008987745, step = 1184200 (2.258 sec) -2022-05-01 18:10:48,893 - tensorflow - INFO - global_step/sec: 88.8941 -2022-05-01 18:10:48,893 - tensorflow - INFO - loss = 0.019046573, step = 1184400 (2.250 sec) -2022-05-01 18:10:51,153 - tensorflow - INFO - global_step/sec: 88.4934 -2022-05-01 18:10:51,153 - tensorflow - INFO - loss = 0.015017347, step = 1184600 (2.260 sec) -2022-05-01 18:10:53,404 - tensorflow - INFO - global_step/sec: 88.8355 -2022-05-01 18:10:53,405 - tensorflow - INFO - loss = 0.020354364, step = 1184800 (2.251 sec) -2022-05-01 18:10:55,658 - tensorflow - INFO - global_step/sec: 88.7297 -2022-05-01 18:10:55,659 - tensorflow - INFO - loss = 0.023749266, step = 1185000 (2.254 sec) -2022-05-01 18:10:57,919 - tensorflow - INFO - global_step/sec: 88.4582 -2022-05-01 18:10:57,920 - tensorflow - INFO - loss = 0.042302318, step = 1185200 (2.261 sec) -2022-05-01 18:11:00,176 - tensorflow - INFO - global_step/sec: 88.6145 -2022-05-01 18:11:00,177 - tensorflow - INFO - loss = 0.2269616, step = 1185400 (2.257 sec) -2022-05-01 18:11:02,430 - tensorflow - INFO - global_step/sec: 88.7532 -2022-05-01 18:11:02,430 - tensorflow - INFO - loss = 0.023176527, step = 1185600 (2.254 sec) -2022-05-01 18:11:04,686 - tensorflow - INFO - global_step/sec: 88.6265 -2022-05-01 18:11:04,687 - tensorflow - INFO - loss = 0.044826522, step = 1185800 (2.257 sec) -2022-05-01 18:11:06,943 - tensorflow - INFO - global_step/sec: 88.6347 -2022-05-01 18:11:06,943 - tensorflow - INFO - loss = 0.03250031, step = 1186000 (2.256 sec) -2022-05-01 18:11:09,199 - tensorflow - INFO - global_step/sec: 88.6546 -2022-05-01 18:11:09,199 - tensorflow - INFO - loss = 0.062487267, step = 1186200 (2.256 sec) -2022-05-01 18:11:11,462 - tensorflow - INFO - global_step/sec: 88.3802 -2022-05-01 18:11:11,462 - tensorflow - INFO - loss = 0.011299405, step = 1186400 (2.263 sec) -2022-05-01 18:11:13,720 - tensorflow - INFO - global_step/sec: 88.558 -2022-05-01 18:11:13,720 - tensorflow - INFO - loss = 0.35987207, step = 1186600 (2.258 sec) -2022-05-01 18:11:15,977 - tensorflow - INFO - global_step/sec: 88.629 -2022-05-01 18:11:15,977 - tensorflow - INFO - loss = 0.48028773, step = 1186800 (2.257 sec) -2022-05-01 18:11:18,230 - tensorflow - INFO - global_step/sec: 88.7715 -2022-05-01 18:11:18,230 - tensorflow - INFO - loss = 0.019366598, step = 1187000 (2.253 sec) -2022-05-01 18:11:20,484 - tensorflow - INFO - global_step/sec: 88.7232 -2022-05-01 18:11:20,484 - tensorflow - INFO - loss = 0.04313879, step = 1187200 (2.254 sec) -2022-05-01 18:11:22,742 - tensorflow - INFO - global_step/sec: 88.5613 -2022-05-01 18:11:22,743 - tensorflow - INFO - loss = 0.007858479, step = 1187400 (2.258 sec) -2022-05-01 18:11:25,003 - tensorflow - INFO - global_step/sec: 88.4462 -2022-05-01 18:11:25,004 - tensorflow - INFO - loss = 0.113287, step = 1187600 (2.261 sec) -2022-05-01 18:11:27,261 - tensorflow - INFO - global_step/sec: 88.5974 -2022-05-01 18:11:27,261 - tensorflow - INFO - loss = 0.010161064, step = 1187800 (2.257 sec) -2022-05-01 18:11:29,518 - tensorflow - INFO - global_step/sec: 88.6209 -2022-05-01 18:11:29,518 - tensorflow - INFO - loss = 0.018867392, step = 1188000 (2.257 sec) -2022-05-01 18:11:31,780 - tensorflow - INFO - global_step/sec: 88.4049 -2022-05-01 18:11:31,780 - tensorflow - INFO - loss = 0.061028652, step = 1188200 (2.262 sec) -2022-05-01 18:11:34,039 - tensorflow - INFO - global_step/sec: 88.5441 -2022-05-01 18:11:34,039 - tensorflow - INFO - loss = 0.03321492, step = 1188400 (2.259 sec) -2022-05-01 18:11:36,301 - tensorflow - INFO - global_step/sec: 88.4174 -2022-05-01 18:11:36,301 - tensorflow - INFO - loss = 0.066412434, step = 1188600 (2.262 sec) -2022-05-01 18:11:38,550 - tensorflow - INFO - global_step/sec: 88.9089 -2022-05-01 18:11:38,551 - tensorflow - INFO - loss = 0.017991543, step = 1188800 (2.250 sec) -2022-05-01 18:11:40,807 - tensorflow - INFO - global_step/sec: 88.6189 -2022-05-01 18:11:40,807 - tensorflow - INFO - loss = 0.027934484, step = 1189000 (2.257 sec) -2022-05-01 18:11:43,058 - tensorflow - INFO - global_step/sec: 88.8359 -2022-05-01 18:11:43,059 - tensorflow - INFO - loss = 0.014947686, step = 1189200 (2.251 sec) -2022-05-01 18:11:45,312 - tensorflow - INFO - global_step/sec: 88.7526 -2022-05-01 18:11:45,312 - tensorflow - INFO - loss = 0.019492371, step = 1189400 (2.253 sec) -2022-05-01 18:11:47,565 - tensorflow - INFO - global_step/sec: 88.7467 -2022-05-01 18:11:47,566 - tensorflow - INFO - loss = 0.036995653, step = 1189600 (2.254 sec) -2022-05-01 18:11:49,825 - tensorflow - INFO - global_step/sec: 88.5181 -2022-05-01 18:11:49,825 - tensorflow - INFO - loss = 0.09084523, step = 1189800 (2.259 sec) -2022-05-01 18:11:52,078 - tensorflow - INFO - global_step/sec: 88.748 -2022-05-01 18:11:52,079 - tensorflow - INFO - loss = 0.10673227, step = 1190000 (2.254 sec) -2022-05-01 18:11:54,329 - tensorflow - INFO - global_step/sec: 88.8723 -2022-05-01 18:11:54,329 - tensorflow - INFO - loss = 0.0142885335, step = 1190200 (2.250 sec) -2022-05-01 18:11:56,583 - tensorflow - INFO - global_step/sec: 88.7303 -2022-05-01 18:11:56,583 - tensorflow - INFO - loss = 0.43347728, step = 1190400 (2.254 sec) -2022-05-01 18:11:58,840 - tensorflow - INFO - global_step/sec: 88.6206 -2022-05-01 18:11:58,840 - tensorflow - INFO - loss = 0.030480046, step = 1190600 (2.257 sec) -2022-05-01 18:12:01,093 - tensorflow - INFO - global_step/sec: 88.7518 -2022-05-01 18:12:01,094 - tensorflow - INFO - loss = 0.08956727, step = 1190800 (2.253 sec) -2022-05-01 18:12:03,353 - tensorflow - INFO - global_step/sec: 88.4922 -2022-05-01 18:12:03,354 - tensorflow - INFO - loss = 0.034298472, step = 1191000 (2.260 sec) -2022-05-01 18:12:05,608 - tensorflow - INFO - global_step/sec: 88.6941 -2022-05-01 18:12:05,609 - tensorflow - INFO - loss = 0.04235449, step = 1191200 (2.255 sec) -2022-05-01 18:12:07,865 - tensorflow - INFO - global_step/sec: 88.6266 -2022-05-01 18:12:07,865 - tensorflow - INFO - loss = 0.018376255, step = 1191400 (2.257 sec) -2022-05-01 18:12:10,118 - tensorflow - INFO - global_step/sec: 88.7677 -2022-05-01 18:12:10,118 - tensorflow - INFO - loss = 0.03255583, step = 1191600 (2.253 sec) -2022-05-01 18:12:12,377 - tensorflow - INFO - global_step/sec: 88.5139 -2022-05-01 18:12:12,378 - tensorflow - INFO - loss = 0.012742184, step = 1191800 (2.260 sec) -2022-05-01 18:12:14,641 - tensorflow - INFO - global_step/sec: 88.3719 -2022-05-01 18:12:14,641 - tensorflow - INFO - loss = 0.010812651, step = 1192000 (2.263 sec) -2022-05-01 18:12:16,898 - tensorflow - INFO - global_step/sec: 88.6071 -2022-05-01 18:12:16,898 - tensorflow - INFO - loss = 0.12380754, step = 1192200 (2.257 sec) -2022-05-01 18:12:19,146 - tensorflow - INFO - global_step/sec: 88.9405 -2022-05-01 18:12:19,147 - tensorflow - INFO - loss = 0.017856931, step = 1192400 (2.249 sec) -2022-05-01 18:12:21,399 - tensorflow - INFO - global_step/sec: 88.8056 -2022-05-01 18:12:21,399 - tensorflow - INFO - loss = 0.006084865, step = 1192600 (2.252 sec) -2022-05-01 18:12:23,656 - tensorflow - INFO - global_step/sec: 88.5832 -2022-05-01 18:12:23,657 - tensorflow - INFO - loss = 0.019839931, step = 1192800 (2.258 sec) -2022-05-01 18:12:25,910 - tensorflow - INFO - global_step/sec: 88.747 -2022-05-01 18:12:25,910 - tensorflow - INFO - loss = 0.057857707, step = 1193000 (2.254 sec) -2022-05-01 18:12:28,167 - tensorflow - INFO - global_step/sec: 88.6283 -2022-05-01 18:12:28,167 - tensorflow - INFO - loss = 0.026098432, step = 1193200 (2.257 sec) -2022-05-01 18:12:30,425 - tensorflow - INFO - global_step/sec: 88.5675 -2022-05-01 18:12:30,425 - tensorflow - INFO - loss = 0.017955255, step = 1193400 (2.258 sec) -2022-05-01 18:12:32,676 - tensorflow - INFO - global_step/sec: 88.8284 -2022-05-01 18:12:32,677 - tensorflow - INFO - loss = 14.991538, step = 1193600 (2.251 sec) -2022-05-01 18:12:34,933 - tensorflow - INFO - global_step/sec: 88.6301 -2022-05-01 18:12:34,933 - tensorflow - INFO - loss = 0.021420287, step = 1193800 (2.257 sec) -2022-05-01 18:12:37,184 - tensorflow - INFO - global_step/sec: 88.8432 -2022-05-01 18:12:37,184 - tensorflow - INFO - loss = 0.025775535, step = 1194000 (2.251 sec) -2022-05-01 18:12:39,442 - tensorflow - INFO - global_step/sec: 88.5622 -2022-05-01 18:12:39,443 - tensorflow - INFO - loss = 0.016893541, step = 1194200 (2.258 sec) -2022-05-01 18:12:41,697 - tensorflow - INFO - global_step/sec: 88.713 -2022-05-01 18:12:41,697 - tensorflow - INFO - loss = 0.033774905, step = 1194400 (2.254 sec) -2022-05-01 18:12:43,948 - tensorflow - INFO - global_step/sec: 88.8447 -2022-05-01 18:12:43,948 - tensorflow - INFO - loss = 0.009096233, step = 1194600 (2.251 sec) -2022-05-01 18:12:46,203 - tensorflow - INFO - global_step/sec: 88.6816 -2022-05-01 18:12:46,204 - tensorflow - INFO - loss = 0.061590552, step = 1194800 (2.255 sec) -2022-05-01 18:12:48,458 - tensorflow - INFO - global_step/sec: 88.6798 -2022-05-01 18:12:48,459 - tensorflow - INFO - loss = 0.56076455, step = 1195000 (2.255 sec) -2022-05-01 18:12:50,717 - tensorflow - INFO - global_step/sec: 88.5356 -2022-05-01 18:12:50,718 - tensorflow - INFO - loss = 0.047898788, step = 1195200 (2.259 sec) -2022-05-01 18:12:52,976 - tensorflow - INFO - global_step/sec: 88.5673 -2022-05-01 18:12:52,976 - tensorflow - INFO - loss = 0.037085086, step = 1195400 (2.258 sec) -2022-05-01 18:12:55,230 - tensorflow - INFO - global_step/sec: 88.716 -2022-05-01 18:12:55,230 - tensorflow - INFO - loss = 0.056205664, step = 1195600 (2.254 sec) -2022-05-01 18:12:57,491 - tensorflow - INFO - global_step/sec: 88.4358 -2022-05-01 18:12:57,492 - tensorflow - INFO - loss = 0.03376495, step = 1195800 (2.261 sec) -2022-05-01 18:12:59,749 - tensorflow - INFO - global_step/sec: 88.5909 -2022-05-01 18:12:59,749 - tensorflow - INFO - loss = 0.0069675804, step = 1196000 (2.258 sec) -2022-05-01 18:13:02,015 - tensorflow - INFO - global_step/sec: 88.2758 -2022-05-01 18:13:02,015 - tensorflow - INFO - loss = 0.09626718, step = 1196200 (2.266 sec) -2022-05-01 18:13:04,278 - tensorflow - INFO - global_step/sec: 88.384 -2022-05-01 18:13:04,278 - tensorflow - INFO - loss = 0.28406236, step = 1196400 (2.263 sec) -2022-05-01 18:13:06,539 - tensorflow - INFO - global_step/sec: 88.4227 -2022-05-01 18:13:06,540 - tensorflow - INFO - loss = 0.013516074, step = 1196600 (2.262 sec) -2022-05-01 18:13:08,799 - tensorflow - INFO - global_step/sec: 88.5036 -2022-05-01 18:13:08,800 - tensorflow - INFO - loss = 0.037272662, step = 1196800 (2.260 sec) -2022-05-01 18:13:11,060 - tensorflow - INFO - global_step/sec: 88.4786 -2022-05-01 18:13:11,060 - tensorflow - INFO - loss = 0.020124855, step = 1197000 (2.260 sec) -2022-05-01 18:13:13,325 - tensorflow - INFO - global_step/sec: 88.2711 -2022-05-01 18:13:13,326 - tensorflow - INFO - loss = 0.03263738, step = 1197200 (2.266 sec) -2022-05-01 18:13:15,591 - tensorflow - INFO - global_step/sec: 88.2575 -2022-05-01 18:13:15,592 - tensorflow - INFO - loss = 0.21277907, step = 1197400 (2.266 sec) -2022-05-01 18:13:17,858 - tensorflow - INFO - global_step/sec: 88.2431 -2022-05-01 18:13:17,858 - tensorflow - INFO - loss = 0.011050388, step = 1197600 (2.266 sec) -2022-05-01 18:13:20,124 - tensorflow - INFO - global_step/sec: 88.2652 -2022-05-01 18:13:20,124 - tensorflow - INFO - loss = 0.029462758, step = 1197800 (2.266 sec) -2022-05-01 18:13:22,385 - tensorflow - INFO - global_step/sec: 88.4637 -2022-05-01 18:13:22,385 - tensorflow - INFO - loss = 0.016916715, step = 1198000 (2.261 sec) -2022-05-01 18:13:24,649 - tensorflow - INFO - global_step/sec: 88.3252 -2022-05-01 18:13:24,649 - tensorflow - INFO - loss = 0.01425764, step = 1198200 (2.264 sec) -2022-05-01 18:13:26,914 - tensorflow - INFO - global_step/sec: 88.3187 -2022-05-01 18:13:26,914 - tensorflow - INFO - loss = 0.08497985, step = 1198400 (2.265 sec) -2022-05-01 18:13:29,178 - tensorflow - INFO - global_step/sec: 88.3126 -2022-05-01 18:13:29,179 - tensorflow - INFO - loss = 0.0450411, step = 1198600 (2.265 sec) -2022-05-01 18:13:31,448 - tensorflow - INFO - global_step/sec: 88.1132 -2022-05-01 18:13:31,448 - tensorflow - INFO - loss = 0.033562914, step = 1198800 (2.270 sec) -2022-05-01 18:13:33,706 - tensorflow - INFO - global_step/sec: 88.5748 -2022-05-01 18:13:33,706 - tensorflow - INFO - loss = 0.082803875, step = 1199000 (2.258 sec) -2022-05-01 18:13:35,964 - tensorflow - INFO - global_step/sec: 88.5552 -2022-05-01 18:13:35,965 - tensorflow - INFO - loss = 1.5781188, step = 1199200 (2.258 sec) -2022-05-01 18:13:38,215 - tensorflow - INFO - global_step/sec: 88.8648 -2022-05-01 18:13:38,215 - tensorflow - INFO - loss = 0.0135265775, step = 1199400 (2.251 sec) -2022-05-01 18:13:40,467 - tensorflow - INFO - global_step/sec: 88.8301 -2022-05-01 18:13:40,467 - tensorflow - INFO - loss = 0.009275254, step = 1199600 (2.252 sec) -2022-05-01 18:13:42,718 - tensorflow - INFO - global_step/sec: 88.8346 -2022-05-01 18:13:42,718 - tensorflow - INFO - loss = 0.029307913, step = 1199800 (2.251 sec) -2022-05-01 18:13:44,962 - tensorflow - INFO - Saving checkpoints for 1200000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. -2022-05-01 18:13:44,978 - tensorflow - INFO - model.ckpt-1200000 is not in all_model_checkpoint_paths. Manually adding it. -2022-05-01 18:13:45,518 - tensorflow - INFO - Calling model_fn. -2022-05-01 18:13:45,645 - tensorflow - INFO - Done calling model_fn. -2022-05-01 18:13:45,660 - tensorflow - INFO - Starting evaluation at 2022-05-01T18:13:45Z -2022-05-01 18:13:45,724 - tensorflow - INFO - Graph was finalized. -2022-05-01 18:13:45,727 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1200000 -2022-05-01 18:13:46,008 - tensorflow - INFO - Running local_init_op. -2022-05-01 18:13:46,116 - tensorflow - INFO - Done running local_init_op. -2022-05-01 18:13:56,323 - tensorflow - INFO - Evaluation [3990/39900] -2022-05-01 18:14:05,837 - tensorflow - INFO - Evaluation [7980/39900] -2022-05-01 18:14:15,350 - tensorflow - INFO - Evaluation [11970/39900] -2022-05-01 18:14:24,848 - tensorflow - INFO - Evaluation [15960/39900] -2022-05-01 18:14:34,349 - tensorflow - INFO - Evaluation [19950/39900] -2022-05-01 18:14:43,851 - tensorflow - INFO - Evaluation [23940/39900] -2022-05-01 18:14:53,368 - tensorflow - INFO - Evaluation [27930/39900] -2022-05-01 18:15:02,897 - tensorflow - INFO - Evaluation [31920/39900] -2022-05-01 18:15:12,412 - tensorflow - INFO - Evaluation [35910/39900] -2022-05-01 18:15:21,938 - tensorflow - INFO - Evaluation [39900/39900] -2022-05-01 18:15:22,073 - tensorflow - INFO - Finished evaluation at 2022-05-01-18:15:22 -2022-05-01 18:15:22,074 - tensorflow - INFO - Saving dict for global step 1200000: global_step = 1200000, loss = 0.049551684, mean_degree_err = 8.192199 -2022-05-01 18:15:22,074 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 1200000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1200000 -2022-05-01 18:15:22,088 - tensorflow - INFO - global_step/sec: 2.01268 -2022-05-01 18:15:22,089 - tensorflow - INFO - loss = 0.10029067, step = 1200000 (99.370 sec) -2022-05-01 18:15:24,354 - tensorflow - INFO - global_step/sec: 88.256 -2022-05-01 18:15:24,354 - tensorflow - INFO - loss = 0.03690188, step = 1200200 (2.266 sec) -2022-05-01 18:15:26,611 - tensorflow - INFO - global_step/sec: 88.6113 -2022-05-01 18:15:26,611 - tensorflow - INFO - loss = 0.033221673, step = 1200400 (2.257 sec) -2022-05-01 18:15:28,864 - tensorflow - INFO - global_step/sec: 88.7596 -2022-05-01 18:15:28,865 - tensorflow - INFO - loss = 0.021254009, step = 1200600 (2.253 sec) -2022-05-01 18:15:31,122 - tensorflow - INFO - global_step/sec: 88.5909 -2022-05-01 18:15:31,122 - tensorflow - INFO - loss = 0.03441968, step = 1200800 (2.258 sec) -2022-05-01 18:15:33,376 - tensorflow - INFO - global_step/sec: 88.7153 -2022-05-01 18:15:33,377 - tensorflow - INFO - loss = 0.030785479, step = 1201000 (2.254 sec) -2022-05-01 18:15:35,629 - tensorflow - INFO - global_step/sec: 88.7627 -2022-05-01 18:15:35,630 - tensorflow - INFO - loss = 0.008263586, step = 1201200 (2.253 sec) -2022-05-01 18:15:37,886 - tensorflow - INFO - global_step/sec: 88.6402 -2022-05-01 18:15:37,886 - tensorflow - INFO - loss = 0.08718412, step = 1201400 (2.256 sec) -2022-05-01 18:15:40,141 - tensorflow - INFO - global_step/sec: 88.6754 -2022-05-01 18:15:40,142 - tensorflow - INFO - loss = 0.06642119, step = 1201600 (2.255 sec) -2022-05-01 18:15:42,395 - tensorflow - INFO - global_step/sec: 88.7366 -2022-05-01 18:15:42,395 - tensorflow - INFO - loss = 0.05555537, step = 1201800 (2.254 sec) -2022-05-01 18:15:44,645 - tensorflow - INFO - global_step/sec: 88.9064 -2022-05-01 18:15:44,645 - tensorflow - INFO - loss = 1.1072648, step = 1202000 (2.250 sec) -2022-05-01 18:15:46,905 - tensorflow - INFO - global_step/sec: 88.478 -2022-05-01 18:15:46,905 - tensorflow - INFO - loss = 0.06818071, step = 1202200 (2.260 sec) -2022-05-01 18:15:49,162 - tensorflow - INFO - global_step/sec: 88.6274 -2022-05-01 18:15:49,162 - tensorflow - INFO - loss = 0.02435386, step = 1202400 (2.257 sec) -2022-05-01 18:15:51,425 - tensorflow - INFO - global_step/sec: 88.3848 -2022-05-01 18:15:51,425 - tensorflow - INFO - loss = 0.031496737, step = 1202600 (2.263 sec) -2022-05-01 18:15:53,680 - tensorflow - INFO - global_step/sec: 88.6802 -2022-05-01 18:15:53,680 - tensorflow - INFO - loss = 0.22101256, step = 1202800 (2.255 sec) -2022-05-01 18:15:55,937 - tensorflow - INFO - global_step/sec: 88.6014 -2022-05-01 18:15:55,938 - tensorflow - INFO - loss = 0.010230656, step = 1203000 (2.257 sec) -2022-05-01 18:15:58,194 - tensorflow - INFO - global_step/sec: 88.5991 -2022-05-01 18:15:58,195 - tensorflow - INFO - loss = 2.6677594, step = 1203200 (2.257 sec) -2022-05-01 18:16:00,449 - tensorflow - INFO - global_step/sec: 88.7291 -2022-05-01 18:16:00,449 - tensorflow - INFO - loss = 0.14728445, step = 1203400 (2.254 sec) -2022-05-01 18:16:02,714 - tensorflow - INFO - global_step/sec: 88.2869 -2022-05-01 18:16:02,714 - tensorflow - INFO - loss = 0.03147973, step = 1203600 (2.265 sec) -2022-05-01 18:16:04,972 - tensorflow - INFO - global_step/sec: 88.5771 -2022-05-01 18:16:04,972 - tensorflow - INFO - loss = 0.089283906, step = 1203800 (2.258 sec) -2022-05-01 18:16:07,227 - tensorflow - INFO - global_step/sec: 88.6778 -2022-05-01 18:16:07,228 - tensorflow - INFO - loss = 0.05661044, step = 1204000 (2.255 sec) -2022-05-01 18:16:09,482 - tensorflow - INFO - global_step/sec: 88.6908 -2022-05-01 18:16:09,483 - tensorflow - INFO - loss = 0.018297628, step = 1204200 (2.255 sec) -2022-05-01 18:16:11,740 - tensorflow - INFO - global_step/sec: 88.5849 -2022-05-01 18:16:11,740 - tensorflow - INFO - loss = 0.025900912, step = 1204400 (2.258 sec) -2022-05-01 18:16:14,000 - tensorflow - INFO - global_step/sec: 88.4942 -2022-05-01 18:16:14,000 - tensorflow - INFO - loss = 0.034806952, step = 1204600 (2.260 sec) -2022-05-01 18:16:16,267 - tensorflow - INFO - global_step/sec: 88.2319 -2022-05-01 18:16:16,267 - tensorflow - INFO - loss = 0.018693745, step = 1204800 (2.267 sec) -2022-05-01 18:16:18,518 - tensorflow - INFO - global_step/sec: 88.8386 -2022-05-01 18:16:18,518 - tensorflow - INFO - loss = 0.034952633, step = 1205000 (2.251 sec) -2022-05-01 18:16:20,778 - tensorflow - INFO - global_step/sec: 88.4784 -2022-05-01 18:16:20,779 - tensorflow - INFO - loss = 0.013241578, step = 1205200 (2.260 sec) -2022-05-01 18:16:23,041 - tensorflow - INFO - global_step/sec: 88.3991 -2022-05-01 18:16:23,041 - tensorflow - INFO - loss = 0.011428614, step = 1205400 (2.262 sec) -2022-05-01 18:16:25,300 - tensorflow - INFO - global_step/sec: 88.5366 -2022-05-01 18:16:25,300 - tensorflow - INFO - loss = 0.56577766, step = 1205600 (2.259 sec) -2022-05-01 18:16:27,560 - tensorflow - INFO - global_step/sec: 88.4861 -2022-05-01 18:16:27,560 - tensorflow - INFO - loss = 0.01962918, step = 1205800 (2.260 sec) -2022-05-01 18:16:29,818 - tensorflow - INFO - global_step/sec: 88.5815 -2022-05-01 18:16:29,818 - tensorflow - INFO - loss = 0.077739194, step = 1206000 (2.258 sec) -2022-05-01 18:16:32,082 - tensorflow - INFO - global_step/sec: 88.3441 -2022-05-01 18:16:32,082 - tensorflow - INFO - loss = 0.030087005, step = 1206200 (2.264 sec) -2022-05-01 18:16:34,344 - tensorflow - INFO - global_step/sec: 88.3936 -2022-05-01 18:16:34,345 - tensorflow - INFO - loss = 0.029479984, step = 1206400 (2.263 sec) -2022-05-01 18:16:36,608 - tensorflow - INFO - global_step/sec: 88.3688 -2022-05-01 18:16:36,608 - tensorflow - INFO - loss = 0.13650742, step = 1206600 (2.263 sec) -2022-05-01 18:16:38,871 - tensorflow - INFO - global_step/sec: 88.3564 -2022-05-01 18:16:38,872 - tensorflow - INFO - loss = 0.025181746, step = 1206800 (2.264 sec) -2022-05-01 18:16:41,139 - tensorflow - INFO - global_step/sec: 88.2039 -2022-05-01 18:16:41,139 - tensorflow - INFO - loss = 0.10817782, step = 1207000 (2.268 sec) -2022-05-01 18:16:43,404 - tensorflow - INFO - global_step/sec: 88.2996 -2022-05-01 18:16:43,404 - tensorflow - INFO - loss = 0.024515321, step = 1207200 (2.265 sec) -2022-05-01 18:16:45,668 - tensorflow - INFO - global_step/sec: 88.3078 -2022-05-01 18:16:45,669 - tensorflow - INFO - loss = 0.008551151, step = 1207400 (2.265 sec) -2022-05-01 18:16:47,931 - tensorflow - INFO - global_step/sec: 88.4017 -2022-05-01 18:16:47,931 - tensorflow - 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INFO - global_step/sec: 88.6143 -2022-05-01 18:23:20,660 - tensorflow - INFO - loss = 0.011611406, step = 1242400 (2.257 sec) -2022-05-01 18:23:22,913 - tensorflow - INFO - global_step/sec: 88.7342 -2022-05-01 18:23:22,913 - tensorflow - INFO - loss = 0.015167651, step = 1242600 (2.254 sec) -2022-05-01 18:23:25,170 - tensorflow - INFO - global_step/sec: 88.6087 -2022-05-01 18:23:25,171 - tensorflow - INFO - loss = 0.010836497, step = 1242800 (2.257 sec) -2022-05-01 18:23:27,436 - tensorflow - INFO - global_step/sec: 88.2604 -2022-05-01 18:23:27,437 - tensorflow - INFO - loss = 0.044162713, step = 1243000 (2.266 sec) -2022-05-01 18:23:29,693 - tensorflow - INFO - global_step/sec: 88.6233 -2022-05-01 18:23:29,693 - tensorflow - INFO - loss = 0.08167398, step = 1243200 (2.257 sec) -2022-05-01 18:23:31,963 - tensorflow - INFO - global_step/sec: 88.0938 -2022-05-01 18:23:31,964 - tensorflow - INFO - loss = 0.027459173, step = 1243400 (2.270 sec) -2022-05-01 18:23:34,225 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.5468 -2022-05-01 18:31:16,815 - tensorflow - INFO - loss = 0.03547059, step = 1284600 (2.259 sec) -2022-05-01 18:31:19,072 - tensorflow - INFO - global_step/sec: 88.581 -2022-05-01 18:31:19,073 - tensorflow - INFO - loss = 0.010466327, step = 1284800 (2.258 sec) -2022-05-01 18:31:21,332 - tensorflow - INFO - global_step/sec: 88.5187 -2022-05-01 18:31:21,332 - tensorflow - INFO - loss = 0.013831692, step = 1285000 (2.259 sec) -2022-05-01 18:31:23,589 - tensorflow - INFO - global_step/sec: 88.5789 -2022-05-01 18:31:23,590 - tensorflow - INFO - loss = 0.019581486, step = 1285200 (2.258 sec) -2022-05-01 18:31:25,847 - tensorflow - INFO - global_step/sec: 88.5897 -2022-05-01 18:31:25,847 - tensorflow - INFO - loss = 0.04874866, step = 1285400 (2.258 sec) -2022-05-01 18:31:28,107 - tensorflow - INFO - global_step/sec: 88.5069 -2022-05-01 18:31:28,107 - tensorflow - INFO - loss = 0.036323167, step = 1285600 (2.260 sec) -2022-05-01 18:31:30,366 - tensorflow - INFO - global_step/sec: 88.5367 -2022-05-01 18:31:30,366 - tensorflow - INFO - loss = 0.01787388, step = 1285800 (2.259 sec) -2022-05-01 18:31:32,626 - tensorflow - INFO - global_step/sec: 88.5028 -2022-05-01 18:31:32,626 - tensorflow - INFO - loss = 0.2014732, step = 1286000 (2.260 sec) -2022-05-01 18:31:34,886 - tensorflow - INFO - global_step/sec: 88.4615 -2022-05-01 18:31:34,887 - tensorflow - INFO - loss = 0.010692698, step = 1286200 (2.261 sec) -2022-05-01 18:31:37,139 - tensorflow - INFO - global_step/sec: 88.7775 -2022-05-01 18:31:37,140 - tensorflow - INFO - loss = 0.026695687, step = 1286400 (2.253 sec) -2022-05-01 18:31:39,391 - tensorflow - INFO - global_step/sec: 88.813 -2022-05-01 18:31:39,392 - tensorflow - INFO - loss = 0.051970016, step = 1286600 (2.252 sec) -2022-05-01 18:31:41,645 - tensorflow - INFO - global_step/sec: 88.7399 -2022-05-01 18:31:41,645 - tensorflow - INFO - loss = 0.06618852, step = 1286800 (2.254 sec) -2022-05-01 18:31:43,897 - tensorflow - INFO - global_step/sec: 88.8046 -2022-05-01 18:31:43,897 - tensorflow - INFO - loss = 0.024169106, step = 1287000 (2.252 sec) -2022-05-01 18:31:46,153 - tensorflow - INFO - global_step/sec: 88.6677 -2022-05-01 18:31:46,153 - tensorflow - INFO - loss = 0.009108191, step = 1287200 (2.256 sec) -2022-05-01 18:31:48,406 - tensorflow - INFO - global_step/sec: 88.76 -2022-05-01 18:31:48,406 - tensorflow - INFO - loss = 0.08247757, step = 1287400 (2.253 sec) -2022-05-01 18:31:50,658 - tensorflow - INFO - global_step/sec: 88.82 -2022-05-01 18:31:50,658 - tensorflow - INFO - loss = 0.010382421, step = 1287600 (2.252 sec) -2022-05-01 18:31:52,908 - tensorflow - INFO - global_step/sec: 88.8904 -2022-05-01 18:31:52,908 - tensorflow - INFO - loss = 0.4617443, step = 1287800 (2.250 sec) -2022-05-01 18:31:55,155 - tensorflow - INFO - global_step/sec: 88.9815 -2022-05-01 18:31:55,156 - tensorflow - INFO - loss = 0.018690221, step = 1288000 (2.248 sec) -2022-05-01 18:31:57,423 - tensorflow - INFO - global_step/sec: 88.1855 -2022-05-01 18:31:57,424 - tensorflow - INFO - loss = 0.006660033, step = 1288200 (2.268 sec) -2022-05-01 18:31:59,680 - tensorflow - INFO - global_step/sec: 88.626 -2022-05-01 18:31:59,680 - tensorflow - INFO - loss = 0.08108465, step = 1288400 (2.257 sec) -2022-05-01 18:32:01,939 - tensorflow - INFO - global_step/sec: 88.548 -2022-05-01 18:32:01,939 - tensorflow - INFO - loss = 0.14775956, step = 1288600 (2.259 sec) -2022-05-01 18:32:04,193 - tensorflow - INFO - global_step/sec: 88.7121 -2022-05-01 18:32:04,193 - tensorflow - INFO - loss = 0.024420854, step = 1288800 (2.254 sec) -2022-05-01 18:32:06,444 - tensorflow - INFO - global_step/sec: 88.8523 -2022-05-01 18:32:06,444 - tensorflow - INFO - loss = 2.4564295, step = 1289000 (2.251 sec) -2022-05-01 18:32:08,694 - tensorflow - INFO - global_step/sec: 88.8765 -2022-05-01 18:32:08,695 - tensorflow - INFO - loss = 0.037567124, step = 1289200 (2.250 sec) -2022-05-01 18:32:10,951 - tensorflow - INFO - global_step/sec: 88.6394 -2022-05-01 18:32:10,951 - tensorflow - INFO - loss = 0.020364668, step = 1289400 (2.256 sec) -2022-05-01 18:32:13,204 - tensorflow - INFO - global_step/sec: 88.744 -2022-05-01 18:32:13,205 - tensorflow - INFO - loss = 0.0064563705, step = 1289600 (2.254 sec) -2022-05-01 18:32:15,464 - tensorflow - INFO - global_step/sec: 88.4911 -2022-05-01 18:32:15,465 - tensorflow - INFO - loss = 0.022302406, step = 1289800 (2.260 sec) -2022-05-01 18:32:17,713 - tensorflow - INFO - global_step/sec: 88.9568 -2022-05-01 18:32:17,713 - tensorflow - INFO - loss = 0.03229746, step = 1290000 (2.248 sec) -2022-05-01 18:32:19,963 - tensorflow - INFO - global_step/sec: 88.8878 -2022-05-01 18:32:19,963 - tensorflow - INFO - loss = 0.012207605, step = 1290200 (2.250 sec) -2022-05-01 18:32:22,218 - tensorflow - INFO - global_step/sec: 88.6647 -2022-05-01 18:32:22,219 - tensorflow - INFO - loss = 2.3386588, step = 1290400 (2.256 sec) -2022-05-01 18:32:24,474 - tensorflow - INFO - global_step/sec: 88.6881 -2022-05-01 18:32:24,474 - tensorflow - INFO - loss = 0.012496609, step = 1290600 (2.255 sec) -2022-05-01 18:32:26,729 - tensorflow - INFO - global_step/sec: 88.6877 -2022-05-01 18:32:26,729 - tensorflow - INFO - loss = 0.07285924, step = 1290800 (2.255 sec) -2022-05-01 18:32:28,980 - tensorflow - INFO - global_step/sec: 88.8342 -2022-05-01 18:32:28,980 - tensorflow - INFO - loss = 0.29961962, step = 1291000 (2.251 sec) -2022-05-01 18:32:31,243 - tensorflow - INFO - global_step/sec: 88.3785 -2022-05-01 18:32:31,243 - tensorflow - INFO - loss = 0.15170337, step = 1291200 (2.263 sec) -2022-05-01 18:32:33,499 - tensorflow - INFO - global_step/sec: 88.6536 -2022-05-01 18:32:33,499 - tensorflow - INFO - loss = 0.038761783, step = 1291400 (2.256 sec) -2022-05-01 18:32:35,754 - tensorflow - INFO - global_step/sec: 88.6969 -2022-05-01 18:32:35,754 - tensorflow - INFO - loss = 0.09981961, step = 1291600 (2.255 sec) -2022-05-01 18:32:38,002 - tensorflow - INFO - global_step/sec: 88.9676 -2022-05-01 18:32:38,002 - tensorflow - INFO - loss = 0.2219835, step = 1291800 (2.248 sec) -2022-05-01 18:32:40,252 - tensorflow - INFO - global_step/sec: 88.8749 -2022-05-01 18:32:40,253 - tensorflow - INFO - loss = 0.005805756, step = 1292000 (2.250 sec) -2022-05-01 18:32:42,510 - tensorflow - INFO - global_step/sec: 88.5711 -2022-05-01 18:32:42,511 - tensorflow - INFO - loss = 0.029483391, step = 1292200 (2.258 sec) -2022-05-01 18:32:44,761 - tensorflow - INFO - global_step/sec: 88.8757 -2022-05-01 18:32:44,761 - tensorflow - INFO - loss = 0.014592943, step = 1292400 (2.250 sec) -2022-05-01 18:32:47,018 - tensorflow - INFO - global_step/sec: 88.5939 -2022-05-01 18:32:47,018 - tensorflow - INFO - loss = 0.014249931, step = 1292600 (2.258 sec) -2022-05-01 18:32:49,275 - tensorflow - INFO - global_step/sec: 88.6192 -2022-05-01 18:32:49,275 - tensorflow - INFO - loss = 0.017477775, step = 1292800 (2.257 sec) -2022-05-01 18:32:51,527 - tensorflow - INFO - global_step/sec: 88.8213 -2022-05-01 18:32:51,527 - tensorflow - INFO - loss = 0.011953859, step = 1293000 (2.252 sec) -2022-05-01 18:32:53,778 - tensorflow - INFO - global_step/sec: 88.8499 -2022-05-01 18:32:53,778 - tensorflow - INFO - loss = 0.011802371, step = 1293200 (2.251 sec) -2022-05-01 18:32:56,028 - tensorflow - INFO - global_step/sec: 88.8638 -2022-05-01 18:32:56,029 - tensorflow - INFO - loss = 0.015671462, step = 1293400 (2.251 sec) -2022-05-01 18:32:58,283 - tensorflow - INFO - global_step/sec: 88.709 -2022-05-01 18:32:58,283 - tensorflow - INFO - loss = 0.010309601, step = 1293600 (2.255 sec) -2022-05-01 18:33:00,540 - tensorflow - INFO - global_step/sec: 88.6174 -2022-05-01 18:33:00,540 - tensorflow - INFO - loss = 0.5059539, step = 1293800 (2.257 sec) -2022-05-01 18:33:02,795 - tensorflow - INFO - global_step/sec: 88.6678 -2022-05-01 18:33:02,796 - tensorflow - INFO - loss = 0.029848307, step = 1294000 (2.256 sec) -2022-05-01 18:33:05,054 - tensorflow - INFO - global_step/sec: 88.566 -2022-05-01 18:33:05,054 - tensorflow - INFO - loss = 0.008277052, step = 1294200 (2.258 sec) -2022-05-01 18:33:07,311 - tensorflow - INFO - global_step/sec: 88.5882 -2022-05-01 18:33:07,312 - tensorflow - INFO - loss = 0.20894733, step = 1294400 (2.258 sec) -2022-05-01 18:33:09,563 - tensorflow - INFO - global_step/sec: 88.8015 -2022-05-01 18:33:09,564 - tensorflow - INFO - loss = 0.15793276, step = 1294600 (2.252 sec) -2022-05-01 18:33:11,819 - tensorflow - INFO - global_step/sec: 88.6602 -2022-05-01 18:33:11,820 - tensorflow - INFO - loss = 0.010115347, step = 1294800 (2.256 sec) -2022-05-01 18:33:14,067 - tensorflow - INFO - global_step/sec: 88.9644 -2022-05-01 18:33:14,068 - tensorflow - INFO - loss = 0.01433547, step = 1295000 (2.248 sec) -2022-05-01 18:33:16,320 - tensorflow - INFO - global_step/sec: 88.7784 -2022-05-01 18:33:16,320 - tensorflow - INFO - loss = 0.05959379, step = 1295200 (2.253 sec) -2022-05-01 18:33:18,574 - tensorflow - INFO - global_step/sec: 88.7471 -2022-05-01 18:33:18,574 - tensorflow - INFO - loss = 0.009571571, step = 1295400 (2.254 sec) -2022-05-01 18:33:20,829 - tensorflow - INFO - global_step/sec: 88.688 -2022-05-01 18:33:20,829 - tensorflow - INFO - loss = 0.013059959, step = 1295600 (2.255 sec) -2022-05-01 18:33:23,086 - tensorflow - INFO - global_step/sec: 88.6207 -2022-05-01 18:33:23,086 - tensorflow - INFO - loss = 0.019144017, step = 1295800 (2.257 sec) -2022-05-01 18:33:25,348 - tensorflow - INFO - global_step/sec: 88.4099 -2022-05-01 18:33:25,348 - tensorflow - INFO - loss = 0.012158981, step = 1296000 (2.262 sec) -2022-05-01 18:33:27,601 - tensorflow - INFO - global_step/sec: 88.7429 -2022-05-01 18:33:27,602 - tensorflow - INFO - loss = 0.012564669, step = 1296200 (2.254 sec) -2022-05-01 18:33:29,853 - tensorflow - INFO - global_step/sec: 88.816 -2022-05-01 18:33:29,854 - tensorflow - INFO - loss = 6.0557833, step = 1296400 (2.252 sec) -2022-05-01 18:33:32,110 - tensorflow - INFO - global_step/sec: 88.6116 -2022-05-01 18:33:32,111 - tensorflow - INFO - loss = 0.022484478, step = 1296600 (2.257 sec) -2022-05-01 18:33:34,367 - tensorflow - INFO - global_step/sec: 88.6347 -2022-05-01 18:33:34,367 - tensorflow - INFO - loss = 0.05494872, step = 1296800 (2.256 sec) -2022-05-01 18:33:36,624 - tensorflow - INFO - global_step/sec: 88.5898 -2022-05-01 18:33:36,625 - tensorflow - INFO - loss = 0.01839782, step = 1297000 (2.258 sec) -2022-05-01 18:33:38,880 - tensorflow - INFO - global_step/sec: 88.654 -2022-05-01 18:33:38,881 - tensorflow - INFO - loss = 0.029880244, step = 1297200 (2.256 sec) -2022-05-01 18:33:41,131 - tensorflow - INFO - global_step/sec: 88.8782 -2022-05-01 18:33:41,131 - tensorflow - INFO - loss = 0.018286102, step = 1297400 (2.250 sec) -2022-05-01 18:33:43,382 - tensorflow - INFO - global_step/sec: 88.8519 -2022-05-01 18:33:43,382 - tensorflow - INFO - loss = 0.25781596, step = 1297600 (2.251 sec) -2022-05-01 18:33:45,635 - tensorflow - INFO - global_step/sec: 88.742 -2022-05-01 18:33:45,636 - tensorflow - INFO - loss = 0.016293323, step = 1297800 (2.254 sec) -2022-05-01 18:33:47,887 - tensorflow - INFO - global_step/sec: 88.8093 -2022-05-01 18:33:47,888 - tensorflow - INFO - loss = 0.0564257, step = 1298000 (2.252 sec) -2022-05-01 18:33:50,142 - tensorflow - INFO - global_step/sec: 88.7104 -2022-05-01 18:33:50,142 - tensorflow - INFO - loss = 0.01472992, step = 1298200 (2.255 sec) -2022-05-01 18:33:52,399 - tensorflow - INFO - global_step/sec: 88.6091 -2022-05-01 18:33:52,399 - tensorflow - INFO - loss = 0.21787146, step = 1298400 (2.257 sec) -2022-05-01 18:33:54,656 - tensorflow - INFO - global_step/sec: 88.6034 -2022-05-01 18:33:54,657 - tensorflow - INFO - loss = 0.030389164, step = 1298600 (2.257 sec) -2022-05-01 18:33:56,921 - tensorflow - INFO - global_step/sec: 88.3192 -2022-05-01 18:33:56,921 - tensorflow - INFO - loss = 0.014049642, step = 1298800 (2.264 sec) -2022-05-01 18:33:59,178 - tensorflow - INFO - global_step/sec: 88.611 -2022-05-01 18:33:59,178 - tensorflow - INFO - loss = 0.07121677, step = 1299000 (2.257 sec) -2022-05-01 18:34:01,441 - tensorflow - INFO - global_step/sec: 88.3786 -2022-05-01 18:34:01,441 - tensorflow - INFO - loss = 0.011796599, step = 1299200 (2.263 sec) -2022-05-01 18:34:03,706 - tensorflow - INFO - global_step/sec: 88.3047 -2022-05-01 18:34:03,706 - tensorflow - INFO - loss = 0.02658301, step = 1299400 (2.265 sec) -2022-05-01 18:34:05,964 - tensorflow - INFO - global_step/sec: 88.5573 -2022-05-01 18:34:05,964 - tensorflow - INFO - loss = 0.020927524, step = 1299600 (2.258 sec) -2022-05-01 18:34:08,224 - tensorflow - INFO - global_step/sec: 88.4939 -2022-05-01 18:34:08,225 - tensorflow - INFO - loss = 0.08239248, step = 1299800 (2.260 sec) -2022-05-01 18:34:10,473 - tensorflow - INFO - Saving checkpoints for 1300000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. -2022-05-01 18:34:10,488 - tensorflow - INFO - model.ckpt-1300000 is not in all_model_checkpoint_paths. Manually adding it. -2022-05-01 18:34:11,011 - tensorflow - INFO - Calling model_fn. -2022-05-01 18:34:11,139 - tensorflow - INFO - Done calling model_fn. -2022-05-01 18:34:11,154 - tensorflow - INFO - Starting evaluation at 2022-05-01T18:34:11Z -2022-05-01 18:34:11,218 - tensorflow - INFO - Graph was finalized. -2022-05-01 18:34:11,221 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1300000 -2022-05-01 18:34:11,503 - tensorflow - INFO - Running local_init_op. -2022-05-01 18:34:11,611 - tensorflow - INFO - Done running local_init_op. -2022-05-01 18:34:21,788 - tensorflow - INFO - Evaluation [3990/39900] -2022-05-01 18:34:31,243 - tensorflow - INFO - Evaluation [7980/39900] -2022-05-01 18:34:40,667 - tensorflow - INFO - Evaluation [11970/39900] -2022-05-01 18:34:50,117 - tensorflow - INFO - Evaluation [15960/39900] -2022-05-01 18:34:59,573 - tensorflow - INFO - Evaluation [19950/39900] -2022-05-01 18:35:09,037 - tensorflow - INFO - Evaluation [23940/39900] -2022-05-01 18:35:18,472 - tensorflow - INFO - Evaluation [27930/39900] -2022-05-01 18:35:27,938 - tensorflow - INFO - Evaluation [31920/39900] -2022-05-01 18:35:37,388 - tensorflow - INFO - Evaluation [35910/39900] -2022-05-01 18:35:46,832 - tensorflow - INFO - Evaluation [39900/39900] -2022-05-01 18:35:46,968 - tensorflow - INFO - Finished evaluation at 2022-05-01-18:35:46 -2022-05-01 18:35:46,969 - tensorflow - INFO - Saving dict for global step 1300000: global_step = 1300000, loss = 0.037376873, mean_degree_err = 5.5991697 -2022-05-01 18:35:46,969 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 1300000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1300000 -2022-05-01 18:35:46,983 - tensorflow - INFO - global_step/sec: 2.02513 -2022-05-01 18:35:46,984 - tensorflow - INFO - loss = 0.030853275, step = 1300000 (98.759 sec) -2022-05-01 18:35:49,254 - tensorflow - INFO - global_step/sec: 88.0857 -2022-05-01 18:35:49,254 - tensorflow - INFO - loss = 0.024246916, step = 1300200 (2.270 sec) -2022-05-01 18:35:51,503 - tensorflow - INFO - global_step/sec: 88.8944 -2022-05-01 18:35:51,504 - tensorflow - INFO - loss = 4.38457, step = 1300400 (2.250 sec) -2022-05-01 18:35:53,758 - tensorflow - INFO - global_step/sec: 88.7213 -2022-05-01 18:35:53,758 - tensorflow - INFO - loss = 0.03259698, step = 1300600 (2.254 sec) -2022-05-01 18:35:56,012 - tensorflow - INFO - global_step/sec: 88.7204 -2022-05-01 18:35:56,012 - tensorflow - INFO - loss = 0.026033219, step = 1300800 (2.254 sec) -2022-05-01 18:35:58,264 - tensorflow - INFO - global_step/sec: 88.8051 -2022-05-01 18:35:58,265 - tensorflow - INFO - loss = 0.03283344, step = 1301000 (2.252 sec) -2022-05-01 18:36:00,523 - tensorflow - INFO - global_step/sec: 88.5379 -2022-05-01 18:36:00,523 - tensorflow - INFO - loss = 0.012986472, step = 1301200 (2.259 sec) -2022-05-01 18:36:02,785 - tensorflow - INFO - global_step/sec: 88.4277 -2022-05-01 18:36:02,785 - tensorflow - INFO - loss = 0.078854054, step = 1301400 (2.262 sec) -2022-05-01 18:36:05,037 - tensorflow - INFO - global_step/sec: 88.801 -2022-05-01 18:36:05,037 - tensorflow - INFO - loss = 0.11142617, step = 1301600 (2.252 sec) -2022-05-01 18:36:07,299 - tensorflow - INFO - global_step/sec: 88.4314 -2022-05-01 18:36:07,299 - tensorflow - INFO - loss = 0.04029717, step = 1301800 (2.262 sec) -2022-05-01 18:36:09,551 - tensorflow - INFO - global_step/sec: 88.7994 -2022-05-01 18:36:09,551 - tensorflow - INFO - loss = 0.014742036, step = 1302000 (2.252 sec) -2022-05-01 18:36:11,808 - tensorflow - INFO - global_step/sec: 88.6242 -2022-05-01 18:36:11,808 - tensorflow - INFO - loss = 0.015955841, step = 1302200 (2.257 sec) -2022-05-01 18:36:14,065 - tensorflow - INFO - global_step/sec: 88.6135 -2022-05-01 18:36:14,065 - tensorflow - INFO - loss = 0.013542844, step = 1302400 (2.257 sec) -2022-05-01 18:36:16,323 - tensorflow - INFO - global_step/sec: 88.5507 -2022-05-01 18:36:16,324 - tensorflow - INFO - loss = 0.26178345, step = 1302600 (2.259 sec) -2022-05-01 18:36:18,576 - tensorflow - INFO - global_step/sec: 88.7931 -2022-05-01 18:36:18,576 - tensorflow - INFO - loss = 0.03694377, step = 1302800 (2.252 sec) -2022-05-01 18:36:20,827 - tensorflow - INFO - global_step/sec: 88.8155 -2022-05-01 18:36:20,828 - tensorflow - INFO - loss = 0.15881334, step = 1303000 (2.252 sec) -2022-05-01 18:36:23,081 - tensorflow - INFO - global_step/sec: 88.7524 -2022-05-01 18:36:23,081 - tensorflow - INFO - loss = 0.016012246, step = 1303200 (2.254 sec) -2022-05-01 18:36:25,341 - tensorflow - INFO - global_step/sec: 88.4896 -2022-05-01 18:36:25,342 - tensorflow - INFO - loss = 0.0563306, step = 1303400 (2.260 sec) -2022-05-01 18:36:27,600 - tensorflow - INFO - global_step/sec: 88.5233 -2022-05-01 18:36:27,601 - tensorflow - INFO - loss = 0.00582133, step = 1303600 (2.259 sec) -2022-05-01 18:36:29,857 - tensorflow - INFO - global_step/sec: 88.6167 -2022-05-01 18:36:29,858 - tensorflow - INFO - loss = 0.018234067, step = 1303800 (2.257 sec) -2022-05-01 18:36:32,120 - tensorflow - INFO - global_step/sec: 88.3989 -2022-05-01 18:36:32,120 - tensorflow - INFO - loss = 0.020625934, step = 1304000 (2.262 sec) -2022-05-01 18:36:34,377 - tensorflow - INFO - global_step/sec: 88.6045 -2022-05-01 18:36:34,377 - tensorflow - INFO - loss = 0.03280647, step = 1304200 (2.257 sec) -2022-05-01 18:36:36,635 - tensorflow - INFO - global_step/sec: 88.5891 -2022-05-01 18:36:36,635 - tensorflow - INFO - loss = 0.024327138, step = 1304400 (2.258 sec) -2022-05-01 18:36:38,889 - tensorflow - INFO - global_step/sec: 88.7012 -2022-05-01 18:36:38,890 - tensorflow - INFO - loss = 0.029814724, step = 1304600 (2.255 sec) -2022-05-01 18:36:41,148 - tensorflow - INFO - global_step/sec: 88.5416 -2022-05-01 18:36:41,149 - tensorflow - INFO - loss = 0.5983901, step = 1304800 (2.259 sec) -2022-05-01 18:36:43,405 - tensorflow - INFO - global_step/sec: 88.611 -2022-05-01 18:36:43,406 - tensorflow - INFO - loss = 0.0073133297, step = 1305000 (2.257 sec) -2022-05-01 18:36:45,661 - tensorflow - 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tensorflow - INFO - global_step/sec: 88.6336 -2022-05-01 18:41:52,459 - tensorflow - INFO - loss = 0.85027575, step = 1332400 (2.257 sec) -2022-05-01 18:41:54,719 - tensorflow - INFO - global_step/sec: 88.5084 -2022-05-01 18:41:54,719 - tensorflow - INFO - loss = 0.010502579, step = 1332600 (2.260 sec) -2022-05-01 18:41:56,973 - tensorflow - INFO - global_step/sec: 88.7171 -2022-05-01 18:41:56,973 - tensorflow - INFO - loss = 0.054889064, step = 1332800 (2.254 sec) -2022-05-01 18:41:59,221 - tensorflow - INFO - global_step/sec: 88.9608 -2022-05-01 18:41:59,221 - tensorflow - INFO - loss = 0.013105155, step = 1333000 (2.248 sec) -2022-05-01 18:42:01,472 - tensorflow - INFO - global_step/sec: 88.8684 -2022-05-01 18:42:01,472 - tensorflow - INFO - loss = 0.14711675, step = 1333200 (2.251 sec) -2022-05-01 18:42:03,726 - tensorflow - INFO - global_step/sec: 88.7069 -2022-05-01 18:42:03,727 - tensorflow - INFO - loss = 0.013106503, step = 1333400 (2.255 sec) -2022-05-01 18:42:05,984 - tensorflow - INFO - global_step/sec: 88.5852 -2022-05-01 18:42:05,984 - tensorflow - INFO - loss = 0.013921598, step = 1333600 (2.258 sec) -2022-05-01 18:42:08,240 - tensorflow - INFO - global_step/sec: 88.6409 -2022-05-01 18:42:08,241 - tensorflow - INFO - loss = 0.018557567, step = 1333800 (2.256 sec) -2022-05-01 18:42:10,494 - tensorflow - INFO - global_step/sec: 88.7404 -2022-05-01 18:42:10,494 - tensorflow - INFO - loss = 0.048707053, step = 1334000 (2.254 sec) -2022-05-01 18:42:12,745 - tensorflow - INFO - global_step/sec: 88.8631 -2022-05-01 18:42:12,745 - tensorflow - INFO - loss = 0.071592085, step = 1334200 (2.251 sec) -2022-05-01 18:42:14,997 - tensorflow - INFO - global_step/sec: 88.8115 -2022-05-01 18:42:14,997 - tensorflow - INFO - loss = 0.036467098, step = 1334400 (2.252 sec) -2022-05-01 18:42:17,246 - tensorflow - INFO - global_step/sec: 88.8944 -2022-05-01 18:42:17,247 - tensorflow - INFO - loss = 0.011111818, step = 1334600 (2.250 sec) -2022-05-01 18:42:19,505 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.8989 -2022-05-01 18:43:02,334 - tensorflow - INFO - loss = 0.57656294, step = 1338600 (2.250 sec) -2022-05-01 18:43:04,588 - tensorflow - INFO - global_step/sec: 88.7255 -2022-05-01 18:43:04,589 - tensorflow - INFO - loss = 0.022323834, step = 1338800 (2.254 sec) -2022-05-01 18:43:06,844 - tensorflow - INFO - global_step/sec: 88.645 -2022-05-01 18:43:06,845 - tensorflow - INFO - loss = 0.030625707, step = 1339000 (2.256 sec) -2022-05-01 18:43:09,105 - tensorflow - INFO - global_step/sec: 88.4828 -2022-05-01 18:43:09,105 - tensorflow - INFO - loss = 0.105094716, step = 1339200 (2.260 sec) -2022-05-01 18:43:11,352 - tensorflow - INFO - global_step/sec: 88.9951 -2022-05-01 18:43:11,352 - tensorflow - INFO - loss = 0.08304466, step = 1339400 (2.247 sec) -2022-05-01 18:43:13,604 - tensorflow - INFO - global_step/sec: 88.8167 -2022-05-01 18:43:13,604 - tensorflow - INFO - loss = 0.1481662, step = 1339600 (2.252 sec) -2022-05-01 18:43:15,852 - tensorflow - INFO - global_step/sec: 88.9591 -2022-05-01 18:43:15,853 - tensorflow - INFO - loss = 0.041512046, step = 1339800 (2.248 sec) -2022-05-01 18:43:18,102 - tensorflow - INFO - global_step/sec: 88.8808 -2022-05-01 18:43:18,103 - tensorflow - INFO - loss = 0.0433067, step = 1340000 (2.250 sec) -2022-05-01 18:43:20,353 - tensorflow - INFO - global_step/sec: 88.845 -2022-05-01 18:43:20,354 - tensorflow - INFO - loss = 0.044409916, step = 1340200 (2.251 sec) -2022-05-01 18:43:22,612 - tensorflow - INFO - global_step/sec: 88.5381 -2022-05-01 18:43:22,613 - tensorflow - INFO - loss = 0.018954748, step = 1340400 (2.259 sec) -2022-05-01 18:43:24,870 - tensorflow - INFO - global_step/sec: 88.5734 -2022-05-01 18:43:24,871 - tensorflow - INFO - loss = 0.008331417, step = 1340600 (2.258 sec) -2022-05-01 18:43:27,123 - tensorflow - INFO - global_step/sec: 88.7859 -2022-05-01 18:43:27,123 - tensorflow - INFO - loss = 0.011661217, step = 1340800 (2.253 sec) -2022-05-01 18:43:29,376 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.011366306, step = 1381800 (2.252 sec) -2022-05-01 18:51:11,692 - tensorflow - INFO - global_step/sec: 88.831 -2022-05-01 18:51:11,693 - tensorflow - INFO - loss = 0.016377091, step = 1382000 (2.251 sec) -2022-05-01 18:51:13,945 - tensorflow - INFO - global_step/sec: 88.77 -2022-05-01 18:51:13,946 - tensorflow - INFO - loss = 0.026458615, step = 1382200 (2.253 sec) -2022-05-01 18:51:16,201 - tensorflow - INFO - global_step/sec: 88.655 -2022-05-01 18:51:16,202 - tensorflow - INFO - loss = 0.009050167, step = 1382400 (2.256 sec) -2022-05-01 18:51:18,461 - tensorflow - INFO - global_step/sec: 88.514 -2022-05-01 18:51:18,461 - tensorflow - INFO - loss = 0.01643644, step = 1382600 (2.260 sec) -2022-05-01 18:51:20,716 - tensorflow - INFO - global_step/sec: 88.6844 -2022-05-01 18:51:20,717 - tensorflow - INFO - loss = 0.048719812, step = 1382800 (2.255 sec) -2022-05-01 18:51:22,974 - tensorflow - INFO - global_step/sec: 88.5929 -2022-05-01 18:51:22,974 - tensorflow - INFO - loss = 0.065892175, step = 1383000 (2.257 sec) -2022-05-01 18:51:25,233 - tensorflow - INFO - global_step/sec: 88.5119 -2022-05-01 18:51:25,234 - tensorflow - INFO - loss = 0.023639807, step = 1383200 (2.260 sec) -2022-05-01 18:51:27,491 - tensorflow - INFO - global_step/sec: 88.5873 -2022-05-01 18:51:27,491 - tensorflow - INFO - loss = 0.09788959, step = 1383400 (2.258 sec) -2022-05-01 18:51:29,749 - tensorflow - INFO - global_step/sec: 88.5779 -2022-05-01 18:51:29,749 - tensorflow - INFO - loss = 0.31161013, step = 1383600 (2.258 sec) -2022-05-01 18:51:32,014 - tensorflow - INFO - global_step/sec: 88.2782 -2022-05-01 18:51:32,015 - tensorflow - INFO - loss = 0.018368604, step = 1383800 (2.266 sec) -2022-05-01 18:51:34,270 - tensorflow - INFO - global_step/sec: 88.6773 -2022-05-01 18:51:34,270 - tensorflow - INFO - loss = 0.03938148, step = 1384000 (2.255 sec) -2022-05-01 18:51:36,528 - tensorflow - INFO - global_step/sec: 88.5622 -2022-05-01 18:51:36,528 - tensorflow - INFO - loss = 0.023210814, step = 1384200 (2.258 sec) -2022-05-01 18:51:38,778 - tensorflow - INFO - global_step/sec: 88.8731 -2022-05-01 18:51:38,779 - tensorflow - INFO - loss = 0.009314157, step = 1384400 (2.250 sec) -2022-05-01 18:51:41,030 - tensorflow - INFO - global_step/sec: 88.8453 -2022-05-01 18:51:41,030 - tensorflow - INFO - loss = 0.014635728, step = 1384600 (2.251 sec) -2022-05-01 18:51:43,289 - tensorflow - INFO - global_step/sec: 88.5026 -2022-05-01 18:51:43,290 - tensorflow - INFO - loss = 0.03272478, step = 1384800 (2.260 sec) -2022-05-01 18:51:45,542 - tensorflow - INFO - global_step/sec: 88.7836 -2022-05-01 18:51:45,542 - tensorflow - INFO - loss = 0.17609331, step = 1385000 (2.253 sec) -2022-05-01 18:51:47,805 - tensorflow - INFO - global_step/sec: 88.3729 -2022-05-01 18:51:47,806 - tensorflow - INFO - loss = 0.15155299, step = 1385200 (2.263 sec) -2022-05-01 18:51:50,070 - tensorflow - INFO - global_step/sec: 88.3249 -2022-05-01 18:51:50,070 - tensorflow - INFO - loss = 0.0067518665, step = 1385400 (2.264 sec) -2022-05-01 18:51:52,329 - tensorflow - INFO - global_step/sec: 88.5203 -2022-05-01 18:51:52,329 - tensorflow - INFO - loss = 0.0189787, step = 1385600 (2.259 sec) -2022-05-01 18:51:54,587 - tensorflow - INFO - global_step/sec: 88.5633 -2022-05-01 18:51:54,588 - tensorflow - INFO - loss = 0.02249827, step = 1385800 (2.258 sec) -2022-05-01 18:51:56,853 - tensorflow - INFO - global_step/sec: 88.2833 -2022-05-01 18:51:56,853 - tensorflow - INFO - loss = 0.013075041, step = 1386000 (2.265 sec) -2022-05-01 18:51:59,116 - tensorflow - INFO - global_step/sec: 88.3614 -2022-05-01 18:51:59,116 - tensorflow - INFO - loss = 0.008851925, step = 1386200 (2.263 sec) -2022-05-01 18:52:01,375 - tensorflow - INFO - global_step/sec: 88.5203 -2022-05-01 18:52:01,376 - tensorflow - INFO - loss = 0.109118104, step = 1386400 (2.259 sec) -2022-05-01 18:52:03,636 - tensorflow - INFO - global_step/sec: 88.4737 -2022-05-01 18:52:03,636 - tensorflow - INFO - loss = 0.011091237, step = 1386600 (2.261 sec) -2022-05-01 18:52:05,895 - tensorflow - INFO - global_step/sec: 88.5509 -2022-05-01 18:52:05,895 - tensorflow - INFO - loss = 0.023800887, step = 1386800 (2.259 sec) -2022-05-01 18:52:08,152 - tensorflow - INFO - global_step/sec: 88.6082 -2022-05-01 18:52:08,152 - tensorflow - INFO - loss = 0.011461074, step = 1387000 (2.257 sec) -2022-05-01 18:52:10,415 - tensorflow - INFO - global_step/sec: 88.3837 -2022-05-01 18:52:10,415 - tensorflow - INFO - loss = 0.027946133, step = 1387200 (2.263 sec) -2022-05-01 18:52:12,675 - tensorflow - INFO - global_step/sec: 88.4788 -2022-05-01 18:52:12,675 - tensorflow - INFO - loss = 0.028160535, step = 1387400 (2.260 sec) -2022-05-01 18:52:14,936 - tensorflow - INFO - global_step/sec: 88.4406 -2022-05-01 18:52:14,937 - tensorflow - INFO - loss = 0.037239272, step = 1387600 (2.261 sec) -2022-05-01 18:52:17,193 - tensorflow - INFO - global_step/sec: 88.6221 -2022-05-01 18:52:17,194 - tensorflow - INFO - loss = 0.009626794, step = 1387800 (2.257 sec) -2022-05-01 18:52:19,457 - tensorflow - INFO - global_step/sec: 88.3497 -2022-05-01 18:52:19,457 - tensorflow - INFO - loss = 0.0589523, step = 1388000 (2.264 sec) -2022-05-01 18:52:21,711 - tensorflow - INFO - global_step/sec: 88.7277 -2022-05-01 18:52:21,711 - tensorflow - INFO - loss = 0.069719814, step = 1388200 (2.254 sec) -2022-05-01 18:52:23,963 - tensorflow - INFO - global_step/sec: 88.8262 -2022-05-01 18:52:23,963 - tensorflow - INFO - loss = 0.014002705, step = 1388400 (2.252 sec) -2022-05-01 18:52:26,215 - tensorflow - INFO - global_step/sec: 88.7766 -2022-05-01 18:52:26,216 - tensorflow - INFO - loss = 0.04540629, step = 1388600 (2.253 sec) -2022-05-01 18:52:28,468 - tensorflow - INFO - global_step/sec: 88.7994 -2022-05-01 18:52:28,468 - tensorflow - INFO - loss = 0.02488325, step = 1388800 (2.252 sec) -2022-05-01 18:52:30,723 - tensorflow - INFO - global_step/sec: 88.6864 -2022-05-01 18:52:30,723 - tensorflow - INFO - loss = 0.012708927, step = 1389000 (2.255 sec) -2022-05-01 18:52:32,978 - tensorflow - INFO - global_step/sec: 88.6978 -2022-05-01 18:52:32,978 - tensorflow - INFO - loss = 0.015943076, step = 1389200 (2.255 sec) -2022-05-01 18:52:35,228 - tensorflow - INFO - global_step/sec: 88.8893 -2022-05-01 18:52:35,228 - tensorflow - INFO - loss = 0.20571089, step = 1389400 (2.250 sec) -2022-05-01 18:52:37,486 - tensorflow - INFO - global_step/sec: 88.5603 -2022-05-01 18:52:37,486 - tensorflow - INFO - loss = 0.05094683, step = 1389600 (2.258 sec) -2022-05-01 18:52:39,737 - tensorflow - INFO - global_step/sec: 88.8359 -2022-05-01 18:52:39,738 - tensorflow - INFO - loss = 0.007741022, step = 1389800 (2.251 sec) -2022-05-01 18:52:41,988 - tensorflow - INFO - global_step/sec: 88.845 -2022-05-01 18:52:41,989 - tensorflow - INFO - loss = 0.27258632, step = 1390000 (2.251 sec) -2022-05-01 18:52:44,242 - tensorflow - INFO - global_step/sec: 88.7345 -2022-05-01 18:52:44,243 - tensorflow - INFO - loss = 0.00971758, step = 1390200 (2.254 sec) -2022-05-01 18:52:46,495 - tensorflow - INFO - global_step/sec: 88.799 -2022-05-01 18:52:46,495 - tensorflow - INFO - loss = 0.020727014, step = 1390400 (2.252 sec) -2022-05-01 18:52:48,749 - tensorflow - INFO - global_step/sec: 88.7312 -2022-05-01 18:52:48,749 - tensorflow - INFO - loss = 0.03944362, step = 1390600 (2.254 sec) -2022-05-01 18:52:51,008 - tensorflow - INFO - global_step/sec: 88.5047 -2022-05-01 18:52:51,009 - tensorflow - INFO - loss = 0.041285716, step = 1390800 (2.260 sec) -2022-05-01 18:52:53,265 - tensorflow - INFO - global_step/sec: 88.6425 -2022-05-01 18:52:53,265 - tensorflow - INFO - loss = 0.010349192, step = 1391000 (2.256 sec) -2022-05-01 18:52:55,517 - tensorflow - INFO - global_step/sec: 88.8059 -2022-05-01 18:52:55,517 - tensorflow - INFO - loss = 0.012859952, step = 1391200 (2.252 sec) -2022-05-01 18:52:57,770 - tensorflow - INFO - global_step/sec: 88.7646 -2022-05-01 18:52:57,770 - tensorflow - INFO - loss = 0.017221091, step = 1391400 (2.253 sec) -2022-05-01 18:53:00,022 - tensorflow - INFO - global_step/sec: 88.8126 -2022-05-01 18:53:00,022 - tensorflow - INFO - loss = 0.016328134, step = 1391600 (2.252 sec) -2022-05-01 18:53:02,277 - tensorflow - INFO - global_step/sec: 88.6749 -2022-05-01 18:53:02,278 - tensorflow - INFO - loss = 0.051186584, step = 1391800 (2.255 sec) -2022-05-01 18:53:04,529 - tensorflow - INFO - global_step/sec: 88.8071 -2022-05-01 18:53:04,530 - tensorflow - INFO - loss = 0.07615837, step = 1392000 (2.252 sec) -2022-05-01 18:53:06,783 - tensorflow - INFO - global_step/sec: 88.7597 -2022-05-01 18:53:06,783 - tensorflow - INFO - loss = 0.33934033, step = 1392200 (2.253 sec) -2022-05-01 18:53:09,037 - tensorflow - INFO - global_step/sec: 88.734 -2022-05-01 18:53:09,037 - tensorflow - INFO - loss = 0.030979685, step = 1392400 (2.254 sec) -2022-05-01 18:53:11,287 - tensorflow - INFO - global_step/sec: 88.8638 -2022-05-01 18:53:11,288 - tensorflow - INFO - loss = 0.06840564, step = 1392600 (2.251 sec) -2022-05-01 18:53:13,540 - tensorflow - INFO - global_step/sec: 88.768 -2022-05-01 18:53:13,541 - tensorflow - INFO - loss = 0.06952453, step = 1392800 (2.253 sec) -2022-05-01 18:53:15,797 - tensorflow - INFO - global_step/sec: 88.6284 -2022-05-01 18:53:15,797 - tensorflow - INFO - loss = 0.08090894, step = 1393000 (2.257 sec) -2022-05-01 18:53:18,051 - tensorflow - INFO - global_step/sec: 88.7182 -2022-05-01 18:53:18,052 - tensorflow - INFO - loss = 0.049930155, step = 1393200 (2.254 sec) -2022-05-01 18:53:20,305 - tensorflow - INFO - global_step/sec: 88.7516 -2022-05-01 18:53:20,305 - tensorflow - INFO - loss = 0.007678329, step = 1393400 (2.253 sec) -2022-05-01 18:53:22,556 - tensorflow - INFO - global_step/sec: 88.844 -2022-05-01 18:53:22,556 - tensorflow - INFO - loss = 3.3933983, step = 1393600 (2.251 sec) -2022-05-01 18:53:24,812 - tensorflow - INFO - global_step/sec: 88.6503 -2022-05-01 18:53:24,812 - tensorflow - INFO - loss = 0.50099397, step = 1393800 (2.256 sec) -2022-05-01 18:53:27,063 - tensorflow - INFO - global_step/sec: 88.8246 -2022-05-01 18:53:27,064 - tensorflow - INFO - loss = 0.016014893, step = 1394000 (2.252 sec) -2022-05-01 18:53:29,320 - tensorflow - INFO - global_step/sec: 88.627 -2022-05-01 18:53:29,321 - tensorflow - INFO - loss = 0.6599004, step = 1394200 (2.257 sec) -2022-05-01 18:53:31,580 - tensorflow - INFO - global_step/sec: 88.5192 -2022-05-01 18:53:31,580 - tensorflow - INFO - loss = 0.04345376, step = 1394400 (2.259 sec) -2022-05-01 18:53:33,829 - tensorflow - INFO - global_step/sec: 88.9041 -2022-05-01 18:53:33,830 - tensorflow - INFO - loss = 0.44049037, step = 1394600 (2.250 sec) -2022-05-01 18:53:36,081 - tensorflow - INFO - global_step/sec: 88.7955 -2022-05-01 18:53:36,082 - tensorflow - INFO - loss = 0.012496112, step = 1394800 (2.252 sec) -2022-05-01 18:53:38,329 - tensorflow - INFO - global_step/sec: 88.9786 -2022-05-01 18:53:38,330 - tensorflow - INFO - loss = 0.03480921, step = 1395000 (2.248 sec) -2022-05-01 18:53:40,590 - tensorflow - INFO - global_step/sec: 88.4503 -2022-05-01 18:53:40,591 - tensorflow - INFO - loss = 0.010541502, step = 1395200 (2.261 sec) -2022-05-01 18:53:42,839 - tensorflow - INFO - global_step/sec: 88.9279 -2022-05-01 18:53:42,840 - tensorflow - INFO - loss = 0.020792527, step = 1395400 (2.249 sec) -2022-05-01 18:53:45,087 - tensorflow - INFO - global_step/sec: 88.9803 -2022-05-01 18:53:45,088 - tensorflow - INFO - loss = 0.26173452, step = 1395600 (2.248 sec) -2022-05-01 18:53:47,335 - tensorflow - INFO - global_step/sec: 88.962 -2022-05-01 18:53:47,336 - tensorflow - INFO - loss = 0.01936509, step = 1395800 (2.248 sec) -2022-05-01 18:53:49,590 - tensorflow - INFO - global_step/sec: 88.7004 -2022-05-01 18:53:49,590 - tensorflow - INFO - loss = 0.016364845, step = 1396000 (2.255 sec) -2022-05-01 18:53:51,847 - tensorflow - INFO - global_step/sec: 88.6169 -2022-05-01 18:53:51,847 - tensorflow - INFO - loss = 0.0135764945, step = 1396200 (2.257 sec) -2022-05-01 18:53:54,100 - tensorflow - INFO - global_step/sec: 88.7631 -2022-05-01 18:53:54,101 - tensorflow - INFO - loss = 0.019440118, step = 1396400 (2.253 sec) -2022-05-01 18:53:56,351 - tensorflow - INFO - global_step/sec: 88.8551 -2022-05-01 18:53:56,351 - tensorflow - INFO - loss = 0.024875076, step = 1396600 (2.251 sec) -2022-05-01 18:53:58,600 - tensorflow - INFO - global_step/sec: 88.939 -2022-05-01 18:53:58,600 - tensorflow - INFO - loss = 0.008331047, step = 1396800 (2.249 sec) -2022-05-01 18:54:00,856 - tensorflow - INFO - global_step/sec: 88.6235 -2022-05-01 18:54:00,857 - tensorflow - INFO - loss = 0.009838301, step = 1397000 (2.257 sec) -2022-05-01 18:54:03,110 - tensorflow - INFO - global_step/sec: 88.7341 -2022-05-01 18:54:03,111 - tensorflow - INFO - loss = 0.011992238, step = 1397200 (2.254 sec) -2022-05-01 18:54:05,369 - tensorflow - INFO - global_step/sec: 88.5512 -2022-05-01 18:54:05,369 - tensorflow - INFO - loss = 0.37778002, step = 1397400 (2.259 sec) -2022-05-01 18:54:07,626 - tensorflow - INFO - global_step/sec: 88.6144 -2022-05-01 18:54:07,626 - tensorflow - INFO - loss = 0.020134173, step = 1397600 (2.257 sec) -2022-05-01 18:54:09,882 - tensorflow - INFO - global_step/sec: 88.6686 -2022-05-01 18:54:09,882 - tensorflow - INFO - loss = 0.07450393, step = 1397800 (2.256 sec) -2022-05-01 18:54:12,133 - tensorflow - INFO - global_step/sec: 88.8287 -2022-05-01 18:54:12,133 - tensorflow - INFO - loss = 0.04988601, step = 1398000 (2.252 sec) -2022-05-01 18:54:14,384 - tensorflow - INFO - global_step/sec: 88.8409 -2022-05-01 18:54:14,385 - tensorflow - INFO - loss = 0.022269156, step = 1398200 (2.251 sec) -2022-05-01 18:54:16,641 - tensorflow - INFO - global_step/sec: 88.631 -2022-05-01 18:54:16,641 - tensorflow - INFO - loss = 0.025311477, step = 1398400 (2.257 sec) -2022-05-01 18:54:18,898 - tensorflow - INFO - global_step/sec: 88.5862 -2022-05-01 18:54:18,899 - tensorflow - INFO - loss = 0.43566298, step = 1398600 (2.258 sec) -2022-05-01 18:54:21,153 - tensorflow - INFO - global_step/sec: 88.7096 -2022-05-01 18:54:21,153 - tensorflow - INFO - loss = 0.011970665, step = 1398800 (2.255 sec) -2022-05-01 18:54:23,404 - tensorflow - INFO - global_step/sec: 88.8583 -2022-05-01 18:54:23,404 - tensorflow - INFO - loss = 0.060460646, step = 1399000 (2.251 sec) -2022-05-01 18:54:25,659 - tensorflow - INFO - global_step/sec: 88.698 -2022-05-01 18:54:25,659 - tensorflow - INFO - loss = 0.024868513, step = 1399200 (2.255 sec) -2022-05-01 18:54:27,915 - tensorflow - INFO - global_step/sec: 88.6522 -2022-05-01 18:54:27,915 - tensorflow - INFO - loss = 0.11175607, step = 1399400 (2.256 sec) -2022-05-01 18:54:30,173 - tensorflow - INFO - global_step/sec: 88.5635 -2022-05-01 18:54:30,173 - tensorflow - INFO - loss = 0.008654011, step = 1399600 (2.258 sec) -2022-05-01 18:54:32,427 - tensorflow - INFO - global_step/sec: 88.7413 -2022-05-01 18:54:32,427 - tensorflow - INFO - loss = 0.053722493, step = 1399800 (2.254 sec) -2022-05-01 18:54:34,666 - tensorflow - INFO - Saving checkpoints for 1400000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. -2022-05-01 18:54:34,682 - tensorflow - INFO - model.ckpt-1400000 is not in all_model_checkpoint_paths. Manually adding it. -2022-05-01 18:54:35,205 - tensorflow - INFO - Calling model_fn. -2022-05-01 18:54:35,331 - tensorflow - INFO - Done calling model_fn. -2022-05-01 18:54:35,346 - tensorflow - INFO - Starting evaluation at 2022-05-01T18:54:35Z -2022-05-01 18:54:35,409 - tensorflow - INFO - Graph was finalized. -2022-05-01 18:54:35,412 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1400000 -2022-05-01 18:54:35,695 - tensorflow - INFO - Running local_init_op. -2022-05-01 18:54:35,802 - tensorflow - INFO - Done running local_init_op. -2022-05-01 18:54:45,951 - tensorflow - INFO - Evaluation [3990/39900] -2022-05-01 18:54:55,477 - tensorflow - INFO - Evaluation [7980/39900] -2022-05-01 18:55:04,953 - tensorflow - INFO - Evaluation [11970/39900] -2022-05-01 18:55:14,432 - tensorflow - INFO - Evaluation [15960/39900] -2022-05-01 18:55:23,916 - tensorflow - INFO - Evaluation [19950/39900] -2022-05-01 18:55:33,443 - tensorflow - INFO - Evaluation [23940/39900] -2022-05-01 18:55:42,942 - tensorflow - INFO - Evaluation [27930/39900] -2022-05-01 18:55:52,441 - tensorflow - INFO - Evaluation [31920/39900] -2022-05-01 18:56:01,914 - tensorflow - INFO - Evaluation [35910/39900] -2022-05-01 18:56:11,413 - tensorflow - INFO - Evaluation [39900/39900] -2022-05-01 18:56:11,550 - tensorflow - INFO - Finished evaluation at 2022-05-01-18:56:11 -2022-05-01 18:56:11,550 - tensorflow - INFO - Saving dict for global step 1400000: global_step = 1400000, loss = 0.022675576, mean_degree_err = 4.711262 -2022-05-01 18:56:11,551 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 1400000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1400000 -2022-05-01 18:56:11,564 - tensorflow - INFO - global_step/sec: 2.01739 -2022-05-01 18:56:11,565 - tensorflow - INFO - loss = 1.4852409, step = 1400000 (99.138 sec) -2022-05-01 18:56:13,836 - tensorflow - INFO - global_step/sec: 88.0279 -2022-05-01 18:56:13,837 - tensorflow - INFO - loss = 0.014152582, step = 1400200 (2.272 sec) -2022-05-01 18:56:16,096 - tensorflow - INFO - global_step/sec: 88.4963 -2022-05-01 18:56:16,097 - tensorflow - INFO - loss = 0.034330446, step = 1400400 (2.260 sec) -2022-05-01 18:56:18,349 - tensorflow - INFO - global_step/sec: 88.7976 -2022-05-01 18:56:18,349 - tensorflow - INFO - loss = 0.03530475, step = 1400600 (2.252 sec) -2022-05-01 18:56:20,604 - tensorflow - INFO - global_step/sec: 88.694 -2022-05-01 18:56:20,604 - tensorflow - INFO - loss = 0.029222818, step = 1400800 (2.255 sec) -2022-05-01 18:56:22,862 - tensorflow - INFO - global_step/sec: 88.5766 -2022-05-01 18:56:22,862 - tensorflow - INFO - loss = 0.11862981, step = 1401000 (2.258 sec) -2022-05-01 18:56:25,120 - tensorflow - INFO - global_step/sec: 88.5508 -2022-05-01 18:56:25,121 - tensorflow - INFO - loss = 0.037320793, step = 1401200 (2.259 sec) -2022-05-01 18:56:27,376 - tensorflow - INFO - global_step/sec: 88.6684 -2022-05-01 18:56:27,376 - tensorflow - INFO - loss = 0.3678997, step = 1401400 (2.256 sec) -2022-05-01 18:56:29,631 - tensorflow - INFO - global_step/sec: 88.6882 -2022-05-01 18:56:29,631 - tensorflow - INFO - loss = 0.16894914, step = 1401600 (2.255 sec) -2022-05-01 18:56:31,899 - tensorflow - INFO - global_step/sec: 88.1598 -2022-05-01 18:56:31,900 - tensorflow - INFO - loss = 0.02190847, step = 1401800 (2.269 sec) -2022-05-01 18:56:34,158 - tensorflow - INFO - global_step/sec: 88.5499 -2022-05-01 18:56:34,158 - tensorflow - INFO - loss = 0.0119854715, step = 1402000 (2.259 sec) -2022-05-01 18:56:36,409 - tensorflow - INFO - global_step/sec: 88.8583 -2022-05-01 18:56:36,409 - tensorflow - INFO - loss = 0.020928359, step = 1402200 (2.251 sec) -2022-05-01 18:56:38,663 - tensorflow - INFO - global_step/sec: 88.7287 -2022-05-01 18:56:38,663 - tensorflow - INFO - loss = 0.01655328, step = 1402400 (2.254 sec) -2022-05-01 18:56:40,918 - tensorflow - INFO - global_step/sec: 88.7054 -2022-05-01 18:56:40,918 - tensorflow - INFO - loss = 0.012418087, step = 1402600 (2.255 sec) -2022-05-01 18:56:43,171 - 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INFO - global_step/sec: 88.7635 -2022-05-01 19:02:03,385 - tensorflow - INFO - loss = 0.024886461, step = 1431200 (2.253 sec) -2022-05-01 19:02:05,642 - tensorflow - INFO - global_step/sec: 88.5942 -2022-05-01 19:02:05,643 - tensorflow - INFO - loss = 0.021281244, step = 1431400 (2.258 sec) -2022-05-01 19:02:07,897 - tensorflow - INFO - global_step/sec: 88.7173 -2022-05-01 19:02:07,897 - tensorflow - INFO - loss = 0.057097122, step = 1431600 (2.254 sec) -2022-05-01 19:02:10,154 - tensorflow - INFO - global_step/sec: 88.6021 -2022-05-01 19:02:10,154 - tensorflow - INFO - loss = 0.06532149, step = 1431800 (2.257 sec) -2022-05-01 19:02:12,405 - tensorflow - INFO - global_step/sec: 88.8382 -2022-05-01 19:02:12,406 - tensorflow - INFO - loss = 0.032092087, step = 1432000 (2.251 sec) -2022-05-01 19:02:14,660 - tensorflow - INFO - global_step/sec: 88.7057 -2022-05-01 19:02:14,660 - tensorflow - INFO - loss = 0.065582216, step = 1432200 (2.255 sec) -2022-05-01 19:02:16,914 - tensorflow - INFO - 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INFO - loss = 0.016357116, step = 1480600 (2.256 sec) -2022-05-01 19:11:22,857 - tensorflow - INFO - global_step/sec: 88.592 -2022-05-01 19:11:22,857 - tensorflow - INFO - loss = 0.021436311, step = 1480800 (2.258 sec) -2022-05-01 19:11:25,112 - tensorflow - INFO - global_step/sec: 88.6889 -2022-05-01 19:11:25,112 - tensorflow - INFO - loss = 0.0068689487, step = 1481000 (2.255 sec) -2022-05-01 19:11:27,374 - tensorflow - INFO - global_step/sec: 88.3918 -2022-05-01 19:11:27,375 - tensorflow - INFO - loss = 0.10025742, step = 1481200 (2.263 sec) -2022-05-01 19:11:29,627 - tensorflow - INFO - global_step/sec: 88.7898 -2022-05-01 19:11:29,627 - tensorflow - INFO - loss = 0.019031854, step = 1481400 (2.252 sec) -2022-05-01 19:11:31,890 - tensorflow - INFO - global_step/sec: 88.3739 -2022-05-01 19:11:31,891 - tensorflow - INFO - loss = 0.116369985, step = 1481600 (2.263 sec) -2022-05-01 19:11:34,149 - tensorflow - INFO - global_step/sec: 88.5285 -2022-05-01 19:11:34,150 - tensorflow - INFO - loss = 1.6864299, step = 1481800 (2.259 sec) -2022-05-01 19:11:36,404 - tensorflow - INFO - global_step/sec: 88.6846 -2022-05-01 19:11:36,405 - tensorflow - INFO - loss = 0.38291132, step = 1482000 (2.255 sec) -2022-05-01 19:11:38,658 - tensorflow - INFO - global_step/sec: 88.7496 -2022-05-01 19:11:38,658 - tensorflow - INFO - loss = 0.02460587, step = 1482200 (2.254 sec) -2022-05-01 19:11:40,915 - tensorflow - INFO - global_step/sec: 88.6252 -2022-05-01 19:11:40,915 - tensorflow - INFO - loss = 0.012626898, step = 1482400 (2.257 sec) -2022-05-01 19:11:43,167 - tensorflow - INFO - global_step/sec: 88.8111 -2022-05-01 19:11:43,167 - tensorflow - INFO - loss = 0.047014453, step = 1482600 (2.252 sec) -2022-05-01 19:11:45,420 - tensorflow - INFO - global_step/sec: 88.7618 -2022-05-01 19:11:45,420 - tensorflow - INFO - loss = 0.027172279, step = 1482800 (2.253 sec) -2022-05-01 19:11:47,670 - tensorflow - INFO - global_step/sec: 88.9013 -2022-05-01 19:11:47,670 - tensorflow - INFO - loss = 0.042673394, step = 1483000 (2.250 sec) -2022-05-01 19:11:49,921 - tensorflow - INFO - global_step/sec: 88.8368 -2022-05-01 19:11:49,921 - tensorflow - INFO - loss = 0.01118323, step = 1483200 (2.251 sec) -2022-05-01 19:11:52,175 - tensorflow - INFO - global_step/sec: 88.7255 -2022-05-01 19:11:52,175 - tensorflow - INFO - loss = 0.009533244, step = 1483400 (2.254 sec) -2022-05-01 19:11:54,432 - tensorflow - INFO - global_step/sec: 88.6159 -2022-05-01 19:11:54,432 - tensorflow - INFO - loss = 0.038333315, step = 1483600 (2.257 sec) -2022-05-01 19:11:56,686 - tensorflow - INFO - global_step/sec: 88.709 -2022-05-01 19:11:56,687 - tensorflow - INFO - loss = 0.017258832, step = 1483800 (2.255 sec) -2022-05-01 19:11:58,942 - tensorflow - INFO - global_step/sec: 88.6808 -2022-05-01 19:11:58,942 - tensorflow - INFO - loss = 0.023304895, step = 1484000 (2.255 sec) -2022-05-01 19:12:01,199 - tensorflow - INFO - global_step/sec: 88.6191 -2022-05-01 19:12:01,199 - tensorflow - INFO - loss = 0.016935058, step = 1484200 (2.257 sec) -2022-05-01 19:12:03,454 - tensorflow - INFO - global_step/sec: 88.6904 -2022-05-01 19:12:03,454 - tensorflow - INFO - loss = 0.017758131, step = 1484400 (2.255 sec) -2022-05-01 19:12:05,713 - tensorflow - INFO - global_step/sec: 88.5057 -2022-05-01 19:12:05,714 - tensorflow - INFO - loss = 0.012710876, step = 1484600 (2.260 sec) -2022-05-01 19:12:07,969 - tensorflow - INFO - global_step/sec: 88.6751 -2022-05-01 19:12:07,969 - tensorflow - INFO - loss = 0.01151262, step = 1484800 (2.255 sec) -2022-05-01 19:12:10,225 - tensorflow - INFO - global_step/sec: 88.6448 -2022-05-01 19:12:10,225 - tensorflow - INFO - loss = 0.018936135, step = 1485000 (2.256 sec) -2022-05-01 19:12:12,478 - tensorflow - INFO - global_step/sec: 88.7749 -2022-05-01 19:12:12,478 - tensorflow - INFO - loss = 0.012820639, step = 1485200 (2.253 sec) -2022-05-01 19:12:14,732 - tensorflow - INFO - global_step/sec: 88.7397 -2022-05-01 19:12:14,732 - tensorflow - INFO - loss = 0.018086525, step = 1485400 (2.254 sec) -2022-05-01 19:12:16,987 - tensorflow - INFO - global_step/sec: 88.6785 -2022-05-01 19:12:16,987 - tensorflow - INFO - loss = 0.09597358, step = 1485600 (2.255 sec) -2022-05-01 19:12:19,241 - tensorflow - INFO - global_step/sec: 88.7245 -2022-05-01 19:12:19,242 - tensorflow - INFO - loss = 0.012371361, step = 1485800 (2.254 sec) -2022-05-01 19:12:21,495 - tensorflow - INFO - global_step/sec: 88.7441 -2022-05-01 19:12:21,495 - tensorflow - INFO - loss = 0.01012472, step = 1486000 (2.254 sec) -2022-05-01 19:12:23,750 - tensorflow - INFO - global_step/sec: 88.6763 -2022-05-01 19:12:23,751 - tensorflow - INFO - loss = 0.019150168, step = 1486200 (2.255 sec) -2022-05-01 19:12:26,003 - tensorflow - INFO - global_step/sec: 88.7715 -2022-05-01 19:12:26,004 - tensorflow - INFO - loss = 0.0027024292, step = 1486400 (2.253 sec) -2022-05-01 19:12:28,254 - tensorflow - INFO - global_step/sec: 88.8734 -2022-05-01 19:12:28,254 - tensorflow - INFO - loss = 0.0192898, step = 1486600 (2.250 sec) -2022-05-01 19:12:30,519 - tensorflow - INFO - global_step/sec: 88.291 -2022-05-01 19:12:30,519 - tensorflow - INFO - loss = 0.01304977, step = 1486800 (2.265 sec) -2022-05-01 19:12:32,773 - tensorflow - INFO - global_step/sec: 88.7179 -2022-05-01 19:12:32,774 - tensorflow - INFO - loss = 0.026753342, step = 1487000 (2.254 sec) -2022-05-01 19:12:35,024 - tensorflow - INFO - global_step/sec: 88.841 -2022-05-01 19:12:35,025 - tensorflow - INFO - loss = 0.016424507, step = 1487200 (2.251 sec) -2022-05-01 19:12:37,280 - tensorflow - INFO - global_step/sec: 88.6709 -2022-05-01 19:12:37,280 - tensorflow - INFO - loss = 0.11279587, step = 1487400 (2.256 sec) -2022-05-01 19:12:39,536 - tensorflow - INFO - global_step/sec: 88.6624 -2022-05-01 19:12:39,536 - tensorflow - INFO - loss = 0.11770095, step = 1487600 (2.256 sec) -2022-05-01 19:12:41,788 - tensorflow - INFO - global_step/sec: 88.806 -2022-05-01 19:12:41,788 - tensorflow - INFO - loss = 0.054372743, step = 1487800 (2.252 sec) -2022-05-01 19:12:44,047 - tensorflow - INFO - global_step/sec: 88.5252 -2022-05-01 19:12:44,047 - tensorflow - INFO - loss = 4.644326, step = 1488000 (2.259 sec) -2022-05-01 19:12:46,298 - tensorflow - INFO - global_step/sec: 88.8402 -2022-05-01 19:12:46,299 - tensorflow - INFO - loss = 0.033585217, step = 1488200 (2.251 sec) -2022-05-01 19:12:48,552 - tensorflow - INFO - global_step/sec: 88.757 -2022-05-01 19:12:48,552 - tensorflow - INFO - loss = 0.011081589, step = 1488400 (2.253 sec) -2022-05-01 19:12:50,807 - tensorflow - INFO - global_step/sec: 88.6627 -2022-05-01 19:12:50,808 - tensorflow - INFO - loss = 0.011284853, step = 1488600 (2.256 sec) -2022-05-01 19:12:53,062 - tensorflow - INFO - global_step/sec: 88.698 -2022-05-01 19:12:53,063 - tensorflow - INFO - loss = 0.0142420065, step = 1488800 (2.255 sec) -2022-05-01 19:12:55,319 - tensorflow - INFO - global_step/sec: 88.6163 -2022-05-01 19:12:55,320 - tensorflow - INFO - loss = 0.009824946, step = 1489000 (2.257 sec) -2022-05-01 19:12:57,577 - tensorflow - INFO - global_step/sec: 88.5776 -2022-05-01 19:12:57,577 - tensorflow - INFO - loss = 0.043002818, step = 1489200 (2.258 sec) -2022-05-01 19:12:59,827 - tensorflow - INFO - global_step/sec: 88.8722 -2022-05-01 19:12:59,828 - tensorflow - INFO - loss = 0.04212212, step = 1489400 (2.250 sec) -2022-05-01 19:13:02,081 - tensorflow - INFO - global_step/sec: 88.7289 -2022-05-01 19:13:02,082 - tensorflow - INFO - loss = 0.076076016, step = 1489600 (2.254 sec) -2022-05-01 19:13:04,337 - tensorflow - INFO - global_step/sec: 88.6653 -2022-05-01 19:13:04,338 - tensorflow - INFO - loss = 0.018436994, step = 1489800 (2.256 sec) -2022-05-01 19:13:06,593 - tensorflow - INFO - global_step/sec: 88.6461 -2022-05-01 19:13:06,594 - tensorflow - INFO - loss = 0.03337249, step = 1490000 (2.256 sec) -2022-05-01 19:13:08,851 - tensorflow - INFO - global_step/sec: 88.5903 -2022-05-01 19:13:08,851 - tensorflow - INFO - loss = 0.0954804, step = 1490200 (2.258 sec) -2022-05-01 19:13:11,115 - tensorflow - INFO - global_step/sec: 88.326 -2022-05-01 19:13:11,116 - tensorflow - INFO - loss = 0.038690947, step = 1490400 (2.264 sec) -2022-05-01 19:13:13,376 - tensorflow - INFO - global_step/sec: 88.4515 -2022-05-01 19:13:13,377 - tensorflow - INFO - loss = 0.022575928, step = 1490600 (2.261 sec) -2022-05-01 19:13:15,636 - tensorflow - INFO - global_step/sec: 88.5299 -2022-05-01 19:13:15,636 - tensorflow - INFO - loss = 0.04049495, step = 1490800 (2.259 sec) -2022-05-01 19:13:17,892 - tensorflow - INFO - global_step/sec: 88.6267 -2022-05-01 19:13:17,893 - tensorflow - INFO - loss = 0.053369366, step = 1491000 (2.257 sec) -2022-05-01 19:13:20,155 - tensorflow - INFO - global_step/sec: 88.3868 -2022-05-01 19:13:20,155 - tensorflow - INFO - loss = 0.0073466636, step = 1491200 (2.263 sec) -2022-05-01 19:13:22,417 - tensorflow - INFO - global_step/sec: 88.4281 -2022-05-01 19:13:22,417 - tensorflow - INFO - loss = 0.01530133, step = 1491400 (2.262 sec) -2022-05-01 19:13:24,679 - tensorflow - INFO - global_step/sec: 88.3877 -2022-05-01 19:13:24,680 - tensorflow - INFO - loss = 0.040279463, step = 1491600 (2.263 sec) -2022-05-01 19:13:26,939 - tensorflow - INFO - global_step/sec: 88.5227 -2022-05-01 19:13:26,939 - tensorflow - INFO - loss = 0.019282173, step = 1491800 (2.259 sec) -2022-05-01 19:13:29,198 - tensorflow - INFO - global_step/sec: 88.533 -2022-05-01 19:13:29,198 - tensorflow - INFO - loss = 0.008762198, step = 1492000 (2.259 sec) -2022-05-01 19:13:31,468 - tensorflow - INFO - global_step/sec: 88.1147 -2022-05-01 19:13:31,468 - tensorflow - INFO - loss = 0.56280273, step = 1492200 (2.270 sec) -2022-05-01 19:13:33,724 - tensorflow - INFO - global_step/sec: 88.644 -2022-05-01 19:13:33,724 - tensorflow - INFO - loss = 0.019971997, step = 1492400 (2.256 sec) -2022-05-01 19:13:35,979 - tensorflow - INFO - global_step/sec: 88.6862 -2022-05-01 19:13:35,979 - tensorflow - INFO - loss = 0.017949276, step = 1492600 (2.255 sec) -2022-05-01 19:13:38,232 - tensorflow - INFO - global_step/sec: 88.7758 -2022-05-01 19:13:38,232 - tensorflow - INFO - loss = 0.024088422, step = 1492800 (2.253 sec) -2022-05-01 19:13:40,482 - tensorflow - INFO - global_step/sec: 88.8973 -2022-05-01 19:13:40,482 - tensorflow - INFO - loss = 0.01847333, step = 1493000 (2.250 sec) -2022-05-01 19:13:42,738 - tensorflow - INFO - global_step/sec: 88.6168 -2022-05-01 19:13:42,739 - tensorflow - INFO - loss = 0.0061604944, step = 1493200 (2.257 sec) -2022-05-01 19:13:44,991 - tensorflow - INFO - global_step/sec: 88.8081 -2022-05-01 19:13:44,991 - tensorflow - INFO - loss = 0.031813048, step = 1493400 (2.252 sec) -2022-05-01 19:13:47,246 - tensorflow - INFO - global_step/sec: 88.6719 -2022-05-01 19:13:47,246 - tensorflow - INFO - loss = 0.011895972, step = 1493600 (2.255 sec) -2022-05-01 19:13:49,505 - tensorflow - INFO - global_step/sec: 88.5243 -2022-05-01 19:13:49,506 - tensorflow - INFO - loss = 0.03530208, step = 1493800 (2.259 sec) -2022-05-01 19:13:51,757 - tensorflow - INFO - global_step/sec: 88.8307 -2022-05-01 19:13:51,757 - tensorflow - INFO - loss = 0.042276334, step = 1494000 (2.251 sec) -2022-05-01 19:13:54,003 - tensorflow - INFO - global_step/sec: 89.0302 -2022-05-01 19:13:54,004 - tensorflow - INFO - loss = 0.05104211, step = 1494200 (2.246 sec) -2022-05-01 19:13:56,255 - tensorflow - INFO - global_step/sec: 88.829 -2022-05-01 19:13:56,255 - tensorflow - INFO - loss = 0.044423785, step = 1494400 (2.251 sec) -2022-05-01 19:13:58,512 - tensorflow - INFO - global_step/sec: 88.6044 -2022-05-01 19:13:58,512 - tensorflow - INFO - loss = 0.020210141, step = 1494600 (2.257 sec) -2022-05-01 19:14:00,765 - tensorflow - INFO - global_step/sec: 88.7685 -2022-05-01 19:14:00,765 - tensorflow - INFO - loss = 0.045281194, step = 1494800 (2.253 sec) -2022-05-01 19:14:03,026 - tensorflow - INFO - global_step/sec: 88.4692 -2022-05-01 19:14:03,026 - tensorflow - INFO - loss = 0.015739895, step = 1495000 (2.261 sec) -2022-05-01 19:14:05,279 - tensorflow - INFO - global_step/sec: 88.7514 -2022-05-01 19:14:05,280 - tensorflow - INFO - loss = 0.010020976, step = 1495200 (2.254 sec) -2022-05-01 19:14:07,533 - tensorflow - INFO - global_step/sec: 88.7238 -2022-05-01 19:14:07,534 - tensorflow - INFO - loss = 0.01750209, step = 1495400 (2.254 sec) -2022-05-01 19:14:09,787 - tensorflow - INFO - global_step/sec: 88.7341 -2022-05-01 19:14:09,788 - tensorflow - INFO - loss = 0.06548672, step = 1495600 (2.254 sec) -2022-05-01 19:14:12,040 - tensorflow - INFO - global_step/sec: 88.7937 -2022-05-01 19:14:12,040 - tensorflow - INFO - loss = 0.21525896, step = 1495800 (2.252 sec) -2022-05-01 19:14:14,296 - tensorflow - INFO - global_step/sec: 88.62 -2022-05-01 19:14:14,297 - tensorflow - INFO - loss = 0.014747351, step = 1496000 (2.257 sec) -2022-05-01 19:14:16,555 - tensorflow - INFO - global_step/sec: 88.5362 -2022-05-01 19:14:16,556 - tensorflow - INFO - loss = 0.18117778, step = 1496200 (2.259 sec) -2022-05-01 19:14:18,809 - tensorflow - INFO - global_step/sec: 88.7606 -2022-05-01 19:14:18,809 - tensorflow - INFO - loss = 0.032358292, step = 1496400 (2.253 sec) -2022-05-01 19:14:21,061 - tensorflow - INFO - global_step/sec: 88.7971 -2022-05-01 19:14:21,062 - tensorflow - INFO - loss = 0.012879986, step = 1496600 (2.252 sec) -2022-05-01 19:14:23,316 - tensorflow - INFO - global_step/sec: 88.6747 -2022-05-01 19:14:23,317 - tensorflow - INFO - loss = 0.014577984, step = 1496800 (2.255 sec) -2022-05-01 19:14:25,577 - tensorflow - INFO - global_step/sec: 88.4653 -2022-05-01 19:14:25,578 - tensorflow - INFO - loss = 1.6467451, step = 1497000 (2.261 sec) -2022-05-01 19:14:27,830 - tensorflow - INFO - global_step/sec: 88.7919 -2022-05-01 19:14:27,830 - tensorflow - INFO - loss = 0.04715134, step = 1497200 (2.252 sec) -2022-05-01 19:14:30,079 - tensorflow - INFO - global_step/sec: 88.8975 -2022-05-01 19:14:30,080 - tensorflow - INFO - loss = 0.03892113, step = 1497400 (2.250 sec) -2022-05-01 19:14:32,343 - tensorflow - INFO - global_step/sec: 88.377 -2022-05-01 19:14:32,343 - tensorflow - INFO - loss = 0.034063794, step = 1497600 (2.263 sec) -2022-05-01 19:14:34,602 - tensorflow - INFO - global_step/sec: 88.5231 -2022-05-01 19:14:34,602 - tensorflow - INFO - loss = 0.033480745, step = 1497800 (2.259 sec) -2022-05-01 19:14:36,849 - tensorflow - INFO - global_step/sec: 88.9951 -2022-05-01 19:14:36,850 - tensorflow - INFO - loss = 0.010095269, step = 1498000 (2.247 sec) -2022-05-01 19:14:39,107 - tensorflow - INFO - global_step/sec: 88.5728 -2022-05-01 19:14:39,108 - tensorflow - INFO - loss = 0.02026205, step = 1498200 (2.258 sec) -2022-05-01 19:14:41,358 - tensorflow - INFO - global_step/sec: 88.8423 -2022-05-01 19:14:41,359 - tensorflow - INFO - loss = 0.011833824, step = 1498400 (2.251 sec) -2022-05-01 19:14:43,616 - tensorflow - INFO - global_step/sec: 88.5942 -2022-05-01 19:14:43,616 - tensorflow - INFO - loss = 0.025549715, step = 1498600 (2.258 sec) -2022-05-01 19:14:45,866 - tensorflow - INFO - global_step/sec: 88.871 -2022-05-01 19:14:45,867 - tensorflow - INFO - loss = 0.014869514, step = 1498800 (2.250 sec) -2022-05-01 19:14:48,118 - tensorflow - INFO - global_step/sec: 88.814 -2022-05-01 19:14:48,119 - tensorflow - INFO - loss = 0.013517971, step = 1499000 (2.252 sec) -2022-05-01 19:14:50,376 - tensorflow - INFO - global_step/sec: 88.5686 -2022-05-01 19:14:50,377 - tensorflow - INFO - loss = 0.009027958, step = 1499200 (2.258 sec) -2022-05-01 19:14:52,636 - tensorflow - INFO - global_step/sec: 88.5199 -2022-05-01 19:14:52,636 - tensorflow - INFO - loss = 0.017943226, step = 1499400 (2.259 sec) -2022-05-01 19:14:54,887 - tensorflow - INFO - global_step/sec: 88.83 -2022-05-01 19:14:54,888 - tensorflow - INFO - loss = 19.609617, step = 1499600 (2.252 sec) -2022-05-01 19:14:57,142 - tensorflow - INFO - global_step/sec: 88.7111 -2022-05-01 19:14:57,142 - tensorflow - INFO - loss = 0.034245685, step = 1499800 (2.254 sec) -2022-05-01 19:14:59,381 - tensorflow - INFO - Saving checkpoints for 1500000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. -2022-05-01 19:14:59,397 - tensorflow - INFO - model.ckpt-1500000 is not in all_model_checkpoint_paths. Manually adding it. -2022-05-01 19:14:59,914 - tensorflow - INFO - Calling model_fn. -2022-05-01 19:15:00,041 - tensorflow - INFO - Done calling model_fn. -2022-05-01 19:15:00,056 - tensorflow - INFO - Starting evaluation at 2022-05-01T19:15:00Z -2022-05-01 19:15:00,120 - tensorflow - INFO - Graph was finalized. -2022-05-01 19:15:00,123 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1500000 -2022-05-01 19:15:00,404 - tensorflow - INFO - Running local_init_op. -2022-05-01 19:15:00,511 - tensorflow - INFO - Done running local_init_op. -2022-05-01 19:15:10,766 - tensorflow - INFO - Evaluation [3990/39900] -2022-05-01 19:15:20,318 - tensorflow - INFO - Evaluation [7980/39900] -2022-05-01 19:15:29,860 - tensorflow - INFO - Evaluation [11970/39900] -2022-05-01 19:15:39,412 - tensorflow - INFO - Evaluation [15960/39900] -2022-05-01 19:15:48,935 - tensorflow - INFO - Evaluation [19950/39900] -2022-05-01 19:15:58,483 - tensorflow - INFO - Evaluation [23940/39900] -2022-05-01 19:16:08,019 - tensorflow - INFO - Evaluation [27930/39900] -2022-05-01 19:16:17,585 - tensorflow - INFO - Evaluation [31920/39900] -2022-05-01 19:16:27,118 - tensorflow - INFO - Evaluation [35910/39900] -2022-05-01 19:16:36,684 - tensorflow - INFO - Evaluation [39900/39900] -2022-05-01 19:16:36,821 - tensorflow - INFO - Finished evaluation at 2022-05-01-19:16:36 -2022-05-01 19:16:36,821 - tensorflow - INFO - Saving dict for global step 1500000: global_step = 1500000, loss = 0.019688489, mean_degree_err = 3.836875 -2022-05-01 19:16:36,822 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 1500000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1500000 -2022-05-01 19:16:36,835 - tensorflow - INFO - global_step/sec: 2.00614 -2022-05-01 19:16:36,836 - tensorflow - INFO - loss = 0.016405582, step = 1500000 (99.694 sec) -2022-05-01 19:16:39,110 - tensorflow - INFO - global_step/sec: 87.9407 -2022-05-01 19:16:39,110 - tensorflow - INFO - loss = 0.03980479, step = 1500200 (2.274 sec) -2022-05-01 19:16:41,368 - tensorflow - INFO - global_step/sec: 88.5673 -2022-05-01 19:16:41,368 - tensorflow - INFO - loss = 0.03160281, step = 1500400 (2.258 sec) -2022-05-01 19:16:43,626 - tensorflow - INFO - global_step/sec: 88.5718 -2022-05-01 19:16:43,626 - tensorflow - INFO - loss = 0.010400597, step = 1500600 (2.258 sec) -2022-05-01 19:16:45,885 - tensorflow - INFO - global_step/sec: 88.548 -2022-05-01 19:16:45,885 - tensorflow - INFO - loss = 0.018450955, step = 1500800 (2.259 sec) -2022-05-01 19:16:48,145 - tensorflow - INFO - global_step/sec: 88.4955 -2022-05-01 19:16:48,145 - tensorflow - INFO - loss = 0.039949965, step = 1501000 (2.260 sec) -2022-05-01 19:16:50,403 - tensorflow - INFO - global_step/sec: 88.5761 -2022-05-01 19:16:50,403 - tensorflow - INFO - loss = 0.012375185, step = 1501200 (2.258 sec) -2022-05-01 19:16:52,665 - tensorflow - INFO - global_step/sec: 88.387 -2022-05-01 19:16:52,666 - tensorflow - INFO - loss = 0.013643623, step = 1501400 (2.263 sec) -2022-05-01 19:16:54,927 - tensorflow - INFO - global_step/sec: 88.4466 -2022-05-01 19:16:54,927 - tensorflow - INFO - loss = 0.039375756, step = 1501600 (2.261 sec) -2022-05-01 19:16:57,186 - tensorflow - INFO - global_step/sec: 88.536 -2022-05-01 19:16:57,186 - tensorflow - INFO - loss = 0.0076917754, step = 1501800 (2.259 sec) -2022-05-01 19:16:59,445 - tensorflow - INFO - global_step/sec: 88.5146 -2022-05-01 19:16:59,445 - tensorflow - INFO - loss = 0.009893475, step = 1502000 (2.260 sec) -2022-05-01 19:17:01,705 - tensorflow - INFO - global_step/sec: 88.4975 -2022-05-01 19:17:01,705 - tensorflow - INFO - loss = 0.047156, step = 1502200 (2.260 sec) -2022-05-01 19:17:03,962 - tensorflow - INFO - global_step/sec: 88.5943 -2022-05-01 19:17:03,963 - tensorflow - INFO - loss = 0.021427762, step = 1502400 (2.258 sec) -2022-05-01 19:17:06,219 - tensorflow - INFO - global_step/sec: 88.6152 -2022-05-01 19:17:06,220 - tensorflow - INFO - loss = 0.0681486, step = 1502600 (2.257 sec) -2022-05-01 19:17:08,473 - tensorflow - INFO - global_step/sec: 88.751 -2022-05-01 19:17:08,473 - tensorflow - INFO - loss = 0.028135728, step = 1502800 (2.254 sec) -2022-05-01 19:17:10,724 - tensorflow - INFO - global_step/sec: 88.8541 -2022-05-01 19:17:10,724 - tensorflow - INFO - loss = 0.009688498, step = 1503000 (2.251 sec) -2022-05-01 19:17:12,973 - tensorflow - INFO - global_step/sec: 88.9314 -2022-05-01 19:17:12,973 - tensorflow - INFO - loss = 0.009663465, step = 1503200 (2.249 sec) -2022-05-01 19:17:15,228 - tensorflow - INFO - global_step/sec: 88.6792 -2022-05-01 19:17:15,228 - tensorflow - INFO - loss = 0.010690352, step = 1503400 (2.255 sec) -2022-05-01 19:17:17,479 - tensorflow - INFO - global_step/sec: 88.8556 -2022-05-01 19:17:17,479 - tensorflow - INFO - loss = 0.01446222, step = 1503600 (2.251 sec) -2022-05-01 19:17:19,730 - tensorflow - INFO - global_step/sec: 88.8299 -2022-05-01 19:17:19,731 - tensorflow - INFO - loss = 0.16363442, step = 1503800 (2.251 sec) -2022-05-01 19:17:21,983 - tensorflow - INFO - global_step/sec: 88.7991 -2022-05-01 19:17:21,983 - tensorflow - INFO - loss = 0.24157685, step = 1504000 (2.252 sec) -2022-05-01 19:17:24,237 - tensorflow - INFO - global_step/sec: 88.7346 -2022-05-01 19:17:24,237 - tensorflow - INFO - loss = 0.037840072, step = 1504200 (2.254 sec) -2022-05-01 19:17:26,496 - tensorflow - INFO - global_step/sec: 88.5264 -2022-05-01 19:17:26,496 - tensorflow - INFO - loss = 0.013430537, step = 1504400 (2.259 sec) -2022-05-01 19:17:28,747 - tensorflow - INFO - global_step/sec: 88.8359 -2022-05-01 19:17:28,748 - tensorflow - INFO - loss = 0.0102399755, step = 1504600 (2.251 sec) -2022-05-01 19:17:31,003 - tensorflow - INFO - global_step/sec: 88.6629 -2022-05-01 19:17:31,003 - tensorflow - INFO - loss = 0.47035003, step = 1504800 (2.256 sec) -2022-05-01 19:17:33,258 - tensorflow - INFO - global_step/sec: 88.6973 -2022-05-01 19:17:33,258 - tensorflow - INFO - loss = 0.035697296, step = 1505000 (2.255 sec) -2022-05-01 19:17:35,510 - tensorflow - INFO - global_step/sec: 88.8108 -2022-05-01 19:17:35,510 - tensorflow - INFO - loss = 0.01537087, step = 1505200 (2.252 sec) -2022-05-01 19:17:37,760 - tensorflow - INFO - global_step/sec: 88.86 -2022-05-01 19:17:37,761 - tensorflow - INFO - loss = 0.0061171902, step = 1505400 (2.251 sec) -2022-05-01 19:17:40,012 - tensorflow - INFO - global_step/sec: 88.8074 -2022-05-01 19:17:40,013 - tensorflow - INFO - loss = 0.008986438, step = 1505600 (2.252 sec) -2022-05-01 19:17:42,265 - tensorflow - INFO - global_step/sec: 88.7722 -2022-05-01 19:17:42,266 - tensorflow - INFO - loss = 0.0074248677, step = 1505800 (2.253 sec) -2022-05-01 19:17:44,519 - tensorflow - INFO - global_step/sec: 88.7331 -2022-05-01 19:17:44,520 - tensorflow - INFO - loss = 0.045062054, step = 1506000 (2.254 sec) -2022-05-01 19:17:46,775 - tensorflow - INFO - global_step/sec: 88.6578 -2022-05-01 19:17:46,776 - tensorflow - INFO - loss = 0.049057905, step = 1506200 (2.256 sec) -2022-05-01 19:17:49,031 - tensorflow - INFO - global_step/sec: 88.6698 -2022-05-01 19:17:49,031 - tensorflow - INFO - loss = 9.571878, step = 1506400 (2.256 sec) -2022-05-01 19:17:51,279 - tensorflow - INFO - global_step/sec: 88.9518 -2022-05-01 19:17:51,280 - tensorflow - INFO - loss = 0.010163815, step = 1506600 (2.248 sec) -2022-05-01 19:17:53,537 - tensorflow - INFO - global_step/sec: 88.5769 -2022-05-01 19:17:53,538 - tensorflow - INFO - loss = 0.037897877, step = 1506800 (2.258 sec) -2022-05-01 19:17:55,785 - tensorflow - INFO - global_step/sec: 88.9572 -2022-05-01 19:17:55,786 - tensorflow - INFO - loss = 0.011446608, step = 1507000 (2.248 sec) -2022-05-01 19:17:58,047 - tensorflow - INFO - global_step/sec: 88.4464 -2022-05-01 19:17:58,047 - tensorflow - INFO - loss = 0.030028125, step = 1507200 (2.261 sec) -2022-05-01 19:18:00,304 - tensorflow - INFO - global_step/sec: 88.5904 -2022-05-01 19:18:00,305 - tensorflow - INFO - loss = 0.37124282, step = 1507400 (2.258 sec) -2022-05-01 19:18:02,558 - tensorflow - INFO - global_step/sec: 88.7302 -2022-05-01 19:18:02,559 - tensorflow - INFO - loss = 0.0071982597, step = 1507600 (2.254 sec) -2022-05-01 19:18:04,817 - tensorflow - INFO - global_step/sec: 88.5361 -2022-05-01 19:18:04,818 - tensorflow - INFO - loss = 0.026802769, step = 1507800 (2.259 sec) -2022-05-01 19:18:07,076 - tensorflow - INFO - global_step/sec: 88.5585 -2022-05-01 19:18:07,076 - tensorflow - INFO - loss = 0.01104089, step = 1508000 (2.258 sec) -2022-05-01 19:18:09,330 - tensorflow - INFO - global_step/sec: 88.7186 -2022-05-01 19:18:09,330 - tensorflow - INFO - loss = 0.026884561, step = 1508200 (2.254 sec) -2022-05-01 19:18:11,580 - tensorflow - INFO - global_step/sec: 88.8738 -2022-05-01 19:18:11,581 - tensorflow - INFO - loss = 0.04371132, step = 1508400 (2.250 sec) -2022-05-01 19:18:13,832 - tensorflow - INFO - global_step/sec: 88.8071 -2022-05-01 19:18:13,833 - tensorflow - INFO - loss = 0.35379708, step = 1508600 (2.252 sec) -2022-05-01 19:18:16,083 - tensorflow - INFO - global_step/sec: 88.8594 -2022-05-01 19:18:16,084 - tensorflow - INFO - loss = 0.01931915, step = 1508800 (2.251 sec) -2022-05-01 19:18:18,341 - tensorflow - INFO - global_step/sec: 88.5654 -2022-05-01 19:18:18,342 - tensorflow - INFO - loss = 0.009167142, step = 1509000 (2.258 sec) -2022-05-01 19:18:20,596 - tensorflow - INFO - global_step/sec: 88.6953 -2022-05-01 19:18:20,597 - tensorflow - INFO - loss = 0.010578865, step = 1509200 (2.255 sec) -2022-05-01 19:18:22,845 - tensorflow - INFO - global_step/sec: 88.9425 -2022-05-01 19:18:22,845 - tensorflow - INFO - loss = 0.013854955, step = 1509400 (2.249 sec) -2022-05-01 19:18:25,094 - tensorflow - INFO - global_step/sec: 88.9234 -2022-05-01 19:18:25,095 - tensorflow - INFO - loss = 0.06622584, step = 1509600 (2.249 sec) -2022-05-01 19:18:27,348 - tensorflow - INFO - global_step/sec: 88.7279 -2022-05-01 19:18:27,349 - tensorflow - INFO - loss = 0.8782365, step = 1509800 (2.254 sec) -2022-05-01 19:18:29,604 - tensorflow - INFO - global_step/sec: 88.6551 -2022-05-01 19:18:29,605 - tensorflow - INFO - loss = 0.041533154, step = 1510000 (2.256 sec) -2022-05-01 19:18:31,865 - tensorflow - INFO - global_step/sec: 88.4552 -2022-05-01 19:18:31,866 - tensorflow - INFO - loss = 0.048824005, step = 1510200 (2.261 sec) -2022-05-01 19:18:34,119 - tensorflow - INFO - global_step/sec: 88.75 -2022-05-01 19:18:34,119 - tensorflow - INFO - loss = 0.06452929, step = 1510400 (2.253 sec) -2022-05-01 19:18:36,373 - tensorflow - INFO - global_step/sec: 88.6973 -2022-05-01 19:18:36,374 - tensorflow - INFO - loss = 0.021103691, step = 1510600 (2.255 sec) -2022-05-01 19:18:38,626 - tensorflow - INFO - global_step/sec: 88.7726 -2022-05-01 19:18:38,627 - tensorflow - INFO - loss = 0.03738752, step = 1510800 (2.253 sec) -2022-05-01 19:18:40,874 - tensorflow - INFO - global_step/sec: 89.0037 -2022-05-01 19:18:40,874 - tensorflow - INFO - loss = 0.020102445, step = 1511000 (2.247 sec) -2022-05-01 19:18:43,130 - tensorflow - INFO - global_step/sec: 88.6228 -2022-05-01 19:18:43,131 - tensorflow - INFO - loss = 0.014712809, step = 1511200 (2.257 sec) -2022-05-01 19:18:45,387 - tensorflow - INFO - global_step/sec: 88.6175 -2022-05-01 19:18:45,388 - tensorflow - INFO - loss = 0.115707554, step = 1511400 (2.257 sec) -2022-05-01 19:18:47,637 - tensorflow - INFO - global_step/sec: 88.8917 -2022-05-01 19:18:47,638 - tensorflow - INFO - loss = 0.022547714, step = 1511600 (2.250 sec) -2022-05-01 19:18:49,890 - tensorflow - INFO - global_step/sec: 88.7748 -2022-05-01 19:18:49,890 - tensorflow - INFO - loss = 0.04035278, step = 1511800 (2.253 sec) -2022-05-01 19:18:52,142 - tensorflow - INFO - global_step/sec: 88.8283 -2022-05-01 19:18:52,142 - tensorflow - INFO - loss = 0.011679567, step = 1512000 (2.252 sec) -2022-05-01 19:18:54,395 - tensorflow - INFO - global_step/sec: 88.7433 -2022-05-01 19:18:54,396 - tensorflow - INFO - loss = 0.026710436, step = 1512200 (2.254 sec) -2022-05-01 19:18:56,651 - tensorflow - INFO - global_step/sec: 88.6618 -2022-05-01 19:18:56,651 - tensorflow - INFO - loss = 0.017468259, step = 1512400 (2.256 sec) -2022-05-01 19:18:58,904 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.961452, step = 1513800 (2.248 sec) -2022-05-01 19:19:14,675 - tensorflow - INFO - global_step/sec: 88.8292 -2022-05-01 19:19:14,676 - tensorflow - INFO - loss = 0.005127412, step = 1514000 (2.252 sec) -2022-05-01 19:19:16,930 - tensorflow - INFO - global_step/sec: 88.7034 -2022-05-01 19:19:16,930 - tensorflow - INFO - loss = 0.013860978, step = 1514200 (2.255 sec) -2022-05-01 19:19:19,186 - tensorflow - INFO - global_step/sec: 88.647 -2022-05-01 19:19:19,186 - tensorflow - INFO - loss = 0.009205536, step = 1514400 (2.256 sec) -2022-05-01 19:19:21,441 - tensorflow - INFO - global_step/sec: 88.708 -2022-05-01 19:19:21,441 - tensorflow - INFO - loss = 0.0066144904, step = 1514600 (2.255 sec) -2022-05-01 19:19:23,700 - tensorflow - INFO - global_step/sec: 88.5165 -2022-05-01 19:19:23,701 - tensorflow - INFO - loss = 0.04965475, step = 1514800 (2.259 sec) -2022-05-01 19:19:25,954 - tensorflow - INFO - global_step/sec: 88.7295 -2022-05-01 19:19:25,955 - tensorflow - INFO - loss = 0.017812291, step = 1515000 (2.254 sec) -2022-05-01 19:19:28,204 - tensorflow - INFO - global_step/sec: 88.8944 -2022-05-01 19:19:28,204 - tensorflow - INFO - loss = 0.027830694, step = 1515200 (2.250 sec) -2022-05-01 19:19:30,466 - tensorflow - INFO - global_step/sec: 88.4032 -2022-05-01 19:19:30,467 - tensorflow - INFO - loss = 0.027515944, step = 1515400 (2.262 sec) -2022-05-01 19:19:32,726 - tensorflow - INFO - global_step/sec: 88.5016 -2022-05-01 19:19:32,727 - tensorflow - INFO - loss = 0.11355199, step = 1515600 (2.260 sec) -2022-05-01 19:19:34,977 - tensorflow - INFO - global_step/sec: 88.8434 -2022-05-01 19:19:34,978 - tensorflow - INFO - loss = 0.014939457, step = 1515800 (2.251 sec) -2022-05-01 19:19:37,231 - tensorflow - INFO - global_step/sec: 88.7384 -2022-05-01 19:19:37,232 - tensorflow - INFO - loss = 0.020215426, step = 1516000 (2.254 sec) -2022-05-01 19:19:39,484 - tensorflow - INFO - global_step/sec: 88.7715 -2022-05-01 19:19:39,485 - tensorflow - INFO - loss = 0.01886259, step = 1516200 (2.253 sec) -2022-05-01 19:19:41,740 - tensorflow - INFO - global_step/sec: 88.6547 -2022-05-01 19:19:41,741 - tensorflow - INFO - loss = 0.16277888, step = 1516400 (2.256 sec) -2022-05-01 19:19:43,992 - tensorflow - INFO - global_step/sec: 88.8261 -2022-05-01 19:19:43,992 - tensorflow - INFO - loss = 0.012797175, step = 1516600 (2.252 sec) -2022-05-01 19:19:46,250 - tensorflow - INFO - global_step/sec: 88.5513 -2022-05-01 19:19:46,251 - tensorflow - INFO - loss = 0.011014702, step = 1516800 (2.259 sec) -2022-05-01 19:19:48,500 - tensorflow - INFO - global_step/sec: 88.8827 -2022-05-01 19:19:48,501 - tensorflow - INFO - loss = 0.026667975, step = 1517000 (2.250 sec) -2022-05-01 19:19:50,753 - tensorflow - INFO - global_step/sec: 88.7997 -2022-05-01 19:19:50,753 - tensorflow - INFO - loss = 0.011713488, step = 1517200 (2.252 sec) -2022-05-01 19:19:53,006 - tensorflow - INFO - global_step/sec: 88.7603 -2022-05-01 19:19:53,006 - tensorflow - INFO - loss = 0.028351596, step = 1517400 (2.253 sec) -2022-05-01 19:19:55,254 - tensorflow - INFO - global_step/sec: 88.9736 -2022-05-01 19:19:55,254 - tensorflow - INFO - loss = 0.008203949, step = 1517600 (2.248 sec) -2022-05-01 19:19:57,514 - tensorflow - INFO - global_step/sec: 88.5006 -2022-05-01 19:19:57,514 - tensorflow - INFO - loss = 0.4390188, step = 1517800 (2.260 sec) -2022-05-01 19:19:59,772 - tensorflow - INFO - global_step/sec: 88.5811 -2022-05-01 19:19:59,772 - tensorflow - INFO - loss = 0.016573153, step = 1518000 (2.258 sec) -2022-05-01 19:20:02,026 - tensorflow - INFO - global_step/sec: 88.7131 -2022-05-01 19:20:02,026 - tensorflow - INFO - loss = 0.029199861, step = 1518200 (2.254 sec) -2022-05-01 19:20:04,284 - tensorflow - INFO - global_step/sec: 88.5768 -2022-05-01 19:20:04,284 - tensorflow - INFO - loss = 0.012956169, step = 1518400 (2.258 sec) -2022-05-01 19:20:06,534 - tensorflow - INFO - global_step/sec: 88.8654 -2022-05-01 19:20:06,535 - tensorflow - INFO - loss = 0.028454738, step = 1518600 (2.251 sec) -2022-05-01 19:20:08,792 - tensorflow - 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INFO - global_step/sec: 88.7263 -2022-05-01 19:24:19,237 - tensorflow - INFO - loss = 0.020975178, step = 1541000 (2.254 sec) -2022-05-01 19:24:21,488 - tensorflow - INFO - global_step/sec: 88.8172 -2022-05-01 19:24:21,489 - tensorflow - INFO - loss = 0.028373608, step = 1541200 (2.252 sec) -2022-05-01 19:24:23,742 - tensorflow - INFO - global_step/sec: 88.7457 -2022-05-01 19:24:23,742 - tensorflow - INFO - loss = 0.029991167, step = 1541400 (2.254 sec) -2022-05-01 19:24:25,996 - tensorflow - INFO - global_step/sec: 88.7159 -2022-05-01 19:24:25,997 - tensorflow - INFO - loss = 0.06427478, step = 1541600 (2.254 sec) -2022-05-01 19:24:28,248 - tensorflow - INFO - global_step/sec: 88.8243 -2022-05-01 19:24:28,248 - tensorflow - INFO - loss = 0.009837613, step = 1541800 (2.252 sec) -2022-05-01 19:24:30,509 - tensorflow - INFO - global_step/sec: 88.4659 -2022-05-01 19:24:30,509 - tensorflow - INFO - loss = 0.011918984, step = 1542000 (2.261 sec) -2022-05-01 19:24:32,766 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.7448 -2022-05-01 19:25:15,604 - tensorflow - INFO - loss = 0.020859353, step = 1546000 (2.254 sec) -2022-05-01 19:25:17,854 - tensorflow - INFO - global_step/sec: 88.8677 -2022-05-01 19:25:17,854 - tensorflow - INFO - loss = 0.8780155, step = 1546200 (2.251 sec) -2022-05-01 19:25:20,107 - tensorflow - INFO - global_step/sec: 88.7496 -2022-05-01 19:25:20,108 - tensorflow - INFO - loss = 0.020770505, step = 1546400 (2.254 sec) -2022-05-01 19:25:22,365 - tensorflow - INFO - global_step/sec: 88.5836 -2022-05-01 19:25:22,365 - tensorflow - INFO - loss = 0.02166292, step = 1546600 (2.258 sec) -2022-05-01 19:25:24,619 - tensorflow - INFO - global_step/sec: 88.7166 -2022-05-01 19:25:24,620 - tensorflow - INFO - loss = 0.05862613, step = 1546800 (2.254 sec) -2022-05-01 19:25:26,875 - tensorflow - INFO - global_step/sec: 88.6816 -2022-05-01 19:25:26,875 - tensorflow - INFO - loss = 0.01882706, step = 1547000 (2.255 sec) -2022-05-01 19:25:29,133 - tensorflow - INFO - global_step/sec: 88.569 -2022-05-01 19:25:29,133 - tensorflow - INFO - loss = 0.04019544, step = 1547200 (2.258 sec) -2022-05-01 19:25:31,394 - tensorflow - INFO - global_step/sec: 88.4471 -2022-05-01 19:25:31,394 - tensorflow - INFO - loss = 0.30397993, step = 1547400 (2.261 sec) -2022-05-01 19:25:33,652 - tensorflow - INFO - global_step/sec: 88.5754 -2022-05-01 19:25:33,652 - tensorflow - INFO - loss = 0.10902523, step = 1547600 (2.258 sec) -2022-05-01 19:25:35,907 - tensorflow - INFO - global_step/sec: 88.6866 -2022-05-01 19:25:35,907 - tensorflow - INFO - loss = 0.037281685, step = 1547800 (2.255 sec) -2022-05-01 19:25:38,156 - tensorflow - INFO - global_step/sec: 88.9134 -2022-05-01 19:25:38,157 - tensorflow - INFO - loss = 1.4373074, step = 1548000 (2.249 sec) -2022-05-01 19:25:40,413 - tensorflow - INFO - global_step/sec: 88.6115 -2022-05-01 19:25:40,414 - tensorflow - INFO - loss = 0.019181658, step = 1548200 (2.257 sec) -2022-05-01 19:25:42,666 - tensorflow - INFO - global_step/sec: 88.7797 -2022-05-01 19:25:42,667 - tensorflow - INFO - loss = 0.04345332, step = 1548400 (2.253 sec) -2022-05-01 19:25:44,921 - tensorflow - INFO - global_step/sec: 88.7074 -2022-05-01 19:25:44,921 - tensorflow - INFO - loss = 1.7033477, step = 1548600 (2.255 sec) -2022-05-01 19:25:47,181 - tensorflow - INFO - global_step/sec: 88.5033 -2022-05-01 19:25:47,181 - tensorflow - INFO - loss = 0.022207957, step = 1548800 (2.260 sec) -2022-05-01 19:25:49,433 - tensorflow - INFO - global_step/sec: 88.7784 -2022-05-01 19:25:49,434 - tensorflow - INFO - loss = 0.05524181, step = 1549000 (2.253 sec) -2022-05-01 19:25:51,687 - tensorflow - INFO - global_step/sec: 88.7576 -2022-05-01 19:25:51,687 - tensorflow - INFO - loss = 0.008339899, step = 1549200 (2.253 sec) -2022-05-01 19:25:53,938 - tensorflow - INFO - global_step/sec: 88.8375 -2022-05-01 19:25:53,938 - tensorflow - INFO - loss = 0.09378567, step = 1549400 (2.251 sec) -2022-05-01 19:25:56,194 - tensorflow - INFO - global_step/sec: 88.6386 -2022-05-01 19:25:56,195 - tensorflow - INFO - loss = 0.027930196, step = 1549600 (2.256 sec) -2022-05-01 19:25:58,454 - tensorflow - INFO - global_step/sec: 88.5092 -2022-05-01 19:25:58,455 - tensorflow - INFO - loss = 0.3648109, step = 1549800 (2.260 sec) -2022-05-01 19:26:00,712 - tensorflow - INFO - global_step/sec: 88.5662 -2022-05-01 19:26:00,713 - tensorflow - INFO - loss = 0.02041462, step = 1550000 (2.259 sec) -2022-05-01 19:26:02,966 - tensorflow - INFO - global_step/sec: 88.7315 -2022-05-01 19:26:02,967 - tensorflow - INFO - loss = 1.336118, step = 1550200 (2.254 sec) -2022-05-01 19:26:05,222 - tensorflow - INFO - global_step/sec: 88.6473 -2022-05-01 19:26:05,223 - tensorflow - INFO - loss = 0.04982681, step = 1550400 (2.256 sec) -2022-05-01 19:26:07,478 - tensorflow - INFO - global_step/sec: 88.6657 -2022-05-01 19:26:07,478 - tensorflow - INFO - loss = 0.06480853, step = 1550600 (2.256 sec) -2022-05-01 19:26:09,731 - tensorflow - INFO - global_step/sec: 88.7566 -2022-05-01 19:26:09,732 - tensorflow - INFO - loss = 0.015623615, step = 1550800 (2.253 sec) -2022-05-01 19:26:11,991 - tensorflow - INFO - global_step/sec: 88.5312 -2022-05-01 19:26:11,991 - tensorflow - INFO - loss = 0.0297763, step = 1551000 (2.259 sec) -2022-05-01 19:26:14,254 - tensorflow - INFO - global_step/sec: 88.3703 -2022-05-01 19:26:14,254 - tensorflow - INFO - loss = 0.01136495, step = 1551200 (2.263 sec) -2022-05-01 19:26:16,505 - tensorflow - INFO - global_step/sec: 88.8248 -2022-05-01 19:26:16,506 - tensorflow - INFO - loss = 0.010518458, step = 1551400 (2.252 sec) -2022-05-01 19:26:18,758 - tensorflow - INFO - global_step/sec: 88.7683 -2022-05-01 19:26:18,759 - tensorflow - INFO - loss = 0.014938101, step = 1551600 (2.253 sec) -2022-05-01 19:26:21,009 - tensorflow - INFO - global_step/sec: 88.8808 -2022-05-01 19:26:21,009 - tensorflow - INFO - loss = 0.009529099, step = 1551800 (2.250 sec) -2022-05-01 19:26:23,260 - tensorflow - INFO - global_step/sec: 88.8393 -2022-05-01 19:26:23,260 - tensorflow - INFO - loss = 0.032692857, step = 1552000 (2.251 sec) -2022-05-01 19:26:25,520 - tensorflow - INFO - global_step/sec: 88.4978 -2022-05-01 19:26:25,520 - tensorflow - INFO - loss = 0.022069555, step = 1552200 (2.260 sec) -2022-05-01 19:26:27,778 - tensorflow - INFO - global_step/sec: 88.5814 -2022-05-01 19:26:27,778 - tensorflow - INFO - loss = 0.028814282, step = 1552400 (2.258 sec) -2022-05-01 19:26:30,036 - tensorflow - INFO - global_step/sec: 88.5769 -2022-05-01 19:26:30,036 - tensorflow - INFO - loss = 0.0140770385, step = 1552600 (2.258 sec) -2022-05-01 19:26:32,299 - tensorflow - INFO - global_step/sec: 88.3689 -2022-05-01 19:26:32,299 - tensorflow - INFO - loss = 0.011250533, step = 1552800 (2.263 sec) -2022-05-01 19:26:34,553 - tensorflow - INFO - global_step/sec: 88.7143 -2022-05-01 19:26:34,554 - tensorflow - INFO - loss = 0.010542143, step = 1553000 (2.254 sec) -2022-05-01 19:26:36,814 - tensorflow - INFO - global_step/sec: 88.4834 -2022-05-01 19:26:36,814 - tensorflow - INFO - loss = 0.50717986, step = 1553200 (2.260 sec) -2022-05-01 19:26:39,065 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.035712287, step = 1554600 (2.250 sec) -2022-05-01 19:26:54,850 - tensorflow - INFO - global_step/sec: 88.5326 -2022-05-01 19:26:54,850 - tensorflow - INFO - loss = 0.41522735, step = 1554800 (2.259 sec) -2022-05-01 19:26:57,106 - tensorflow - INFO - global_step/sec: 88.647 -2022-05-01 19:26:57,106 - tensorflow - INFO - loss = 0.030813845, step = 1555000 (2.256 sec) -2022-05-01 19:26:59,361 - tensorflow - INFO - global_step/sec: 88.6715 -2022-05-01 19:26:59,362 - tensorflow - INFO - loss = 0.016018137, step = 1555200 (2.255 sec) -2022-05-01 19:27:01,615 - tensorflow - INFO - global_step/sec: 88.7456 -2022-05-01 19:27:01,615 - tensorflow - INFO - loss = 0.0454354, step = 1555400 (2.254 sec) -2022-05-01 19:27:03,874 - tensorflow - INFO - global_step/sec: 88.5274 -2022-05-01 19:27:03,875 - tensorflow - INFO - loss = 0.009947162, step = 1555600 (2.259 sec) -2022-05-01 19:27:06,128 - tensorflow - INFO - global_step/sec: 88.7262 -2022-05-01 19:27:06,129 - tensorflow - INFO - loss = 0.014390573, step = 1555800 (2.254 sec) -2022-05-01 19:27:08,384 - tensorflow - INFO - global_step/sec: 88.6759 -2022-05-01 19:27:08,384 - tensorflow - INFO - loss = 0.029519245, step = 1556000 (2.255 sec) -2022-05-01 19:27:10,646 - tensorflow - INFO - global_step/sec: 88.4069 -2022-05-01 19:27:10,646 - tensorflow - INFO - loss = 0.020814005, step = 1556200 (2.262 sec) -2022-05-01 19:27:12,905 - tensorflow - INFO - global_step/sec: 88.5466 -2022-05-01 19:27:12,905 - tensorflow - INFO - loss = 0.04717438, step = 1556400 (2.259 sec) -2022-05-01 19:27:15,170 - tensorflow - INFO - global_step/sec: 88.3023 -2022-05-01 19:27:15,170 - tensorflow - INFO - loss = 0.007176837, step = 1556600 (2.265 sec) -2022-05-01 19:27:17,432 - tensorflow - INFO - global_step/sec: 88.3971 -2022-05-01 19:27:17,433 - tensorflow - INFO - loss = 0.023968074, step = 1556800 (2.263 sec) -2022-05-01 19:27:19,695 - tensorflow - INFO - global_step/sec: 88.3919 -2022-05-01 19:27:19,695 - tensorflow - INFO - 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INFO - global_step/sec: 88.7717 -2022-05-01 19:31:18,817 - tensorflow - INFO - loss = 0.18352231, step = 1578200 (2.253 sec) -2022-05-01 19:31:21,074 - tensorflow - INFO - global_step/sec: 88.6043 -2022-05-01 19:31:21,075 - tensorflow - INFO - loss = 0.31989393, step = 1578400 (2.257 sec) -2022-05-01 19:31:23,336 - tensorflow - INFO - global_step/sec: 88.4351 -2022-05-01 19:31:23,336 - tensorflow - INFO - loss = 0.012910461, step = 1578600 (2.262 sec) -2022-05-01 19:31:25,592 - tensorflow - INFO - global_step/sec: 88.6243 -2022-05-01 19:31:25,593 - tensorflow - INFO - loss = 0.024716763, step = 1578800 (2.257 sec) -2022-05-01 19:31:27,849 - tensorflow - INFO - global_step/sec: 88.6312 -2022-05-01 19:31:27,849 - tensorflow - INFO - loss = 0.016229302, step = 1579000 (2.257 sec) -2022-05-01 19:31:30,106 - tensorflow - INFO - global_step/sec: 88.6216 -2022-05-01 19:31:30,106 - tensorflow - INFO - loss = 0.019719245, step = 1579200 (2.257 sec) -2022-05-01 19:31:32,369 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.07760454, step = 1580600 (2.250 sec) -2022-05-01 19:31:48,160 - tensorflow - INFO - global_step/sec: 88.4801 -2022-05-01 19:31:48,160 - tensorflow - INFO - loss = 0.06351081, step = 1580800 (2.260 sec) -2022-05-01 19:31:50,411 - tensorflow - INFO - global_step/sec: 88.8325 -2022-05-01 19:31:50,412 - tensorflow - INFO - loss = 0.0094411755, step = 1581000 (2.251 sec) -2022-05-01 19:31:52,662 - tensorflow - INFO - global_step/sec: 88.8538 -2022-05-01 19:31:52,663 - tensorflow - INFO - loss = 0.017369913, step = 1581200 (2.251 sec) -2022-05-01 19:31:54,918 - tensorflow - INFO - global_step/sec: 88.6613 -2022-05-01 19:31:54,919 - tensorflow - INFO - loss = 0.03887278, step = 1581400 (2.256 sec) -2022-05-01 19:31:57,178 - tensorflow - INFO - global_step/sec: 88.5103 -2022-05-01 19:31:57,178 - tensorflow - INFO - loss = 0.19254777, step = 1581600 (2.260 sec) -2022-05-01 19:31:59,432 - tensorflow - INFO - global_step/sec: 88.7072 -2022-05-01 19:31:59,433 - tensorflow - INFO - loss = 0.0091712065, step = 1581800 (2.255 sec) -2022-05-01 19:32:01,687 - tensorflow - INFO - global_step/sec: 88.7115 -2022-05-01 19:32:01,687 - tensorflow - INFO - loss = 0.8167502, step = 1582000 (2.255 sec) -2022-05-01 19:32:03,937 - tensorflow - INFO - global_step/sec: 88.8941 -2022-05-01 19:32:03,937 - tensorflow - INFO - loss = 0.015261, step = 1582200 (2.250 sec) -2022-05-01 19:32:06,191 - tensorflow - INFO - global_step/sec: 88.7356 -2022-05-01 19:32:06,191 - tensorflow - INFO - loss = 0.07977207, step = 1582400 (2.254 sec) -2022-05-01 19:32:08,443 - tensorflow - INFO - global_step/sec: 88.7754 -2022-05-01 19:32:08,444 - tensorflow - INFO - loss = 0.026426725, step = 1582600 (2.253 sec) -2022-05-01 19:32:10,695 - tensorflow - INFO - global_step/sec: 88.838 -2022-05-01 19:32:10,695 - tensorflow - INFO - loss = 0.035139702, step = 1582800 (2.251 sec) -2022-05-01 19:32:12,955 - tensorflow - INFO - global_step/sec: 88.4909 -2022-05-01 19:32:12,955 - tensorflow - INFO - loss = 0.014947243, step = 1583000 (2.260 sec) -2022-05-01 19:32:15,213 - tensorflow - INFO - global_step/sec: 88.5803 -2022-05-01 19:32:15,213 - tensorflow - INFO - loss = 0.08532808, step = 1583200 (2.258 sec) -2022-05-01 19:32:17,463 - tensorflow - INFO - global_step/sec: 88.8682 -2022-05-01 19:32:17,464 - tensorflow - INFO - loss = 0.020720141, step = 1583400 (2.250 sec) -2022-05-01 19:32:19,721 - tensorflow - INFO - global_step/sec: 88.5699 -2022-05-01 19:32:19,722 - tensorflow - INFO - loss = 0.0035738472, step = 1583600 (2.258 sec) -2022-05-01 19:32:21,972 - tensorflow - INFO - global_step/sec: 88.849 -2022-05-01 19:32:21,973 - tensorflow - INFO - loss = 2.649255, step = 1583800 (2.251 sec) -2022-05-01 19:32:24,224 - tensorflow - INFO - global_step/sec: 88.8165 -2022-05-01 19:32:24,225 - tensorflow - INFO - loss = 0.02071458, step = 1584000 (2.252 sec) -2022-05-01 19:32:26,484 - tensorflow - INFO - global_step/sec: 88.4871 -2022-05-01 19:32:26,485 - tensorflow - INFO - loss = 0.18882996, step = 1584200 (2.260 sec) -2022-05-01 19:32:28,736 - tensorflow - INFO - global_step/sec: 88.8337 -2022-05-01 19:32:28,736 - tensorflow - INFO - loss = 0.0497748, step = 1584400 (2.251 sec) -2022-05-01 19:32:30,990 - tensorflow - INFO - global_step/sec: 88.7308 -2022-05-01 19:32:30,990 - tensorflow - INFO - loss = 1.3267566, step = 1584600 (2.254 sec) -2022-05-01 19:32:33,243 - tensorflow - INFO - global_step/sec: 88.7704 -2022-05-01 19:32:33,243 - tensorflow - INFO - loss = 0.027151179, step = 1584800 (2.253 sec) -2022-05-01 19:32:35,497 - tensorflow - INFO - global_step/sec: 88.7239 -2022-05-01 19:32:35,497 - tensorflow - INFO - loss = 0.04791772, step = 1585000 (2.254 sec) -2022-05-01 19:32:37,749 - tensorflow - INFO - global_step/sec: 88.8205 -2022-05-01 19:32:37,749 - tensorflow - INFO - loss = 0.09944801, step = 1585200 (2.252 sec) -2022-05-01 19:32:40,003 - tensorflow - INFO - global_step/sec: 88.7124 -2022-05-01 19:32:40,004 - tensorflow - INFO - loss = 0.021462023, step = 1585400 (2.254 sec) -2022-05-01 19:32:42,256 - tensorflow - INFO - global_step/sec: 88.7972 -2022-05-01 19:32:42,256 - tensorflow - INFO - loss = 0.03806497, step = 1585600 (2.252 sec) -2022-05-01 19:32:44,510 - tensorflow - INFO - global_step/sec: 88.7148 -2022-05-01 19:32:44,510 - tensorflow - INFO - loss = 0.026090106, step = 1585800 (2.254 sec) -2022-05-01 19:32:46,762 - tensorflow - INFO - global_step/sec: 88.8133 -2022-05-01 19:32:46,762 - tensorflow - INFO - loss = 0.0069897245, step = 1586000 (2.252 sec) -2022-05-01 19:32:49,017 - tensorflow - INFO - global_step/sec: 88.6905 -2022-05-01 19:32:49,017 - tensorflow - INFO - loss = 0.013601289, step = 1586200 (2.255 sec) -2022-05-01 19:32:51,271 - tensorflow - INFO - global_step/sec: 88.7082 -2022-05-01 19:32:51,272 - tensorflow - INFO - loss = 0.006317136, step = 1586400 (2.255 sec) -2022-05-01 19:32:53,527 - tensorflow - INFO - global_step/sec: 88.6746 -2022-05-01 19:32:53,527 - tensorflow - INFO - loss = 0.008498697, step = 1586600 (2.255 sec) -2022-05-01 19:32:55,781 - tensorflow - INFO - global_step/sec: 88.7223 -2022-05-01 19:32:55,782 - tensorflow - INFO - loss = 0.013440279, step = 1586800 (2.254 sec) -2022-05-01 19:32:58,032 - tensorflow - INFO - global_step/sec: 88.8497 -2022-05-01 19:32:58,033 - tensorflow - INFO - loss = 0.026137035, step = 1587000 (2.251 sec) -2022-05-01 19:33:00,284 - tensorflow - INFO - global_step/sec: 88.8006 -2022-05-01 19:33:00,285 - tensorflow - INFO - loss = 0.010146251, step = 1587200 (2.252 sec) -2022-05-01 19:33:02,539 - tensorflow - INFO - global_step/sec: 88.6921 -2022-05-01 19:33:02,540 - tensorflow - INFO - loss = 0.13608116, step = 1587400 (2.255 sec) -2022-05-01 19:33:04,799 - tensorflow - INFO - global_step/sec: 88.527 -2022-05-01 19:33:04,799 - tensorflow - INFO - loss = 0.06195675, step = 1587600 (2.259 sec) -2022-05-01 19:33:07,049 - tensorflow - INFO - global_step/sec: 88.8633 -2022-05-01 19:33:07,050 - tensorflow - INFO - loss = 0.006039193, step = 1587800 (2.251 sec) -2022-05-01 19:33:09,303 - tensorflow - INFO - global_step/sec: 88.7286 -2022-05-01 19:33:09,304 - tensorflow - INFO - loss = 0.0078074397, step = 1588000 (2.254 sec) -2022-05-01 19:33:11,550 - tensorflow - INFO - global_step/sec: 89.0298 -2022-05-01 19:33:11,550 - tensorflow - INFO - loss = 0.020005397, step = 1588200 (2.246 sec) -2022-05-01 19:33:13,803 - tensorflow - INFO - global_step/sec: 88.7499 -2022-05-01 19:33:13,804 - tensorflow - INFO - loss = 0.023765197, step = 1588400 (2.254 sec) -2022-05-01 19:33:16,056 - tensorflow - INFO - global_step/sec: 88.7924 -2022-05-01 19:33:16,056 - tensorflow - INFO - loss = 0.046877913, step = 1588600 (2.252 sec) -2022-05-01 19:33:18,307 - tensorflow - INFO - global_step/sec: 88.8261 -2022-05-01 19:33:18,308 - tensorflow - INFO - loss = 0.028097004, step = 1588800 (2.252 sec) -2022-05-01 19:33:20,564 - tensorflow - INFO - global_step/sec: 88.6399 -2022-05-01 19:33:20,564 - tensorflow - INFO - loss = 0.057888266, step = 1589000 (2.256 sec) -2022-05-01 19:33:22,816 - tensorflow - INFO - global_step/sec: 88.8022 -2022-05-01 19:33:22,816 - tensorflow - INFO - loss = 0.05316, step = 1589200 (2.252 sec) -2022-05-01 19:33:25,064 - tensorflow - INFO - global_step/sec: 88.9413 -2022-05-01 19:33:25,065 - tensorflow - INFO - loss = 0.101530254, step = 1589400 (2.249 sec) -2022-05-01 19:33:27,315 - tensorflow - INFO - global_step/sec: 88.8613 -2022-05-01 19:33:27,316 - tensorflow - INFO - loss = 0.055434287, step = 1589600 (2.251 sec) -2022-05-01 19:33:29,567 - tensorflow - INFO - global_step/sec: 88.8333 -2022-05-01 19:33:29,567 - tensorflow - INFO - loss = 0.015367792, step = 1589800 (2.251 sec) -2022-05-01 19:33:31,822 - tensorflow - INFO - global_step/sec: 88.676 -2022-05-01 19:33:31,822 - tensorflow - INFO - loss = 0.07796347, step = 1590000 (2.255 sec) -2022-05-01 19:33:34,075 - tensorflow - INFO - global_step/sec: 88.7751 -2022-05-01 19:33:34,075 - tensorflow - INFO - loss = 0.024588708, step = 1590200 (2.253 sec) -2022-05-01 19:33:36,330 - tensorflow - INFO - global_step/sec: 88.6726 -2022-05-01 19:33:36,331 - tensorflow - INFO - loss = 0.009122549, step = 1590400 (2.256 sec) -2022-05-01 19:33:38,585 - tensorflow - INFO - global_step/sec: 88.7176 -2022-05-01 19:33:38,585 - tensorflow - INFO - loss = 0.01820556, step = 1590600 (2.254 sec) -2022-05-01 19:33:40,838 - tensorflow - INFO - global_step/sec: 88.7469 -2022-05-01 19:33:40,839 - tensorflow - INFO - loss = 0.13771403, step = 1590800 (2.254 sec) -2022-05-01 19:33:43,097 - tensorflow - INFO - global_step/sec: 88.5529 -2022-05-01 19:33:43,097 - tensorflow - INFO - loss = 0.01528587, step = 1591000 (2.259 sec) -2022-05-01 19:33:45,344 - tensorflow - INFO - global_step/sec: 89.003 -2022-05-01 19:33:45,344 - tensorflow - INFO - loss = 0.11659643, step = 1591200 (2.247 sec) -2022-05-01 19:33:47,595 - tensorflow - INFO - global_step/sec: 88.846 -2022-05-01 19:33:47,595 - tensorflow - INFO - loss = 0.016851345, step = 1591400 (2.251 sec) -2022-05-01 19:33:49,846 - tensorflow - INFO - global_step/sec: 88.8568 -2022-05-01 19:33:49,846 - tensorflow - INFO - loss = 0.013559494, step = 1591600 (2.251 sec) -2022-05-01 19:33:52,102 - tensorflow - INFO - global_step/sec: 88.6615 -2022-05-01 19:33:52,102 - tensorflow - INFO - loss = 0.04798165, step = 1591800 (2.256 sec) -2022-05-01 19:33:54,358 - tensorflow - INFO - global_step/sec: 88.6353 -2022-05-01 19:33:54,358 - tensorflow - INFO - loss = 0.018655952, step = 1592000 (2.256 sec) -2022-05-01 19:33:56,620 - tensorflow - INFO - global_step/sec: 88.4314 -2022-05-01 19:33:56,620 - tensorflow - INFO - loss = 0.03391049, step = 1592200 (2.262 sec) -2022-05-01 19:33:58,869 - tensorflow - INFO - global_step/sec: 88.9314 -2022-05-01 19:33:58,869 - tensorflow - INFO - loss = 0.069250464, step = 1592400 (2.249 sec) -2022-05-01 19:34:01,118 - tensorflow - INFO - global_step/sec: 88.9064 -2022-05-01 19:34:01,119 - tensorflow - INFO - loss = 0.035049617, step = 1592600 (2.250 sec) -2022-05-01 19:34:03,371 - tensorflow - INFO - global_step/sec: 88.7631 -2022-05-01 19:34:03,372 - tensorflow - INFO - loss = 0.009838936, step = 1592800 (2.253 sec) -2022-05-01 19:34:05,623 - tensorflow - INFO - global_step/sec: 88.8397 -2022-05-01 19:34:05,623 - tensorflow - INFO - loss = 0.047494456, step = 1593000 (2.251 sec) -2022-05-01 19:34:07,880 - tensorflow - INFO - global_step/sec: 88.5972 -2022-05-01 19:34:07,880 - tensorflow - INFO - loss = 0.09272047, step = 1593200 (2.257 sec) -2022-05-01 19:34:10,136 - tensorflow - INFO - global_step/sec: 88.6715 -2022-05-01 19:34:10,136 - tensorflow - INFO - loss = 0.0153245935, step = 1593400 (2.256 sec) -2022-05-01 19:34:12,394 - tensorflow - INFO - global_step/sec: 88.5618 -2022-05-01 19:34:12,394 - tensorflow - INFO - loss = 1.4519277, step = 1593600 (2.258 sec) -2022-05-01 19:34:14,657 - tensorflow - INFO - global_step/sec: 88.37 -2022-05-01 19:34:14,658 - tensorflow - INFO - loss = 0.58025074, step = 1593800 (2.263 sec) -2022-05-01 19:34:16,918 - tensorflow - INFO - global_step/sec: 88.4433 -2022-05-01 19:34:16,919 - tensorflow - INFO - loss = 0.018323332, step = 1594000 (2.261 sec) -2022-05-01 19:34:19,181 - tensorflow - INFO - global_step/sec: 88.3756 -2022-05-01 19:34:19,182 - tensorflow - INFO - loss = 0.027733069, step = 1594200 (2.263 sec) -2022-05-01 19:34:21,442 - tensorflow - INFO - global_step/sec: 88.4649 -2022-05-01 19:34:21,443 - tensorflow - INFO - loss = 0.021599734, step = 1594400 (2.261 sec) -2022-05-01 19:34:23,709 - tensorflow - INFO - global_step/sec: 88.2266 -2022-05-01 19:34:23,710 - tensorflow - INFO - loss = 0.020881085, step = 1594600 (2.267 sec) -2022-05-01 19:34:25,971 - tensorflow - INFO - global_step/sec: 88.4053 -2022-05-01 19:34:25,972 - tensorflow - INFO - loss = 0.034342658, step = 1594800 (2.262 sec) -2022-05-01 19:34:28,234 - tensorflow - INFO - global_step/sec: 88.3982 -2022-05-01 19:34:28,234 - tensorflow - INFO - loss = 0.025880713, step = 1595000 (2.262 sec) -2022-05-01 19:34:30,498 - tensorflow - INFO - global_step/sec: 88.3272 -2022-05-01 19:34:30,499 - tensorflow - INFO - loss = 0.130944, step = 1595200 (2.264 sec) -2022-05-01 19:34:32,759 - tensorflow - INFO - global_step/sec: 88.4758 -2022-05-01 19:34:32,759 - tensorflow - INFO - loss = 0.010540167, step = 1595400 (2.261 sec) -2022-05-01 19:34:35,021 - tensorflow - INFO - global_step/sec: 88.4221 -2022-05-01 19:34:35,021 - tensorflow - INFO - loss = 0.007979, step = 1595600 (2.262 sec) -2022-05-01 19:34:37,283 - tensorflow - INFO - global_step/sec: 88.4207 -2022-05-01 19:34:37,283 - tensorflow - INFO - loss = 0.018332234, step = 1595800 (2.262 sec) -2022-05-01 19:34:39,544 - tensorflow - INFO - global_step/sec: 88.4203 -2022-05-01 19:34:39,545 - tensorflow - INFO - loss = 0.029539935, step = 1596000 (2.262 sec) -2022-05-01 19:34:41,798 - tensorflow - INFO - global_step/sec: 88.7637 -2022-05-01 19:34:41,798 - tensorflow - INFO - loss = 0.010827591, step = 1596200 (2.253 sec) -2022-05-01 19:34:44,052 - tensorflow - INFO - global_step/sec: 88.709 -2022-05-01 19:34:44,053 - tensorflow - INFO - loss = 0.02367578, step = 1596400 (2.255 sec) -2022-05-01 19:34:46,302 - tensorflow - INFO - global_step/sec: 88.8811 -2022-05-01 19:34:46,303 - tensorflow - INFO - loss = 0.029965963, step = 1596600 (2.250 sec) -2022-05-01 19:34:48,561 - tensorflow - INFO - global_step/sec: 88.5344 -2022-05-01 19:34:48,562 - tensorflow - INFO - loss = 0.0040046135, step = 1596800 (2.259 sec) -2022-05-01 19:34:50,816 - tensorflow - INFO - global_step/sec: 88.7208 -2022-05-01 19:34:50,816 - tensorflow - INFO - loss = 0.013771693, step = 1597000 (2.254 sec) -2022-05-01 19:34:53,066 - tensorflow - INFO - global_step/sec: 88.8668 -2022-05-01 19:34:53,067 - tensorflow - INFO - loss = 0.0055685453, step = 1597200 (2.251 sec) -2022-05-01 19:34:55,317 - tensorflow - INFO - global_step/sec: 88.8493 -2022-05-01 19:34:55,318 - tensorflow - INFO - loss = 0.29111502, step = 1597400 (2.251 sec) -2022-05-01 19:34:57,567 - tensorflow - INFO - global_step/sec: 88.9108 -2022-05-01 19:34:57,567 - tensorflow - INFO - loss = 0.034654275, step = 1597600 (2.249 sec) -2022-05-01 19:34:59,819 - tensorflow - INFO - global_step/sec: 88.7871 -2022-05-01 19:34:59,820 - tensorflow - INFO - loss = 0.045142382, step = 1597800 (2.253 sec) -2022-05-01 19:35:02,077 - tensorflow - INFO - global_step/sec: 88.5762 -2022-05-01 19:35:02,078 - tensorflow - INFO - loss = 0.02174133, step = 1598000 (2.258 sec) -2022-05-01 19:35:04,335 - tensorflow - INFO - global_step/sec: 88.5995 -2022-05-01 19:35:04,335 - tensorflow - INFO - loss = 0.034647133, step = 1598200 (2.257 sec) -2022-05-01 19:35:06,584 - tensorflow - INFO - global_step/sec: 88.8987 -2022-05-01 19:35:06,585 - tensorflow - INFO - loss = 0.048062347, step = 1598400 (2.250 sec) -2022-05-01 19:35:08,836 - tensorflow - INFO - global_step/sec: 88.808 -2022-05-01 19:35:08,837 - tensorflow - INFO - loss = 0.06000845, step = 1598600 (2.252 sec) -2022-05-01 19:35:11,090 - tensorflow - INFO - global_step/sec: 88.738 -2022-05-01 19:35:11,091 - tensorflow - INFO - loss = 0.014896672, step = 1598800 (2.254 sec) -2022-05-01 19:35:13,348 - tensorflow - INFO - global_step/sec: 88.5689 -2022-05-01 19:35:13,349 - tensorflow - INFO - loss = 0.11341639, step = 1599000 (2.258 sec) -2022-05-01 19:35:15,607 - tensorflow - INFO - global_step/sec: 88.5451 -2022-05-01 19:35:15,607 - tensorflow - INFO - loss = 0.017968118, step = 1599200 (2.259 sec) -2022-05-01 19:35:17,859 - tensorflow - INFO - global_step/sec: 88.7948 -2022-05-01 19:35:17,860 - tensorflow - INFO - loss = 0.02949712, step = 1599400 (2.252 sec) -2022-05-01 19:35:20,112 - tensorflow - INFO - global_step/sec: 88.7764 -2022-05-01 19:35:20,113 - tensorflow - INFO - loss = 0.016649557, step = 1599600 (2.253 sec) -2022-05-01 19:35:22,367 - tensorflow - INFO - global_step/sec: 88.6975 -2022-05-01 19:35:22,368 - tensorflow - INFO - loss = 0.019204346, step = 1599800 (2.255 sec) -2022-05-01 19:35:24,612 - tensorflow - INFO - Saving checkpoints for 1600000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. -2022-05-01 19:35:24,629 - tensorflow - INFO - model.ckpt-1600000 is not in all_model_checkpoint_paths. Manually adding it. -2022-05-01 19:35:25,147 - tensorflow - INFO - Calling model_fn. -2022-05-01 19:35:25,275 - tensorflow - INFO - Done calling model_fn. -2022-05-01 19:35:25,290 - tensorflow - INFO - Starting evaluation at 2022-05-01T19:35:25Z -2022-05-01 19:35:25,354 - tensorflow - INFO - Graph was finalized. -2022-05-01 19:35:25,357 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1600000 -2022-05-01 19:35:25,639 - tensorflow - INFO - Running local_init_op. -2022-05-01 19:35:25,747 - tensorflow - INFO - Done running local_init_op. -2022-05-01 19:35:35,989 - tensorflow - INFO - Evaluation [3990/39900] -2022-05-01 19:35:45,552 - tensorflow - INFO - Evaluation [7980/39900] -2022-05-01 19:35:55,109 - tensorflow - INFO - Evaluation [11970/39900] -2022-05-01 19:36:04,674 - tensorflow - INFO - Evaluation [15960/39900] -2022-05-01 19:36:14,213 - tensorflow - INFO - Evaluation [19950/39900] -2022-05-01 19:36:23,777 - tensorflow - INFO - Evaluation [23940/39900] -2022-05-01 19:36:33,343 - tensorflow - INFO - Evaluation [27930/39900] -2022-05-01 19:36:42,896 - tensorflow - INFO - Evaluation [31920/39900] -2022-05-01 19:36:52,449 - tensorflow - INFO - Evaluation [35910/39900] -2022-05-01 19:37:02,022 - tensorflow - INFO - Evaluation [39900/39900] -2022-05-01 19:37:02,157 - tensorflow - INFO - Finished evaluation at 2022-05-01-19:37:02 -2022-05-01 19:37:02,157 - tensorflow - INFO - Saving dict for global step 1600000: global_step = 1600000, loss = 0.0288886, mean_degree_err = 5.0725417 -2022-05-01 19:37:02,157 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 1600000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1600000 -2022-05-01 19:37:02,171 - tensorflow - INFO - global_step/sec: 2.00392 -2022-05-01 19:37:02,172 - tensorflow - INFO - loss = 0.036867063, step = 1600000 (99.805 sec) -2022-05-01 19:37:04,446 - tensorflow - INFO - global_step/sec: 87.9391 -2022-05-01 19:37:04,446 - tensorflow - INFO - loss = 0.040967178, step = 1600200 (2.274 sec) -2022-05-01 19:37:06,704 - tensorflow - INFO - global_step/sec: 88.5445 -2022-05-01 19:37:06,705 - tensorflow - INFO - loss = 0.22369778, step = 1600400 (2.259 sec) -2022-05-01 19:37:08,963 - tensorflow - INFO - global_step/sec: 88.5558 -2022-05-01 19:37:08,963 - tensorflow - INFO - loss = 0.04972737, step = 1600600 (2.258 sec) -2022-05-01 19:37:11,221 - tensorflow - INFO - global_step/sec: 88.5523 -2022-05-01 19:37:11,222 - tensorflow - INFO - loss = 0.012088019, step = 1600800 (2.259 sec) -2022-05-01 19:37:13,476 - tensorflow - INFO - global_step/sec: 88.7227 -2022-05-01 19:37:13,476 - tensorflow - INFO - loss = 0.027309652, step = 1601000 (2.254 sec) -2022-05-01 19:37:15,733 - tensorflow - INFO - global_step/sec: 88.6028 -2022-05-01 19:37:15,733 - tensorflow - INFO - loss = 0.010206912, step = 1601200 (2.257 sec) -2022-05-01 19:37:17,986 - tensorflow - INFO - global_step/sec: 88.7572 -2022-05-01 19:37:17,987 - tensorflow - INFO - loss = 0.005925677, step = 1601400 (2.253 sec) -2022-05-01 19:37:20,241 - tensorflow - INFO - global_step/sec: 88.712 -2022-05-01 19:37:20,241 - tensorflow - INFO - loss = 0.01557181, step = 1601600 (2.254 sec) -2022-05-01 19:37:22,502 - tensorflow - INFO - global_step/sec: 88.4272 -2022-05-01 19:37:22,503 - tensorflow - INFO - loss = 0.012669121, step = 1601800 (2.262 sec) -2022-05-01 19:37:24,760 - tensorflow - INFO - global_step/sec: 88.6035 -2022-05-01 19:37:24,760 - tensorflow - INFO - loss = 0.010645354, step = 1602000 (2.257 sec) -2022-05-01 19:37:27,014 - tensorflow - INFO - global_step/sec: 88.7156 -2022-05-01 19:37:27,014 - tensorflow - INFO - loss = 0.017084083, step = 1602200 (2.254 sec) -2022-05-01 19:37:29,268 - tensorflow - INFO - global_step/sec: 88.7194 -2022-05-01 19:37:29,269 - tensorflow - INFO - loss = 0.025699474, step = 1602400 (2.254 sec) -2022-05-01 19:37:31,527 - tensorflow - INFO - global_step/sec: 88.5463 -2022-05-01 19:37:31,527 - tensorflow - INFO - loss = 0.022008544, step = 1602600 (2.259 sec) -2022-05-01 19:37:33,783 - tensorflow - INFO - global_step/sec: 88.6539 -2022-05-01 19:37:33,783 - tensorflow - INFO - loss = 0.010230547, step = 1602800 (2.256 sec) -2022-05-01 19:37:36,036 - tensorflow - INFO - global_step/sec: 88.7676 -2022-05-01 19:37:36,037 - tensorflow - INFO - loss = 0.029947232, step = 1603000 (2.253 sec) -2022-05-01 19:37:38,289 - tensorflow - INFO - global_step/sec: 88.7648 -2022-05-01 19:37:38,290 - tensorflow - INFO - loss = 0.007518686, step = 1603200 (2.253 sec) -2022-05-01 19:37:40,547 - tensorflow - INFO - global_step/sec: 88.585 -2022-05-01 19:37:40,547 - tensorflow - INFO - loss = 0.0097355675, step = 1603400 (2.258 sec) -2022-05-01 19:37:42,809 - tensorflow - INFO - global_step/sec: 88.4294 -2022-05-01 19:37:42,809 - tensorflow - INFO - loss = 0.074278854, step = 1603600 (2.262 sec) -2022-05-01 19:37:45,073 - tensorflow - INFO - global_step/sec: 88.3324 -2022-05-01 19:37:45,073 - tensorflow - INFO - loss = 0.017599702, step = 1603800 (2.264 sec) -2022-05-01 19:37:47,338 - tensorflow - INFO - global_step/sec: 88.3053 -2022-05-01 19:37:47,338 - tensorflow - INFO - loss = 0.06725284, step = 1604000 (2.265 sec) -2022-05-01 19:37:49,600 - tensorflow - INFO - global_step/sec: 88.4228 -2022-05-01 19:37:49,600 - tensorflow - INFO - loss = 0.06788803, step = 1604200 (2.262 sec) -2022-05-01 19:37:51,865 - tensorflow - INFO - global_step/sec: 88.2895 -2022-05-01 19:37:51,865 - tensorflow - INFO - loss = 0.024817625, step = 1604400 (2.265 sec) -2022-05-01 19:37:54,130 - tensorflow - INFO - global_step/sec: 88.3108 -2022-05-01 19:37:54,130 - tensorflow - INFO - loss = 0.011394728, step = 1604600 (2.265 sec) -2022-05-01 19:37:56,394 - tensorflow - INFO - global_step/sec: 88.3047 -2022-05-01 19:37:56,395 - tensorflow - INFO - loss = 0.07237558, step = 1604800 (2.265 sec) -2022-05-01 19:37:58,658 - tensorflow - INFO - global_step/sec: 88.3465 -2022-05-01 19:37:58,659 - tensorflow - INFO - loss = 0.08910976, step = 1605000 (2.264 sec) -2022-05-01 19:38:00,919 - tensorflow - INFO - global_step/sec: 88.4754 -2022-05-01 19:38:00,919 - tensorflow - INFO - loss = 0.10602189, step = 1605200 (2.261 sec) -2022-05-01 19:38:03,187 - tensorflow - INFO - global_step/sec: 88.1663 -2022-05-01 19:38:03,188 - tensorflow - INFO - loss = 0.35692906, step = 1605400 (2.268 sec) -2022-05-01 19:38:05,451 - tensorflow - INFO - global_step/sec: 88.3521 -2022-05-01 19:38:05,451 - tensorflow - INFO - loss = 0.025508648, step = 1605600 (2.264 sec) -2022-05-01 19:38:07,710 - tensorflow - INFO - global_step/sec: 88.531 -2022-05-01 19:38:07,710 - tensorflow - INFO - loss = 0.020784743, step = 1605800 (2.259 sec) -2022-05-01 19:38:09,971 - tensorflow - INFO - global_step/sec: 88.4562 -2022-05-01 19:38:09,971 - tensorflow - INFO - loss = 0.009046527, step = 1606000 (2.261 sec) -2022-05-01 19:38:12,230 - tensorflow - INFO - global_step/sec: 88.5493 -2022-05-01 19:38:12,230 - tensorflow - INFO - loss = 0.12052645, step = 1606200 (2.259 sec) -2022-05-01 19:38:14,487 - tensorflow - INFO - global_step/sec: 88.6104 -2022-05-01 19:38:14,487 - tensorflow - INFO - loss = 0.010093826, step = 1606400 (2.257 sec) -2022-05-01 19:38:16,748 - tensorflow - INFO - global_step/sec: 88.4417 -2022-05-01 19:38:16,748 - tensorflow - INFO - loss = 0.060307868, step = 1606600 (2.261 sec) -2022-05-01 19:38:19,003 - tensorflow - INFO - global_step/sec: 88.7118 -2022-05-01 19:38:19,003 - tensorflow - INFO - loss = 0.0069079367, step = 1606800 (2.255 sec) -2022-05-01 19:38:21,254 - tensorflow - INFO - global_step/sec: 88.8224 -2022-05-01 19:38:21,255 - tensorflow - INFO - loss = 0.040427912, step = 1607000 (2.252 sec) -2022-05-01 19:38:23,512 - tensorflow - INFO - global_step/sec: 88.6025 -2022-05-01 19:38:23,512 - tensorflow - INFO - loss = 0.04088958, step = 1607200 (2.257 sec) -2022-05-01 19:38:25,767 - tensorflow - INFO - global_step/sec: 88.6759 -2022-05-01 19:38:25,767 - tensorflow - INFO - loss = 0.4712106, step = 1607400 (2.255 sec) -2022-05-01 19:38:28,028 - tensorflow - INFO - global_step/sec: 88.4476 -2022-05-01 19:38:28,029 - tensorflow - INFO - loss = 0.11803105, step = 1607600 (2.261 sec) -2022-05-01 19:38:30,286 - tensorflow - INFO - global_step/sec: 88.5953 -2022-05-01 19:38:30,286 - tensorflow - INFO - loss = 0.0077702906, step = 1607800 (2.257 sec) -2022-05-01 19:38:32,542 - tensorflow - INFO - global_step/sec: 88.6321 -2022-05-01 19:38:32,543 - tensorflow - INFO - loss = 0.07963283, step = 1608000 (2.256 sec) -2022-05-01 19:38:34,799 - tensorflow - INFO - global_step/sec: 88.6339 -2022-05-01 19:38:34,799 - tensorflow - INFO - loss = 0.28873613, step = 1608200 (2.256 sec) -2022-05-01 19:38:37,053 - tensorflow - INFO - global_step/sec: 88.7161 -2022-05-01 19:38:37,053 - tensorflow - INFO - loss = 0.05215706, step = 1608400 (2.254 sec) -2022-05-01 19:38:39,307 - tensorflow - INFO - global_step/sec: 88.7456 -2022-05-01 19:38:39,307 - tensorflow - INFO - loss = 0.0728301, step = 1608600 (2.254 sec) -2022-05-01 19:38:41,563 - tensorflow - INFO - global_step/sec: 88.6317 -2022-05-01 19:38:41,564 - tensorflow - INFO - loss = 0.05030317, step = 1608800 (2.257 sec) -2022-05-01 19:38:43,819 - tensorflow - INFO - global_step/sec: 88.6564 -2022-05-01 19:38:43,819 - tensorflow - INFO - loss = 0.013282448, step = 1609000 (2.256 sec) -2022-05-01 19:38:46,071 - tensorflow - INFO - global_step/sec: 88.8167 -2022-05-01 19:38:46,071 - tensorflow - INFO - loss = 0.019527066, step = 1609200 (2.252 sec) -2022-05-01 19:38:48,327 - tensorflow - INFO - global_step/sec: 88.6506 -2022-05-01 19:38:48,327 - tensorflow - INFO - loss = 0.009463694, step = 1609400 (2.256 sec) -2022-05-01 19:38:50,578 - tensorflow - INFO - global_step/sec: 88.849 -2022-05-01 19:38:50,578 - tensorflow - INFO - loss = 0.014914087, step = 1609600 (2.251 sec) -2022-05-01 19:38:52,834 - tensorflow - INFO - global_step/sec: 88.6385 -2022-05-01 19:38:52,835 - tensorflow - INFO - loss = 0.03069091, step = 1609800 (2.256 sec) -2022-05-01 19:38:55,093 - tensorflow - INFO - global_step/sec: 88.5442 -2022-05-01 19:38:55,093 - tensorflow - INFO - loss = 0.015568502, step = 1610000 (2.259 sec) -2022-05-01 19:38:57,350 - tensorflow - INFO - global_step/sec: 88.6284 -2022-05-01 19:38:57,350 - tensorflow - INFO - loss = 0.026304193, step = 1610200 (2.257 sec) -2022-05-01 19:38:59,600 - tensorflow - INFO - global_step/sec: 88.8798 -2022-05-01 19:38:59,600 - tensorflow - INFO - loss = 0.01577914, step = 1610400 (2.250 sec) -2022-05-01 19:39:01,853 - tensorflow - INFO - global_step/sec: 88.7517 -2022-05-01 19:39:01,854 - tensorflow - INFO - loss = 0.010001397, step = 1610600 (2.253 sec) -2022-05-01 19:39:04,111 - tensorflow - INFO - global_step/sec: 88.6022 -2022-05-01 19:39:04,111 - tensorflow - INFO - loss = 0.16669104, step = 1610800 (2.257 sec) -2022-05-01 19:39:06,372 - tensorflow - INFO - global_step/sec: 88.4573 -2022-05-01 19:39:06,372 - tensorflow - INFO - loss = 0.086048484, step = 1611000 (2.261 sec) -2022-05-01 19:39:08,626 - tensorflow - INFO - global_step/sec: 88.7357 -2022-05-01 19:39:08,626 - tensorflow - INFO - loss = 0.015150867, step = 1611200 (2.254 sec) -2022-05-01 19:39:10,874 - tensorflow - INFO - global_step/sec: 88.9594 -2022-05-01 19:39:10,874 - tensorflow - INFO - loss = 0.029532706, step = 1611400 (2.248 sec) -2022-05-01 19:39:13,123 - tensorflow - INFO - global_step/sec: 88.9277 -2022-05-01 19:39:13,123 - tensorflow - INFO - loss = 0.0062228935, step = 1611600 (2.249 sec) -2022-05-01 19:39:15,379 - tensorflow - INFO - global_step/sec: 88.631 -2022-05-01 19:39:15,380 - tensorflow - INFO - loss = 0.02429897, step = 1611800 (2.257 sec) -2022-05-01 19:39:17,634 - tensorflow - INFO - global_step/sec: 88.7114 -2022-05-01 19:39:17,634 - tensorflow - INFO - loss = 0.012944584, step = 1612000 (2.254 sec) -2022-05-01 19:39:19,890 - tensorflow - INFO - global_step/sec: 88.6536 -2022-05-01 19:39:19,890 - tensorflow - INFO - loss = 0.12568352, step = 1612200 (2.256 sec) -2022-05-01 19:39:22,144 - tensorflow - INFO - global_step/sec: 88.7101 -2022-05-01 19:39:22,145 - tensorflow - INFO - loss = 0.5938471, step = 1612400 (2.255 sec) -2022-05-01 19:39:24,396 - tensorflow - INFO - global_step/sec: 88.8153 -2022-05-01 19:39:24,397 - tensorflow - INFO - loss = 0.034583516, step = 1612600 (2.252 sec) -2022-05-01 19:39:26,652 - tensorflow - INFO - global_step/sec: 88.6671 -2022-05-01 19:39:26,652 - tensorflow - INFO - loss = 0.022513185, step = 1612800 (2.256 sec) -2022-05-01 19:39:28,906 - tensorflow - INFO - global_step/sec: 88.7386 -2022-05-01 19:39:28,906 - tensorflow - INFO - loss = 0.017508483, step = 1613000 (2.254 sec) -2022-05-01 19:39:31,167 - tensorflow - INFO - global_step/sec: 88.4312 -2022-05-01 19:39:31,168 - tensorflow - INFO - loss = 0.004814804, step = 1613200 (2.262 sec) -2022-05-01 19:39:33,421 - tensorflow - INFO - global_step/sec: 88.7257 -2022-05-01 19:39:33,422 - tensorflow - INFO - loss = 0.02977703, step = 1613400 (2.254 sec) -2022-05-01 19:39:35,669 - tensorflow - INFO - global_step/sec: 88.9721 -2022-05-01 19:39:35,670 - tensorflow - INFO - loss = 0.026092917, step = 1613600 (2.248 sec) -2022-05-01 19:39:37,918 - tensorflow - INFO - global_step/sec: 88.9591 -2022-05-01 19:39:37,918 - tensorflow - INFO - loss = 0.022681203, step = 1613800 (2.248 sec) -2022-05-01 19:39:40,174 - tensorflow - INFO - global_step/sec: 88.6215 -2022-05-01 19:39:40,175 - tensorflow - INFO - loss = 0.031323865, step = 1614000 (2.257 sec) -2022-05-01 19:39:42,425 - tensorflow - INFO - global_step/sec: 88.8421 -2022-05-01 19:39:42,426 - tensorflow - INFO - loss = 0.010317453, step = 1614200 (2.251 sec) -2022-05-01 19:39:44,681 - tensorflow - INFO - global_step/sec: 88.6888 -2022-05-01 19:39:44,681 - tensorflow - INFO - loss = 0.053331293, step = 1614400 (2.255 sec) -2022-05-01 19:39:46,930 - tensorflow - INFO - global_step/sec: 88.8947 -2022-05-01 19:39:46,931 - tensorflow - INFO - loss = 0.0056943605, step = 1614600 (2.250 sec) -2022-05-01 19:39:49,187 - tensorflow - INFO - global_step/sec: 88.6119 -2022-05-01 19:39:49,188 - tensorflow - INFO - loss = 0.042760827, step = 1614800 (2.257 sec) -2022-05-01 19:39:51,442 - tensorflow - INFO - global_step/sec: 88.7208 -2022-05-01 19:39:51,442 - tensorflow - INFO - loss = 0.09538596, step = 1615000 (2.254 sec) -2022-05-01 19:39:53,698 - tensorflow - INFO - global_step/sec: 88.6245 -2022-05-01 19:39:53,699 - tensorflow - INFO - loss = 0.0061489423, step = 1615200 (2.257 sec) -2022-05-01 19:39:55,959 - tensorflow - INFO - global_step/sec: 88.4757 -2022-05-01 19:39:55,959 - tensorflow - INFO - loss = 0.025436368, step = 1615400 (2.261 sec) -2022-05-01 19:39:58,211 - tensorflow - INFO - global_step/sec: 88.8139 -2022-05-01 19:39:58,211 - tensorflow - INFO - loss = 0.019615395, step = 1615600 (2.252 sec) -2022-05-01 19:40:00,468 - tensorflow - INFO - global_step/sec: 88.6208 -2022-05-01 19:40:00,468 - tensorflow - INFO - loss = 0.008789796, step = 1615800 (2.257 sec) -2022-05-01 19:40:02,720 - tensorflow - INFO - global_step/sec: 88.7873 -2022-05-01 19:40:02,721 - tensorflow - INFO - loss = 0.014913684, step = 1616000 (2.253 sec) -2022-05-01 19:40:04,974 - tensorflow - INFO - global_step/sec: 88.7429 -2022-05-01 19:40:04,974 - tensorflow - INFO - loss = 0.07278446, step = 1616200 (2.254 sec) -2022-05-01 19:40:07,226 - tensorflow - INFO - global_step/sec: 88.8158 -2022-05-01 19:40:07,226 - tensorflow - INFO - loss = 0.12501572, step = 1616400 (2.252 sec) -2022-05-01 19:40:09,481 - tensorflow - INFO - global_step/sec: 88.6723 -2022-05-01 19:40:09,482 - tensorflow - INFO - loss = 0.28377822, step = 1616600 (2.256 sec) -2022-05-01 19:40:11,732 - tensorflow - INFO - global_step/sec: 88.8551 -2022-05-01 19:40:11,733 - tensorflow - INFO - loss = 0.021106012, step = 1616800 (2.251 sec) -2022-05-01 19:40:13,987 - tensorflow - INFO - global_step/sec: 88.7023 -2022-05-01 19:40:13,987 - tensorflow - INFO - loss = 8.388082, step = 1617000 (2.255 sec) -2022-05-01 19:40:16,238 - tensorflow - INFO - global_step/sec: 88.8266 -2022-05-01 19:40:16,239 - tensorflow - INFO - loss = 0.019565396, step = 1617200 (2.252 sec) -2022-05-01 19:40:18,491 - tensorflow - INFO - global_step/sec: 88.7973 -2022-05-01 19:40:18,491 - tensorflow - INFO - loss = 0.03616175, step = 1617400 (2.252 sec) -2022-05-01 19:40:20,743 - tensorflow - INFO - global_step/sec: 88.807 -2022-05-01 19:40:20,743 - tensorflow - INFO - loss = 0.05423862, step = 1617600 (2.252 sec) -2022-05-01 19:40:22,997 - tensorflow - INFO - global_step/sec: 88.7125 -2022-05-01 19:40:22,998 - tensorflow - INFO - loss = 0.042066444, step = 1617800 (2.255 sec) -2022-05-01 19:40:25,250 - tensorflow - INFO - global_step/sec: 88.7986 -2022-05-01 19:40:25,250 - tensorflow - INFO - loss = 0.011787547, step = 1618000 (2.252 sec) -2022-05-01 19:40:27,505 - tensorflow - INFO - global_step/sec: 88.6943 -2022-05-01 19:40:27,505 - tensorflow - INFO - loss = 0.04883994, step = 1618200 (2.255 sec) -2022-05-01 19:40:29,760 - tensorflow - INFO - global_step/sec: 88.677 -2022-05-01 19:40:29,760 - tensorflow - INFO - loss = 0.012506776, step = 1618400 (2.255 sec) -2022-05-01 19:40:32,022 - tensorflow - INFO - global_step/sec: 88.4313 -2022-05-01 19:40:32,022 - tensorflow - INFO - loss = 0.025670322, step = 1618600 (2.262 sec) -2022-05-01 19:40:34,279 - tensorflow - INFO - global_step/sec: 88.5886 -2022-05-01 19:40:34,280 - tensorflow - INFO - loss = 0.04210098, step = 1618800 (2.258 sec) -2022-05-01 19:40:36,528 - tensorflow - INFO - global_step/sec: 88.9197 -2022-05-01 19:40:36,529 - tensorflow - INFO - loss = 0.016680093, step = 1619000 (2.249 sec) -2022-05-01 19:40:38,775 - tensorflow - INFO - global_step/sec: 89.0187 -2022-05-01 19:40:38,775 - tensorflow - INFO - loss = 0.46585515, step = 1619200 (2.247 sec) -2022-05-01 19:40:41,031 - tensorflow - INFO - global_step/sec: 88.6695 -2022-05-01 19:40:41,031 - tensorflow - INFO - loss = 0.023283059, step = 1619400 (2.256 sec) -2022-05-01 19:40:43,283 - tensorflow - INFO - global_step/sec: 88.7917 -2022-05-01 19:40:43,284 - tensorflow - INFO - loss = 0.015406001, step = 1619600 (2.252 sec) -2022-05-01 19:40:45,537 - tensorflow - INFO - global_step/sec: 88.7295 -2022-05-01 19:40:45,538 - tensorflow - INFO - loss = 0.021344887, step = 1619800 (2.254 sec) -2022-05-01 19:40:47,795 - tensorflow - INFO - global_step/sec: 88.5796 -2022-05-01 19:40:47,795 - tensorflow - 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INFO - loss = 0.009885623, step = 1698000 (2.259 sec) -2022-05-01 19:55:29,711 - tensorflow - INFO - global_step/sec: 88.2778 -2022-05-01 19:55:29,712 - tensorflow - INFO - loss = 0.02006259, step = 1698200 (2.266 sec) -2022-05-01 19:55:31,979 - tensorflow - INFO - global_step/sec: 88.188 -2022-05-01 19:55:31,980 - tensorflow - INFO - loss = 0.0032196615, step = 1698400 (2.268 sec) -2022-05-01 19:55:34,236 - tensorflow - INFO - global_step/sec: 88.6414 -2022-05-01 19:55:34,236 - tensorflow - INFO - loss = 0.010056755, step = 1698600 (2.256 sec) -2022-05-01 19:55:36,494 - tensorflow - INFO - global_step/sec: 88.5485 -2022-05-01 19:55:36,495 - tensorflow - INFO - loss = 0.053509187, step = 1698800 (2.259 sec) -2022-05-01 19:55:38,753 - tensorflow - INFO - global_step/sec: 88.5453 -2022-05-01 19:55:38,753 - tensorflow - INFO - loss = 0.08257486, step = 1699000 (2.259 sec) -2022-05-01 19:55:41,011 - tensorflow - INFO - global_step/sec: 88.5622 -2022-05-01 19:55:41,012 - tensorflow - INFO - loss = 0.037976272, step = 1699200 (2.258 sec) -2022-05-01 19:55:43,270 - tensorflow - INFO - global_step/sec: 88.554 -2022-05-01 19:55:43,270 - tensorflow - INFO - loss = 0.017978225, step = 1699400 (2.258 sec) -2022-05-01 19:55:45,528 - tensorflow - INFO - global_step/sec: 88.5821 -2022-05-01 19:55:45,528 - tensorflow - INFO - loss = 0.012758279, step = 1699600 (2.258 sec) -2022-05-01 19:55:47,786 - tensorflow - INFO - global_step/sec: 88.5669 -2022-05-01 19:55:47,786 - tensorflow - INFO - loss = 0.022172417, step = 1699800 (2.258 sec) -2022-05-01 19:55:50,034 - tensorflow - INFO - Saving checkpoints for 1700000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. -2022-05-01 19:55:50,049 - tensorflow - INFO - model.ckpt-1700000 is not in all_model_checkpoint_paths. Manually adding it. -2022-05-01 19:55:50,567 - tensorflow - INFO - Calling model_fn. -2022-05-01 19:55:50,695 - tensorflow - INFO - Done calling model_fn. -2022-05-01 19:55:50,710 - tensorflow - INFO - Starting evaluation at 2022-05-01T19:55:50Z -2022-05-01 19:55:50,773 - tensorflow - INFO - Graph was finalized. -2022-05-01 19:55:50,777 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1700000 -2022-05-01 19:55:51,056 - tensorflow - INFO - Running local_init_op. -2022-05-01 19:55:51,164 - tensorflow - INFO - Done running local_init_op. -2022-05-01 19:56:01,425 - tensorflow - INFO - Evaluation [3990/39900] -2022-05-01 19:56:10,995 - tensorflow - INFO - Evaluation [7980/39900] -2022-05-01 19:56:20,542 - tensorflow - INFO - Evaluation [11970/39900] -2022-05-01 19:56:30,094 - tensorflow - INFO - Evaluation [15960/39900] -2022-05-01 19:56:39,656 - tensorflow - INFO - Evaluation [19950/39900] -2022-05-01 19:56:49,226 - tensorflow - INFO - Evaluation [23940/39900] -2022-05-01 19:56:58,785 - tensorflow - INFO - Evaluation [27930/39900] -2022-05-01 19:57:08,350 - tensorflow - INFO - Evaluation [31920/39900] -2022-05-01 19:57:17,900 - tensorflow - INFO - Evaluation [35910/39900] -2022-05-01 19:57:27,483 - tensorflow - INFO - Evaluation [39900/39900] -2022-05-01 19:57:27,620 - tensorflow - INFO - Finished evaluation at 2022-05-01-19:57:27 -2022-05-01 19:57:27,620 - tensorflow - INFO - Saving dict for global step 1700000: global_step = 1700000, loss = 0.016528208, mean_degree_err = 3.3223944 -2022-05-01 19:57:27,621 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 1700000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1700000 -2022-05-01 19:57:27,635 - tensorflow - INFO - global_step/sec: 2.00302 -2022-05-01 19:57:27,635 - tensorflow - INFO - loss = 0.018213827, step = 1700000 (99.849 sec) -2022-05-01 19:57:29,903 - tensorflow - INFO - global_step/sec: 88.1838 -2022-05-01 19:57:29,903 - tensorflow - INFO - loss = 0.021835033, step = 1700200 (2.268 sec) -2022-05-01 19:57:32,156 - tensorflow - INFO - global_step/sec: 88.7664 -2022-05-01 19:57:32,156 - tensorflow - INFO - loss = 0.041633718, step = 1700400 (2.253 sec) -2022-05-01 19:57:34,411 - tensorflow - INFO - global_step/sec: 88.6858 -2022-05-01 19:57:34,411 - tensorflow - INFO - loss = 0.10517413, step = 1700600 (2.255 sec) -2022-05-01 19:57:36,664 - tensorflow - INFO - global_step/sec: 88.7511 -2022-05-01 19:57:36,665 - tensorflow - INFO - loss = 0.037491966, step = 1700800 (2.254 sec) -2022-05-01 19:57:38,916 - tensorflow - INFO - global_step/sec: 88.8348 -2022-05-01 19:57:38,916 - tensorflow - INFO - loss = 0.031109095, step = 1701000 (2.251 sec) -2022-05-01 19:57:41,173 - tensorflow - INFO - global_step/sec: 88.623 -2022-05-01 19:57:41,173 - tensorflow - INFO - loss = 0.040808327, step = 1701200 (2.257 sec) -2022-05-01 19:57:43,428 - tensorflow - INFO - global_step/sec: 88.6723 -2022-05-01 19:57:43,428 - tensorflow - INFO - loss = 0.47118276, step = 1701400 (2.256 sec) -2022-05-01 19:57:45,677 - tensorflow - INFO - global_step/sec: 88.942 -2022-05-01 19:57:45,677 - tensorflow - INFO - loss = 0.040171977, step = 1701600 (2.249 sec) -2022-05-01 19:57:47,929 - tensorflow - INFO - global_step/sec: 88.8062 -2022-05-01 19:57:47,929 - tensorflow - INFO - loss = 0.01732021, step = 1701800 (2.252 sec) -2022-05-01 19:57:50,183 - tensorflow - INFO - global_step/sec: 88.7345 -2022-05-01 19:57:50,183 - tensorflow - INFO - loss = 0.013448552, step = 1702000 (2.254 sec) -2022-05-01 19:57:52,438 - tensorflow - INFO - global_step/sec: 88.6815 -2022-05-01 19:57:52,438 - tensorflow - INFO - loss = 0.017138971, step = 1702200 (2.255 sec) -2022-05-01 19:57:54,692 - tensorflow - INFO - global_step/sec: 88.7224 -2022-05-01 19:57:54,693 - tensorflow - INFO - loss = 0.01174594, step = 1702400 (2.254 sec) -2022-05-01 19:57:56,946 - tensorflow - INFO - global_step/sec: 88.7225 -2022-05-01 19:57:56,947 - tensorflow - INFO - loss = 0.041543134, step = 1702600 (2.254 sec) -2022-05-01 19:57:59,199 - tensorflow - INFO - global_step/sec: 88.7761 -2022-05-01 19:57:59,200 - tensorflow - INFO - loss = 0.048404578, step = 1702800 (2.253 sec) -2022-05-01 19:58:01,454 - tensorflow - INFO - global_step/sec: 88.7005 -2022-05-01 19:58:01,454 - tensorflow - INFO - loss = 0.14215983, step = 1703000 (2.255 sec) -2022-05-01 19:58:03,707 - tensorflow - INFO - global_step/sec: 88.7618 -2022-05-01 19:58:03,708 - tensorflow - INFO - loss = 0.027555201, step = 1703200 (2.253 sec) -2022-05-01 19:58:05,972 - tensorflow - INFO - global_step/sec: 88.3134 -2022-05-01 19:58:05,972 - tensorflow - INFO - loss = 0.045765974, step = 1703400 (2.265 sec) -2022-05-01 19:58:08,230 - tensorflow - INFO - global_step/sec: 88.5631 -2022-05-01 19:58:08,231 - tensorflow - INFO - loss = 0.037620254, step = 1703600 (2.258 sec) -2022-05-01 19:58:10,487 - tensorflow - INFO - global_step/sec: 88.638 -2022-05-01 19:58:10,487 - tensorflow - INFO - loss = 0.03932178, step = 1703800 (2.256 sec) -2022-05-01 19:58:12,738 - tensorflow - INFO - global_step/sec: 88.8331 -2022-05-01 19:58:12,738 - tensorflow - INFO - loss = 0.024784284, step = 1704000 (2.251 sec) -2022-05-01 19:58:14,990 - tensorflow - INFO - global_step/sec: 88.8245 -2022-05-01 19:58:14,990 - tensorflow - INFO - loss = 0.03867591, step = 1704200 (2.252 sec) -2022-05-01 19:58:17,242 - tensorflow - INFO - global_step/sec: 88.8119 -2022-05-01 19:58:17,242 - tensorflow - INFO - loss = 0.17101954, step = 1704400 (2.252 sec) -2022-05-01 19:58:19,502 - tensorflow - INFO - global_step/sec: 88.4969 -2022-05-01 19:58:19,502 - tensorflow - INFO - loss = 0.12228063, step = 1704600 (2.260 sec) -2022-05-01 19:58:21,751 - tensorflow - INFO - global_step/sec: 88.8995 -2022-05-01 19:58:21,752 - tensorflow - INFO - loss = 0.020781545, step = 1704800 (2.250 sec) -2022-05-01 19:58:24,001 - tensorflow - INFO - global_step/sec: 88.8852 -2022-05-01 19:58:24,002 - tensorflow - INFO - loss = 0.016060911, step = 1705000 (2.250 sec) -2022-05-01 19:58:26,257 - tensorflow - INFO - global_step/sec: 88.6791 -2022-05-01 19:58:26,257 - tensorflow - INFO - loss = 0.013135418, step = 1705200 (2.255 sec) -2022-05-01 19:58:28,508 - tensorflow - INFO - global_step/sec: 88.8238 -2022-05-01 19:58:28,509 - tensorflow - INFO - loss = 0.033608403, step = 1705400 (2.252 sec) -2022-05-01 19:58:30,774 - tensorflow - INFO - global_step/sec: 88.2905 -2022-05-01 19:58:30,774 - tensorflow - INFO - loss = 0.02455812, step = 1705600 (2.265 sec) -2022-05-01 19:58:33,024 - tensorflow - INFO - global_step/sec: 88.8609 -2022-05-01 19:58:33,025 - tensorflow - INFO - loss = 0.73054725, step = 1705800 (2.251 sec) -2022-05-01 19:58:35,271 - tensorflow - INFO - global_step/sec: 89.0251 -2022-05-01 19:58:35,271 - tensorflow - INFO - loss = 0.46992564, step = 1706000 (2.247 sec) -2022-05-01 19:58:37,521 - tensorflow - INFO - global_step/sec: 88.8733 -2022-05-01 19:58:37,522 - tensorflow - INFO - loss = 0.008819725, step = 1706200 (2.250 sec) -2022-05-01 19:58:39,774 - tensorflow - INFO - global_step/sec: 88.7783 -2022-05-01 19:58:39,774 - tensorflow - INFO - loss = 0.05270659, step = 1706400 (2.253 sec) -2022-05-01 19:58:42,030 - tensorflow - INFO - global_step/sec: 88.6664 -2022-05-01 19:58:42,030 - tensorflow - INFO - loss = 0.06409759, step = 1706600 (2.256 sec) -2022-05-01 19:58:44,289 - tensorflow - INFO - global_step/sec: 88.5222 -2022-05-01 19:58:44,289 - tensorflow - INFO - loss = 0.017197635, step = 1706800 (2.259 sec) -2022-05-01 19:58:46,544 - tensorflow - INFO - global_step/sec: 88.7045 -2022-05-01 19:58:46,544 - tensorflow - INFO - loss = 0.0068109254, step = 1707000 (2.255 sec) -2022-05-01 19:58:48,793 - tensorflow - INFO - global_step/sec: 88.9021 -2022-05-01 19:58:48,794 - tensorflow - INFO - loss = 0.005135089, step = 1707200 (2.250 sec) -2022-05-01 19:58:51,047 - tensorflow - INFO - global_step/sec: 88.7387 -2022-05-01 19:58:51,048 - tensorflow - INFO - loss = 0.015555227, step = 1707400 (2.254 sec) -2022-05-01 19:58:53,301 - tensorflow - INFO - global_step/sec: 88.7398 -2022-05-01 19:58:53,301 - tensorflow - INFO - loss = 0.008056469, step = 1707600 (2.254 sec) -2022-05-01 19:58:55,556 - tensorflow - INFO - global_step/sec: 88.6893 -2022-05-01 19:58:55,556 - tensorflow - INFO - loss = 11.810549, step = 1707800 (2.255 sec) -2022-05-01 19:58:57,820 - tensorflow - INFO - global_step/sec: 88.3409 -2022-05-01 19:58:57,820 - tensorflow - INFO - loss = 0.63033855, step = 1708000 (2.264 sec) -2022-05-01 19:59:00,084 - tensorflow - INFO - global_step/sec: 88.3282 -2022-05-01 19:59:00,085 - tensorflow - INFO - loss = 0.02858428, step = 1708200 (2.264 sec) -2022-05-01 19:59:02,342 - tensorflow - INFO - global_step/sec: 88.5962 -2022-05-01 19:59:02,342 - tensorflow - INFO - loss = 0.028509991, step = 1708400 (2.257 sec) -2022-05-01 19:59:04,599 - tensorflow - INFO - global_step/sec: 88.5975 -2022-05-01 19:59:04,599 - tensorflow - INFO - loss = 0.043081976, step = 1708600 (2.257 sec) -2022-05-01 19:59:06,856 - tensorflow - INFO - global_step/sec: 88.6067 -2022-05-01 19:59:06,857 - tensorflow - INFO - loss = 0.6489007, step = 1708800 (2.257 sec) -2022-05-01 19:59:09,115 - tensorflow - INFO - global_step/sec: 88.5532 -2022-05-01 19:59:09,115 - tensorflow - INFO - loss = 0.06143674, step = 1709000 (2.259 sec) -2022-05-01 19:59:11,374 - tensorflow - INFO - global_step/sec: 88.5094 -2022-05-01 19:59:11,375 - tensorflow - INFO - loss = 0.014285991, step = 1709200 (2.260 sec) -2022-05-01 19:59:13,635 - tensorflow - INFO - global_step/sec: 88.482 -2022-05-01 19:59:13,635 - tensorflow - INFO - loss = 0.02394065, step = 1709400 (2.260 sec) -2022-05-01 19:59:15,894 - tensorflow - INFO - global_step/sec: 88.521 -2022-05-01 19:59:15,895 - tensorflow - INFO - loss = 0.040852383, step = 1709600 (2.259 sec) -2022-05-01 19:59:18,155 - tensorflow - INFO - global_step/sec: 88.4788 -2022-05-01 19:59:18,155 - tensorflow - INFO - loss = 0.020867106, step = 1709800 (2.260 sec) -2022-05-01 19:59:20,416 - tensorflow - INFO - global_step/sec: 88.444 -2022-05-01 19:59:20,416 - tensorflow - INFO - loss = 0.015351091, step = 1710000 (2.261 sec) -2022-05-01 19:59:22,678 - tensorflow - INFO - global_step/sec: 88.4161 -2022-05-01 19:59:22,678 - tensorflow - INFO - loss = 0.008525239, step = 1710200 (2.262 sec) -2022-05-01 19:59:24,939 - tensorflow - INFO - global_step/sec: 88.4628 -2022-05-01 19:59:24,939 - tensorflow - INFO - loss = 0.041885566, step = 1710400 (2.261 sec) -2022-05-01 19:59:27,199 - tensorflow - INFO - global_step/sec: 88.4711 -2022-05-01 19:59:27,200 - tensorflow - INFO - loss = 0.018942935, step = 1710600 (2.261 sec) -2022-05-01 19:59:29,452 - tensorflow - INFO - global_step/sec: 88.8002 -2022-05-01 19:59:29,452 - tensorflow - INFO - loss = 0.008305543, step = 1710800 (2.252 sec) -2022-05-01 19:59:31,714 - tensorflow - INFO - global_step/sec: 88.3904 -2022-05-01 19:59:31,715 - tensorflow - INFO - loss = 0.02622455, step = 1711000 (2.263 sec) -2022-05-01 19:59:33,966 - tensorflow - INFO - global_step/sec: 88.8314 -2022-05-01 19:59:33,966 - tensorflow - INFO - loss = 0.03469228, step = 1711200 (2.251 sec) -2022-05-01 19:59:36,219 - tensorflow - INFO - global_step/sec: 88.7587 -2022-05-01 19:59:36,219 - tensorflow - INFO - loss = 0.012422364, step = 1711400 (2.253 sec) -2022-05-01 19:59:38,473 - tensorflow - INFO - global_step/sec: 88.7485 -2022-05-01 19:59:38,473 - tensorflow - INFO - loss = 0.012812477, step = 1711600 (2.254 sec) -2022-05-01 19:59:40,725 - tensorflow - INFO - global_step/sec: 88.7892 -2022-05-01 19:59:40,726 - tensorflow - INFO - loss = 0.028063836, step = 1711800 (2.253 sec) -2022-05-01 19:59:42,975 - tensorflow - INFO - global_step/sec: 88.9101 -2022-05-01 19:59:42,975 - tensorflow - INFO - loss = 0.010674957, step = 1712000 (2.249 sec) -2022-05-01 19:59:45,226 - tensorflow - INFO - global_step/sec: 88.832 -2022-05-01 19:59:45,226 - tensorflow - INFO - loss = 0.020355092, step = 1712200 (2.251 sec) -2022-05-01 19:59:47,477 - tensorflow - INFO - global_step/sec: 88.8343 -2022-05-01 19:59:47,478 - tensorflow - INFO - loss = 0.029279368, step = 1712400 (2.251 sec) -2022-05-01 19:59:49,735 - tensorflow - INFO - global_step/sec: 88.6006 -2022-05-01 19:59:49,735 - tensorflow - INFO - loss = 0.006299399, step = 1712600 (2.257 sec) -2022-05-01 19:59:51,983 - tensorflow - INFO - global_step/sec: 88.9729 -2022-05-01 19:59:51,983 - tensorflow - INFO - loss = 0.0369786, step = 1712800 (2.248 sec) -2022-05-01 19:59:54,232 - tensorflow - INFO - global_step/sec: 88.9232 -2022-05-01 19:59:54,232 - tensorflow - INFO - loss = 0.052752845, step = 1713000 (2.249 sec) -2022-05-01 19:59:56,482 - tensorflow - INFO - global_step/sec: 88.8903 -2022-05-01 19:59:56,482 - tensorflow - INFO - loss = 0.022305928, step = 1713200 (2.250 sec) -2022-05-01 19:59:58,729 - tensorflow - INFO - global_step/sec: 88.9813 -2022-05-01 19:59:58,730 - tensorflow - INFO - loss = 0.102901265, step = 1713400 (2.248 sec) -2022-05-01 20:00:00,985 - tensorflow - INFO - global_step/sec: 88.6524 -2022-05-01 20:00:00,986 - tensorflow - INFO - loss = 0.012466687, step = 1713600 (2.256 sec) -2022-05-01 20:00:03,245 - tensorflow - INFO - global_step/sec: 88.4958 -2022-05-01 20:00:03,246 - tensorflow - INFO - loss = 0.041081473, step = 1713800 (2.260 sec) -2022-05-01 20:00:05,498 - tensorflow - INFO - global_step/sec: 88.8039 -2022-05-01 20:00:05,498 - tensorflow - INFO - loss = 0.021235213, step = 1714000 (2.252 sec) -2022-05-01 20:00:07,749 - tensorflow - INFO - global_step/sec: 88.8408 -2022-05-01 20:00:07,749 - tensorflow - INFO - loss = 0.10852687, step = 1714200 (2.251 sec) -2022-05-01 20:00:10,004 - tensorflow - INFO - global_step/sec: 88.693 -2022-05-01 20:00:10,004 - tensorflow - INFO - loss = 0.14167748, step = 1714400 (2.255 sec) -2022-05-01 20:00:12,255 - tensorflow - INFO - global_step/sec: 88.8499 -2022-05-01 20:00:12,255 - tensorflow - INFO - loss = 0.012458287, step = 1714600 (2.251 sec) -2022-05-01 20:00:14,507 - tensorflow - INFO - global_step/sec: 88.7877 -2022-05-01 20:00:14,508 - tensorflow - INFO - loss = 0.15571158, step = 1714800 (2.253 sec) -2022-05-01 20:00:16,765 - tensorflow - INFO - global_step/sec: 88.5666 -2022-05-01 20:00:16,766 - tensorflow - INFO - loss = 0.019827556, step = 1715000 (2.258 sec) -2022-05-01 20:00:19,014 - tensorflow - INFO - global_step/sec: 88.9583 -2022-05-01 20:00:19,014 - tensorflow - INFO - loss = 3.7223036, step = 1715200 (2.248 sec) -2022-05-01 20:00:21,263 - tensorflow - INFO - global_step/sec: 88.9078 -2022-05-01 20:00:21,264 - tensorflow - INFO - loss = 0.014220778, step = 1715400 (2.249 sec) -2022-05-01 20:00:23,512 - tensorflow - INFO - global_step/sec: 88.9291 -2022-05-01 20:00:23,513 - tensorflow - INFO - loss = 0.034245614, step = 1715600 (2.249 sec) -2022-05-01 20:00:25,766 - tensorflow - INFO - global_step/sec: 88.7206 -2022-05-01 20:00:25,767 - tensorflow - INFO - loss = 0.025689885, step = 1715800 (2.254 sec) -2022-05-01 20:00:28,026 - tensorflow - INFO - global_step/sec: 88.531 -2022-05-01 20:00:28,026 - tensorflow - INFO - loss = 0.012042551, step = 1716000 (2.259 sec) -2022-05-01 20:00:30,281 - tensorflow - INFO - global_step/sec: 88.6762 -2022-05-01 20:00:30,281 - tensorflow - INFO - loss = 0.011883922, step = 1716200 (2.255 sec) -2022-05-01 20:00:32,541 - tensorflow - INFO - global_step/sec: 88.4955 -2022-05-01 20:00:32,541 - tensorflow - INFO - loss = 0.038108338, step = 1716400 (2.260 sec) -2022-05-01 20:00:34,790 - tensorflow - INFO - global_step/sec: 88.9393 -2022-05-01 20:00:34,790 - tensorflow - INFO - loss = 0.021661714, step = 1716600 (2.249 sec) -2022-05-01 20:00:37,047 - tensorflow - INFO - global_step/sec: 88.586 -2022-05-01 20:00:37,048 - tensorflow - INFO - loss = 0.0092684915, step = 1716800 (2.258 sec) -2022-05-01 20:00:39,299 - tensorflow - INFO - global_step/sec: 88.8071 -2022-05-01 20:00:39,300 - tensorflow - INFO - loss = 0.07145426, step = 1717000 (2.252 sec) -2022-05-01 20:00:41,551 - tensorflow - INFO - global_step/sec: 88.8133 -2022-05-01 20:00:41,552 - tensorflow - INFO - loss = 0.011701191, step = 1717200 (2.252 sec) -2022-05-01 20:00:43,805 - tensorflow - INFO - global_step/sec: 88.7525 -2022-05-01 20:00:43,805 - tensorflow - INFO - loss = 0.038168073, step = 1717400 (2.253 sec) -2022-05-01 20:00:46,059 - tensorflow - INFO - 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INFO - global_step/sec: 88.6817 -2022-05-01 20:09:35,854 - tensorflow - INFO - loss = 0.051668994, step = 1764600 (2.255 sec) -2022-05-01 20:09:38,110 - tensorflow - INFO - global_step/sec: 88.6415 -2022-05-01 20:09:38,110 - tensorflow - INFO - loss = 0.015650654, step = 1764800 (2.256 sec) -2022-05-01 20:09:40,369 - tensorflow - INFO - global_step/sec: 88.5277 -2022-05-01 20:09:40,369 - tensorflow - INFO - loss = 0.009082341, step = 1765000 (2.259 sec) -2022-05-01 20:09:42,626 - tensorflow - INFO - global_step/sec: 88.5888 -2022-05-01 20:09:42,627 - tensorflow - INFO - loss = 0.019312901, step = 1765200 (2.258 sec) -2022-05-01 20:09:44,888 - tensorflow - INFO - global_step/sec: 88.4305 -2022-05-01 20:09:44,888 - tensorflow - INFO - loss = 0.015152554, step = 1765400 (2.262 sec) -2022-05-01 20:09:47,148 - tensorflow - INFO - global_step/sec: 88.4924 -2022-05-01 20:09:47,149 - tensorflow - INFO - loss = 0.046553165, step = 1765600 (2.260 sec) -2022-05-01 20:09:49,405 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.7209 -2022-05-01 20:11:55,692 - tensorflow - INFO - loss = 0.0155015765, step = 1777000 (2.254 sec) -2022-05-01 20:11:57,946 - tensorflow - INFO - global_step/sec: 88.7392 -2022-05-01 20:11:57,946 - tensorflow - INFO - loss = 0.14066681, step = 1777200 (2.254 sec) -2022-05-01 20:12:00,196 - tensorflow - INFO - global_step/sec: 88.8755 -2022-05-01 20:12:00,196 - tensorflow - INFO - loss = 0.019969832, step = 1777400 (2.250 sec) -2022-05-01 20:12:02,449 - tensorflow - INFO - global_step/sec: 88.7876 -2022-05-01 20:12:02,449 - tensorflow - INFO - loss = 0.01277157, step = 1777600 (2.253 sec) -2022-05-01 20:12:04,704 - tensorflow - INFO - global_step/sec: 88.6616 -2022-05-01 20:12:04,705 - tensorflow - INFO - loss = 0.015029792, step = 1777800 (2.256 sec) -2022-05-01 20:12:06,956 - tensorflow - INFO - global_step/sec: 88.8082 -2022-05-01 20:12:06,957 - tensorflow - INFO - loss = 0.034628175, step = 1778000 (2.252 sec) -2022-05-01 20:12:09,210 - tensorflow - INFO - global_step/sec: 88.7385 -2022-05-01 20:12:09,211 - tensorflow - INFO - loss = 0.020785347, step = 1778200 (2.254 sec) -2022-05-01 20:12:11,462 - tensorflow - INFO - global_step/sec: 88.8232 -2022-05-01 20:12:11,462 - tensorflow - INFO - loss = 0.014245909, step = 1778400 (2.252 sec) -2022-05-01 20:12:13,711 - tensorflow - INFO - global_step/sec: 88.9194 -2022-05-01 20:12:13,712 - tensorflow - INFO - loss = 0.017461497, step = 1778600 (2.249 sec) -2022-05-01 20:12:15,969 - tensorflow - INFO - global_step/sec: 88.593 -2022-05-01 20:12:15,969 - tensorflow - INFO - loss = 0.04171947, step = 1778800 (2.258 sec) -2022-05-01 20:12:18,225 - tensorflow - INFO - global_step/sec: 88.6212 -2022-05-01 20:12:18,226 - tensorflow - INFO - loss = 0.053099677, step = 1779000 (2.257 sec) -2022-05-01 20:12:20,478 - tensorflow - INFO - global_step/sec: 88.787 -2022-05-01 20:12:20,478 - tensorflow - INFO - loss = 0.010349859, step = 1779200 (2.253 sec) -2022-05-01 20:12:22,729 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.022772396, step = 1780600 (2.253 sec) -2022-05-01 20:12:38,520 - tensorflow - INFO - global_step/sec: 88.7063 -2022-05-01 20:12:38,520 - tensorflow - INFO - loss = 0.013392142, step = 1780800 (2.255 sec) -2022-05-01 20:12:40,769 - tensorflow - INFO - global_step/sec: 88.9085 -2022-05-01 20:12:40,770 - tensorflow - INFO - loss = 0.021318369, step = 1781000 (2.249 sec) -2022-05-01 20:12:43,033 - tensorflow - INFO - global_step/sec: 88.3635 -2022-05-01 20:12:43,033 - tensorflow - INFO - loss = 0.030087322, step = 1781200 (2.263 sec) -2022-05-01 20:12:45,288 - tensorflow - INFO - global_step/sec: 88.6784 -2022-05-01 20:12:45,288 - tensorflow - INFO - loss = 0.02462101, step = 1781400 (2.255 sec) -2022-05-01 20:12:47,542 - tensorflow - INFO - global_step/sec: 88.7298 -2022-05-01 20:12:47,543 - tensorflow - INFO - loss = 0.07925145, step = 1781600 (2.254 sec) -2022-05-01 20:12:49,800 - tensorflow - INFO - global_step/sec: 88.5814 -2022-05-01 20:12:49,800 - tensorflow - INFO - loss = 0.008063761, step = 1781800 (2.258 sec) -2022-05-01 20:12:52,055 - tensorflow - INFO - global_step/sec: 88.7008 -2022-05-01 20:12:52,055 - tensorflow - INFO - loss = 0.018648414, step = 1782000 (2.255 sec) -2022-05-01 20:12:54,313 - tensorflow - INFO - global_step/sec: 88.5547 -2022-05-01 20:12:54,314 - tensorflow - INFO - loss = 0.009507858, step = 1782200 (2.259 sec) -2022-05-01 20:12:56,565 - tensorflow - INFO - global_step/sec: 88.8153 -2022-05-01 20:12:56,565 - tensorflow - INFO - loss = 0.013911311, step = 1782400 (2.252 sec) -2022-05-01 20:12:58,815 - tensorflow - INFO - global_step/sec: 88.8714 -2022-05-01 20:12:58,816 - tensorflow - INFO - loss = 0.015068855, step = 1782600 (2.250 sec) -2022-05-01 20:13:01,068 - tensorflow - INFO - global_step/sec: 88.7709 -2022-05-01 20:13:01,069 - tensorflow - INFO - loss = 0.021220945, step = 1782800 (2.253 sec) -2022-05-01 20:13:03,329 - tensorflow - INFO - global_step/sec: 88.4902 -2022-05-01 20:13:03,329 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.3074 -2022-05-01 20:13:19,143 - tensorflow - INFO - loss = 0.012970069, step = 1784400 (2.265 sec) -2022-05-01 20:13:21,408 - tensorflow - INFO - global_step/sec: 88.2976 -2022-05-01 20:13:21,408 - tensorflow - INFO - loss = 0.008985808, step = 1784600 (2.265 sec) -2022-05-01 20:13:23,670 - tensorflow - INFO - global_step/sec: 88.4183 -2022-05-01 20:13:23,670 - tensorflow - INFO - loss = 0.036576655, step = 1784800 (2.262 sec) -2022-05-01 20:13:25,930 - tensorflow - INFO - global_step/sec: 88.4635 -2022-05-01 20:13:25,931 - tensorflow - INFO - loss = 0.026095899, step = 1785000 (2.261 sec) -2022-05-01 20:13:28,188 - tensorflow - INFO - global_step/sec: 88.5763 -2022-05-01 20:13:28,189 - tensorflow - INFO - loss = 0.039014556, step = 1785200 (2.258 sec) -2022-05-01 20:13:30,452 - tensorflow - INFO - global_step/sec: 88.3553 -2022-05-01 20:13:30,452 - tensorflow - INFO - loss = 0.010799349, step = 1785400 (2.264 sec) -2022-05-01 20:13:32,714 - tensorflow - INFO - global_step/sec: 88.3979 -2022-05-01 20:13:32,715 - tensorflow - INFO - loss = 0.07767479, step = 1785600 (2.263 sec) -2022-05-01 20:13:34,977 - tensorflow - INFO - global_step/sec: 88.3776 -2022-05-01 20:13:34,978 - tensorflow - INFO - loss = 0.013945446, step = 1785800 (2.263 sec) -2022-05-01 20:13:37,242 - tensorflow - INFO - global_step/sec: 88.3042 -2022-05-01 20:13:37,243 - tensorflow - INFO - loss = 0.007430071, step = 1786000 (2.265 sec) -2022-05-01 20:13:39,502 - tensorflow - INFO - global_step/sec: 88.5204 -2022-05-01 20:13:39,502 - tensorflow - INFO - loss = 0.07640466, step = 1786200 (2.259 sec) -2022-05-01 20:13:41,756 - tensorflow - INFO - global_step/sec: 88.7186 -2022-05-01 20:13:41,756 - tensorflow - INFO - loss = 0.009463412, step = 1786400 (2.254 sec) -2022-05-01 20:13:44,011 - tensorflow - INFO - global_step/sec: 88.6931 -2022-05-01 20:13:44,011 - tensorflow - INFO - loss = 0.11465584, step = 1786600 (2.255 sec) -2022-05-01 20:13:46,264 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.05434043, step = 1788000 (2.250 sec) -2022-05-01 20:14:02,062 - tensorflow - INFO - global_step/sec: 88.506 -2022-05-01 20:14:02,063 - tensorflow - INFO - loss = 0.02158013, step = 1788200 (2.260 sec) -2022-05-01 20:14:04,318 - tensorflow - INFO - global_step/sec: 88.672 -2022-05-01 20:14:04,318 - tensorflow - INFO - loss = 0.036688764, step = 1788400 (2.255 sec) -2022-05-01 20:14:06,567 - tensorflow - INFO - global_step/sec: 88.9208 -2022-05-01 20:14:06,567 - tensorflow - INFO - loss = 0.036543354, step = 1788600 (2.249 sec) -2022-05-01 20:14:08,825 - tensorflow - INFO - global_step/sec: 88.5745 -2022-05-01 20:14:08,825 - tensorflow - INFO - loss = 4.63417, step = 1788800 (2.258 sec) -2022-05-01 20:14:11,082 - tensorflow - INFO - global_step/sec: 88.6162 -2022-05-01 20:14:11,082 - tensorflow - INFO - loss = 0.0054372754, step = 1789000 (2.257 sec) -2022-05-01 20:14:13,337 - tensorflow - INFO - global_step/sec: 88.6694 -2022-05-01 20:14:13,338 - tensorflow - INFO - loss = 0.07487969, step = 1789200 (2.256 sec) -2022-05-01 20:14:15,595 - tensorflow - INFO - global_step/sec: 88.5954 -2022-05-01 20:14:15,595 - tensorflow - INFO - loss = 0.018776996, step = 1789400 (2.257 sec) -2022-05-01 20:14:17,852 - tensorflow - INFO - global_step/sec: 88.595 -2022-05-01 20:14:17,853 - tensorflow - INFO - loss = 0.022459345, step = 1789600 (2.257 sec) -2022-05-01 20:14:20,104 - tensorflow - INFO - global_step/sec: 88.8163 -2022-05-01 20:14:20,104 - tensorflow - INFO - loss = 0.05700142, step = 1789800 (2.252 sec) -2022-05-01 20:14:22,361 - tensorflow - INFO - global_step/sec: 88.597 -2022-05-01 20:14:22,362 - tensorflow - INFO - loss = 0.024788637, step = 1790000 (2.257 sec) -2022-05-01 20:14:24,617 - tensorflow - INFO - global_step/sec: 88.6738 -2022-05-01 20:14:24,617 - tensorflow - INFO - loss = 0.089785844, step = 1790200 (2.255 sec) -2022-05-01 20:14:26,878 - tensorflow - INFO - global_step/sec: 88.4581 -2022-05-01 20:14:26,878 - tensorflow - INFO - loss = 0.47366673, step = 1790400 (2.261 sec) -2022-05-01 20:14:29,133 - tensorflow - INFO - global_step/sec: 88.6972 -2022-05-01 20:14:29,133 - tensorflow - INFO - loss = 0.030992618, step = 1790600 (2.255 sec) -2022-05-01 20:14:31,390 - tensorflow - INFO - global_step/sec: 88.5982 -2022-05-01 20:14:31,391 - tensorflow - INFO - loss = 0.034015767, step = 1790800 (2.257 sec) -2022-05-01 20:14:33,644 - tensorflow - INFO - global_step/sec: 88.731 -2022-05-01 20:14:33,645 - tensorflow - INFO - loss = 0.071194686, step = 1791000 (2.254 sec) -2022-05-01 20:14:35,895 - tensorflow - INFO - global_step/sec: 88.8645 -2022-05-01 20:14:35,895 - tensorflow - INFO - loss = 0.008461665, step = 1791200 (2.251 sec) -2022-05-01 20:14:38,149 - tensorflow - INFO - global_step/sec: 88.7362 -2022-05-01 20:14:38,149 - tensorflow - INFO - loss = 0.040716104, step = 1791400 (2.254 sec) -2022-05-01 20:14:40,402 - tensorflow - INFO - global_step/sec: 88.7425 -2022-05-01 20:14:40,403 - tensorflow - INFO - loss = 0.053414673, step = 1791600 (2.254 sec) -2022-05-01 20:14:42,654 - tensorflow - INFO - global_step/sec: 88.8393 -2022-05-01 20:14:42,654 - tensorflow - INFO - loss = 0.016931232, step = 1791800 (2.251 sec) -2022-05-01 20:14:44,906 - tensorflow - INFO - global_step/sec: 88.7935 -2022-05-01 20:14:44,906 - tensorflow - INFO - loss = 0.037635088, step = 1792000 (2.252 sec) -2022-05-01 20:14:47,157 - tensorflow - INFO - global_step/sec: 88.8472 -2022-05-01 20:14:47,158 - tensorflow - INFO - loss = 0.621138, step = 1792200 (2.251 sec) -2022-05-01 20:14:49,409 - tensorflow - INFO - global_step/sec: 88.826 -2022-05-01 20:14:49,409 - tensorflow - INFO - loss = 0.011683818, step = 1792400 (2.252 sec) -2022-05-01 20:14:51,668 - tensorflow - INFO - global_step/sec: 88.5339 -2022-05-01 20:14:51,668 - tensorflow - INFO - loss = 0.06398842, step = 1792600 (2.259 sec) -2022-05-01 20:14:53,926 - tensorflow - INFO - global_step/sec: 88.5539 -2022-05-01 20:14:53,927 - tensorflow - INFO - loss = 0.0077382503, step = 1792800 (2.259 sec) -2022-05-01 20:14:56,178 - tensorflow - INFO - global_step/sec: 88.8049 -2022-05-01 20:14:56,179 - tensorflow - INFO - loss = 0.0059882104, step = 1793000 (2.252 sec) -2022-05-01 20:14:58,433 - tensorflow - INFO - global_step/sec: 88.7048 -2022-05-01 20:14:58,433 - tensorflow - INFO - loss = 0.0121996235, step = 1793200 (2.255 sec) -2022-05-01 20:15:00,692 - tensorflow - INFO - global_step/sec: 88.5164 -2022-05-01 20:15:00,693 - tensorflow - INFO - loss = 0.013352605, step = 1793400 (2.259 sec) -2022-05-01 20:15:02,947 - tensorflow - INFO - global_step/sec: 88.7274 -2022-05-01 20:15:02,947 - tensorflow - INFO - loss = 0.35117516, step = 1793600 (2.254 sec) -2022-05-01 20:15:05,204 - tensorflow - INFO - global_step/sec: 88.6128 -2022-05-01 20:15:05,204 - tensorflow - INFO - loss = 0.017232269, step = 1793800 (2.257 sec) -2022-05-01 20:15:07,457 - tensorflow - INFO - global_step/sec: 88.76 -2022-05-01 20:15:07,457 - tensorflow - INFO - loss = 0.06693153, step = 1794000 (2.253 sec) -2022-05-01 20:15:09,709 - tensorflow - INFO - global_step/sec: 88.7869 -2022-05-01 20:15:09,710 - tensorflow - INFO - loss = 0.056630254, step = 1794200 (2.253 sec) -2022-05-01 20:15:11,960 - tensorflow - INFO - global_step/sec: 88.8683 -2022-05-01 20:15:11,960 - tensorflow - INFO - loss = 0.031748325, step = 1794400 (2.251 sec) -2022-05-01 20:15:14,210 - tensorflow - INFO - global_step/sec: 88.8958 -2022-05-01 20:15:14,210 - tensorflow - INFO - loss = 0.021562433, step = 1794600 (2.250 sec) -2022-05-01 20:15:16,462 - tensorflow - INFO - global_step/sec: 88.7856 -2022-05-01 20:15:16,463 - tensorflow - INFO - loss = 0.014114676, step = 1794800 (2.253 sec) -2022-05-01 20:15:18,724 - tensorflow - INFO - global_step/sec: 88.441 -2022-05-01 20:15:18,724 - tensorflow - INFO - loss = 0.017012898, step = 1795000 (2.261 sec) -2022-05-01 20:15:20,976 - tensorflow - INFO - global_step/sec: 88.7909 -2022-05-01 20:15:20,977 - tensorflow - INFO - loss = 0.3222133, step = 1795200 (2.252 sec) -2022-05-01 20:15:23,231 - tensorflow - INFO - global_step/sec: 88.7104 -2022-05-01 20:15:23,231 - tensorflow - INFO - loss = 0.019055504, step = 1795400 (2.255 sec) -2022-05-01 20:15:25,485 - tensorflow - INFO - global_step/sec: 88.7088 -2022-05-01 20:15:25,486 - tensorflow - INFO - loss = 0.009157332, step = 1795600 (2.255 sec) -2022-05-01 20:15:27,738 - tensorflow - INFO - global_step/sec: 88.8014 -2022-05-01 20:15:27,738 - tensorflow - INFO - loss = 0.014342883, step = 1795800 (2.252 sec) -2022-05-01 20:15:29,991 - tensorflow - INFO - global_step/sec: 88.7719 -2022-05-01 20:15:29,991 - tensorflow - INFO - loss = 0.013180383, step = 1796000 (2.253 sec) -2022-05-01 20:15:32,253 - tensorflow - INFO - global_step/sec: 88.4086 -2022-05-01 20:15:32,253 - tensorflow - INFO - loss = 0.0068212757, step = 1796200 (2.262 sec) -2022-05-01 20:15:34,515 - tensorflow - INFO - global_step/sec: 88.4121 -2022-05-01 20:15:34,515 - tensorflow - INFO - loss = 0.017810522, step = 1796400 (2.262 sec) -2022-05-01 20:15:36,763 - tensorflow - INFO - global_step/sec: 88.9589 -2022-05-01 20:15:36,764 - tensorflow - INFO - loss = 0.06250075, step = 1796600 (2.248 sec) -2022-05-01 20:15:39,023 - tensorflow - INFO - global_step/sec: 88.4817 -2022-05-01 20:15:39,024 - tensorflow - INFO - loss = 0.010002321, step = 1796800 (2.260 sec) -2022-05-01 20:15:41,274 - tensorflow - INFO - global_step/sec: 88.8512 -2022-05-01 20:15:41,275 - tensorflow - INFO - loss = 0.004910208, step = 1797000 (2.251 sec) -2022-05-01 20:15:43,528 - tensorflow - INFO - global_step/sec: 88.745 -2022-05-01 20:15:43,529 - tensorflow - INFO - loss = 0.96549255, step = 1797200 (2.254 sec) -2022-05-01 20:15:45,778 - tensorflow - INFO - global_step/sec: 88.9052 -2022-05-01 20:15:45,778 - tensorflow - INFO - loss = 0.012416754, step = 1797400 (2.250 sec) -2022-05-01 20:15:48,032 - tensorflow - INFO - global_step/sec: 88.7304 -2022-05-01 20:15:48,032 - tensorflow - INFO - loss = 0.25109956, step = 1797600 (2.254 sec) -2022-05-01 20:15:50,285 - tensorflow - INFO - global_step/sec: 88.7773 -2022-05-01 20:15:50,285 - tensorflow - INFO - loss = 0.017483495, step = 1797800 (2.253 sec) -2022-05-01 20:15:52,541 - tensorflow - INFO - global_step/sec: 88.6378 -2022-05-01 20:15:52,541 - tensorflow - INFO - loss = 0.028559688, step = 1798000 (2.256 sec) -2022-05-01 20:15:54,800 - tensorflow - INFO - global_step/sec: 88.5397 -2022-05-01 20:15:54,800 - tensorflow - INFO - loss = 1.5846872, step = 1798200 (2.259 sec) -2022-05-01 20:15:57,053 - tensorflow - INFO - global_step/sec: 88.7447 -2022-05-01 20:15:57,054 - tensorflow - INFO - loss = 0.008915515, step = 1798400 (2.254 sec) -2022-05-01 20:15:59,308 - tensorflow - INFO - global_step/sec: 88.7048 -2022-05-01 20:15:59,308 - tensorflow - INFO - loss = 0.01911306, step = 1798600 (2.255 sec) -2022-05-01 20:16:01,564 - tensorflow - INFO - global_step/sec: 88.6598 -2022-05-01 20:16:01,564 - tensorflow - INFO - loss = 0.0072809146, step = 1798800 (2.256 sec) -2022-05-01 20:16:03,820 - tensorflow - INFO - global_step/sec: 88.6346 -2022-05-01 20:16:03,821 - tensorflow - INFO - loss = 0.044288322, step = 1799000 (2.256 sec) -2022-05-01 20:16:06,074 - tensorflow - INFO - global_step/sec: 88.7646 -2022-05-01 20:16:06,074 - tensorflow - INFO - loss = 0.23373622, step = 1799200 (2.253 sec) -2022-05-01 20:16:08,332 - tensorflow - INFO - global_step/sec: 88.5687 -2022-05-01 20:16:08,332 - tensorflow - INFO - loss = 0.05972874, step = 1799400 (2.258 sec) -2022-05-01 20:16:10,588 - tensorflow - INFO - global_step/sec: 88.6542 -2022-05-01 20:16:10,588 - tensorflow - INFO - loss = 0.061447047, step = 1799600 (2.256 sec) -2022-05-01 20:16:12,838 - tensorflow - INFO - global_step/sec: 88.8724 -2022-05-01 20:16:12,838 - tensorflow - INFO - loss = 0.01870015, step = 1799800 (2.250 sec) -2022-05-01 20:16:15,078 - tensorflow - INFO - Saving checkpoints for 1800000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. -2022-05-01 20:16:15,094 - tensorflow - INFO - model.ckpt-1800000 is not in all_model_checkpoint_paths. Manually adding it. -2022-05-01 20:16:15,614 - tensorflow - INFO - Calling model_fn. -2022-05-01 20:16:15,741 - tensorflow - INFO - Done calling model_fn. -2022-05-01 20:16:15,756 - tensorflow - INFO - Starting evaluation at 2022-05-01T20:16:15Z -2022-05-01 20:16:15,820 - tensorflow - INFO - Graph was finalized. -2022-05-01 20:16:15,823 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1800000 -2022-05-01 20:16:16,107 - tensorflow - INFO - Running local_init_op. -2022-05-01 20:16:16,216 - tensorflow - INFO - Done running local_init_op. -2022-05-01 20:16:26,353 - tensorflow - INFO - Evaluation [3990/39900] -2022-05-01 20:16:35,826 - tensorflow - INFO - Evaluation [7980/39900] -2022-05-01 20:16:45,282 - tensorflow - INFO - Evaluation [11970/39900] -2022-05-01 20:16:54,752 - tensorflow - INFO - Evaluation [15960/39900] -2022-05-01 20:17:04,229 - tensorflow - INFO - Evaluation [19950/39900] -2022-05-01 20:17:13,709 - tensorflow - INFO - Evaluation [23940/39900] -2022-05-01 20:17:23,164 - tensorflow - INFO - Evaluation [27930/39900] -2022-05-01 20:17:32,617 - tensorflow - INFO - Evaluation [31920/39900] -2022-05-01 20:17:42,064 - tensorflow - INFO - Evaluation [35910/39900] -2022-05-01 20:17:51,527 - tensorflow - INFO - Evaluation [39900/39900] -2022-05-01 20:17:51,666 - tensorflow - INFO - Finished evaluation at 2022-05-01-20:17:51 -2022-05-01 20:17:51,666 - tensorflow - INFO - Saving dict for global step 1800000: global_step = 1800000, loss = 0.016763782, mean_degree_err = 3.5563521 -2022-05-01 20:17:51,667 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 1800000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1800000 -2022-05-01 20:17:51,681 - tensorflow - INFO - global_step/sec: 2.02342 -2022-05-01 20:17:51,682 - tensorflow - INFO - loss = 0.027914403, step = 1800000 (98.843 sec) -2022-05-01 20:17:53,950 - tensorflow - INFO - global_step/sec: 88.1532 -2022-05-01 20:17:53,950 - tensorflow - INFO - loss = 0.021869771, step = 1800200 (2.268 sec) -2022-05-01 20:17:56,207 - tensorflow - INFO - global_step/sec: 88.5779 -2022-05-01 20:17:56,208 - tensorflow - INFO - loss = 0.092984244, step = 1800400 (2.258 sec) -2022-05-01 20:17:58,460 - tensorflow - INFO - global_step/sec: 88.8035 -2022-05-01 20:17:58,460 - tensorflow - INFO - loss = 0.013734039, step = 1800600 (2.252 sec) -2022-05-01 20:18:00,715 - tensorflow - INFO - global_step/sec: 88.671 -2022-05-01 20:18:00,716 - tensorflow - INFO - loss = 0.024841797, step = 1800800 (2.256 sec) -2022-05-01 20:18:02,971 - tensorflow - INFO - global_step/sec: 88.6527 -2022-05-01 20:18:02,972 - tensorflow - INFO - loss = 0.022523532, step = 1801000 (2.256 sec) -2022-05-01 20:18:05,226 - tensorflow - INFO - global_step/sec: 88.6917 -2022-05-01 20:18:05,227 - tensorflow - INFO - loss = 0.019885017, step = 1801200 (2.255 sec) -2022-05-01 20:18:07,485 - tensorflow - INFO - global_step/sec: 88.5244 -2022-05-01 20:18:07,486 - tensorflow - INFO - loss = 0.035318907, step = 1801400 (2.259 sec) -2022-05-01 20:18:09,741 - tensorflow - INFO - global_step/sec: 88.6836 -2022-05-01 20:18:09,741 - tensorflow - INFO - loss = 0.010549978, step = 1801600 (2.255 sec) -2022-05-01 20:18:12,000 - tensorflow - INFO - global_step/sec: 88.5235 -2022-05-01 20:18:12,000 - tensorflow - INFO - loss = 0.30921438, step = 1801800 (2.259 sec) -2022-05-01 20:18:14,257 - tensorflow - INFO - global_step/sec: 88.6201 -2022-05-01 20:18:14,257 - tensorflow - INFO - loss = 0.060837887, step = 1802000 (2.257 sec) -2022-05-01 20:18:16,514 - tensorflow - INFO - global_step/sec: 88.6091 -2022-05-01 20:18:16,514 - tensorflow - INFO - loss = 0.073248446, step = 1802200 (2.257 sec) -2022-05-01 20:18:18,769 - tensorflow - INFO - global_step/sec: 88.7003 -2022-05-01 20:18:18,769 - tensorflow - INFO - loss = 0.020106304, step = 1802400 (2.255 sec) -2022-05-01 20:18:21,026 - tensorflow - INFO - global_step/sec: 88.609 -2022-05-01 20:18:21,026 - tensorflow - INFO - loss = 0.34301686, step = 1802600 (2.257 sec) -2022-05-01 20:18:23,277 - tensorflow - INFO - global_step/sec: 88.851 -2022-05-01 20:18:23,277 - tensorflow - INFO - loss = 0.03422773, step = 1802800 (2.251 sec) -2022-05-01 20:18:25,536 - tensorflow - INFO - global_step/sec: 88.5365 -2022-05-01 20:18:25,536 - tensorflow - INFO - loss = 0.010858849, step = 1803000 (2.259 sec) -2022-05-01 20:18:27,794 - tensorflow - INFO - global_step/sec: 88.5571 -2022-05-01 20:18:27,794 - tensorflow - INFO - loss = 0.28296545, step = 1803200 (2.258 sec) -2022-05-01 20:18:30,056 - tensorflow - INFO - global_step/sec: 88.4263 -2022-05-01 20:18:30,056 - tensorflow - INFO - loss = 0.04658611, step = 1803400 (2.262 sec) -2022-05-01 20:18:32,319 - tensorflow - INFO - global_step/sec: 88.3909 -2022-05-01 20:18:32,319 - tensorflow - INFO - loss = 0.048911583, step = 1803600 (2.263 sec) -2022-05-01 20:18:34,577 - tensorflow - INFO - global_step/sec: 88.5663 -2022-05-01 20:18:34,577 - tensorflow - INFO - loss = 0.01163725, step = 1803800 (2.258 sec) -2022-05-01 20:18:36,830 - tensorflow - INFO - global_step/sec: 88.7513 -2022-05-01 20:18:36,831 - tensorflow - INFO - loss = 9.297604, step = 1804000 (2.254 sec) -2022-05-01 20:18:39,083 - tensorflow - INFO - global_step/sec: 88.7746 -2022-05-01 20:18:39,084 - tensorflow - INFO - loss = 0.0068070027, step = 1804200 (2.253 sec) -2022-05-01 20:18:41,337 - tensorflow - INFO - global_step/sec: 88.7427 -2022-05-01 20:18:41,337 - tensorflow - INFO - loss = 0.015604509, step = 1804400 (2.254 sec) -2022-05-01 20:18:43,593 - tensorflow - INFO - global_step/sec: 88.6336 -2022-05-01 20:18:43,594 - tensorflow - INFO - loss = 0.0144014135, step = 1804600 (2.256 sec) -2022-05-01 20:18:45,845 - tensorflow - INFO - global_step/sec: 88.8138 -2022-05-01 20:18:45,846 - tensorflow - INFO - loss = 0.02853755, step = 1804800 (2.252 sec) -2022-05-01 20:18:48,096 - tensorflow - INFO - global_step/sec: 88.8374 -2022-05-01 20:18:48,097 - tensorflow - INFO - loss = 0.008257154, step = 1805000 (2.251 sec) -2022-05-01 20:18:50,349 - tensorflow - INFO - global_step/sec: 88.7729 -2022-05-01 20:18:50,350 - tensorflow - INFO - loss = 0.02656951, step = 1805200 (2.253 sec) -2022-05-01 20:18:52,603 - tensorflow - INFO - global_step/sec: 88.755 -2022-05-01 20:18:52,603 - tensorflow - INFO - loss = 0.01831774, step = 1805400 (2.253 sec) -2022-05-01 20:18:54,856 - tensorflow - INFO - global_step/sec: 88.7477 -2022-05-01 20:18:54,857 - tensorflow - INFO - loss = 0.023801386, step = 1805600 (2.254 sec) -2022-05-01 20:18:57,118 - tensorflow - INFO - global_step/sec: 88.4459 -2022-05-01 20:18:57,118 - tensorflow - INFO - loss = 0.65763414, step = 1805800 (2.261 sec) -2022-05-01 20:18:59,372 - tensorflow - INFO - global_step/sec: 88.7027 -2022-05-01 20:18:59,373 - tensorflow - INFO - loss = 0.101669826, step = 1806000 (2.255 sec) -2022-05-01 20:19:01,624 - tensorflow - INFO - global_step/sec: 88.82 -2022-05-01 20:19:01,625 - tensorflow - INFO - loss = 0.012000534, step = 1806200 (2.252 sec) -2022-05-01 20:19:03,880 - tensorflow - INFO - global_step/sec: 88.6715 -2022-05-01 20:19:03,880 - tensorflow - INFO - loss = 0.026554633, step = 1806400 (2.255 sec) -2022-05-01 20:19:06,133 - tensorflow - INFO - global_step/sec: 88.7724 -2022-05-01 20:19:06,133 - tensorflow - INFO - loss = 0.013799883, step = 1806600 (2.253 sec) -2022-05-01 20:19:08,392 - tensorflow - INFO - global_step/sec: 88.5263 -2022-05-01 20:19:08,392 - tensorflow - INFO - loss = 0.30768314, step = 1806800 (2.259 sec) -2022-05-01 20:19:10,646 - tensorflow - INFO - global_step/sec: 88.7053 -2022-05-01 20:19:10,647 - tensorflow - INFO - loss = 0.025185607, step = 1807000 (2.255 sec) -2022-05-01 20:19:12,902 - tensorflow - INFO - global_step/sec: 88.6882 -2022-05-01 20:19:12,902 - tensorflow - INFO - loss = 0.008936843, step = 1807200 (2.255 sec) -2022-05-01 20:19:15,156 - tensorflow - INFO - global_step/sec: 88.7325 -2022-05-01 20:19:15,156 - tensorflow - INFO - loss = 0.036230564, step = 1807400 (2.254 sec) -2022-05-01 20:19:17,411 - tensorflow - INFO - global_step/sec: 88.6543 -2022-05-01 20:19:17,412 - tensorflow - INFO - loss = 0.011483357, step = 1807600 (2.256 sec) -2022-05-01 20:19:19,671 - tensorflow - INFO - global_step/sec: 88.5007 -2022-05-01 20:19:19,672 - tensorflow - INFO - loss = 0.024457522, step = 1807800 (2.260 sec) -2022-05-01 20:19:21,933 - tensorflow - INFO - global_step/sec: 88.4353 -2022-05-01 20:19:21,933 - tensorflow - INFO - loss = 0.00525497, step = 1808000 (2.262 sec) -2022-05-01 20:19:24,185 - tensorflow - INFO - global_step/sec: 88.8016 -2022-05-01 20:19:24,186 - tensorflow - INFO - loss = 0.012021458, step = 1808200 (2.252 sec) -2022-05-01 20:19:26,438 - tensorflow - INFO - global_step/sec: 88.7816 -2022-05-01 20:19:26,438 - tensorflow - INFO - loss = 0.025556244, step = 1808400 (2.253 sec) -2022-05-01 20:19:28,695 - tensorflow - INFO - global_step/sec: 88.6239 -2022-05-01 20:19:28,695 - tensorflow - INFO - loss = 0.05274012, step = 1808600 (2.257 sec) -2022-05-01 20:19:30,950 - tensorflow - INFO - global_step/sec: 88.6846 -2022-05-01 20:19:30,950 - tensorflow - INFO - loss = 0.10206439, step = 1808800 (2.255 sec) -2022-05-01 20:19:33,206 - tensorflow - INFO - global_step/sec: 88.6274 -2022-05-01 20:19:33,207 - tensorflow - INFO - loss = 0.061892245, step = 1809000 (2.257 sec) -2022-05-01 20:19:35,468 - tensorflow - INFO - global_step/sec: 88.4147 -2022-05-01 20:19:35,469 - tensorflow - INFO - loss = 0.010666244, step = 1809200 (2.262 sec) -2022-05-01 20:19:37,716 - tensorflow - INFO - global_step/sec: 88.9914 -2022-05-01 20:19:37,716 - tensorflow - INFO - loss = 0.059726246, step = 1809400 (2.247 sec) -2022-05-01 20:19:39,964 - tensorflow - INFO - global_step/sec: 88.9669 -2022-05-01 20:19:39,964 - tensorflow - INFO - loss = 0.015109569, step = 1809600 (2.248 sec) -2022-05-01 20:19:42,218 - tensorflow - INFO - global_step/sec: 88.7319 -2022-05-01 20:19:42,218 - tensorflow - INFO - loss = 0.010054525, step = 1809800 (2.254 sec) -2022-05-01 20:19:44,470 - tensorflow - INFO - global_step/sec: 88.7908 -2022-05-01 20:19:44,471 - tensorflow - INFO - loss = 0.018031895, step = 1810000 (2.252 sec) -2022-05-01 20:19:46,722 - tensorflow - INFO - global_step/sec: 88.8368 -2022-05-01 20:19:46,722 - tensorflow - INFO - loss = 0.016368955, step = 1810200 (2.251 sec) -2022-05-01 20:19:48,981 - tensorflow - INFO - global_step/sec: 88.5389 -2022-05-01 20:19:48,981 - tensorflow - INFO - loss = 0.007853529, step = 1810400 (2.259 sec) -2022-05-01 20:19:51,232 - tensorflow - INFO - global_step/sec: 88.8182 -2022-05-01 20:19:51,233 - tensorflow - INFO - loss = 0.0068646725, step = 1810600 (2.252 sec) -2022-05-01 20:19:53,485 - tensorflow - INFO - global_step/sec: 88.767 -2022-05-01 20:19:53,486 - tensorflow - INFO - loss = 0.21444012, step = 1810800 (2.253 sec) -2022-05-01 20:19:55,739 - tensorflow - INFO - global_step/sec: 88.7615 -2022-05-01 20:19:55,739 - tensorflow - INFO - loss = 0.049558554, step = 1811000 (2.253 sec) -2022-05-01 20:19:57,990 - tensorflow - INFO - global_step/sec: 88.8386 -2022-05-01 20:19:57,990 - tensorflow - INFO - loss = 0.0171601, step = 1811200 (2.251 sec) -2022-05-01 20:20:00,245 - tensorflow - INFO - global_step/sec: 88.685 -2022-05-01 20:20:00,246 - tensorflow - INFO - loss = 0.01656272, step = 1811400 (2.255 sec) -2022-05-01 20:20:02,500 - tensorflow - INFO - global_step/sec: 88.694 -2022-05-01 20:20:02,500 - tensorflow - INFO - loss = 0.006385114, step = 1811600 (2.255 sec) -2022-05-01 20:20:04,751 - tensorflow - INFO - global_step/sec: 88.8453 -2022-05-01 20:20:04,752 - tensorflow - INFO - loss = 0.009432913, step = 1811800 (2.251 sec) -2022-05-01 20:20:07,009 - tensorflow - INFO - global_step/sec: 88.5886 -2022-05-01 20:20:07,009 - tensorflow - INFO - loss = 0.04209045, step = 1812000 (2.258 sec) -2022-05-01 20:20:09,258 - tensorflow - INFO - global_step/sec: 88.915 -2022-05-01 20:20:09,259 - tensorflow - INFO - loss = 0.009847219, step = 1812200 (2.249 sec) -2022-05-01 20:20:11,510 - tensorflow - INFO - global_step/sec: 88.8152 -2022-05-01 20:20:11,510 - tensorflow - INFO - loss = 0.051155195, step = 1812400 (2.252 sec) -2022-05-01 20:20:13,773 - tensorflow - INFO - global_step/sec: 88.3861 -2022-05-01 20:20:13,773 - tensorflow - INFO - loss = 0.010624302, step = 1812600 (2.263 sec) -2022-05-01 20:20:16,034 - tensorflow - INFO - global_step/sec: 88.4405 -2022-05-01 20:20:16,035 - tensorflow - INFO - loss = 0.007253981, step = 1812800 (2.261 sec) -2022-05-01 20:20:18,291 - tensorflow - INFO - global_step/sec: 88.6121 -2022-05-01 20:20:18,292 - tensorflow - INFO - loss = 0.006397931, step = 1813000 (2.257 sec) -2022-05-01 20:20:20,552 - tensorflow - INFO - global_step/sec: 88.4662 -2022-05-01 20:20:20,552 - tensorflow - INFO - loss = 0.038472924, step = 1813200 (2.261 sec) -2022-05-01 20:20:22,814 - tensorflow - INFO - global_step/sec: 88.4175 -2022-05-01 20:20:22,814 - tensorflow - INFO - loss = 0.025957687, step = 1813400 (2.262 sec) -2022-05-01 20:20:25,078 - tensorflow - INFO - global_step/sec: 88.3259 -2022-05-01 20:20:25,079 - tensorflow - INFO - loss = 0.01257656, step = 1813600 (2.264 sec) -2022-05-01 20:20:27,343 - tensorflow - INFO - global_step/sec: 88.2963 -2022-05-01 20:20:27,344 - tensorflow - INFO - loss = 0.0098339375, step = 1813800 (2.265 sec) -2022-05-01 20:20:29,605 - tensorflow - INFO - global_step/sec: 88.452 -2022-05-01 20:20:29,605 - tensorflow - INFO - loss = 0.003876243, step = 1814000 (2.261 sec) -2022-05-01 20:20:31,866 - tensorflow - INFO - global_step/sec: 88.4305 -2022-05-01 20:20:31,867 - tensorflow - INFO - loss = 4.306312, step = 1814200 (2.262 sec) -2022-05-01 20:20:34,129 - tensorflow - INFO - global_step/sec: 88.37 -2022-05-01 20:20:34,130 - tensorflow - INFO - loss = 0.016175754, step = 1814400 (2.263 sec) -2022-05-01 20:20:36,390 - tensorflow - INFO - global_step/sec: 88.4749 -2022-05-01 20:20:36,390 - tensorflow - INFO - loss = 0.031760797, step = 1814600 (2.261 sec) -2022-05-01 20:20:38,654 - tensorflow - INFO - global_step/sec: 88.336 -2022-05-01 20:20:38,654 - tensorflow - INFO - loss = 0.013849026, step = 1814800 (2.264 sec) -2022-05-01 20:20:40,916 - tensorflow - INFO - global_step/sec: 88.4109 -2022-05-01 20:20:40,917 - tensorflow - INFO - loss = 0.014158366, step = 1815000 (2.262 sec) -2022-05-01 20:20:43,176 - tensorflow - INFO - global_step/sec: 88.4899 -2022-05-01 20:20:43,177 - tensorflow - INFO - loss = 0.18197498, step = 1815200 (2.260 sec) -2022-05-01 20:20:45,427 - tensorflow - INFO - global_step/sec: 88.8548 -2022-05-01 20:20:45,428 - tensorflow - INFO - loss = 0.01803931, step = 1815400 (2.251 sec) -2022-05-01 20:20:47,680 - tensorflow - INFO - global_step/sec: 88.7659 -2022-05-01 20:20:47,681 - tensorflow - INFO - loss = 0.023806624, step = 1815600 (2.253 sec) -2022-05-01 20:20:49,936 - tensorflow - INFO - global_step/sec: 88.6643 -2022-05-01 20:20:49,936 - tensorflow - INFO - loss = 0.008687656, step = 1815800 (2.256 sec) -2022-05-01 20:20:52,192 - tensorflow - INFO - global_step/sec: 88.6655 -2022-05-01 20:20:52,192 - tensorflow - INFO - loss = 0.018411316, step = 1816000 (2.256 sec) -2022-05-01 20:20:54,451 - tensorflow - INFO - global_step/sec: 88.5323 -2022-05-01 20:20:54,451 - tensorflow - INFO - loss = 0.013286571, step = 1816200 (2.259 sec) -2022-05-01 20:20:56,707 - tensorflow - 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tensorflow - INFO - global_step/sec: 88.7831 -2022-05-01 20:24:26,490 - tensorflow - INFO - loss = 0.01560775, step = 1835000 (2.253 sec) -2022-05-01 20:24:28,742 - tensorflow - INFO - global_step/sec: 88.8244 -2022-05-01 20:24:28,742 - tensorflow - INFO - loss = 0.022258969, step = 1835200 (2.252 sec) -2022-05-01 20:24:31,006 - tensorflow - INFO - global_step/sec: 88.3116 -2022-05-01 20:24:31,007 - tensorflow - INFO - loss = 0.0053165057, step = 1835400 (2.265 sec) -2022-05-01 20:24:33,261 - tensorflow - INFO - global_step/sec: 88.7107 -2022-05-01 20:24:33,261 - tensorflow - INFO - loss = 0.06763758, step = 1835600 (2.255 sec) -2022-05-01 20:24:35,515 - tensorflow - INFO - global_step/sec: 88.7253 -2022-05-01 20:24:35,515 - tensorflow - INFO - loss = 0.015103186, step = 1835800 (2.254 sec) -2022-05-01 20:24:37,766 - tensorflow - INFO - global_step/sec: 88.8569 -2022-05-01 20:24:37,766 - tensorflow - INFO - loss = 0.03623424, step = 1836000 (2.251 sec) -2022-05-01 20:24:40,020 - tensorflow - INFO - global_step/sec: 88.7192 -2022-05-01 20:24:40,021 - tensorflow - INFO - loss = 0.032539867, step = 1836200 (2.254 sec) -2022-05-01 20:24:42,272 - tensorflow - INFO - global_step/sec: 88.8284 -2022-05-01 20:24:42,272 - tensorflow - INFO - loss = 0.06612409, step = 1836400 (2.252 sec) -2022-05-01 20:24:44,529 - tensorflow - INFO - global_step/sec: 88.5875 -2022-05-01 20:24:44,530 - tensorflow - INFO - loss = 0.007893974, step = 1836600 (2.258 sec) -2022-05-01 20:24:46,785 - tensorflow - INFO - global_step/sec: 88.6538 -2022-05-01 20:24:46,786 - tensorflow - INFO - loss = 0.014295866, step = 1836800 (2.256 sec) -2022-05-01 20:24:49,035 - tensorflow - INFO - global_step/sec: 88.8826 -2022-05-01 20:24:49,036 - tensorflow - INFO - loss = 0.02057194, step = 1837000 (2.250 sec) -2022-05-01 20:24:51,289 - tensorflow - INFO - global_step/sec: 88.7366 -2022-05-01 20:24:51,290 - tensorflow - INFO - loss = 0.046822, step = 1837200 (2.254 sec) -2022-05-01 20:24:53,546 - tensorflow - INFO - global_step/sec: 88.6409 -2022-05-01 20:24:53,546 - tensorflow - INFO - loss = 0.012756139, step = 1837400 (2.256 sec) -2022-05-01 20:24:55,801 - tensorflow - INFO - global_step/sec: 88.6943 -2022-05-01 20:24:55,801 - tensorflow - INFO - loss = 0.007280142, step = 1837600 (2.255 sec) -2022-05-01 20:24:58,057 - tensorflow - INFO - global_step/sec: 88.6528 -2022-05-01 20:24:58,057 - tensorflow - INFO - loss = 0.0045169657, step = 1837800 (2.256 sec) -2022-05-01 20:25:00,309 - tensorflow - INFO - global_step/sec: 88.791 -2022-05-01 20:25:00,309 - tensorflow - INFO - loss = 0.010652699, step = 1838000 (2.252 sec) -2022-05-01 20:25:02,561 - tensorflow - INFO - global_step/sec: 88.8252 -2022-05-01 20:25:02,561 - tensorflow - INFO - loss = 0.026743425, step = 1838200 (2.252 sec) -2022-05-01 20:25:04,817 - tensorflow - INFO - global_step/sec: 88.6541 -2022-05-01 20:25:04,817 - tensorflow - INFO - loss = 0.07407653, step = 1838400 (2.256 sec) -2022-05-01 20:25:07,069 - tensorflow - INFO - global_step/sec: 88.8024 -2022-05-01 20:25:07,069 - tensorflow - INFO - loss = 4.821314, step = 1838600 (2.252 sec) -2022-05-01 20:25:09,329 - tensorflow - INFO - global_step/sec: 88.4997 -2022-05-01 20:25:09,329 - tensorflow - INFO - loss = 0.056913167, step = 1838800 (2.260 sec) -2022-05-01 20:25:11,580 - tensorflow - INFO - global_step/sec: 88.843 -2022-05-01 20:25:11,580 - tensorflow - INFO - loss = 0.018655833, step = 1839000 (2.251 sec) -2022-05-01 20:25:13,831 - tensorflow - INFO - global_step/sec: 88.8633 -2022-05-01 20:25:13,831 - tensorflow - INFO - loss = 0.010940998, step = 1839200 (2.251 sec) -2022-05-01 20:25:16,086 - tensorflow - INFO - global_step/sec: 88.6838 -2022-05-01 20:25:16,086 - tensorflow - INFO - loss = 0.011907165, step = 1839400 (2.255 sec) -2022-05-01 20:25:18,343 - tensorflow - INFO - global_step/sec: 88.612 -2022-05-01 20:25:18,343 - tensorflow - INFO - loss = 0.011977665, step = 1839600 (2.257 sec) -2022-05-01 20:25:20,597 - tensorflow - INFO - global_step/sec: 88.7328 -2022-05-01 20:25:20,597 - tensorflow - INFO - loss = 0.006268484, step = 1839800 (2.254 sec) -2022-05-01 20:25:22,854 - tensorflow - INFO - global_step/sec: 88.5894 -2022-05-01 20:25:22,855 - tensorflow - INFO - loss = 0.015248191, step = 1840000 (2.258 sec) -2022-05-01 20:25:25,109 - tensorflow - INFO - global_step/sec: 88.7118 -2022-05-01 20:25:25,109 - tensorflow - INFO - loss = 0.016756605, step = 1840200 (2.255 sec) -2022-05-01 20:25:27,364 - tensorflow - INFO - global_step/sec: 88.6919 -2022-05-01 20:25:27,364 - tensorflow - INFO - loss = 0.11232264, step = 1840400 (2.255 sec) -2022-05-01 20:25:29,616 - tensorflow - INFO - global_step/sec: 88.7934 -2022-05-01 20:25:29,617 - tensorflow - INFO - loss = 0.018364131, step = 1840600 (2.252 sec) -2022-05-01 20:25:31,872 - tensorflow - INFO - global_step/sec: 88.6449 -2022-05-01 20:25:31,873 - tensorflow - INFO - loss = 0.006125507, step = 1840800 (2.256 sec) -2022-05-01 20:25:34,131 - tensorflow - INFO - global_step/sec: 88.5571 -2022-05-01 20:25:34,131 - tensorflow - INFO - loss = 0.005172074, step = 1841000 (2.258 sec) -2022-05-01 20:25:36,388 - tensorflow - INFO - global_step/sec: 88.5967 -2022-05-01 20:25:36,389 - tensorflow - INFO - loss = 0.005981467, step = 1841200 (2.257 sec) -2022-05-01 20:25:38,639 - tensorflow - INFO - global_step/sec: 88.8647 -2022-05-01 20:25:38,639 - tensorflow - INFO - loss = 0.039633546, step = 1841400 (2.251 sec) -2022-05-01 20:25:40,897 - tensorflow - INFO - global_step/sec: 88.5703 -2022-05-01 20:25:40,897 - tensorflow - INFO - loss = 0.054717924, step = 1841600 (2.258 sec) -2022-05-01 20:25:43,149 - tensorflow - INFO - global_step/sec: 88.8282 -2022-05-01 20:25:43,149 - tensorflow - INFO - loss = 0.015850564, step = 1841800 (2.252 sec) -2022-05-01 20:25:45,399 - tensorflow - INFO - global_step/sec: 88.8804 -2022-05-01 20:25:45,399 - tensorflow - INFO - loss = 0.009141061, step = 1842000 (2.250 sec) -2022-05-01 20:25:47,654 - tensorflow - INFO - global_step/sec: 88.6671 -2022-05-01 20:25:47,655 - tensorflow - INFO - loss = 0.013469338, step = 1842200 (2.256 sec) -2022-05-01 20:25:49,906 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.0918156, step = 1843600 (2.250 sec) -2022-05-01 20:26:05,676 - tensorflow - INFO - global_step/sec: 88.6945 -2022-05-01 20:26:05,677 - tensorflow - INFO - loss = 0.025549937, step = 1843800 (2.255 sec) -2022-05-01 20:26:07,932 - tensorflow - INFO - global_step/sec: 88.6866 -2022-05-01 20:26:07,932 - tensorflow - INFO - loss = 0.027432416, step = 1844000 (2.255 sec) -2022-05-01 20:26:10,188 - tensorflow - INFO - global_step/sec: 88.6408 -2022-05-01 20:26:10,188 - tensorflow - INFO - loss = 0.011867199, step = 1844200 (2.256 sec) -2022-05-01 20:26:12,447 - tensorflow - INFO - global_step/sec: 88.5122 -2022-05-01 20:26:12,448 - tensorflow - INFO - loss = 0.012199122, step = 1844400 (2.260 sec) -2022-05-01 20:26:14,700 - tensorflow - INFO - global_step/sec: 88.788 -2022-05-01 20:26:14,700 - tensorflow - INFO - loss = 0.00509081, step = 1844600 (2.253 sec) -2022-05-01 20:26:16,952 - tensorflow - INFO - global_step/sec: 88.8005 -2022-05-01 20:26:16,953 - tensorflow - INFO - loss = 0.78227735, step = 1844800 (2.252 sec) -2022-05-01 20:26:19,206 - tensorflow - INFO - global_step/sec: 88.7443 -2022-05-01 20:26:19,206 - tensorflow - INFO - loss = 0.0076343045, step = 1845000 (2.254 sec) -2022-05-01 20:26:21,458 - tensorflow - INFO - global_step/sec: 88.8079 -2022-05-01 20:26:21,458 - tensorflow - INFO - loss = 0.00985547, step = 1845200 (2.252 sec) -2022-05-01 20:26:23,722 - tensorflow - INFO - global_step/sec: 88.338 -2022-05-01 20:26:23,722 - tensorflow - INFO - loss = 0.025111364, step = 1845400 (2.264 sec) -2022-05-01 20:26:25,982 - tensorflow - INFO - global_step/sec: 88.4837 -2022-05-01 20:26:25,983 - tensorflow - INFO - loss = 0.08325562, step = 1845600 (2.260 sec) -2022-05-01 20:26:28,242 - tensorflow - INFO - global_step/sec: 88.5013 -2022-05-01 20:26:28,243 - tensorflow - INFO - loss = 0.086955115, step = 1845800 (2.260 sec) -2022-05-01 20:26:30,504 - tensorflow - INFO - global_step/sec: 88.4293 -2022-05-01 20:26:30,504 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.8645 -2022-05-01 20:26:46,300 - tensorflow - INFO - loss = 0.0078629125, step = 1847400 (2.251 sec) -2022-05-01 20:26:48,554 - tensorflow - INFO - global_step/sec: 88.7161 -2022-05-01 20:26:48,555 - tensorflow - INFO - loss = 21.127949, step = 1847600 (2.254 sec) -2022-05-01 20:26:50,813 - tensorflow - INFO - global_step/sec: 88.5408 -2022-05-01 20:26:50,814 - tensorflow - INFO - loss = 0.010781923, step = 1847800 (2.259 sec) -2022-05-01 20:26:53,063 - tensorflow - INFO - global_step/sec: 88.8844 -2022-05-01 20:26:53,064 - tensorflow - INFO - loss = 0.0041205594, step = 1848000 (2.250 sec) -2022-05-01 20:26:55,322 - tensorflow - INFO - global_step/sec: 88.5485 -2022-05-01 20:26:55,322 - tensorflow - INFO - loss = 0.011492386, step = 1848200 (2.259 sec) -2022-05-01 20:26:57,577 - tensorflow - INFO - global_step/sec: 88.683 -2022-05-01 20:26:57,578 - tensorflow - INFO - loss = 0.011655846, step = 1848400 (2.255 sec) -2022-05-01 20:26:59,830 - tensorflow - INFO - global_step/sec: 88.7821 -2022-05-01 20:26:59,830 - tensorflow - INFO - loss = 0.0073967315, step = 1848600 (2.253 sec) -2022-05-01 20:27:02,092 - tensorflow - INFO - global_step/sec: 88.4243 -2022-05-01 20:27:02,092 - tensorflow - INFO - loss = 0.123996615, step = 1848800 (2.262 sec) -2022-05-01 20:27:04,348 - tensorflow - INFO - global_step/sec: 88.6468 -2022-05-01 20:27:04,348 - tensorflow - INFO - loss = 0.013393193, step = 1849000 (2.256 sec) -2022-05-01 20:27:06,602 - tensorflow - INFO - global_step/sec: 88.7481 -2022-05-01 20:27:06,602 - tensorflow - INFO - loss = 0.03099203, step = 1849200 (2.254 sec) -2022-05-01 20:27:08,854 - tensorflow - INFO - global_step/sec: 88.7932 -2022-05-01 20:27:08,854 - tensorflow - INFO - loss = 0.02929867, step = 1849400 (2.252 sec) -2022-05-01 20:27:11,110 - tensorflow - INFO - global_step/sec: 88.6343 -2022-05-01 20:27:11,111 - tensorflow - INFO - loss = 0.013980811, step = 1849600 (2.256 sec) -2022-05-01 20:27:13,367 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.15137419, step = 1851000 (2.265 sec) -2022-05-01 20:27:29,210 - tensorflow - INFO - global_step/sec: 88.3522 -2022-05-01 20:27:29,211 - tensorflow - INFO - loss = 0.0065481705, step = 1851200 (2.264 sec) -2022-05-01 20:27:31,478 - tensorflow - INFO - global_step/sec: 88.213 -2022-05-01 20:27:31,478 - tensorflow - INFO - loss = 0.019686084, step = 1851400 (2.267 sec) -2022-05-01 20:27:33,739 - tensorflow - INFO - global_step/sec: 88.4619 -2022-05-01 20:27:33,739 - tensorflow - INFO - loss = 0.004503956, step = 1851600 (2.261 sec) -2022-05-01 20:27:36,001 - tensorflow - INFO - global_step/sec: 88.3995 -2022-05-01 20:27:36,001 - tensorflow - INFO - loss = 0.08106224, step = 1851800 (2.262 sec) -2022-05-01 20:27:38,261 - tensorflow - INFO - global_step/sec: 88.4841 -2022-05-01 20:27:38,262 - tensorflow - INFO - loss = 0.028569568, step = 1852000 (2.260 sec) -2022-05-01 20:27:40,525 - tensorflow - INFO - global_step/sec: 88.3676 -2022-05-01 20:27:40,525 - tensorflow - INFO - loss = 0.012765411, step = 1852200 (2.263 sec) -2022-05-01 20:27:42,787 - tensorflow - INFO - global_step/sec: 88.3998 -2022-05-01 20:27:42,788 - tensorflow - INFO - loss = 0.02419498, step = 1852400 (2.262 sec) -2022-05-01 20:27:45,045 - tensorflow - INFO - global_step/sec: 88.5608 -2022-05-01 20:27:45,046 - tensorflow - INFO - loss = 0.07054446, step = 1852600 (2.258 sec) -2022-05-01 20:27:47,296 - tensorflow - INFO - global_step/sec: 88.8835 -2022-05-01 20:27:47,296 - tensorflow - INFO - loss = 0.011235343, step = 1852800 (2.250 sec) -2022-05-01 20:27:49,549 - tensorflow - INFO - global_step/sec: 88.7393 -2022-05-01 20:27:49,550 - tensorflow - INFO - loss = 0.09467631, step = 1853000 (2.254 sec) -2022-05-01 20:27:51,806 - tensorflow - INFO - global_step/sec: 88.6149 -2022-05-01 20:27:51,807 - tensorflow - INFO - loss = 0.022562038, step = 1853200 (2.257 sec) -2022-05-01 20:27:54,059 - tensorflow - INFO - global_step/sec: 88.7968 -2022-05-01 20:27:54,059 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.6778 -2022-05-01 20:28:09,848 - tensorflow - INFO - loss = 1.252865, step = 1854800 (2.255 sec) -2022-05-01 20:28:12,100 - tensorflow - INFO - global_step/sec: 88.7969 -2022-05-01 20:28:12,101 - tensorflow - INFO - loss = 0.027452521, step = 1855000 (2.252 sec) -2022-05-01 20:28:14,354 - tensorflow - INFO - global_step/sec: 88.7332 -2022-05-01 20:28:14,354 - tensorflow - INFO - loss = 0.01079275, step = 1855200 (2.254 sec) -2022-05-01 20:28:16,604 - tensorflow - INFO - global_step/sec: 88.8892 -2022-05-01 20:28:16,604 - tensorflow - INFO - loss = 0.0138835525, step = 1855400 (2.250 sec) -2022-05-01 20:28:18,858 - tensorflow - INFO - global_step/sec: 88.7494 -2022-05-01 20:28:18,858 - tensorflow - INFO - loss = 0.002622583, step = 1855600 (2.254 sec) -2022-05-01 20:28:21,118 - tensorflow - INFO - global_step/sec: 88.472 -2022-05-01 20:28:21,119 - tensorflow - INFO - loss = 0.27811888, step = 1855800 (2.261 sec) -2022-05-01 20:28:23,367 - tensorflow - INFO - global_step/sec: 88.9264 -2022-05-01 20:28:23,368 - tensorflow - INFO - loss = 0.038571924, step = 1856000 (2.249 sec) -2022-05-01 20:28:25,620 - tensorflow - INFO - global_step/sec: 88.7633 -2022-05-01 20:28:25,621 - tensorflow - INFO - loss = 0.008832814, step = 1856200 (2.253 sec) -2022-05-01 20:28:27,869 - tensorflow - INFO - global_step/sec: 88.9429 -2022-05-01 20:28:27,869 - tensorflow - INFO - loss = 0.012104606, step = 1856400 (2.249 sec) -2022-05-01 20:28:30,129 - tensorflow - INFO - global_step/sec: 88.5071 -2022-05-01 20:28:30,129 - tensorflow - INFO - loss = 0.2690666, step = 1856600 (2.260 sec) -2022-05-01 20:28:32,384 - tensorflow - INFO - global_step/sec: 88.6681 -2022-05-01 20:28:32,385 - tensorflow - INFO - loss = 0.012544476, step = 1856800 (2.256 sec) -2022-05-01 20:28:34,637 - tensorflow - INFO - global_step/sec: 88.8032 -2022-05-01 20:28:34,637 - tensorflow - INFO - loss = 0.033771485, step = 1857000 (2.252 sec) -2022-05-01 20:28:36,887 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.6166 -2022-05-01 20:29:33,247 - tensorflow - INFO - loss = 0.025657427, step = 1862200 (2.257 sec) -2022-05-01 20:29:35,504 - tensorflow - INFO - global_step/sec: 88.5842 -2022-05-01 20:29:35,505 - tensorflow - INFO - loss = 0.037618686, step = 1862400 (2.258 sec) -2022-05-01 20:29:37,760 - tensorflow - INFO - global_step/sec: 88.6551 -2022-05-01 20:29:37,761 - tensorflow - INFO - loss = 0.0073780203, step = 1862600 (2.256 sec) -2022-05-01 20:29:40,012 - tensorflow - INFO - global_step/sec: 88.8074 -2022-05-01 20:29:40,013 - tensorflow - INFO - loss = 0.0057529695, step = 1862800 (2.252 sec) -2022-05-01 20:29:42,260 - tensorflow - INFO - global_step/sec: 88.9563 -2022-05-01 20:29:42,261 - tensorflow - INFO - loss = 0.061805457, step = 1863000 (2.248 sec) -2022-05-01 20:29:44,511 - tensorflow - INFO - global_step/sec: 88.8643 -2022-05-01 20:29:44,512 - tensorflow - INFO - loss = 0.14968938, step = 1863200 (2.251 sec) -2022-05-01 20:29:46,764 - tensorflow - INFO - global_step/sec: 88.7697 -2022-05-01 20:29:46,765 - tensorflow - INFO - loss = 0.38842642, step = 1863400 (2.253 sec) -2022-05-01 20:29:49,022 - tensorflow - INFO - global_step/sec: 88.5753 -2022-05-01 20:29:49,023 - tensorflow - INFO - loss = 0.04474082, step = 1863600 (2.258 sec) -2022-05-01 20:29:51,280 - tensorflow - INFO - global_step/sec: 88.5699 -2022-05-01 20:29:51,281 - tensorflow - INFO - loss = 0.100732185, step = 1863800 (2.258 sec) -2022-05-01 20:29:53,530 - tensorflow - INFO - global_step/sec: 88.9017 -2022-05-01 20:29:53,530 - tensorflow - INFO - loss = 0.01562722, step = 1864000 (2.250 sec) -2022-05-01 20:29:55,784 - tensorflow - INFO - global_step/sec: 88.7161 -2022-05-01 20:29:55,785 - tensorflow - INFO - loss = 0.009844961, step = 1864200 (2.254 sec) -2022-05-01 20:29:58,041 - tensorflow - INFO - global_step/sec: 88.63 -2022-05-01 20:29:58,041 - tensorflow - INFO - loss = 0.019376265, step = 1864400 (2.257 sec) -2022-05-01 20:30:00,297 - tensorflow - INFO - global_step/sec: 88.6414 -2022-05-01 20:30:00,298 - tensorflow - INFO - loss = 0.025746921, step = 1864600 (2.256 sec) -2022-05-01 20:30:02,561 - tensorflow - INFO - global_step/sec: 88.337 -2022-05-01 20:30:02,562 - tensorflow - INFO - loss = 0.016607694, step = 1864800 (2.264 sec) -2022-05-01 20:30:04,817 - tensorflow - INFO - global_step/sec: 88.6651 -2022-05-01 20:30:04,817 - tensorflow - INFO - loss = 0.006056394, step = 1865000 (2.256 sec) -2022-05-01 20:30:07,075 - tensorflow - INFO - global_step/sec: 88.5722 -2022-05-01 20:30:07,075 - tensorflow - INFO - loss = 0.012430912, step = 1865200 (2.258 sec) -2022-05-01 20:30:09,325 - tensorflow - INFO - global_step/sec: 88.8686 -2022-05-01 20:30:09,326 - tensorflow - INFO - loss = 0.014980311, step = 1865400 (2.251 sec) -2022-05-01 20:30:11,579 - tensorflow - INFO - global_step/sec: 88.7415 -2022-05-01 20:30:11,580 - tensorflow - INFO - loss = 0.022223497, step = 1865600 (2.254 sec) -2022-05-01 20:30:13,829 - tensorflow - INFO - global_step/sec: 88.88 -2022-05-01 20:30:13,830 - tensorflow - INFO - loss = 0.006417212, step = 1865800 (2.250 sec) -2022-05-01 20:30:16,086 - tensorflow - INFO - global_step/sec: 88.6469 -2022-05-01 20:30:16,086 - tensorflow - INFO - loss = 0.022596888, step = 1866000 (2.256 sec) -2022-05-01 20:30:18,340 - tensorflow - INFO - global_step/sec: 88.7191 -2022-05-01 20:30:18,340 - tensorflow - INFO - loss = 0.019118773, step = 1866200 (2.254 sec) -2022-05-01 20:30:20,593 - tensorflow - INFO - global_step/sec: 88.7686 -2022-05-01 20:30:20,593 - tensorflow - INFO - loss = 0.08572295, step = 1866400 (2.253 sec) -2022-05-01 20:30:22,845 - tensorflow - INFO - global_step/sec: 88.8008 -2022-05-01 20:30:22,846 - tensorflow - INFO - loss = 0.018673077, step = 1866600 (2.252 sec) -2022-05-01 20:30:25,105 - tensorflow - INFO - global_step/sec: 88.5006 -2022-05-01 20:30:25,105 - tensorflow - INFO - loss = 0.08109714, step = 1866800 (2.260 sec) -2022-05-01 20:30:27,358 - tensorflow - INFO - global_step/sec: 88.7613 -2022-05-01 20:30:27,359 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.7237 -2022-05-01 20:30:43,145 - tensorflow - INFO - loss = 0.32599005, step = 1868400 (2.254 sec) -2022-05-01 20:30:45,396 - tensorflow - INFO - global_step/sec: 88.8409 -2022-05-01 20:30:45,396 - tensorflow - INFO - loss = 0.011387359, step = 1868600 (2.251 sec) -2022-05-01 20:30:47,658 - tensorflow - INFO - global_step/sec: 88.4094 -2022-05-01 20:30:47,658 - tensorflow - INFO - loss = 0.03894508, step = 1868800 (2.262 sec) -2022-05-01 20:30:49,923 - tensorflow - INFO - global_step/sec: 88.2932 -2022-05-01 20:30:49,923 - tensorflow - INFO - loss = 0.030715233, step = 1869000 (2.265 sec) -2022-05-01 20:30:52,186 - tensorflow - INFO - global_step/sec: 88.3924 -2022-05-01 20:30:52,186 - tensorflow - INFO - loss = 0.007998239, step = 1869200 (2.263 sec) -2022-05-01 20:30:54,451 - tensorflow - INFO - global_step/sec: 88.2957 -2022-05-01 20:30:54,451 - tensorflow - INFO - loss = 0.011327565, step = 1869400 (2.265 sec) -2022-05-01 20:30:56,716 - tensorflow - INFO - global_step/sec: 88.3105 -2022-05-01 20:30:56,716 - tensorflow - INFO - loss = 0.0057827244, step = 1869600 (2.265 sec) -2022-05-01 20:30:58,983 - tensorflow - INFO - global_step/sec: 88.2174 -2022-05-01 20:30:58,983 - tensorflow - INFO - loss = 0.013923317, step = 1869800 (2.267 sec) -2022-05-01 20:31:01,246 - tensorflow - INFO - global_step/sec: 88.3611 -2022-05-01 20:31:01,247 - tensorflow - INFO - loss = 0.0124671925, step = 1870000 (2.263 sec) -2022-05-01 20:31:03,512 - tensorflow - INFO - global_step/sec: 88.2563 -2022-05-01 20:31:03,513 - tensorflow - INFO - loss = 0.029464753, step = 1870200 (2.266 sec) -2022-05-01 20:31:05,775 - tensorflow - INFO - global_step/sec: 88.3983 -2022-05-01 20:31:05,775 - tensorflow - INFO - loss = 0.12631345, step = 1870400 (2.262 sec) -2022-05-01 20:31:08,038 - tensorflow - INFO - global_step/sec: 88.3824 -2022-05-01 20:31:08,038 - tensorflow - INFO - loss = 0.019793203, step = 1870600 (2.263 sec) -2022-05-01 20:31:10,304 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.67305064, step = 1894200 (2.256 sec) -2022-05-01 20:35:36,582 - tensorflow - INFO - global_step/sec: 88.5272 -2022-05-01 20:35:36,583 - tensorflow - INFO - loss = 0.098160475, step = 1894400 (2.259 sec) -2022-05-01 20:35:38,836 - tensorflow - INFO - global_step/sec: 88.7334 -2022-05-01 20:35:38,837 - tensorflow - INFO - loss = 0.008926311, step = 1894600 (2.254 sec) -2022-05-01 20:35:41,086 - tensorflow - INFO - global_step/sec: 88.8908 -2022-05-01 20:35:41,087 - tensorflow - INFO - loss = 0.010282107, step = 1894800 (2.250 sec) -2022-05-01 20:35:43,338 - tensorflow - INFO - global_step/sec: 88.8349 -2022-05-01 20:35:43,338 - tensorflow - INFO - loss = 0.025060002, step = 1895000 (2.251 sec) -2022-05-01 20:35:45,592 - tensorflow - INFO - global_step/sec: 88.7108 -2022-05-01 20:35:45,593 - tensorflow - INFO - loss = 0.030853648, step = 1895200 (2.254 sec) -2022-05-01 20:35:47,849 - tensorflow - INFO - global_step/sec: 88.6236 -2022-05-01 20:35:47,849 - tensorflow - INFO - loss = 0.24151732, step = 1895400 (2.257 sec) -2022-05-01 20:35:50,105 - tensorflow - INFO - global_step/sec: 88.6622 -2022-05-01 20:35:50,105 - tensorflow - INFO - loss = 0.026341088, step = 1895600 (2.256 sec) -2022-05-01 20:35:52,356 - tensorflow - INFO - global_step/sec: 88.8508 -2022-05-01 20:35:52,356 - tensorflow - INFO - loss = 0.019398818, step = 1895800 (2.251 sec) -2022-05-01 20:35:54,609 - tensorflow - INFO - global_step/sec: 88.7598 -2022-05-01 20:35:54,609 - tensorflow - INFO - loss = 0.018978981, step = 1896000 (2.253 sec) -2022-05-01 20:35:56,861 - tensorflow - INFO - global_step/sec: 88.8227 -2022-05-01 20:35:56,861 - tensorflow - INFO - loss = 0.015217569, step = 1896200 (2.252 sec) -2022-05-01 20:35:59,111 - tensorflow - INFO - global_step/sec: 88.8691 -2022-05-01 20:35:59,111 - tensorflow - INFO - loss = 0.018522281, step = 1896400 (2.251 sec) -2022-05-01 20:36:01,371 - tensorflow - INFO - global_step/sec: 88.4987 -2022-05-01 20:36:01,371 - tensorflow - INFO - loss = 0.035829056, step = 1896600 (2.260 sec) -2022-05-01 20:36:03,624 - tensorflow - INFO - global_step/sec: 88.7635 -2022-05-01 20:36:03,625 - tensorflow - INFO - loss = 0.01608836, step = 1896800 (2.253 sec) -2022-05-01 20:36:05,882 - tensorflow - INFO - global_step/sec: 88.5936 -2022-05-01 20:36:05,882 - tensorflow - INFO - loss = 0.025285728, step = 1897000 (2.258 sec) -2022-05-01 20:36:08,132 - tensorflow - INFO - global_step/sec: 88.8569 -2022-05-01 20:36:08,133 - tensorflow - INFO - loss = 0.00848588, step = 1897200 (2.251 sec) -2022-05-01 20:36:10,387 - tensorflow - INFO - global_step/sec: 88.7181 -2022-05-01 20:36:10,387 - tensorflow - INFO - loss = 0.013154034, step = 1897400 (2.254 sec) -2022-05-01 20:36:12,640 - tensorflow - INFO - global_step/sec: 88.7697 -2022-05-01 20:36:12,640 - tensorflow - INFO - loss = 0.0072760545, step = 1897600 (2.253 sec) -2022-05-01 20:36:14,900 - tensorflow - INFO - global_step/sec: 88.4853 -2022-05-01 20:36:14,901 - tensorflow - INFO - loss = 0.00759117, step = 1897800 (2.260 sec) -2022-05-01 20:36:17,156 - tensorflow - INFO - global_step/sec: 88.667 -2022-05-01 20:36:17,156 - tensorflow - INFO - loss = 0.05357273, step = 1898000 (2.256 sec) -2022-05-01 20:36:19,405 - tensorflow - INFO - global_step/sec: 88.9256 -2022-05-01 20:36:19,405 - tensorflow - INFO - loss = 0.013738202, step = 1898200 (2.249 sec) -2022-05-01 20:36:21,661 - tensorflow - INFO - global_step/sec: 88.6619 -2022-05-01 20:36:21,661 - tensorflow - INFO - loss = 0.6070651, step = 1898400 (2.256 sec) -2022-05-01 20:36:23,918 - tensorflow - INFO - global_step/sec: 88.5968 -2022-05-01 20:36:23,918 - tensorflow - INFO - loss = 0.014128826, step = 1898600 (2.257 sec) -2022-05-01 20:36:26,177 - tensorflow - INFO - global_step/sec: 88.521 -2022-05-01 20:36:26,178 - tensorflow - INFO - loss = 0.0088235345, step = 1898800 (2.259 sec) -2022-05-01 20:36:28,436 - tensorflow - INFO - global_step/sec: 88.5464 -2022-05-01 20:36:28,436 - tensorflow - INFO - loss = 0.13586804, step = 1899000 (2.259 sec) -2022-05-01 20:36:30,696 - tensorflow - INFO - global_step/sec: 88.5006 -2022-05-01 20:36:30,696 - tensorflow - INFO - loss = 0.010024523, step = 1899200 (2.260 sec) -2022-05-01 20:36:32,957 - tensorflow - INFO - global_step/sec: 88.4388 -2022-05-01 20:36:32,958 - tensorflow - INFO - loss = 0.011715906, step = 1899400 (2.261 sec) -2022-05-01 20:36:35,209 - tensorflow - INFO - global_step/sec: 88.8165 -2022-05-01 20:36:35,210 - tensorflow - INFO - loss = 0.030111665, step = 1899600 (2.252 sec) -2022-05-01 20:36:37,466 - tensorflow - INFO - global_step/sec: 88.6293 -2022-05-01 20:36:37,466 - tensorflow - INFO - loss = 0.03254773, step = 1899800 (2.257 sec) -2022-05-01 20:36:39,716 - tensorflow - INFO - Saving checkpoints for 1900000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. -2022-05-01 20:36:39,731 - tensorflow - INFO - model.ckpt-1900000 is not in all_model_checkpoint_paths. Manually adding it. -2022-05-01 20:36:40,255 - tensorflow - INFO - Calling model_fn. -2022-05-01 20:36:40,383 - tensorflow - INFO - Done calling model_fn. -2022-05-01 20:36:40,398 - tensorflow - INFO - Starting evaluation at 2022-05-01T20:36:40Z -2022-05-01 20:36:40,461 - tensorflow - INFO - Graph was finalized. -2022-05-01 20:36:40,465 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1900000 -2022-05-01 20:36:40,746 - tensorflow - INFO - Running local_init_op. -2022-05-01 20:36:40,853 - tensorflow - INFO - Done running local_init_op. -2022-05-01 20:36:50,996 - tensorflow - INFO - Evaluation [3990/39900] -2022-05-01 20:37:00,456 - tensorflow - INFO - Evaluation [7980/39900] -2022-05-01 20:37:09,895 - tensorflow - INFO - Evaluation [11970/39900] -2022-05-01 20:37:19,351 - tensorflow - INFO - Evaluation [15960/39900] -2022-05-01 20:37:28,792 - tensorflow - INFO - Evaluation [19950/39900] -2022-05-01 20:37:38,253 - tensorflow - INFO - Evaluation [23940/39900] -2022-05-01 20:37:47,688 - tensorflow - INFO - Evaluation [27930/39900] -2022-05-01 20:37:57,152 - tensorflow - INFO - Evaluation [31920/39900] -2022-05-01 20:38:06,602 - tensorflow - INFO - Evaluation [35910/39900] -2022-05-01 20:38:16,082 - tensorflow - INFO - Evaluation [39900/39900] -2022-05-01 20:38:16,222 - tensorflow - INFO - Finished evaluation at 2022-05-01-20:38:16 -2022-05-01 20:38:16,222 - tensorflow - INFO - Saving dict for global step 1900000: global_step = 1900000, loss = 0.021781396, mean_degree_err = 3.919044 -2022-05-01 20:38:16,223 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 1900000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-1900000 -2022-05-01 20:38:16,236 - tensorflow - INFO - global_step/sec: 2.0249 -2022-05-01 20:38:16,237 - tensorflow - INFO - loss = 0.010318757, step = 1900000 (98.771 sec) -2022-05-01 20:38:18,518 - tensorflow - INFO - global_step/sec: 87.665 -2022-05-01 20:38:18,518 - tensorflow - INFO - loss = 0.05919086, step = 1900200 (2.281 sec) -2022-05-01 20:38:20,775 - tensorflow - INFO - global_step/sec: 88.6049 -2022-05-01 20:38:20,775 - tensorflow - INFO - loss = 0.0043990593, step = 1900400 (2.257 sec) -2022-05-01 20:38:23,037 - tensorflow - INFO - global_step/sec: 88.411 -2022-05-01 20:38:23,038 - tensorflow - INFO - loss = 0.014942108, step = 1900600 (2.262 sec) -2022-05-01 20:38:25,292 - tensorflow - INFO - global_step/sec: 88.6773 -2022-05-01 20:38:25,293 - tensorflow - INFO - loss = 0.018904645, step = 1900800 (2.255 sec) -2022-05-01 20:38:27,548 - tensorflow - INFO - global_step/sec: 88.656 -2022-05-01 20:38:27,549 - tensorflow - INFO - loss = 0.014355497, step = 1901000 (2.256 sec) -2022-05-01 20:38:29,801 - tensorflow - INFO - global_step/sec: 88.7702 -2022-05-01 20:38:29,802 - tensorflow - INFO - loss = 0.022026083, step = 1901200 (2.253 sec) -2022-05-01 20:38:32,061 - tensorflow - INFO - global_step/sec: 88.511 -2022-05-01 20:38:32,061 - tensorflow - INFO - loss = 0.026987147, step = 1901400 (2.260 sec) -2022-05-01 20:38:34,317 - tensorflow - INFO - global_step/sec: 88.64 -2022-05-01 20:38:34,318 - tensorflow - INFO - loss = 0.016082386, step = 1901600 (2.256 sec) -2022-05-01 20:38:36,573 - tensorflow - INFO - global_step/sec: 88.6813 -2022-05-01 20:38:36,573 - tensorflow - INFO - loss = 0.021116573, step = 1901800 (2.255 sec) -2022-05-01 20:38:38,827 - tensorflow - INFO - global_step/sec: 88.7107 -2022-05-01 20:38:38,828 - tensorflow - INFO - loss = 0.17873903, step = 1902000 (2.255 sec) -2022-05-01 20:38:41,079 - tensorflow - INFO - global_step/sec: 88.8149 -2022-05-01 20:38:41,079 - tensorflow - INFO - loss = 0.010280648, step = 1902200 (2.252 sec) -2022-05-01 20:38:43,339 - tensorflow - INFO - global_step/sec: 88.4946 -2022-05-01 20:38:43,339 - tensorflow - INFO - loss = 0.007372737, step = 1902400 (2.260 sec) -2022-05-01 20:38:45,596 - tensorflow - INFO - global_step/sec: 88.6306 -2022-05-01 20:38:45,596 - tensorflow - INFO - loss = 0.069900736, step = 1902600 (2.257 sec) -2022-05-01 20:38:47,853 - tensorflow - INFO - global_step/sec: 88.5961 -2022-05-01 20:38:47,853 - tensorflow - INFO - loss = 0.025056195, step = 1902800 (2.257 sec) -2022-05-01 20:38:50,112 - tensorflow - INFO - global_step/sec: 88.5546 -2022-05-01 20:38:50,112 - tensorflow - INFO - loss = 0.09052631, step = 1903000 (2.258 sec) -2022-05-01 20:38:52,368 - tensorflow - INFO - global_step/sec: 88.614 -2022-05-01 20:38:52,369 - tensorflow - INFO - loss = 0.006703171, step = 1903200 (2.257 sec) -2022-05-01 20:38:54,621 - tensorflow - INFO - global_step/sec: 88.7818 -2022-05-01 20:38:54,622 - tensorflow - INFO - loss = 1.4233687, step = 1903400 (2.253 sec) -2022-05-01 20:38:56,877 - tensorflow - INFO - global_step/sec: 88.6577 -2022-05-01 20:38:56,877 - tensorflow - INFO - loss = 0.008456467, step = 1903600 (2.256 sec) -2022-05-01 20:38:59,128 - tensorflow - INFO - global_step/sec: 88.8706 -2022-05-01 20:38:59,128 - tensorflow - INFO - loss = 0.0235586, step = 1903800 (2.250 sec) -2022-05-01 20:39:01,390 - tensorflow - INFO - global_step/sec: 88.3861 -2022-05-01 20:39:01,391 - tensorflow - INFO - loss = 0.028131448, step = 1904000 (2.263 sec) -2022-05-01 20:39:03,646 - tensorflow - INFO - global_step/sec: 88.6654 -2022-05-01 20:39:03,646 - tensorflow - INFO - loss = 0.3278071, step = 1904200 (2.256 sec) -2022-05-01 20:39:05,902 - tensorflow - INFO - global_step/sec: 88.6647 -2022-05-01 20:39:05,902 - tensorflow - INFO - loss = 0.061649084, step = 1904400 (2.256 sec) -2022-05-01 20:39:08,156 - tensorflow - INFO - global_step/sec: 88.7094 -2022-05-01 20:39:08,157 - tensorflow - INFO - loss = 0.011733083, step = 1904600 (2.255 sec) -2022-05-01 20:39:10,409 - tensorflow - INFO - global_step/sec: 88.7938 -2022-05-01 20:39:10,409 - tensorflow - INFO - loss = 0.09543198, step = 1904800 (2.252 sec) -2022-05-01 20:39:12,669 - tensorflow - INFO - global_step/sec: 88.4762 -2022-05-01 20:39:12,670 - tensorflow - INFO - loss = 0.011331211, step = 1905000 (2.261 sec) -2022-05-01 20:39:14,932 - tensorflow - INFO - global_step/sec: 88.3948 -2022-05-01 20:39:14,932 - tensorflow - INFO - loss = 0.14373742, step = 1905200 (2.262 sec) -2022-05-01 20:39:17,182 - tensorflow - INFO - global_step/sec: 88.8763 -2022-05-01 20:39:17,182 - tensorflow - INFO - loss = 0.059360716, step = 1905400 (2.250 sec) -2022-05-01 20:39:19,435 - tensorflow - INFO - global_step/sec: 88.7831 -2022-05-01 20:39:19,435 - tensorflow - INFO - loss = 0.010062706, step = 1905600 (2.253 sec) -2022-05-01 20:39:21,692 - tensorflow - INFO - global_step/sec: 88.6162 -2022-05-01 20:39:21,692 - tensorflow - INFO - loss = 0.032864153, step = 1905800 (2.257 sec) -2022-05-01 20:39:23,948 - tensorflow - INFO - global_step/sec: 88.6232 -2022-05-01 20:39:23,949 - tensorflow - INFO - loss = 0.003971881, step = 1906000 (2.257 sec) -2022-05-01 20:39:26,206 - tensorflow - INFO - global_step/sec: 88.6007 -2022-05-01 20:39:26,206 - tensorflow - INFO - loss = 0.04123966, step = 1906200 (2.257 sec) -2022-05-01 20:39:28,463 - tensorflow - INFO - global_step/sec: 88.5905 -2022-05-01 20:39:28,464 - tensorflow - INFO - loss = 0.014023264, step = 1906400 (2.258 sec) -2022-05-01 20:39:30,727 - tensorflow - INFO - global_step/sec: 88.3625 -2022-05-01 20:39:30,727 - tensorflow - INFO - loss = 0.019056926, step = 1906600 (2.263 sec) -2022-05-01 20:39:32,987 - tensorflow - INFO - global_step/sec: 88.4871 -2022-05-01 20:39:32,987 - tensorflow - INFO - loss = 0.03874336, step = 1906800 (2.260 sec) -2022-05-01 20:39:35,239 - tensorflow - INFO - global_step/sec: 88.7957 -2022-05-01 20:39:35,240 - tensorflow - INFO - loss = 0.011484627, step = 1907000 (2.252 sec) -2022-05-01 20:39:37,493 - tensorflow - INFO - global_step/sec: 88.7349 -2022-05-01 20:39:37,494 - tensorflow - INFO - loss = 0.40345308, step = 1907200 (2.254 sec) -2022-05-01 20:39:39,751 - tensorflow - INFO - global_step/sec: 88.5682 -2022-05-01 20:39:39,752 - tensorflow - INFO - loss = 0.2545933, step = 1907400 (2.258 sec) -2022-05-01 20:39:42,002 - tensorflow - INFO - global_step/sec: 88.8538 -2022-05-01 20:39:42,003 - tensorflow - INFO - loss = 0.030244552, step = 1907600 (2.251 sec) -2022-05-01 20:39:44,256 - tensorflow - INFO - global_step/sec: 88.752 -2022-05-01 20:39:44,256 - tensorflow - INFO - loss = 0.07354349, step = 1907800 (2.253 sec) -2022-05-01 20:39:46,511 - tensorflow - INFO - global_step/sec: 88.6774 -2022-05-01 20:39:46,511 - tensorflow - INFO - loss = 0.19981001, step = 1908000 (2.255 sec) -2022-05-01 20:39:48,768 - tensorflow - INFO - global_step/sec: 88.6124 -2022-05-01 20:39:48,768 - tensorflow - INFO - loss = 0.01842366, step = 1908200 (2.257 sec) -2022-05-01 20:39:51,025 - tensorflow - INFO - global_step/sec: 88.61 -2022-05-01 20:39:51,026 - tensorflow - INFO - loss = 0.029061416, step = 1908400 (2.257 sec) -2022-05-01 20:39:53,274 - tensorflow - INFO - global_step/sec: 88.9391 -2022-05-01 20:39:53,274 - tensorflow - INFO - loss = 0.49992847, step = 1908600 (2.249 sec) -2022-05-01 20:39:55,530 - tensorflow - INFO - global_step/sec: 88.6647 -2022-05-01 20:39:55,530 - tensorflow - INFO - loss = 0.0052447515, step = 1908800 (2.256 sec) -2022-05-01 20:39:57,782 - tensorflow - INFO - global_step/sec: 88.7969 -2022-05-01 20:39:57,782 - tensorflow - INFO - loss = 0.05780475, step = 1909000 (2.252 sec) -2022-05-01 20:40:00,037 - tensorflow - INFO - global_step/sec: 88.701 -2022-05-01 20:40:00,037 - tensorflow - INFO - loss = 0.014669808, step = 1909200 (2.255 sec) -2022-05-01 20:40:02,288 - tensorflow - INFO - global_step/sec: 88.83 -2022-05-01 20:40:02,289 - tensorflow - INFO - loss = 0.021751998, step = 1909400 (2.251 sec) -2022-05-01 20:40:04,547 - tensorflow - INFO - global_step/sec: 88.5593 -2022-05-01 20:40:04,547 - tensorflow - INFO - loss = 0.09459678, step = 1909600 (2.258 sec) -2022-05-01 20:40:06,803 - tensorflow - INFO - global_step/sec: 88.6219 -2022-05-01 20:40:06,804 - tensorflow - INFO - loss = 0.09258586, step = 1909800 (2.257 sec) -2022-05-01 20:40:09,053 - tensorflow - INFO - global_step/sec: 88.8807 -2022-05-01 20:40:09,054 - tensorflow - INFO - loss = 0.039172817, step = 1910000 (2.250 sec) -2022-05-01 20:40:11,304 - tensorflow - INFO - global_step/sec: 88.879 -2022-05-01 20:40:11,304 - tensorflow - INFO - loss = 0.0074850977, step = 1910200 (2.250 sec) -2022-05-01 20:40:13,556 - tensorflow - INFO - global_step/sec: 88.7955 -2022-05-01 20:40:13,557 - tensorflow - INFO - loss = 0.048138943, step = 1910400 (2.252 sec) -2022-05-01 20:40:15,812 - tensorflow - INFO - global_step/sec: 88.6453 -2022-05-01 20:40:15,813 - tensorflow - INFO - loss = 0.025071843, step = 1910600 (2.256 sec) -2022-05-01 20:40:18,067 - tensorflow - INFO - global_step/sec: 88.6936 -2022-05-01 20:40:18,068 - tensorflow - INFO - loss = 0.021888126, step = 1910800 (2.255 sec) -2022-05-01 20:40:20,321 - tensorflow - INFO - global_step/sec: 88.7523 -2022-05-01 20:40:20,321 - tensorflow - INFO - loss = 0.022394136, step = 1911000 (2.253 sec) -2022-05-01 20:40:22,574 - tensorflow - INFO - global_step/sec: 88.7484 -2022-05-01 20:40:22,575 - tensorflow - INFO - loss = 0.011828977, step = 1911200 (2.254 sec) -2022-05-01 20:40:24,823 - tensorflow - INFO - global_step/sec: 88.9248 -2022-05-01 20:40:24,824 - tensorflow - INFO - loss = 0.02373368, step = 1911400 (2.249 sec) -2022-05-01 20:40:27,075 - tensorflow - INFO - global_step/sec: 88.8402 -2022-05-01 20:40:27,075 - tensorflow - INFO - loss = 0.035408095, step = 1911600 (2.251 sec) -2022-05-01 20:40:29,330 - tensorflow - INFO - global_step/sec: 88.6673 -2022-05-01 20:40:29,331 - tensorflow - INFO - loss = 0.016631411, step = 1911800 (2.256 sec) -2022-05-01 20:40:31,587 - tensorflow - INFO - global_step/sec: 88.64 -2022-05-01 20:40:31,587 - tensorflow - INFO - loss = 0.0070046624, step = 1912000 (2.256 sec) -2022-05-01 20:40:33,841 - tensorflow - INFO - global_step/sec: 88.7216 -2022-05-01 20:40:33,841 - tensorflow - INFO - loss = 0.06245377, step = 1912200 (2.254 sec) -2022-05-01 20:40:36,094 - tensorflow - INFO - global_step/sec: 88.7758 -2022-05-01 20:40:36,094 - tensorflow - INFO - loss = 0.013396024, step = 1912400 (2.253 sec) -2022-05-01 20:40:38,353 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.01927559, step = 1913800 (2.249 sec) -2022-05-01 20:40:54,126 - tensorflow - INFO - global_step/sec: 88.4858 -2022-05-01 20:40:54,127 - tensorflow - INFO - loss = 9.369586, step = 1914000 (2.260 sec) -2022-05-01 20:40:56,381 - tensorflow - INFO - global_step/sec: 88.7036 -2022-05-01 20:40:56,382 - tensorflow - INFO - loss = 0.01684592, step = 1914200 (2.255 sec) -2022-05-01 20:40:58,637 - tensorflow - INFO - global_step/sec: 88.6603 -2022-05-01 20:40:58,637 - tensorflow - INFO - loss = 0.101945385, step = 1914400 (2.256 sec) -2022-05-01 20:41:00,890 - tensorflow - INFO - global_step/sec: 88.7815 -2022-05-01 20:41:00,890 - tensorflow - INFO - loss = 0.0064888103, step = 1914600 (2.253 sec) -2022-05-01 20:41:03,142 - tensorflow - INFO - global_step/sec: 88.7776 -2022-05-01 20:41:03,143 - tensorflow - INFO - loss = 0.021903522, step = 1914800 (2.253 sec) -2022-05-01 20:41:05,398 - tensorflow - INFO - global_step/sec: 88.6585 -2022-05-01 20:41:05,399 - tensorflow - 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tensorflow - INFO - loss = 0.0121766105, step = 1997800 (2.254 sec) -2022-05-01 20:56:41,465 - tensorflow - INFO - global_step/sec: 88.6415 -2022-05-01 20:56:41,465 - tensorflow - INFO - loss = 0.047288876, step = 1998000 (2.256 sec) -2022-05-01 20:56:43,718 - tensorflow - INFO - global_step/sec: 88.7616 -2022-05-01 20:56:43,719 - tensorflow - INFO - loss = 0.062814854, step = 1998200 (2.253 sec) -2022-05-01 20:56:45,978 - tensorflow - INFO - global_step/sec: 88.4877 -2022-05-01 20:56:45,979 - tensorflow - INFO - loss = 0.020641323, step = 1998400 (2.260 sec) -2022-05-01 20:56:48,228 - tensorflow - INFO - global_step/sec: 88.8852 -2022-05-01 20:56:48,229 - tensorflow - INFO - loss = 16.469042, step = 1998600 (2.250 sec) -2022-05-01 20:56:50,483 - tensorflow - INFO - global_step/sec: 88.7054 -2022-05-01 20:56:50,483 - tensorflow - INFO - loss = 0.011845652, step = 1998800 (2.255 sec) -2022-05-01 20:56:52,740 - tensorflow - INFO - global_step/sec: 88.6063 -2022-05-01 20:56:52,741 - tensorflow - INFO - loss = 0.0357652, step = 1999000 (2.257 sec) -2022-05-01 20:56:54,994 - tensorflow - INFO - global_step/sec: 88.7291 -2022-05-01 20:56:54,995 - tensorflow - INFO - loss = 0.07935603, step = 1999200 (2.254 sec) -2022-05-01 20:56:57,256 - tensorflow - INFO - global_step/sec: 88.4201 -2022-05-01 20:56:57,257 - tensorflow - INFO - loss = 0.012287976, step = 1999400 (2.262 sec) -2022-05-01 20:56:59,509 - tensorflow - INFO - global_step/sec: 88.7909 -2022-05-01 20:56:59,509 - tensorflow - INFO - loss = 0.0061325766, step = 1999600 (2.252 sec) -2022-05-01 20:57:01,759 - tensorflow - INFO - global_step/sec: 88.8596 -2022-05-01 20:57:01,760 - tensorflow - INFO - loss = 0.0115417605, step = 1999800 (2.251 sec) -2022-05-01 20:57:04,005 - tensorflow - INFO - Saving checkpoints for 2000000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. -2022-05-01 20:57:04,023 - tensorflow - INFO - model.ckpt-2000000 is not in all_model_checkpoint_paths. Manually adding it. -2022-05-01 20:57:04,551 - tensorflow - INFO - Calling model_fn. -2022-05-01 20:57:04,678 - tensorflow - INFO - Done calling model_fn. -2022-05-01 20:57:04,693 - tensorflow - INFO - Starting evaluation at 2022-05-01T20:57:04Z -2022-05-01 20:57:05,038 - tensorflow - INFO - Graph was finalized. -2022-05-01 20:57:05,041 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-2000000 -2022-05-01 20:57:05,323 - tensorflow - INFO - Running local_init_op. -2022-05-01 20:57:05,432 - tensorflow - INFO - Done running local_init_op. -2022-05-01 20:57:15,574 - tensorflow - INFO - Evaluation [3990/39900] -2022-05-01 20:57:25,021 - tensorflow - INFO - Evaluation [7980/39900] -2022-05-01 20:57:34,462 - tensorflow - INFO - Evaluation [11970/39900] -2022-05-01 20:57:43,894 - tensorflow - INFO - Evaluation [15960/39900] -2022-05-01 20:57:53,335 - tensorflow - INFO - Evaluation [19950/39900] -2022-05-01 20:58:02,783 - tensorflow - INFO - Evaluation [23940/39900] -2022-05-01 20:58:12,227 - tensorflow - INFO - Evaluation [27930/39900] -2022-05-01 20:58:21,686 - tensorflow - INFO - Evaluation [31920/39900] -2022-05-01 20:58:31,130 - tensorflow - INFO - Evaluation [35910/39900] -2022-05-01 20:58:40,568 - tensorflow - INFO - Evaluation [39900/39900] -2022-05-01 20:58:40,705 - tensorflow - INFO - Finished evaluation at 2022-05-01-20:58:40 -2022-05-01 20:58:40,706 - tensorflow - INFO - Saving dict for global step 2000000: global_step = 2000000, loss = 0.015859876, mean_degree_err = 3.1788845 -2022-05-01 20:58:40,706 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 2000000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-2000000 -2022-05-01 20:58:40,720 - tensorflow - INFO - global_step/sec: 2.02101 -2022-05-01 20:58:40,721 - tensorflow - INFO - loss = 0.01467032, step = 2000000 (98.961 sec) -2022-05-01 20:58:42,981 - tensorflow - INFO - global_step/sec: 88.4545 -2022-05-01 20:58:42,982 - tensorflow - INFO - loss = 0.013274206, step = 2000200 (2.261 sec) -2022-05-01 20:58:45,239 - tensorflow - INFO - global_step/sec: 88.558 -2022-05-01 20:58:45,240 - tensorflow - INFO - loss = 0.026724704, step = 2000400 (2.258 sec) -2022-05-01 20:58:47,492 - tensorflow - INFO - global_step/sec: 88.7754 -2022-05-01 20:58:47,493 - tensorflow - INFO - loss = 0.007345394, step = 2000600 (2.253 sec) -2022-05-01 20:58:49,751 - tensorflow - INFO - global_step/sec: 88.5343 -2022-05-01 20:58:49,752 - tensorflow - INFO - loss = 0.008276798, step = 2000800 (2.259 sec) -2022-05-01 20:58:52,005 - tensorflow - INFO - global_step/sec: 88.7607 -2022-05-01 20:58:52,005 - tensorflow - INFO - loss = 0.037647825, step = 2001000 (2.253 sec) -2022-05-01 20:58:54,264 - tensorflow - INFO - global_step/sec: 88.5243 -2022-05-01 20:58:54,264 - tensorflow - INFO - loss = 0.014356632, step = 2001200 (2.259 sec) -2022-05-01 20:58:56,527 - tensorflow - INFO - global_step/sec: 88.3761 -2022-05-01 20:58:56,527 - tensorflow - INFO - loss = 0.018045077, step = 2001400 (2.263 sec) -2022-05-01 20:58:58,788 - tensorflow - INFO - global_step/sec: 88.4435 -2022-05-01 20:58:58,789 - tensorflow - INFO - loss = 0.25831372, step = 2001600 (2.261 sec) -2022-05-01 20:59:01,052 - tensorflow - INFO - global_step/sec: 88.3513 -2022-05-01 20:59:01,052 - tensorflow - INFO - loss = 0.0193472, step = 2001800 (2.264 sec) -2022-05-01 20:59:03,311 - tensorflow - INFO - global_step/sec: 88.5515 -2022-05-01 20:59:03,311 - tensorflow - INFO - loss = 0.039867893, step = 2002000 (2.259 sec) -2022-05-01 20:59:05,575 - tensorflow - INFO - global_step/sec: 88.3249 -2022-05-01 20:59:05,575 - tensorflow - INFO - loss = 0.021221787, step = 2002200 (2.264 sec) -2022-05-01 20:59:07,836 - tensorflow - INFO - global_step/sec: 88.4694 -2022-05-01 20:59:07,836 - tensorflow - INFO - loss = 0.04830868, step = 2002400 (2.261 sec) -2022-05-01 20:59:10,096 - tensorflow - INFO - global_step/sec: 88.4924 -2022-05-01 20:59:10,096 - tensorflow - INFO - loss = 1.0361093, step = 2002600 (2.260 sec) -2022-05-01 20:59:12,354 - tensorflow - INFO - global_step/sec: 88.5539 -2022-05-01 20:59:12,355 - tensorflow - INFO - loss = 0.018668309, step = 2002800 (2.259 sec) -2022-05-01 20:59:14,616 - tensorflow - INFO - global_step/sec: 88.4391 -2022-05-01 20:59:14,616 - tensorflow - INFO - loss = 0.014582523, step = 2003000 (2.261 sec) -2022-05-01 20:59:16,875 - tensorflow - INFO - global_step/sec: 88.5287 -2022-05-01 20:59:16,875 - tensorflow - INFO - loss = 0.34214815, step = 2003200 (2.259 sec) -2022-05-01 20:59:19,141 - tensorflow - INFO - global_step/sec: 88.2529 -2022-05-01 20:59:19,141 - tensorflow - INFO - loss = 0.027201526, step = 2003400 (2.266 sec) -2022-05-01 20:59:21,399 - tensorflow - INFO - global_step/sec: 88.5729 -2022-05-01 20:59:21,399 - tensorflow - INFO - loss = 0.017191805, step = 2003600 (2.258 sec) -2022-05-01 20:59:23,659 - tensorflow - INFO - global_step/sec: 88.5133 -2022-05-01 20:59:23,659 - tensorflow - INFO - loss = 0.040490415, step = 2003800 (2.260 sec) -2022-05-01 20:59:25,911 - tensorflow - INFO - global_step/sec: 88.8008 -2022-05-01 20:59:25,911 - tensorflow - INFO - loss = 0.019516265, step = 2004000 (2.252 sec) -2022-05-01 20:59:28,164 - tensorflow - INFO - global_step/sec: 88.7503 -2022-05-01 20:59:28,165 - tensorflow - INFO - loss = 0.025298692, step = 2004200 (2.254 sec) -2022-05-01 20:59:30,427 - tensorflow - INFO - global_step/sec: 88.3838 -2022-05-01 20:59:30,428 - tensorflow - INFO - loss = 0.02187489, step = 2004400 (2.263 sec) -2022-05-01 20:59:32,683 - tensorflow - INFO - global_step/sec: 88.6602 -2022-05-01 20:59:32,683 - tensorflow - INFO - loss = 0.008246376, step = 2004600 (2.256 sec) -2022-05-01 20:59:34,936 - tensorflow - INFO - global_step/sec: 88.7644 -2022-05-01 20:59:34,936 - tensorflow - INFO - loss = 0.00793772, step = 2004800 (2.253 sec) -2022-05-01 20:59:37,186 - tensorflow - INFO - global_step/sec: 88.9049 -2022-05-01 20:59:37,186 - tensorflow - INFO - loss = 0.009742752, step = 2005000 (2.250 sec) -2022-05-01 20:59:39,439 - tensorflow - INFO - global_step/sec: 88.7513 -2022-05-01 20:59:39,440 - tensorflow - INFO - loss = 0.019376889, step = 2005200 (2.253 sec) -2022-05-01 20:59:41,694 - tensorflow - INFO - global_step/sec: 88.6935 -2022-05-01 20:59:41,695 - tensorflow - INFO - loss = 0.0048106383, step = 2005400 (2.255 sec) -2022-05-01 20:59:43,958 - tensorflow - INFO - global_step/sec: 88.3361 -2022-05-01 20:59:43,959 - tensorflow - INFO - loss = 0.13213453, step = 2005600 (2.264 sec) -2022-05-01 20:59:46,211 - tensorflow - INFO - global_step/sec: 88.7763 -2022-05-01 20:59:46,211 - tensorflow - INFO - loss = 0.050713982, step = 2005800 (2.253 sec) -2022-05-01 20:59:48,461 - tensorflow - INFO - global_step/sec: 88.8766 -2022-05-01 20:59:48,462 - tensorflow - INFO - loss = 0.027069667, step = 2006000 (2.250 sec) -2022-05-01 20:59:50,712 - tensorflow - INFO - global_step/sec: 88.8684 -2022-05-01 20:59:50,712 - tensorflow - INFO - loss = 0.2192508, step = 2006200 (2.251 sec) -2022-05-01 20:59:52,968 - tensorflow - INFO - global_step/sec: 88.629 -2022-05-01 20:59:52,969 - tensorflow - INFO - loss = 0.02215605, step = 2006400 (2.257 sec) -2022-05-01 20:59:55,224 - tensorflow - INFO - global_step/sec: 88.688 -2022-05-01 20:59:55,224 - tensorflow - INFO - loss = 0.015829494, step = 2006600 (2.255 sec) -2022-05-01 20:59:57,483 - tensorflow - INFO - global_step/sec: 88.5101 -2022-05-01 20:59:57,484 - tensorflow - INFO - loss = 0.020551346, step = 2006800 (2.260 sec) -2022-05-01 20:59:59,737 - tensorflow - INFO - global_step/sec: 88.7463 -2022-05-01 20:59:59,737 - tensorflow - INFO - loss = 2.006907, step = 2007000 (2.254 sec) -2022-05-01 21:00:01,992 - tensorflow - INFO - global_step/sec: 88.6747 -2022-05-01 21:00:01,993 - tensorflow - INFO - loss = 0.009550948, step = 2007200 (2.255 sec) -2022-05-01 21:00:04,245 - tensorflow - INFO - global_step/sec: 88.7846 -2022-05-01 21:00:04,245 - tensorflow - INFO - loss = 0.03207432, step = 2007400 (2.253 sec) -2022-05-01 21:00:06,496 - tensorflow - INFO - global_step/sec: 88.8523 -2022-05-01 21:00:06,496 - tensorflow - INFO - loss = 0.008671386, step = 2007600 (2.251 sec) -2022-05-01 21:00:08,752 - tensorflow - INFO - global_step/sec: 88.6274 -2022-05-01 21:00:08,753 - tensorflow - INFO - loss = 0.081116244, step = 2007800 (2.257 sec) -2022-05-01 21:00:11,003 - tensorflow - INFO - global_step/sec: 88.8742 -2022-05-01 21:00:11,003 - tensorflow - INFO - loss = 0.024560489, step = 2008000 (2.250 sec) -2022-05-01 21:00:13,260 - tensorflow - INFO - global_step/sec: 88.625 -2022-05-01 21:00:13,260 - tensorflow - INFO - loss = 0.054812565, step = 2008200 (2.257 sec) -2022-05-01 21:00:15,514 - tensorflow - INFO - global_step/sec: 88.7045 -2022-05-01 21:00:15,515 - tensorflow - INFO - loss = 0.021981124, step = 2008400 (2.255 sec) -2022-05-01 21:00:17,765 - tensorflow - INFO - global_step/sec: 88.8768 -2022-05-01 21:00:17,765 - tensorflow - INFO - loss = 0.024330994, step = 2008600 (2.250 sec) -2022-05-01 21:00:20,021 - tensorflow - INFO - global_step/sec: 88.6422 -2022-05-01 21:00:20,021 - tensorflow - INFO - loss = 0.121265806, step = 2008800 (2.256 sec) -2022-05-01 21:00:22,279 - tensorflow - INFO - global_step/sec: 88.5661 -2022-05-01 21:00:22,279 - tensorflow - INFO - loss = 0.013724765, step = 2009000 (2.258 sec) -2022-05-01 21:00:24,529 - tensorflow - INFO - global_step/sec: 88.8898 -2022-05-01 21:00:24,529 - tensorflow - INFO - loss = 0.07820247, step = 2009200 (2.250 sec) -2022-05-01 21:00:26,781 - tensorflow - INFO - global_step/sec: 88.8091 -2022-05-01 21:00:26,781 - tensorflow - INFO - loss = 0.069547355, step = 2009400 (2.252 sec) -2022-05-01 21:00:29,039 - tensorflow - INFO - global_step/sec: 88.5597 -2022-05-01 21:00:29,040 - tensorflow - INFO - loss = 0.032171834, step = 2009600 (2.258 sec) -2022-05-01 21:00:31,300 - tensorflow - INFO - global_step/sec: 88.4701 -2022-05-01 21:00:31,300 - tensorflow - INFO - loss = 0.074208364, step = 2009800 (2.261 sec) -2022-05-01 21:00:33,551 - tensorflow - INFO - global_step/sec: 88.8576 -2022-05-01 21:00:33,551 - tensorflow - INFO - loss = 0.014253901, step = 2010000 (2.251 sec) -2022-05-01 21:00:35,801 - tensorflow - INFO - global_step/sec: 88.8754 -2022-05-01 21:00:35,802 - tensorflow - INFO - loss = 0.107017614, step = 2010200 (2.250 sec) -2022-05-01 21:00:38,052 - tensorflow - INFO - global_step/sec: 88.8646 -2022-05-01 21:00:38,052 - tensorflow - INFO - loss = 0.02333238, step = 2010400 (2.251 sec) -2022-05-01 21:00:40,304 - tensorflow - INFO - global_step/sec: 88.8074 -2022-05-01 21:00:40,304 - tensorflow - INFO - loss = 0.013060355, step = 2010600 (2.252 sec) -2022-05-01 21:00:42,550 - tensorflow - INFO - global_step/sec: 89.0439 -2022-05-01 21:00:42,550 - tensorflow - INFO - loss = 0.05624281, step = 2010800 (2.246 sec) -2022-05-01 21:00:44,804 - tensorflow - INFO - global_step/sec: 88.7119 -2022-05-01 21:00:44,805 - tensorflow - INFO - loss = 0.09383938, step = 2011000 (2.254 sec) -2022-05-01 21:00:47,063 - tensorflow - INFO - global_step/sec: 88.5444 -2022-05-01 21:00:47,064 - tensorflow - INFO - loss = 0.026484538, step = 2011200 (2.259 sec) -2022-05-01 21:00:49,314 - tensorflow - INFO - global_step/sec: 88.8738 -2022-05-01 21:00:49,314 - tensorflow - INFO - loss = 0.010374699, step = 2011400 (2.250 sec) -2022-05-01 21:00:51,568 - tensorflow - INFO - global_step/sec: 88.7215 -2022-05-01 21:00:51,568 - tensorflow - INFO - loss = 0.11623212, step = 2011600 (2.254 sec) -2022-05-01 21:00:53,815 - tensorflow - INFO - global_step/sec: 89.0112 -2022-05-01 21:00:53,815 - tensorflow - INFO - loss = 0.0196031, step = 2011800 (2.247 sec) -2022-05-01 21:00:56,068 - tensorflow - INFO - global_step/sec: 88.7529 -2022-05-01 21:00:56,069 - tensorflow - INFO - loss = 0.024542011, step = 2012000 (2.253 sec) -2022-05-01 21:00:58,320 - tensorflow - INFO - global_step/sec: 88.8249 -2022-05-01 21:00:58,320 - tensorflow - INFO - loss = 0.008934006, step = 2012200 (2.252 sec) -2022-05-01 21:01:00,578 - tensorflow - INFO - global_step/sec: 88.5485 -2022-05-01 21:01:00,579 - tensorflow - INFO - loss = 0.014190808, step = 2012400 (2.259 sec) -2022-05-01 21:01:02,830 - tensorflow - INFO - global_step/sec: 88.8435 -2022-05-01 21:01:02,830 - tensorflow - INFO - loss = 0.011069354, step = 2012600 (2.251 sec) -2022-05-01 21:01:05,082 - tensorflow - INFO - global_step/sec: 88.7971 -2022-05-01 21:01:05,082 - tensorflow - INFO - loss = 0.0054627806, step = 2012800 (2.252 sec) -2022-05-01 21:01:07,332 - tensorflow - INFO - global_step/sec: 88.8756 -2022-05-01 21:01:07,333 - tensorflow - INFO - loss = 0.03997647, step = 2013000 (2.250 sec) -2022-05-01 21:01:09,580 - tensorflow - INFO - global_step/sec: 88.9896 -2022-05-01 21:01:09,580 - tensorflow - INFO - loss = 0.07273039, step = 2013200 (2.247 sec) -2022-05-01 21:01:11,836 - tensorflow - INFO - global_step/sec: 88.6371 -2022-05-01 21:01:11,836 - tensorflow - INFO - loss = 0.07716078, step = 2013400 (2.256 sec) -2022-05-01 21:01:14,090 - tensorflow - INFO - global_step/sec: 88.7507 -2022-05-01 21:01:14,090 - tensorflow - INFO - loss = 0.00919323, step = 2013600 (2.253 sec) -2022-05-01 21:01:16,345 - tensorflow - INFO - global_step/sec: 88.6708 -2022-05-01 21:01:16,345 - tensorflow - INFO - loss = 0.006531185, step = 2013800 (2.256 sec) -2022-05-01 21:01:18,593 - tensorflow - INFO - global_step/sec: 88.9757 -2022-05-01 21:01:18,593 - tensorflow - INFO - loss = 0.009378989, step = 2014000 (2.248 sec) -2022-05-01 21:01:20,847 - tensorflow - INFO - global_step/sec: 88.7404 -2022-05-01 21:01:20,847 - tensorflow - INFO - loss = 0.010182643, step = 2014200 (2.254 sec) -2022-05-01 21:01:23,103 - tensorflow - INFO - global_step/sec: 88.6379 -2022-05-01 21:01:23,103 - tensorflow - INFO - loss = 0.03052396, step = 2014400 (2.256 sec) -2022-05-01 21:01:25,362 - tensorflow - INFO - global_step/sec: 88.5363 -2022-05-01 21:01:25,362 - tensorflow - INFO - loss = 0.021367367, step = 2014600 (2.259 sec) -2022-05-01 21:01:27,618 - tensorflow - INFO - global_step/sec: 88.632 -2022-05-01 21:01:27,619 - tensorflow - INFO - loss = 0.03744563, step = 2014800 (2.256 sec) -2022-05-01 21:01:29,875 - tensorflow - INFO - global_step/sec: 88.6486 -2022-05-01 21:01:29,875 - tensorflow - 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tensorflow - INFO - global_step/sec: 88.7349 -2022-05-01 21:01:45,653 - tensorflow - INFO - loss = 0.048359454, step = 2016400 (2.254 sec) -2022-05-01 21:01:47,908 - tensorflow - INFO - global_step/sec: 88.6848 -2022-05-01 21:01:47,908 - tensorflow - INFO - loss = 0.7631538, step = 2016600 (2.255 sec) -2022-05-01 21:01:50,160 - tensorflow - INFO - global_step/sec: 88.798 -2022-05-01 21:01:50,161 - tensorflow - INFO - loss = 0.010982657, step = 2016800 (2.252 sec) -2022-05-01 21:01:52,414 - tensorflow - INFO - global_step/sec: 88.7327 -2022-05-01 21:01:52,415 - tensorflow - INFO - loss = 0.015912972, step = 2017000 (2.254 sec) -2022-05-01 21:01:54,670 - tensorflow - INFO - global_step/sec: 88.6593 -2022-05-01 21:01:54,671 - tensorflow - INFO - loss = 0.047597427, step = 2017200 (2.256 sec) -2022-05-01 21:01:56,924 - tensorflow - INFO - global_step/sec: 88.7278 -2022-05-01 21:01:56,925 - tensorflow - INFO - loss = 0.005005228, step = 2017400 (2.254 sec) -2022-05-01 21:01:59,174 - tensorflow - 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INFO - global_step/sec: 88.5894 -2022-05-01 21:09:25,430 - tensorflow - INFO - loss = 0.009835939, step = 2057200 (2.258 sec) -2022-05-01 21:09:27,691 - tensorflow - INFO - global_step/sec: 88.4177 -2022-05-01 21:09:27,692 - tensorflow - INFO - loss = 0.06272012, step = 2057400 (2.262 sec) -2022-05-01 21:09:29,956 - tensorflow - INFO - global_step/sec: 88.3026 -2022-05-01 21:09:29,957 - tensorflow - INFO - loss = 0.03090753, step = 2057600 (2.265 sec) -2022-05-01 21:09:32,224 - tensorflow - INFO - global_step/sec: 88.1899 -2022-05-01 21:09:32,224 - tensorflow - INFO - loss = 0.01705765, step = 2057800 (2.268 sec) -2022-05-01 21:09:34,486 - tensorflow - INFO - global_step/sec: 88.4149 -2022-05-01 21:09:34,486 - tensorflow - INFO - loss = 0.014951571, step = 2058000 (2.262 sec) -2022-05-01 21:09:36,748 - tensorflow - INFO - global_step/sec: 88.3998 -2022-05-01 21:09:36,749 - tensorflow - INFO - loss = 0.12008022, step = 2058200 (2.262 sec) -2022-05-01 21:09:39,007 - tensorflow - INFO - global_step/sec: 88.539 -2022-05-01 21:09:39,008 - 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tensorflow - INFO - global_step/sec: 88.7619 -2022-05-01 21:10:08,350 - tensorflow - INFO - loss = 0.019503139, step = 2061000 (2.253 sec) -2022-05-01 21:10:10,605 - tensorflow - INFO - global_step/sec: 88.6836 -2022-05-01 21:10:10,605 - tensorflow - INFO - loss = 0.014946849, step = 2061200 (2.255 sec) -2022-05-01 21:10:12,856 - tensorflow - INFO - global_step/sec: 88.8291 -2022-05-01 21:10:12,857 - tensorflow - INFO - loss = 0.06617184, step = 2061400 (2.252 sec) -2022-05-01 21:10:15,105 - tensorflow - INFO - global_step/sec: 88.9473 -2022-05-01 21:10:15,105 - tensorflow - INFO - loss = 0.017059457, step = 2061600 (2.249 sec) -2022-05-01 21:10:17,356 - tensorflow - INFO - global_step/sec: 88.8569 -2022-05-01 21:10:17,356 - tensorflow - INFO - loss = 0.01815191, step = 2061800 (2.251 sec) -2022-05-01 21:10:19,608 - tensorflow - INFO - global_step/sec: 88.7956 -2022-05-01 21:10:19,608 - tensorflow - INFO - loss = 0.013575856, step = 2062000 (2.252 sec) -2022-05-01 21:10:21,863 - tensorflow - 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INFO - loss = 0.009364031, step = 2065800 (2.254 sec) -2022-05-01 21:11:04,653 - tensorflow - INFO - global_step/sec: 88.9472 -2022-05-01 21:11:04,654 - tensorflow - INFO - loss = 0.031356715, step = 2066000 (2.249 sec) -2022-05-01 21:11:06,904 - tensorflow - INFO - global_step/sec: 88.8554 -2022-05-01 21:11:06,905 - tensorflow - INFO - loss = 0.028149718, step = 2066200 (2.251 sec) -2022-05-01 21:11:09,151 - tensorflow - INFO - global_step/sec: 89.0089 -2022-05-01 21:11:09,152 - tensorflow - INFO - loss = 0.023787953, step = 2066400 (2.247 sec) -2022-05-01 21:11:11,405 - tensorflow - INFO - global_step/sec: 88.7357 -2022-05-01 21:11:11,406 - tensorflow - INFO - loss = 0.036262065, step = 2066600 (2.254 sec) -2022-05-01 21:11:13,659 - tensorflow - INFO - global_step/sec: 88.7255 -2022-05-01 21:11:13,660 - tensorflow - INFO - loss = 0.013351564, step = 2066800 (2.254 sec) -2022-05-01 21:11:15,913 - tensorflow - INFO - global_step/sec: 88.7623 -2022-05-01 21:11:15,913 - tensorflow - INFO - 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INFO - global_step/sec: 88.8301 -2022-05-01 21:11:45,214 - tensorflow - INFO - loss = 0.5638597, step = 2069600 (2.251 sec) -2022-05-01 21:11:47,465 - tensorflow - INFO - global_step/sec: 88.8383 -2022-05-01 21:11:47,465 - tensorflow - INFO - loss = 0.043907017, step = 2069800 (2.251 sec) -2022-05-01 21:11:49,719 - tensorflow - INFO - global_step/sec: 88.7031 -2022-05-01 21:11:49,720 - tensorflow - INFO - loss = 0.015858615, step = 2070000 (2.255 sec) -2022-05-01 21:11:51,978 - tensorflow - INFO - global_step/sec: 88.5345 -2022-05-01 21:11:51,979 - tensorflow - INFO - loss = 0.013738412, step = 2070200 (2.259 sec) -2022-05-01 21:11:54,228 - tensorflow - INFO - global_step/sec: 88.8952 -2022-05-01 21:11:54,229 - tensorflow - INFO - loss = 0.01958806, step = 2070400 (2.250 sec) -2022-05-01 21:11:56,485 - tensorflow - INFO - global_step/sec: 88.6396 -2022-05-01 21:11:56,485 - tensorflow - INFO - loss = 0.2860499, step = 2070600 (2.256 sec) -2022-05-01 21:11:58,737 - tensorflow - INFO - global_step/sec: 88.7878 -2022-05-01 21:11:58,738 - tensorflow - INFO - loss = 0.011893651, step = 2070800 (2.253 sec) -2022-05-01 21:12:00,986 - tensorflow - INFO - global_step/sec: 88.9309 -2022-05-01 21:12:00,987 - tensorflow - INFO - loss = 0.01323004, step = 2071000 (2.249 sec) -2022-05-01 21:12:03,243 - tensorflow - INFO - global_step/sec: 88.6124 -2022-05-01 21:12:03,244 - tensorflow - INFO - loss = 0.0049747634, step = 2071200 (2.257 sec) -2022-05-01 21:12:05,494 - tensorflow - INFO - global_step/sec: 88.8724 -2022-05-01 21:12:05,494 - tensorflow - INFO - loss = 0.007261668, step = 2071400 (2.250 sec) -2022-05-01 21:12:07,749 - tensorflow - INFO - global_step/sec: 88.6939 -2022-05-01 21:12:07,749 - tensorflow - INFO - loss = 0.02347025, step = 2071600 (2.255 sec) -2022-05-01 21:12:10,000 - tensorflow - INFO - global_step/sec: 88.8393 -2022-05-01 21:12:10,000 - tensorflow - INFO - loss = 0.021576278, step = 2071800 (2.251 sec) -2022-05-01 21:12:12,250 - tensorflow - INFO - global_step/sec: 88.8652 -2022-05-01 21:12:12,251 - tensorflow - INFO - loss = 0.13531554, step = 2072000 (2.251 sec) -2022-05-01 21:12:14,505 - tensorflow - INFO - global_step/sec: 88.7201 -2022-05-01 21:12:14,505 - tensorflow - INFO - loss = 0.089086235, step = 2072200 (2.254 sec) -2022-05-01 21:12:16,758 - tensorflow - INFO - global_step/sec: 88.7744 -2022-05-01 21:12:16,758 - tensorflow - INFO - loss = 0.014256991, step = 2072400 (2.253 sec) -2022-05-01 21:12:19,005 - tensorflow - INFO - global_step/sec: 88.9976 -2022-05-01 21:12:19,005 - tensorflow - INFO - loss = 0.019302256, step = 2072600 (2.247 sec) -2022-05-01 21:12:21,257 - tensorflow - INFO - global_step/sec: 88.7845 -2022-05-01 21:12:21,258 - tensorflow - INFO - loss = 0.02729341, step = 2072800 (2.253 sec) -2022-05-01 21:12:23,507 - tensorflow - INFO - global_step/sec: 88.8893 -2022-05-01 21:12:23,508 - tensorflow - INFO - loss = 0.0073949606, step = 2073000 (2.250 sec) -2022-05-01 21:12:25,766 - tensorflow - INFO - global_step/sec: 88.5397 -2022-05-01 21:12:25,767 - tensorflow - INFO - 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INFO - global_step/sec: 88.7154 -2022-05-01 21:12:55,046 - tensorflow - INFO - loss = 0.46384215, step = 2075800 (2.254 sec) -2022-05-01 21:12:57,316 - tensorflow - INFO - global_step/sec: 88.0912 -2022-05-01 21:12:57,316 - tensorflow - INFO - loss = 0.01076333, step = 2076000 (2.270 sec) -2022-05-01 21:12:59,575 - tensorflow - INFO - global_step/sec: 88.5378 -2022-05-01 21:12:59,575 - tensorflow - INFO - loss = 0.014261659, step = 2076200 (2.259 sec) -2022-05-01 21:13:01,839 - tensorflow - INFO - global_step/sec: 88.3265 -2022-05-01 21:13:01,839 - tensorflow - INFO - loss = 0.008098021, step = 2076400 (2.264 sec) -2022-05-01 21:13:04,101 - tensorflow - INFO - global_step/sec: 88.3948 -2022-05-01 21:13:04,102 - tensorflow - INFO - loss = 0.01590208, step = 2076600 (2.263 sec) -2022-05-01 21:13:06,354 - tensorflow - INFO - global_step/sec: 88.805 -2022-05-01 21:13:06,354 - tensorflow - INFO - loss = 0.031782128, step = 2076800 (2.252 sec) -2022-05-01 21:13:08,609 - tensorflow - INFO - global_step/sec: 88.6621 -2022-05-01 21:13:08,610 - 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INFO - global_step/sec: 88.5093 -2022-05-01 21:16:24,656 - tensorflow - INFO - loss = 0.01545664, step = 2094400 (2.260 sec) -2022-05-01 21:16:26,912 - tensorflow - INFO - global_step/sec: 88.6031 -2022-05-01 21:16:26,913 - tensorflow - INFO - loss = 0.023175605, step = 2094600 (2.257 sec) -2022-05-01 21:16:29,174 - tensorflow - INFO - global_step/sec: 88.4327 -2022-05-01 21:16:29,174 - tensorflow - INFO - loss = 0.24491048, step = 2094800 (2.262 sec) -2022-05-01 21:16:31,445 - tensorflow - INFO - global_step/sec: 88.0511 -2022-05-01 21:16:31,446 - tensorflow - INFO - loss = 0.01627911, step = 2095000 (2.271 sec) -2022-05-01 21:16:33,716 - tensorflow - INFO - global_step/sec: 88.0973 -2022-05-01 21:16:33,716 - tensorflow - INFO - loss = 0.07918907, step = 2095200 (2.270 sec) -2022-05-01 21:16:35,974 - tensorflow - INFO - global_step/sec: 88.5521 -2022-05-01 21:16:35,975 - tensorflow - INFO - loss = 0.012776617, step = 2095400 (2.259 sec) -2022-05-01 21:16:38,239 - tensorflow - INFO - global_step/sec: 88.3085 -2022-05-01 21:16:38,239 - tensorflow - INFO - loss = 0.026352443, step = 2095600 (2.265 sec) -2022-05-01 21:16:40,497 - tensorflow - INFO - global_step/sec: 88.555 -2022-05-01 21:16:40,498 - tensorflow - INFO - loss = 0.012043992, step = 2095800 (2.259 sec) -2022-05-01 21:16:42,759 - tensorflow - INFO - global_step/sec: 88.4462 -2022-05-01 21:16:42,759 - tensorflow - INFO - loss = 0.023362797, step = 2096000 (2.261 sec) -2022-05-01 21:16:45,021 - tensorflow - INFO - global_step/sec: 88.4093 -2022-05-01 21:16:45,021 - tensorflow - INFO - loss = 0.03033696, step = 2096200 (2.262 sec) -2022-05-01 21:16:47,281 - tensorflow - INFO - global_step/sec: 88.499 -2022-05-01 21:16:47,281 - tensorflow - INFO - loss = 0.0076375394, step = 2096400 (2.260 sec) -2022-05-01 21:16:49,542 - tensorflow - INFO - global_step/sec: 88.4654 -2022-05-01 21:16:49,542 - tensorflow - INFO - loss = 0.008694179, step = 2096600 (2.261 sec) -2022-05-01 21:16:51,800 - tensorflow - INFO - global_step/sec: 88.5724 -2022-05-01 21:16:51,800 - tensorflow - INFO - loss = 0.037878785, step = 2096800 (2.258 sec) -2022-05-01 21:16:54,059 - tensorflow - INFO - global_step/sec: 88.5216 -2022-05-01 21:16:54,059 - tensorflow - INFO - loss = 0.12967177, step = 2097000 (2.259 sec) -2022-05-01 21:16:56,321 - tensorflow - INFO - global_step/sec: 88.4241 -2022-05-01 21:16:56,321 - tensorflow - INFO - loss = 0.017660087, step = 2097200 (2.262 sec) -2022-05-01 21:16:58,580 - tensorflow - INFO - global_step/sec: 88.5298 -2022-05-01 21:16:58,580 - tensorflow - INFO - loss = 0.010364544, step = 2097400 (2.259 sec) -2022-05-01 21:17:00,841 - tensorflow - INFO - global_step/sec: 88.4503 -2022-05-01 21:17:00,842 - tensorflow - INFO - loss = 0.01831447, step = 2097600 (2.261 sec) -2022-05-01 21:17:03,098 - tensorflow - INFO - global_step/sec: 88.6275 -2022-05-01 21:17:03,098 - tensorflow - INFO - loss = 0.04949817, step = 2097800 (2.257 sec) -2022-05-01 21:17:05,350 - tensorflow - INFO - global_step/sec: 88.8195 -2022-05-01 21:17:05,350 - tensorflow - INFO - loss = 0.04070931, step = 2098000 (2.252 sec) -2022-05-01 21:17:07,604 - tensorflow - INFO - global_step/sec: 88.7045 -2022-05-01 21:17:07,605 - tensorflow - INFO - loss = 0.035523713, step = 2098200 (2.255 sec) -2022-05-01 21:17:09,859 - tensorflow - INFO - global_step/sec: 88.6824 -2022-05-01 21:17:09,860 - tensorflow - INFO - loss = 0.021236856, step = 2098400 (2.255 sec) -2022-05-01 21:17:12,119 - tensorflow - INFO - global_step/sec: 88.5007 -2022-05-01 21:17:12,120 - tensorflow - INFO - loss = 0.016438108, step = 2098600 (2.260 sec) -2022-05-01 21:17:14,369 - tensorflow - INFO - global_step/sec: 88.8847 -2022-05-01 21:17:14,370 - tensorflow - INFO - loss = 0.00890751, step = 2098800 (2.250 sec) -2022-05-01 21:17:16,623 - tensorflow - INFO - global_step/sec: 88.7402 -2022-05-01 21:17:16,624 - tensorflow - INFO - loss = 0.005814164, step = 2099000 (2.254 sec) -2022-05-01 21:17:18,879 - tensorflow - INFO - global_step/sec: 88.6581 -2022-05-01 21:17:18,879 - tensorflow - INFO - loss = 0.009963096, step = 2099200 (2.256 sec) -2022-05-01 21:17:21,129 - tensorflow - INFO - global_step/sec: 88.8753 -2022-05-01 21:17:21,130 - tensorflow - INFO - loss = 0.008472566, step = 2099400 (2.250 sec) -2022-05-01 21:17:23,383 - tensorflow - INFO - global_step/sec: 88.7272 -2022-05-01 21:17:23,384 - tensorflow - INFO - loss = 0.008451465, step = 2099600 (2.254 sec) -2022-05-01 21:17:25,635 - tensorflow - INFO - global_step/sec: 88.8399 -2022-05-01 21:17:25,635 - tensorflow - INFO - loss = 0.028972732, step = 2099800 (2.251 sec) -2022-05-01 21:17:27,870 - tensorflow - INFO - Saving checkpoints for 2100000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. -2022-05-01 21:17:27,885 - tensorflow - INFO - model.ckpt-2100000 is not in all_model_checkpoint_paths. Manually adding it. -2022-05-01 21:17:28,408 - tensorflow - INFO - Calling model_fn. -2022-05-01 21:17:28,535 - tensorflow - INFO - Done calling model_fn. -2022-05-01 21:17:28,550 - tensorflow - INFO - Starting evaluation at 2022-05-01T21:17:28Z -2022-05-01 21:17:28,618 - tensorflow - INFO - Graph was finalized. -2022-05-01 21:17:28,621 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-2100000 -2022-05-01 21:17:28,903 - tensorflow - INFO - Running local_init_op. -2022-05-01 21:17:29,010 - tensorflow - INFO - Done running local_init_op. -2022-05-01 21:17:39,236 - tensorflow - INFO - Evaluation [3990/39900] -2022-05-01 21:17:48,745 - tensorflow - INFO - Evaluation [7980/39900] -2022-05-01 21:17:58,242 - tensorflow - INFO - Evaluation [11970/39900] -2022-05-01 21:18:07,766 - tensorflow - INFO - Evaluation [15960/39900] -2022-05-01 21:18:17,302 - tensorflow - INFO - Evaluation [19950/39900] -2022-05-01 21:18:26,848 - tensorflow - INFO - Evaluation [23940/39900] -2022-05-01 21:18:36,360 - tensorflow - INFO - Evaluation [27930/39900] -2022-05-01 21:18:45,842 - tensorflow - INFO - Evaluation [31920/39900] -2022-05-01 21:18:55,344 - tensorflow - INFO - Evaluation [35910/39900] -2022-05-01 21:19:04,884 - tensorflow - INFO - Evaluation [39900/39900] -2022-05-01 21:19:05,022 - tensorflow - INFO - Finished evaluation at 2022-05-01-21:19:05 -2022-05-01 21:19:05,022 - tensorflow - INFO - Saving dict for global step 2100000: global_step = 2100000, loss = 0.016458333, mean_degree_err = 3.3069553 -2022-05-01 21:19:05,022 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 2100000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-2100000 -2022-05-01 21:19:05,036 - tensorflow - INFO - global_step/sec: 2.01204 -2022-05-01 21:19:05,037 - tensorflow - INFO - loss = 0.05436354, step = 2100000 (99.402 sec) -2022-05-01 21:19:07,310 - tensorflow - INFO - global_step/sec: 87.9633 -2022-05-01 21:19:07,310 - tensorflow - INFO - loss = 0.038885124, step = 2100200 (2.273 sec) -2022-05-01 21:19:09,564 - tensorflow - INFO - global_step/sec: 88.7438 -2022-05-01 21:19:09,564 - tensorflow - INFO - loss = 0.0110758245, step = 2100400 (2.254 sec) -2022-05-01 21:19:11,816 - tensorflow - INFO - global_step/sec: 88.8034 -2022-05-01 21:19:11,816 - tensorflow - INFO - loss = 0.02910995, step = 2100600 (2.252 sec) -2022-05-01 21:19:14,067 - tensorflow - INFO - global_step/sec: 88.8318 -2022-05-01 21:19:14,068 - tensorflow - INFO - loss = 0.019992223, step = 2100800 (2.251 sec) -2022-05-01 21:19:16,325 - tensorflow - INFO - global_step/sec: 88.5787 -2022-05-01 21:19:16,325 - tensorflow - INFO - loss = 0.010165205, step = 2101000 (2.258 sec) -2022-05-01 21:19:18,576 - tensorflow - INFO - global_step/sec: 88.8618 -2022-05-01 21:19:18,576 - tensorflow - INFO - loss = 0.013112518, step = 2101200 (2.251 sec) -2022-05-01 21:19:20,828 - tensorflow - INFO - global_step/sec: 88.8129 -2022-05-01 21:19:20,828 - tensorflow - INFO - loss = 0.01900612, step = 2101400 (2.252 sec) -2022-05-01 21:19:23,079 - tensorflow - INFO - global_step/sec: 88.8353 -2022-05-01 21:19:23,079 - tensorflow - INFO - loss = 0.05495621, step = 2101600 (2.251 sec) -2022-05-01 21:19:25,329 - tensorflow - INFO - global_step/sec: 88.9022 -2022-05-01 21:19:25,329 - tensorflow - INFO - loss = 0.05240432, step = 2101800 (2.250 sec) -2022-05-01 21:19:27,584 - tensorflow - INFO - global_step/sec: 88.69 -2022-05-01 21:19:27,584 - tensorflow - INFO - loss = 0.0105040055, step = 2102000 (2.255 sec) -2022-05-01 21:19:29,834 - tensorflow - INFO - global_step/sec: 88.86 -2022-05-01 21:19:29,835 - tensorflow - INFO - loss = 0.070999, step = 2102200 (2.251 sec) -2022-05-01 21:19:32,095 - tensorflow - INFO - global_step/sec: 88.4786 -2022-05-01 21:19:32,095 - tensorflow - INFO - loss = 0.016450152, step = 2102400 (2.260 sec) -2022-05-01 21:19:34,353 - tensorflow - INFO - global_step/sec: 88.5747 -2022-05-01 21:19:34,353 - tensorflow - INFO - loss = 0.009405328, step = 2102600 (2.258 sec) -2022-05-01 21:19:36,604 - tensorflow - INFO - global_step/sec: 88.8532 -2022-05-01 21:19:36,604 - tensorflow - INFO - loss = 0.09677004, step = 2102800 (2.251 sec) -2022-05-01 21:19:38,856 - tensorflow - INFO - global_step/sec: 88.8127 -2022-05-01 21:19:38,856 - tensorflow - INFO - loss = 0.010431, step = 2103000 (2.252 sec) -2022-05-01 21:19:41,114 - tensorflow - INFO - global_step/sec: 88.5512 -2022-05-01 21:19:41,115 - tensorflow - INFO - loss = 0.009252382, step = 2103200 (2.259 sec) -2022-05-01 21:19:43,362 - tensorflow - INFO - global_step/sec: 88.9678 -2022-05-01 21:19:43,363 - tensorflow - INFO - loss = 0.010826778, step = 2103400 (2.248 sec) -2022-05-01 21:19:45,612 - tensorflow - INFO - global_step/sec: 88.8826 -2022-05-01 21:19:45,613 - tensorflow - INFO - loss = 0.042679265, step = 2103600 (2.250 sec) -2022-05-01 21:19:47,860 - tensorflow - INFO - global_step/sec: 88.9817 -2022-05-01 21:19:47,860 - tensorflow - INFO - loss = 0.00809348, step = 2103800 (2.248 sec) -2022-05-01 21:19:50,115 - tensorflow - INFO - global_step/sec: 88.6784 -2022-05-01 21:19:50,116 - tensorflow - INFO - loss = 0.0068497146, step = 2104000 (2.255 sec) -2022-05-01 21:19:52,372 - tensorflow - INFO - global_step/sec: 88.617 -2022-05-01 21:19:52,373 - tensorflow - INFO - loss = 0.010443541, step = 2104200 (2.257 sec) -2022-05-01 21:19:54,626 - tensorflow - INFO - global_step/sec: 88.7574 -2022-05-01 21:19:54,626 - tensorflow - INFO - loss = 0.02146333, step = 2104400 (2.253 sec) -2022-05-01 21:19:56,883 - tensorflow - INFO - global_step/sec: 88.6164 -2022-05-01 21:19:56,883 - tensorflow - INFO - loss = 0.006963673, step = 2104600 (2.257 sec) -2022-05-01 21:19:59,136 - tensorflow - INFO - global_step/sec: 88.7392 -2022-05-01 21:19:59,137 - tensorflow - INFO - loss = 0.05600545, step = 2104800 (2.254 sec) -2022-05-01 21:20:01,392 - tensorflow - INFO - global_step/sec: 88.6644 -2022-05-01 21:20:01,392 - tensorflow - INFO - loss = 0.07515754, step = 2105000 (2.256 sec) -2022-05-01 21:20:03,651 - tensorflow - INFO - global_step/sec: 88.5435 -2022-05-01 21:20:03,651 - tensorflow - INFO - loss = 0.010558774, step = 2105200 (2.259 sec) -2022-05-01 21:20:05,915 - tensorflow - INFO - global_step/sec: 88.3282 -2022-05-01 21:20:05,916 - tensorflow - INFO - loss = 0.014063058, step = 2105400 (2.264 sec) -2022-05-01 21:20:08,178 - tensorflow - INFO - global_step/sec: 88.3788 -2022-05-01 21:20:08,179 - tensorflow - INFO - loss = 0.037848994, step = 2105600 (2.263 sec) -2022-05-01 21:20:10,439 - tensorflow - INFO - global_step/sec: 88.4571 -2022-05-01 21:20:10,439 - tensorflow - INFO - loss = 0.009343641, step = 2105800 (2.261 sec) -2022-05-01 21:20:12,700 - tensorflow - INFO - global_step/sec: 88.4639 -2022-05-01 21:20:12,700 - tensorflow - INFO - loss = 0.020438509, step = 2106000 (2.261 sec) -2022-05-01 21:20:14,959 - tensorflow - INFO - global_step/sec: 88.5247 -2022-05-01 21:20:14,960 - tensorflow - INFO - loss = 0.0067908484, step = 2106200 (2.259 sec) -2022-05-01 21:20:17,222 - tensorflow - INFO - global_step/sec: 88.3955 -2022-05-01 21:20:17,222 - tensorflow - INFO - loss = 0.10189227, step = 2106400 (2.263 sec) -2022-05-01 21:20:19,477 - tensorflow - INFO - global_step/sec: 88.6961 -2022-05-01 21:20:19,477 - tensorflow - INFO - loss = 0.029760987, step = 2106600 (2.255 sec) -2022-05-01 21:20:21,742 - tensorflow - INFO - global_step/sec: 88.2739 -2022-05-01 21:20:21,743 - tensorflow - INFO - loss = 0.034703635, step = 2106800 (2.266 sec) -2022-05-01 21:20:24,005 - tensorflow - INFO - global_step/sec: 88.3958 -2022-05-01 21:20:24,005 - tensorflow - INFO - loss = 0.017966729, step = 2107000 (2.263 sec) -2022-05-01 21:20:26,265 - tensorflow - INFO - global_step/sec: 88.4732 -2022-05-01 21:20:26,266 - tensorflow - INFO - loss = 0.019259527, step = 2107200 (2.261 sec) -2022-05-01 21:20:28,528 - tensorflow - INFO - global_step/sec: 88.4109 -2022-05-01 21:20:28,528 - tensorflow - INFO - loss = 0.010687232, step = 2107400 (2.262 sec) -2022-05-01 21:20:30,790 - tensorflow - INFO - global_step/sec: 88.4049 -2022-05-01 21:20:30,790 - tensorflow - INFO - loss = 0.03140063, step = 2107600 (2.262 sec) -2022-05-01 21:20:33,050 - tensorflow - INFO - global_step/sec: 88.4724 -2022-05-01 21:20:33,051 - tensorflow - INFO - loss = 0.0077216662, step = 2107800 (2.261 sec) -2022-05-01 21:20:35,303 - tensorflow - INFO - global_step/sec: 88.7959 -2022-05-01 21:20:35,303 - tensorflow - INFO - loss = 0.058895633, step = 2108000 (2.252 sec) -2022-05-01 21:20:37,555 - tensorflow - INFO - global_step/sec: 88.79 -2022-05-01 21:20:37,556 - tensorflow - INFO - loss = 0.2672078, step = 2108200 (2.252 sec) -2022-05-01 21:20:39,809 - tensorflow - INFO - global_step/sec: 88.7541 -2022-05-01 21:20:39,809 - tensorflow - INFO - loss = 0.0117831575, step = 2108400 (2.253 sec) -2022-05-01 21:20:42,062 - tensorflow - INFO - global_step/sec: 88.7505 -2022-05-01 21:20:42,063 - tensorflow - INFO - loss = 0.028337851, step = 2108600 (2.254 sec) -2022-05-01 21:20:44,316 - tensorflow - INFO - global_step/sec: 88.7299 -2022-05-01 21:20:44,317 - tensorflow - INFO - loss = 0.067769766, step = 2108800 (2.254 sec) -2022-05-01 21:20:46,570 - tensorflow - INFO - global_step/sec: 88.7534 -2022-05-01 21:20:46,570 - tensorflow - INFO - loss = 0.16715415, step = 2109000 (2.253 sec) -2022-05-01 21:20:48,816 - tensorflow - INFO - global_step/sec: 89.0504 -2022-05-01 21:20:48,816 - tensorflow - INFO - loss = 0.2685939, step = 2109200 (2.246 sec) -2022-05-01 21:20:51,065 - tensorflow - INFO - global_step/sec: 88.9068 -2022-05-01 21:20:51,066 - tensorflow - INFO - loss = 0.50303036, step = 2109400 (2.250 sec) -2022-05-01 21:20:53,315 - tensorflow - INFO - global_step/sec: 88.8834 -2022-05-01 21:20:53,316 - tensorflow - INFO - loss = 0.015402426, step = 2109600 (2.250 sec) -2022-05-01 21:20:55,571 - tensorflow - INFO - global_step/sec: 88.6581 -2022-05-01 21:20:55,572 - tensorflow - INFO - loss = 0.019315388, step = 2109800 (2.256 sec) -2022-05-01 21:20:57,832 - tensorflow - INFO - global_step/sec: 88.4478 -2022-05-01 21:20:57,833 - tensorflow - INFO - loss = 0.02295776, step = 2110000 (2.261 sec) -2022-05-01 21:21:00,090 - tensorflow - INFO - global_step/sec: 88.5985 -2022-05-01 21:21:00,090 - tensorflow - INFO - loss = 0.006488925, step = 2110200 (2.257 sec) -2022-05-01 21:21:02,341 - tensorflow - INFO - global_step/sec: 88.8574 -2022-05-01 21:21:02,341 - tensorflow - INFO - loss = 0.021128904, step = 2110400 (2.251 sec) -2022-05-01 21:21:04,590 - tensorflow - INFO - global_step/sec: 88.9032 -2022-05-01 21:21:04,591 - tensorflow - INFO - loss = 2.7757092, step = 2110600 (2.250 sec) -2022-05-01 21:21:06,843 - tensorflow - INFO - global_step/sec: 88.7744 -2022-05-01 21:21:06,844 - tensorflow - INFO - loss = 0.04770029, step = 2110800 (2.253 sec) -2022-05-01 21:21:09,096 - tensorflow - INFO - global_step/sec: 88.784 -2022-05-01 21:21:09,096 - tensorflow - INFO - loss = 0.26345018, step = 2111000 (2.253 sec) -2022-05-01 21:21:11,358 - tensorflow - INFO - global_step/sec: 88.4278 -2022-05-01 21:21:11,358 - tensorflow - INFO - loss = 0.006933829, step = 2111200 (2.262 sec) -2022-05-01 21:21:13,614 - tensorflow - INFO - global_step/sec: 88.6263 -2022-05-01 21:21:13,615 - tensorflow - INFO - loss = 0.03251392, step = 2111400 (2.257 sec) -2022-05-01 21:21:15,865 - tensorflow - INFO - global_step/sec: 88.8368 -2022-05-01 21:21:15,866 - tensorflow - INFO - loss = 0.0056138346, step = 2111600 (2.251 sec) -2022-05-01 21:21:18,114 - tensorflow - INFO - global_step/sec: 88.9652 -2022-05-01 21:21:18,114 - tensorflow - INFO - loss = 0.047357827, step = 2111800 (2.248 sec) -2022-05-01 21:21:20,365 - tensorflow - INFO - global_step/sec: 88.8384 -2022-05-01 21:21:20,365 - tensorflow - INFO - loss = 0.018748086, step = 2112000 (2.251 sec) -2022-05-01 21:21:22,621 - tensorflow - INFO - global_step/sec: 88.6269 -2022-05-01 21:21:22,622 - tensorflow - INFO - loss = 0.04558868, step = 2112200 (2.257 sec) -2022-05-01 21:21:24,885 - tensorflow - INFO - global_step/sec: 88.3769 -2022-05-01 21:21:24,885 - tensorflow - INFO - loss = 0.010202223, step = 2112400 (2.263 sec) -2022-05-01 21:21:27,139 - tensorflow - INFO - global_step/sec: 88.7074 -2022-05-01 21:21:27,140 - tensorflow - INFO - loss = 0.017460253, step = 2112600 (2.255 sec) -2022-05-01 21:21:29,397 - tensorflow - INFO - global_step/sec: 88.581 -2022-05-01 21:21:29,397 - tensorflow - INFO - loss = 0.012965627, step = 2112800 (2.258 sec) -2022-05-01 21:21:31,655 - tensorflow - INFO - global_step/sec: 88.5678 -2022-05-01 21:21:31,656 - tensorflow - INFO - loss = 0.0047509475, step = 2113000 (2.258 sec) -2022-05-01 21:21:33,915 - tensorflow - INFO - global_step/sec: 88.4957 -2022-05-01 21:21:33,915 - tensorflow - INFO - loss = 0.018873725, step = 2113200 (2.260 sec) -2022-05-01 21:21:36,166 - tensorflow - INFO - global_step/sec: 88.8707 -2022-05-01 21:21:36,166 - tensorflow - INFO - loss = 0.036171153, step = 2113400 (2.251 sec) -2022-05-01 21:21:38,424 - tensorflow - INFO - global_step/sec: 88.54 -2022-05-01 21:21:38,425 - tensorflow - INFO - loss = 0.031342074, step = 2113600 (2.259 sec) -2022-05-01 21:21:40,670 - tensorflow - INFO - global_step/sec: 89.0725 -2022-05-01 21:21:40,670 - tensorflow - INFO - loss = 0.076487854, step = 2113800 (2.245 sec) -2022-05-01 21:21:42,922 - tensorflow - INFO - global_step/sec: 88.8055 -2022-05-01 21:21:42,922 - tensorflow - INFO - loss = 0.008787336, step = 2114000 (2.252 sec) -2022-05-01 21:21:45,178 - tensorflow - INFO - global_step/sec: 88.659 -2022-05-01 21:21:45,178 - tensorflow - INFO - loss = 0.07260836, step = 2114200 (2.256 sec) -2022-05-01 21:21:47,429 - tensorflow - INFO - global_step/sec: 88.8457 -2022-05-01 21:21:47,429 - tensorflow - INFO - loss = 0.0040681423, step = 2114400 (2.251 sec) -2022-05-01 21:21:49,683 - tensorflow - INFO - global_step/sec: 88.7175 -2022-05-01 21:21:49,684 - tensorflow - INFO - loss = 0.015764127, step = 2114600 (2.254 sec) -2022-05-01 21:21:51,940 - tensorflow - INFO - global_step/sec: 88.6369 -2022-05-01 21:21:51,940 - tensorflow - INFO - loss = 0.021431446, step = 2114800 (2.256 sec) -2022-05-01 21:21:54,189 - tensorflow - INFO - global_step/sec: 88.9288 -2022-05-01 21:21:54,189 - tensorflow - 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INFO - global_step/sec: 88.4057 -2022-05-01 21:23:46,972 - tensorflow - INFO - loss = 0.0226475, step = 2125000 (2.263 sec) -2022-05-01 21:23:49,231 - tensorflow - INFO - global_step/sec: 88.4982 -2022-05-01 21:23:49,231 - tensorflow - INFO - loss = 0.005193279, step = 2125200 (2.260 sec) -2022-05-01 21:23:51,497 - tensorflow - INFO - global_step/sec: 88.2605 -2022-05-01 21:23:51,497 - tensorflow - INFO - loss = 0.0506152, step = 2125400 (2.266 sec) -2022-05-01 21:23:53,758 - tensorflow - INFO - global_step/sec: 88.4408 -2022-05-01 21:23:53,759 - tensorflow - INFO - loss = 0.07199786, step = 2125600 (2.261 sec) -2022-05-01 21:23:56,023 - tensorflow - INFO - global_step/sec: 88.3033 -2022-05-01 21:23:56,024 - tensorflow - INFO - loss = 0.008661807, step = 2125800 (2.265 sec) -2022-05-01 21:23:58,289 - tensorflow - INFO - global_step/sec: 88.2882 -2022-05-01 21:23:58,289 - tensorflow - INFO - loss = 0.025556296, step = 2126000 (2.265 sec) -2022-05-01 21:24:00,542 - tensorflow - INFO - global_step/sec: 88.7625 -2022-05-01 21:24:00,542 - 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tensorflow - INFO - global_step/sec: 88.8146 -2022-05-01 21:24:43,362 - tensorflow - INFO - loss = 0.016302548, step = 2130000 (2.252 sec) -2022-05-01 21:24:45,612 - tensorflow - INFO - global_step/sec: 88.8898 -2022-05-01 21:24:45,612 - tensorflow - INFO - loss = 0.019901492, step = 2130200 (2.250 sec) -2022-05-01 21:24:47,871 - tensorflow - INFO - global_step/sec: 88.5235 -2022-05-01 21:24:47,872 - tensorflow - INFO - loss = 0.016128661, step = 2130400 (2.259 sec) -2022-05-01 21:24:50,126 - tensorflow - INFO - global_step/sec: 88.6914 -2022-05-01 21:24:50,127 - tensorflow - INFO - loss = 0.005283707, step = 2130600 (2.255 sec) -2022-05-01 21:24:52,385 - tensorflow - INFO - global_step/sec: 88.5514 -2022-05-01 21:24:52,385 - tensorflow - INFO - loss = 0.098066896, step = 2130800 (2.259 sec) -2022-05-01 21:24:54,635 - tensorflow - INFO - global_step/sec: 88.8872 -2022-05-01 21:24:54,635 - tensorflow - INFO - loss = 0.068171926, step = 2131000 (2.250 sec) -2022-05-01 21:24:56,894 - tensorflow - INFO - global_step/sec: 88.5439 -2022-05-01 21:24:56,894 - tensorflow - INFO - loss = 0.032036074, step = 2131200 (2.259 sec) -2022-05-01 21:24:59,148 - tensorflow - INFO - global_step/sec: 88.7262 -2022-05-01 21:24:59,148 - tensorflow - INFO - loss = 1.1063821, step = 2131400 (2.254 sec) -2022-05-01 21:25:01,405 - tensorflow - INFO - global_step/sec: 88.6084 -2022-05-01 21:25:01,405 - tensorflow - INFO - loss = 0.18810025, step = 2131600 (2.257 sec) -2022-05-01 21:25:03,659 - tensorflow - INFO - global_step/sec: 88.7325 -2022-05-01 21:25:03,659 - tensorflow - INFO - loss = 0.0030217841, step = 2131800 (2.254 sec) -2022-05-01 21:25:05,910 - tensorflow - INFO - global_step/sec: 88.8345 -2022-05-01 21:25:05,911 - tensorflow - INFO - loss = 0.013679769, step = 2132000 (2.251 sec) -2022-05-01 21:25:08,162 - tensorflow - INFO - global_step/sec: 88.8263 -2022-05-01 21:25:08,162 - tensorflow - INFO - loss = 0.009654799, step = 2132200 (2.252 sec) -2022-05-01 21:25:10,415 - tensorflow - INFO - 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INFO - global_step/sec: 88.6723 -2022-05-01 21:26:20,289 - tensorflow - INFO - loss = 0.01930915, step = 2138600 (2.255 sec) -2022-05-01 21:26:22,544 - tensorflow - INFO - global_step/sec: 88.668 -2022-05-01 21:26:22,545 - tensorflow - INFO - loss = 0.43397862, step = 2138800 (2.256 sec) -2022-05-01 21:26:24,796 - tensorflow - INFO - global_step/sec: 88.8055 -2022-05-01 21:26:24,797 - tensorflow - INFO - loss = 0.008880379, step = 2139000 (2.252 sec) -2022-05-01 21:26:27,054 - tensorflow - INFO - global_step/sec: 88.5911 -2022-05-01 21:26:27,054 - tensorflow - INFO - loss = 0.021737663, step = 2139200 (2.258 sec) -2022-05-01 21:26:29,313 - tensorflow - INFO - global_step/sec: 88.5524 -2022-05-01 21:26:29,313 - tensorflow - INFO - loss = 0.017832357, step = 2139400 (2.258 sec) -2022-05-01 21:26:31,573 - tensorflow - INFO - global_step/sec: 88.4781 -2022-05-01 21:26:31,573 - tensorflow - INFO - loss = 0.008692442, step = 2139600 (2.260 sec) -2022-05-01 21:26:33,827 - tensorflow - INFO - 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INFO - loss = 0.053463686, step = 2142200 (2.261 sec) -2022-05-01 21:27:03,140 - tensorflow - INFO - global_step/sec: 88.4919 -2022-05-01 21:27:03,141 - tensorflow - INFO - loss = 0.013493532, step = 2142400 (2.260 sec) -2022-05-01 21:27:05,396 - tensorflow - INFO - global_step/sec: 88.6661 -2022-05-01 21:27:05,396 - tensorflow - INFO - loss = 0.014751355, step = 2142600 (2.256 sec) -2022-05-01 21:27:07,659 - tensorflow - INFO - global_step/sec: 88.3626 -2022-05-01 21:27:07,660 - tensorflow - INFO - loss = 0.015253401, step = 2142800 (2.263 sec) -2022-05-01 21:27:09,917 - tensorflow - INFO - global_step/sec: 88.5915 -2022-05-01 21:27:09,917 - tensorflow - INFO - loss = 0.030651044, step = 2143000 (2.258 sec) -2022-05-01 21:27:12,177 - tensorflow - INFO - global_step/sec: 88.5046 -2022-05-01 21:27:12,178 - tensorflow - INFO - loss = 0.010999289, step = 2143200 (2.261 sec) -2022-05-01 21:27:14,435 - tensorflow - INFO - global_step/sec: 88.5682 -2022-05-01 21:27:14,435 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.4172 -2022-05-01 21:27:30,264 - tensorflow - INFO - loss = 0.007630715, step = 2144800 (2.262 sec) -2022-05-01 21:27:32,529 - tensorflow - INFO - global_step/sec: 88.3023 -2022-05-01 21:27:32,529 - tensorflow - INFO - loss = 0.02547647, step = 2145000 (2.265 sec) -2022-05-01 21:27:34,782 - tensorflow - INFO - global_step/sec: 88.7504 -2022-05-01 21:27:34,783 - tensorflow - INFO - loss = 0.03039402, step = 2145200 (2.253 sec) -2022-05-01 21:27:37,037 - tensorflow - INFO - global_step/sec: 88.6971 -2022-05-01 21:27:37,038 - tensorflow - INFO - loss = 0.010505091, step = 2145400 (2.255 sec) -2022-05-01 21:27:39,292 - tensorflow - INFO - global_step/sec: 88.6855 -2022-05-01 21:27:39,293 - tensorflow - INFO - loss = 0.018424356, step = 2145600 (2.255 sec) -2022-05-01 21:27:41,545 - tensorflow - INFO - global_step/sec: 88.7705 -2022-05-01 21:27:41,546 - tensorflow - INFO - loss = 0.009579858, step = 2145800 (2.253 sec) -2022-05-01 21:27:43,799 - tensorflow - INFO - global_step/sec: 88.74 -2022-05-01 21:27:43,800 - tensorflow - INFO - loss = 0.013186602, step = 2146000 (2.254 sec) -2022-05-01 21:27:46,051 - tensorflow - INFO - global_step/sec: 88.8089 -2022-05-01 21:27:46,052 - tensorflow - INFO - loss = 0.13267243, step = 2146200 (2.252 sec) -2022-05-01 21:27:48,306 - tensorflow - INFO - global_step/sec: 88.7174 -2022-05-01 21:27:48,306 - tensorflow - INFO - loss = 0.01090483, step = 2146400 (2.254 sec) -2022-05-01 21:27:50,558 - tensorflow - INFO - global_step/sec: 88.7801 -2022-05-01 21:27:50,559 - tensorflow - INFO - loss = 0.021057162, step = 2146600 (2.253 sec) -2022-05-01 21:27:52,810 - tensorflow - INFO - global_step/sec: 88.8242 -2022-05-01 21:27:52,810 - tensorflow - INFO - loss = 0.006197462, step = 2146800 (2.252 sec) -2022-05-01 21:27:55,063 - tensorflow - INFO - global_step/sec: 88.7603 -2022-05-01 21:27:55,064 - tensorflow - INFO - loss = 0.039617598, step = 2147000 (2.253 sec) -2022-05-01 21:27:57,320 - tensorflow - INFO - global_step/sec: 88.6118 -2022-05-01 21:27:57,321 - tensorflow - INFO - loss = 0.020207167, step = 2147200 (2.257 sec) -2022-05-01 21:27:59,574 - tensorflow - INFO - global_step/sec: 88.7511 -2022-05-01 21:27:59,574 - tensorflow - INFO - loss = 0.013427195, step = 2147400 (2.254 sec) -2022-05-01 21:28:01,833 - tensorflow - INFO - global_step/sec: 88.5078 -2022-05-01 21:28:01,834 - tensorflow - INFO - loss = 0.017240942, step = 2147600 (2.260 sec) -2022-05-01 21:28:04,088 - tensorflow - INFO - global_step/sec: 88.7135 -2022-05-01 21:28:04,088 - tensorflow - INFO - loss = 0.010838108, step = 2147800 (2.254 sec) -2022-05-01 21:28:06,344 - tensorflow - INFO - global_step/sec: 88.63 -2022-05-01 21:28:06,345 - tensorflow - INFO - loss = 0.015569428, step = 2148000 (2.257 sec) -2022-05-01 21:28:08,600 - tensorflow - INFO - global_step/sec: 88.6835 -2022-05-01 21:28:08,600 - tensorflow - INFO - loss = 0.010146435, step = 2148200 (2.255 sec) -2022-05-01 21:28:10,852 - tensorflow - INFO - global_step/sec: 88.7926 -2022-05-01 21:28:10,853 - tensorflow - 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tensorflow - INFO - global_step/sec: 88.647 -2022-05-01 21:28:40,153 - tensorflow - INFO - loss = 0.0049577425, step = 2151000 (2.256 sec) -2022-05-01 21:28:42,408 - tensorflow - INFO - global_step/sec: 88.6683 -2022-05-01 21:28:42,408 - tensorflow - INFO - loss = 0.009653422, step = 2151200 (2.256 sec) -2022-05-01 21:28:44,663 - tensorflow - INFO - global_step/sec: 88.6838 -2022-05-01 21:28:44,663 - tensorflow - INFO - loss = 0.0048831427, step = 2151400 (2.255 sec) -2022-05-01 21:28:46,914 - tensorflow - INFO - global_step/sec: 88.8404 -2022-05-01 21:28:46,915 - tensorflow - INFO - loss = 0.007938244, step = 2151600 (2.251 sec) -2022-05-01 21:28:49,167 - tensorflow - INFO - global_step/sec: 88.7756 -2022-05-01 21:28:49,167 - tensorflow - INFO - loss = 0.004820142, step = 2151800 (2.253 sec) -2022-05-01 21:28:51,424 - tensorflow - INFO - global_step/sec: 88.6287 -2022-05-01 21:28:51,424 - tensorflow - INFO - loss = 0.01035807, step = 2152000 (2.257 sec) -2022-05-01 21:28:53,680 - tensorflow - 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INFO - global_step/sec: 88.5602 -2022-05-01 21:31:27,024 - tensorflow - INFO - loss = 0.010295693, step = 2165800 (2.258 sec) -2022-05-01 21:31:29,280 - tensorflow - INFO - global_step/sec: 88.6396 -2022-05-01 21:31:29,281 - tensorflow - INFO - loss = 0.025834952, step = 2166000 (2.256 sec) -2022-05-01 21:31:31,541 - tensorflow - INFO - global_step/sec: 88.4723 -2022-05-01 21:31:31,541 - tensorflow - INFO - loss = 0.009997468, step = 2166200 (2.261 sec) -2022-05-01 21:31:33,800 - tensorflow - INFO - global_step/sec: 88.5112 -2022-05-01 21:31:33,801 - tensorflow - INFO - loss = 0.1724355, step = 2166400 (2.260 sec) -2022-05-01 21:31:36,060 - tensorflow - INFO - global_step/sec: 88.5197 -2022-05-01 21:31:36,060 - tensorflow - INFO - loss = 0.012560392, step = 2166600 (2.259 sec) -2022-05-01 21:31:38,314 - tensorflow - INFO - global_step/sec: 88.734 -2022-05-01 21:31:38,314 - tensorflow - INFO - loss = 0.013011185, step = 2166800 (2.254 sec) -2022-05-01 21:31:40,572 - tensorflow - INFO - 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loss = 0.015364771, step = 2191600 (2.261 sec) -2022-05-01 21:36:20,185 - tensorflow - INFO - global_step/sec: 88.6835 -2022-05-01 21:36:20,186 - tensorflow - INFO - loss = 0.13073684, step = 2191800 (2.255 sec) -2022-05-01 21:36:22,446 - tensorflow - INFO - global_step/sec: 88.4874 -2022-05-01 21:36:22,446 - tensorflow - INFO - loss = 0.05943086, step = 2192000 (2.260 sec) -2022-05-01 21:36:24,701 - tensorflow - INFO - global_step/sec: 88.6829 -2022-05-01 21:36:24,701 - tensorflow - INFO - loss = 0.010933407, step = 2192200 (2.255 sec) -2022-05-01 21:36:26,954 - tensorflow - INFO - global_step/sec: 88.7558 -2022-05-01 21:36:26,955 - tensorflow - INFO - loss = 0.005767065, step = 2192400 (2.253 sec) -2022-05-01 21:36:29,206 - tensorflow - INFO - global_step/sec: 88.8305 -2022-05-01 21:36:29,206 - tensorflow - INFO - loss = 0.07748546, step = 2192600 (2.251 sec) -2022-05-01 21:36:31,470 - tensorflow - INFO - global_step/sec: 88.3346 -2022-05-01 21:36:31,470 - tensorflow - INFO - loss = 0.030347891, step = 2192800 (2.264 sec) -2022-05-01 21:36:33,726 - tensorflow - INFO - global_step/sec: 88.6374 -2022-05-01 21:36:33,727 - tensorflow - INFO - loss = 0.0073875445, step = 2193000 (2.256 sec) -2022-05-01 21:36:35,984 - tensorflow - INFO - global_step/sec: 88.58 -2022-05-01 21:36:35,985 - tensorflow - INFO - loss = 0.020786371, step = 2193200 (2.258 sec) -2022-05-01 21:36:38,232 - tensorflow - INFO - global_step/sec: 88.9812 -2022-05-01 21:36:38,232 - tensorflow - INFO - loss = 0.047285732, step = 2193400 (2.248 sec) -2022-05-01 21:36:40,484 - tensorflow - INFO - global_step/sec: 88.8151 -2022-05-01 21:36:40,484 - tensorflow - INFO - loss = 0.014292076, step = 2193600 (2.252 sec) -2022-05-01 21:36:42,739 - tensorflow - INFO - global_step/sec: 88.6817 -2022-05-01 21:36:42,739 - tensorflow - INFO - loss = 0.025393961, step = 2193800 (2.255 sec) -2022-05-01 21:36:44,993 - tensorflow - INFO - global_step/sec: 88.7232 -2022-05-01 21:36:44,994 - tensorflow - INFO - loss = 0.023196246, step = 2194000 (2.254 sec) -2022-05-01 21:36:47,244 - tensorflow - INFO - global_step/sec: 88.8493 -2022-05-01 21:36:47,245 - tensorflow - INFO - loss = 0.016532872, step = 2194200 (2.251 sec) -2022-05-01 21:36:49,500 - tensorflow - INFO - global_step/sec: 88.6702 -2022-05-01 21:36:49,500 - tensorflow - INFO - loss = 0.005099818, step = 2194400 (2.256 sec) -2022-05-01 21:36:51,756 - tensorflow - INFO - global_step/sec: 88.6475 -2022-05-01 21:36:51,756 - tensorflow - INFO - loss = 0.014568393, step = 2194600 (2.256 sec) -2022-05-01 21:36:54,003 - tensorflow - INFO - global_step/sec: 89.0025 -2022-05-01 21:36:54,003 - tensorflow - INFO - loss = 0.00928764, step = 2194800 (2.247 sec) -2022-05-01 21:36:56,258 - tensorflow - INFO - global_step/sec: 88.6935 -2022-05-01 21:36:56,258 - tensorflow - INFO - loss = 0.0219753, step = 2195000 (2.255 sec) -2022-05-01 21:36:58,511 - tensorflow - INFO - global_step/sec: 88.779 -2022-05-01 21:36:58,511 - tensorflow - INFO - loss = 1.0612338, step = 2195200 (2.253 sec) -2022-05-01 21:37:00,761 - tensorflow - INFO - global_step/sec: 88.8767 -2022-05-01 21:37:00,761 - tensorflow - INFO - loss = 0.31271794, step = 2195400 (2.250 sec) -2022-05-01 21:37:03,013 - tensorflow - INFO - global_step/sec: 88.8259 -2022-05-01 21:37:03,013 - tensorflow - INFO - loss = 0.07092461, step = 2195600 (2.252 sec) -2022-05-01 21:37:05,266 - tensorflow - INFO - global_step/sec: 88.766 -2022-05-01 21:37:05,266 - tensorflow - INFO - loss = 0.02657806, step = 2195800 (2.253 sec) -2022-05-01 21:37:07,522 - tensorflow - INFO - global_step/sec: 88.624 -2022-05-01 21:37:07,523 - tensorflow - INFO - loss = 0.012601734, step = 2196000 (2.257 sec) -2022-05-01 21:37:09,780 - tensorflow - INFO - global_step/sec: 88.5875 -2022-05-01 21:37:09,780 - tensorflow - INFO - loss = 0.010346203, step = 2196200 (2.258 sec) -2022-05-01 21:37:12,029 - tensorflow - INFO - global_step/sec: 88.94 -2022-05-01 21:37:12,029 - tensorflow - INFO - loss = 0.0631614, step = 2196400 (2.249 sec) -2022-05-01 21:37:14,278 - tensorflow - INFO - global_step/sec: 88.9297 -2022-05-01 21:37:14,278 - tensorflow - INFO - loss = 0.02143442, step = 2196600 (2.249 sec) -2022-05-01 21:37:16,528 - tensorflow - INFO - global_step/sec: 88.8974 -2022-05-01 21:37:16,528 - tensorflow - INFO - loss = 0.013591796, step = 2196800 (2.250 sec) -2022-05-01 21:37:18,783 - tensorflow - INFO - global_step/sec: 88.6885 -2022-05-01 21:37:18,783 - tensorflow - INFO - loss = 0.009670453, step = 2197000 (2.255 sec) -2022-05-01 21:37:21,040 - tensorflow - INFO - global_step/sec: 88.618 -2022-05-01 21:37:21,040 - tensorflow - INFO - loss = 0.012457259, step = 2197200 (2.257 sec) -2022-05-01 21:37:23,288 - tensorflow - INFO - global_step/sec: 88.938 -2022-05-01 21:37:23,289 - tensorflow - INFO - loss = 0.026262462, step = 2197400 (2.249 sec) -2022-05-01 21:37:25,540 - tensorflow - INFO - global_step/sec: 88.8215 -2022-05-01 21:37:25,540 - tensorflow - INFO - loss = 0.0077347336, step = 2197600 (2.252 sec) -2022-05-01 21:37:27,791 - tensorflow - INFO - global_step/sec: 88.8591 -2022-05-01 21:37:27,791 - tensorflow - INFO - loss = 0.01425202, step = 2197800 (2.251 sec) -2022-05-01 21:37:30,051 - tensorflow - INFO - global_step/sec: 88.4816 -2022-05-01 21:37:30,052 - tensorflow - INFO - loss = 0.07255714, step = 2198000 (2.260 sec) -2022-05-01 21:37:32,311 - tensorflow - INFO - global_step/sec: 88.4938 -2022-05-01 21:37:32,312 - tensorflow - INFO - loss = 0.20931415, step = 2198200 (2.260 sec) -2022-05-01 21:37:34,571 - tensorflow - INFO - global_step/sec: 88.4917 -2022-05-01 21:37:34,572 - tensorflow - INFO - loss = 0.037890192, step = 2198400 (2.260 sec) -2022-05-01 21:37:36,839 - tensorflow - INFO - global_step/sec: 88.1986 -2022-05-01 21:37:36,839 - tensorflow - INFO - loss = 0.037103213, step = 2198600 (2.268 sec) -2022-05-01 21:37:39,100 - tensorflow - INFO - global_step/sec: 88.4457 -2022-05-01 21:37:39,101 - tensorflow - INFO - loss = 0.021963807, step = 2198800 (2.261 sec) -2022-05-01 21:37:41,359 - tensorflow - INFO - global_step/sec: 88.5571 -2022-05-01 21:37:41,359 - tensorflow - INFO - loss = 0.017868139, step = 2199000 (2.258 sec) -2022-05-01 21:37:43,620 - tensorflow - INFO - global_step/sec: 88.4404 -2022-05-01 21:37:43,620 - tensorflow - INFO - loss = 0.08600532, step = 2199200 (2.261 sec) -2022-05-01 21:37:45,884 - tensorflow - INFO - global_step/sec: 88.3505 -2022-05-01 21:37:45,884 - tensorflow - INFO - loss = 0.014971986, step = 2199400 (2.264 sec) -2022-05-01 21:37:48,149 - tensorflow - INFO - global_step/sec: 88.2993 -2022-05-01 21:37:48,149 - tensorflow - INFO - loss = 0.009885784, step = 2199600 (2.265 sec) -2022-05-01 21:37:50,409 - tensorflow - INFO - global_step/sec: 88.4647 -2022-05-01 21:37:50,410 - tensorflow - INFO - loss = 0.16105652, step = 2199800 (2.261 sec) -2022-05-01 21:37:52,664 - tensorflow - INFO - Saving checkpoints for 2200000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. -2022-05-01 21:37:52,680 - tensorflow - INFO - model.ckpt-2200000 is not in all_model_checkpoint_paths. Manually adding it. -2022-05-01 21:37:53,206 - tensorflow - INFO - Calling model_fn. -2022-05-01 21:37:53,334 - tensorflow - INFO - Done calling model_fn. -2022-05-01 21:37:53,349 - tensorflow - INFO - Starting evaluation at 2022-05-01T21:37:53Z -2022-05-01 21:37:53,417 - tensorflow - INFO - Graph was finalized. -2022-05-01 21:37:53,421 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-2200000 -2022-05-01 21:37:53,703 - tensorflow - INFO - Running local_init_op. -2022-05-01 21:37:53,812 - tensorflow - INFO - Done running local_init_op. -2022-05-01 21:38:04,130 - tensorflow - INFO - Evaluation [3990/39900] -2022-05-01 21:38:13,663 - tensorflow - INFO - Evaluation [7980/39900] -2022-05-01 21:38:23,220 - tensorflow - INFO - Evaluation [11970/39900] -2022-05-01 21:38:32,772 - tensorflow - INFO - Evaluation [15960/39900] -2022-05-01 21:38:42,320 - tensorflow - INFO - Evaluation [19950/39900] -2022-05-01 21:38:51,892 - tensorflow - INFO - Evaluation [23940/39900] -2022-05-01 21:39:01,423 - tensorflow - INFO - Evaluation [27930/39900] -2022-05-01 21:39:10,981 - tensorflow - INFO - Evaluation [31920/39900] -2022-05-01 21:39:20,545 - tensorflow - INFO - Evaluation [35910/39900] -2022-05-01 21:39:30,119 - tensorflow - INFO - Evaluation [39900/39900] -2022-05-01 21:39:30,258 - tensorflow - INFO - Finished evaluation at 2022-05-01-21:39:30 -2022-05-01 21:39:30,259 - tensorflow - INFO - Saving dict for global step 2200000: global_step = 2200000, loss = 0.015515498, mean_degree_err = 3.1324177 -2022-05-01 21:39:30,259 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 2200000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-2200000 -2022-05-01 21:39:30,273 - tensorflow - INFO - global_step/sec: 2.00273 -2022-05-01 21:39:30,274 - tensorflow - INFO - loss = 0.016591474, step = 2200000 (99.864 sec) -2022-05-01 21:39:32,548 - tensorflow - INFO - global_step/sec: 87.916 -2022-05-01 21:39:32,548 - tensorflow - INFO - loss = 0.04369278, step = 2200200 (2.274 sec) -2022-05-01 21:39:34,804 - tensorflow - INFO - global_step/sec: 88.6638 -2022-05-01 21:39:34,804 - tensorflow - INFO - loss = 0.035334364, step = 2200400 (2.256 sec) -2022-05-01 21:39:37,057 - tensorflow - INFO - global_step/sec: 88.7553 -2022-05-01 21:39:37,057 - tensorflow - INFO - loss = 0.0069875494, step = 2200600 (2.253 sec) -2022-05-01 21:39:39,309 - tensorflow - INFO - global_step/sec: 88.8105 -2022-05-01 21:39:39,309 - tensorflow - INFO - loss = 0.041061778, step = 2200800 (2.252 sec) -2022-05-01 21:39:41,567 - tensorflow - INFO - global_step/sec: 88.579 -2022-05-01 21:39:41,567 - tensorflow - INFO - loss = 0.011567708, step = 2201000 (2.258 sec) -2022-05-01 21:39:43,822 - tensorflow - INFO - global_step/sec: 88.6912 -2022-05-01 21:39:43,822 - tensorflow - INFO - loss = 0.014715085, step = 2201200 (2.255 sec) -2022-05-01 21:39:46,073 - tensorflow - INFO - global_step/sec: 88.8445 -2022-05-01 21:39:46,073 - tensorflow - INFO - loss = 0.009093689, step = 2201400 (2.251 sec) -2022-05-01 21:39:48,322 - tensorflow - INFO - global_step/sec: 88.9168 -2022-05-01 21:39:48,323 - tensorflow - INFO - loss = 1.0612983, step = 2201600 (2.249 sec) -2022-05-01 21:39:50,575 - tensorflow - INFO - global_step/sec: 88.7781 -2022-05-01 21:39:50,575 - tensorflow - INFO - loss = 0.009138137, step = 2201800 (2.253 sec) -2022-05-01 21:39:52,827 - tensorflow - INFO - global_step/sec: 88.8019 -2022-05-01 21:39:52,828 - tensorflow - INFO - loss = 0.010925783, step = 2202000 (2.252 sec) -2022-05-01 21:39:55,085 - tensorflow - INFO - global_step/sec: 88.5793 -2022-05-01 21:39:55,086 - tensorflow - INFO - loss = 0.014196128, step = 2202200 (2.258 sec) -2022-05-01 21:39:57,344 - tensorflow - INFO - global_step/sec: 88.5409 -2022-05-01 21:39:57,344 - tensorflow - INFO - loss = 0.1022256, step = 2202400 (2.259 sec) -2022-05-01 21:39:59,595 - tensorflow - INFO - global_step/sec: 88.8293 -2022-05-01 21:39:59,596 - tensorflow - INFO - loss = 0.12285152, step = 2202600 (2.252 sec) -2022-05-01 21:40:01,849 - tensorflow - INFO - global_step/sec: 88.7609 -2022-05-01 21:40:01,849 - tensorflow - INFO - loss = 0.00926359, step = 2202800 (2.253 sec) -2022-05-01 21:40:04,101 - tensorflow - INFO - global_step/sec: 88.7822 -2022-05-01 21:40:04,102 - tensorflow - INFO - loss = 0.15393083, step = 2203000 (2.253 sec) -2022-05-01 21:40:06,353 - tensorflow - INFO - global_step/sec: 88.8288 -2022-05-01 21:40:06,353 - tensorflow - INFO - loss = 0.01870555, step = 2203200 (2.252 sec) -2022-05-01 21:40:08,613 - tensorflow - INFO - global_step/sec: 88.5019 -2022-05-01 21:40:08,613 - tensorflow - INFO - loss = 0.020982817, step = 2203400 (2.260 sec) -2022-05-01 21:40:10,864 - tensorflow - INFO - global_step/sec: 88.8411 -2022-05-01 21:40:10,864 - tensorflow - INFO - loss = 0.03838037, step = 2203600 (2.251 sec) -2022-05-01 21:40:13,117 - tensorflow - INFO - global_step/sec: 88.769 -2022-05-01 21:40:13,117 - tensorflow - INFO - loss = 0.019582264, step = 2203800 (2.253 sec) -2022-05-01 21:40:15,369 - tensorflow - INFO - global_step/sec: 88.8261 -2022-05-01 21:40:15,369 - tensorflow - INFO - loss = 0.0038917055, step = 2204000 (2.252 sec) -2022-05-01 21:40:17,623 - tensorflow - INFO - global_step/sec: 88.7092 -2022-05-01 21:40:17,624 - tensorflow - INFO - loss = 0.012383769, step = 2204200 (2.255 sec) -2022-05-01 21:40:19,879 - tensorflow - INFO - global_step/sec: 88.6498 -2022-05-01 21:40:19,880 - tensorflow - INFO - loss = 0.006668681, step = 2204400 (2.256 sec) -2022-05-01 21:40:22,129 - tensorflow - INFO - global_step/sec: 88.9148 -2022-05-01 21:40:22,129 - tensorflow - INFO - loss = 0.008942205, step = 2204600 (2.249 sec) -2022-05-01 21:40:24,380 - tensorflow - INFO - global_step/sec: 88.8245 -2022-05-01 21:40:24,381 - tensorflow - INFO - loss = 0.10828018, step = 2204800 (2.252 sec) -2022-05-01 21:40:26,635 - tensorflow - INFO - global_step/sec: 88.7117 -2022-05-01 21:40:26,635 - tensorflow - INFO - loss = 0.026557386, step = 2205000 (2.255 sec) -2022-05-01 21:40:28,884 - tensorflow - INFO - global_step/sec: 88.9343 -2022-05-01 21:40:28,884 - tensorflow - INFO - loss = 0.009144509, step = 2205200 (2.249 sec) -2022-05-01 21:40:31,141 - tensorflow - INFO - global_step/sec: 88.6109 -2022-05-01 21:40:31,141 - tensorflow - INFO - loss = 0.018995715, step = 2205400 (2.257 sec) -2022-05-01 21:40:33,394 - tensorflow - INFO - global_step/sec: 88.7683 -2022-05-01 21:40:33,394 - tensorflow - INFO - loss = 0.02026128, step = 2205600 (2.253 sec) -2022-05-01 21:40:35,649 - tensorflow - INFO - global_step/sec: 88.6789 -2022-05-01 21:40:35,649 - tensorflow - INFO - loss = 0.052717946, step = 2205800 (2.255 sec) -2022-05-01 21:40:37,899 - tensorflow - INFO - global_step/sec: 88.9058 -2022-05-01 21:40:37,899 - tensorflow - INFO - loss = 0.11664444, step = 2206000 (2.250 sec) -2022-05-01 21:40:40,151 - tensorflow - INFO - global_step/sec: 88.7921 -2022-05-01 21:40:40,151 - tensorflow - INFO - loss = 0.0046029845, step = 2206200 (2.252 sec) -2022-05-01 21:40:42,401 - tensorflow - INFO - global_step/sec: 88.8739 -2022-05-01 21:40:42,402 - tensorflow - INFO - loss = 0.04805688, step = 2206400 (2.250 sec) -2022-05-01 21:40:44,653 - tensorflow - INFO - global_step/sec: 88.8337 -2022-05-01 21:40:44,653 - tensorflow - INFO - loss = 0.006403135, step = 2206600 (2.251 sec) -2022-05-01 21:40:46,909 - tensorflow - INFO - global_step/sec: 88.6538 -2022-05-01 21:40:46,909 - tensorflow - INFO - loss = 0.11942297, step = 2206800 (2.256 sec) -2022-05-01 21:40:49,163 - tensorflow - INFO - global_step/sec: 88.7151 -2022-05-01 21:40:49,164 - tensorflow - INFO - loss = 18.514679, step = 2207000 (2.254 sec) -2022-05-01 21:40:51,414 - tensorflow - INFO - global_step/sec: 88.8687 -2022-05-01 21:40:51,414 - tensorflow - INFO - loss = 0.026035413, step = 2207200 (2.251 sec) -2022-05-01 21:40:53,661 - tensorflow - INFO - global_step/sec: 88.9766 -2022-05-01 21:40:53,662 - tensorflow - INFO - loss = 0.9963044, step = 2207400 (2.248 sec) -2022-05-01 21:40:55,918 - tensorflow - INFO - global_step/sec: 88.6148 -2022-05-01 21:40:55,919 - tensorflow - INFO - loss = 0.063365005, step = 2207600 (2.257 sec) -2022-05-01 21:40:58,179 - tensorflow - INFO - global_step/sec: 88.4572 -2022-05-01 21:40:58,180 - tensorflow - INFO - loss = 0.17814486, step = 2207800 (2.261 sec) -2022-05-01 21:41:00,433 - tensorflow - INFO - global_step/sec: 88.7593 -2022-05-01 21:41:00,433 - tensorflow - INFO - loss = 0.010942705, step = 2208000 (2.253 sec) -2022-05-01 21:41:02,690 - tensorflow - INFO - global_step/sec: 88.5825 -2022-05-01 21:41:02,691 - tensorflow - INFO - loss = 0.014380009, step = 2208200 (2.258 sec) -2022-05-01 21:41:04,950 - tensorflow - INFO - global_step/sec: 88.5144 -2022-05-01 21:41:04,950 - tensorflow - INFO - loss = 0.0478812, step = 2208400 (2.259 sec) -2022-05-01 21:41:07,209 - tensorflow - INFO - global_step/sec: 88.5299 -2022-05-01 21:41:07,210 - tensorflow - INFO - loss = 0.07858298, step = 2208600 (2.259 sec) -2022-05-01 21:41:09,468 - tensorflow - INFO - global_step/sec: 88.5227 -2022-05-01 21:41:09,469 - tensorflow - INFO - loss = 0.021009544, step = 2208800 (2.259 sec) -2022-05-01 21:41:11,730 - tensorflow - INFO - global_step/sec: 88.417 -2022-05-01 21:41:11,731 - tensorflow - INFO - loss = 0.020054996, step = 2209000 (2.262 sec) -2022-05-01 21:41:13,986 - tensorflow - INFO - global_step/sec: 88.6608 -2022-05-01 21:41:13,987 - tensorflow - INFO - loss = 0.008389406, step = 2209200 (2.256 sec) -2022-05-01 21:41:16,247 - tensorflow - INFO - global_step/sec: 88.4481 -2022-05-01 21:41:16,248 - tensorflow - INFO - loss = 0.013282923, step = 2209400 (2.261 sec) -2022-05-01 21:41:18,505 - tensorflow - INFO - global_step/sec: 88.5847 -2022-05-01 21:41:18,506 - tensorflow - INFO - loss = 0.010178466, step = 2209600 (2.258 sec) -2022-05-01 21:41:20,769 - tensorflow - INFO - global_step/sec: 88.3552 -2022-05-01 21:41:20,769 - tensorflow - INFO - loss = 0.013363117, step = 2209800 (2.264 sec) -2022-05-01 21:41:23,029 - tensorflow - INFO - global_step/sec: 88.4893 -2022-05-01 21:41:23,029 - tensorflow - INFO - loss = 0.035251763, step = 2210000 (2.260 sec) -2022-05-01 21:41:25,289 - tensorflow - INFO - global_step/sec: 88.4995 -2022-05-01 21:41:25,289 - tensorflow - INFO - loss = 0.0061641047, step = 2210200 (2.260 sec) -2022-05-01 21:41:27,552 - tensorflow - INFO - global_step/sec: 88.3811 -2022-05-01 21:41:27,552 - tensorflow - INFO - loss = 0.045407366, step = 2210400 (2.263 sec) -2022-05-01 21:41:29,817 - tensorflow - INFO - global_step/sec: 88.3095 -2022-05-01 21:41:29,817 - tensorflow - INFO - loss = 0.023515439, step = 2210600 (2.265 sec) -2022-05-01 21:41:32,083 - tensorflow - INFO - global_step/sec: 88.2607 -2022-05-01 21:41:32,083 - tensorflow - INFO - loss = 0.009892464, step = 2210800 (2.266 sec) -2022-05-01 21:41:34,340 - tensorflow - INFO - global_step/sec: 88.6008 -2022-05-01 21:41:34,340 - tensorflow - INFO - loss = 0.055669792, step = 2211000 (2.257 sec) -2022-05-01 21:41:36,601 - tensorflow - INFO - global_step/sec: 88.4679 -2022-05-01 21:41:36,601 - tensorflow - INFO - loss = 0.005568575, step = 2211200 (2.261 sec) -2022-05-01 21:41:38,861 - tensorflow - INFO - 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INFO - global_step/sec: 88.606 -2022-05-01 21:49:32,387 - tensorflow - INFO - loss = 0.032762483, step = 2253400 (2.257 sec) -2022-05-01 21:49:34,643 - tensorflow - INFO - global_step/sec: 88.6362 -2022-05-01 21:49:34,644 - tensorflow - INFO - loss = 0.07266872, step = 2253600 (2.256 sec) -2022-05-01 21:49:36,896 - tensorflow - INFO - global_step/sec: 88.7893 -2022-05-01 21:49:36,896 - tensorflow - INFO - loss = 0.016507518, step = 2253800 (2.253 sec) -2022-05-01 21:49:39,154 - tensorflow - INFO - global_step/sec: 88.5677 -2022-05-01 21:49:39,154 - tensorflow - INFO - loss = 0.018191723, step = 2254000 (2.258 sec) -2022-05-01 21:49:41,411 - tensorflow - INFO - global_step/sec: 88.6125 -2022-05-01 21:49:41,411 - tensorflow - INFO - loss = 0.0047383253, step = 2254200 (2.257 sec) -2022-05-01 21:49:43,664 - tensorflow - INFO - global_step/sec: 88.7601 -2022-05-01 21:49:43,665 - tensorflow - INFO - loss = 0.013006965, step = 2254400 (2.253 sec) -2022-05-01 21:49:45,916 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.77 -2022-05-01 21:50:28,749 - tensorflow - INFO - loss = 0.022165522, step = 2258400 (2.253 sec) -2022-05-01 21:50:31,006 - tensorflow - INFO - global_step/sec: 88.589 -2022-05-01 21:50:31,006 - tensorflow - INFO - loss = 0.02005813, step = 2258600 (2.258 sec) -2022-05-01 21:50:33,257 - tensorflow - INFO - global_step/sec: 88.8548 -2022-05-01 21:50:33,257 - tensorflow - INFO - loss = 0.0063831746, step = 2258800 (2.251 sec) -2022-05-01 21:50:35,513 - tensorflow - INFO - global_step/sec: 88.6299 -2022-05-01 21:50:35,514 - tensorflow - INFO - loss = 0.005107364, step = 2259000 (2.257 sec) -2022-05-01 21:50:37,767 - tensorflow - INFO - global_step/sec: 88.7591 -2022-05-01 21:50:37,767 - tensorflow - INFO - loss = 0.046872757, step = 2259200 (2.253 sec) -2022-05-01 21:50:40,022 - tensorflow - INFO - global_step/sec: 88.6742 -2022-05-01 21:50:40,023 - tensorflow - INFO - loss = 0.01797883, step = 2259400 (2.256 sec) -2022-05-01 21:50:42,274 - tensorflow - INFO - global_step/sec: 88.8139 -2022-05-01 21:50:42,274 - tensorflow - INFO - loss = 0.025002211, step = 2259600 (2.252 sec) -2022-05-01 21:50:44,526 - tensorflow - INFO - global_step/sec: 88.8089 -2022-05-01 21:50:44,526 - tensorflow - INFO - loss = 0.021525549, step = 2259800 (2.252 sec) -2022-05-01 21:50:46,780 - tensorflow - INFO - global_step/sec: 88.7497 -2022-05-01 21:50:46,780 - tensorflow - INFO - loss = 0.009225631, step = 2260000 (2.254 sec) -2022-05-01 21:50:49,037 - tensorflow - INFO - global_step/sec: 88.6101 -2022-05-01 21:50:49,037 - tensorflow - INFO - loss = 0.022248156, step = 2260200 (2.257 sec) -2022-05-01 21:50:51,292 - tensorflow - INFO - global_step/sec: 88.6772 -2022-05-01 21:50:51,292 - tensorflow - INFO - loss = 0.0394319, step = 2260400 (2.255 sec) -2022-05-01 21:50:53,545 - tensorflow - INFO - global_step/sec: 88.7701 -2022-05-01 21:50:53,545 - tensorflow - INFO - loss = 0.03016043, step = 2260600 (2.253 sec) -2022-05-01 21:50:55,802 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.011728222, step = 2262000 (2.251 sec) -2022-05-01 21:51:11,581 - tensorflow - INFO - global_step/sec: 88.717 -2022-05-01 21:51:11,582 - tensorflow - INFO - loss = 0.17968774, step = 2262200 (2.254 sec) -2022-05-01 21:51:13,838 - tensorflow - INFO - global_step/sec: 88.6033 -2022-05-01 21:51:13,839 - tensorflow - INFO - loss = 0.022172717, step = 2262400 (2.257 sec) -2022-05-01 21:51:16,088 - tensorflow - INFO - global_step/sec: 88.9105 -2022-05-01 21:51:16,088 - tensorflow - INFO - loss = 0.012855254, step = 2262600 (2.249 sec) -2022-05-01 21:51:18,346 - tensorflow - INFO - global_step/sec: 88.5533 -2022-05-01 21:51:18,347 - tensorflow - INFO - loss = 0.029036885, step = 2262800 (2.259 sec) -2022-05-01 21:51:20,603 - tensorflow - INFO - global_step/sec: 88.6229 -2022-05-01 21:51:20,603 - tensorflow - INFO - loss = 0.017841196, step = 2263000 (2.257 sec) -2022-05-01 21:51:22,860 - tensorflow - INFO - global_step/sec: 88.623 -2022-05-01 21:51:22,860 - tensorflow - INFO - loss = 0.01587489, step = 2263200 (2.257 sec) -2022-05-01 21:51:25,119 - tensorflow - INFO - global_step/sec: 88.5378 -2022-05-01 21:51:25,119 - tensorflow - INFO - loss = 0.007913903, step = 2263400 (2.259 sec) -2022-05-01 21:51:27,372 - tensorflow - INFO - global_step/sec: 88.7433 -2022-05-01 21:51:27,373 - tensorflow - INFO - loss = 0.012880603, step = 2263600 (2.254 sec) -2022-05-01 21:51:29,632 - tensorflow - INFO - global_step/sec: 88.5223 -2022-05-01 21:51:29,632 - tensorflow - INFO - loss = 0.003497982, step = 2263800 (2.259 sec) -2022-05-01 21:51:31,894 - tensorflow - INFO - global_step/sec: 88.4241 -2022-05-01 21:51:31,894 - tensorflow - INFO - loss = 0.022918161, step = 2264000 (2.262 sec) -2022-05-01 21:51:34,150 - tensorflow - INFO - global_step/sec: 88.6285 -2022-05-01 21:51:34,151 - tensorflow - INFO - loss = 0.13521606, step = 2264200 (2.257 sec) -2022-05-01 21:51:36,407 - tensorflow - INFO - global_step/sec: 88.6375 -2022-05-01 21:51:36,407 - tensorflow - INFO - 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INFO - global_step/sec: 88.4546 -2022-05-01 21:52:05,799 - tensorflow - INFO - loss = 0.0666922, step = 2267000 (2.261 sec) -2022-05-01 21:52:08,060 - tensorflow - INFO - global_step/sec: 88.4182 -2022-05-01 21:52:08,061 - tensorflow - INFO - loss = 0.01974915, step = 2267200 (2.262 sec) -2022-05-01 21:52:10,316 - tensorflow - INFO - global_step/sec: 88.6516 -2022-05-01 21:52:10,317 - tensorflow - INFO - loss = 0.93610466, step = 2267400 (2.256 sec) -2022-05-01 21:52:12,574 - tensorflow - INFO - global_step/sec: 88.6105 -2022-05-01 21:52:12,574 - tensorflow - INFO - loss = 0.022423476, step = 2267600 (2.257 sec) -2022-05-01 21:52:14,825 - tensorflow - INFO - global_step/sec: 88.8362 -2022-05-01 21:52:14,825 - tensorflow - INFO - loss = 0.011413089, step = 2267800 (2.251 sec) -2022-05-01 21:52:17,077 - tensorflow - INFO - global_step/sec: 88.8095 -2022-05-01 21:52:17,077 - tensorflow - INFO - loss = 0.013983531, step = 2268000 (2.252 sec) -2022-05-01 21:52:19,330 - tensorflow - INFO - global_step/sec: 88.7678 -2022-05-01 21:52:19,330 - tensorflow - INFO - loss = 0.0028574385, step = 2268200 (2.253 sec) -2022-05-01 21:52:21,582 - tensorflow - INFO - global_step/sec: 88.7979 -2022-05-01 21:52:21,583 - tensorflow - INFO - loss = 0.04333497, step = 2268400 (2.252 sec) -2022-05-01 21:52:23,831 - tensorflow - INFO - global_step/sec: 88.9502 -2022-05-01 21:52:23,831 - tensorflow - INFO - loss = 0.33113492, step = 2268600 (2.248 sec) -2022-05-01 21:52:26,083 - tensorflow - INFO - global_step/sec: 88.7816 -2022-05-01 21:52:26,084 - tensorflow - INFO - loss = 0.02726505, step = 2268800 (2.253 sec) -2022-05-01 21:52:28,335 - tensorflow - INFO - global_step/sec: 88.8254 -2022-05-01 21:52:28,335 - tensorflow - INFO - loss = 0.011160085, step = 2269000 (2.252 sec) -2022-05-01 21:52:30,595 - tensorflow - INFO - global_step/sec: 88.4921 -2022-05-01 21:52:30,596 - tensorflow - INFO - loss = 0.11342676, step = 2269200 (2.260 sec) -2022-05-01 21:52:32,850 - tensorflow - INFO - global_step/sec: 88.7093 -2022-05-01 21:52:32,850 - tensorflow - INFO - loss = 0.032502107, step = 2269400 (2.255 sec) -2022-05-01 21:52:35,103 - tensorflow - INFO - global_step/sec: 88.7527 -2022-05-01 21:52:35,103 - tensorflow - INFO - loss = 0.010498369, step = 2269600 (2.253 sec) -2022-05-01 21:52:37,350 - tensorflow - INFO - global_step/sec: 89.014 -2022-05-01 21:52:37,350 - tensorflow - INFO - loss = 0.04888901, step = 2269800 (2.247 sec) -2022-05-01 21:52:39,605 - tensorflow - INFO - global_step/sec: 88.6936 -2022-05-01 21:52:39,605 - tensorflow - INFO - loss = 0.032412484, step = 2270000 (2.255 sec) -2022-05-01 21:52:41,856 - tensorflow - INFO - global_step/sec: 88.8539 -2022-05-01 21:52:41,856 - tensorflow - INFO - loss = 0.009196581, step = 2270200 (2.251 sec) -2022-05-01 21:52:44,117 - tensorflow - INFO - global_step/sec: 88.4408 -2022-05-01 21:52:44,118 - tensorflow - INFO - loss = 0.07453966, step = 2270400 (2.261 sec) -2022-05-01 21:52:46,371 - tensorflow - INFO - global_step/sec: 88.7549 -2022-05-01 21:52:46,371 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.862 -2022-05-01 21:53:02,151 - tensorflow - INFO - loss = 0.016846657, step = 2272000 (2.251 sec) -2022-05-01 21:53:04,405 - tensorflow - INFO - global_step/sec: 88.7363 -2022-05-01 21:53:04,405 - tensorflow - INFO - loss = 0.060905166, step = 2272200 (2.254 sec) -2022-05-01 21:53:06,656 - tensorflow - INFO - global_step/sec: 88.8552 -2022-05-01 21:53:06,656 - tensorflow - INFO - loss = 0.012017032, step = 2272400 (2.251 sec) -2022-05-01 21:53:08,915 - tensorflow - INFO - global_step/sec: 88.5177 -2022-05-01 21:53:08,915 - tensorflow - INFO - loss = 0.0040350785, step = 2272600 (2.259 sec) -2022-05-01 21:53:11,169 - tensorflow - INFO - global_step/sec: 88.7429 -2022-05-01 21:53:11,169 - tensorflow - INFO - loss = 0.02077328, step = 2272800 (2.254 sec) -2022-05-01 21:53:13,423 - tensorflow - INFO - global_step/sec: 88.7306 -2022-05-01 21:53:13,423 - tensorflow - INFO - loss = 0.015841708, step = 2273000 (2.254 sec) -2022-05-01 21:53:15,674 - tensorflow - INFO - global_step/sec: 88.8567 -2022-05-01 21:53:15,674 - tensorflow - INFO - loss = 0.019090988, step = 2273200 (2.251 sec) -2022-05-01 21:53:17,926 - tensorflow - INFO - global_step/sec: 88.8015 -2022-05-01 21:53:17,926 - tensorflow - INFO - loss = 0.035035364, step = 2273400 (2.252 sec) -2022-05-01 21:53:20,181 - tensorflow - INFO - global_step/sec: 88.6723 -2022-05-01 21:53:20,182 - tensorflow - INFO - loss = 0.009551028, step = 2273600 (2.255 sec) -2022-05-01 21:53:22,438 - tensorflow - INFO - global_step/sec: 88.6157 -2022-05-01 21:53:22,439 - tensorflow - INFO - loss = 0.4296293, step = 2273800 (2.257 sec) -2022-05-01 21:53:24,693 - tensorflow - INFO - global_step/sec: 88.7129 -2022-05-01 21:53:24,693 - tensorflow - INFO - loss = 0.017614042, step = 2274000 (2.255 sec) -2022-05-01 21:53:26,943 - tensorflow - INFO - global_step/sec: 88.8563 -2022-05-01 21:53:26,944 - tensorflow - INFO - loss = 0.021202384, step = 2274200 (2.251 sec) -2022-05-01 21:53:29,202 - tensorflow - INFO - 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INFO - loss = 0.02268545, step = 2276800 (2.259 sec) -2022-05-01 21:53:58,493 - tensorflow - INFO - global_step/sec: 88.6152 -2022-05-01 21:53:58,494 - tensorflow - INFO - loss = 0.0313809, step = 2277000 (2.257 sec) -2022-05-01 21:54:00,748 - tensorflow - INFO - global_step/sec: 88.7119 -2022-05-01 21:54:00,748 - tensorflow - INFO - loss = 0.010058243, step = 2277200 (2.254 sec) -2022-05-01 21:54:03,000 - tensorflow - INFO - global_step/sec: 88.7937 -2022-05-01 21:54:03,001 - tensorflow - INFO - loss = 0.04645252, step = 2277400 (2.252 sec) -2022-05-01 21:54:05,253 - tensorflow - INFO - global_step/sec: 88.7785 -2022-05-01 21:54:05,253 - tensorflow - INFO - loss = 0.010454596, step = 2277600 (2.253 sec) -2022-05-01 21:54:07,509 - tensorflow - INFO - global_step/sec: 88.6569 -2022-05-01 21:54:07,509 - tensorflow - INFO - loss = 0.050151728, step = 2277800 (2.256 sec) -2022-05-01 21:54:09,762 - tensorflow - INFO - global_step/sec: 88.7449 -2022-05-01 21:54:09,763 - tensorflow - INFO - loss = 0.017808698, step = 2278000 (2.254 sec) -2022-05-01 21:54:12,017 - tensorflow - INFO - global_step/sec: 88.6915 -2022-05-01 21:54:12,018 - tensorflow - INFO - loss = 0.4646956, step = 2278200 (2.255 sec) -2022-05-01 21:54:14,270 - tensorflow - INFO - global_step/sec: 88.7932 -2022-05-01 21:54:14,270 - tensorflow - INFO - loss = 0.04662407, step = 2278400 (2.252 sec) -2022-05-01 21:54:16,526 - tensorflow - INFO - global_step/sec: 88.6609 -2022-05-01 21:54:16,526 - tensorflow - INFO - loss = 0.009046311, step = 2278600 (2.256 sec) -2022-05-01 21:54:18,775 - tensorflow - INFO - global_step/sec: 88.8987 -2022-05-01 21:54:18,776 - tensorflow - INFO - loss = 0.007356795, step = 2278800 (2.250 sec) -2022-05-01 21:54:21,026 - tensorflow - INFO - global_step/sec: 88.8807 -2022-05-01 21:54:21,026 - tensorflow - INFO - loss = 0.14606391, step = 2279000 (2.250 sec) -2022-05-01 21:54:23,281 - tensorflow - INFO - global_step/sec: 88.6939 -2022-05-01 21:54:23,281 - tensorflow - INFO - loss = 0.041655574, step = 2279200 (2.255 sec) -2022-05-01 21:54:25,532 - tensorflow - INFO - global_step/sec: 88.8313 -2022-05-01 21:54:25,532 - tensorflow - INFO - loss = 0.010438031, step = 2279400 (2.251 sec) -2022-05-01 21:54:27,788 - tensorflow - INFO - global_step/sec: 88.6481 -2022-05-01 21:54:27,789 - tensorflow - INFO - loss = 0.023894245, step = 2279600 (2.256 sec) -2022-05-01 21:54:30,045 - tensorflow - INFO - global_step/sec: 88.6244 -2022-05-01 21:54:30,045 - tensorflow - INFO - loss = 0.03804998, step = 2279800 (2.257 sec) -2022-05-01 21:54:32,303 - tensorflow - INFO - global_step/sec: 88.5512 -2022-05-01 21:54:32,304 - tensorflow - INFO - loss = 0.012431679, step = 2280000 (2.259 sec) -2022-05-01 21:54:34,554 - tensorflow - INFO - global_step/sec: 88.8645 -2022-05-01 21:54:34,555 - tensorflow - INFO - loss = 0.009730356, step = 2280200 (2.251 sec) -2022-05-01 21:54:36,809 - tensorflow - INFO - global_step/sec: 88.6889 -2022-05-01 21:54:36,810 - tensorflow - INFO - loss = 0.012002792, step = 2280400 (2.255 sec) -2022-05-01 21:54:39,056 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.02516003, step = 2281800 (2.251 sec) -2022-05-01 21:54:54,826 - tensorflow - INFO - global_step/sec: 88.991 -2022-05-01 21:54:54,826 - tensorflow - INFO - loss = 0.007950349, step = 2282000 (2.247 sec) -2022-05-01 21:54:57,081 - tensorflow - INFO - global_step/sec: 88.6804 -2022-05-01 21:54:57,081 - tensorflow - INFO - loss = 0.03578655, step = 2282200 (2.255 sec) -2022-05-01 21:54:59,334 - tensorflow - INFO - global_step/sec: 88.7636 -2022-05-01 21:54:59,334 - tensorflow - INFO - loss = 0.0075575593, step = 2282400 (2.253 sec) -2022-05-01 21:55:01,591 - tensorflow - INFO - global_step/sec: 88.6215 -2022-05-01 21:55:01,591 - tensorflow - INFO - loss = 0.0053380663, step = 2282600 (2.257 sec) -2022-05-01 21:55:03,851 - tensorflow - INFO - global_step/sec: 88.4811 -2022-05-01 21:55:03,852 - tensorflow - INFO - loss = 0.007805185, step = 2282800 (2.260 sec) -2022-05-01 21:55:06,104 - tensorflow - INFO - global_step/sec: 88.7876 -2022-05-01 21:55:06,104 - tensorflow - INFO - loss = 0.0065984176, step = 2283000 (2.253 sec) -2022-05-01 21:55:08,358 - tensorflow - INFO - global_step/sec: 88.738 -2022-05-01 21:55:08,358 - tensorflow - INFO - loss = 0.07137847, step = 2283200 (2.254 sec) -2022-05-01 21:55:10,615 - tensorflow - INFO - global_step/sec: 88.5968 -2022-05-01 21:55:10,615 - tensorflow - INFO - loss = 0.012754681, step = 2283400 (2.257 sec) -2022-05-01 21:55:12,874 - tensorflow - INFO - global_step/sec: 88.5431 -2022-05-01 21:55:12,874 - tensorflow - INFO - loss = 0.03999576, step = 2283600 (2.259 sec) -2022-05-01 21:55:15,138 - tensorflow - INFO - global_step/sec: 88.3463 -2022-05-01 21:55:15,138 - tensorflow - INFO - loss = 0.019726895, step = 2283800 (2.264 sec) -2022-05-01 21:55:17,395 - tensorflow - INFO - global_step/sec: 88.5942 -2022-05-01 21:55:17,395 - tensorflow - INFO - loss = 0.012661542, step = 2284000 (2.257 sec) -2022-05-01 21:55:19,655 - tensorflow - INFO - global_step/sec: 88.5009 -2022-05-01 21:55:19,655 - tensorflow - INFO - 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INFO - global_step/sec: 88.7376 -2022-05-01 21:55:49,024 - tensorflow - INFO - loss = 0.049257375, step = 2286800 (2.254 sec) -2022-05-01 21:55:51,275 - tensorflow - INFO - global_step/sec: 88.8473 -2022-05-01 21:55:51,275 - tensorflow - INFO - loss = 0.022474559, step = 2287000 (2.251 sec) -2022-05-01 21:55:53,529 - tensorflow - INFO - global_step/sec: 88.718 -2022-05-01 21:55:53,530 - tensorflow - INFO - loss = 0.015099445, step = 2287200 (2.254 sec) -2022-05-01 21:55:55,790 - tensorflow - INFO - global_step/sec: 88.4526 -2022-05-01 21:55:55,791 - tensorflow - INFO - loss = 0.0758746, step = 2287400 (2.261 sec) -2022-05-01 21:55:58,047 - tensorflow - INFO - global_step/sec: 88.6278 -2022-05-01 21:55:58,047 - tensorflow - INFO - loss = 0.33961636, step = 2287600 (2.257 sec) -2022-05-01 21:56:00,298 - tensorflow - INFO - global_step/sec: 88.8317 -2022-05-01 21:56:00,299 - tensorflow - INFO - loss = 0.049325325, step = 2287800 (2.251 sec) -2022-05-01 21:56:02,553 - tensorflow - INFO - global_step/sec: 88.7139 -2022-05-01 21:56:02,553 - tensorflow - INFO - loss = 0.008577246, step = 2288000 (2.254 sec) -2022-05-01 21:56:04,808 - tensorflow - INFO - global_step/sec: 88.6691 -2022-05-01 21:56:04,809 - tensorflow - INFO - loss = 0.004305575, step = 2288200 (2.256 sec) -2022-05-01 21:56:07,061 - tensorflow - INFO - global_step/sec: 88.7987 -2022-05-01 21:56:07,061 - tensorflow - INFO - loss = 0.05146283, step = 2288400 (2.252 sec) -2022-05-01 21:56:09,314 - tensorflow - INFO - global_step/sec: 88.7583 -2022-05-01 21:56:09,314 - tensorflow - INFO - loss = 0.007753142, step = 2288600 (2.253 sec) -2022-05-01 21:56:11,572 - tensorflow - INFO - global_step/sec: 88.5821 -2022-05-01 21:56:11,572 - tensorflow - INFO - loss = 0.03586986, step = 2288800 (2.258 sec) -2022-05-01 21:56:13,823 - tensorflow - INFO - global_step/sec: 88.8218 -2022-05-01 21:56:13,824 - tensorflow - INFO - loss = 0.0688177, step = 2289000 (2.252 sec) -2022-05-01 21:56:16,075 - tensorflow - INFO - global_step/sec: 88.8439 -2022-05-01 21:56:16,075 - tensorflow - INFO - loss = 0.00816809, step = 2289200 (2.251 sec) -2022-05-01 21:56:18,327 - tensorflow - INFO - global_step/sec: 88.81 -2022-05-01 21:56:18,327 - tensorflow - INFO - loss = 0.07801333, step = 2289400 (2.252 sec) -2022-05-01 21:56:20,581 - tensorflow - INFO - global_step/sec: 88.718 -2022-05-01 21:56:20,581 - tensorflow - INFO - loss = 0.048330776, step = 2289600 (2.254 sec) -2022-05-01 21:56:22,843 - tensorflow - INFO - global_step/sec: 88.4233 -2022-05-01 21:56:22,843 - tensorflow - INFO - loss = 0.05577566, step = 2289800 (2.262 sec) -2022-05-01 21:56:25,099 - tensorflow - INFO - global_step/sec: 88.6608 -2022-05-01 21:56:25,099 - tensorflow - INFO - loss = 0.042707544, step = 2290000 (2.256 sec) -2022-05-01 21:56:27,358 - tensorflow - INFO - global_step/sec: 88.534 -2022-05-01 21:56:27,358 - tensorflow - INFO - loss = 0.007859388, step = 2290200 (2.259 sec) -2022-05-01 21:56:29,618 - tensorflow - INFO - global_step/sec: 88.4927 -2022-05-01 21:56:29,618 - tensorflow - INFO - loss = 0.022069693, step = 2290400 (2.260 sec) -2022-05-01 21:56:31,886 - tensorflow - INFO - global_step/sec: 88.1805 -2022-05-01 21:56:31,886 - tensorflow - INFO - loss = 0.02007738, step = 2290600 (2.268 sec) -2022-05-01 21:56:34,146 - tensorflow - INFO - global_step/sec: 88.4982 -2022-05-01 21:56:34,146 - tensorflow - INFO - loss = 0.006746944, step = 2290800 (2.260 sec) -2022-05-01 21:56:36,398 - tensorflow - INFO - global_step/sec: 88.8038 -2022-05-01 21:56:36,398 - tensorflow - INFO - loss = 0.005979812, step = 2291000 (2.252 sec) -2022-05-01 21:56:38,648 - tensorflow - INFO - global_step/sec: 88.8831 -2022-05-01 21:56:38,648 - tensorflow - INFO - loss = 0.007480228, step = 2291200 (2.250 sec) -2022-05-01 21:56:40,901 - tensorflow - INFO - global_step/sec: 88.7637 -2022-05-01 21:56:40,902 - tensorflow - INFO - loss = 0.04220979, step = 2291400 (2.253 sec) -2022-05-01 21:56:43,148 - tensorflow - INFO - global_step/sec: 89.0311 -2022-05-01 21:56:43,148 - tensorflow - INFO - loss = 0.037302103, step = 2291600 (2.246 sec) -2022-05-01 21:56:45,405 - tensorflow - INFO - global_step/sec: 88.5843 -2022-05-01 21:56:45,406 - tensorflow - INFO - loss = 0.2169662, step = 2291800 (2.258 sec) -2022-05-01 21:56:47,662 - tensorflow - INFO - global_step/sec: 88.6128 -2022-05-01 21:56:47,663 - tensorflow - INFO - loss = 0.07506015, step = 2292000 (2.257 sec) -2022-05-01 21:56:49,917 - tensorflow - INFO - global_step/sec: 88.7082 -2022-05-01 21:56:49,917 - tensorflow - INFO - loss = 0.011692755, step = 2292200 (2.255 sec) -2022-05-01 21:56:52,167 - tensorflow - INFO - global_step/sec: 88.9001 -2022-05-01 21:56:52,167 - tensorflow - INFO - loss = 0.044270314, step = 2292400 (2.250 sec) -2022-05-01 21:56:54,418 - tensorflow - INFO - global_step/sec: 88.8406 -2022-05-01 21:56:54,418 - tensorflow - INFO - loss = 0.0096690245, step = 2292600 (2.251 sec) -2022-05-01 21:56:56,671 - tensorflow - INFO - global_step/sec: 88.7609 -2022-05-01 21:56:56,671 - tensorflow - INFO - loss = 0.015168619, step = 2292800 (2.253 sec) -2022-05-01 21:56:58,929 - tensorflow - INFO - global_step/sec: 88.5879 -2022-05-01 21:56:58,929 - tensorflow - INFO - loss = 0.0038347745, step = 2293000 (2.258 sec) -2022-05-01 21:57:01,179 - tensorflow - INFO - global_step/sec: 88.8582 -2022-05-01 21:57:01,180 - tensorflow - INFO - loss = 0.014080549, step = 2293200 (2.251 sec) -2022-05-01 21:57:03,439 - tensorflow - INFO - global_step/sec: 88.5001 -2022-05-01 21:57:03,440 - tensorflow - INFO - loss = 0.0055178367, step = 2293400 (2.260 sec) -2022-05-01 21:57:05,691 - tensorflow - INFO - global_step/sec: 88.8284 -2022-05-01 21:57:05,691 - tensorflow - INFO - loss = 0.031844318, step = 2293600 (2.252 sec) -2022-05-01 21:57:07,944 - tensorflow - INFO - global_step/sec: 88.7622 -2022-05-01 21:57:07,945 - tensorflow - INFO - loss = 1.3870256, step = 2293800 (2.253 sec) -2022-05-01 21:57:10,202 - tensorflow - INFO - global_step/sec: 88.5595 -2022-05-01 21:57:10,203 - tensorflow - INFO - loss = 0.010294217, step = 2294000 (2.258 sec) -2022-05-01 21:57:12,453 - tensorflow - INFO - global_step/sec: 88.8624 -2022-05-01 21:57:12,454 - tensorflow - INFO - loss = 0.014056441, step = 2294200 (2.251 sec) -2022-05-01 21:57:14,700 - tensorflow - INFO - global_step/sec: 89.0314 -2022-05-01 21:57:14,700 - tensorflow - INFO - loss = 0.028425688, step = 2294400 (2.246 sec) -2022-05-01 21:57:16,956 - tensorflow - INFO - global_step/sec: 88.6322 -2022-05-01 21:57:16,956 - tensorflow - INFO - loss = 0.14513147, step = 2294600 (2.257 sec) -2022-05-01 21:57:19,212 - tensorflow - INFO - global_step/sec: 88.6421 -2022-05-01 21:57:19,213 - tensorflow - INFO - loss = 0.018518772, step = 2294800 (2.256 sec) -2022-05-01 21:57:21,462 - tensorflow - INFO - global_step/sec: 88.8915 -2022-05-01 21:57:21,463 - tensorflow - INFO - loss = 0.0112281665, step = 2295000 (2.250 sec) -2022-05-01 21:57:23,724 - tensorflow - INFO - global_step/sec: 88.4416 -2022-05-01 21:57:23,724 - tensorflow - INFO - loss = 0.033915814, step = 2295200 (2.261 sec) -2022-05-01 21:57:25,974 - tensorflow - INFO - global_step/sec: 88.8701 -2022-05-01 21:57:25,975 - tensorflow - INFO - loss = 0.045164473, step = 2295400 (2.250 sec) -2022-05-01 21:57:28,221 - tensorflow - INFO - global_step/sec: 88.998 -2022-05-01 21:57:28,222 - tensorflow - INFO - loss = 0.015295318, step = 2295600 (2.247 sec) -2022-05-01 21:57:30,477 - tensorflow - INFO - global_step/sec: 88.6528 -2022-05-01 21:57:30,478 - tensorflow - INFO - loss = 0.055552155, step = 2295800 (2.256 sec) -2022-05-01 21:57:32,728 - tensorflow - INFO - global_step/sec: 88.8457 -2022-05-01 21:57:32,729 - tensorflow - INFO - loss = 0.007291389, step = 2296000 (2.251 sec) -2022-05-01 21:57:34,982 - tensorflow - INFO - global_step/sec: 88.7465 -2022-05-01 21:57:34,983 - tensorflow - INFO - loss = 0.009181721, step = 2296200 (2.254 sec) -2022-05-01 21:57:37,241 - tensorflow - INFO - global_step/sec: 88.5505 -2022-05-01 21:57:37,241 - tensorflow - INFO - loss = 0.015211635, step = 2296400 (2.259 sec) -2022-05-01 21:57:39,493 - tensorflow - INFO - global_step/sec: 88.8017 -2022-05-01 21:57:39,493 - tensorflow - INFO - loss = 0.07218133, step = 2296600 (2.252 sec) -2022-05-01 21:57:41,741 - tensorflow - INFO - global_step/sec: 88.98 -2022-05-01 21:57:41,741 - tensorflow - INFO - loss = 0.2888913, step = 2296800 (2.248 sec) -2022-05-01 21:57:43,991 - tensorflow - INFO - global_step/sec: 88.8873 -2022-05-01 21:57:43,991 - tensorflow - INFO - loss = 0.011046013, step = 2297000 (2.250 sec) -2022-05-01 21:57:46,238 - tensorflow - INFO - global_step/sec: 88.9803 -2022-05-01 21:57:46,239 - tensorflow - INFO - loss = 0.14288104, step = 2297200 (2.248 sec) -2022-05-01 21:57:48,495 - tensorflow - INFO - global_step/sec: 88.6299 -2022-05-01 21:57:48,495 - tensorflow - INFO - loss = 0.010061863, step = 2297400 (2.257 sec) -2022-05-01 21:57:50,747 - tensorflow - INFO - global_step/sec: 88.7933 -2022-05-01 21:57:50,748 - tensorflow - INFO - loss = 0.011489116, step = 2297600 (2.252 sec) -2022-05-01 21:57:52,996 - tensorflow - INFO - global_step/sec: 88.9238 -2022-05-01 21:57:52,997 - tensorflow - INFO - loss = 0.004584601, step = 2297800 (2.249 sec) -2022-05-01 21:57:55,247 - tensorflow - INFO - global_step/sec: 88.8742 -2022-05-01 21:57:55,247 - tensorflow - INFO - loss = 0.010511144, step = 2298000 (2.250 sec) -2022-05-01 21:57:57,500 - tensorflow - INFO - global_step/sec: 88.7639 -2022-05-01 21:57:57,500 - tensorflow - INFO - loss = 0.0056808935, step = 2298200 (2.253 sec) -2022-05-01 21:57:59,753 - tensorflow - INFO - global_step/sec: 88.7811 -2022-05-01 21:57:59,753 - tensorflow - INFO - loss = 0.01763389, step = 2298400 (2.253 sec) -2022-05-01 21:58:02,004 - tensorflow - INFO - global_step/sec: 88.8373 -2022-05-01 21:58:02,004 - tensorflow - INFO - loss = 0.11997159, step = 2298600 (2.251 sec) -2022-05-01 21:58:04,258 - tensorflow - INFO - global_step/sec: 88.7125 -2022-05-01 21:58:04,259 - tensorflow - INFO - loss = 0.06352675, step = 2298800 (2.254 sec) -2022-05-01 21:58:06,511 - tensorflow - INFO - global_step/sec: 88.7991 -2022-05-01 21:58:06,511 - tensorflow - INFO - loss = 0.18617159, step = 2299000 (2.252 sec) -2022-05-01 21:58:08,764 - tensorflow - INFO - global_step/sec: 88.7447 -2022-05-01 21:58:08,765 - tensorflow - INFO - loss = 0.010075515, step = 2299200 (2.254 sec) -2022-05-01 21:58:11,020 - tensorflow - INFO - global_step/sec: 88.6623 -2022-05-01 21:58:11,021 - tensorflow - INFO - loss = 0.07672944, step = 2299400 (2.256 sec) -2022-05-01 21:58:13,274 - tensorflow - INFO - global_step/sec: 88.7209 -2022-05-01 21:58:13,275 - tensorflow - INFO - loss = 0.012490427, step = 2299600 (2.254 sec) -2022-05-01 21:58:15,521 - tensorflow - INFO - global_step/sec: 89.0263 -2022-05-01 21:58:15,521 - tensorflow - INFO - loss = 0.084134504, step = 2299800 (2.246 sec) -2022-05-01 21:58:17,758 - tensorflow - INFO - Saving checkpoints for 2300000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. -2022-05-01 21:58:17,774 - tensorflow - INFO - model.ckpt-2300000 is not in all_model_checkpoint_paths. Manually adding it. -2022-05-01 21:58:18,309 - tensorflow - INFO - Calling model_fn. -2022-05-01 21:58:18,436 - tensorflow - INFO - Done calling model_fn. -2022-05-01 21:58:18,451 - tensorflow - INFO - Starting evaluation at 2022-05-01T21:58:18Z -2022-05-01 21:58:18,514 - tensorflow - INFO - Graph was finalized. -2022-05-01 21:58:18,517 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-2300000 -2022-05-01 21:58:18,797 - tensorflow - INFO - Running local_init_op. -2022-05-01 21:58:18,905 - tensorflow - INFO - Done running local_init_op. -2022-05-01 21:58:29,046 - tensorflow - INFO - Evaluation [3990/39900] -2022-05-01 21:58:38,480 - tensorflow - INFO - Evaluation [7980/39900] -2022-05-01 21:58:47,929 - tensorflow - INFO - Evaluation [11970/39900] -2022-05-01 21:58:57,409 - tensorflow - INFO - Evaluation [15960/39900] -2022-05-01 21:59:06,885 - tensorflow - INFO - Evaluation [19950/39900] -2022-05-01 21:59:16,344 - tensorflow - INFO - Evaluation [23940/39900] -2022-05-01 21:59:25,817 - tensorflow - INFO - Evaluation [27930/39900] -2022-05-01 21:59:35,288 - tensorflow - INFO - Evaluation [31920/39900] -2022-05-01 21:59:44,735 - tensorflow - INFO - Evaluation [35910/39900] -2022-05-01 21:59:54,202 - tensorflow - INFO - Evaluation [39900/39900] -2022-05-01 21:59:54,339 - tensorflow - INFO - Finished evaluation at 2022-05-01-21:59:54 -2022-05-01 21:59:54,339 - tensorflow - INFO - Saving dict for global step 2300000: global_step = 2300000, loss = 0.017903524, mean_degree_err = 3.449092 -2022-05-01 21:59:54,340 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 2300000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-2300000 -2022-05-01 21:59:54,353 - tensorflow - INFO - global_step/sec: 2.02363 -2022-05-01 21:59:54,354 - tensorflow - INFO - loss = 0.0091372905, step = 2300000 (98.833 sec) -2022-05-01 21:59:56,633 - tensorflow - INFO - global_step/sec: 87.7322 -2022-05-01 21:59:56,633 - tensorflow - INFO - loss = 0.024899326, step = 2300200 (2.279 sec) -2022-05-01 21:59:58,886 - tensorflow - INFO - global_step/sec: 88.7735 -2022-05-01 21:59:58,886 - tensorflow - INFO - loss = 0.016124004, step = 2300400 (2.253 sec) -2022-05-01 22:00:01,143 - tensorflow - INFO - global_step/sec: 88.6166 -2022-05-01 22:00:01,143 - tensorflow - INFO - loss = 0.01588392, step = 2300600 (2.257 sec) -2022-05-01 22:00:03,397 - tensorflow - INFO - global_step/sec: 88.7324 -2022-05-01 22:00:03,397 - tensorflow - INFO - loss = 0.010000843, step = 2300800 (2.254 sec) -2022-05-01 22:00:05,651 - tensorflow - INFO - global_step/sec: 88.7368 -2022-05-01 22:00:05,651 - tensorflow - INFO - loss = 0.059633113, step = 2301000 (2.254 sec) -2022-05-01 22:00:07,911 - tensorflow - INFO - global_step/sec: 88.4922 -2022-05-01 22:00:07,911 - tensorflow - INFO - loss = 0.0062330198, step = 2301200 (2.260 sec) -2022-05-01 22:00:10,169 - tensorflow - INFO - global_step/sec: 88.568 -2022-05-01 22:00:10,169 - tensorflow - INFO - loss = 0.23845582, step = 2301400 (2.258 sec) -2022-05-01 22:00:12,422 - tensorflow - INFO - global_step/sec: 88.7789 -2022-05-01 22:00:12,422 - tensorflow - INFO - loss = 0.011025475, step = 2301600 (2.253 sec) -2022-05-01 22:00:14,674 - tensorflow - INFO - global_step/sec: 88.8013 -2022-05-01 22:00:14,674 - tensorflow - INFO - loss = 0.0045896634, step = 2301800 (2.252 sec) -2022-05-01 22:00:16,926 - tensorflow - INFO - global_step/sec: 88.8024 -2022-05-01 22:00:16,927 - tensorflow - INFO - loss = 0.0063096797, step = 2302000 (2.252 sec) -2022-05-01 22:00:19,178 - tensorflow - INFO - global_step/sec: 88.8262 -2022-05-01 22:00:19,178 - tensorflow - INFO - loss = 0.006863452, step = 2302200 (2.252 sec) -2022-05-01 22:00:21,436 - tensorflow - INFO - global_step/sec: 88.5556 -2022-05-01 22:00:21,437 - tensorflow - INFO - loss = 0.048675433, step = 2302400 (2.259 sec) -2022-05-01 22:00:23,687 - tensorflow - INFO - global_step/sec: 88.8532 -2022-05-01 22:00:23,687 - tensorflow - INFO - loss = 0.0055150455, step = 2302600 (2.251 sec) -2022-05-01 22:00:25,941 - tensorflow - INFO - global_step/sec: 88.7483 -2022-05-01 22:00:25,941 - tensorflow - INFO - loss = 0.01271037, step = 2302800 (2.254 sec) -2022-05-01 22:00:28,193 - tensorflow - INFO - global_step/sec: 88.7814 -2022-05-01 22:00:28,194 - tensorflow - INFO - loss = 0.009561626, step = 2303000 (2.253 sec) -2022-05-01 22:00:30,452 - tensorflow - INFO - global_step/sec: 88.5499 -2022-05-01 22:00:30,452 - tensorflow - INFO - loss = 0.10002799, step = 2303200 (2.259 sec) -2022-05-01 22:00:32,708 - tensorflow - INFO - global_step/sec: 88.6695 -2022-05-01 22:00:32,708 - tensorflow - INFO - loss = 0.005737627, step = 2303400 (2.256 sec) -2022-05-01 22:00:34,966 - tensorflow - INFO - global_step/sec: 88.5713 -2022-05-01 22:00:34,966 - tensorflow - INFO - loss = 0.04569764, step = 2303600 (2.258 sec) -2022-05-01 22:00:37,215 - tensorflow - INFO - global_step/sec: 88.9007 -2022-05-01 22:00:37,216 - tensorflow - INFO - loss = 0.004944335, step = 2303800 (2.250 sec) -2022-05-01 22:00:39,464 - tensorflow - INFO - global_step/sec: 88.9471 -2022-05-01 22:00:39,464 - tensorflow - INFO - loss = 0.009186769, step = 2304000 (2.249 sec) -2022-05-01 22:00:41,715 - tensorflow - INFO - global_step/sec: 88.8285 -2022-05-01 22:00:41,716 - tensorflow - INFO - loss = 0.1479854, step = 2304200 (2.252 sec) -2022-05-01 22:00:43,969 - tensorflow - INFO - global_step/sec: 88.7419 -2022-05-01 22:00:43,969 - tensorflow - INFO - loss = 0.022605823, step = 2304400 (2.254 sec) -2022-05-01 22:00:46,227 - tensorflow - INFO - global_step/sec: 88.5717 -2022-05-01 22:00:46,228 - tensorflow - INFO - loss = 0.021883419, step = 2304600 (2.258 sec) -2022-05-01 22:00:48,478 - tensorflow - INFO - global_step/sec: 88.8408 -2022-05-01 22:00:48,479 - tensorflow - INFO - loss = 0.009133572, step = 2304800 (2.251 sec) -2022-05-01 22:00:50,734 - tensorflow - INFO - global_step/sec: 88.6823 -2022-05-01 22:00:50,734 - tensorflow - INFO - loss = 0.011011441, step = 2305000 (2.255 sec) -2022-05-01 22:00:52,985 - tensorflow - INFO - global_step/sec: 88.8334 -2022-05-01 22:00:52,985 - tensorflow - INFO - loss = 0.021263834, step = 2305200 (2.251 sec) -2022-05-01 22:00:55,239 - tensorflow - INFO - global_step/sec: 88.7191 -2022-05-01 22:00:55,240 - tensorflow - INFO - loss = 3.0099537, step = 2305400 (2.254 sec) -2022-05-01 22:00:57,496 - tensorflow - INFO - global_step/sec: 88.6135 -2022-05-01 22:00:57,497 - tensorflow - INFO - loss = 0.00849932, step = 2305600 (2.257 sec) -2022-05-01 22:00:59,751 - tensorflow - INFO - global_step/sec: 88.7239 -2022-05-01 22:00:59,751 - tensorflow - INFO - loss = 0.009330539, step = 2305800 (2.254 sec) -2022-05-01 22:01:02,003 - tensorflow - INFO - global_step/sec: 88.8097 -2022-05-01 22:01:02,003 - tensorflow - INFO - loss = 0.25501916, step = 2306000 (2.252 sec) -2022-05-01 22:01:04,259 - tensorflow - INFO - global_step/sec: 88.6387 -2022-05-01 22:01:04,259 - tensorflow - INFO - loss = 0.0076217586, step = 2306200 (2.256 sec) -2022-05-01 22:01:06,512 - tensorflow - INFO - global_step/sec: 88.7569 -2022-05-01 22:01:06,513 - tensorflow - INFO - loss = 0.2942369, step = 2306400 (2.253 sec) -2022-05-01 22:01:08,766 - tensorflow - INFO - global_step/sec: 88.7346 -2022-05-01 22:01:08,767 - tensorflow - INFO - loss = 0.007971099, step = 2306600 (2.254 sec) -2022-05-01 22:01:11,026 - tensorflow - INFO - global_step/sec: 88.4867 -2022-05-01 22:01:11,027 - tensorflow - INFO - loss = 0.0067938194, step = 2306800 (2.260 sec) -2022-05-01 22:01:13,279 - tensorflow - INFO - global_step/sec: 88.7929 -2022-05-01 22:01:13,279 - tensorflow - INFO - loss = 0.024233544, step = 2307000 (2.252 sec) -2022-05-01 22:01:15,529 - tensorflow - INFO - global_step/sec: 88.8989 -2022-05-01 22:01:15,529 - tensorflow - INFO - loss = 0.030882917, step = 2307200 (2.250 sec) -2022-05-01 22:01:17,782 - tensorflow - INFO - global_step/sec: 88.7576 -2022-05-01 22:01:17,782 - tensorflow - INFO - loss = 0.39104134, step = 2307400 (2.253 sec) -2022-05-01 22:01:20,035 - tensorflow - INFO - global_step/sec: 88.7829 -2022-05-01 22:01:20,035 - tensorflow - INFO - loss = 0.02334808, step = 2307600 (2.253 sec) -2022-05-01 22:01:22,294 - tensorflow - INFO - global_step/sec: 88.5144 -2022-05-01 22:01:22,294 - tensorflow - INFO - loss = 0.017138243, step = 2307800 (2.260 sec) -2022-05-01 22:01:24,555 - tensorflow - INFO - global_step/sec: 88.4762 -2022-05-01 22:01:24,555 - tensorflow - INFO - loss = 0.090016745, step = 2308000 (2.261 sec) -2022-05-01 22:01:26,813 - tensorflow - INFO - global_step/sec: 88.5436 -2022-05-01 22:01:26,814 - tensorflow - INFO - loss = 0.030420732, step = 2308200 (2.259 sec) -2022-05-01 22:01:29,070 - tensorflow - INFO - global_step/sec: 88.6409 -2022-05-01 22:01:29,070 - tensorflow - INFO - loss = 0.09480525, step = 2308400 (2.256 sec) -2022-05-01 22:01:31,332 - tensorflow - INFO - global_step/sec: 88.3843 -2022-05-01 22:01:31,333 - tensorflow - INFO - loss = 0.009265394, step = 2308600 (2.263 sec) -2022-05-01 22:01:33,594 - tensorflow - INFO - global_step/sec: 88.446 -2022-05-01 22:01:33,594 - tensorflow - INFO - loss = 0.0055280607, step = 2308800 (2.261 sec) -2022-05-01 22:01:35,854 - tensorflow - INFO - global_step/sec: 88.4888 -2022-05-01 22:01:35,854 - tensorflow - INFO - loss = 0.081085324, step = 2309000 (2.260 sec) -2022-05-01 22:01:38,103 - tensorflow - INFO - global_step/sec: 88.918 -2022-05-01 22:01:38,104 - tensorflow - INFO - loss = 0.026840352, step = 2309200 (2.249 sec) -2022-05-01 22:01:40,356 - tensorflow - INFO - global_step/sec: 88.7619 -2022-05-01 22:01:40,357 - tensorflow - INFO - loss = 0.010899147, step = 2309400 (2.253 sec) -2022-05-01 22:01:42,612 - tensorflow - INFO - global_step/sec: 88.6616 -2022-05-01 22:01:42,613 - tensorflow - INFO - loss = 0.030948, step = 2309600 (2.256 sec) -2022-05-01 22:01:44,864 - tensorflow - INFO - global_step/sec: 88.8096 -2022-05-01 22:01:44,865 - tensorflow - INFO - loss = 0.071116395, step = 2309800 (2.252 sec) -2022-05-01 22:01:47,115 - tensorflow - INFO - global_step/sec: 88.8501 -2022-05-01 22:01:47,116 - tensorflow - INFO - loss = 0.012341282, step = 2310000 (2.251 sec) -2022-05-01 22:01:49,374 - tensorflow - INFO - global_step/sec: 88.5234 -2022-05-01 22:01:49,375 - tensorflow - INFO - loss = 0.05269678, step = 2310200 (2.259 sec) -2022-05-01 22:01:51,632 - tensorflow - INFO - global_step/sec: 88.5832 -2022-05-01 22:01:51,633 - tensorflow - INFO - loss = 0.012764389, step = 2310400 (2.258 sec) -2022-05-01 22:01:53,879 - tensorflow - INFO - global_step/sec: 89.0218 -2022-05-01 22:01:53,879 - tensorflow - INFO - loss = 0.2302305, step = 2310600 (2.247 sec) -2022-05-01 22:01:56,133 - tensorflow - INFO - global_step/sec: 88.7124 -2022-05-01 22:01:56,134 - tensorflow - INFO - loss = 0.17189549, step = 2310800 (2.255 sec) -2022-05-01 22:01:58,387 - tensorflow - INFO - global_step/sec: 88.7537 -2022-05-01 22:01:58,387 - tensorflow - INFO - loss = 0.007894407, step = 2311000 (2.253 sec) -2022-05-01 22:02:00,641 - tensorflow - INFO - global_step/sec: 88.705 -2022-05-01 22:02:00,642 - tensorflow - INFO - loss = 0.18904224, step = 2311200 (2.255 sec) -2022-05-01 22:02:02,902 - 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INFO - global_step/sec: 88.6366 -2022-05-01 22:05:32,620 - tensorflow - INFO - loss = 0.037553925, step = 2330000 (2.256 sec) -2022-05-01 22:05:34,872 - tensorflow - INFO - global_step/sec: 88.8133 -2022-05-01 22:05:34,872 - tensorflow - INFO - loss = 0.008088087, step = 2330200 (2.252 sec) -2022-05-01 22:05:37,123 - tensorflow - INFO - global_step/sec: 88.834 -2022-05-01 22:05:37,124 - tensorflow - INFO - loss = 0.00979968, step = 2330400 (2.251 sec) -2022-05-01 22:05:39,374 - tensorflow - INFO - global_step/sec: 88.8699 -2022-05-01 22:05:39,374 - tensorflow - INFO - loss = 0.015699673, step = 2330600 (2.250 sec) -2022-05-01 22:05:41,630 - tensorflow - INFO - global_step/sec: 88.6432 -2022-05-01 22:05:41,630 - tensorflow - INFO - loss = 0.044774663, step = 2330800 (2.256 sec) -2022-05-01 22:05:43,894 - tensorflow - INFO - global_step/sec: 88.3547 -2022-05-01 22:05:43,894 - tensorflow - INFO - loss = 0.060779974, step = 2331000 (2.264 sec) -2022-05-01 22:05:46,155 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.3842 -2022-05-01 22:06:29,067 - tensorflow - INFO - loss = 0.01186622, step = 2335000 (2.263 sec) -2022-05-01 22:06:31,329 - tensorflow - INFO - global_step/sec: 88.4146 -2022-05-01 22:06:31,329 - tensorflow - INFO - loss = 0.08158864, step = 2335200 (2.262 sec) -2022-05-01 22:06:33,582 - tensorflow - INFO - global_step/sec: 88.778 -2022-05-01 22:06:33,582 - tensorflow - INFO - loss = 0.016213449, step = 2335400 (2.253 sec) -2022-05-01 22:06:35,837 - tensorflow - INFO - global_step/sec: 88.671 -2022-05-01 22:06:35,838 - tensorflow - INFO - loss = 0.1896997, step = 2335600 (2.256 sec) -2022-05-01 22:06:38,092 - tensorflow - INFO - global_step/sec: 88.7013 -2022-05-01 22:06:38,092 - tensorflow - INFO - loss = 0.039793912, step = 2335800 (2.255 sec) -2022-05-01 22:06:40,346 - tensorflow - INFO - global_step/sec: 88.7092 -2022-05-01 22:06:40,347 - tensorflow - INFO - loss = 0.5908584, step = 2336000 (2.255 sec) -2022-05-01 22:06:42,607 - tensorflow - INFO - global_step/sec: 88.4873 -2022-05-01 22:06:42,607 - tensorflow - INFO - loss = 0.009805529, step = 2336200 (2.260 sec) -2022-05-01 22:06:44,856 - tensorflow - INFO - global_step/sec: 88.9053 -2022-05-01 22:06:44,857 - tensorflow - INFO - loss = 0.019799821, step = 2336400 (2.250 sec) -2022-05-01 22:06:47,109 - tensorflow - INFO - global_step/sec: 88.7871 -2022-05-01 22:06:47,109 - tensorflow - INFO - loss = 0.011163163, step = 2336600 (2.253 sec) -2022-05-01 22:06:49,359 - tensorflow - INFO - global_step/sec: 88.8939 -2022-05-01 22:06:49,359 - tensorflow - INFO - loss = 0.057771083, step = 2336800 (2.250 sec) -2022-05-01 22:06:51,612 - tensorflow - INFO - global_step/sec: 88.7643 -2022-05-01 22:06:51,612 - tensorflow - INFO - loss = 0.009727901, step = 2337000 (2.253 sec) -2022-05-01 22:06:53,868 - tensorflow - INFO - global_step/sec: 88.6472 -2022-05-01 22:06:53,868 - tensorflow - INFO - loss = 0.0070720967, step = 2337200 (2.256 sec) -2022-05-01 22:06:56,126 - tensorflow - INFO - 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tensorflow - INFO - loss = 11.913254, step = 2338600 (2.255 sec) -2022-05-01 22:07:11,903 - tensorflow - INFO - global_step/sec: 88.6866 -2022-05-01 22:07:11,903 - tensorflow - INFO - loss = 0.0029664468, step = 2338800 (2.255 sec) -2022-05-01 22:07:14,156 - tensorflow - INFO - global_step/sec: 88.7648 -2022-05-01 22:07:14,156 - tensorflow - INFO - loss = 0.0143400775, step = 2339000 (2.253 sec) -2022-05-01 22:07:16,408 - tensorflow - INFO - global_step/sec: 88.8178 -2022-05-01 22:07:16,408 - tensorflow - INFO - loss = 0.023873892, step = 2339200 (2.252 sec) -2022-05-01 22:07:18,665 - tensorflow - INFO - global_step/sec: 88.6026 -2022-05-01 22:07:18,666 - tensorflow - INFO - loss = 0.01951231, step = 2339400 (2.257 sec) -2022-05-01 22:07:20,921 - tensorflow - INFO - global_step/sec: 88.662 -2022-05-01 22:07:20,921 - tensorflow - INFO - loss = 0.033171035, step = 2339600 (2.256 sec) -2022-05-01 22:07:23,172 - tensorflow - INFO - global_step/sec: 88.8626 -2022-05-01 22:07:23,172 - tensorflow - INFO - loss = 0.06758325, step = 2339800 (2.251 sec) -2022-05-01 22:07:25,422 - tensorflow - INFO - global_step/sec: 88.8592 -2022-05-01 22:07:25,423 - tensorflow - INFO - loss = 0.054566626, step = 2340000 (2.251 sec) -2022-05-01 22:07:27,673 - tensorflow - INFO - global_step/sec: 88.8646 -2022-05-01 22:07:27,673 - tensorflow - INFO - loss = 0.018394904, step = 2340200 (2.251 sec) -2022-05-01 22:07:29,926 - tensorflow - INFO - global_step/sec: 88.7599 -2022-05-01 22:07:29,927 - tensorflow - INFO - loss = 0.012832569, step = 2340400 (2.253 sec) -2022-05-01 22:07:32,189 - tensorflow - INFO - global_step/sec: 88.39 -2022-05-01 22:07:32,189 - tensorflow - INFO - loss = 0.005976406, step = 2340600 (2.263 sec) -2022-05-01 22:07:34,444 - tensorflow - INFO - global_step/sec: 88.6942 -2022-05-01 22:07:34,444 - tensorflow - INFO - loss = 0.003158088, step = 2340800 (2.255 sec) -2022-05-01 22:07:36,692 - tensorflow - INFO - global_step/sec: 88.9451 -2022-05-01 22:07:36,693 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.6672 -2022-05-01 22:07:52,474 - tensorflow - INFO - loss = 0.005737345, step = 2342400 (2.256 sec) -2022-05-01 22:07:54,726 - tensorflow - INFO - global_step/sec: 88.8181 -2022-05-01 22:07:54,726 - tensorflow - INFO - loss = 0.33173925, step = 2342600 (2.252 sec) -2022-05-01 22:07:56,988 - tensorflow - INFO - global_step/sec: 88.4059 -2022-05-01 22:07:56,988 - tensorflow - INFO - loss = 0.0030213078, step = 2342800 (2.262 sec) -2022-05-01 22:07:59,238 - tensorflow - INFO - global_step/sec: 88.8644 -2022-05-01 22:07:59,239 - tensorflow - INFO - loss = 0.019566808, step = 2343000 (2.251 sec) -2022-05-01 22:08:01,489 - tensorflow - INFO - global_step/sec: 88.8676 -2022-05-01 22:08:01,490 - tensorflow - INFO - loss = 0.04868641, step = 2343200 (2.251 sec) -2022-05-01 22:08:03,747 - tensorflow - INFO - global_step/sec: 88.5743 -2022-05-01 22:08:03,747 - tensorflow - INFO - loss = 0.02816845, step = 2343400 (2.258 sec) -2022-05-01 22:08:06,009 - tensorflow - INFO - global_step/sec: 88.4288 -2022-05-01 22:08:06,009 - tensorflow - INFO - loss = 0.0069576884, step = 2343600 (2.262 sec) -2022-05-01 22:08:08,262 - tensorflow - INFO - global_step/sec: 88.7771 -2022-05-01 22:08:08,262 - tensorflow - INFO - loss = 0.059992794, step = 2343800 (2.253 sec) -2022-05-01 22:08:10,518 - tensorflow - INFO - global_step/sec: 88.6328 -2022-05-01 22:08:10,518 - tensorflow - INFO - loss = 0.021908706, step = 2344000 (2.257 sec) -2022-05-01 22:08:12,771 - tensorflow - INFO - global_step/sec: 88.7547 -2022-05-01 22:08:12,772 - tensorflow - INFO - loss = 0.029149998, step = 2344200 (2.253 sec) -2022-05-01 22:08:15,026 - tensorflow - INFO - global_step/sec: 88.692 -2022-05-01 22:08:15,027 - tensorflow - INFO - loss = 0.0133520365, step = 2344400 (2.255 sec) -2022-05-01 22:08:17,276 - tensorflow - INFO - global_step/sec: 88.8985 -2022-05-01 22:08:17,277 - tensorflow - INFO - loss = 0.026598422, step = 2344600 (2.250 sec) -2022-05-01 22:08:19,526 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.00861906, step = 2346000 (2.253 sec) -2022-05-01 22:08:35,320 - tensorflow - INFO - global_step/sec: 88.3982 -2022-05-01 22:08:35,321 - tensorflow - INFO - loss = 0.0022816355, step = 2346200 (2.262 sec) -2022-05-01 22:08:37,574 - tensorflow - INFO - global_step/sec: 88.7508 -2022-05-01 22:08:37,574 - tensorflow - INFO - loss = 0.04020985, step = 2346400 (2.253 sec) -2022-05-01 22:08:39,830 - tensorflow - INFO - global_step/sec: 88.6572 -2022-05-01 22:08:39,830 - tensorflow - INFO - loss = 0.010818541, step = 2346600 (2.256 sec) -2022-05-01 22:08:42,080 - tensorflow - INFO - global_step/sec: 88.8779 -2022-05-01 22:08:42,080 - tensorflow - INFO - loss = 0.033512853, step = 2346800 (2.250 sec) -2022-05-01 22:08:44,335 - tensorflow - INFO - global_step/sec: 88.6902 -2022-05-01 22:08:44,335 - tensorflow - INFO - loss = 0.040175766, step = 2347000 (2.255 sec) -2022-05-01 22:08:46,585 - tensorflow - INFO - global_step/sec: 88.8838 -2022-05-01 22:08:46,586 - tensorflow - INFO - loss = 0.04513892, step = 2347200 (2.250 sec) -2022-05-01 22:08:48,844 - tensorflow - INFO - global_step/sec: 88.5486 -2022-05-01 22:08:48,844 - tensorflow - INFO - loss = 0.006276906, step = 2347400 (2.259 sec) -2022-05-01 22:08:51,100 - tensorflow - INFO - global_step/sec: 88.6439 -2022-05-01 22:08:51,100 - tensorflow - INFO - loss = 0.012032225, step = 2347600 (2.256 sec) -2022-05-01 22:08:53,355 - tensorflow - INFO - global_step/sec: 88.6893 -2022-05-01 22:08:53,356 - tensorflow - INFO - loss = 0.032703087, step = 2347800 (2.255 sec) -2022-05-01 22:08:55,609 - tensorflow - INFO - global_step/sec: 88.7573 -2022-05-01 22:08:55,609 - tensorflow - INFO - loss = 0.025609188, step = 2348000 (2.253 sec) -2022-05-01 22:08:57,869 - tensorflow - INFO - global_step/sec: 88.4827 -2022-05-01 22:08:57,869 - tensorflow - INFO - loss = 0.009650145, step = 2348200 (2.260 sec) -2022-05-01 22:09:00,123 - tensorflow - INFO - global_step/sec: 88.7084 -2022-05-01 22:09:00,124 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.3017 -2022-05-01 22:09:15,930 - tensorflow - INFO - loss = 0.017859217, step = 2349800 (2.265 sec) -2022-05-01 22:09:18,193 - tensorflow - INFO - global_step/sec: 88.363 -2022-05-01 22:09:18,193 - tensorflow - INFO - loss = 0.045248006, step = 2350000 (2.264 sec) -2022-05-01 22:09:20,452 - tensorflow - INFO - global_step/sec: 88.5298 -2022-05-01 22:09:20,452 - tensorflow - INFO - loss = 0.006212191, step = 2350200 (2.259 sec) -2022-05-01 22:09:22,716 - tensorflow - INFO - global_step/sec: 88.3369 -2022-05-01 22:09:22,716 - tensorflow - INFO - loss = 0.013777234, step = 2350400 (2.264 sec) -2022-05-01 22:09:24,982 - tensorflow - INFO - global_step/sec: 88.2669 -2022-05-01 22:09:24,982 - tensorflow - INFO - loss = 0.05128285, step = 2350600 (2.266 sec) -2022-05-01 22:09:27,241 - tensorflow - INFO - global_step/sec: 88.528 -2022-05-01 22:09:27,241 - tensorflow - INFO - loss = 0.0340871, step = 2350800 (2.259 sec) -2022-05-01 22:09:29,502 - tensorflow - INFO - global_step/sec: 88.4397 -2022-05-01 22:09:29,503 - tensorflow - INFO - loss = 0.0099432655, step = 2351000 (2.261 sec) -2022-05-01 22:09:31,770 - tensorflow - INFO - global_step/sec: 88.1938 -2022-05-01 22:09:31,770 - tensorflow - INFO - loss = 0.008760823, step = 2351200 (2.268 sec) -2022-05-01 22:09:34,030 - tensorflow - INFO - global_step/sec: 88.4807 -2022-05-01 22:09:34,031 - tensorflow - INFO - loss = 0.0542563, step = 2351400 (2.260 sec) -2022-05-01 22:09:36,289 - tensorflow - INFO - global_step/sec: 88.5482 -2022-05-01 22:09:36,290 - tensorflow - INFO - loss = 0.007148968, step = 2351600 (2.259 sec) -2022-05-01 22:09:38,550 - tensorflow - INFO - global_step/sec: 88.4466 -2022-05-01 22:09:38,551 - tensorflow - INFO - loss = 0.02096405, step = 2351800 (2.261 sec) -2022-05-01 22:09:40,814 - tensorflow - INFO - global_step/sec: 88.345 -2022-05-01 22:09:40,815 - tensorflow - INFO - loss = 0.0086912485, step = 2352000 (2.264 sec) -2022-05-01 22:09:43,074 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.030029457, step = 2353400 (2.254 sec) -2022-05-01 22:09:58,850 - tensorflow - INFO - global_step/sec: 88.7795 -2022-05-01 22:09:58,851 - tensorflow - INFO - loss = 0.0619104, step = 2353600 (2.253 sec) -2022-05-01 22:10:01,105 - tensorflow - INFO - global_step/sec: 88.7066 -2022-05-01 22:10:01,105 - tensorflow - INFO - loss = 0.007638214, step = 2353800 (2.255 sec) -2022-05-01 22:10:03,363 - tensorflow - INFO - global_step/sec: 88.5804 -2022-05-01 22:10:03,363 - tensorflow - INFO - loss = 0.117859095, step = 2354000 (2.258 sec) -2022-05-01 22:10:05,620 - tensorflow - INFO - global_step/sec: 88.6156 -2022-05-01 22:10:05,620 - tensorflow - INFO - loss = 0.052432656, step = 2354200 (2.257 sec) -2022-05-01 22:10:07,880 - tensorflow - INFO - global_step/sec: 88.4916 -2022-05-01 22:10:07,880 - tensorflow - INFO - loss = 0.0035383592, step = 2354400 (2.260 sec) -2022-05-01 22:10:10,132 - tensorflow - INFO - global_step/sec: 88.813 -2022-05-01 22:10:10,132 - tensorflow - INFO - loss = 0.028265294, step = 2354600 (2.252 sec) -2022-05-01 22:10:12,383 - tensorflow - INFO - global_step/sec: 88.8454 -2022-05-01 22:10:12,383 - tensorflow - INFO - loss = 0.0070477324, step = 2354800 (2.251 sec) -2022-05-01 22:10:14,633 - tensorflow - INFO - global_step/sec: 88.8861 -2022-05-01 22:10:14,633 - tensorflow - INFO - loss = 0.036895104, step = 2355000 (2.250 sec) -2022-05-01 22:10:16,889 - tensorflow - INFO - global_step/sec: 88.6651 -2022-05-01 22:10:16,889 - tensorflow - INFO - loss = 0.011889763, step = 2355200 (2.256 sec) -2022-05-01 22:10:19,146 - tensorflow - INFO - global_step/sec: 88.5931 -2022-05-01 22:10:19,146 - tensorflow - INFO - loss = 0.03548288, step = 2355400 (2.257 sec) -2022-05-01 22:10:21,402 - tensorflow - INFO - global_step/sec: 88.6655 -2022-05-01 22:10:21,402 - tensorflow - INFO - loss = 0.019216398, step = 2355600 (2.256 sec) -2022-05-01 22:10:23,658 - tensorflow - INFO - global_step/sec: 88.6541 -2022-05-01 22:10:23,658 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.5 -2022-05-01 22:10:39,449 - tensorflow - INFO - loss = 0.019714009, step = 2357200 (2.260 sec) -2022-05-01 22:10:41,703 - tensorflow - INFO - global_step/sec: 88.7033 -2022-05-01 22:10:41,704 - tensorflow - INFO - loss = 0.0466834, step = 2357400 (2.255 sec) -2022-05-01 22:10:43,957 - tensorflow - INFO - global_step/sec: 88.742 -2022-05-01 22:10:43,957 - tensorflow - INFO - loss = 0.01898101, step = 2357600 (2.254 sec) -2022-05-01 22:10:46,212 - tensorflow - INFO - global_step/sec: 88.6683 -2022-05-01 22:10:46,213 - tensorflow - INFO - loss = 0.016786704, step = 2357800 (2.256 sec) -2022-05-01 22:10:48,463 - tensorflow - INFO - global_step/sec: 88.8727 -2022-05-01 22:10:48,463 - tensorflow - INFO - loss = 0.067788795, step = 2358000 (2.250 sec) -2022-05-01 22:10:50,720 - tensorflow - INFO - global_step/sec: 88.6091 -2022-05-01 22:10:50,720 - tensorflow - INFO - loss = 0.021722503, step = 2358200 (2.257 sec) -2022-05-01 22:10:52,975 - tensorflow - INFO - global_step/sec: 88.6863 -2022-05-01 22:10:52,975 - tensorflow - INFO - loss = 0.033839982, step = 2358400 (2.255 sec) -2022-05-01 22:10:55,232 - tensorflow - INFO - global_step/sec: 88.6125 -2022-05-01 22:10:55,233 - tensorflow - INFO - loss = 0.08963957, step = 2358600 (2.257 sec) -2022-05-01 22:10:57,491 - tensorflow - INFO - global_step/sec: 88.5485 -2022-05-01 22:10:57,491 - tensorflow - INFO - loss = 0.009970225, step = 2358800 (2.259 sec) -2022-05-01 22:10:59,744 - tensorflow - INFO - global_step/sec: 88.7576 -2022-05-01 22:10:59,744 - tensorflow - INFO - loss = 0.01552359, step = 2359000 (2.253 sec) -2022-05-01 22:11:01,993 - tensorflow - INFO - global_step/sec: 88.9441 -2022-05-01 22:11:01,993 - tensorflow - INFO - loss = 0.11977928, step = 2359200 (2.249 sec) -2022-05-01 22:11:04,248 - tensorflow - INFO - global_step/sec: 88.686 -2022-05-01 22:11:04,248 - tensorflow - INFO - loss = 0.009839378, step = 2359400 (2.255 sec) -2022-05-01 22:11:06,501 - tensorflow - INFO - global_step/sec: 88.7465 -2022-05-01 22:11:06,502 - tensorflow - INFO - loss = 0.03091337, step = 2359600 (2.254 sec) -2022-05-01 22:11:08,759 - tensorflow - INFO - global_step/sec: 88.6063 -2022-05-01 22:11:08,759 - tensorflow - INFO - loss = 0.0106901685, step = 2359800 (2.257 sec) -2022-05-01 22:11:11,012 - tensorflow - INFO - global_step/sec: 88.7435 -2022-05-01 22:11:11,013 - tensorflow - INFO - loss = 0.14742582, step = 2360000 (2.254 sec) -2022-05-01 22:11:13,267 - tensorflow - INFO - global_step/sec: 88.7031 -2022-05-01 22:11:13,267 - tensorflow - INFO - loss = 0.009636853, step = 2360200 (2.255 sec) -2022-05-01 22:11:15,517 - tensorflow - INFO - global_step/sec: 88.892 -2022-05-01 22:11:15,517 - tensorflow - INFO - loss = 0.010644963, step = 2360400 (2.250 sec) -2022-05-01 22:11:17,774 - tensorflow - INFO - global_step/sec: 88.6064 -2022-05-01 22:11:17,775 - tensorflow - INFO - loss = 0.051432684, step = 2360600 (2.257 sec) -2022-05-01 22:11:20,027 - tensorflow - INFO - global_step/sec: 88.7765 -2022-05-01 22:11:20,027 - tensorflow - INFO - loss = 0.061627805, step = 2360800 (2.253 sec) -2022-05-01 22:11:22,283 - tensorflow - INFO - global_step/sec: 88.6341 -2022-05-01 22:11:22,284 - tensorflow - INFO - loss = 0.09752448, step = 2361000 (2.257 sec) -2022-05-01 22:11:24,541 - tensorflow - INFO - global_step/sec: 88.578 -2022-05-01 22:11:24,542 - tensorflow - INFO - loss = 0.021298578, step = 2361200 (2.258 sec) -2022-05-01 22:11:26,800 - tensorflow - INFO - global_step/sec: 88.5334 -2022-05-01 22:11:26,801 - tensorflow - INFO - loss = 0.01599053, step = 2361400 (2.259 sec) -2022-05-01 22:11:29,061 - tensorflow - INFO - global_step/sec: 88.479 -2022-05-01 22:11:29,061 - tensorflow - INFO - loss = 0.019439112, step = 2361600 (2.260 sec) -2022-05-01 22:11:31,325 - tensorflow - INFO - global_step/sec: 88.3495 -2022-05-01 22:11:31,325 - tensorflow - INFO - loss = 0.011619351, step = 2361800 (2.264 sec) -2022-05-01 22:11:33,584 - tensorflow - INFO - global_step/sec: 88.5199 -2022-05-01 22:11:33,584 - tensorflow - INFO - loss = 0.01157747, step = 2362000 (2.259 sec) -2022-05-01 22:11:35,843 - tensorflow - INFO - global_step/sec: 88.5389 -2022-05-01 22:11:35,843 - tensorflow - INFO - loss = 0.012894679, step = 2362200 (2.259 sec) -2022-05-01 22:11:38,096 - tensorflow - INFO - global_step/sec: 88.777 -2022-05-01 22:11:38,096 - tensorflow - INFO - loss = 0.119117185, step = 2362400 (2.253 sec) -2022-05-01 22:11:40,350 - tensorflow - INFO - global_step/sec: 88.7025 -2022-05-01 22:11:40,351 - tensorflow - INFO - loss = 0.01409577, step = 2362600 (2.255 sec) -2022-05-01 22:11:42,605 - tensorflow - INFO - global_step/sec: 88.6949 -2022-05-01 22:11:42,606 - tensorflow - INFO - loss = 0.029172141, step = 2362800 (2.255 sec) -2022-05-01 22:11:44,859 - tensorflow - INFO - global_step/sec: 88.7246 -2022-05-01 22:11:44,860 - tensorflow - INFO - loss = 0.012926551, step = 2363000 (2.254 sec) -2022-05-01 22:11:47,116 - tensorflow - INFO - global_step/sec: 88.6195 -2022-05-01 22:11:47,117 - tensorflow - INFO - loss = 0.019551968, step = 2363200 (2.257 sec) -2022-05-01 22:11:49,370 - tensorflow - INFO - global_step/sec: 88.7435 -2022-05-01 22:11:49,370 - tensorflow - INFO - loss = 0.0049020173, step = 2363400 (2.254 sec) -2022-05-01 22:11:51,621 - tensorflow - INFO - global_step/sec: 88.853 -2022-05-01 22:11:51,621 - tensorflow - INFO - loss = 0.026439264, step = 2363600 (2.251 sec) -2022-05-01 22:11:53,871 - tensorflow - INFO - global_step/sec: 88.8866 -2022-05-01 22:11:53,871 - tensorflow - INFO - loss = 0.010113217, step = 2363800 (2.250 sec) -2022-05-01 22:11:56,129 - tensorflow - INFO - global_step/sec: 88.559 -2022-05-01 22:11:56,130 - tensorflow - INFO - loss = 0.021224327, step = 2364000 (2.258 sec) -2022-05-01 22:11:58,377 - tensorflow - INFO - global_step/sec: 88.9714 -2022-05-01 22:11:58,378 - tensorflow - INFO - loss = 0.18730974, step = 2364200 (2.248 sec) -2022-05-01 22:12:00,636 - tensorflow - INFO - global_step/sec: 88.5341 -2022-05-01 22:12:00,637 - tensorflow - INFO - loss = 0.09812194, step = 2364400 (2.259 sec) -2022-05-01 22:12:02,890 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.03492358, step = 2365800 (2.254 sec) -2022-05-01 22:12:18,670 - tensorflow - INFO - global_step/sec: 88.6344 -2022-05-01 22:12:18,671 - tensorflow - INFO - loss = 0.10256855, step = 2366000 (2.256 sec) -2022-05-01 22:12:20,927 - tensorflow - INFO - global_step/sec: 88.6174 -2022-05-01 22:12:20,928 - tensorflow - INFO - loss = 0.0069527263, step = 2366200 (2.257 sec) -2022-05-01 22:12:23,185 - tensorflow - INFO - global_step/sec: 88.6066 -2022-05-01 22:12:23,185 - tensorflow - INFO - loss = 0.006643961, step = 2366400 (2.257 sec) -2022-05-01 22:12:25,442 - tensorflow - INFO - global_step/sec: 88.577 -2022-05-01 22:12:25,443 - tensorflow - INFO - loss = 0.023996007, step = 2366600 (2.258 sec) -2022-05-01 22:12:27,692 - tensorflow - INFO - global_step/sec: 88.8936 -2022-05-01 22:12:27,693 - tensorflow - INFO - loss = 0.017353734, step = 2366800 (2.250 sec) -2022-05-01 22:12:29,943 - tensorflow - INFO - global_step/sec: 88.8612 -2022-05-01 22:12:29,943 - tensorflow - INFO - loss = 0.012825327, step = 2367000 (2.251 sec) -2022-05-01 22:12:32,201 - tensorflow - INFO - global_step/sec: 88.5717 -2022-05-01 22:12:32,201 - tensorflow - INFO - loss = 0.0131017435, step = 2367200 (2.258 sec) -2022-05-01 22:12:34,457 - tensorflow - INFO - global_step/sec: 88.6408 -2022-05-01 22:12:34,458 - tensorflow - INFO - loss = 0.072256595, step = 2367400 (2.256 sec) -2022-05-01 22:12:36,713 - tensorflow - INFO - global_step/sec: 88.674 -2022-05-01 22:12:36,713 - tensorflow - INFO - loss = 0.014600789, step = 2367600 (2.255 sec) -2022-05-01 22:12:38,966 - tensorflow - INFO - global_step/sec: 88.7686 -2022-05-01 22:12:38,966 - tensorflow - INFO - loss = 0.019045288, step = 2367800 (2.253 sec) -2022-05-01 22:12:41,217 - tensorflow - INFO - global_step/sec: 88.8284 -2022-05-01 22:12:41,218 - tensorflow - INFO - loss = 0.0063287104, step = 2368000 (2.252 sec) -2022-05-01 22:12:43,480 - tensorflow - INFO - global_step/sec: 88.4123 -2022-05-01 22:12:43,480 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.4426 -2022-05-01 22:12:59,326 - tensorflow - INFO - loss = 0.0074239303, step = 2369600 (2.261 sec) -2022-05-01 22:13:01,588 - tensorflow - INFO - global_step/sec: 88.3978 -2022-05-01 22:13:01,588 - tensorflow - INFO - loss = 0.05968262, step = 2369800 (2.262 sec) -2022-05-01 22:13:03,846 - tensorflow - INFO - global_step/sec: 88.5701 -2022-05-01 22:13:03,846 - tensorflow - INFO - loss = 0.0046462216, step = 2370000 (2.258 sec) -2022-05-01 22:13:06,108 - tensorflow - INFO - global_step/sec: 88.3953 -2022-05-01 22:13:06,109 - tensorflow - INFO - loss = 0.0052085514, step = 2370200 (2.263 sec) -2022-05-01 22:13:08,373 - tensorflow - INFO - global_step/sec: 88.3005 -2022-05-01 22:13:08,374 - tensorflow - INFO - loss = 0.009080168, step = 2370400 (2.265 sec) -2022-05-01 22:13:10,635 - tensorflow - INFO - global_step/sec: 88.4213 -2022-05-01 22:13:10,636 - tensorflow - INFO - loss = 0.012946348, step = 2370600 (2.262 sec) -2022-05-01 22:13:12,898 - tensorflow - INFO - global_step/sec: 88.3653 -2022-05-01 22:13:12,899 - tensorflow - INFO - loss = 0.02769855, step = 2370800 (2.263 sec) -2022-05-01 22:13:15,158 - tensorflow - INFO - global_step/sec: 88.5008 -2022-05-01 22:13:15,159 - tensorflow - INFO - loss = 0.010072215, step = 2371000 (2.260 sec) -2022-05-01 22:13:17,412 - tensorflow - INFO - global_step/sec: 88.7519 -2022-05-01 22:13:17,412 - tensorflow - INFO - loss = 0.008546825, step = 2371200 (2.253 sec) -2022-05-01 22:13:19,670 - tensorflow - INFO - global_step/sec: 88.5802 -2022-05-01 22:13:19,670 - tensorflow - INFO - loss = 0.008905073, step = 2371400 (2.258 sec) -2022-05-01 22:13:21,920 - tensorflow - INFO - global_step/sec: 88.8881 -2022-05-01 22:13:21,920 - tensorflow - INFO - loss = 0.03151849, step = 2371600 (2.250 sec) -2022-05-01 22:13:24,172 - tensorflow - INFO - global_step/sec: 88.8046 -2022-05-01 22:13:24,172 - tensorflow - INFO - loss = 0.052477755, step = 2371800 (2.252 sec) -2022-05-01 22:13:26,423 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.040376134, step = 2373200 (2.252 sec) -2022-05-01 22:13:42,208 - tensorflow - INFO - global_step/sec: 88.6747 -2022-05-01 22:13:42,209 - tensorflow - INFO - loss = 0.67771184, step = 2373400 (2.255 sec) -2022-05-01 22:13:44,472 - tensorflow - INFO - global_step/sec: 88.3397 -2022-05-01 22:13:44,473 - tensorflow - INFO - loss = 0.03562181, step = 2373600 (2.264 sec) -2022-05-01 22:13:46,730 - tensorflow - INFO - global_step/sec: 88.5723 -2022-05-01 22:13:46,731 - tensorflow - INFO - loss = 0.0781373, step = 2373800 (2.258 sec) -2022-05-01 22:13:48,979 - tensorflow - INFO - global_step/sec: 88.937 -2022-05-01 22:13:48,980 - tensorflow - INFO - loss = 0.007866849, step = 2374000 (2.249 sec) -2022-05-01 22:13:51,238 - tensorflow - INFO - global_step/sec: 88.546 -2022-05-01 22:13:51,238 - tensorflow - INFO - loss = 0.10566403, step = 2374200 (2.259 sec) -2022-05-01 22:13:53,492 - tensorflow - INFO - global_step/sec: 88.7318 -2022-05-01 22:13:53,492 - tensorflow - INFO - loss = 0.012510798, step = 2374400 (2.254 sec) -2022-05-01 22:13:55,751 - tensorflow - INFO - global_step/sec: 88.5141 -2022-05-01 22:13:55,752 - tensorflow - INFO - loss = 0.21246701, step = 2374600 (2.260 sec) -2022-05-01 22:13:58,004 - tensorflow - INFO - global_step/sec: 88.7723 -2022-05-01 22:13:58,005 - tensorflow - INFO - loss = 0.008742791, step = 2374800 (2.253 sec) -2022-05-01 22:14:00,254 - tensorflow - INFO - global_step/sec: 88.8976 -2022-05-01 22:14:00,255 - tensorflow - INFO - loss = 0.012201543, step = 2375000 (2.250 sec) -2022-05-01 22:14:02,508 - tensorflow - INFO - global_step/sec: 88.7503 -2022-05-01 22:14:02,508 - tensorflow - INFO - loss = 0.2981624, step = 2375200 (2.253 sec) -2022-05-01 22:14:04,764 - tensorflow - INFO - global_step/sec: 88.6274 -2022-05-01 22:14:04,765 - tensorflow - INFO - loss = 0.31821147, step = 2375400 (2.257 sec) -2022-05-01 22:14:07,018 - tensorflow - INFO - global_step/sec: 88.7507 -2022-05-01 22:14:07,018 - tensorflow - INFO - loss = 0.036498245, step = 2375600 (2.254 sec) -2022-05-01 22:14:09,276 - tensorflow - INFO - global_step/sec: 88.5602 -2022-05-01 22:14:09,276 - tensorflow - INFO - loss = 0.017628154, step = 2375800 (2.258 sec) -2022-05-01 22:14:11,525 - tensorflow - INFO - global_step/sec: 88.9153 -2022-05-01 22:14:11,526 - tensorflow - INFO - loss = 0.017715462, step = 2376000 (2.249 sec) -2022-05-01 22:14:13,779 - tensorflow - INFO - global_step/sec: 88.7279 -2022-05-01 22:14:13,780 - tensorflow - INFO - loss = 0.02486781, step = 2376200 (2.254 sec) -2022-05-01 22:14:16,036 - tensorflow - INFO - global_step/sec: 88.6359 -2022-05-01 22:14:16,036 - tensorflow - INFO - loss = 0.009574259, step = 2376400 (2.256 sec) -2022-05-01 22:14:18,295 - tensorflow - INFO - global_step/sec: 88.5367 -2022-05-01 22:14:18,295 - tensorflow - INFO - loss = 0.037036, step = 2376600 (2.259 sec) -2022-05-01 22:14:20,557 - tensorflow - INFO - global_step/sec: 88.4025 -2022-05-01 22:14:20,558 - tensorflow - INFO - loss = 0.020934843, step = 2376800 (2.262 sec) -2022-05-01 22:14:22,810 - tensorflow - INFO - global_step/sec: 88.7867 -2022-05-01 22:14:22,810 - tensorflow - INFO - loss = 0.036132008, step = 2377000 (2.252 sec) -2022-05-01 22:14:25,056 - tensorflow - INFO - global_step/sec: 89.0351 -2022-05-01 22:14:25,057 - tensorflow - INFO - loss = 0.033706702, step = 2377200 (2.246 sec) -2022-05-01 22:14:27,310 - tensorflow - INFO - global_step/sec: 88.7539 -2022-05-01 22:14:27,310 - tensorflow - INFO - loss = 0.01699885, step = 2377400 (2.253 sec) -2022-05-01 22:14:29,563 - tensorflow - INFO - global_step/sec: 88.7562 -2022-05-01 22:14:29,563 - tensorflow - INFO - loss = 0.022424644, step = 2377600 (2.253 sec) -2022-05-01 22:14:31,821 - tensorflow - INFO - global_step/sec: 88.58 -2022-05-01 22:14:31,821 - tensorflow - INFO - loss = 0.01934461, step = 2377800 (2.258 sec) -2022-05-01 22:14:34,078 - tensorflow - INFO - global_step/sec: 88.6221 -2022-05-01 22:14:34,078 - tensorflow - INFO - loss = 0.0123040015, step = 2378000 (2.257 sec) -2022-05-01 22:14:36,333 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.01770787, step = 2379400 (2.252 sec) -2022-05-01 22:14:52,100 - tensorflow - INFO - global_step/sec: 88.835 -2022-05-01 22:14:52,100 - tensorflow - INFO - loss = 0.0119537, step = 2379600 (2.251 sec) -2022-05-01 22:14:54,354 - tensorflow - INFO - global_step/sec: 88.7293 -2022-05-01 22:14:54,354 - tensorflow - INFO - loss = 0.076885924, step = 2379800 (2.254 sec) -2022-05-01 22:14:56,608 - tensorflow - INFO - global_step/sec: 88.7097 -2022-05-01 22:14:56,609 - tensorflow - INFO - loss = 0.01063635, step = 2380000 (2.255 sec) -2022-05-01 22:14:58,868 - tensorflow - INFO - global_step/sec: 88.5057 -2022-05-01 22:14:58,868 - tensorflow - INFO - loss = 0.029029515, step = 2380200 (2.260 sec) -2022-05-01 22:15:01,113 - tensorflow - INFO - global_step/sec: 89.0694 -2022-05-01 22:15:01,114 - tensorflow - INFO - loss = 0.04142113, step = 2380400 (2.245 sec) -2022-05-01 22:15:03,371 - tensorflow - INFO - global_step/sec: 88.5906 -2022-05-01 22:15:03,371 - tensorflow - INFO - loss = 0.008125562, step = 2380600 (2.258 sec) -2022-05-01 22:15:05,625 - tensorflow - INFO - global_step/sec: 88.7348 -2022-05-01 22:15:05,625 - tensorflow - INFO - loss = 0.7975176, step = 2380800 (2.254 sec) -2022-05-01 22:15:07,883 - tensorflow - INFO - global_step/sec: 88.5644 -2022-05-01 22:15:07,884 - tensorflow - INFO - loss = 0.0044040154, step = 2381000 (2.258 sec) -2022-05-01 22:15:10,136 - tensorflow - INFO - global_step/sec: 88.784 -2022-05-01 22:15:10,136 - tensorflow - INFO - loss = 0.039620418, step = 2381200 (2.253 sec) -2022-05-01 22:15:12,395 - tensorflow - INFO - global_step/sec: 88.5206 -2022-05-01 22:15:12,396 - tensorflow - INFO - loss = 0.017591197, step = 2381400 (2.259 sec) -2022-05-01 22:15:14,647 - tensorflow - INFO - global_step/sec: 88.8092 -2022-05-01 22:15:14,648 - tensorflow - INFO - loss = 0.097436376, step = 2381600 (2.252 sec) -2022-05-01 22:15:16,901 - tensorflow - INFO - global_step/sec: 88.7243 -2022-05-01 22:15:16,902 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.7508 -2022-05-01 22:15:32,684 - tensorflow - INFO - loss = 0.02424026, step = 2383200 (2.253 sec) -2022-05-01 22:15:34,936 - tensorflow - INFO - global_step/sec: 88.8125 -2022-05-01 22:15:34,936 - tensorflow - INFO - loss = 0.04004424, step = 2383400 (2.252 sec) -2022-05-01 22:15:37,192 - tensorflow - INFO - global_step/sec: 88.6456 -2022-05-01 22:15:37,192 - tensorflow - INFO - loss = 0.06911096, step = 2383600 (2.256 sec) -2022-05-01 22:15:39,448 - tensorflow - INFO - global_step/sec: 88.6641 -2022-05-01 22:15:39,448 - tensorflow - INFO - loss = 0.0045621977, step = 2383800 (2.256 sec) -2022-05-01 22:15:41,704 - tensorflow - INFO - global_step/sec: 88.6504 -2022-05-01 22:15:41,704 - tensorflow - INFO - loss = 0.01261645, step = 2384000 (2.256 sec) -2022-05-01 22:15:43,957 - tensorflow - INFO - global_step/sec: 88.7578 -2022-05-01 22:15:43,957 - tensorflow - INFO - loss = 0.0063984967, step = 2384200 (2.253 sec) -2022-05-01 22:15:46,210 - tensorflow - INFO - global_step/sec: 88.7579 -2022-05-01 22:15:46,211 - tensorflow - INFO - loss = 0.09875474, step = 2384400 (2.253 sec) -2022-05-01 22:15:48,463 - tensorflow - INFO - global_step/sec: 88.7832 -2022-05-01 22:15:48,463 - tensorflow - INFO - loss = 0.0077966563, step = 2384600 (2.253 sec) -2022-05-01 22:15:50,723 - tensorflow - INFO - global_step/sec: 88.5068 -2022-05-01 22:15:50,723 - tensorflow - INFO - loss = 0.0064509623, step = 2384800 (2.260 sec) -2022-05-01 22:15:52,979 - tensorflow - INFO - global_step/sec: 88.6624 -2022-05-01 22:15:52,979 - tensorflow - INFO - loss = 0.016903702, step = 2385000 (2.256 sec) -2022-05-01 22:15:55,232 - tensorflow - INFO - global_step/sec: 88.748 -2022-05-01 22:15:55,232 - tensorflow - INFO - loss = 0.0073978687, step = 2385200 (2.254 sec) -2022-05-01 22:15:57,488 - tensorflow - INFO - global_step/sec: 88.656 -2022-05-01 22:15:57,488 - tensorflow - INFO - loss = 0.04489147, step = 2385400 (2.256 sec) -2022-05-01 22:15:59,742 - tensorflow - INFO - global_step/sec: 88.7456 -2022-05-01 22:15:59,742 - tensorflow - INFO - loss = 0.016343482, step = 2385600 (2.254 sec) -2022-05-01 22:16:02,001 - tensorflow - INFO - global_step/sec: 88.5057 -2022-05-01 22:16:02,002 - tensorflow - INFO - loss = 0.008009482, step = 2385800 (2.260 sec) -2022-05-01 22:16:04,257 - tensorflow - INFO - global_step/sec: 88.6761 -2022-05-01 22:16:04,257 - tensorflow - INFO - loss = 0.0510852, step = 2386000 (2.255 sec) -2022-05-01 22:16:06,510 - tensorflow - INFO - global_step/sec: 88.7768 -2022-05-01 22:16:06,510 - tensorflow - INFO - loss = 0.058420647, step = 2386200 (2.253 sec) -2022-05-01 22:16:08,761 - tensorflow - INFO - global_step/sec: 88.8516 -2022-05-01 22:16:08,761 - tensorflow - INFO - loss = 0.018244365, step = 2386400 (2.251 sec) -2022-05-01 22:16:11,013 - tensorflow - INFO - global_step/sec: 88.8016 -2022-05-01 22:16:11,013 - tensorflow - INFO - loss = 0.07714485, step = 2386600 (2.252 sec) -2022-05-01 22:16:13,266 - tensorflow - INFO - global_step/sec: 88.7739 -2022-05-01 22:16:13,266 - tensorflow - INFO - loss = 0.1699921, step = 2386800 (2.253 sec) -2022-05-01 22:16:15,529 - tensorflow - INFO - global_step/sec: 88.3722 -2022-05-01 22:16:15,529 - tensorflow - INFO - loss = 0.03827375, step = 2387000 (2.263 sec) -2022-05-01 22:16:17,789 - tensorflow - INFO - global_step/sec: 88.5068 -2022-05-01 22:16:17,789 - tensorflow - INFO - loss = 0.046373516, step = 2387200 (2.260 sec) -2022-05-01 22:16:20,055 - tensorflow - INFO - global_step/sec: 88.2586 -2022-05-01 22:16:20,055 - tensorflow - INFO - loss = 0.013266853, step = 2387400 (2.266 sec) -2022-05-01 22:16:22,317 - tensorflow - INFO - global_step/sec: 88.3962 -2022-05-01 22:16:22,318 - tensorflow - INFO - loss = 0.021442072, step = 2387600 (2.263 sec) -2022-05-01 22:16:24,578 - tensorflow - INFO - global_step/sec: 88.4747 -2022-05-01 22:16:24,578 - tensorflow - INFO - loss = 0.10181264, step = 2387800 (2.260 sec) -2022-05-01 22:16:26,841 - tensorflow - INFO - global_step/sec: 88.3802 -2022-05-01 22:16:26,841 - tensorflow - INFO - loss = 0.05081415, step = 2388000 (2.263 sec) -2022-05-01 22:16:29,106 - tensorflow - INFO - global_step/sec: 88.3035 -2022-05-01 22:16:29,106 - tensorflow - INFO - loss = 0.025215954, step = 2388200 (2.265 sec) -2022-05-01 22:16:31,376 - tensorflow - INFO - global_step/sec: 88.071 -2022-05-01 22:16:31,377 - tensorflow - INFO - loss = 0.017948493, step = 2388400 (2.271 sec) -2022-05-01 22:16:33,645 - tensorflow - INFO - global_step/sec: 88.1614 -2022-05-01 22:16:33,645 - tensorflow - INFO - loss = 0.008095983, step = 2388600 (2.269 sec) -2022-05-01 22:16:35,909 - tensorflow - INFO - global_step/sec: 88.3501 -2022-05-01 22:16:35,909 - tensorflow - INFO - loss = 0.030127618, step = 2388800 (2.264 sec) -2022-05-01 22:16:38,171 - tensorflow - INFO - global_step/sec: 88.4056 -2022-05-01 22:16:38,171 - tensorflow - INFO - loss = 0.12454489, step = 2389000 (2.262 sec) -2022-05-01 22:16:40,432 - tensorflow - INFO - global_step/sec: 88.4749 -2022-05-01 22:16:40,432 - tensorflow - INFO - loss = 0.013886469, step = 2389200 (2.260 sec) -2022-05-01 22:16:42,694 - tensorflow - INFO - global_step/sec: 88.405 -2022-05-01 22:16:42,694 - tensorflow - INFO - loss = 0.005526525, step = 2389400 (2.262 sec) -2022-05-01 22:16:44,958 - tensorflow - INFO - global_step/sec: 88.3333 -2022-05-01 22:16:44,958 - tensorflow - INFO - loss = 0.018395491, step = 2389600 (2.264 sec) -2022-05-01 22:16:47,219 - tensorflow - INFO - global_step/sec: 88.4527 -2022-05-01 22:16:47,220 - tensorflow - INFO - loss = 0.00997955, step = 2389800 (2.261 sec) -2022-05-01 22:16:49,470 - tensorflow - INFO - global_step/sec: 88.8441 -2022-05-01 22:16:49,471 - tensorflow - INFO - loss = 0.0037690697, step = 2390000 (2.251 sec) -2022-05-01 22:16:51,721 - tensorflow - INFO - global_step/sec: 88.863 -2022-05-01 22:16:51,721 - tensorflow - INFO - loss = 0.013914542, step = 2390200 (2.251 sec) -2022-05-01 22:16:53,980 - tensorflow - INFO - global_step/sec: 88.5495 -2022-05-01 22:16:53,980 - tensorflow - INFO - loss = 0.009430278, step = 2390400 (2.259 sec) -2022-05-01 22:16:56,239 - tensorflow - INFO - global_step/sec: 88.5315 -2022-05-01 22:16:56,239 - tensorflow - INFO - loss = 0.024905298, step = 2390600 (2.259 sec) -2022-05-01 22:16:58,494 - tensorflow - INFO - global_step/sec: 88.6647 -2022-05-01 22:16:58,495 - tensorflow - INFO - loss = 0.0072831106, step = 2390800 (2.256 sec) -2022-05-01 22:17:00,745 - tensorflow - INFO - global_step/sec: 88.8468 -2022-05-01 22:17:00,746 - tensorflow - INFO - loss = 0.03236274, step = 2391000 (2.251 sec) -2022-05-01 22:17:03,000 - tensorflow - INFO - global_step/sec: 88.7116 -2022-05-01 22:17:03,000 - tensorflow - INFO - loss = 0.015077673, step = 2391200 (2.255 sec) -2022-05-01 22:17:05,257 - tensorflow - INFO - global_step/sec: 88.5927 -2022-05-01 22:17:05,258 - tensorflow - INFO - loss = 0.0067395577, step = 2391400 (2.258 sec) -2022-05-01 22:17:07,509 - tensorflow - INFO - global_step/sec: 88.8246 -2022-05-01 22:17:07,509 - tensorflow - INFO - loss = 0.019622741, step = 2391600 (2.252 sec) -2022-05-01 22:17:09,765 - tensorflow - INFO - global_step/sec: 88.6531 -2022-05-01 22:17:09,765 - tensorflow - INFO - loss = 0.014148858, step = 2391800 (2.256 sec) -2022-05-01 22:17:12,020 - tensorflow - INFO - global_step/sec: 88.6967 -2022-05-01 22:17:12,020 - tensorflow - INFO - loss = 0.005203164, step = 2392000 (2.255 sec) -2022-05-01 22:17:14,276 - tensorflow - INFO - global_step/sec: 88.645 -2022-05-01 22:17:14,277 - tensorflow - INFO - loss = 0.040775083, step = 2392200 (2.256 sec) -2022-05-01 22:17:16,531 - tensorflow - INFO - global_step/sec: 88.705 -2022-05-01 22:17:16,531 - tensorflow - INFO - loss = 0.07990815, step = 2392400 (2.255 sec) -2022-05-01 22:17:18,782 - tensorflow - INFO - global_step/sec: 88.8364 -2022-05-01 22:17:18,782 - tensorflow - INFO - loss = 0.041910116, step = 2392600 (2.251 sec) -2022-05-01 22:17:21,034 - tensorflow - INFO - global_step/sec: 88.8098 -2022-05-01 22:17:21,034 - tensorflow - INFO - loss = 0.022378735, step = 2392800 (2.252 sec) -2022-05-01 22:17:23,292 - tensorflow - INFO - global_step/sec: 88.5926 -2022-05-01 22:17:23,292 - tensorflow - INFO - loss = 0.22880238, step = 2393000 (2.258 sec) -2022-05-01 22:17:25,543 - tensorflow - INFO - global_step/sec: 88.8418 -2022-05-01 22:17:25,543 - tensorflow - INFO - loss = 0.029437074, step = 2393200 (2.251 sec) -2022-05-01 22:17:27,794 - tensorflow - INFO - global_step/sec: 88.8368 -2022-05-01 22:17:27,795 - tensorflow - INFO - loss = 0.02802345, step = 2393400 (2.251 sec) -2022-05-01 22:17:30,049 - tensorflow - INFO - global_step/sec: 88.6802 -2022-05-01 22:17:30,050 - tensorflow - INFO - loss = 0.063665904, step = 2393600 (2.255 sec) -2022-05-01 22:17:32,307 - tensorflow - INFO - global_step/sec: 88.5758 -2022-05-01 22:17:32,308 - tensorflow - INFO - loss = 0.021801218, step = 2393800 (2.258 sec) -2022-05-01 22:17:34,568 - tensorflow - INFO - global_step/sec: 88.4643 -2022-05-01 22:17:34,569 - tensorflow - INFO - loss = 0.11958213, step = 2394000 (2.261 sec) -2022-05-01 22:17:36,821 - tensorflow - INFO - global_step/sec: 88.7855 -2022-05-01 22:17:36,821 - tensorflow - INFO - loss = 0.028965376, step = 2394200 (2.253 sec) -2022-05-01 22:17:39,071 - tensorflow - INFO - global_step/sec: 88.8764 -2022-05-01 22:17:39,072 - tensorflow - INFO - loss = 0.0116002485, step = 2394400 (2.250 sec) -2022-05-01 22:17:41,326 - tensorflow - INFO - global_step/sec: 88.6799 -2022-05-01 22:17:41,327 - tensorflow - INFO - loss = 0.01459889, step = 2394600 (2.255 sec) -2022-05-01 22:17:43,582 - tensorflow - INFO - global_step/sec: 88.6758 -2022-05-01 22:17:43,582 - tensorflow - INFO - loss = 0.077938415, step = 2394800 (2.255 sec) -2022-05-01 22:17:45,836 - tensorflow - INFO - global_step/sec: 88.7167 -2022-05-01 22:17:45,837 - tensorflow - INFO - loss = 0.032044075, step = 2395000 (2.254 sec) -2022-05-01 22:17:48,097 - tensorflow - INFO - global_step/sec: 88.4702 -2022-05-01 22:17:48,097 - tensorflow - INFO - loss = 0.03634771, step = 2395200 (2.261 sec) -2022-05-01 22:17:50,352 - tensorflow - INFO - global_step/sec: 88.6998 -2022-05-01 22:17:50,352 - tensorflow - INFO - loss = 0.07824456, step = 2395400 (2.255 sec) -2022-05-01 22:17:52,603 - tensorflow - INFO - global_step/sec: 88.8301 -2022-05-01 22:17:52,603 - tensorflow - INFO - loss = 0.0139762, step = 2395600 (2.251 sec) -2022-05-01 22:17:54,855 - tensorflow - INFO - global_step/sec: 88.8065 -2022-05-01 22:17:54,856 - tensorflow - INFO - loss = 0.009006003, step = 2395800 (2.252 sec) -2022-05-01 22:17:57,107 - tensorflow - INFO - global_step/sec: 88.804 -2022-05-01 22:17:57,108 - tensorflow - INFO - loss = 0.019991372, step = 2396000 (2.252 sec) -2022-05-01 22:17:59,358 - tensorflow - INFO - global_step/sec: 88.8513 -2022-05-01 22:17:59,359 - tensorflow - INFO - loss = 0.5126882, step = 2396200 (2.251 sec) -2022-05-01 22:18:01,617 - tensorflow - INFO - global_step/sec: 88.5474 -2022-05-01 22:18:01,617 - tensorflow - INFO - loss = 0.01509488, step = 2396400 (2.259 sec) -2022-05-01 22:18:03,870 - tensorflow - INFO - global_step/sec: 88.7642 -2022-05-01 22:18:03,871 - tensorflow - INFO - loss = 0.01103553, step = 2396600 (2.253 sec) -2022-05-01 22:18:06,124 - tensorflow - INFO - global_step/sec: 88.7297 -2022-05-01 22:18:06,125 - tensorflow - INFO - loss = 0.01015729, step = 2396800 (2.254 sec) -2022-05-01 22:18:08,379 - tensorflow - INFO - global_step/sec: 88.7154 -2022-05-01 22:18:08,379 - tensorflow - INFO - loss = 0.01095845, step = 2397000 (2.254 sec) -2022-05-01 22:18:10,628 - tensorflow - INFO - global_step/sec: 88.9017 -2022-05-01 22:18:10,629 - tensorflow - INFO - loss = 0.019706031, step = 2397200 (2.250 sec) -2022-05-01 22:18:12,888 - tensorflow - INFO - global_step/sec: 88.5183 -2022-05-01 22:18:12,888 - tensorflow - INFO - loss = 0.01793155, step = 2397400 (2.259 sec) -2022-05-01 22:18:15,137 - tensorflow - INFO - global_step/sec: 88.9022 -2022-05-01 22:18:15,138 - tensorflow - INFO - loss = 0.0090178745, step = 2397600 (2.250 sec) -2022-05-01 22:18:17,389 - tensorflow - INFO - global_step/sec: 88.8423 -2022-05-01 22:18:17,389 - tensorflow - INFO - loss = 0.004080846, step = 2397800 (2.251 sec) -2022-05-01 22:18:19,643 - tensorflow - INFO - global_step/sec: 88.7274 -2022-05-01 22:18:19,643 - tensorflow - INFO - loss = 0.013300663, step = 2398000 (2.254 sec) -2022-05-01 22:18:21,900 - tensorflow - INFO - global_step/sec: 88.5801 -2022-05-01 22:18:21,901 - tensorflow - INFO - loss = 0.029440576, step = 2398200 (2.258 sec) -2022-05-01 22:18:24,157 - tensorflow - INFO - global_step/sec: 88.6256 -2022-05-01 22:18:24,158 - tensorflow - INFO - loss = 0.021608679, step = 2398400 (2.257 sec) -2022-05-01 22:18:26,413 - tensorflow - INFO - global_step/sec: 88.6579 -2022-05-01 22:18:26,413 - tensorflow - INFO - loss = 0.013700175, step = 2398600 (2.256 sec) -2022-05-01 22:18:28,669 - tensorflow - INFO - global_step/sec: 88.662 -2022-05-01 22:18:28,669 - tensorflow - INFO - loss = 0.018216874, step = 2398800 (2.256 sec) -2022-05-01 22:18:30,923 - tensorflow - INFO - global_step/sec: 88.7059 -2022-05-01 22:18:30,924 - tensorflow - INFO - loss = 0.087347396, step = 2399000 (2.255 sec) -2022-05-01 22:18:33,175 - tensorflow - INFO - global_step/sec: 88.8152 -2022-05-01 22:18:33,176 - tensorflow - INFO - loss = 0.010069039, step = 2399200 (2.252 sec) -2022-05-01 22:18:35,430 - tensorflow - INFO - global_step/sec: 88.7169 -2022-05-01 22:18:35,430 - tensorflow - INFO - loss = 0.0043498473, step = 2399400 (2.254 sec) -2022-05-01 22:18:37,685 - tensorflow - INFO - global_step/sec: 88.6671 -2022-05-01 22:18:37,686 - tensorflow - INFO - loss = 0.045280192, step = 2399600 (2.256 sec) -2022-05-01 22:18:39,940 - tensorflow - INFO - global_step/sec: 88.7167 -2022-05-01 22:18:39,940 - tensorflow - INFO - loss = 0.009058259, step = 2399800 (2.254 sec) -2022-05-01 22:18:42,178 - tensorflow - INFO - Saving checkpoints for 2400000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. -2022-05-01 22:18:42,193 - tensorflow - INFO - model.ckpt-2400000 is not in all_model_checkpoint_paths. Manually adding it. -2022-05-01 22:18:42,743 - tensorflow - INFO - Calling model_fn. -2022-05-01 22:18:42,869 - tensorflow - INFO - Done calling model_fn. -2022-05-01 22:18:42,884 - tensorflow - INFO - Starting evaluation at 2022-05-01T22:18:42Z -2022-05-01 22:18:42,947 - tensorflow - INFO - Graph was finalized. -2022-05-01 22:18:42,951 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-2400000 -2022-05-01 22:18:43,230 - tensorflow - INFO - Running local_init_op. -2022-05-01 22:18:43,338 - tensorflow - INFO - Done running local_init_op. -2022-05-01 22:18:53,570 - tensorflow - INFO - Evaluation [3990/39900] -2022-05-01 22:19:03,090 - tensorflow - INFO - Evaluation [7980/39900] -2022-05-01 22:19:12,586 - tensorflow - INFO - Evaluation [11970/39900] -2022-05-01 22:19:22,093 - tensorflow - INFO - Evaluation [15960/39900] -2022-05-01 22:19:31,631 - tensorflow - INFO - Evaluation [19950/39900] -2022-05-01 22:19:41,171 - tensorflow - INFO - Evaluation [23940/39900] -2022-05-01 22:19:50,691 - tensorflow - INFO - Evaluation [27930/39900] -2022-05-01 22:20:00,276 - tensorflow - INFO - Evaluation [31920/39900] -2022-05-01 22:20:09,832 - tensorflow - INFO - Evaluation [35910/39900] -2022-05-01 22:20:19,401 - tensorflow - INFO - Evaluation [39900/39900] -2022-05-01 22:20:19,539 - tensorflow - INFO - Finished evaluation at 2022-05-01-22:20:19 -2022-05-01 22:20:19,539 - tensorflow - INFO - Saving dict for global step 2400000: global_step = 2400000, loss = 0.0163621, mean_degree_err = 3.2771058 -2022-05-01 22:20:19,540 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 2400000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-2400000 -2022-05-01 22:20:19,553 - tensorflow - INFO - global_step/sec: 2.00776 -2022-05-01 22:20:19,554 - tensorflow - INFO - loss = 0.017489435, step = 2400000 (99.614 sec) -2022-05-01 22:20:21,826 - tensorflow - INFO - global_step/sec: 87.9881 -2022-05-01 22:20:21,827 - tensorflow - INFO - loss = 0.07216262, step = 2400200 (2.273 sec) -2022-05-01 22:20:24,081 - tensorflow - INFO - global_step/sec: 88.6938 -2022-05-01 22:20:24,082 - tensorflow - INFO - loss = 0.092333764, step = 2400400 (2.255 sec) -2022-05-01 22:20:26,335 - tensorflow - INFO - global_step/sec: 88.7409 -2022-05-01 22:20:26,336 - tensorflow - INFO - loss = 0.0081663225, step = 2400600 (2.254 sec) -2022-05-01 22:20:28,588 - tensorflow - INFO - global_step/sec: 88.7691 -2022-05-01 22:20:28,589 - tensorflow - INFO - loss = 0.008921932, step = 2400800 (2.253 sec) -2022-05-01 22:20:30,851 - tensorflow - INFO - global_step/sec: 88.3872 -2022-05-01 22:20:30,851 - tensorflow - INFO - loss = 0.020071022, step = 2401000 (2.263 sec) -2022-05-01 22:20:33,106 - tensorflow - INFO - global_step/sec: 88.6718 -2022-05-01 22:20:33,107 - tensorflow - INFO - loss = 0.029509716, step = 2401200 (2.256 sec) -2022-05-01 22:20:35,366 - tensorflow - INFO - global_step/sec: 88.5227 -2022-05-01 22:20:35,366 - tensorflow - INFO - loss = 0.021414602, step = 2401400 (2.259 sec) -2022-05-01 22:20:37,615 - tensorflow - INFO - global_step/sec: 88.9051 -2022-05-01 22:20:37,616 - tensorflow - INFO - loss = 0.0034311865, step = 2401600 (2.250 sec) -2022-05-01 22:20:39,872 - tensorflow - INFO - global_step/sec: 88.6275 -2022-05-01 22:20:39,872 - tensorflow - INFO - loss = 0.045313366, step = 2401800 (2.257 sec) -2022-05-01 22:20:42,122 - tensorflow - INFO - global_step/sec: 88.9013 -2022-05-01 22:20:42,122 - tensorflow - INFO - loss = 0.018083166, step = 2402000 (2.250 sec) -2022-05-01 22:20:44,372 - tensorflow - INFO - global_step/sec: 88.8594 -2022-05-01 22:20:44,373 - tensorflow - INFO - loss = 0.011318088, step = 2402200 (2.251 sec) -2022-05-01 22:20:46,626 - tensorflow - INFO - global_step/sec: 88.7272 -2022-05-01 22:20:46,627 - tensorflow - INFO - loss = 0.011177439, step = 2402400 (2.254 sec) -2022-05-01 22:20:48,879 - tensorflow - INFO - global_step/sec: 88.7988 -2022-05-01 22:20:48,879 - tensorflow - INFO - loss = 0.036629625, step = 2402600 (2.252 sec) -2022-05-01 22:20:51,136 - tensorflow - INFO - global_step/sec: 88.5901 -2022-05-01 22:20:51,137 - tensorflow - INFO - loss = 0.09368414, step = 2402800 (2.258 sec) -2022-05-01 22:20:53,387 - tensorflow - INFO - global_step/sec: 88.8506 -2022-05-01 22:20:53,388 - tensorflow - INFO - loss = 0.07625322, step = 2403000 (2.251 sec) -2022-05-01 22:20:55,643 - tensorflow - INFO - global_step/sec: 88.6679 -2022-05-01 22:20:55,643 - tensorflow - INFO - loss = 0.0075386334, step = 2403200 (2.256 sec) -2022-05-01 22:20:57,896 - tensorflow - INFO - global_step/sec: 88.7743 -2022-05-01 22:20:57,896 - tensorflow - INFO - loss = 0.01009267, step = 2403400 (2.253 sec) -2022-05-01 22:21:00,153 - tensorflow - INFO - global_step/sec: 88.6053 -2022-05-01 22:21:00,153 - tensorflow - INFO - loss = 0.021714091, step = 2403600 (2.257 sec) -2022-05-01 22:21:02,406 - tensorflow - INFO - global_step/sec: 88.7812 -2022-05-01 22:21:02,406 - tensorflow - INFO - loss = 0.03484129, step = 2403800 (2.253 sec) -2022-05-01 22:21:04,653 - tensorflow - INFO - global_step/sec: 88.9997 -2022-05-01 22:21:04,653 - tensorflow - INFO - loss = 0.046384007, step = 2404000 (2.247 sec) -2022-05-01 22:21:06,901 - tensorflow - INFO - global_step/sec: 88.9751 -2022-05-01 22:21:06,901 - tensorflow - INFO - loss = 0.017480731, step = 2404200 (2.248 sec) -2022-05-01 22:21:09,153 - tensorflow - INFO - global_step/sec: 88.8041 -2022-05-01 22:21:09,153 - tensorflow - INFO - loss = 0.02646036, step = 2404400 (2.252 sec) -2022-05-01 22:21:11,411 - tensorflow - INFO - global_step/sec: 88.5687 -2022-05-01 22:21:11,411 - tensorflow - INFO - loss = 0.01452543, step = 2404600 (2.258 sec) -2022-05-01 22:21:13,662 - tensorflow - INFO - global_step/sec: 88.8672 -2022-05-01 22:21:13,662 - tensorflow - INFO - loss = 0.00674339, step = 2404800 (2.251 sec) -2022-05-01 22:21:15,916 - tensorflow - INFO - global_step/sec: 88.7083 -2022-05-01 22:21:15,917 - tensorflow - INFO - loss = 0.013400199, step = 2405000 (2.255 sec) -2022-05-01 22:21:18,168 - tensorflow - INFO - global_step/sec: 88.8052 -2022-05-01 22:21:18,169 - tensorflow - INFO - loss = 0.010974922, step = 2405200 (2.252 sec) -2022-05-01 22:21:20,419 - tensorflow - INFO - global_step/sec: 88.8626 -2022-05-01 22:21:20,419 - tensorflow - INFO - loss = 0.013504675, step = 2405400 (2.251 sec) -2022-05-01 22:21:22,675 - tensorflow - INFO - global_step/sec: 88.6573 -2022-05-01 22:21:22,675 - tensorflow - INFO - loss = 0.07476987, step = 2405600 (2.256 sec) -2022-05-01 22:21:24,939 - tensorflow - INFO - global_step/sec: 88.3407 -2022-05-01 22:21:24,939 - tensorflow - INFO - loss = 0.07896981, step = 2405800 (2.264 sec) -2022-05-01 22:21:27,199 - tensorflow - INFO - global_step/sec: 88.4758 -2022-05-01 22:21:27,200 - tensorflow - INFO - loss = 0.022436867, step = 2406000 (2.260 sec) -2022-05-01 22:21:29,450 - tensorflow - INFO - global_step/sec: 88.8492 -2022-05-01 22:21:29,451 - tensorflow - INFO - loss = 0.012631004, step = 2406200 (2.251 sec) -2022-05-01 22:21:31,712 - tensorflow - INFO - global_step/sec: 88.4304 -2022-05-01 22:21:31,712 - tensorflow - INFO - loss = 0.039282817, step = 2406400 (2.262 sec) -2022-05-01 22:21:33,969 - tensorflow - INFO - global_step/sec: 88.6097 -2022-05-01 22:21:33,969 - tensorflow - INFO - loss = 0.032221712, step = 2406600 (2.257 sec) -2022-05-01 22:21:36,221 - tensorflow - INFO - global_step/sec: 88.7976 -2022-05-01 22:21:36,222 - tensorflow - INFO - loss = 0.025849657, step = 2406800 (2.252 sec) -2022-05-01 22:21:38,481 - tensorflow - INFO - global_step/sec: 88.505 -2022-05-01 22:21:38,482 - tensorflow - INFO - loss = 0.0321095, step = 2407000 (2.260 sec) -2022-05-01 22:21:40,732 - tensorflow - INFO - global_step/sec: 88.8545 -2022-05-01 22:21:40,732 - tensorflow - INFO - loss = 0.024980348, step = 2407200 (2.251 sec) -2022-05-01 22:21:42,986 - tensorflow - INFO - global_step/sec: 88.7484 -2022-05-01 22:21:42,986 - tensorflow - INFO - loss = 0.28917772, step = 2407400 (2.254 sec) -2022-05-01 22:21:45,239 - tensorflow - INFO - global_step/sec: 88.7736 -2022-05-01 22:21:45,239 - tensorflow - INFO - loss = 0.005209112, step = 2407600 (2.253 sec) -2022-05-01 22:21:47,492 - tensorflow - INFO - global_step/sec: 88.7671 -2022-05-01 22:21:47,492 - tensorflow - INFO - loss = 0.020049214, step = 2407800 (2.253 sec) -2022-05-01 22:21:49,750 - tensorflow - INFO - global_step/sec: 88.5526 -2022-05-01 22:21:49,751 - tensorflow - INFO - loss = 0.032050703, step = 2408000 (2.259 sec) -2022-05-01 22:21:52,005 - tensorflow - INFO - global_step/sec: 88.7151 -2022-05-01 22:21:52,005 - tensorflow - INFO - loss = 0.011353227, step = 2408200 (2.254 sec) -2022-05-01 22:21:54,262 - tensorflow - INFO - global_step/sec: 88.6049 -2022-05-01 22:21:54,262 - tensorflow - INFO - loss = 0.00505063, step = 2408400 (2.257 sec) -2022-05-01 22:21:56,519 - tensorflow - INFO - global_step/sec: 88.614 -2022-05-01 22:21:56,519 - tensorflow - INFO - loss = 0.07129796, step = 2408600 (2.257 sec) -2022-05-01 22:21:58,773 - tensorflow - INFO - global_step/sec: 88.7389 -2022-05-01 22:21:58,773 - tensorflow - INFO - loss = 0.04968427, step = 2408800 (2.254 sec) -2022-05-01 22:22:01,032 - tensorflow - INFO - global_step/sec: 88.5163 -2022-05-01 22:22:01,032 - tensorflow - INFO - loss = 0.080510594, step = 2409000 (2.260 sec) -2022-05-01 22:22:03,290 - tensorflow - INFO - global_step/sec: 88.5805 -2022-05-01 22:22:03,290 - tensorflow - INFO - loss = 0.015240785, step = 2409200 (2.258 sec) -2022-05-01 22:22:05,541 - tensorflow - INFO - global_step/sec: 88.8291 -2022-05-01 22:22:05,542 - tensorflow - INFO - loss = 0.041671883, step = 2409400 (2.252 sec) -2022-05-01 22:22:07,792 - tensorflow - INFO - global_step/sec: 88.8674 -2022-05-01 22:22:07,792 - tensorflow - INFO - loss = 0.016763734, step = 2409600 (2.251 sec) -2022-05-01 22:22:10,045 - tensorflow - INFO - global_step/sec: 88.7713 -2022-05-01 22:22:10,045 - tensorflow - INFO - loss = 0.022382911, step = 2409800 (2.253 sec) -2022-05-01 22:22:12,304 - tensorflow - INFO - global_step/sec: 88.517 -2022-05-01 22:22:12,305 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.4631 -2022-05-01 22:22:28,084 - tensorflow - INFO - loss = 0.0067545497, step = 2411400 (2.261 sec) -2022-05-01 22:22:30,341 - tensorflow - INFO - global_step/sec: 88.5863 -2022-05-01 22:22:30,341 - tensorflow - INFO - loss = 0.0063256193, step = 2411600 (2.258 sec) -2022-05-01 22:22:32,599 - tensorflow - INFO - global_step/sec: 88.5583 -2022-05-01 22:22:32,600 - tensorflow - INFO - loss = 0.044202406, step = 2411800 (2.258 sec) -2022-05-01 22:22:34,850 - tensorflow - INFO - global_step/sec: 88.8734 -2022-05-01 22:22:34,850 - tensorflow - INFO - loss = 0.05210459, step = 2412000 (2.250 sec) -2022-05-01 22:22:37,102 - tensorflow - INFO - global_step/sec: 88.8103 -2022-05-01 22:22:37,102 - tensorflow - INFO - loss = 0.07206778, step = 2412200 (2.252 sec) -2022-05-01 22:22:39,351 - tensorflow - INFO - global_step/sec: 88.9343 -2022-05-01 22:22:39,351 - tensorflow - INFO - loss = 0.028545413, step = 2412400 (2.249 sec) -2022-05-01 22:22:41,604 - tensorflow - INFO - global_step/sec: 88.7339 -2022-05-01 22:22:41,605 - tensorflow - INFO - loss = 0.00634375, step = 2412600 (2.254 sec) -2022-05-01 22:22:43,859 - tensorflow - INFO - global_step/sec: 88.7042 -2022-05-01 22:22:43,860 - tensorflow - INFO - loss = 0.016428966, step = 2412800 (2.255 sec) -2022-05-01 22:22:46,108 - tensorflow - INFO - global_step/sec: 88.9321 -2022-05-01 22:22:46,108 - tensorflow - INFO - loss = 0.007920577, step = 2413000 (2.249 sec) -2022-05-01 22:22:48,365 - tensorflow - INFO - global_step/sec: 88.626 -2022-05-01 22:22:48,365 - tensorflow - INFO - loss = 0.04073445, step = 2413200 (2.257 sec) -2022-05-01 22:22:50,617 - tensorflow - INFO - global_step/sec: 88.7912 -2022-05-01 22:22:50,618 - tensorflow - INFO - loss = 0.013052452, step = 2413400 (2.253 sec) -2022-05-01 22:22:52,872 - tensorflow - INFO - global_step/sec: 88.6946 -2022-05-01 22:22:52,873 - tensorflow - INFO - loss = 0.01770258, step = 2413600 (2.255 sec) -2022-05-01 22:22:55,129 - tensorflow - INFO - global_step/sec: 88.6314 -2022-05-01 22:22:55,129 - tensorflow - INFO - loss = 0.051142707, step = 2413800 (2.257 sec) -2022-05-01 22:22:57,382 - tensorflow - INFO - global_step/sec: 88.7708 -2022-05-01 22:22:57,382 - tensorflow - INFO - loss = 4.831999, step = 2414000 (2.253 sec) -2022-05-01 22:22:59,637 - tensorflow - INFO - global_step/sec: 88.6601 -2022-05-01 22:22:59,638 - tensorflow - INFO - loss = 0.01260927, step = 2414200 (2.256 sec) -2022-05-01 22:23:01,888 - tensorflow - INFO - global_step/sec: 88.8853 -2022-05-01 22:23:01,888 - tensorflow - INFO - loss = 0.010449916, step = 2414400 (2.250 sec) -2022-05-01 22:23:04,140 - tensorflow - INFO - global_step/sec: 88.7775 -2022-05-01 22:23:04,141 - tensorflow - INFO - loss = 0.010623086, step = 2414600 (2.253 sec) -2022-05-01 22:23:06,398 - tensorflow - INFO - global_step/sec: 88.5814 -2022-05-01 22:23:06,399 - tensorflow - INFO - loss = 0.016910218, step = 2414800 (2.258 sec) -2022-05-01 22:23:08,657 - tensorflow - INFO - global_step/sec: 88.5485 -2022-05-01 22:23:08,657 - tensorflow - INFO - loss = 0.013798049, step = 2415000 (2.259 sec) -2022-05-01 22:23:10,910 - tensorflow - INFO - global_step/sec: 88.7463 -2022-05-01 22:23:10,911 - tensorflow - INFO - loss = 0.0064181024, step = 2415200 (2.254 sec) -2022-05-01 22:23:13,160 - tensorflow - INFO - global_step/sec: 88.9038 -2022-05-01 22:23:13,160 - tensorflow - INFO - loss = 0.007887255, step = 2415400 (2.250 sec) -2022-05-01 22:23:15,415 - tensorflow - INFO - global_step/sec: 88.6954 -2022-05-01 22:23:15,415 - tensorflow - INFO - loss = 0.010746698, step = 2415600 (2.255 sec) -2022-05-01 22:23:17,675 - tensorflow - INFO - global_step/sec: 88.4763 -2022-05-01 22:23:17,676 - tensorflow - INFO - loss = 0.033685423, step = 2415800 (2.260 sec) -2022-05-01 22:23:19,928 - tensorflow - INFO - global_step/sec: 88.7741 -2022-05-01 22:23:19,929 - tensorflow - INFO - loss = 0.010350782, step = 2416000 (2.253 sec) -2022-05-01 22:23:22,191 - tensorflow - INFO - global_step/sec: 88.4083 -2022-05-01 22:23:22,191 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.5215 -2022-05-01 22:23:38,032 - tensorflow - INFO - loss = 0.010946408, step = 2417600 (2.259 sec) -2022-05-01 22:23:40,292 - tensorflow - INFO - global_step/sec: 88.5035 -2022-05-01 22:23:40,292 - tensorflow - INFO - loss = 0.040977396, step = 2417800 (2.260 sec) -2022-05-01 22:23:42,552 - tensorflow - INFO - global_step/sec: 88.5014 -2022-05-01 22:23:42,552 - tensorflow - INFO - loss = 0.019095192, step = 2418000 (2.260 sec) -2022-05-01 22:23:44,816 - tensorflow - INFO - global_step/sec: 88.3294 -2022-05-01 22:23:44,816 - tensorflow - INFO - loss = 0.030950455, step = 2418200 (2.264 sec) -2022-05-01 22:23:47,082 - tensorflow - INFO - global_step/sec: 88.2481 -2022-05-01 22:23:47,083 - tensorflow - INFO - loss = 0.025897225, step = 2418400 (2.266 sec) -2022-05-01 22:23:49,340 - tensorflow - INFO - global_step/sec: 88.5658 -2022-05-01 22:23:49,341 - tensorflow - INFO - loss = 0.04686994, step = 2418600 (2.258 sec) -2022-05-01 22:23:51,593 - tensorflow - INFO - global_step/sec: 88.7811 -2022-05-01 22:23:51,594 - tensorflow - INFO - loss = 0.012097688, step = 2418800 (2.253 sec) -2022-05-01 22:23:53,845 - tensorflow - INFO - global_step/sec: 88.8195 -2022-05-01 22:23:53,845 - tensorflow - INFO - loss = 0.018058829, step = 2419000 (2.252 sec) -2022-05-01 22:23:56,101 - tensorflow - INFO - global_step/sec: 88.6559 -2022-05-01 22:23:56,101 - tensorflow - INFO - loss = 0.005407006, step = 2419200 (2.256 sec) -2022-05-01 22:23:58,361 - tensorflow - INFO - global_step/sec: 88.4868 -2022-05-01 22:23:58,362 - tensorflow - INFO - loss = 0.010135386, step = 2419400 (2.260 sec) -2022-05-01 22:24:00,615 - tensorflow - INFO - global_step/sec: 88.7319 -2022-05-01 22:24:00,616 - tensorflow - INFO - loss = 0.010889774, step = 2419600 (2.254 sec) -2022-05-01 22:24:02,879 - tensorflow - INFO - global_step/sec: 88.332 -2022-05-01 22:24:02,880 - tensorflow - INFO - loss = 0.014974509, step = 2419800 (2.264 sec) -2022-05-01 22:24:05,134 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.06388452, step = 2421200 (2.252 sec) -2022-05-01 22:24:20,916 - tensorflow - INFO - global_step/sec: 88.7929 -2022-05-01 22:24:20,917 - tensorflow - INFO - loss = 0.025522318, step = 2421400 (2.252 sec) -2022-05-01 22:24:23,175 - tensorflow - INFO - global_step/sec: 88.5368 -2022-05-01 22:24:23,176 - tensorflow - INFO - loss = 0.01230228, step = 2421600 (2.259 sec) -2022-05-01 22:24:25,431 - tensorflow - INFO - global_step/sec: 88.6837 -2022-05-01 22:24:25,431 - tensorflow - INFO - loss = 0.075653605, step = 2421800 (2.255 sec) -2022-05-01 22:24:27,681 - tensorflow - INFO - global_step/sec: 88.8626 -2022-05-01 22:24:27,682 - tensorflow - INFO - loss = 0.025689162, step = 2422000 (2.251 sec) -2022-05-01 22:24:29,939 - tensorflow - INFO - global_step/sec: 88.6005 -2022-05-01 22:24:29,939 - tensorflow - INFO - loss = 0.3590022, step = 2422200 (2.257 sec) -2022-05-01 22:24:32,203 - tensorflow - INFO - global_step/sec: 88.33 -2022-05-01 22:24:32,203 - tensorflow - INFO - loss = 0.04820884, step = 2422400 (2.264 sec) -2022-05-01 22:24:34,456 - tensorflow - INFO - global_step/sec: 88.743 -2022-05-01 22:24:34,457 - tensorflow - INFO - loss = 0.00656184, step = 2422600 (2.254 sec) -2022-05-01 22:24:36,711 - tensorflow - INFO - global_step/sec: 88.693 -2022-05-01 22:24:36,712 - tensorflow - INFO - loss = 0.007785233, step = 2422800 (2.255 sec) -2022-05-01 22:24:38,963 - tensorflow - INFO - global_step/sec: 88.8087 -2022-05-01 22:24:38,964 - tensorflow - INFO - loss = 0.018070709, step = 2423000 (2.252 sec) -2022-05-01 22:24:41,218 - tensorflow - INFO - global_step/sec: 88.7246 -2022-05-01 22:24:41,218 - tensorflow - INFO - loss = 0.007902175, step = 2423200 (2.254 sec) -2022-05-01 22:24:43,470 - tensorflow - INFO - global_step/sec: 88.7828 -2022-05-01 22:24:43,471 - tensorflow - INFO - loss = 0.018189617, step = 2423400 (2.253 sec) -2022-05-01 22:24:45,724 - tensorflow - INFO - global_step/sec: 88.7473 -2022-05-01 22:24:45,724 - tensorflow - INFO - loss = 0.03778987, step = 2423600 (2.254 sec) -2022-05-01 22:24:47,980 - tensorflow - INFO - global_step/sec: 88.6356 -2022-05-01 22:24:47,981 - tensorflow - INFO - loss = 0.009193819, step = 2423800 (2.256 sec) -2022-05-01 22:24:50,236 - tensorflow - INFO - global_step/sec: 88.6787 -2022-05-01 22:24:50,236 - tensorflow - INFO - loss = 0.22480245, step = 2424000 (2.255 sec) -2022-05-01 22:24:52,490 - tensorflow - INFO - global_step/sec: 88.7344 -2022-05-01 22:24:52,490 - tensorflow - INFO - loss = 0.037477303, step = 2424200 (2.254 sec) -2022-05-01 22:24:54,742 - tensorflow - INFO - global_step/sec: 88.7901 -2022-05-01 22:24:54,742 - tensorflow - INFO - loss = 0.065649144, step = 2424400 (2.253 sec) -2022-05-01 22:24:56,996 - tensorflow - INFO - global_step/sec: 88.747 -2022-05-01 22:24:56,996 - tensorflow - INFO - loss = 0.014988281, step = 2424600 (2.254 sec) -2022-05-01 22:24:59,247 - tensorflow - INFO - global_step/sec: 88.8217 -2022-05-01 22:24:59,248 - tensorflow - INFO - loss = 0.010490783, step = 2424800 (2.252 sec) -2022-05-01 22:25:01,505 - tensorflow - INFO - global_step/sec: 88.5808 -2022-05-01 22:25:01,506 - tensorflow - INFO - loss = 0.01600422, step = 2425000 (2.258 sec) -2022-05-01 22:25:03,761 - tensorflow - INFO - global_step/sec: 88.6668 -2022-05-01 22:25:03,761 - tensorflow - INFO - loss = 0.021280637, step = 2425200 (2.255 sec) -2022-05-01 22:25:06,014 - tensorflow - INFO - global_step/sec: 88.7725 -2022-05-01 22:25:06,014 - tensorflow - INFO - loss = 0.039950836, step = 2425400 (2.253 sec) -2022-05-01 22:25:08,268 - tensorflow - INFO - global_step/sec: 88.7166 -2022-05-01 22:25:08,269 - tensorflow - INFO - loss = 0.11885853, step = 2425600 (2.254 sec) -2022-05-01 22:25:10,520 - tensorflow - INFO - global_step/sec: 88.821 -2022-05-01 22:25:10,520 - tensorflow - INFO - loss = 0.0047355983, step = 2425800 (2.252 sec) -2022-05-01 22:25:12,776 - tensorflow - INFO - global_step/sec: 88.6627 -2022-05-01 22:25:12,776 - tensorflow - INFO - loss = 0.015703008, step = 2426000 (2.256 sec) -2022-05-01 22:25:15,026 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.022055803, step = 2427400 (2.258 sec) -2022-05-01 22:25:30,819 - tensorflow - INFO - global_step/sec: 88.3754 -2022-05-01 22:25:30,819 - tensorflow - INFO - loss = 0.0149949975, step = 2427600 (2.263 sec) -2022-05-01 22:25:33,079 - tensorflow - INFO - global_step/sec: 88.4682 -2022-05-01 22:25:33,080 - tensorflow - INFO - loss = 0.030170036, step = 2427800 (2.261 sec) -2022-05-01 22:25:35,335 - tensorflow - INFO - global_step/sec: 88.6683 -2022-05-01 22:25:35,335 - tensorflow - INFO - loss = 0.033823244, step = 2428000 (2.256 sec) -2022-05-01 22:25:37,592 - tensorflow - INFO - global_step/sec: 88.6136 -2022-05-01 22:25:37,592 - tensorflow - INFO - loss = 0.36012655, step = 2428200 (2.257 sec) -2022-05-01 22:25:39,849 - tensorflow - INFO - global_step/sec: 88.5972 -2022-05-01 22:25:39,850 - tensorflow - INFO - loss = 0.020032069, step = 2428400 (2.257 sec) -2022-05-01 22:25:42,103 - tensorflow - INFO - global_step/sec: 88.725 -2022-05-01 22:25:42,104 - tensorflow - INFO - loss = 0.012113841, step = 2428600 (2.254 sec) -2022-05-01 22:25:44,358 - tensorflow - INFO - global_step/sec: 88.7113 -2022-05-01 22:25:44,358 - tensorflow - INFO - loss = 0.0061306115, step = 2428800 (2.255 sec) -2022-05-01 22:25:46,609 - tensorflow - INFO - global_step/sec: 88.8417 -2022-05-01 22:25:46,610 - tensorflow - INFO - loss = 0.010476081, step = 2429000 (2.251 sec) -2022-05-01 22:25:48,860 - tensorflow - INFO - global_step/sec: 88.8504 -2022-05-01 22:25:48,861 - tensorflow - INFO - loss = 0.012106378, step = 2429200 (2.251 sec) -2022-05-01 22:25:51,122 - tensorflow - INFO - global_step/sec: 88.4123 -2022-05-01 22:25:51,123 - tensorflow - INFO - loss = 0.12133789, step = 2429400 (2.262 sec) -2022-05-01 22:25:53,375 - tensorflow - INFO - global_step/sec: 88.786 -2022-05-01 22:25:53,375 - tensorflow - INFO - loss = 0.013075585, step = 2429600 (2.253 sec) -2022-05-01 22:25:55,626 - tensorflow - INFO - global_step/sec: 88.8334 -2022-05-01 22:25:55,627 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.9791 -2022-05-01 22:26:11,413 - tensorflow - INFO - loss = 0.008713246, step = 2431200 (2.248 sec) -2022-05-01 22:26:13,670 - tensorflow - INFO - global_step/sec: 88.5915 -2022-05-01 22:26:13,670 - tensorflow - INFO - loss = 0.016446285, step = 2431400 (2.258 sec) -2022-05-01 22:26:15,931 - tensorflow - INFO - global_step/sec: 88.4519 -2022-05-01 22:26:15,931 - tensorflow - INFO - loss = 0.32633343, step = 2431600 (2.261 sec) -2022-05-01 22:26:18,183 - tensorflow - INFO - global_step/sec: 88.8223 -2022-05-01 22:26:18,183 - tensorflow - INFO - loss = 0.008611464, step = 2431800 (2.252 sec) -2022-05-01 22:26:20,434 - tensorflow - INFO - global_step/sec: 88.8315 -2022-05-01 22:26:20,435 - tensorflow - INFO - loss = 0.014535236, step = 2432000 (2.251 sec) -2022-05-01 22:26:22,691 - tensorflow - INFO - global_step/sec: 88.6179 -2022-05-01 22:26:22,692 - tensorflow - INFO - loss = 0.17081915, step = 2432200 (2.257 sec) -2022-05-01 22:26:24,948 - tensorflow - INFO - global_step/sec: 88.6224 -2022-05-01 22:26:24,948 - tensorflow - INFO - loss = 0.004440788, step = 2432400 (2.257 sec) -2022-05-01 22:26:27,203 - tensorflow - INFO - global_step/sec: 88.6937 -2022-05-01 22:26:27,203 - tensorflow - INFO - loss = 0.014876054, step = 2432600 (2.255 sec) -2022-05-01 22:26:29,463 - tensorflow - INFO - global_step/sec: 88.4734 -2022-05-01 22:26:29,464 - tensorflow - INFO - loss = 0.011966743, step = 2432800 (2.261 sec) -2022-05-01 22:26:31,726 - tensorflow - INFO - global_step/sec: 88.3975 -2022-05-01 22:26:31,726 - tensorflow - INFO - loss = 0.0031124237, step = 2433000 (2.263 sec) -2022-05-01 22:26:33,982 - tensorflow - INFO - global_step/sec: 88.6325 -2022-05-01 22:26:33,983 - tensorflow - INFO - loss = 0.24724612, step = 2433200 (2.257 sec) -2022-05-01 22:26:36,240 - tensorflow - INFO - global_step/sec: 88.5953 -2022-05-01 22:26:36,240 - tensorflow - INFO - loss = 0.07949506, step = 2433400 (2.257 sec) -2022-05-01 22:26:38,497 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.038247883, step = 2434800 (2.260 sec) -2022-05-01 22:26:54,297 - tensorflow - INFO - global_step/sec: 88.3733 -2022-05-01 22:26:54,297 - tensorflow - INFO - loss = 0.008712643, step = 2435000 (2.263 sec) -2022-05-01 22:26:56,564 - tensorflow - INFO - global_step/sec: 88.2227 -2022-05-01 22:26:56,564 - tensorflow - INFO - loss = 0.017594587, step = 2435200 (2.267 sec) -2022-05-01 22:26:58,821 - tensorflow - INFO - global_step/sec: 88.6141 -2022-05-01 22:26:58,821 - tensorflow - INFO - loss = 0.0076810466, step = 2435400 (2.257 sec) -2022-05-01 22:27:01,082 - tensorflow - INFO - global_step/sec: 88.4511 -2022-05-01 22:27:01,082 - tensorflow - INFO - loss = 0.0088895075, step = 2435600 (2.261 sec) -2022-05-01 22:27:03,342 - tensorflow - INFO - global_step/sec: 88.4914 -2022-05-01 22:27:03,342 - tensorflow - INFO - loss = 0.009010309, step = 2435800 (2.260 sec) -2022-05-01 22:27:05,605 - tensorflow - INFO - global_step/sec: 88.3706 -2022-05-01 22:27:05,605 - tensorflow - INFO - loss = 0.39461958, step = 2436000 (2.263 sec) -2022-05-01 22:27:07,864 - tensorflow - INFO - global_step/sec: 88.52 -2022-05-01 22:27:07,865 - tensorflow - INFO - loss = 0.22795394, step = 2436200 (2.259 sec) -2022-05-01 22:27:10,124 - tensorflow - INFO - global_step/sec: 88.5009 -2022-05-01 22:27:10,125 - tensorflow - INFO - loss = 0.026495546, step = 2436400 (2.260 sec) -2022-05-01 22:27:12,384 - tensorflow - INFO - global_step/sec: 88.5125 -2022-05-01 22:27:12,384 - tensorflow - INFO - loss = 0.015892439, step = 2436600 (2.260 sec) -2022-05-01 22:27:14,644 - tensorflow - INFO - global_step/sec: 88.4907 -2022-05-01 22:27:14,644 - tensorflow - INFO - loss = 0.009224594, step = 2436800 (2.260 sec) -2022-05-01 22:27:16,904 - tensorflow - INFO - global_step/sec: 88.4822 -2022-05-01 22:27:16,905 - tensorflow - INFO - loss = 0.020486783, step = 2437000 (2.260 sec) -2022-05-01 22:27:19,167 - tensorflow - INFO - global_step/sec: 88.4028 -2022-05-01 22:27:19,167 - tensorflow - INFO - loss = 0.25998735, step = 2437200 (2.262 sec) -2022-05-01 22:27:21,423 - tensorflow - INFO - global_step/sec: 88.6533 -2022-05-01 22:27:21,423 - tensorflow - INFO - loss = 0.037896115, step = 2437400 (2.256 sec) -2022-05-01 22:27:23,685 - tensorflow - INFO - global_step/sec: 88.3866 -2022-05-01 22:27:23,686 - tensorflow - INFO - loss = 0.022613788, step = 2437600 (2.263 sec) -2022-05-01 22:27:25,938 - tensorflow - INFO - global_step/sec: 88.7694 -2022-05-01 22:27:25,939 - tensorflow - INFO - loss = 0.011877377, step = 2437800 (2.253 sec) -2022-05-01 22:27:28,192 - tensorflow - INFO - global_step/sec: 88.7644 -2022-05-01 22:27:28,192 - tensorflow - INFO - loss = 0.01136734, step = 2438000 (2.253 sec) -2022-05-01 22:27:30,448 - tensorflow - INFO - global_step/sec: 88.6327 -2022-05-01 22:27:30,448 - tensorflow - INFO - loss = 0.102835014, step = 2438200 (2.257 sec) -2022-05-01 22:27:32,702 - tensorflow - INFO - global_step/sec: 88.7245 -2022-05-01 22:27:32,703 - tensorflow - INFO - loss = 0.024789814, step = 2438400 (2.254 sec) -2022-05-01 22:27:34,959 - tensorflow - INFO - global_step/sec: 88.6221 -2022-05-01 22:27:34,959 - tensorflow - INFO - loss = 0.60774803, step = 2438600 (2.257 sec) -2022-05-01 22:27:37,213 - tensorflow - INFO - global_step/sec: 88.7369 -2022-05-01 22:27:37,213 - tensorflow - INFO - loss = 0.009254277, step = 2438800 (2.254 sec) -2022-05-01 22:27:39,467 - tensorflow - INFO - global_step/sec: 88.7076 -2022-05-01 22:27:39,468 - tensorflow - INFO - loss = 0.019108947, step = 2439000 (2.255 sec) -2022-05-01 22:27:41,722 - tensorflow - INFO - global_step/sec: 88.716 -2022-05-01 22:27:41,722 - tensorflow - INFO - loss = 0.021962412, step = 2439200 (2.254 sec) -2022-05-01 22:27:43,972 - tensorflow - INFO - global_step/sec: 88.8979 -2022-05-01 22:27:43,972 - tensorflow - INFO - loss = 0.0046605812, step = 2439400 (2.250 sec) -2022-05-01 22:27:46,223 - tensorflow - INFO - global_step/sec: 88.823 -2022-05-01 22:27:46,224 - tensorflow - INFO - loss = 0.02036841, step = 2439600 (2.252 sec) -2022-05-01 22:27:48,481 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.018889189, step = 2441000 (2.259 sec) -2022-05-01 22:28:04,258 - tensorflow - INFO - global_step/sec: 88.8125 -2022-05-01 22:28:04,258 - tensorflow - INFO - loss = 0.020466503, step = 2441200 (2.252 sec) -2022-05-01 22:28:06,520 - tensorflow - INFO - global_step/sec: 88.4021 -2022-05-01 22:28:06,521 - tensorflow - INFO - loss = 0.14971241, step = 2441400 (2.262 sec) -2022-05-01 22:28:08,777 - tensorflow - INFO - global_step/sec: 88.6368 -2022-05-01 22:28:08,777 - tensorflow - INFO - loss = 0.015328262, step = 2441600 (2.256 sec) -2022-05-01 22:28:11,028 - tensorflow - INFO - global_step/sec: 88.8224 -2022-05-01 22:28:11,029 - tensorflow - INFO - loss = 0.03621527, step = 2441800 (2.252 sec) -2022-05-01 22:28:13,284 - tensorflow - INFO - global_step/sec: 88.6748 -2022-05-01 22:28:13,284 - tensorflow - INFO - loss = 0.021771828, step = 2442000 (2.255 sec) -2022-05-01 22:28:15,537 - tensorflow - INFO - global_step/sec: 88.7476 -2022-05-01 22:28:15,538 - tensorflow - INFO - loss = 0.013880587, step = 2442200 (2.254 sec) -2022-05-01 22:28:17,790 - tensorflow - INFO - global_step/sec: 88.8041 -2022-05-01 22:28:17,790 - tensorflow - INFO - loss = 0.004753161, step = 2442400 (2.252 sec) -2022-05-01 22:28:20,040 - tensorflow - INFO - global_step/sec: 88.8679 -2022-05-01 22:28:20,041 - tensorflow - INFO - loss = 0.0054917885, step = 2442600 (2.251 sec) -2022-05-01 22:28:22,294 - tensorflow - INFO - global_step/sec: 88.7443 -2022-05-01 22:28:22,294 - tensorflow - INFO - loss = 0.007898835, step = 2442800 (2.254 sec) -2022-05-01 22:28:24,546 - tensorflow - INFO - global_step/sec: 88.8188 -2022-05-01 22:28:24,546 - tensorflow - INFO - loss = 0.028259933, step = 2443000 (2.252 sec) -2022-05-01 22:28:26,802 - tensorflow - INFO - global_step/sec: 88.6237 -2022-05-01 22:28:26,803 - tensorflow - INFO - loss = 0.013659278, step = 2443200 (2.257 sec) -2022-05-01 22:28:29,057 - tensorflow - INFO - global_step/sec: 88.719 -2022-05-01 22:28:29,057 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.7621 -2022-05-01 22:28:44,837 - tensorflow - INFO - loss = 0.01237338, step = 2444800 (2.253 sec) -2022-05-01 22:28:47,088 - tensorflow - INFO - global_step/sec: 88.8243 -2022-05-01 22:28:47,088 - tensorflow - INFO - loss = 0.010459521, step = 2445000 (2.252 sec) -2022-05-01 22:28:49,344 - tensorflow - INFO - global_step/sec: 88.6343 -2022-05-01 22:28:49,345 - tensorflow - INFO - loss = 0.02367907, step = 2445200 (2.256 sec) -2022-05-01 22:28:51,604 - tensorflow - INFO - global_step/sec: 88.5057 -2022-05-01 22:28:51,604 - tensorflow - INFO - loss = 0.023524862, step = 2445400 (2.260 sec) -2022-05-01 22:28:53,855 - tensorflow - INFO - global_step/sec: 88.8334 -2022-05-01 22:28:53,856 - tensorflow - INFO - loss = 0.07011649, step = 2445600 (2.251 sec) -2022-05-01 22:28:56,108 - tensorflow - INFO - global_step/sec: 88.7745 -2022-05-01 22:28:56,109 - tensorflow - INFO - loss = 0.011394534, step = 2445800 (2.253 sec) -2022-05-01 22:28:58,360 - tensorflow - INFO - global_step/sec: 88.8141 -2022-05-01 22:28:58,361 - tensorflow - INFO - loss = 0.010070166, step = 2446000 (2.252 sec) -2022-05-01 22:29:00,611 - tensorflow - INFO - global_step/sec: 88.8679 -2022-05-01 22:29:00,611 - tensorflow - INFO - loss = 0.016251925, step = 2446200 (2.251 sec) -2022-05-01 22:29:02,868 - tensorflow - INFO - global_step/sec: 88.6177 -2022-05-01 22:29:02,868 - tensorflow - INFO - loss = 0.023029368, step = 2446400 (2.257 sec) -2022-05-01 22:29:05,119 - tensorflow - INFO - global_step/sec: 88.8504 -2022-05-01 22:29:05,119 - tensorflow - INFO - loss = 0.029826157, step = 2446600 (2.251 sec) -2022-05-01 22:29:07,378 - tensorflow - INFO - global_step/sec: 88.5366 -2022-05-01 22:29:07,378 - tensorflow - INFO - loss = 0.012460985, step = 2446800 (2.259 sec) -2022-05-01 22:29:09,629 - tensorflow - INFO - global_step/sec: 88.8393 -2022-05-01 22:29:09,629 - tensorflow - INFO - loss = 0.052463446, step = 2447000 (2.251 sec) -2022-05-01 22:29:11,884 - tensorflow - INFO - 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INFO - loss = 0.0068839383, step = 2449600 (2.251 sec) -2022-05-01 22:29:41,179 - tensorflow - INFO - global_step/sec: 88.6605 -2022-05-01 22:29:41,180 - tensorflow - INFO - loss = 0.02295091, step = 2449800 (2.256 sec) -2022-05-01 22:29:43,429 - tensorflow - INFO - global_step/sec: 88.9152 -2022-05-01 22:29:43,430 - tensorflow - INFO - loss = 0.0068595465, step = 2450000 (2.250 sec) -2022-05-01 22:29:45,680 - tensorflow - INFO - global_step/sec: 88.8375 -2022-05-01 22:29:45,681 - tensorflow - INFO - loss = 0.004242505, step = 2450200 (2.251 sec) -2022-05-01 22:29:47,933 - tensorflow - INFO - global_step/sec: 88.7797 -2022-05-01 22:29:47,933 - tensorflow - INFO - loss = 0.018911486, step = 2450400 (2.253 sec) -2022-05-01 22:29:50,188 - tensorflow - INFO - global_step/sec: 88.6806 -2022-05-01 22:29:50,189 - tensorflow - INFO - loss = 0.015513633, step = 2450600 (2.255 sec) -2022-05-01 22:29:52,439 - tensorflow - INFO - global_step/sec: 88.8485 -2022-05-01 22:29:52,440 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.561 -2022-05-01 22:30:08,230 - tensorflow - INFO - loss = 0.014634468, step = 2452200 (2.258 sec) -2022-05-01 22:30:10,483 - tensorflow - INFO - global_step/sec: 88.773 -2022-05-01 22:30:10,483 - tensorflow - INFO - loss = 0.01357374, step = 2452400 (2.253 sec) -2022-05-01 22:30:12,732 - tensorflow - INFO - global_step/sec: 88.9067 -2022-05-01 22:30:12,733 - tensorflow - INFO - loss = 0.018299773, step = 2452600 (2.250 sec) -2022-05-01 22:30:14,986 - tensorflow - INFO - global_step/sec: 88.741 -2022-05-01 22:30:14,986 - tensorflow - INFO - loss = 0.006838115, step = 2452800 (2.254 sec) -2022-05-01 22:30:17,240 - tensorflow - INFO - global_step/sec: 88.746 -2022-05-01 22:30:17,240 - tensorflow - INFO - loss = 0.12840974, step = 2453000 (2.254 sec) -2022-05-01 22:30:19,492 - tensorflow - INFO - global_step/sec: 88.7793 -2022-05-01 22:30:19,493 - tensorflow - INFO - loss = 0.011005908, step = 2453200 (2.253 sec) -2022-05-01 22:30:21,751 - tensorflow - INFO - global_step/sec: 88.5557 -2022-05-01 22:30:21,751 - tensorflow - INFO - loss = 0.054185353, step = 2453400 (2.258 sec) -2022-05-01 22:30:24,014 - tensorflow - INFO - global_step/sec: 88.3793 -2022-05-01 22:30:24,014 - tensorflow - INFO - loss = 0.050072975, step = 2453600 (2.263 sec) -2022-05-01 22:30:26,272 - tensorflow - INFO - global_step/sec: 88.5699 -2022-05-01 22:30:26,272 - tensorflow - INFO - loss = 0.017814076, step = 2453800 (2.258 sec) -2022-05-01 22:30:28,533 - tensorflow - INFO - global_step/sec: 88.4725 -2022-05-01 22:30:28,533 - tensorflow - INFO - loss = 0.0115552675, step = 2454000 (2.261 sec) -2022-05-01 22:30:30,803 - tensorflow - INFO - global_step/sec: 88.1052 -2022-05-01 22:30:30,803 - tensorflow - INFO - loss = 0.025704335, step = 2454200 (2.270 sec) -2022-05-01 22:30:33,063 - tensorflow - INFO - global_step/sec: 88.4666 -2022-05-01 22:30:33,064 - tensorflow - INFO - loss = 0.007582742, step = 2454400 (2.261 sec) -2022-05-01 22:30:35,327 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.02097673, step = 2455800 (2.260 sec) -2022-05-01 22:30:51,164 - tensorflow - INFO - global_step/sec: 88.6419 -2022-05-01 22:30:51,165 - tensorflow - INFO - loss = 0.012825205, step = 2456000 (2.256 sec) -2022-05-01 22:30:53,414 - tensorflow - INFO - global_step/sec: 88.8868 -2022-05-01 22:30:53,415 - tensorflow - INFO - loss = 0.015099917, step = 2456200 (2.250 sec) -2022-05-01 22:30:55,674 - tensorflow - INFO - global_step/sec: 88.5171 -2022-05-01 22:30:55,674 - tensorflow - INFO - loss = 0.010448134, step = 2456400 (2.260 sec) -2022-05-01 22:30:57,931 - tensorflow - INFO - global_step/sec: 88.5842 -2022-05-01 22:30:57,932 - tensorflow - INFO - loss = 0.010750689, step = 2456600 (2.258 sec) -2022-05-01 22:31:00,187 - tensorflow - INFO - global_step/sec: 88.6547 -2022-05-01 22:31:00,188 - tensorflow - INFO - loss = 0.03213253, step = 2456800 (2.256 sec) -2022-05-01 22:31:02,438 - tensorflow - INFO - global_step/sec: 88.866 -2022-05-01 22:31:02,438 - tensorflow - INFO - loss = 0.030992215, step = 2457000 (2.251 sec) -2022-05-01 22:31:04,693 - tensorflow - INFO - global_step/sec: 88.6988 -2022-05-01 22:31:04,693 - tensorflow - INFO - loss = 0.019934999, step = 2457200 (2.255 sec) -2022-05-01 22:31:06,949 - tensorflow - INFO - global_step/sec: 88.6582 -2022-05-01 22:31:06,949 - tensorflow - INFO - loss = 0.009189779, step = 2457400 (2.256 sec) -2022-05-01 22:31:09,201 - tensorflow - INFO - global_step/sec: 88.7951 -2022-05-01 22:31:09,201 - tensorflow - INFO - loss = 0.021401089, step = 2457600 (2.252 sec) -2022-05-01 22:31:11,456 - tensorflow - INFO - global_step/sec: 88.6949 -2022-05-01 22:31:11,456 - tensorflow - INFO - loss = 0.020945026, step = 2457800 (2.255 sec) -2022-05-01 22:31:13,708 - tensorflow - INFO - global_step/sec: 88.8239 -2022-05-01 22:31:13,708 - tensorflow - INFO - loss = 0.025445186, step = 2458000 (2.252 sec) -2022-05-01 22:31:15,960 - tensorflow - INFO - global_step/sec: 88.7913 -2022-05-01 22:31:15,960 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.3728 -2022-05-01 22:31:31,760 - tensorflow - INFO - loss = 0.0067091575, step = 2459600 (2.263 sec) -2022-05-01 22:31:34,010 - tensorflow - INFO - global_step/sec: 88.8492 -2022-05-01 22:31:34,011 - tensorflow - INFO - loss = 0.11180801, step = 2459800 (2.251 sec) -2022-05-01 22:31:36,270 - tensorflow - INFO - global_step/sec: 88.4965 -2022-05-01 22:31:36,271 - tensorflow - INFO - loss = 0.0076923836, step = 2460000 (2.260 sec) -2022-05-01 22:31:38,527 - tensorflow - INFO - global_step/sec: 88.6318 -2022-05-01 22:31:38,527 - tensorflow - INFO - loss = 0.003023584, step = 2460200 (2.256 sec) -2022-05-01 22:31:40,780 - tensorflow - INFO - global_step/sec: 88.7695 -2022-05-01 22:31:40,780 - tensorflow - INFO - loss = 0.016734779, step = 2460400 (2.253 sec) -2022-05-01 22:31:43,032 - tensorflow - INFO - global_step/sec: 88.8001 -2022-05-01 22:31:43,033 - tensorflow - INFO - loss = 0.009536225, step = 2460600 (2.252 sec) -2022-05-01 22:31:45,289 - tensorflow - INFO - global_step/sec: 88.6013 -2022-05-01 22:31:45,290 - tensorflow - INFO - loss = 0.00845288, step = 2460800 (2.257 sec) -2022-05-01 22:31:47,544 - tensorflow - INFO - global_step/sec: 88.717 -2022-05-01 22:31:47,544 - tensorflow - INFO - loss = 0.049344245, step = 2461000 (2.254 sec) -2022-05-01 22:31:49,802 - tensorflow - INFO - global_step/sec: 88.5711 -2022-05-01 22:31:49,802 - tensorflow - INFO - loss = 0.030603003, step = 2461200 (2.258 sec) -2022-05-01 22:31:52,060 - tensorflow - INFO - global_step/sec: 88.554 -2022-05-01 22:31:52,061 - tensorflow - INFO - loss = 0.00895907, step = 2461400 (2.258 sec) -2022-05-01 22:31:54,316 - tensorflow - INFO - global_step/sec: 88.6735 -2022-05-01 22:31:54,316 - tensorflow - INFO - loss = 0.015357697, step = 2461600 (2.256 sec) -2022-05-01 22:31:56,575 - tensorflow - INFO - global_step/sec: 88.5328 -2022-05-01 22:31:56,575 - tensorflow - INFO - loss = 0.02388647, step = 2461800 (2.259 sec) -2022-05-01 22:31:58,830 - tensorflow - INFO - global_step/sec: 88.6927 -2022-05-01 22:31:58,830 - tensorflow - INFO - loss = 0.023603702, step = 2462000 (2.255 sec) -2022-05-01 22:32:01,080 - tensorflow - INFO - global_step/sec: 88.901 -2022-05-01 22:32:01,080 - tensorflow - INFO - loss = 0.0283974, step = 2462200 (2.250 sec) -2022-05-01 22:32:03,344 - tensorflow - INFO - global_step/sec: 88.3334 -2022-05-01 22:32:03,344 - tensorflow - INFO - loss = 0.068761826, step = 2462400 (2.264 sec) -2022-05-01 22:32:05,596 - tensorflow - INFO - global_step/sec: 88.8006 -2022-05-01 22:32:05,596 - tensorflow - INFO - loss = 0.013406992, step = 2462600 (2.252 sec) -2022-05-01 22:32:07,848 - tensorflow - INFO - global_step/sec: 88.8213 -2022-05-01 22:32:07,848 - tensorflow - INFO - loss = 0.0074104825, step = 2462800 (2.252 sec) -2022-05-01 22:32:10,102 - tensorflow - INFO - global_step/sec: 88.7141 -2022-05-01 22:32:10,102 - tensorflow - INFO - loss = 0.07611355, step = 2463000 (2.254 sec) -2022-05-01 22:32:12,354 - tensorflow - INFO - global_step/sec: 88.8007 -2022-05-01 22:32:12,355 - tensorflow - INFO - loss = 0.019530449, step = 2463200 (2.252 sec) -2022-05-01 22:32:14,607 - tensorflow - INFO - global_step/sec: 88.7737 -2022-05-01 22:32:14,608 - tensorflow - INFO - loss = 0.062303066, step = 2463400 (2.253 sec) -2022-05-01 22:32:16,863 - tensorflow - INFO - global_step/sec: 88.6468 -2022-05-01 22:32:16,864 - tensorflow - INFO - loss = 0.053038966, step = 2463600 (2.256 sec) -2022-05-01 22:32:19,114 - tensorflow - INFO - global_step/sec: 88.879 -2022-05-01 22:32:19,114 - tensorflow - INFO - loss = 0.022775115, step = 2463800 (2.250 sec) -2022-05-01 22:32:21,362 - tensorflow - INFO - global_step/sec: 88.9374 -2022-05-01 22:32:21,363 - tensorflow - INFO - loss = 0.040615294, step = 2464000 (2.249 sec) -2022-05-01 22:32:23,621 - tensorflow - INFO - global_step/sec: 88.5326 -2022-05-01 22:32:23,622 - tensorflow - INFO - loss = 0.0070890756, step = 2464200 (2.259 sec) -2022-05-01 22:32:25,878 - tensorflow - INFO - global_step/sec: 88.6446 -2022-05-01 22:32:25,878 - tensorflow - INFO - loss = 0.010253248, step = 2464400 (2.256 sec) -2022-05-01 22:32:28,140 - tensorflow - INFO - global_step/sec: 88.3912 -2022-05-01 22:32:28,141 - tensorflow - INFO - loss = 0.022638936, step = 2464600 (2.263 sec) -2022-05-01 22:32:30,398 - tensorflow - INFO - global_step/sec: 88.5675 -2022-05-01 22:32:30,399 - tensorflow - INFO - loss = 0.014777252, step = 2464800 (2.258 sec) -2022-05-01 22:32:32,655 - tensorflow - INFO - global_step/sec: 88.633 -2022-05-01 22:32:32,655 - tensorflow - INFO - loss = 0.0031788982, step = 2465000 (2.256 sec) -2022-05-01 22:32:34,905 - tensorflow - INFO - global_step/sec: 88.8968 -2022-05-01 22:32:34,905 - tensorflow - INFO - loss = 0.0057559973, step = 2465200 (2.250 sec) -2022-05-01 22:32:37,157 - tensorflow - INFO - global_step/sec: 88.8177 -2022-05-01 22:32:37,157 - tensorflow - INFO - loss = 0.025105083, step = 2465400 (2.252 sec) -2022-05-01 22:32:39,413 - tensorflow - INFO - global_step/sec: 88.6544 -2022-05-01 22:32:39,413 - tensorflow - INFO - loss = 3.044093, step = 2465600 (2.256 sec) -2022-05-01 22:32:41,667 - tensorflow - INFO - global_step/sec: 88.7003 -2022-05-01 22:32:41,668 - tensorflow - INFO - loss = 0.022113722, step = 2465800 (2.255 sec) -2022-05-01 22:32:43,923 - tensorflow - INFO - global_step/sec: 88.6783 -2022-05-01 22:32:43,923 - tensorflow - INFO - loss = 0.11212695, step = 2466000 (2.255 sec) -2022-05-01 22:32:46,176 - tensorflow - INFO - global_step/sec: 88.7475 -2022-05-01 22:32:46,177 - tensorflow - INFO - loss = 0.017850898, step = 2466200 (2.254 sec) -2022-05-01 22:32:48,431 - tensorflow - INFO - global_step/sec: 88.7069 -2022-05-01 22:32:48,431 - tensorflow - INFO - loss = 0.03084237, step = 2466400 (2.255 sec) -2022-05-01 22:32:50,684 - tensorflow - INFO - global_step/sec: 88.7483 -2022-05-01 22:32:50,685 - tensorflow - INFO - loss = 0.014584415, step = 2466600 (2.254 sec) -2022-05-01 22:32:52,939 - tensorflow - INFO - global_step/sec: 88.7025 -2022-05-01 22:32:52,940 - tensorflow - INFO - loss = 0.0087754745, step = 2466800 (2.255 sec) -2022-05-01 22:32:55,194 - tensorflow - INFO - global_step/sec: 88.6887 -2022-05-01 22:32:55,195 - tensorflow - INFO - loss = 0.21998209, step = 2467000 (2.255 sec) -2022-05-01 22:32:57,446 - tensorflow - INFO - global_step/sec: 88.8013 -2022-05-01 22:32:57,447 - tensorflow - INFO - loss = 0.018761933, step = 2467200 (2.252 sec) -2022-05-01 22:32:59,700 - tensorflow - INFO - global_step/sec: 88.737 -2022-05-01 22:32:59,701 - tensorflow - INFO - loss = 0.0160272, step = 2467400 (2.254 sec) -2022-05-01 22:33:01,958 - tensorflow - INFO - global_step/sec: 88.5996 -2022-05-01 22:33:01,958 - tensorflow - INFO - loss = 0.015052554, step = 2467600 (2.257 sec) -2022-05-01 22:33:04,212 - tensorflow - INFO - global_step/sec: 88.7208 -2022-05-01 22:33:04,212 - tensorflow - INFO - loss = 0.032359116, step = 2467800 (2.254 sec) -2022-05-01 22:33:06,473 - tensorflow - INFO - global_step/sec: 88.4461 -2022-05-01 22:33:06,474 - tensorflow - INFO - loss = 0.12189176, step = 2468000 (2.261 sec) -2022-05-01 22:33:08,725 - tensorflow - INFO - global_step/sec: 88.8208 -2022-05-01 22:33:08,725 - 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INFO - global_step/sec: 88.6425 -2022-05-01 22:34:05,126 - tensorflow - INFO - loss = 0.046241548, step = 2473200 (2.256 sec) -2022-05-01 22:34:07,388 - tensorflow - INFO - global_step/sec: 88.4257 -2022-05-01 22:34:07,388 - tensorflow - INFO - loss = 0.01648511, step = 2473400 (2.262 sec) -2022-05-01 22:34:09,647 - tensorflow - INFO - global_step/sec: 88.5198 -2022-05-01 22:34:09,648 - tensorflow - INFO - loss = 0.060620237, step = 2473600 (2.259 sec) -2022-05-01 22:34:11,910 - tensorflow - INFO - global_step/sec: 88.3943 -2022-05-01 22:34:11,910 - tensorflow - INFO - loss = 0.010266771, step = 2473800 (2.263 sec) -2022-05-01 22:34:14,174 - tensorflow - INFO - global_step/sec: 88.3261 -2022-05-01 22:34:14,174 - tensorflow - INFO - loss = 0.009591879, step = 2474000 (2.264 sec) -2022-05-01 22:34:16,437 - tensorflow - INFO - global_step/sec: 88.3703 -2022-05-01 22:34:16,438 - tensorflow - INFO - loss = 0.0050626313, step = 2474200 (2.263 sec) -2022-05-01 22:34:18,699 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.008434938, step = 2475600 (2.256 sec) -2022-05-01 22:34:34,483 - tensorflow - INFO - global_step/sec: 88.8282 -2022-05-01 22:34:34,484 - tensorflow - INFO - loss = 0.0056113084, step = 2475800 (2.251 sec) -2022-05-01 22:34:36,737 - tensorflow - INFO - global_step/sec: 88.7215 -2022-05-01 22:34:36,738 - tensorflow - INFO - loss = 0.016700625, step = 2476000 (2.254 sec) -2022-05-01 22:34:38,991 - tensorflow - INFO - global_step/sec: 88.7505 -2022-05-01 22:34:38,991 - tensorflow - INFO - loss = 0.014226502, step = 2476200 (2.254 sec) -2022-05-01 22:34:41,243 - tensorflow - INFO - global_step/sec: 88.8209 -2022-05-01 22:34:41,243 - tensorflow - INFO - loss = 0.0097963195, step = 2476400 (2.252 sec) -2022-05-01 22:34:43,500 - tensorflow - INFO - global_step/sec: 88.5915 -2022-05-01 22:34:43,501 - tensorflow - INFO - loss = 0.14534214, step = 2476600 (2.258 sec) -2022-05-01 22:34:45,754 - tensorflow - INFO - global_step/sec: 88.7335 -2022-05-01 22:34:45,755 - tensorflow - INFO - loss = 0.21932498, step = 2476800 (2.254 sec) -2022-05-01 22:34:48,005 - tensorflow - INFO - global_step/sec: 88.8431 -2022-05-01 22:34:48,006 - tensorflow - INFO - loss = 0.019473905, step = 2477000 (2.251 sec) -2022-05-01 22:34:50,267 - tensorflow - INFO - global_step/sec: 88.425 -2022-05-01 22:34:50,267 - tensorflow - INFO - loss = 0.0079299025, step = 2477200 (2.262 sec) -2022-05-01 22:34:52,519 - tensorflow - INFO - global_step/sec: 88.7958 -2022-05-01 22:34:52,520 - tensorflow - INFO - loss = 0.011020434, step = 2477400 (2.252 sec) -2022-05-01 22:34:54,771 - tensorflow - INFO - global_step/sec: 88.8472 -2022-05-01 22:34:54,771 - tensorflow - INFO - loss = 0.011515933, step = 2477600 (2.251 sec) -2022-05-01 22:34:57,029 - tensorflow - INFO - global_step/sec: 88.5652 -2022-05-01 22:34:57,029 - tensorflow - INFO - loss = 0.0076202303, step = 2477800 (2.258 sec) -2022-05-01 22:34:59,281 - tensorflow - INFO - global_step/sec: 88.8007 -2022-05-01 22:34:59,281 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.6998 -2022-05-01 22:35:15,069 - tensorflow - INFO - loss = 0.5366832, step = 2479400 (2.255 sec) -2022-05-01 22:35:17,321 - tensorflow - INFO - global_step/sec: 88.7707 -2022-05-01 22:35:17,322 - tensorflow - INFO - loss = 0.02988251, step = 2479600 (2.253 sec) -2022-05-01 22:35:19,575 - tensorflow - INFO - global_step/sec: 88.755 -2022-05-01 22:35:19,575 - tensorflow - INFO - loss = 0.055242106, step = 2479800 (2.253 sec) -2022-05-01 22:35:21,828 - tensorflow - INFO - global_step/sec: 88.7484 -2022-05-01 22:35:21,829 - tensorflow - INFO - loss = 0.017433317, step = 2480000 (2.254 sec) -2022-05-01 22:35:24,087 - tensorflow - INFO - global_step/sec: 88.5587 -2022-05-01 22:35:24,087 - tensorflow - INFO - loss = 0.05514112, step = 2480200 (2.258 sec) -2022-05-01 22:35:26,337 - tensorflow - INFO - global_step/sec: 88.872 -2022-05-01 22:35:26,337 - tensorflow - INFO - loss = 0.0416425, step = 2480400 (2.250 sec) -2022-05-01 22:35:28,595 - tensorflow - INFO - global_step/sec: 88.5884 -2022-05-01 22:35:28,595 - tensorflow - INFO - loss = 0.0061695157, step = 2480600 (2.258 sec) -2022-05-01 22:35:30,856 - tensorflow - INFO - global_step/sec: 88.4619 -2022-05-01 22:35:30,856 - tensorflow - INFO - loss = 0.01364998, step = 2480800 (2.261 sec) -2022-05-01 22:35:33,112 - tensorflow - INFO - global_step/sec: 88.6304 -2022-05-01 22:35:33,113 - tensorflow - INFO - loss = 0.0075875903, step = 2481000 (2.257 sec) -2022-05-01 22:35:35,373 - tensorflow - INFO - global_step/sec: 88.4488 -2022-05-01 22:35:35,374 - tensorflow - INFO - loss = 0.009406906, step = 2481200 (2.261 sec) -2022-05-01 22:35:37,631 - tensorflow - INFO - global_step/sec: 88.5808 -2022-05-01 22:35:37,631 - tensorflow - INFO - loss = 0.013360513, step = 2481400 (2.258 sec) -2022-05-01 22:35:39,885 - tensorflow - INFO - global_step/sec: 88.7445 -2022-05-01 22:35:39,885 - tensorflow - INFO - loss = 0.016177379, step = 2481600 (2.254 sec) -2022-05-01 22:35:42,139 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.03847104, step = 2483000 (2.256 sec) -2022-05-01 22:35:57,933 - tensorflow - INFO - global_step/sec: 88.6801 -2022-05-01 22:35:57,933 - tensorflow - INFO - loss = 0.02437882, step = 2483200 (2.255 sec) -2022-05-01 22:36:00,182 - tensorflow - INFO - global_step/sec: 88.935 -2022-05-01 22:36:00,182 - tensorflow - INFO - loss = 0.021544535, step = 2483400 (2.249 sec) -2022-05-01 22:36:02,433 - tensorflow - INFO - global_step/sec: 88.8342 -2022-05-01 22:36:02,433 - tensorflow - INFO - loss = 0.010321522, step = 2483600 (2.251 sec) -2022-05-01 22:36:04,690 - tensorflow - INFO - global_step/sec: 88.6144 -2022-05-01 22:36:04,690 - tensorflow - INFO - loss = 0.07524426, step = 2483800 (2.257 sec) -2022-05-01 22:36:06,947 - tensorflow - INFO - global_step/sec: 88.6021 -2022-05-01 22:36:06,948 - tensorflow - INFO - loss = 0.01738942, step = 2484000 (2.257 sec) -2022-05-01 22:36:09,204 - tensorflow - INFO - global_step/sec: 88.6336 -2022-05-01 22:36:09,204 - tensorflow - INFO - loss = 0.015435829, step = 2484200 (2.256 sec) -2022-05-01 22:36:11,455 - tensorflow - INFO - global_step/sec: 88.8429 -2022-05-01 22:36:11,455 - tensorflow - INFO - loss = 0.13900219, step = 2484400 (2.251 sec) -2022-05-01 22:36:13,708 - tensorflow - INFO - global_step/sec: 88.7728 -2022-05-01 22:36:13,708 - tensorflow - INFO - loss = 0.022148643, step = 2484600 (2.253 sec) -2022-05-01 22:36:15,962 - tensorflow - INFO - global_step/sec: 88.7212 -2022-05-01 22:36:15,962 - tensorflow - INFO - loss = 0.009017587, step = 2484800 (2.254 sec) -2022-05-01 22:36:18,218 - tensorflow - INFO - global_step/sec: 88.6473 -2022-05-01 22:36:18,219 - tensorflow - INFO - loss = 0.030413501, step = 2485000 (2.256 sec) -2022-05-01 22:36:20,469 - tensorflow - INFO - global_step/sec: 88.8619 -2022-05-01 22:36:20,469 - tensorflow - INFO - loss = 0.008298157, step = 2485200 (2.251 sec) -2022-05-01 22:36:22,724 - tensorflow - INFO - global_step/sec: 88.6869 -2022-05-01 22:36:22,724 - tensorflow - INFO - loss = 0.0050102044, step = 2485400 (2.255 sec) -2022-05-01 22:36:24,979 - tensorflow - INFO - global_step/sec: 88.6926 -2022-05-01 22:36:24,979 - tensorflow - INFO - loss = 0.011594127, step = 2485600 (2.255 sec) -2022-05-01 22:36:27,234 - tensorflow - INFO - global_step/sec: 88.6906 -2022-05-01 22:36:27,234 - tensorflow - INFO - loss = 0.006304825, step = 2485800 (2.255 sec) -2022-05-01 22:36:29,490 - tensorflow - INFO - global_step/sec: 88.6356 -2022-05-01 22:36:29,491 - tensorflow - INFO - loss = 2.7082615, step = 2486000 (2.256 sec) -2022-05-01 22:36:31,756 - tensorflow - INFO - global_step/sec: 88.2866 -2022-05-01 22:36:31,756 - tensorflow - INFO - loss = 0.015781693, step = 2486200 (2.265 sec) -2022-05-01 22:36:34,013 - tensorflow - INFO - global_step/sec: 88.6173 -2022-05-01 22:36:34,013 - tensorflow - INFO - loss = 0.2971293, step = 2486400 (2.257 sec) -2022-05-01 22:36:36,267 - tensorflow - INFO - global_step/sec: 88.7221 -2022-05-01 22:36:36,267 - tensorflow - INFO - loss = 0.004865778, step = 2486600 (2.254 sec) -2022-05-01 22:36:38,517 - tensorflow - INFO - global_step/sec: 88.8855 -2022-05-01 22:36:38,517 - tensorflow - INFO - loss = 0.008420302, step = 2486800 (2.250 sec) -2022-05-01 22:36:40,768 - tensorflow - INFO - global_step/sec: 88.8477 -2022-05-01 22:36:40,768 - tensorflow - INFO - loss = 0.07152127, step = 2487000 (2.251 sec) -2022-05-01 22:36:43,024 - tensorflow - INFO - global_step/sec: 88.6567 -2022-05-01 22:36:43,024 - tensorflow - INFO - loss = 0.009115946, step = 2487200 (2.256 sec) -2022-05-01 22:36:45,277 - tensorflow - INFO - global_step/sec: 88.7715 -2022-05-01 22:36:45,277 - tensorflow - INFO - loss = 0.005687272, step = 2487400 (2.253 sec) -2022-05-01 22:36:47,526 - tensorflow - INFO - global_step/sec: 88.9087 -2022-05-01 22:36:47,527 - tensorflow - INFO - loss = 0.19188553, step = 2487600 (2.249 sec) -2022-05-01 22:36:49,780 - tensorflow - INFO - global_step/sec: 88.7629 -2022-05-01 22:36:49,780 - tensorflow - INFO - loss = 0.023123372, step = 2487800 (2.253 sec) -2022-05-01 22:36:52,035 - tensorflow - INFO - global_step/sec: 88.6651 -2022-05-01 22:36:52,036 - tensorflow - INFO - loss = 1.4362056, step = 2488000 (2.256 sec) -2022-05-01 22:36:54,287 - tensorflow - INFO - global_step/sec: 88.8069 -2022-05-01 22:36:54,288 - tensorflow - INFO - loss = 0.49593955, step = 2488200 (2.252 sec) -2022-05-01 22:36:56,547 - tensorflow - INFO - global_step/sec: 88.5042 -2022-05-01 22:36:56,547 - tensorflow - INFO - loss = 0.0058627683, step = 2488400 (2.260 sec) -2022-05-01 22:36:58,797 - tensorflow - INFO - global_step/sec: 88.8873 -2022-05-01 22:36:58,798 - tensorflow - INFO - loss = 0.04569395, step = 2488600 (2.250 sec) -2022-05-01 22:37:01,050 - tensorflow - INFO - global_step/sec: 88.7574 -2022-05-01 22:37:01,051 - tensorflow - INFO - loss = 0.1335107, step = 2488800 (2.253 sec) -2022-05-01 22:37:03,305 - tensorflow - INFO - global_step/sec: 88.6996 -2022-05-01 22:37:03,306 - tensorflow - INFO - loss = 0.01614601, step = 2489000 (2.255 sec) -2022-05-01 22:37:05,558 - tensorflow - INFO - global_step/sec: 88.7925 -2022-05-01 22:37:05,558 - tensorflow - INFO - loss = 0.007931199, step = 2489200 (2.252 sec) -2022-05-01 22:37:07,816 - tensorflow - INFO - global_step/sec: 88.5643 -2022-05-01 22:37:07,816 - tensorflow - INFO - loss = 0.05090235, step = 2489400 (2.258 sec) -2022-05-01 22:37:10,076 - tensorflow - INFO - global_step/sec: 88.504 -2022-05-01 22:37:10,076 - tensorflow - INFO - loss = 0.017152905, step = 2489600 (2.260 sec) -2022-05-01 22:37:12,325 - tensorflow - INFO - global_step/sec: 88.9359 -2022-05-01 22:37:12,325 - tensorflow - INFO - loss = 0.010166051, step = 2489800 (2.249 sec) -2022-05-01 22:37:14,576 - tensorflow - INFO - global_step/sec: 88.8497 -2022-05-01 22:37:14,576 - tensorflow - INFO - loss = 0.008025802, step = 2490000 (2.251 sec) -2022-05-01 22:37:16,828 - tensorflow - INFO - global_step/sec: 88.7933 -2022-05-01 22:37:16,828 - tensorflow - INFO - loss = 0.02635665, step = 2490200 (2.252 sec) -2022-05-01 22:37:19,082 - tensorflow - INFO - global_step/sec: 88.7142 -2022-05-01 22:37:19,083 - tensorflow - INFO - loss = 0.05959672, step = 2490400 (2.254 sec) -2022-05-01 22:37:21,338 - tensorflow - INFO - global_step/sec: 88.6671 -2022-05-01 22:37:21,338 - tensorflow - INFO - loss = 0.007345924, step = 2490600 (2.256 sec) -2022-05-01 22:37:23,601 - tensorflow - INFO - global_step/sec: 88.3764 -2022-05-01 22:37:23,601 - tensorflow - INFO - loss = 0.6229799, step = 2490800 (2.263 sec) -2022-05-01 22:37:25,862 - tensorflow - INFO - global_step/sec: 88.4762 -2022-05-01 22:37:25,862 - tensorflow - INFO - loss = 0.023782894, step = 2491000 (2.260 sec) -2022-05-01 22:37:28,124 - tensorflow - INFO - global_step/sec: 88.3935 -2022-05-01 22:37:28,125 - tensorflow - INFO - loss = 0.017684136, step = 2491200 (2.263 sec) -2022-05-01 22:37:30,389 - tensorflow - INFO - global_step/sec: 88.3029 -2022-05-01 22:37:30,390 - tensorflow - INFO - loss = 0.029672848, step = 2491400 (2.265 sec) -2022-05-01 22:37:32,652 - tensorflow - INFO - global_step/sec: 88.3824 -2022-05-01 22:37:32,652 - tensorflow - INFO - loss = 0.018330775, step = 2491600 (2.263 sec) -2022-05-01 22:37:34,917 - tensorflow - INFO - global_step/sec: 88.3055 -2022-05-01 22:37:34,917 - tensorflow - INFO - loss = 0.19323613, step = 2491800 (2.265 sec) -2022-05-01 22:37:37,180 - tensorflow - INFO - global_step/sec: 88.3689 -2022-05-01 22:37:37,181 - tensorflow - INFO - loss = 0.0035518394, step = 2492000 (2.263 sec) -2022-05-01 22:37:39,442 - tensorflow - INFO - global_step/sec: 88.4067 -2022-05-01 22:37:39,443 - tensorflow - INFO - loss = 0.0065744384, step = 2492200 (2.262 sec) -2022-05-01 22:37:41,703 - tensorflow - INFO - global_step/sec: 88.453 -2022-05-01 22:37:41,704 - tensorflow - INFO - loss = 0.007689911, step = 2492400 (2.261 sec) -2022-05-01 22:37:43,967 - tensorflow - INFO - global_step/sec: 88.3656 -2022-05-01 22:37:43,967 - tensorflow - INFO - loss = 0.03819196, step = 2492600 (2.263 sec) -2022-05-01 22:37:46,227 - tensorflow - INFO - global_step/sec: 88.476 -2022-05-01 22:37:46,228 - tensorflow - INFO - loss = 0.047451183, step = 2492800 (2.261 sec) -2022-05-01 22:37:48,487 - tensorflow - INFO - global_step/sec: 88.5072 -2022-05-01 22:37:48,487 - tensorflow - INFO - loss = 0.014459378, step = 2493000 (2.260 sec) -2022-05-01 22:37:50,747 - tensorflow - INFO - global_step/sec: 88.5062 -2022-05-01 22:37:50,747 - tensorflow - INFO - loss = 0.20836158, step = 2493200 (2.260 sec) -2022-05-01 22:37:53,006 - tensorflow - INFO - global_step/sec: 88.5224 -2022-05-01 22:37:53,006 - tensorflow - INFO - loss = 0.03848868, step = 2493400 (2.259 sec) -2022-05-01 22:37:55,263 - tensorflow - INFO - global_step/sec: 88.6129 -2022-05-01 22:37:55,263 - tensorflow - INFO - loss = 0.17222637, step = 2493600 (2.257 sec) -2022-05-01 22:37:57,517 - tensorflow - INFO - global_step/sec: 88.7318 -2022-05-01 22:37:57,517 - tensorflow - INFO - loss = 0.012568856, step = 2493800 (2.254 sec) -2022-05-01 22:37:59,768 - tensorflow - INFO - global_step/sec: 88.8338 -2022-05-01 22:37:59,769 - tensorflow - INFO - loss = 0.036384873, step = 2494000 (2.251 sec) -2022-05-01 22:38:02,022 - tensorflow - INFO - global_step/sec: 88.7564 -2022-05-01 22:38:02,022 - tensorflow - INFO - loss = 0.010013607, step = 2494200 (2.253 sec) -2022-05-01 22:38:04,274 - tensorflow - INFO - global_step/sec: 88.7988 -2022-05-01 22:38:04,274 - tensorflow - INFO - loss = 0.011140664, step = 2494400 (2.252 sec) -2022-05-01 22:38:06,524 - tensorflow - INFO - global_step/sec: 88.8942 -2022-05-01 22:38:06,524 - tensorflow - INFO - loss = 0.00332475, step = 2494600 (2.250 sec) -2022-05-01 22:38:08,779 - tensorflow - INFO - global_step/sec: 88.6708 -2022-05-01 22:38:08,780 - tensorflow - INFO - loss = 0.03149625, step = 2494800 (2.256 sec) -2022-05-01 22:38:11,030 - tensorflow - INFO - global_step/sec: 88.8648 -2022-05-01 22:38:11,030 - tensorflow - INFO - loss = 0.008571399, step = 2495000 (2.251 sec) -2022-05-01 22:38:13,281 - tensorflow - INFO - global_step/sec: 88.8418 -2022-05-01 22:38:13,282 - tensorflow - INFO - loss = 0.024274005, step = 2495200 (2.251 sec) -2022-05-01 22:38:15,539 - tensorflow - INFO - global_step/sec: 88.5933 -2022-05-01 22:38:15,539 - tensorflow - INFO - loss = 0.026418442, step = 2495400 (2.257 sec) -2022-05-01 22:38:17,792 - tensorflow - INFO - global_step/sec: 88.7513 -2022-05-01 22:38:17,793 - tensorflow - INFO - loss = 0.019441668, step = 2495600 (2.253 sec) -2022-05-01 22:38:20,042 - tensorflow - INFO - global_step/sec: 88.8905 -2022-05-01 22:38:20,043 - tensorflow - INFO - loss = 0.010732288, step = 2495800 (2.250 sec) -2022-05-01 22:38:22,287 - tensorflow - INFO - global_step/sec: 89.1048 -2022-05-01 22:38:22,287 - tensorflow - INFO - loss = 0.03142559, step = 2496000 (2.245 sec) -2022-05-01 22:38:24,533 - tensorflow - INFO - global_step/sec: 89.0417 -2022-05-01 22:38:24,533 - tensorflow - INFO - loss = 0.059488736, step = 2496200 (2.246 sec) -2022-05-01 22:38:26,792 - tensorflow - INFO - global_step/sec: 88.5427 -2022-05-01 22:38:26,792 - tensorflow - INFO - loss = 0.014048614, step = 2496400 (2.259 sec) -2022-05-01 22:38:29,042 - tensorflow - INFO - global_step/sec: 88.8708 -2022-05-01 22:38:29,043 - tensorflow - INFO - loss = 0.047158502, step = 2496600 (2.250 sec) -2022-05-01 22:38:31,297 - tensorflow - INFO - global_step/sec: 88.7053 -2022-05-01 22:38:31,297 - tensorflow - INFO - loss = 0.019878263, step = 2496800 (2.255 sec) -2022-05-01 22:38:33,548 - tensorflow - INFO - global_step/sec: 88.8446 -2022-05-01 22:38:33,548 - tensorflow - INFO - loss = 0.0071397563, step = 2497000 (2.251 sec) -2022-05-01 22:38:35,800 - tensorflow - INFO - global_step/sec: 88.7965 -2022-05-01 22:38:35,801 - tensorflow - INFO - loss = 0.7433055, step = 2497200 (2.252 sec) -2022-05-01 22:38:38,053 - tensorflow - INFO - global_step/sec: 88.7709 -2022-05-01 22:38:38,054 - tensorflow - INFO - loss = 0.0055767056, step = 2497400 (2.253 sec) -2022-05-01 22:38:40,309 - tensorflow - INFO - global_step/sec: 88.661 -2022-05-01 22:38:40,309 - tensorflow - INFO - loss = 0.012999417, step = 2497600 (2.256 sec) -2022-05-01 22:38:42,559 - tensorflow - INFO - global_step/sec: 88.8825 -2022-05-01 22:38:42,560 - tensorflow - INFO - loss = 0.009196865, step = 2497800 (2.250 sec) -2022-05-01 22:38:44,817 - tensorflow - INFO - global_step/sec: 88.5973 -2022-05-01 22:38:44,817 - tensorflow - INFO - loss = 0.019697893, step = 2498000 (2.257 sec) -2022-05-01 22:38:47,067 - tensorflow - INFO - global_step/sec: 88.8669 -2022-05-01 22:38:47,068 - tensorflow - INFO - loss = 0.0067554098, step = 2498200 (2.251 sec) -2022-05-01 22:38:49,321 - tensorflow - INFO - global_step/sec: 88.7454 -2022-05-01 22:38:49,321 - tensorflow - INFO - loss = 0.012799524, step = 2498400 (2.254 sec) -2022-05-01 22:38:51,579 - tensorflow - INFO - global_step/sec: 88.5476 -2022-05-01 22:38:51,580 - tensorflow - INFO - loss = 0.04408863, step = 2498600 (2.259 sec) -2022-05-01 22:38:53,828 - tensorflow - INFO - global_step/sec: 88.95 -2022-05-01 22:38:53,828 - tensorflow - INFO - loss = 0.056328095, step = 2498800 (2.248 sec) -2022-05-01 22:38:56,084 - tensorflow - INFO - global_step/sec: 88.6324 -2022-05-01 22:38:56,085 - tensorflow - INFO - loss = 0.011546372, step = 2499000 (2.256 sec) -2022-05-01 22:38:58,335 - tensorflow - INFO - global_step/sec: 88.8491 -2022-05-01 22:38:58,336 - tensorflow - INFO - loss = 0.0033892635, step = 2499200 (2.251 sec) -2022-05-01 22:39:00,593 - tensorflow - INFO - global_step/sec: 88.6082 -2022-05-01 22:39:00,593 - tensorflow - INFO - loss = 0.004750331, step = 2499400 (2.257 sec) -2022-05-01 22:39:02,853 - tensorflow - INFO - global_step/sec: 88.4876 -2022-05-01 22:39:02,853 - tensorflow - INFO - loss = 0.007829336, step = 2499600 (2.260 sec) -2022-05-01 22:39:05,113 - tensorflow - INFO - global_step/sec: 88.4993 -2022-05-01 22:39:05,113 - tensorflow - INFO - loss = 0.024423394, step = 2499800 (2.260 sec) -2022-05-01 22:39:07,351 - tensorflow - INFO - Saving checkpoints for 2500000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. -2022-05-01 22:39:07,369 - tensorflow - INFO - model.ckpt-2500000 is not in all_model_checkpoint_paths. Manually adding it. -2022-05-01 22:39:07,900 - tensorflow - INFO - Calling model_fn. -2022-05-01 22:39:08,027 - tensorflow - INFO - Done calling model_fn. -2022-05-01 22:39:08,042 - tensorflow - INFO - Starting evaluation at 2022-05-01T22:39:08Z -2022-05-01 22:39:08,106 - tensorflow - INFO - Graph was finalized. -2022-05-01 22:39:08,109 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-2500000 -2022-05-01 22:39:08,391 - tensorflow - INFO - Running local_init_op. -2022-05-01 22:39:08,499 - tensorflow - INFO - Done running local_init_op. -2022-05-01 22:39:18,635 - tensorflow - INFO - Evaluation [3990/39900] -2022-05-01 22:39:28,069 - tensorflow - INFO - Evaluation [7980/39900] -2022-05-01 22:39:37,498 - tensorflow - INFO - Evaluation [11970/39900] -2022-05-01 22:39:46,922 - tensorflow - INFO - Evaluation [15960/39900] -2022-05-01 22:39:56,366 - tensorflow - INFO - Evaluation [19950/39900] -2022-05-01 22:40:05,832 - tensorflow - INFO - Evaluation [23940/39900] -2022-05-01 22:40:15,280 - tensorflow - INFO - Evaluation [27930/39900] -2022-05-01 22:40:24,719 - tensorflow - INFO - Evaluation [31920/39900] -2022-05-01 22:40:34,151 - tensorflow - INFO - Evaluation [35910/39900] -2022-05-01 22:40:43,604 - tensorflow - INFO - Evaluation [39900/39900] -2022-05-01 22:40:43,741 - tensorflow - INFO - Finished evaluation at 2022-05-01-22:40:43 -2022-05-01 22:40:43,741 - tensorflow - INFO - Saving dict for global step 2500000: global_step = 2500000, loss = 0.01607213, mean_degree_err = 3.0870223 -2022-05-01 22:40:43,742 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 2500000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-2500000 -2022-05-01 22:40:43,755 - tensorflow - INFO - global_step/sec: 2.02752 -2022-05-01 22:40:43,756 - tensorflow - INFO - loss = 0.028217498, step = 2500000 (98.643 sec) -2022-05-01 22:40:46,023 - tensorflow - INFO - global_step/sec: 88.1845 -2022-05-01 22:40:46,024 - tensorflow - INFO - loss = 0.01755622, step = 2500200 (2.268 sec) -2022-05-01 22:40:48,279 - tensorflow - INFO - global_step/sec: 88.675 -2022-05-01 22:40:48,279 - tensorflow - INFO - loss = 0.015188168, step = 2500400 (2.255 sec) -2022-05-01 22:40:50,536 - tensorflow - INFO - global_step/sec: 88.597 -2022-05-01 22:40:50,537 - tensorflow - INFO - loss = 0.011648623, step = 2500600 (2.257 sec) -2022-05-01 22:40:52,787 - tensorflow - INFO - global_step/sec: 88.8475 -2022-05-01 22:40:52,788 - tensorflow - INFO - loss = 0.08985994, step = 2500800 (2.251 sec) -2022-05-01 22:40:55,051 - tensorflow - INFO - global_step/sec: 88.3396 -2022-05-01 22:40:55,052 - tensorflow - INFO - loss = 0.0031446307, step = 2501000 (2.264 sec) -2022-05-01 22:40:57,317 - tensorflow - INFO - global_step/sec: 88.2696 -2022-05-01 22:40:57,317 - tensorflow - INFO - loss = 0.007888079, step = 2501200 (2.266 sec) -2022-05-01 22:40:59,584 - tensorflow - INFO - global_step/sec: 88.2311 -2022-05-01 22:40:59,584 - tensorflow - INFO - loss = 0.0051378547, step = 2501400 (2.267 sec) -2022-05-01 22:41:01,844 - tensorflow - INFO - global_step/sec: 88.5079 -2022-05-01 22:41:01,844 - tensorflow - INFO - loss = 0.013122154, step = 2501600 (2.260 sec) -2022-05-01 22:41:04,105 - tensorflow - INFO - global_step/sec: 88.4465 -2022-05-01 22:41:04,105 - tensorflow - INFO - loss = 0.01367711, step = 2501800 (2.261 sec) -2022-05-01 22:41:06,365 - tensorflow - INFO - global_step/sec: 88.4751 -2022-05-01 22:41:06,366 - tensorflow - INFO - loss = 0.014673911, step = 2502000 (2.261 sec) -2022-05-01 22:41:08,624 - tensorflow - INFO - global_step/sec: 88.5279 -2022-05-01 22:41:08,625 - tensorflow - INFO - loss = 0.010844378, step = 2502200 (2.259 sec) -2022-05-01 22:41:10,879 - tensorflow - INFO - global_step/sec: 88.6954 -2022-05-01 22:41:10,880 - tensorflow - INFO - loss = 0.011447811, step = 2502400 (2.255 sec) -2022-05-01 22:41:13,145 - tensorflow - INFO - global_step/sec: 88.2901 -2022-05-01 22:41:13,145 - tensorflow - INFO - loss = 0.043297, step = 2502600 (2.265 sec) -2022-05-01 22:41:15,407 - tensorflow - INFO - global_step/sec: 88.4005 -2022-05-01 22:41:15,407 - tensorflow - INFO - loss = 0.027249252, step = 2502800 (2.262 sec) -2022-05-01 22:41:17,667 - tensorflow - INFO - global_step/sec: 88.4985 -2022-05-01 22:41:17,667 - tensorflow - INFO - loss = 0.01280329, step = 2503000 (2.260 sec) -2022-05-01 22:41:19,928 - tensorflow - INFO - global_step/sec: 88.4401 -2022-05-01 22:41:19,929 - tensorflow - INFO - loss = 0.01926428, step = 2503200 (2.261 sec) -2022-05-01 22:41:22,187 - tensorflow - INFO - global_step/sec: 88.5565 -2022-05-01 22:41:22,187 - tensorflow - INFO - loss = 0.07693437, step = 2503400 (2.258 sec) -2022-05-01 22:41:24,446 - tensorflow - INFO - global_step/sec: 88.5255 -2022-05-01 22:41:24,447 - tensorflow - INFO - loss = 0.89175195, step = 2503600 (2.259 sec) -2022-05-01 22:41:26,705 - tensorflow - INFO - global_step/sec: 88.5356 -2022-05-01 22:41:26,705 - tensorflow - INFO - loss = 0.10346782, step = 2503800 (2.259 sec) -2022-05-01 22:41:28,960 - tensorflow - INFO - global_step/sec: 88.6761 -2022-05-01 22:41:28,961 - tensorflow - INFO - loss = 0.09175624, step = 2504000 (2.255 sec) -2022-05-01 22:41:31,216 - tensorflow - INFO - global_step/sec: 88.6662 -2022-05-01 22:41:31,217 - tensorflow - INFO - loss = 0.050385676, step = 2504200 (2.256 sec) -2022-05-01 22:41:33,467 - tensorflow - INFO - global_step/sec: 88.8368 -2022-05-01 22:41:33,468 - tensorflow - INFO - loss = 0.010782375, step = 2504400 (2.251 sec) -2022-05-01 22:41:35,722 - tensorflow - INFO - global_step/sec: 88.7187 -2022-05-01 22:41:35,722 - tensorflow - INFO - loss = 0.0145096, step = 2504600 (2.254 sec) -2022-05-01 22:41:37,976 - tensorflow - INFO - global_step/sec: 88.7105 -2022-05-01 22:41:37,977 - tensorflow - INFO - loss = 0.10901194, step = 2504800 (2.254 sec) -2022-05-01 22:41:40,233 - tensorflow - INFO - global_step/sec: 88.6185 -2022-05-01 22:41:40,234 - tensorflow - INFO - loss = 0.030011967, step = 2505000 (2.257 sec) -2022-05-01 22:41:42,481 - tensorflow - INFO - global_step/sec: 88.9853 -2022-05-01 22:41:42,481 - tensorflow - INFO - loss = 0.041071665, step = 2505200 (2.248 sec) -2022-05-01 22:41:44,733 - tensorflow - INFO - global_step/sec: 88.815 -2022-05-01 22:41:44,733 - tensorflow - INFO - loss = 0.007870996, step = 2505400 (2.252 sec) -2022-05-01 22:41:46,982 - tensorflow - INFO - global_step/sec: 88.9093 -2022-05-01 22:41:46,983 - tensorflow - INFO - loss = 0.0217721, step = 2505600 (2.250 sec) -2022-05-01 22:41:49,234 - tensorflow - INFO - global_step/sec: 88.8162 -2022-05-01 22:41:49,234 - tensorflow - INFO - loss = 0.0058401357, step = 2505800 (2.252 sec) -2022-05-01 22:41:51,491 - tensorflow - INFO - global_step/sec: 88.6144 -2022-05-01 22:41:51,491 - tensorflow - INFO - loss = 0.010875938, step = 2506000 (2.257 sec) -2022-05-01 22:41:53,748 - tensorflow - INFO - global_step/sec: 88.5955 -2022-05-01 22:41:53,749 - tensorflow - INFO - loss = 0.018075192, step = 2506200 (2.257 sec) -2022-05-01 22:41:56,006 - tensorflow - INFO - 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INFO - global_step/sec: 88.8338 -2022-05-01 22:43:05,851 - tensorflow - INFO - loss = 0.032996435, step = 2512600 (2.251 sec) -2022-05-01 22:43:08,105 - tensorflow - INFO - global_step/sec: 88.7179 -2022-05-01 22:43:08,106 - tensorflow - INFO - loss = 0.011973894, step = 2512800 (2.254 sec) -2022-05-01 22:43:10,365 - tensorflow - INFO - global_step/sec: 88.5172 -2022-05-01 22:43:10,365 - tensorflow - INFO - loss = 0.021260012, step = 2513000 (2.259 sec) -2022-05-01 22:43:12,613 - tensorflow - INFO - global_step/sec: 88.97 -2022-05-01 22:43:12,613 - tensorflow - INFO - loss = 0.06239277, step = 2513200 (2.248 sec) -2022-05-01 22:43:14,858 - tensorflow - INFO - global_step/sec: 89.0595 -2022-05-01 22:43:14,859 - tensorflow - INFO - loss = 0.011447022, step = 2513400 (2.246 sec) -2022-05-01 22:43:17,114 - tensorflow - INFO - global_step/sec: 88.6814 -2022-05-01 22:43:17,114 - tensorflow - INFO - loss = 0.01663798, step = 2513600 (2.255 sec) -2022-05-01 22:43:19,366 - tensorflow - INFO - global_step/sec: 88.7862 -2022-05-01 22:43:19,367 - tensorflow - INFO - loss = 0.0050092377, step = 2513800 (2.253 sec) -2022-05-01 22:43:21,616 - tensorflow - INFO - global_step/sec: 88.9065 -2022-05-01 22:43:21,616 - tensorflow - INFO - loss = 0.026145354, step = 2514000 (2.250 sec) -2022-05-01 22:43:23,873 - tensorflow - INFO - global_step/sec: 88.6185 -2022-05-01 22:43:23,873 - tensorflow - INFO - loss = 0.021748584, step = 2514200 (2.257 sec) -2022-05-01 22:43:26,124 - tensorflow - INFO - global_step/sec: 88.8162 -2022-05-01 22:43:26,125 - tensorflow - INFO - loss = 0.011702653, step = 2514400 (2.252 sec) -2022-05-01 22:43:28,380 - tensorflow - INFO - global_step/sec: 88.6809 -2022-05-01 22:43:28,380 - tensorflow - INFO - loss = 0.022134826, step = 2514600 (2.255 sec) -2022-05-01 22:43:30,637 - tensorflow - INFO - global_step/sec: 88.587 -2022-05-01 22:43:30,638 - tensorflow - INFO - loss = 0.007043754, step = 2514800 (2.258 sec) -2022-05-01 22:43:32,891 - tensorflow - INFO - global_step/sec: 88.7408 -2022-05-01 22:43:32,892 - tensorflow - INFO - loss = 0.015608694, step = 2515000 (2.254 sec) -2022-05-01 22:43:35,148 - tensorflow - INFO - global_step/sec: 88.6108 -2022-05-01 22:43:35,149 - tensorflow - INFO - loss = 0.008972997, step = 2515200 (2.257 sec) -2022-05-01 22:43:37,399 - tensorflow - INFO - global_step/sec: 88.859 -2022-05-01 22:43:37,399 - tensorflow - INFO - loss = 0.009503961, step = 2515400 (2.251 sec) -2022-05-01 22:43:39,649 - tensorflow - INFO - global_step/sec: 88.8693 -2022-05-01 22:43:39,650 - tensorflow - INFO - loss = 0.0039321072, step = 2515600 (2.251 sec) -2022-05-01 22:43:41,901 - tensorflow - INFO - global_step/sec: 88.8226 -2022-05-01 22:43:41,902 - tensorflow - INFO - loss = 0.014092447, step = 2515800 (2.252 sec) -2022-05-01 22:43:44,151 - tensorflow - INFO - global_step/sec: 88.9042 -2022-05-01 22:43:44,151 - tensorflow - INFO - loss = 0.005770107, step = 2516000 (2.250 sec) -2022-05-01 22:43:46,408 - tensorflow - INFO - global_step/sec: 88.6123 -2022-05-01 22:43:46,408 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.8257 -2022-05-01 22:44:02,194 - tensorflow - INFO - loss = 0.1319748, step = 2517600 (2.252 sec) -2022-05-01 22:44:04,447 - tensorflow - INFO - global_step/sec: 88.7198 -2022-05-01 22:44:04,448 - tensorflow - INFO - loss = 0.0036735244, step = 2517800 (2.254 sec) -2022-05-01 22:44:06,698 - tensorflow - INFO - global_step/sec: 88.8619 -2022-05-01 22:44:06,699 - tensorflow - INFO - loss = 0.047612377, step = 2518000 (2.251 sec) -2022-05-01 22:44:08,948 - tensorflow - INFO - global_step/sec: 88.8772 -2022-05-01 22:44:08,949 - tensorflow - INFO - loss = 0.0066236276, step = 2518200 (2.250 sec) -2022-05-01 22:44:11,202 - tensorflow - INFO - global_step/sec: 88.7675 -2022-05-01 22:44:11,202 - tensorflow - INFO - loss = 0.0067285597, step = 2518400 (2.253 sec) -2022-05-01 22:44:13,457 - tensorflow - INFO - global_step/sec: 88.6561 -2022-05-01 22:44:13,458 - tensorflow - INFO - loss = 0.018304791, step = 2518600 (2.256 sec) -2022-05-01 22:44:15,709 - tensorflow - 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INFO - loss = 0.00386234, step = 2522400 (2.257 sec) -2022-05-01 22:44:58,644 - tensorflow - INFO - global_step/sec: 88.7059 -2022-05-01 22:44:58,645 - tensorflow - INFO - loss = 0.019720316, step = 2522600 (2.255 sec) -2022-05-01 22:45:00,900 - tensorflow - INFO - global_step/sec: 88.6435 -2022-05-01 22:45:00,901 - tensorflow - INFO - loss = 0.0074373996, step = 2522800 (2.256 sec) -2022-05-01 22:45:03,157 - tensorflow - INFO - global_step/sec: 88.6473 -2022-05-01 22:45:03,157 - tensorflow - INFO - loss = 0.09727685, step = 2523000 (2.256 sec) -2022-05-01 22:45:05,411 - tensorflow - INFO - global_step/sec: 88.7344 -2022-05-01 22:45:05,411 - tensorflow - INFO - loss = 0.03645045, step = 2523200 (2.254 sec) -2022-05-01 22:45:07,663 - tensorflow - INFO - global_step/sec: 88.8097 -2022-05-01 22:45:07,663 - tensorflow - INFO - loss = 0.018618433, step = 2523400 (2.252 sec) -2022-05-01 22:45:09,911 - tensorflow - INFO - global_step/sec: 88.9577 -2022-05-01 22:45:09,911 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.7759 -2022-05-01 22:45:25,690 - tensorflow - INFO - loss = 0.054611653, step = 2525000 (2.253 sec) -2022-05-01 22:45:27,944 - tensorflow - INFO - global_step/sec: 88.684 -2022-05-01 22:45:27,945 - tensorflow - INFO - loss = 0.0085484935, step = 2525200 (2.255 sec) -2022-05-01 22:45:30,203 - tensorflow - INFO - global_step/sec: 88.5647 -2022-05-01 22:45:30,203 - tensorflow - INFO - loss = 0.017433915, step = 2525400 (2.258 sec) -2022-05-01 22:45:32,466 - tensorflow - INFO - global_step/sec: 88.3641 -2022-05-01 22:45:32,466 - tensorflow - INFO - loss = 0.02791887, step = 2525600 (2.263 sec) -2022-05-01 22:45:34,720 - tensorflow - INFO - global_step/sec: 88.7419 -2022-05-01 22:45:34,720 - tensorflow - INFO - loss = 0.065674186, step = 2525800 (2.254 sec) -2022-05-01 22:45:36,968 - tensorflow - INFO - global_step/sec: 88.9489 -2022-05-01 22:45:36,968 - tensorflow - INFO - loss = 0.02218478, step = 2526000 (2.248 sec) -2022-05-01 22:45:39,216 - tensorflow - INFO - global_step/sec: 88.9606 -2022-05-01 22:45:39,217 - tensorflow - INFO - loss = 0.008726221, step = 2526200 (2.248 sec) -2022-05-01 22:45:41,469 - tensorflow - INFO - global_step/sec: 88.7926 -2022-05-01 22:45:41,469 - tensorflow - INFO - loss = 0.035256118, step = 2526400 (2.252 sec) -2022-05-01 22:45:43,729 - tensorflow - INFO - global_step/sec: 88.4914 -2022-05-01 22:45:43,729 - tensorflow - INFO - loss = 0.015490872, step = 2526600 (2.260 sec) -2022-05-01 22:45:45,984 - tensorflow - INFO - global_step/sec: 88.7003 -2022-05-01 22:45:45,984 - tensorflow - INFO - loss = 0.0068812855, step = 2526800 (2.255 sec) -2022-05-01 22:45:48,234 - tensorflow - INFO - global_step/sec: 88.8671 -2022-05-01 22:45:48,235 - tensorflow - INFO - loss = 0.040634274, step = 2527000 (2.251 sec) -2022-05-01 22:45:50,491 - tensorflow - INFO - global_step/sec: 88.6024 -2022-05-01 22:45:50,492 - tensorflow - INFO - loss = 0.08081141, step = 2527200 (2.257 sec) -2022-05-01 22:45:52,740 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.023867954, step = 2528600 (2.254 sec) -2022-05-01 22:46:08,531 - tensorflow - INFO - global_step/sec: 88.5211 -2022-05-01 22:46:08,531 - tensorflow - INFO - loss = 0.021360926, step = 2528800 (2.259 sec) -2022-05-01 22:46:10,786 - tensorflow - INFO - global_step/sec: 88.6647 -2022-05-01 22:46:10,787 - tensorflow - INFO - loss = 0.0070251855, step = 2529000 (2.256 sec) -2022-05-01 22:46:13,041 - tensorflow - INFO - global_step/sec: 88.6977 -2022-05-01 22:46:13,042 - tensorflow - INFO - loss = 0.020961937, step = 2529200 (2.255 sec) -2022-05-01 22:46:15,294 - tensorflow - INFO - global_step/sec: 88.7715 -2022-05-01 22:46:15,295 - tensorflow - INFO - loss = 0.00983898, step = 2529400 (2.253 sec) -2022-05-01 22:46:17,548 - tensorflow - INFO - global_step/sec: 88.74 -2022-05-01 22:46:17,548 - tensorflow - INFO - loss = 0.021158015, step = 2529600 (2.254 sec) -2022-05-01 22:46:19,803 - tensorflow - INFO - global_step/sec: 88.7043 -2022-05-01 22:46:19,803 - tensorflow - INFO - loss = 0.1289204, step = 2529800 (2.255 sec) -2022-05-01 22:46:22,063 - tensorflow - INFO - global_step/sec: 88.474 -2022-05-01 22:46:22,064 - tensorflow - INFO - loss = 0.00733544, step = 2530000 (2.261 sec) -2022-05-01 22:46:24,321 - tensorflow - INFO - global_step/sec: 88.5594 -2022-05-01 22:46:24,322 - tensorflow - INFO - loss = 0.006650264, step = 2530200 (2.258 sec) -2022-05-01 22:46:26,578 - tensorflow - INFO - global_step/sec: 88.619 -2022-05-01 22:46:26,579 - tensorflow - INFO - loss = 1.1148852, step = 2530400 (2.257 sec) -2022-05-01 22:46:28,839 - tensorflow - INFO - global_step/sec: 88.4881 -2022-05-01 22:46:28,839 - tensorflow - INFO - loss = 0.025489006, step = 2530600 (2.260 sec) -2022-05-01 22:46:31,099 - tensorflow - INFO - global_step/sec: 88.4711 -2022-05-01 22:46:31,100 - tensorflow - INFO - loss = 0.026309218, step = 2530800 (2.261 sec) -2022-05-01 22:46:33,362 - tensorflow - INFO - global_step/sec: 88.3953 -2022-05-01 22:46:33,362 - tensorflow - INFO - loss = 0.037741657, step = 2531000 (2.263 sec) -2022-05-01 22:46:35,616 - tensorflow - INFO - global_step/sec: 88.7372 -2022-05-01 22:46:35,616 - tensorflow - INFO - loss = 0.0073168147, step = 2531200 (2.254 sec) -2022-05-01 22:46:37,869 - tensorflow - INFO - global_step/sec: 88.7679 -2022-05-01 22:46:37,869 - tensorflow - INFO - loss = 0.07530931, step = 2531400 (2.253 sec) -2022-05-01 22:46:40,124 - tensorflow - INFO - global_step/sec: 88.6971 -2022-05-01 22:46:40,124 - tensorflow - INFO - loss = 0.004444405, step = 2531600 (2.255 sec) -2022-05-01 22:46:42,376 - tensorflow - INFO - global_step/sec: 88.8019 -2022-05-01 22:46:42,376 - tensorflow - INFO - loss = 0.033372402, step = 2531800 (2.252 sec) -2022-05-01 22:46:44,626 - tensorflow - INFO - global_step/sec: 88.8685 -2022-05-01 22:46:44,627 - tensorflow - INFO - loss = 0.005551937, step = 2532000 (2.251 sec) -2022-05-01 22:46:46,886 - tensorflow - INFO - global_step/sec: 88.5173 -2022-05-01 22:46:46,886 - tensorflow - INFO - loss = 0.013973923, step = 2532200 (2.259 sec) -2022-05-01 22:46:49,139 - tensorflow - INFO - global_step/sec: 88.7619 -2022-05-01 22:46:49,139 - tensorflow - INFO - loss = 0.007855685, step = 2532400 (2.253 sec) -2022-05-01 22:46:51,391 - tensorflow - INFO - global_step/sec: 88.7898 -2022-05-01 22:46:51,392 - tensorflow - INFO - loss = 0.013866673, step = 2532600 (2.252 sec) -2022-05-01 22:46:53,644 - tensorflow - INFO - global_step/sec: 88.7947 -2022-05-01 22:46:53,644 - tensorflow - INFO - loss = 0.010676412, step = 2532800 (2.252 sec) -2022-05-01 22:46:55,901 - tensorflow - INFO - global_step/sec: 88.5894 -2022-05-01 22:46:55,902 - tensorflow - INFO - loss = 0.01469085, step = 2533000 (2.258 sec) -2022-05-01 22:46:58,165 - tensorflow - INFO - global_step/sec: 88.3471 -2022-05-01 22:46:58,166 - tensorflow - INFO - loss = 0.013090211, step = 2533200 (2.264 sec) -2022-05-01 22:47:00,422 - tensorflow - INFO - global_step/sec: 88.6138 -2022-05-01 22:47:00,423 - tensorflow - INFO - loss = 0.009685187, step = 2533400 (2.257 sec) -2022-05-01 22:47:02,674 - tensorflow - INFO - global_step/sec: 88.8319 -2022-05-01 22:47:02,674 - tensorflow - INFO - loss = 0.016568955, step = 2533600 (2.251 sec) -2022-05-01 22:47:04,926 - tensorflow - INFO - global_step/sec: 88.7756 -2022-05-01 22:47:04,927 - tensorflow - INFO - loss = 0.087907135, step = 2533800 (2.253 sec) -2022-05-01 22:47:07,187 - tensorflow - INFO - global_step/sec: 88.4786 -2022-05-01 22:47:07,187 - tensorflow - INFO - loss = 0.0128628425, step = 2534000 (2.260 sec) -2022-05-01 22:47:09,440 - tensorflow - INFO - global_step/sec: 88.7678 -2022-05-01 22:47:09,440 - tensorflow - INFO - loss = 0.039572977, step = 2534200 (2.253 sec) -2022-05-01 22:47:11,694 - tensorflow - INFO - global_step/sec: 88.7353 -2022-05-01 22:47:11,694 - tensorflow - INFO - loss = 0.031185715, step = 2534400 (2.254 sec) -2022-05-01 22:47:13,951 - tensorflow - INFO - global_step/sec: 88.6052 -2022-05-01 22:47:13,952 - tensorflow - INFO - loss = 0.014337154, step = 2534600 (2.257 sec) -2022-05-01 22:47:16,205 - tensorflow - INFO - global_step/sec: 88.7285 -2022-05-01 22:47:16,206 - tensorflow - INFO - loss = 0.012679648, step = 2534800 (2.254 sec) -2022-05-01 22:47:18,460 - tensorflow - INFO - global_step/sec: 88.6938 -2022-05-01 22:47:18,461 - tensorflow - INFO - loss = 0.0075245304, step = 2535000 (2.255 sec) -2022-05-01 22:47:20,714 - tensorflow - INFO - global_step/sec: 88.7246 -2022-05-01 22:47:20,715 - tensorflow - INFO - loss = 0.010959899, step = 2535200 (2.254 sec) -2022-05-01 22:47:22,968 - tensorflow - INFO - global_step/sec: 88.7615 -2022-05-01 22:47:22,968 - tensorflow - INFO - loss = 0.0083835805, step = 2535400 (2.253 sec) -2022-05-01 22:47:25,221 - tensorflow - INFO - global_step/sec: 88.7635 -2022-05-01 22:47:25,221 - tensorflow - INFO - loss = 0.11738077, step = 2535600 (2.253 sec) -2022-05-01 22:47:27,473 - tensorflow - INFO - global_step/sec: 88.8011 -2022-05-01 22:47:27,473 - tensorflow - INFO - loss = 0.00811711, step = 2535800 (2.252 sec) -2022-05-01 22:47:29,723 - tensorflow - INFO - global_step/sec: 88.8746 -2022-05-01 22:47:29,724 - tensorflow - INFO - loss = 0.05884538, step = 2536000 (2.250 sec) -2022-05-01 22:47:31,980 - tensorflow - INFO - global_step/sec: 88.6219 -2022-05-01 22:47:31,980 - tensorflow - INFO - loss = 0.050516434, step = 2536200 (2.257 sec) -2022-05-01 22:47:34,234 - tensorflow - INFO - global_step/sec: 88.7353 -2022-05-01 22:47:34,234 - tensorflow - INFO - loss = 0.031141484, step = 2536400 (2.254 sec) -2022-05-01 22:47:36,495 - tensorflow - INFO - global_step/sec: 88.4556 -2022-05-01 22:47:36,495 - tensorflow - INFO - loss = 0.029849585, step = 2536600 (2.261 sec) -2022-05-01 22:47:38,746 - tensorflow - INFO - global_step/sec: 88.8618 -2022-05-01 22:47:38,746 - tensorflow - INFO - loss = 0.014284352, step = 2536800 (2.251 sec) -2022-05-01 22:47:40,995 - tensorflow - INFO - global_step/sec: 88.9144 -2022-05-01 22:47:40,995 - tensorflow - INFO - loss = 0.014337454, step = 2537000 (2.249 sec) -2022-05-01 22:47:43,249 - tensorflow - INFO - global_step/sec: 88.7188 -2022-05-01 22:47:43,250 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.3167 -2022-05-01 22:47:59,054 - tensorflow - INFO - loss = 0.06591574, step = 2538600 (2.265 sec) -2022-05-01 22:48:01,316 - tensorflow - INFO - global_step/sec: 88.3856 -2022-05-01 22:48:01,317 - tensorflow - INFO - loss = 0.010528269, step = 2538800 (2.263 sec) -2022-05-01 22:48:03,575 - tensorflow - INFO - global_step/sec: 88.5373 -2022-05-01 22:48:03,575 - tensorflow - INFO - loss = 0.0035607866, step = 2539000 (2.259 sec) -2022-05-01 22:48:05,836 - tensorflow - INFO - global_step/sec: 88.4755 -2022-05-01 22:48:05,836 - tensorflow - INFO - loss = 0.0062068263, step = 2539200 (2.261 sec) -2022-05-01 22:48:08,100 - tensorflow - INFO - global_step/sec: 88.3318 -2022-05-01 22:48:08,100 - tensorflow - INFO - loss = 0.014400102, step = 2539400 (2.264 sec) -2022-05-01 22:48:10,362 - tensorflow - INFO - global_step/sec: 88.4241 -2022-05-01 22:48:10,362 - tensorflow - INFO - loss = 0.016748605, step = 2539600 (2.262 sec) -2022-05-01 22:48:12,622 - tensorflow - INFO - global_step/sec: 88.4698 -2022-05-01 22:48:12,623 - tensorflow - INFO - loss = 0.009805948, step = 2539800 (2.261 sec) -2022-05-01 22:48:14,883 - tensorflow - INFO - global_step/sec: 88.4636 -2022-05-01 22:48:14,883 - tensorflow - INFO - loss = 0.0056213485, step = 2540000 (2.261 sec) -2022-05-01 22:48:17,149 - tensorflow - INFO - global_step/sec: 88.2496 -2022-05-01 22:48:17,150 - tensorflow - INFO - loss = 0.01510028, step = 2540200 (2.266 sec) -2022-05-01 22:48:19,412 - tensorflow - INFO - global_step/sec: 88.3843 -2022-05-01 22:48:19,413 - tensorflow - INFO - loss = 0.038076065, step = 2540400 (2.263 sec) -2022-05-01 22:48:21,679 - tensorflow - INFO - global_step/sec: 88.2475 -2022-05-01 22:48:21,679 - tensorflow - INFO - loss = 0.018373983, step = 2540600 (2.266 sec) -2022-05-01 22:48:23,936 - tensorflow - INFO - global_step/sec: 88.5829 -2022-05-01 22:48:23,937 - tensorflow - INFO - loss = 0.0557938, step = 2540800 (2.258 sec) -2022-05-01 22:48:26,198 - tensorflow - INFO - global_step/sec: 88.4091 -2022-05-01 22:48:26,199 - tensorflow - INFO - loss = 0.07594441, step = 2541000 (2.262 sec) -2022-05-01 22:48:28,450 - tensorflow - INFO - global_step/sec: 88.8371 -2022-05-01 22:48:28,450 - tensorflow - INFO - loss = 0.00945507, step = 2541200 (2.251 sec) -2022-05-01 22:48:30,705 - tensorflow - INFO - global_step/sec: 88.6965 -2022-05-01 22:48:30,705 - tensorflow - INFO - loss = 0.029454486, step = 2541400 (2.255 sec) -2022-05-01 22:48:32,953 - tensorflow - INFO - global_step/sec: 88.9577 -2022-05-01 22:48:32,953 - tensorflow - INFO - loss = 0.013326672, step = 2541600 (2.248 sec) -2022-05-01 22:48:35,197 - tensorflow - INFO - global_step/sec: 89.1064 -2022-05-01 22:48:35,198 - tensorflow - INFO - loss = 0.016604941, step = 2541800 (2.244 sec) -2022-05-01 22:48:37,451 - tensorflow - INFO - global_step/sec: 88.7576 -2022-05-01 22:48:37,451 - tensorflow - INFO - loss = 0.0717519, step = 2542000 (2.253 sec) -2022-05-01 22:48:39,707 - tensorflow - INFO - global_step/sec: 88.6594 -2022-05-01 22:48:39,707 - tensorflow - INFO - loss = 0.019117307, step = 2542200 (2.256 sec) -2022-05-01 22:48:41,966 - tensorflow - INFO - global_step/sec: 88.5254 -2022-05-01 22:48:41,966 - tensorflow - INFO - loss = 0.014296472, step = 2542400 (2.259 sec) -2022-05-01 22:48:44,217 - tensorflow - INFO - global_step/sec: 88.8361 -2022-05-01 22:48:44,218 - tensorflow - INFO - loss = 0.005062365, step = 2542600 (2.251 sec) -2022-05-01 22:48:46,468 - tensorflow - INFO - global_step/sec: 88.8366 -2022-05-01 22:48:46,469 - tensorflow - INFO - loss = 0.038442142, step = 2542800 (2.251 sec) -2022-05-01 22:48:48,720 - tensorflow - INFO - global_step/sec: 88.8246 -2022-05-01 22:48:48,721 - tensorflow - INFO - loss = 0.23086584, step = 2543000 (2.252 sec) -2022-05-01 22:48:50,974 - tensorflow - INFO - global_step/sec: 88.7239 -2022-05-01 22:48:50,975 - tensorflow - INFO - loss = 0.015884178, step = 2543200 (2.254 sec) -2022-05-01 22:48:53,235 - tensorflow - INFO - global_step/sec: 88.4688 -2022-05-01 22:48:53,235 - tensorflow - INFO - loss = 0.010811222, step = 2543400 (2.261 sec) -2022-05-01 22:48:55,485 - tensorflow - INFO - global_step/sec: 88.8903 -2022-05-01 22:48:55,485 - tensorflow - INFO - loss = 0.006758169, step = 2543600 (2.250 sec) -2022-05-01 22:48:57,747 - tensorflow - INFO - global_step/sec: 88.4124 -2022-05-01 22:48:57,748 - tensorflow - INFO - loss = 0.081859335, step = 2543800 (2.262 sec) -2022-05-01 22:48:59,996 - tensorflow - INFO - global_step/sec: 88.9153 -2022-05-01 22:48:59,997 - tensorflow - INFO - loss = 0.005912382, step = 2544000 (2.249 sec) -2022-05-01 22:49:02,255 - tensorflow - INFO - global_step/sec: 88.5666 -2022-05-01 22:49:02,255 - tensorflow - INFO - loss = 0.04825076, step = 2544200 (2.258 sec) -2022-05-01 22:49:04,510 - tensorflow - INFO - global_step/sec: 88.6872 -2022-05-01 22:49:04,510 - tensorflow - INFO - loss = 0.0073155835, step = 2544400 (2.255 sec) -2022-05-01 22:49:06,767 - tensorflow - INFO - global_step/sec: 88.6002 -2022-05-01 22:49:06,767 - tensorflow - INFO - 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INFO - global_step/sec: 88.8661 -2022-05-01 22:49:36,071 - tensorflow - INFO - loss = 0.008072868, step = 2547200 (2.251 sec) -2022-05-01 22:49:38,325 - tensorflow - INFO - global_step/sec: 88.7389 -2022-05-01 22:49:38,325 - tensorflow - INFO - loss = 0.004705961, step = 2547400 (2.254 sec) -2022-05-01 22:49:40,581 - tensorflow - INFO - global_step/sec: 88.6195 -2022-05-01 22:49:40,582 - tensorflow - INFO - loss = 0.040594444, step = 2547600 (2.257 sec) -2022-05-01 22:49:42,832 - tensorflow - INFO - global_step/sec: 88.8781 -2022-05-01 22:49:42,832 - tensorflow - INFO - loss = 0.0070745815, step = 2547800 (2.250 sec) -2022-05-01 22:49:45,092 - tensorflow - INFO - global_step/sec: 88.4629 -2022-05-01 22:49:45,093 - tensorflow - INFO - loss = 0.006131235, step = 2548000 (2.261 sec) -2022-05-01 22:49:47,343 - tensorflow - INFO - global_step/sec: 88.8685 -2022-05-01 22:49:47,343 - tensorflow - INFO - loss = 0.03124081, step = 2548200 (2.250 sec) -2022-05-01 22:49:49,593 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.6074 -2022-05-01 22:50:32,430 - tensorflow - INFO - loss = 0.83199763, step = 2552200 (2.257 sec) -2022-05-01 22:50:34,687 - tensorflow - INFO - global_step/sec: 88.5733 -2022-05-01 22:50:34,688 - tensorflow - INFO - loss = 0.021190714, step = 2552400 (2.258 sec) -2022-05-01 22:50:36,938 - tensorflow - INFO - global_step/sec: 88.8412 -2022-05-01 22:50:36,939 - tensorflow - INFO - loss = 0.0023580533, step = 2552600 (2.251 sec) -2022-05-01 22:50:39,191 - tensorflow - INFO - global_step/sec: 88.7684 -2022-05-01 22:50:39,192 - tensorflow - INFO - loss = 0.011092663, step = 2552800 (2.253 sec) -2022-05-01 22:50:41,446 - tensorflow - INFO - global_step/sec: 88.7127 -2022-05-01 22:50:41,446 - tensorflow - INFO - loss = 0.11473206, step = 2553000 (2.254 sec) -2022-05-01 22:50:43,696 - tensorflow - INFO - global_step/sec: 88.8791 -2022-05-01 22:50:43,697 - tensorflow - INFO - loss = 0.010452032, step = 2553200 (2.250 sec) -2022-05-01 22:50:45,956 - tensorflow - INFO - global_step/sec: 88.5052 -2022-05-01 22:50:45,956 - tensorflow - INFO - loss = 0.016556587, step = 2553400 (2.260 sec) -2022-05-01 22:50:48,207 - tensorflow - INFO - global_step/sec: 88.8291 -2022-05-01 22:50:48,208 - tensorflow - INFO - loss = 0.33795607, step = 2553600 (2.252 sec) -2022-05-01 22:50:50,460 - tensorflow - INFO - global_step/sec: 88.7849 -2022-05-01 22:50:50,460 - tensorflow - INFO - loss = 0.026521377, step = 2553800 (2.253 sec) -2022-05-01 22:50:52,710 - tensorflow - INFO - global_step/sec: 88.9013 -2022-05-01 22:50:52,710 - tensorflow - INFO - loss = 0.03137654, step = 2554000 (2.250 sec) -2022-05-01 22:50:54,967 - tensorflow - INFO - global_step/sec: 88.6091 -2022-05-01 22:50:54,967 - tensorflow - INFO - loss = 0.01058252, step = 2554200 (2.257 sec) -2022-05-01 22:50:57,218 - tensorflow - INFO - global_step/sec: 88.8363 -2022-05-01 22:50:57,219 - tensorflow - INFO - loss = 0.008173717, step = 2554400 (2.251 sec) -2022-05-01 22:50:59,476 - tensorflow - INFO - 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tensorflow - INFO - loss = 0.042044383, step = 2555800 (2.262 sec) -2022-05-01 22:51:15,253 - tensorflow - INFO - global_step/sec: 88.7395 -2022-05-01 22:51:15,253 - tensorflow - INFO - loss = 0.026508592, step = 2556000 (2.254 sec) -2022-05-01 22:51:17,504 - tensorflow - INFO - global_step/sec: 88.8343 -2022-05-01 22:51:17,505 - tensorflow - INFO - loss = 0.014341069, step = 2556200 (2.251 sec) -2022-05-01 22:51:19,754 - tensorflow - INFO - global_step/sec: 88.9068 -2022-05-01 22:51:19,754 - tensorflow - INFO - loss = 0.04941809, step = 2556400 (2.250 sec) -2022-05-01 22:51:22,009 - tensorflow - INFO - global_step/sec: 88.6659 -2022-05-01 22:51:22,010 - tensorflow - INFO - loss = 0.016061569, step = 2556600 (2.256 sec) -2022-05-01 22:51:24,272 - tensorflow - INFO - global_step/sec: 88.3814 -2022-05-01 22:51:24,273 - tensorflow - INFO - loss = 0.0072139446, step = 2556800 (2.263 sec) -2022-05-01 22:51:26,531 - tensorflow - INFO - global_step/sec: 88.5473 -2022-05-01 22:51:26,531 - tensorflow - INFO - loss = 0.021163104, step = 2557000 (2.259 sec) -2022-05-01 22:51:28,788 - tensorflow - INFO - global_step/sec: 88.6157 -2022-05-01 22:51:28,788 - tensorflow - INFO - loss = 0.0047359876, step = 2557200 (2.257 sec) -2022-05-01 22:51:31,056 - tensorflow - INFO - global_step/sec: 88.185 -2022-05-01 22:51:31,056 - tensorflow - INFO - loss = 0.0071138768, step = 2557400 (2.268 sec) -2022-05-01 22:51:33,315 - tensorflow - INFO - global_step/sec: 88.5097 -2022-05-01 22:51:33,316 - tensorflow - INFO - loss = 0.014201727, step = 2557600 (2.260 sec) -2022-05-01 22:51:35,581 - tensorflow - INFO - global_step/sec: 88.2699 -2022-05-01 22:51:35,582 - tensorflow - INFO - loss = 0.03632896, step = 2557800 (2.266 sec) -2022-05-01 22:51:37,845 - tensorflow - INFO - global_step/sec: 88.3564 -2022-05-01 22:51:37,845 - tensorflow - INFO - loss = 0.043422718, step = 2558000 (2.264 sec) -2022-05-01 22:51:40,106 - tensorflow - INFO - global_step/sec: 88.4415 -2022-05-01 22:51:40,107 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.3785 -2022-05-01 22:51:55,942 - tensorflow - INFO - loss = 0.010030072, step = 2559600 (2.263 sec) -2022-05-01 22:51:58,199 - tensorflow - INFO - global_step/sec: 88.6044 -2022-05-01 22:51:58,199 - tensorflow - INFO - loss = 0.1980426, step = 2559800 (2.257 sec) -2022-05-01 22:52:00,453 - tensorflow - INFO - global_step/sec: 88.7164 -2022-05-01 22:52:00,454 - tensorflow - INFO - loss = 0.019649642, step = 2560000 (2.254 sec) -2022-05-01 22:52:02,706 - tensorflow - INFO - global_step/sec: 88.7925 -2022-05-01 22:52:02,706 - tensorflow - INFO - loss = 0.013645825, step = 2560200 (2.252 sec) -2022-05-01 22:52:04,962 - tensorflow - INFO - global_step/sec: 88.6468 -2022-05-01 22:52:04,962 - tensorflow - INFO - loss = 0.008293188, step = 2560400 (2.256 sec) -2022-05-01 22:52:07,216 - tensorflow - INFO - global_step/sec: 88.7106 -2022-05-01 22:52:07,217 - tensorflow - INFO - loss = 0.032863967, step = 2560600 (2.255 sec) -2022-05-01 22:52:09,465 - tensorflow - INFO - global_step/sec: 88.9278 -2022-05-01 22:52:09,466 - tensorflow - INFO - loss = 0.010850931, step = 2560800 (2.249 sec) -2022-05-01 22:52:11,720 - tensorflow - INFO - global_step/sec: 88.6977 -2022-05-01 22:52:11,720 - tensorflow - INFO - loss = 0.0175879, step = 2561000 (2.255 sec) -2022-05-01 22:52:13,974 - tensorflow - INFO - global_step/sec: 88.7345 -2022-05-01 22:52:13,974 - tensorflow - INFO - loss = 0.014139969, step = 2561200 (2.254 sec) -2022-05-01 22:52:16,227 - tensorflow - INFO - global_step/sec: 88.7889 -2022-05-01 22:52:16,227 - tensorflow - INFO - loss = 0.009444745, step = 2561400 (2.253 sec) -2022-05-01 22:52:18,480 - tensorflow - INFO - global_step/sec: 88.7545 -2022-05-01 22:52:18,480 - tensorflow - INFO - loss = 0.020438613, step = 2561600 (2.253 sec) -2022-05-01 22:52:20,734 - tensorflow - INFO - global_step/sec: 88.7219 -2022-05-01 22:52:20,735 - tensorflow - INFO - loss = 0.003945601, step = 2561800 (2.254 sec) -2022-05-01 22:52:22,986 - tensorflow - INFO - 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tensorflow - INFO - global_step/sec: 88.833 -2022-05-01 22:54:42,731 - tensorflow - INFO - loss = 0.005400113, step = 2574400 (2.251 sec) -2022-05-01 22:54:44,984 - tensorflow - INFO - global_step/sec: 88.7463 -2022-05-01 22:54:44,984 - tensorflow - INFO - loss = 0.0032447493, step = 2574600 (2.254 sec) -2022-05-01 22:54:47,237 - tensorflow - INFO - global_step/sec: 88.7822 -2022-05-01 22:54:47,237 - tensorflow - INFO - loss = 0.03992021, step = 2574800 (2.253 sec) -2022-05-01 22:54:49,495 - tensorflow - INFO - global_step/sec: 88.5497 -2022-05-01 22:54:49,496 - tensorflow - INFO - loss = 0.006976843, step = 2575000 (2.259 sec) -2022-05-01 22:54:51,751 - tensorflow - INFO - global_step/sec: 88.6778 -2022-05-01 22:54:51,751 - tensorflow - INFO - loss = 0.022812184, step = 2575200 (2.255 sec) -2022-05-01 22:54:54,009 - tensorflow - INFO - global_step/sec: 88.568 -2022-05-01 22:54:54,009 - tensorflow - INFO - loss = 0.0229411, step = 2575400 (2.258 sec) -2022-05-01 22:54:56,261 - tensorflow - INFO - global_step/sec: 88.784 -2022-05-01 22:54:56,262 - tensorflow - INFO - loss = 0.037632674, step = 2575600 (2.253 sec) -2022-05-01 22:54:58,518 - tensorflow - INFO - global_step/sec: 88.6304 -2022-05-01 22:54:58,518 - tensorflow - INFO - loss = 0.055111006, step = 2575800 (2.256 sec) -2022-05-01 22:55:00,782 - tensorflow - INFO - global_step/sec: 88.3433 -2022-05-01 22:55:00,782 - tensorflow - INFO - loss = 0.05147831, step = 2576000 (2.264 sec) -2022-05-01 22:55:03,046 - tensorflow - INFO - global_step/sec: 88.3119 -2022-05-01 22:55:03,047 - tensorflow - INFO - loss = 0.048165888, step = 2576200 (2.265 sec) -2022-05-01 22:55:05,307 - tensorflow - INFO - global_step/sec: 88.4672 -2022-05-01 22:55:05,308 - tensorflow - INFO - loss = 0.010406117, step = 2576400 (2.261 sec) -2022-05-01 22:55:07,566 - tensorflow - INFO - global_step/sec: 88.5268 -2022-05-01 22:55:07,567 - tensorflow - INFO - loss = 0.021043975, step = 2576600 (2.259 sec) -2022-05-01 22:55:09,825 - tensorflow - INFO - 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INFO - loss = 0.009138454, step = 2579200 (2.252 sec) -2022-05-01 22:55:39,210 - tensorflow - INFO - global_step/sec: 88.9324 -2022-05-01 22:55:39,210 - tensorflow - INFO - loss = 0.011386326, step = 2579400 (2.249 sec) -2022-05-01 22:55:41,466 - tensorflow - INFO - global_step/sec: 88.659 -2022-05-01 22:55:41,466 - tensorflow - INFO - loss = 0.05404347, step = 2579600 (2.256 sec) -2022-05-01 22:55:43,721 - tensorflow - INFO - global_step/sec: 88.6752 -2022-05-01 22:55:43,722 - tensorflow - INFO - loss = 0.05870767, step = 2579800 (2.255 sec) -2022-05-01 22:55:45,979 - tensorflow - INFO - global_step/sec: 88.5692 -2022-05-01 22:55:45,980 - tensorflow - INFO - loss = 0.013865546, step = 2580000 (2.258 sec) -2022-05-01 22:55:48,231 - tensorflow - INFO - global_step/sec: 88.805 -2022-05-01 22:55:48,232 - tensorflow - INFO - loss = 0.020384759, step = 2580200 (2.252 sec) -2022-05-01 22:55:50,490 - tensorflow - INFO - global_step/sec: 88.5525 -2022-05-01 22:55:50,490 - tensorflow - INFO - loss = 0.03880316, step = 2580400 (2.259 sec) -2022-05-01 22:55:52,740 - tensorflow - INFO - global_step/sec: 88.8892 -2022-05-01 22:55:52,740 - tensorflow - INFO - loss = 0.18459275, step = 2580600 (2.250 sec) -2022-05-01 22:55:54,996 - tensorflow - INFO - global_step/sec: 88.6499 -2022-05-01 22:55:54,996 - tensorflow - INFO - loss = 0.022431366, step = 2580800 (2.256 sec) -2022-05-01 22:55:57,256 - tensorflow - INFO - global_step/sec: 88.4933 -2022-05-01 22:55:57,257 - tensorflow - INFO - loss = 0.010495337, step = 2581000 (2.260 sec) -2022-05-01 22:55:59,508 - tensorflow - INFO - global_step/sec: 88.7956 -2022-05-01 22:55:59,509 - tensorflow - INFO - loss = 0.009700634, step = 2581200 (2.252 sec) -2022-05-01 22:56:01,765 - tensorflow - INFO - global_step/sec: 88.6344 -2022-05-01 22:56:01,765 - tensorflow - INFO - loss = 0.00733386, step = 2581400 (2.256 sec) -2022-05-01 22:56:04,023 - tensorflow - INFO - global_step/sec: 88.5672 -2022-05-01 22:56:04,023 - tensorflow - INFO - loss = 0.01729981, step = 2581600 (2.258 sec) -2022-05-01 22:56:06,277 - tensorflow - INFO - global_step/sec: 88.7544 -2022-05-01 22:56:06,277 - tensorflow - INFO - loss = 0.0063993847, step = 2581800 (2.253 sec) -2022-05-01 22:56:08,534 - tensorflow - INFO - global_step/sec: 88.5805 -2022-05-01 22:56:08,535 - tensorflow - INFO - loss = 0.0073182443, step = 2582000 (2.258 sec) -2022-05-01 22:56:10,786 - tensorflow - INFO - global_step/sec: 88.8317 -2022-05-01 22:56:10,786 - tensorflow - INFO - loss = 0.021946015, step = 2582200 (2.251 sec) -2022-05-01 22:56:13,039 - tensorflow - INFO - global_step/sec: 88.7672 -2022-05-01 22:56:13,039 - tensorflow - INFO - loss = 0.0060194437, step = 2582400 (2.253 sec) -2022-05-01 22:56:15,292 - tensorflow - INFO - global_step/sec: 88.7481 -2022-05-01 22:56:15,293 - tensorflow - INFO - loss = 2.427406, step = 2582600 (2.254 sec) -2022-05-01 22:56:17,543 - tensorflow - INFO - global_step/sec: 88.8731 -2022-05-01 22:56:17,543 - tensorflow - INFO - loss = 0.14152369, step = 2582800 (2.250 sec) -2022-05-01 22:56:19,800 - tensorflow - INFO - global_step/sec: 88.6059 -2022-05-01 22:56:19,800 - tensorflow - INFO - loss = 0.016190255, step = 2583000 (2.257 sec) -2022-05-01 22:56:22,059 - tensorflow - INFO - global_step/sec: 88.5428 -2022-05-01 22:56:22,059 - tensorflow - INFO - loss = 0.013177842, step = 2583200 (2.259 sec) -2022-05-01 22:56:24,315 - tensorflow - INFO - global_step/sec: 88.659 -2022-05-01 22:56:24,315 - tensorflow - INFO - loss = 0.36855096, step = 2583400 (2.256 sec) -2022-05-01 22:56:26,568 - tensorflow - INFO - global_step/sec: 88.7576 -2022-05-01 22:56:26,568 - tensorflow - INFO - loss = 0.0063442616, step = 2583600 (2.253 sec) -2022-05-01 22:56:28,827 - tensorflow - INFO - global_step/sec: 88.527 -2022-05-01 22:56:28,828 - tensorflow - INFO - loss = 0.0069246194, step = 2583800 (2.259 sec) -2022-05-01 22:56:31,091 - tensorflow - INFO - global_step/sec: 88.3343 -2022-05-01 22:56:31,092 - tensorflow - INFO - loss = 0.03587559, step = 2584000 (2.264 sec) -2022-05-01 22:56:33,348 - tensorflow - INFO - global_step/sec: 88.6069 -2022-05-01 22:56:33,349 - tensorflow - INFO - loss = 0.011723658, step = 2584200 (2.257 sec) -2022-05-01 22:56:35,606 - tensorflow - INFO - global_step/sec: 88.5769 -2022-05-01 22:56:35,607 - tensorflow - INFO - loss = 0.04543133, step = 2584400 (2.258 sec) -2022-05-01 22:56:37,854 - tensorflow - INFO - global_step/sec: 88.9811 -2022-05-01 22:56:37,854 - tensorflow - INFO - loss = 0.109359056, step = 2584600 (2.248 sec) -2022-05-01 22:56:40,106 - tensorflow - INFO - global_step/sec: 88.8278 -2022-05-01 22:56:40,106 - tensorflow - INFO - loss = 0.019882528, step = 2584800 (2.252 sec) -2022-05-01 22:56:42,356 - tensorflow - INFO - global_step/sec: 88.8675 -2022-05-01 22:56:42,357 - tensorflow - INFO - loss = 0.29308128, step = 2585000 (2.251 sec) -2022-05-01 22:56:44,607 - tensorflow - INFO - global_step/sec: 88.8432 -2022-05-01 22:56:44,608 - tensorflow - INFO - loss = 0.011806726, step = 2585200 (2.251 sec) -2022-05-01 22:56:46,862 - tensorflow - INFO - global_step/sec: 88.6902 -2022-05-01 22:56:46,863 - tensorflow - INFO - loss = 0.06566036, step = 2585400 (2.255 sec) -2022-05-01 22:56:49,111 - tensorflow - INFO - global_step/sec: 88.9266 -2022-05-01 22:56:49,112 - tensorflow - INFO - loss = 0.014203774, step = 2585600 (2.249 sec) -2022-05-01 22:56:51,365 - tensorflow - INFO - global_step/sec: 88.7547 -2022-05-01 22:56:51,365 - tensorflow - INFO - loss = 0.013998233, step = 2585800 (2.253 sec) -2022-05-01 22:56:53,618 - tensorflow - INFO - global_step/sec: 88.7695 -2022-05-01 22:56:53,618 - tensorflow - INFO - loss = 0.012911035, step = 2586000 (2.253 sec) -2022-05-01 22:56:55,873 - tensorflow - INFO - global_step/sec: 88.6755 -2022-05-01 22:56:55,874 - tensorflow - INFO - loss = 0.013721411, step = 2586200 (2.255 sec) -2022-05-01 22:56:58,133 - tensorflow - INFO - global_step/sec: 88.523 -2022-05-01 22:56:58,133 - tensorflow - INFO - loss = 0.0067030694, step = 2586400 (2.259 sec) -2022-05-01 22:57:00,384 - tensorflow - INFO - global_step/sec: 88.8318 -2022-05-01 22:57:00,384 - tensorflow - INFO - loss = 0.009887196, step = 2586600 (2.251 sec) -2022-05-01 22:57:02,643 - tensorflow - INFO - global_step/sec: 88.547 -2022-05-01 22:57:02,643 - tensorflow - INFO - loss = 0.31959456, step = 2586800 (2.259 sec) -2022-05-01 22:57:04,896 - tensorflow - INFO - global_step/sec: 88.7475 -2022-05-01 22:57:04,897 - tensorflow - INFO - loss = 0.018577524, step = 2587000 (2.254 sec) -2022-05-01 22:57:07,148 - tensorflow - INFO - global_step/sec: 88.8043 -2022-05-01 22:57:07,149 - tensorflow - INFO - loss = 0.0077270763, step = 2587200 (2.252 sec) -2022-05-01 22:57:09,406 - tensorflow - INFO - global_step/sec: 88.5889 -2022-05-01 22:57:09,406 - tensorflow - INFO - loss = 0.057520434, step = 2587400 (2.258 sec) -2022-05-01 22:57:11,659 - tensorflow - INFO - global_step/sec: 88.7908 -2022-05-01 22:57:11,659 - tensorflow - INFO - loss = 0.15892008, step = 2587600 (2.253 sec) -2022-05-01 22:57:13,909 - tensorflow - INFO - global_step/sec: 88.8624 -2022-05-01 22:57:13,910 - tensorflow - INFO - loss = 0.03677657, step = 2587800 (2.251 sec) -2022-05-01 22:57:16,159 - tensorflow - INFO - global_step/sec: 88.9004 -2022-05-01 22:57:16,159 - tensorflow - INFO - loss = 0.011227585, step = 2588000 (2.250 sec) -2022-05-01 22:57:18,409 - tensorflow - INFO - global_step/sec: 88.8677 -2022-05-01 22:57:18,410 - tensorflow - INFO - loss = 0.050311897, step = 2588200 (2.251 sec) -2022-05-01 22:57:20,664 - tensorflow - INFO - global_step/sec: 88.6989 -2022-05-01 22:57:20,665 - tensorflow - INFO - loss = 0.0051274067, step = 2588400 (2.255 sec) -2022-05-01 22:57:22,915 - tensorflow - INFO - global_step/sec: 88.8735 -2022-05-01 22:57:22,915 - tensorflow - INFO - loss = 0.031046556, step = 2588600 (2.250 sec) -2022-05-01 22:57:25,170 - tensorflow - INFO - global_step/sec: 88.6787 -2022-05-01 22:57:25,170 - tensorflow - INFO - loss = 0.008272649, step = 2588800 (2.255 sec) -2022-05-01 22:57:27,428 - tensorflow - INFO - global_step/sec: 88.5865 -2022-05-01 22:57:27,428 - tensorflow - INFO - loss = 0.0075667934, step = 2589000 (2.258 sec) -2022-05-01 22:57:29,683 - tensorflow - INFO - global_step/sec: 88.6647 -2022-05-01 22:57:29,684 - tensorflow - INFO - loss = 0.03137114, step = 2589200 (2.256 sec) -2022-05-01 22:57:31,937 - tensorflow - INFO - global_step/sec: 88.749 -2022-05-01 22:57:31,937 - tensorflow - INFO - loss = 0.0040960596, step = 2589400 (2.254 sec) -2022-05-01 22:57:34,189 - tensorflow - INFO - global_step/sec: 88.7869 -2022-05-01 22:57:34,190 - tensorflow - INFO - loss = 0.01772147, step = 2589600 (2.253 sec) -2022-05-01 22:57:36,445 - tensorflow - INFO - global_step/sec: 88.6831 -2022-05-01 22:57:36,445 - tensorflow - INFO - loss = 0.050396413, step = 2589800 (2.255 sec) -2022-05-01 22:57:38,696 - tensorflow - INFO - global_step/sec: 88.8194 -2022-05-01 22:57:38,697 - tensorflow - INFO - loss = 0.01502031, step = 2590000 (2.252 sec) -2022-05-01 22:57:40,950 - tensorflow - INFO - global_step/sec: 88.768 -2022-05-01 22:57:40,950 - tensorflow - INFO - loss = 0.009751061, step = 2590200 (2.253 sec) -2022-05-01 22:57:43,205 - tensorflow - INFO - global_step/sec: 88.6663 -2022-05-01 22:57:43,206 - tensorflow - INFO - loss = 0.021950861, step = 2590400 (2.256 sec) -2022-05-01 22:57:45,458 - tensorflow - INFO - global_step/sec: 88.7737 -2022-05-01 22:57:45,458 - tensorflow - INFO - loss = 0.022604462, step = 2590600 (2.253 sec) -2022-05-01 22:57:47,712 - tensorflow - INFO - global_step/sec: 88.7264 -2022-05-01 22:57:47,713 - tensorflow - INFO - loss = 0.0073060235, step = 2590800 (2.254 sec) -2022-05-01 22:57:49,968 - tensorflow - INFO - global_step/sec: 88.671 -2022-05-01 22:57:49,968 - tensorflow - INFO - loss = 0.013161661, step = 2591000 (2.256 sec) -2022-05-01 22:57:52,221 - tensorflow - INFO - global_step/sec: 88.76 -2022-05-01 22:57:52,221 - tensorflow - INFO - loss = 0.038956575, step = 2591200 (2.253 sec) -2022-05-01 22:57:54,471 - tensorflow - INFO - global_step/sec: 88.8912 -2022-05-01 22:57:54,471 - tensorflow - INFO - loss = 0.0062237633, step = 2591400 (2.250 sec) -2022-05-01 22:57:56,727 - tensorflow - INFO - global_step/sec: 88.6646 -2022-05-01 22:57:56,727 - tensorflow - INFO - loss = 0.013718772, step = 2591600 (2.256 sec) -2022-05-01 22:57:58,979 - tensorflow - INFO - global_step/sec: 88.7919 -2022-05-01 22:57:58,980 - tensorflow - INFO - loss = 0.7088275, step = 2591800 (2.252 sec) -2022-05-01 22:58:01,239 - tensorflow - INFO - global_step/sec: 88.5091 -2022-05-01 22:58:01,239 - tensorflow - INFO - loss = 0.006887424, step = 2592000 (2.260 sec) -2022-05-01 22:58:03,492 - tensorflow - INFO - global_step/sec: 88.7505 -2022-05-01 22:58:03,493 - tensorflow - INFO - loss = 0.11528784, step = 2592200 (2.254 sec) -2022-05-01 22:58:05,743 - tensorflow - INFO - global_step/sec: 88.8667 -2022-05-01 22:58:05,743 - tensorflow - INFO - loss = 0.009931682, step = 2592400 (2.250 sec) -2022-05-01 22:58:07,996 - tensorflow - INFO - global_step/sec: 88.7633 -2022-05-01 22:58:07,996 - tensorflow - INFO - loss = 0.013525553, step = 2592600 (2.253 sec) -2022-05-01 22:58:10,249 - tensorflow - INFO - global_step/sec: 88.7874 -2022-05-01 22:58:10,249 - tensorflow - INFO - loss = 0.007436597, step = 2592800 (2.253 sec) -2022-05-01 22:58:12,503 - tensorflow - INFO - global_step/sec: 88.7123 -2022-05-01 22:58:12,503 - tensorflow - INFO - loss = 0.0054980563, step = 2593000 (2.254 sec) -2022-05-01 22:58:14,757 - tensorflow - INFO - global_step/sec: 88.7201 -2022-05-01 22:58:14,758 - tensorflow - INFO - loss = 0.0050919726, step = 2593200 (2.254 sec) -2022-05-01 22:58:17,015 - tensorflow - INFO - global_step/sec: 88.6058 -2022-05-01 22:58:17,015 - tensorflow - INFO - loss = 0.0148495585, step = 2593400 (2.257 sec) -2022-05-01 22:58:19,265 - tensorflow - INFO - global_step/sec: 88.8656 -2022-05-01 22:58:19,266 - tensorflow - INFO - loss = 0.10807957, step = 2593600 (2.251 sec) -2022-05-01 22:58:21,516 - tensorflow - INFO - global_step/sec: 88.8721 -2022-05-01 22:58:21,516 - tensorflow - INFO - loss = 0.03904371, step = 2593800 (2.250 sec) -2022-05-01 22:58:23,771 - tensorflow - INFO - global_step/sec: 88.6903 -2022-05-01 22:58:23,771 - tensorflow - INFO - loss = 0.010118168, step = 2594000 (2.255 sec) -2022-05-01 22:58:26,028 - tensorflow - INFO - global_step/sec: 88.6155 -2022-05-01 22:58:26,028 - tensorflow - INFO - loss = 0.026852738, step = 2594200 (2.257 sec) -2022-05-01 22:58:28,280 - tensorflow - INFO - global_step/sec: 88.7723 -2022-05-01 22:58:28,281 - tensorflow - INFO - loss = 0.01683753, step = 2594400 (2.253 sec) -2022-05-01 22:58:30,540 - tensorflow - INFO - global_step/sec: 88.5013 -2022-05-01 22:58:30,541 - tensorflow - INFO - loss = 0.033845864, step = 2594600 (2.260 sec) -2022-05-01 22:58:32,804 - tensorflow - INFO - global_step/sec: 88.3342 -2022-05-01 22:58:32,805 - tensorflow - INFO - loss = 0.12980647, step = 2594800 (2.264 sec) -2022-05-01 22:58:35,062 - tensorflow - INFO - global_step/sec: 88.5774 -2022-05-01 22:58:35,063 - tensorflow - INFO - loss = 0.008408929, step = 2595000 (2.258 sec) -2022-05-01 22:58:37,324 - tensorflow - INFO - global_step/sec: 88.417 -2022-05-01 22:58:37,325 - tensorflow - INFO - loss = 0.018310972, step = 2595200 (2.262 sec) -2022-05-01 22:58:39,589 - tensorflow - INFO - global_step/sec: 88.3019 -2022-05-01 22:58:39,590 - tensorflow - INFO - loss = 0.013260505, step = 2595400 (2.265 sec) -2022-05-01 22:58:41,845 - tensorflow - INFO - global_step/sec: 88.6845 -2022-05-01 22:58:41,845 - tensorflow - INFO - loss = 0.019862723, step = 2595600 (2.255 sec) -2022-05-01 22:58:44,108 - tensorflow - INFO - global_step/sec: 88.3498 -2022-05-01 22:58:44,109 - tensorflow - INFO - loss = 0.0025866749, step = 2595800 (2.264 sec) -2022-05-01 22:58:46,372 - tensorflow - INFO - global_step/sec: 88.3395 -2022-05-01 22:58:46,373 - tensorflow - INFO - loss = 0.013068748, step = 2596000 (2.264 sec) -2022-05-01 22:58:48,631 - tensorflow - INFO - global_step/sec: 88.5417 -2022-05-01 22:58:48,631 - tensorflow - INFO - loss = 0.018583922, step = 2596200 (2.259 sec) -2022-05-01 22:58:50,891 - tensorflow - INFO - global_step/sec: 88.4857 -2022-05-01 22:58:50,892 - tensorflow - INFO - loss = 0.019659905, step = 2596400 (2.260 sec) -2022-05-01 22:58:53,154 - tensorflow - INFO - global_step/sec: 88.3723 -2022-05-01 22:58:53,155 - tensorflow - INFO - loss = 0.06279365, step = 2596600 (2.263 sec) -2022-05-01 22:58:55,418 - tensorflow - INFO - global_step/sec: 88.3445 -2022-05-01 22:58:55,419 - tensorflow - INFO - loss = 0.017705481, step = 2596800 (2.264 sec) -2022-05-01 22:58:57,682 - tensorflow - INFO - global_step/sec: 88.3529 -2022-05-01 22:58:57,682 - tensorflow - INFO - loss = 0.01881864, step = 2597000 (2.264 sec) -2022-05-01 22:58:59,948 - tensorflow - INFO - global_step/sec: 88.2484 -2022-05-01 22:58:59,949 - tensorflow - INFO - loss = 0.0077836798, step = 2597200 (2.266 sec) -2022-05-01 22:59:02,204 - tensorflow - INFO - global_step/sec: 88.6699 -2022-05-01 22:59:02,204 - tensorflow - INFO - loss = 0.16888902, step = 2597400 (2.255 sec) -2022-05-01 22:59:04,459 - tensorflow - INFO - global_step/sec: 88.6693 -2022-05-01 22:59:04,460 - tensorflow - INFO - loss = 0.023880437, step = 2597600 (2.256 sec) -2022-05-01 22:59:06,720 - tensorflow - INFO - global_step/sec: 88.4835 -2022-05-01 22:59:06,720 - tensorflow - INFO - loss = 0.0019687468, step = 2597800 (2.260 sec) -2022-05-01 22:59:08,973 - tensorflow - INFO - global_step/sec: 88.772 -2022-05-01 22:59:08,973 - tensorflow - INFO - loss = 0.009858871, step = 2598000 (2.253 sec) -2022-05-01 22:59:11,226 - tensorflow - INFO - global_step/sec: 88.751 -2022-05-01 22:59:11,227 - tensorflow - INFO - loss = 0.03571446, step = 2598200 (2.253 sec) -2022-05-01 22:59:13,477 - tensorflow - INFO - global_step/sec: 88.8674 -2022-05-01 22:59:13,477 - tensorflow - INFO - loss = 0.004305632, step = 2598400 (2.251 sec) -2022-05-01 22:59:15,728 - tensorflow - INFO - global_step/sec: 88.8317 -2022-05-01 22:59:15,729 - tensorflow - INFO - loss = 0.011081892, step = 2598600 (2.251 sec) -2022-05-01 22:59:17,983 - tensorflow - INFO - global_step/sec: 88.6949 -2022-05-01 22:59:17,984 - tensorflow - INFO - loss = 0.029049512, step = 2598800 (2.255 sec) -2022-05-01 22:59:20,237 - tensorflow - INFO - global_step/sec: 88.7534 -2022-05-01 22:59:20,237 - tensorflow - INFO - loss = 0.045791313, step = 2599000 (2.253 sec) -2022-05-01 22:59:22,487 - tensorflow - INFO - global_step/sec: 88.881 -2022-05-01 22:59:22,487 - tensorflow - INFO - loss = 0.016003875, step = 2599200 (2.250 sec) -2022-05-01 22:59:24,740 - tensorflow - INFO - global_step/sec: 88.7705 -2022-05-01 22:59:24,740 - tensorflow - INFO - loss = 0.007047058, step = 2599400 (2.253 sec) -2022-05-01 22:59:26,994 - tensorflow - INFO - global_step/sec: 88.7231 -2022-05-01 22:59:26,994 - tensorflow - INFO - loss = 0.1277457, step = 2599600 (2.254 sec) -2022-05-01 22:59:29,242 - tensorflow - INFO - global_step/sec: 88.9479 -2022-05-01 22:59:29,243 - tensorflow - INFO - loss = 0.031332817, step = 2599800 (2.249 sec) -2022-05-01 22:59:31,492 - tensorflow - INFO - Saving checkpoints for 2600000 into /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt. -2022-05-01 22:59:31,508 - tensorflow - INFO - model.ckpt-2600000 is not in all_model_checkpoint_paths. Manually adding it. -2022-05-01 22:59:32,027 - tensorflow - INFO - Calling model_fn. -2022-05-01 22:59:32,158 - tensorflow - INFO - Done calling model_fn. -2022-05-01 22:59:32,173 - tensorflow - INFO - Starting evaluation at 2022-05-01T22:59:32Z -2022-05-01 22:59:32,238 - tensorflow - INFO - Graph was finalized. -2022-05-01 22:59:32,241 - tensorflow - INFO - Restoring parameters from /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-2600000 -2022-05-01 22:59:32,519 - tensorflow - INFO - Running local_init_op. -2022-05-01 22:59:32,625 - tensorflow - INFO - Done running local_init_op. -2022-05-01 22:59:42,867 - tensorflow - INFO - Evaluation [3990/39900] -2022-05-01 22:59:52,420 - tensorflow - INFO - Evaluation [7980/39900] -2022-05-01 23:00:01,983 - tensorflow - INFO - Evaluation [11970/39900] -2022-05-01 23:00:11,571 - tensorflow - INFO - Evaluation [15960/39900] -2022-05-01 23:00:21,147 - tensorflow - INFO - Evaluation [19950/39900] -2022-05-01 23:00:30,752 - tensorflow - INFO - Evaluation [23940/39900] -2022-05-01 23:00:40,323 - tensorflow - INFO - Evaluation [27930/39900] -2022-05-01 23:00:49,929 - tensorflow - INFO - Evaluation [31920/39900] -2022-05-01 23:00:59,510 - tensorflow - INFO - Evaluation [35910/39900] -2022-05-01 23:01:09,073 - tensorflow - INFO - Evaluation [39900/39900] -2022-05-01 23:01:09,208 - tensorflow - INFO - Finished evaluation at 2022-05-01-23:01:09 -2022-05-01 23:01:09,208 - tensorflow - INFO - Saving dict for global step 2600000: global_step = 2600000, loss = 0.020267775, mean_degree_err = 3.9795845 -2022-05-01 23:01:09,209 - tensorflow - INFO - Saving 'checkpoint_path' summary for global step 2600000: /root/RotationContinuity-master2/shapenet/data/pc_plane/to/model51/model.ckpt-2600000 -2022-05-01 23:01:09,209 - tensorflow - DEBUG - Calling exporter with the `is_the_final_export=True`. -2022-05-01 23:01:09,230 - tensorflow - INFO - Loss for final step: 0.01898054. \ No newline at end of file diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/main_point_cloud_gpu.py b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/main_point_cloud_gpu.py deleted file mode 100644 index a3cbfe37d..000000000 --- a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/main_point_cloud_gpu.py +++ /dev/null @@ -1,431 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ - -# coding=utf-8 -# Copyright 2021 The Google Research Authors. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -"""Training and evaluation for the point cloud alignment experiment.""" -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import utils_gpu -import time -from absl import flags -import numpy as np -import tensorflow as tf -import logging -FLAGS = flags.FLAGS -log = logging.getLogger('tensorflow') -log.setLevel(logging.DEBUG) - -# General flags. -flags.DEFINE_string('method', 'svd', - 'Specifies the method to use for predicting rotations. ' - 'Choices are "svd", "svd-inf", or "gs".') -flags.DEFINE_string('checkpoint_dir', '', - 'Locations for checkpoints, summaries, etc.') -flags.DEFINE_integer('train_steps', 2600000, 'Number of training iterations.') -flags.DEFINE_integer('save_checkpoints_steps', 100000, - 'How often to save checkpoints') -flags.DEFINE_integer('log_step_count', 200, 'How often to log the step count') -flags.DEFINE_integer('save_summaries_steps', 25000, - 'How often to save summaries.') -flags.DEFINE_float('learning_rate', 1e-5, 'Learning rate') -flags.DEFINE_boolean('lr_decay', False, 'Decay the learning rate if True.') -flags.DEFINE_integer('lr_decay_steps', 35000, - 'Learning rate decays steps.') -flags.DEFINE_float('lr_decay_rate', 0.95, - 'Learning rate decays rate.') -flags.DEFINE_boolean('predict_all_test', False, - 'If true, runs an eval job on latest checkpoint and ' - 'prints the error for each input.') -flags.DEFINE_integer('eval_examples', 39900, 'Number of test examples.') -flags.DEFINE_boolean('print_variable_names', False, - 'Print model variable names.') - -# Flags only used in the point cloud alignment experiment. -flags.DEFINE_integer('num_train_augmentations', 10, - 'Number of random rotations for augmenting each input ' - 'point cloud.') -flags.DEFINE_string('pt_cloud_train_files', '', - 'Expression matching all training point files, e.g. ' - '/path/to/files/pc_plane/points/*.pts') -flags.DEFINE_string('pt_cloud_test_files', '', - 'Expression matching all modified test point files, e.g. ' - '/path/to/files/pc_plane/points_test/*.pts') -flags.DEFINE_boolean('random_rotation_axang', True, - 'If true, samples random rotations using the method ' - 'from the original benchmark code. Otherwise samples ' - 'by Haar measure.') - - -def pt_features(batch_pts): - """Input shape: [B, N, 3], output shape: [B, 1024].""" - with tf.variable_scope('ptenc', reuse=tf.AUTO_REUSE): - f1 = tf.layers.conv1d(inputs=batch_pts, filters=64, kernel_size=1) - f1 = tf.nn.leaky_relu(f1) - f2 = tf.layers.conv1d(inputs=f1, filters=128, kernel_size=1) - f2 = tf.nn.leaky_relu(f2) - f3 = tf.layers.conv1d(inputs=f2, filters=1024, kernel_size=1) - - f = tf.reduce_max(f3, axis=1, keep_dims=False) - return f - - -def regress_from_features(batch_features, out_dim): - """Regress to a rotation representation from point cloud encodings. - - In Zhou et al, CVPR19, the paper describes this regression network as an MLP - mapping 2048->512->512->out_dim, but the associated code implements it with - one less layer: 2048->512->out_dim. We mimic the code. - - Args: - batch_features: [batch_size, in_dim]. - out_dim: desired output dimensionality. - - Returns: - A [batch_size, out_dim] tensor. - """ - f1 = tf.layers.dense(batch_features, 512) - f1 = tf.nn.leaky_relu(f1) - f2 = tf.layers.dense(f1, out_dim) - return f2 - - -def net_point_cloud(points1, points2, mode): - """Predict a relative rotation given two point clouds. - - Args: - points1: [batch_size, N, 3] float tensor. - points2: [batch_size, N, 3] float tensor. - mode: tf.estimator.ModeKeys. - - Returns: - [batch_size, 3, 3] matrices. - """ - f1 = pt_features(points1) - f2 = pt_features(points2) - f = tf.concat([f1, f2], axis=-1) - - if FLAGS.method == 'svd': - p = regress_from_features(f, 9) - return utils_gpu.symmetric_orthogonalization(p) - - if FLAGS.method == 'svd-inf': - p = regress_from_features(f, 9) - if mode == tf.estimator.ModeKeys.TRAIN: - return tf.reshape(p, (-1, 3, 3)) - else: - return utils_gpu.symmetric_orthogonalization(p) - - if FLAGS.method == 'gs': - p = regress_from_features(f, 6) - return utils_gpu.gs_orthogonalization(p) - - -def model_fn(features, labels, mode, params): - """The model_fn used to construct the tf.Estimator.""" - del labels, params # Unused. - if mode == tf.estimator.ModeKeys.TRAIN: - # Training data has point cloud of size [1, N, 3] and random rotations - # of size [1, FLAGS.num_train_augmentations, 3, 3] - rot = features['rot'][0] - num_rot = FLAGS.num_train_augmentations - batch_pts1 = tf.tile(features['data'], [num_rot, 1, 1]) - #batch_pts1 = tf.reshape(batch_pts1,(num_rot,-1,3)) - # In this experiment it does not matter if we pre or post-multiply the - # rotation as long as we are consistent between training and eval. - batch_pts2 = tf.matmul(batch_pts1, rot) # post-multiplying! - else: - # Test data has point cloud of size [1, N, 3] and a single random - # rotation of size [1, 3, 3] - batch_pts1 = features['data'] - rot = features['rot'] - batch_pts2 = tf.matmul(batch_pts1, rot) - rot = tf.reshape(rot, (-1, 3, 3)) - - # Predict the rotation. - r = net_point_cloud(batch_pts1, batch_pts2, mode) - - # Compute the loss. - loss = tf.nn.l2_loss(rot - r) - - # Compute the relative angle in radians. - theta = utils_gpu.relative_angle(r, rot) - - # Mean angle error over the batch. - mean_theta = tf.reduce_mean(theta) - mean_theta_deg = mean_theta * 180.0 / np.pi - - # Train, eval, or predict depending on mode. - if mode == tf.estimator.ModeKeys.TRAIN: - tf.summary.scalar('train/loss', loss) - tf.summary.scalar('train/theta', mean_theta_deg) - global_step = tf.train.get_or_create_global_step() - - if FLAGS.lr_decay: - learning_rate = tf.train.exponential_decay( - FLAGS.learning_rate, - global_step, - FLAGS.lr_decay_steps, - FLAGS.lr_decay_rate) - else: - learning_rate = FLAGS.learning_rate - - tf.summary.scalar('train/learning_rate', learning_rate) - optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate) - update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) - with tf.control_dependencies(update_ops): - train_op = optimizer.minimize(loss, global_step=global_step) - return tf.estimator.EstimatorSpec( - mode=mode, - loss=loss, - train_op=train_op) - - if mode == tf.estimator.ModeKeys.EVAL: - if FLAGS.predict_all_test: - print_error_op = tf.print('error:', mean_theta_deg) - with tf.control_dependencies([print_error_op]): - eval_metric_ops = { - 'mean_degree_err': tf.metrics.mean(mean_theta_deg), - } - else: - eval_metric_ops = { - 'mean_degree_err': tf.metrics.mean(mean_theta_deg), - } - - return tf.estimator.EstimatorSpec( - mode=mode, - loss=loss, - eval_metric_ops=eval_metric_ops) - - if mode == tf.estimator.ModeKeys.PREDICT: - pred = {'error': mean_theta_deg} - return tf.estimator.EstimatorSpec( - mode=mode, - predictions=pred) - - -def train_input_fn(): - """Generate training data iterator from the .pts files.""" - def _file_to_matrix(pts_path): - """Read Nx3 point cloud from a .pts file.""" - file_buffer = tf.read_file(pts_path) - lines = tf.string_split([file_buffer], delimiter='\n') - lines1 = tf.string_split(lines.values, delimiter='\r') - values = tf.stack(tf.decode_csv(lines1.values, - record_defaults=[[0.0], [0.0], [0.0]], - field_delim=' ')) - values = tf.transpose(values)# 3xN --> Nx3 - values = values[:(tf.shape(values)[0]// 2), :] - diff_num = 1500-tf.shape(values)[0] - repeat_pts = tf.tile(tf.reshape(values[1,:],(1,-1)),[diff_num,1]) - values = tf.concat([values,repeat_pts],axis=0) - # The experiment code in - # github.com/papagina/RotationContinuity/.../shapenet/code/train_pointnet.py - # only used the first half of the points in each file. - return values - - - - def _random_rotation(pts): - """Attach N random rotations to a point cloud.""" - if FLAGS.random_rotation_axang: - rotations = utils_gpu.random_rotation_benchmark(FLAGS.num_train_augmentations) - else: - rotations = utils_gpu.random_rotation(FLAGS.num_train_augmentations) - return pts, rotations - - pts_paths = tf.gfile.Glob(FLAGS.pt_cloud_train_files) - dataset = tf.data.Dataset.from_tensor_slices(pts_paths) - dataset = dataset.map(_file_to_matrix) - dataset = dataset.cache() # Comment out if memory cannot hold all the data. - dataset = dataset.shuffle(buffer_size=50, reshuffle_each_iteration=True) - dataset = dataset.repeat() - dataset = dataset.map(_random_rotation) - dataset = dataset.batch(1) - iterator = tf.data.make_one_shot_iterator(dataset) - batch_data, batch_rot = iterator.get_next() - features_dict = {'data': batch_data, 'rot': batch_rot} - batch_size = tf.shape(batch_data)[0] - batch_labels_dummy = tf.zeros(shape=(batch_size, 1)) - return (features_dict, batch_labels_dummy) - - - - -def eval_input_fn(): - """Generate test data from *modified* .pts files. - - See README and comments below for details on how the data is modified. - - Returns: - A tuple of features and associated labels. - """ - def _file_to_matrix(pts_path): - """Read Nx3 point cloud and 3x3 rotation matrix from a .pts file. - - The test data is a modified version of the original files. For each .pts - file we have (1) added a 3x3 rotation matrix for testing, and (2) removed - the second half of the point cloud since it is not used at all. - - Args: - pts_path: path to a .pts file. - - Returns: - A Nx3 point cloud. - A 3x3 rotation matrix. - """ - file_buffer = tf.read_file(pts_path) - lines = tf.string_split([file_buffer], delimiter='\n') - lines1 = tf.string_split(lines.values, delimiter='\r') - values = tf.stack(tf.decode_csv(lines1.values, - record_defaults=[[0.0], [0.0], [0.0]], field_delim=' ')) - values = tf.transpose(values) # 3xN --> Nx3. - diff_num = 1414-tf.shape(values)[0] - repeat_pts = tf.tile(tf.reshape(values[4,:],(1,-1)),[diff_num,1]) - - values = tf.concat([values,repeat_pts],axis=0) - # First three rows are the rotation matrix, remaining rows the point cloud. - rot = values[:3, :] - return values[4:, :], rot - - pts_paths = tf.gfile.Glob(FLAGS.pt_cloud_test_files) - dataset = tf.data.Dataset.from_tensor_slices(pts_paths) - dataset = dataset.map(_file_to_matrix) - dataset = dataset.batch(1) - iterator = tf.data.make_one_shot_iterator(dataset) - batch_data, batch_rot = iterator.get_next() - features_dict = {'data': batch_data, 'rot': batch_rot} - batch_size = tf.shape(batch_data)[0] - batch_labels_dummy = tf.zeros(shape=(batch_size, 1)) - return (features_dict, batch_labels_dummy) - - -def print_variable_names(): - """Print variable names in a model.""" - params = {'dummy': 0} - estimator = tf.estimator.Estimator( - model_fn=model_fn, - model_dir=FLAGS.checkpoint_dir, - params=params) - names = estimator.get_variable_names() - for name in names: - print(name) - - -def predict_all_test(): - """Print error statistics for the test dataset.""" - params = {'dummy': 0} - estimator = tf.estimator.Estimator( - model_fn=model_fn, - model_dir=FLAGS.checkpoint_dir, - params=params) - evals = estimator.predict(input_fn=eval_input_fn, yield_single_examples=False) - - # Print error statistics. - all_errors = [x['error'] for x in evals] - errors = np.array(all_errors) - print('Evaluated %d examples'%np.size(errors)) - print('Mean error: %f degrees', np.mean(errors)) - print('Median error: %f degrees', np.median(errors)) - print('Std: %f degrees', np.std(errors)) - sorted_errors = np.sort(errors) - n = np.size(sorted_errors) - print('\nPercentiles:') - for perc in range(1, 101): - index = np.int32(np.float32(n * perc) / 100.0) - 1 - print('%3d%%: %f'%(perc, sorted_errors[index])) - - -def train_and_eval(): - """Train and evaluate a model.""" - # create formatter and add it to the handlers - formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') - - # create file handler which logs even debug messages - fh = logging.FileHandler('loss.log') - fh.setLevel(logging.DEBUG) - fh.setFormatter(formatter) - log.addHandler(fh) - - save_summary_steps = FLAGS.save_summaries_steps - save_checkpoints_steps = FLAGS.save_checkpoints_steps - log_step_count = FLAGS.log_step_count - - config = tf.estimator.RunConfig( - save_summary_steps=save_summary_steps, - save_checkpoints_steps=save_checkpoints_steps, - log_step_count_steps=log_step_count, - keep_checkpoint_max=None) - - params = {'dummy': 0} - estimator = tf.estimator.Estimator( - model_fn=model_fn, - model_dir=FLAGS.checkpoint_dir, - config=config, - params=params) - - train_spec = tf.estimator.TrainSpec( - input_fn=train_input_fn, - max_steps=FLAGS.train_steps) - - eval_spec = tf.estimator.EvalSpec(input_fn=eval_input_fn, - start_delay_secs=60, - steps=FLAGS.eval_examples, - throttle_secs=60) - - tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec) - - -def main(argv=None): # pylint: disable=unused-argument - if FLAGS.print_variable_names: - print_variable_names() - return - - if FLAGS.predict_all_test: - predict_all_test() - else: - train_and_eval() - - -if __name__ == '__main__': - tf.app.run() diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/requirements.txt b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/requirements.txt deleted file mode 100644 index de8ee8edb..000000000 --- a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/requirements.txt +++ /dev/null @@ -1,69 +0,0 @@ -absl-py==0.11.0 - -astunparse==1.6.3 - -cachetools==4.1.1 - -certifi==2020.6.20 - -chardet==3.0.4 - -gast==0.3.3 - -google-auth==1.23.0 - -google-auth-oauthlib==0.4.2 - -google-pasta==0.2.0 - -grpcio==1.33.2 - -h5py==2.10.0 - -idna==2.10 - -importlib-metadata==2.0.0 - -Keras-Preprocessing==1.1.2 - -Markdown==3.3.3 - -numpy==1.18.5 - -oauthlib==3.1.0 - -opt-einsum==3.3.0 - -protobuf==3.13.0 - -pyasn1==0.4.8 - -pyasn1-modules==0.2.8 - -requests==2.24.0 - -requests-oauthlib==1.3.0 - -rsa==4.6 - -scipy==1.5.3 - -six==1.15.0 - -tensorboard==2.3.0 - -tensorboard-plugin-wit==1.7.0 - -tensorflow==2.3.1 - -tensorflow-estimator==2.3.0 - -termcolor==1.1.0 - -urllib3==1.25.11 - -Werkzeug==1.0.1 - -wrapt==1.12.1 - -zipp==3.4.0 diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/utils_gpu.py b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/utils_gpu.py deleted file mode 100644 index a08b4fa97..000000000 --- a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/GPU/utils_gpu.py +++ /dev/null @@ -1,144 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ - -# coding=utf-8 -# Copyright 2021 The Google Research Authors. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""Utility functions.""" -import numpy as np -from scipy.stats import special_ortho_group -import tensorflow as tf - - -def relative_angle(r1, r2): - """Relative angle (radians) between 3D rotation matrices.""" - rel_rot = tf.matmul(tf.transpose(r1, perm=[0, 2, 1]), r2) - trace = rel_rot[:, 0, 0] + rel_rot[:, 1, 1] + rel_rot[:, 2, 2] - cos_theta = (trace - 1.0) / 2.0 - cos_theta = tf.minimum(cos_theta, tf.ones_like(cos_theta)) - cos_theta = tf.maximum(cos_theta, (-1.0) * tf.ones_like(cos_theta)) - theta = tf.acos(cos_theta) - return theta - - -def random_rotation_benchmark_np(n): - """Sample a random 3D rotation by method used in Zhou et al, CVPR19. - - This numpy function is a copy of the PyTorch function - get_sampled_rotation_matrices_by_axisAngle() in the code made available - for Zhou et al, CVPR19, at https://github.com/papagina/RotationContinuity/. - - Args: - n: the number of rotation matrices to return. - - Returns: - [n, 3, 3] np array. - """ - theta = np.random.uniform(-1, 1, n) * np.pi - sin = np.sin(theta) - axis = np.random.randn(n, 3) - axis = axis / np.maximum(np.linalg.norm(axis, axis=-1, keepdims=True), 1e-7) - qw = np.cos(theta) - qx = axis[:, 0] * sin - qy = axis[:, 1] * sin - qz = axis[:, 2] * sin - - xx = qx*qx - yy = qy*qy - zz = qz*qz - xy = qx*qy - xz = qx*qz - yz = qy*qz - xw = qx*qw - yw = qy*qw - zw = qz*qw - - row0 = np.stack((1-2*yy-2*zz, 2*xy-2*zw, 2*xz+2*yw), axis=-1) - row1 = np.stack((2*xy+2*zw, 1-2*xx-2*zz, 2*yz-2*xw), axis=-1) - row2 = np.stack((2*xz-2*yw, 2*yz+2*xw, 1-2*xx-2*yy), axis=-1) - matrix = np.stack((row0, row1, row2), axis=1) - - return matrix - - -def random_rotation_benchmark(n): - """A TF wrapper for random_rotation_benchmark_np().""" - mat = tf.py_func( - func=lambda t: np.float32(random_rotation_benchmark_np(t)), - inp=[n], - Tout=tf.float32, - stateful=True) - return tf.reshape(mat, (n, 3, 3)) - - -def random_rotation(n): - """Sample rotations from a uniform distribution on SO(3).""" - mat = tf.py_func( - func=lambda t: np.float32(special_ortho_group.rvs(3, size=t)), - inp=[n], - Tout=tf.float32, - stateful=True) - return tf.reshape(mat, (n, 3, 3)) - - -def symmetric_orthogonalization(x): - """Maps 9D input vectors onto SO(3) via symmetric orthogonalization.""" - # Innner dimensions of the input should be 3x3 matrices. - m = tf.reshape(x, (-1, 3, 3)) - _, u, v = tf.svd(m) - det = tf.linalg.det(tf.matmul(u, v, transpose_b=True)) - r = tf.matmul( - tf.concat([u[:, :, :-1], u[:, :, -1:] * tf.reshape(det, [-1, 1, 1])], 2), - v, transpose_b=True) - return r - - -def gs_orthogonalization(p6): - """Gram-Schmidt orthogonalization from 6D input.""" - # Input should be [batch_size, 6] - x = p6[:, 0:3] - y = p6[:, 3:6] - xn = tf.math.l2_normalize(x, axis=-1) - z = tf.linalg.cross(xn, y) - zn = tf.math.l2_normalize(z, axis=-1) - y = tf.linalg.cross(zn, xn) - r = tf.stack([xn, y, zn], -1) - return r -- Gitee From d615fb98697a2cc6c416078587b7f08615c1ffe8 Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Sun, 8 May 2022 14:36:20 +0000 Subject: [PATCH 47/88] =?UTF-8?q?NPU=E7=9B=B8=E5=85=B3=E7=A0=81=E6=BA=90?= =?UTF-8?q?=E5=92=8C=E7=BB=93=E6=9E=9C?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../NPU\347\262\276\345\272\246.txt" | 106 +++++ .../genStatistical.sh | 203 +++++++++ .../SVD_ID2019_for_Tensorflow/genTestData.sh | 203 +++++++++ .../gen_pt_test_data.py | 72 +++ .../main_point_cloud.py | 419 ++++++++++++++++++ .../modelarts_entry_stat.py | 40 ++ .../train_full_1p.sh | 209 +++++++++ .../cv/SVD_ID2019_for_Tensorflow/utils.py | 116 +++++ 8 files changed, 1368 insertions(+) create mode 100644 "TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU\347\262\276\345\272\246.txt" create mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/genStatistical.sh create mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/genTestData.sh create mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/gen_pt_test_data.py create mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/main_point_cloud.py create mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/modelarts_entry_stat.py create mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/train_full_1p.sh create mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/utils.py diff --git "a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU\347\262\276\345\272\246.txt" "b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU\347\262\276\345\272\246.txt" new file mode 100644 index 000000000..954bca432 --- /dev/null +++ "b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU\347\262\276\345\272\246.txt" @@ -0,0 +1,106 @@ +Evaluated 39900 examples +Mean error: %f degrees 2.9278567 +Median error: %f degrees 1.7031833 +Std: %f degrees 8.454575 + +Percentiles: + 1%: 0.322641 + 2%: 0.414959 + 3%: 0.483760 + 4%: 0.539592 + 5%: 0.581482 + 6%: 0.623068 + 7%: 0.659679 + 8%: 0.691538 + 9%: 0.723617 + 10%: 0.751734 + 11%: 0.778837 + 12%: 0.803569 + 13%: 0.829923 + 14%: 0.853175 + 15%: 0.875810 + 16%: 0.898746 + 17%: 0.920686 + 18%: 0.941284 + 19%: 0.963068 + 20%: 0.985562 + 21%: 1.006582 + 22%: 1.029266 + 23%: 1.050157 + 24%: 1.071371 + 25%: 1.092174 + 26%: 1.113466 + 27%: 1.135911 + 28%: 1.157751 + 29%: 1.178524 + 30%: 1.201056 + 31%: 1.223973 + 32%: 1.247724 + 33%: 1.270570 + 34%: 1.294524 + 35%: 1.317301 + 36%: 1.340275 + 37%: 1.363005 + 38%: 1.388467 + 39%: 1.411946 + 40%: 1.436132 + 41%: 1.460185 + 42%: 1.484376 + 43%: 1.511031 + 44%: 1.535951 + 45%: 1.562352 + 46%: 1.589054 + 47%: 1.618583 + 48%: 1.644610 + 49%: 1.671404 + 50%: 1.703183 + 51%: 1.733704 + 52%: 1.763252 + 53%: 1.791003 + 54%: 1.822308 + 55%: 1.854563 + 56%: 1.886785 + 57%: 1.922339 + 58%: 1.957048 + 59%: 1.992330 + 60%: 2.030376 + 61%: 2.073393 + 62%: 2.113592 + 63%: 2.152860 + 64%: 2.195707 + 65%: 2.241841 + 66%: 2.286874 + 67%: 2.330366 + 68%: 2.379650 + 69%: 2.427613 + 70%: 2.480492 + 71%: 2.534432 + 72%: 2.593293 + 73%: 2.657559 + 74%: 2.726347 + 75%: 2.795964 + 76%: 2.867306 + 77%: 2.933115 + 78%: 3.011294 + 79%: 3.097182 + 80%: 3.192182 + 81%: 3.284318 + 82%: 3.385412 + 83%: 3.501069 + 84%: 3.619581 + 85%: 3.753794 + 86%: 3.906153 + 87%: 4.080823 + 88%: 4.264343 + 89%: 4.474133 + 90%: 4.726048 + 91%: 5.013212 + 92%: 5.333844 + 93%: 5.743374 + 94%: 6.223213 + 95%: 6.846590 + 96%: 7.720470 + 97%: 9.113199 + 98%: 11.549816 + 99%: 18.274883 +100%: 179.585983 \ No newline at end of file diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/genStatistical.sh b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/genStatistical.sh new file mode 100644 index 000000000..990ca29a5 --- /dev/null +++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/genStatistical.sh @@ -0,0 +1,203 @@ +#!/bin/bash + +########################################################## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +########################################################## +# shell脚本所在路径 +cur_path=`echo $(cd $(dirname $0);pwd)` + +# 判断当前shell是否是performance +perf_flag=`echo $0 | grep performance | wc -l` + +# 当前执行网络的名称 +Network=`echo $(cd $(dirname $0);pwd) | awk -F"/" '{print $(NF-1)}'` + +export RANK_SIZE=1 +export RANK_ID=0 +export JOB_ID=10087 + +# 路径参数初始化 +data_path="" +output_path="" + +# 帮助信息，不需要修改 +if [[ $1 == --help || $1 == -h ]];then + echo"usage:./train_performance_1P.sh " + echo " " + echo "parameter explain: + --data_path # dataset of training + --output_path # output of training + --train_steps # max_step for training + --train_epochs # max_epoch for training + --batch_size # batch size + -h/--help show help message + " + exit 1 +fi + +# 参数校验，不需要修改 +for para in $* +do + if [[ $para == --data_path* ]];then + data_path=`echo ${para#*=}` + elif [[ $para == --output_path* ]];then + output_path=`echo ${para#*=}` + elif [[ $para == --train_steps* ]];then + train_steps=`echo ${para#*=}` + elif [[ $para == --train_epochs* ]];then + train_epochs=`echo ${para#*=}` + elif [[ $para == --batch_size* ]];then + batch_size=`echo ${para#*=}` + fi +done + +# 校验是否传入data_path,不需要修改 +if [[ $data_path == "" ]];then + echo "[Error] para \"data_path\" must be config" + exit 1 +fi + +# 校验是否传入output_path,不需要修改 +if [[ $output_path == "" ]];then + output_path="./test/output/${ASCEND_DEVICE_ID}" +fi + +# 设置打屏日志文件名，请保留，文件名为${print_log} +print_log="./test/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log" +etp_flag=${etp_running_flag} +if [ x"${etp_flag}" != xtrue ]; +then + echo "running without etp..." + print_log_name=`ls /home/ma-user/modelarts/log/ | grep proc-rank` + print_log="/home/ma-user/modelarts/log/${print_log_name}" +fi +echo ${print_log} + +CaseName="" +function get_casename() +{ + if [ x"${perf_flag}" = x1 ]; + then + CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'perf' + else + CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'acc' + fi +} + +# 跳转到code目录 +cd ${cur_path}/../ +rm -rf ./test/output/${ASCEND_DEVICE_ID} +mkdir -p ./test/output/${ASCEND_DEVICE_ID} + +# 训练开始时间记录，不需要修改 +start_time=$(date +%s) +########################################################## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +########################################################## + +#========================================================= +#========================================================= +#========训练执行命令，需要根据您的网络进行修改============== +#========================================================= +#========================================================= +# 您的训练数据集在${data_path}路径下，请直接使用这个变量获取 +# 您的训练输出目录在${output_path}路径下，请直接使用这个变量获取 +# 您的其他基础参数，可以自定义增加，但是batch_size请保留，并且设置正确的值 +batch_size=1 + +echo "------------------***PRINT DATA PATH***-----------------" + +docuClass="test_points_modified/*.pts" +modelPath="output" + +testData_Path="$data_path$docuClass" +outputPath="$data_path$modelPath" + +echo "$outputPath" +echo "$testData_Path" + + +if [ x"${modelarts_flag}" != x ]; +then + python /home/ma-user/modelarts/user-job-dir/code/main_point_cloud.py \ + --method=svd \ + --checkpoint_dir=${outputPath} \ + --pt_cloud_test_files=${testData_Path} \ + --predict_all_test=True +else + python /home/ma-user/modelarts/user-job-dir/code/main_point_cloud.py \ + --method=svd \ + --checkpoint_dir=${outputPath} \ + --pt_cloud_test_files=${testData_Path} \ + --predict_all_test=True +fi + + +# 性能相关数据计算 +StepTime=`grep "sec/step :" ${print_log} | tail -n 10 | awk '{print $NF}' | awk '{sum+=$1} END {print sum/NR}'` +FPS=`awk 'BEGIN{ "%.2f\n", '${batch_size}'/'${StepTime}'}'` + +# 精度相关数据计算 +train_accuracy=`grep "Final Accuracy accuracy" ${print_log} | awk '{print $NF}'` +# 提取所有loss打印信息 +grep "loss :" ${print_log} | awk -F ":" '{print $4}' | awk -F "-" '{print $1}' > ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt + + +########################################################### +#########后面的所有内容请不要修改########################### +#########后面的所有内容请不要修改########################### +#########后面的所有内容请不要修改########################### +########################################################### + +# 判断本次执行是否正确使用Ascend NPU +use_npu_flag=`grep "The model has been compiled on the Ascend AI processor" ${print_log} | wc -l` +if [ x"${use_npu_flag}" == x0 ]; +then + echo "------------------ ERROR NOTICE START ------------------" + echo "ERROR, your task haven't used Ascend NPU, please check your npu Migration." + echo "------------------ ERROR NOTICE END------------------" +else + echo "------------------ INFO NOTICE START------------------" + echo "INFO, your task have used Ascend NPU, please check your result." + echo "------------------ INFO NOTICE END------------------" +fi + +# 获取最终的casename，请保留，case文件名为${CaseName} +get_casename + +# 重命名loss文件 +if [ -f ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ]; +then + mv ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ./test/output/${ASCEND_DEVICE_ID}/${CaseName}_loss.txt +fi + +# 训练端到端耗时 +end_time=$(date +%s) +e2e_time=$(( $end_time - $start_time )) + +echo "------------------ Final result ------------------" +# 输出性能FPS/单step耗时/端到端耗时 +echo "Final Performance images/sec : $FPS" +echo "Final Performance sec/step : $StepTime" +echo "E2E Training Duration sec : $e2e_time" + +# 输出训练精度 +echo "Final Train Accuracy : ${train_accuracy}" + +# 最后一个迭代loss值，不需要修改 +ActualLoss=(`awk 'END {print $NF}' $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}_loss.txt`) + +#关键信息打印到${CaseName}.log中，不需要修改 +echo "Network = ${Network}" > $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "RankSize = ${RANK_SIZE}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "BatchSize = ${batch_size}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "DeviceType = `uname -m`" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualFPS = ${FPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "TrainingTime = ${StepTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log \ No newline at end of file diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/genTestData.sh b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/genTestData.sh new file mode 100644 index 000000000..416b71b04 --- /dev/null +++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/genTestData.sh @@ -0,0 +1,203 @@ +#!/bin/bash + +########################################################## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +########################################################## +# shell脚本所在路径 +cur_path=`echo $(cd $(dirname $0);pwd)` + +# 判断当前shell是否是performance +perf_flag=`echo $0 | grep performance | wc -l` + +# 当前执行网络的名称 +Network=`echo $(cd $(dirname $0);pwd) | awk -F"/" '{print $(NF-1)}'` + +export RANK_SIZE=1 +export RANK_ID=0 +export JOB_ID=10087 + +# 路径参数初始化 +data_path="" +output_path="" + +# 帮助信息，不需要修改 +if [[ $1 == --help || $1 == -h ]];then + echo"usage:./train_performance_1P.sh " + echo " " + echo "parameter explain: + --data_path # dataset of training + --output_path # output of training + --train_steps # max_step for training + --train_epochs # max_epoch for training + --batch_size # batch size + -h/--help show help message + " + exit 1 +fi + +# 参数校验，不需要修改 +for para in $* +do + if [[ $para == --data_path* ]];then + data_path=`echo ${para#*=}` + elif [[ $para == --output_path* ]];then + output_path=`echo ${para#*=}` + elif [[ $para == --train_steps* ]];then + train_steps=`echo ${para#*=}` + elif [[ $para == --train_epochs* ]];then + train_epochs=`echo ${para#*=}` + elif [[ $para == --batch_size* ]];then + batch_size=`echo ${para#*=}` + fi +done + +# 校验是否传入data_path,不需要修改 +if [[ $data_path == "" ]];then + echo "[Error] para \"data_path\" must be config" + exit 1 +fi + +# 校验是否传入output_path,不需要修改 +if [[ $output_path == "" ]];then + output_path="./test/output/${ASCEND_DEVICE_ID}" +fi + +# 设置打屏日志文件名，请保留，文件名为${print_log} +print_log="./test/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log" +etp_flag=${etp_running_flag} +if [ x"${etp_flag}" != xtrue ]; +then + echo "running without etp..." + print_log_name=`ls /home/ma-user/modelarts/log/ | grep proc-rank` + print_log="/home/ma-user/modelarts/log/${print_log_name}" +fi +echo ${print_log} + +CaseName="" +function get_casename() +{ + if [ x"${perf_flag}" = x1 ]; + then + CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'perf' + else + CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'acc' + fi +} + +# 跳转到code目录 +cd ${cur_path}/../ +rm -rf ./test/output/${ASCEND_DEVICE_ID} +mkdir -p ./test/output/${ASCEND_DEVICE_ID} + +# 训练开始时间记录，不需要修改 +start_time=$(date +%s) +########################################################## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +########################################################## + +#========================================================= +#========================================================= +#========训练执行命令，需要根据您的网络进行修改============== +#========================================================= +#========================================================= +# 您的训练数据集在${data_path}路径下，请直接使用这个变量获取 +# 您的训练输出目录在${output_path}路径下，请直接使用这个变量获取 +# 您的其他基础参数，可以自定义增加，但是batch_size请保留，并且设置正确的值 +batch_size=1 + +echo "------------------***PRINT DATA PATH***-----------------" + +inputDataPath="points_test/*.pts" +TestdataPath="test_points_modified" + +Data_Path="$data_path$inputDataPath" +outputPath="$output_path$TestdataPath" +AXANG_SAMPLIE="True" + + +echo "$outputPath" +echo "$testData_Path" + + +if [ x"${modelarts_flag}" != x ]; +then + python /home/ma-user/modelarts/user-job-dir/code/gen_pt_test_data.py\ + --input_test_files=${Data_Path} \ + --output_directory=${outputPath} \ + --random_rotation_axang=${AXANG_SAMPLIE} +else + python /home/ma-user/modelarts/user-job-dir/code/gen_pt_test_data.py\ + --input_test_files=${Data_Path} \ + --output_directory=${outputPath} \ + --random_rotation_axang=${AXANG_SAMPLIE} +fi + + +# 性能相关数据计算 +StepTime=`grep "sec/step :" ${print_log} | tail -n 10 | awk '{print $NF}' | awk '{sum+=$1} END {print sum/NR}'` +FPS=`awk 'BEGIN{ "%.2f\n", '${batch_size}'/'${StepTime}'}'` + +# 精度相关数据计算 +train_accuracy=`grep "Final Accuracy accuracy" ${print_log} | awk '{print $NF}'` +# 提取所有loss打印信息 +grep "loss :" ${print_log} | awk -F ":" '{print $4}' | awk -F "-" '{print $1}' > ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt + + +########################################################### +#########后面的所有内容请不要修改########################### +#########后面的所有内容请不要修改########################### +#########后面的所有内容请不要修改########################### +########################################################### + +# 判断本次执行是否正确使用Ascend NPU +use_npu_flag=`grep "The model has been compiled on the Ascend AI processor" ${print_log} | wc -l` +if [ x"${use_npu_flag}" == x0 ]; +then + echo "------------------ ERROR NOTICE START ------------------" + echo "ERROR, your task haven't used Ascend NPU, please check your npu Migration." + echo "------------------ ERROR NOTICE END------------------" +else + echo "------------------ INFO NOTICE START------------------" + echo "INFO, your task have used Ascend NPU, please check your result." + echo "------------------ INFO NOTICE END------------------" +fi + +# 获取最终的casename，请保留，case文件名为${CaseName} +get_casename + +# 重命名loss文件 +if [ -f ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ]; +then + mv ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ./test/output/${ASCEND_DEVICE_ID}/${CaseName}_loss.txt +fi + +# 训练端到端耗时 +end_time=$(date +%s) +e2e_time=$(( $end_time - $start_time )) + +echo "------------------ Final result ------------------" +# 输出性能FPS/单step耗时/端到端耗时 +echo "Final Performance images/sec : $FPS" +echo "Final Performance sec/step : $StepTime" +echo "E2E Training Duration sec : $e2e_time" + +# 输出训练精度 +echo "Final Train Accuracy : ${train_accuracy}" + +# 最后一个迭代loss值，不需要修改 +ActualLoss=(`awk 'END {print $NF}' $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}_loss.txt`) + +#关键信息打印到${CaseName}.log中，不需要修改 +echo "Network = ${Network}" > $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "RankSize = ${RANK_SIZE}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "BatchSize = ${batch_size}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "DeviceType = `uname -m`" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualFPS = ${FPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "TrainingTime = ${StepTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log \ No newline at end of file diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/gen_pt_test_data.py b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/gen_pt_test_data.py new file mode 100644 index 000000000..c1c299409 --- /dev/null +++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/gen_pt_test_data.py @@ -0,0 +1,72 @@ +# coding=utf-8 +# Copyright 2021 The Google Research Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Generate modified point cloud test data with fixed random rotations.""" +import glob +import os +import pathlib + +import utils +from absl import app +from absl import flags +import numpy as np +from scipy.stats import special_ortho_group +from npu_bridge.npu_init import * + +FLAGS = flags.FLAGS +flags.DEFINE_string('input_test_files', '', + 'Regular expression for the original input test files.') +flags.DEFINE_string('output_directory', '', + 'Output directory where new test files will be stored.') +flags.DEFINE_integer('num_rotations_per_file', 100, + 'Number of random rotation augmentations per test point ' + 'cloud.') +flags.DEFINE_boolean('random_rotation_axang', True, + 'If true, samples random rotations using the method ' + 'from the original benchmark code. Otherwise samples ' + 'by Haar measure.') + + +def gen_test_data(): + """Generate the new (modified) test data.""" + # Create output directory. + os.makedirs(FLAGS.output_directory, exist_ok=True) + + # Get all test point cloud files in the original dataset. + input_test_files = glob.glob(FLAGS.input_test_files) + + for in_file in input_test_files: + out_file_prefix = pathlib.Path(in_file).stem + pts = np.loadtxt(in_file) # N x 3 + num_pts_to_keep = pts.shape[0] // 2 + pts = pts[:num_pts_to_keep, :] # N//2 x 3. + + for k in range(FLAGS.num_rotations_per_file): + if FLAGS.random_rotation_axang: + r = utils.random_rotation_benchmark_np(1) + r = r[0] + else: + r = special_ortho_group.rvs(3) + joined = np.float32(np.concatenate((r, pts), axis=0)) # (N//2+3) x 3. + out_file = os.path.join( + FLAGS.output_directory, '%s_r%03d.pts'%(out_file_prefix, k)) + np.savetxt(out_file, joined) + + +def main(unused_argv): + gen_test_data() + +if __name__ == '__main__': + app.run(main) diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/main_point_cloud.py b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/main_point_cloud.py new file mode 100644 index 000000000..a3b4e9a22 --- /dev/null +++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/main_point_cloud.py @@ -0,0 +1,419 @@ +# coding=utf-8 +# Copyright 2021 The Google Research Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Training and evaluation for the point cloud alignment experiment.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import utils +from absl import flags +import numpy as np +import tensorflow as tf +import glob +import pathlib +import datetime +from npu_bridge.npu_init import * +from npu_bridge.estimator.npu.npu_estimator import NPUEstimatorSpec +from npu_bridge.estimator.npu.npu_estimator import NPUEstimator +from npu_bridge.estimator.npu.npu_config import NPURunConfig +#from npu_bridge.estimator.npu.npu_config import ProfilingConfig +import os +FLAGS = tf.app.flags.FLAGS + +# General flags. +flags.DEFINE_string('method', 'svd', + 'Specifies the method to use for predicting rotations. ' + 'Choices are "svd", "svd-inf", or "gs".') +flags.DEFINE_string('checkpoint_dir', '', + 'Locations for checkpoints, summaries, etc.') +flags.DEFINE_integer('train_steps', 2600000, 'Number of training iterations.') +flags.DEFINE_integer('save_checkpoints_steps', 10000, + 'How often to save checkpoints') +flags.DEFINE_integer('log_step_count', 500, 'How often to log the step count') +flags.DEFINE_integer('save_summaries_steps', 5000, + 'How often to save summaries.') +flags.DEFINE_float('learning_rate', 1e-5, 'Learning rate') +flags.DEFINE_boolean('lr_decay', False, 'Decay the learning rate if True.') +flags.DEFINE_integer('lr_decay_steps', 35000, + 'Learning rate decays steps.') +flags.DEFINE_float('lr_decay_rate', 0.95, + 'Learning rate decays rate.') +flags.DEFINE_boolean('predict_all_test', False, + 'If true, runs an eval job on latest checkpoint and ' + 'prints the error for each input.') +flags.DEFINE_integer('eval_examples', 39900, 'Number of test examples.') +flags.DEFINE_boolean('print_variable_names', False, + 'Print model variable names.') + +# Flags only used in the point cloud alignment experiment. +flags.DEFINE_integer('num_train_augmentations', 10, + 'Number of random rotations for augmenting each input ' + 'point cloud.') +flags.DEFINE_string('pt_cloud_train_files', '', + 'Expression matching all training point files, e.g. ' + '/path/to/files/pc_plane/points/*.pts') +flags.DEFINE_string('pt_cloud_test_files','', + 'Expression matching all modified test point files, e.g. ' + '/path/to/files/pc_plane/points_test/*.pts') +flags.DEFINE_boolean('random_rotation_axang', True, + 'If true, samples random rotations using the method ' + 'from the original benchmark code. Otherwise samples ' + 'by Haar measure.') + + +def pt_features(batch_pts): + """Input shape: [B, N, 3], output shape: [B, 1024].""" + with tf.variable_scope('ptenc', reuse=tf.AUTO_REUSE): + f1 = tf.layers.conv1d(inputs=batch_pts, filters=64, kernel_size=1) + f1 = tf.nn.leaky_relu(f1) + f2 = tf.layers.conv1d(inputs=f1, filters=128, kernel_size=1) + f2 = tf.nn.leaky_relu(f2) + f3 = tf.layers.conv1d(inputs=f2, filters=1024, kernel_size=1) + f = tf.reduce_max(f3, axis=1, keep_dims=False) + return f + + +def regress_from_features(batch_features, out_dim): + """Regress to a rotation representation from point cloud encodings. + + In Zhou et al, CVPR19, the paper describes this regression network as an MLP + mapping 2048->512->512->out_dim, but the associated code implements it with + one less layer: 2048->512->out_dim. We mimic the code. + + Args: + batch_features: [batch_size, in_dim]. + out_dim: desired output dimensionality. + + Returns: + A [batch_size, out_dim] tensor. + """ + f1 = tf.layers.dense(batch_features, 512) + f1 = tf.nn.leaky_relu(f1) + f2 = tf.layers.dense(f1, out_dim) + return f2 + + +def net_point_cloud(points1, points2, mode): + """Predict a relative rotation given two point clouds. + + Args: + points1: [batch_size, N, 3] float tensor. + points2: [batch_size, N, 3] float tensor. + mode: tf.estimator.ModeKeys. + + Returns: + [batch_size, 3, 3] matrices. + """ + f1 = pt_features(points1) + f2 = pt_features(points2) + f = tf.concat([f1, f2], axis=-1) + + if FLAGS.method == 'svd': + p = regress_from_features(f, 9) + return utils.symmetric_orthogonalization(p) + + if FLAGS.method == 'svd-inf': + p = regress_from_features(f, 9) + if mode == tf.estimator.ModeKeys.TRAIN: + return tf.reshape(p, (-1, 3, 3)) + else: + return utils.symmetric_orthogonalization(p) + + if FLAGS.method == 'gs': + p = regress_from_features(f, 6) + return utils.gs_orthogonalization(p) + + +def model_fn(features, labels, mode, params): + """The model_fn used to construct the tf.Estimator.""" + del labels, params # Unused. + if mode == tf.estimator.ModeKeys.TRAIN: + # Training data has point cloud of size [1, N, 3] and random rotations + # of size [1, FLAGS.num_train_augmentations, 3, 3] + rot = features['rot'][0] + num_rot = FLAGS.num_train_augmentations + batch_pts1 = tf.tile(features['data'], [num_rot, 1, 1]) + #batch_pts1 = tf.reshape(batch_pts1,(num_rot,-1,3)) + # In this experiment it does not matter if we pre or post-multiply the + # rotation as long as we are consistent between training and eval. + batch_pts2 = tf.matmul(batch_pts1, rot) # post-multiplying! + else: + # Test data has point cloud of size [1, N, 3] and a single random + # rotation of size [1, 3, 3] + batch_pts1 = features['data'] + rot = features['rot'] + batch_pts2 = tf.matmul(batch_pts1, rot) + rot = tf.reshape(rot, (-1, 3, 3)) + + # Predict the rotation. + r = net_point_cloud(batch_pts1, batch_pts2, mode) + + # Compute the loss. + loss = tf.nn.l2_loss(rot - r) + + # Compute the relative angle in radians. + theta = utils.relative_angle(r, rot) + + # Mean angle error over the batch. + mean_theta = tf.reduce_mean(theta) + mean_theta_deg = mean_theta * 180.0 / np.pi + + # Train, eval, or predict depending on mode. + if mode == tf.estimator.ModeKeys.TRAIN: + tf.summary.scalar('train/loss', loss) + tf.summary.scalar('train/theta', mean_theta_deg) + global_step = tf.train.get_or_create_global_step() + + if FLAGS.lr_decay: + learning_rate = tf.train.exponential_decay( + FLAGS.learning_rate, + global_step, + FLAGS.lr_decay_steps, + FLAGS.lr_decay_rate) + else: + learning_rate = FLAGS.learning_rate + + tf.summary.scalar('train/learning_rate', learning_rate) + optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate) + update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) + with tf.control_dependencies(update_ops): + train_op = optimizer.minimize(loss, global_step=global_step) + #train_op = util.set_graph_exec_config(train_op, dynamic_input=True,dynamic_graph_execute_mode="lazy_recompile") + + return NPUEstimatorSpec( + mode=mode, + loss=loss, + train_op=train_op) + + if mode == tf.estimator.ModeKeys.EVAL: + if FLAGS.predict_all_test: + print_error_op = tf.print('error:', mean_theta_deg) + with tf.control_dependencies([print_error_op]): + eval_metric_ops = { + 'mean_degree_err': tf.metrics.mean(mean_theta_deg), + } + else: + eval_metric_ops = { + 'mean_degree_err': tf.metrics.mean(mean_theta_deg), + } + + return NPUEstimatorSpec( + mode=mode, + loss=loss, + eval_metric_ops=eval_metric_ops) + + if mode == tf.estimator.ModeKeys.PREDICT: + pred = {'error': mean_theta_deg} + return NPUEstimatorSpec( + mode=mode, + predictions=pred) + + +def train_input_fn(): + """Generate training data iterator from the .pts files.""" + def _file_to_matrix(pts_path): + """Read Nx3 point cloud from a .pts file.""" + file_buffer = tf.read_file(pts_path) + lines = tf.string_split([file_buffer], delimiter='\n') + lines1 = tf.string_split(lines.values, delimiter='\r') + values = tf.stack(tf.decode_csv(lines1.values, + record_defaults=[[0.0], [0.0], [0.0]], + field_delim=' ')) + values = tf.transpose(values) # 3xN --> Nx3. + values = values[:tf.shape(values)[0] // 2, :] + + # modified by Lyoung + diff_num = 1500-tf.shape(values)[0] + repeat_pts = tf.tile(tf.reshape(values[1, :], (1, -1)), [diff_num, 1]) + values = tf.concat([values,repeat_pts], axis=0) + # The experiment code in + # github.com/papagina/RotationContinuity/.../shapenet/code/train_pointnet.py + # only used the first half of the points in each file. + return values + + def _random_rotation(pts): + """Attach N random rotations to a point cloud.""" + if FLAGS.random_rotation_axang: + rotations = utils.random_rotation_benchmark(FLAGS.num_train_augmentations) + else: + rotations = utils.random_rotation(FLAGS.num_train_augmentations) + return pts,rotations + + pts_paths = tf.gfile.Glob(FLAGS.pt_cloud_train_files) + dataset = tf.data.Dataset.from_tensor_slices(pts_paths) + dataset = dataset.map(_file_to_matrix) + dataset = dataset.cache() # Comment out if memory cannot hold all the data. + dataset = dataset.shuffle(buffer_size=50, reshuffle_each_iteration=True) + dataset = dataset.repeat() + dataset = dataset.map(_random_rotation) + dataset = dataset.batch(1) + #dataset = dataset.prefetch(buffer_size=buffer_size) + iterator = tf.data.make_one_shot_iterator(dataset) + batch_data, batch_rot = iterator.get_next() + features_dict = {'data': batch_data, 'rot': batch_rot} + batch_size = tf.shape(batch_data)[0] + batch_labels_dummy = tf.zeros(shape=(batch_size, 1)) + return (features_dict, batch_labels_dummy) + + +def eval_input_fn(): + """Generate test data from *modified* .pts files. + + See README and comments below for details on how the data is modified. + + Returns: + A tuple of features and associated labels. + """ + def _file_to_matrix(pts_path): + """Read Nx3 point cloud and 3x3 rotation matrix from a .pts file. + + The test data is a modified version of the original files. For each .pts + file we have (1) added a 3x3 rotation matrix for testing, and (2) removed + the second half of the point cloud since it is not used at all. + + Args: + pts_path: path to a .pts file. + + Returns: + A Nx3 point cloud. + A 3x3 rotation matrix. + """ + file_buffer = tf.read_file(pts_path) + lines = tf.string_split([file_buffer], delimiter='\n') + lines1 = tf.string_split(lines.values,delimiter='\r') + values = tf.stack(tf.decode_csv(lines1.values, + record_defaults=[[0.0], [0.0], [0.0]],field_delim=' ')) + values = tf.transpose(values)# 3xN --> Nx3. + + #modified by Lyoung + diff_num = 1414-tf.shape(values)[0] + repeat_pts = tf.tile(tf.reshape(values[4, :], (1, -1)), [diff_num, 1]) + values = tf.concat([values, repeat_pts], axis=0) + # First three rows are the rotation matrix, remaining rows the point cloud. + rot = values[:3, :] + + return values[4:, :], rot + + pts_paths = tf.io.gfile.glob(FLAGS.pt_cloud_test_files) + dataset = tf.data.Dataset.from_tensor_slices(pts_paths) + dataset = dataset.map(_file_to_matrix) + dataset = dataset.batch(1) + iterator = tf.data.make_one_shot_iterator(dataset) + batch_data, batch_rot = iterator.get_next() + + features_dict = {'data': batch_data, 'rot': batch_rot} + batch_size = tf.shape(batch_data)[0] + batch_labels_dummy = tf.zeros(shape=(batch_size, 1)) + return (features_dict, batch_labels_dummy) + + +def print_variable_names(): + """Print variable names in a model.""" + params = {'dummy': 0} + estimator = NPUEstimator( + model_fn=model_fn, + model_dir=FLAGS.checkpoint_dir, + params=params) + + names = estimator.get_variable_names() + for name in names: + print(name) + + +def predict_all_test(): + """Print error statistics for the test dataset.""" + + config = NPURunConfig( + save_summary_steps=None, + save_checkpoints_steps=None, + log_step_count_steps=None, + keep_checkpoint_max=None) + + params = {'dummy': 0} + estimator = NPUEstimator( + model_fn=model_fn, + model_dir=FLAGS.checkpoint_dir, + params=params, + config=config) + evals = estimator.predict(input_fn=eval_input_fn, yield_single_examples=True) + + # Print error statistics. + + all_errors = [x['error'] for x in evals] + errors = np.array(all_errors) + print('Evaluated %d examples'%np.size(errors)) + print('Mean error: %f degrees',np.mean(errors)) + print('Median error: %f degrees', np.median(errors)) + print('Std: %f degrees', np.std(errors)) + sorted_errors = np.sort(errors) + n = np.size(sorted_errors) + print('\nPercentiles:') + for perc in range(1, 101): + index = np.int32(np.float32(n * perc) / 100.0) - 1 + print('%3d%%: %f'%(perc, sorted_errors[index])) + + +def train_and_eval(): + """Train and evaluate a model.""" + save_summary_steps = FLAGS.save_summaries_steps + save_checkpoints_steps = FLAGS.save_checkpoints_steps + log_step_count = FLAGS.log_step_count + + config = NPURunConfig( + save_summary_steps=save_summary_steps, + save_checkpoints_steps=save_checkpoints_steps, + log_step_count_steps=log_step_count, + keep_checkpoint_max=None, + precision_mode = "all_mix_precision") + + params = {'dummy': 0} + estimator = NPUEstimator( + model_fn=model_fn, + model_dir=FLAGS.checkpoint_dir, + config=config, + params=params) + + + train_spec = tf.estimator.TrainSpec( + input_fn=train_input_fn, + max_steps=FLAGS.train_steps) + + + eval_spec = tf.estimator.EvalSpec(input_fn=eval_input_fn, + start_delay_secs=60, + steps=FLAGS.eval_examples, + throttle_secs=60) + + + + tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec) + + +def main(argv=None): # pylint: disable=unused-argument + + if FLAGS.print_variable_names: + print_variable_names() + return + + if FLAGS.predict_all_test: + predict_all_test() + else: + train_and_eval() + + +if __name__ == '__main__': + + tf.app.run() diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/modelarts_entry_stat.py b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/modelarts_entry_stat.py new file mode 100644 index 000000000..78debea21 --- /dev/null +++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/modelarts_entry_stat.py @@ -0,0 +1,40 @@ +import os +import argparse +import sys + +# 解析输入参数data_url +parser = argparse.ArgumentParser() +parser.add_argument("--data_url", type=str, default="/home/ma-user/modelarts/inputs/data_url_0") +parser.add_argument("--train_url", type=str, default="/home/ma-user/modelarts/outputs/train_url_0/") +config = parser.parse_args() + +print("[CANN-Modelzoo] code_dir path is [%s]" % (sys.path[0])) +code_dir = sys.path[0] +os.chdir(code_dir) +print("[CANN-Modelzoo] work_dir path is [%s]" % (os.getcwd())) + +print("[CANN-Modelzoo] before train - list my run files:") +os.system("ls -al /usr/local/Ascend/ascend-toolkit/") + +print("[CANN-Modelzoo] before train - list my dataset files:") +os.system("ls -al %s" % config.data_url) + +files = os.listdir(config.data_url) +print("===>>>Files:", files) + + + +print("[CANN-Modelzoo] start run train shell") +# 设置sh文件格式为linux可执行 +os.system("dos2unix ./test/*") + +# 执行train_full_1p.sh或者train_performance_1p.sh，需要用户自己指定 +# full和performance的差异，performance只需要执行很少的step，控制在15分钟以内，主要关注性能FPS +os.system("bash /home/ma-user/modelarts/user-job-dir/code/genStatistical.sh --data_path=%s --output_path=%s " % (config.data_url, config.train_url)) + +print("[CANN-Modelzoo] finish run train shell") + +# 将当前执行目录所有文件拷贝到obs的output进行备份 +print("[CANN-Modelzoo] after train - list my output files:") +os.system("cp -r %s %s " % (code_dir, config.train_url)) +os.system("ls -al %s" % config.train_url) \ No newline at end of file diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/train_full_1p.sh b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/train_full_1p.sh new file mode 100644 index 000000000..edd5eed9a --- /dev/null +++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/train_full_1p.sh @@ -0,0 +1,209 @@ +#!/bin/bash + +########################################################## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +########################################################## +# shell脚本所在路径 +cur_path=`echo $(cd $(dirname $0);pwd)` + +# 判断当前shell是否是performance +perf_flag=`echo $0 | grep performance | wc -l` + +# 当前执行网络的名称 +Network=`echo $(cd $(dirname $0);pwd) | awk -F"/" '{print $(NF-1)}'` + +export RANK_SIZE=1 +export RANK_ID=0 +export JOB_ID=10087 + +# 路径参数初始化 +data_path="" +output_path="" + +# 帮助信息，不需要修改 +if [[ $1 == --help || $1 == -h ]];then + echo"usage:./train_performance_1P.sh " + echo " " + echo "parameter explain: + --data_path # dataset of training + --output_path # output of training + --train_steps # max_step for training + --train_epochs # max_epoch for training + --batch_size # batch size + -h/--help show help message + " + exit 1 +fi + +# 参数校验，不需要修改 +for para in $* +do + if [[ $para == --data_path* ]];then + data_path=`echo ${para#*=}` + elif [[ $para == --output_path* ]];then + output_path=`echo ${para#*=}` + elif [[ $para == --train_steps* ]];then + train_steps=`echo ${para#*=}` + elif [[ $para == --train_epochs* ]];then + train_epochs=`echo ${para#*=}` + elif [[ $para == --batch_size* ]];then + batch_size=`echo ${para#*=}` + fi +done + +# 校验是否传入data_path,不需要修改 +if [[ $data_path == "" ]];then + echo "[Error] para \"data_path\" must be config" + exit 1 +fi + +# 校验是否传入output_path,不需要修改 +if [[ $output_path == "" ]];then + output_path="./test/output/${ASCEND_DEVICE_ID}" +fi + +# 设置打屏日志文件名，请保留，文件名为${print_log} +print_log="./test/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log" +etp_flag=${etp_running_flag} +if [ x"${etp_flag}" != xtrue ]; +then + echo "running without etp..." + print_log_name=`ls /home/ma-user/modelarts/log/ | grep proc-rank` + print_log="/home/ma-user/modelarts/log/${print_log_name}" +fi +echo ${print_log} + +CaseName="" +function get_casename() +{ + if [ x"${perf_flag}" = x1 ]; + then + CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'perf' + else + CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'acc' + fi +} + +# 跳转到code目录 +cd ${cur_path}/../ +rm -rf ./test/output/${ASCEND_DEVICE_ID} +mkdir -p ./test/output/${ASCEND_DEVICE_ID} + +# 训练开始时间记录，不需要修改 +start_time=$(date +%s) +########################################################## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +########################################################## + +#========================================================= +#========================================================= +#========训练执行命令，需要根据您的网络进行修改============== +#========================================================= +#========================================================= +# 您的训练数据集在${data_path}路径下，请直接使用这个变量获取 +# 您的训练输出目录在${output_path}路径下，请直接使用这个变量获取 +# 您的其他基础参数，可以自定义增加，但是batch_size请保留，并且设置正确的值 +batch_size=1 + +echo "------------------***PRINT DATA PATH***-----------------" +train_data="points/*.pts" +test_data="test_points_modified/*.pts" + +trainData_Path="$data_path$train_data" +testData_Path="$data_path$test_data" + + +if [ x"${modelarts_flag}" != x ]; +then + python3.7 /home/ma-user/modelarts/user-job-dir/code/main_point_cloud_boostPerf.py \ + --method=svd \ + --checkpoint_dir=${output_path} \ + --log_step_count=200 \ + --save_summaries_steps=25000 \ + --pt_cloud_train_files=${trainData_Path} \ + --pt_cloud_test_files=${testData_Path} \ + --train_steps=2600000 \ + --save_checkpoints_steps=100000 \ + --eval_examples=39900 +else + python3.7 /home/ma-user/modelarts/user-job-dir/code/main_point_cloud_boostPerf.py \ + --method=svd \ + --checkpoint_dir=${output_path} \ + --log_step_count=200 \ + --save_summaries_steps=25000 \ + --pt_cloud_train_files=${trainData_Path}\ + --pt_cloud_test_files=${testData_Path} \ + --train_steps=2600000 \ + --save_checkpoints_steps=100000 \ + --eval_examples=39900 > ${print_log} +fi + + +# 性能相关数据计算 +StepTime=`grep "sec/step :" ${print_log} | tail -n 10 | awk '{print $NF}' | awk '{sum+=$1} END {print sum/NR}'` +FPS=`awk 'BEGIN{ "%.2f\n", '${batch_size}'/'${StepTime}'}'` + +# 精度相关数据计算 +train_accuracy=`grep "Final Accuracy accuracy" ${print_log} | awk '{print $NF}'` +# 提取所有loss打印信息 +grep "loss :" ${print_log} | awk -F ":" '{print $4}' | awk -F "-" '{print $1}' > ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt + + +########################################################### +#########后面的所有内容请不要修改########################### +#########后面的所有内容请不要修改########################### +#########后面的所有内容请不要修改########################### +########################################################### + +# 判断本次执行是否正确使用Ascend NPU +use_npu_flag=`grep "The model has been compiled on the Ascend AI processor" ${print_log} | wc -l` +if [ x"${use_npu_flag}" == x0 ]; +then + echo "------------------ ERROR NOTICE START ------------------" + echo "ERROR, your task haven't used Ascend NPU, please check your npu Migration." + echo "------------------ ERROR NOTICE END------------------" +else + echo "------------------ INFO NOTICE START------------------" + echo "INFO, your task have used Ascend NPU, please check your result." + echo "------------------ INFO NOTICE END------------------" +fi + +# 获取最终的casename，请保留，case文件名为${CaseName} +get_casename + +# 重命名loss文件 +if [ -f ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ]; +then + mv ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ./test/output/${ASCEND_DEVICE_ID}/${CaseName}_loss.txt +fi + +# 训练端到端耗时 +end_time=$(date +%s) +e2e_time=$(( $end_time - $start_time )) + +echo "------------------ Final result ------------------" +# 输出性能FPS/单step耗时/端到端耗时 +echo "Final Performance images/sec : $FPS" +echo "Final Performance sec/step : $StepTime" +echo "E2E Training Duration sec : $e2e_time" + +# 输出训练精度 +echo "Final Train Accuracy : ${train_accuracy}" + +# 最后一个迭代loss值，不需要修改 +ActualLoss=(`awk 'END {print $NF}' $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}_loss.txt`) + +#关键信息打印到${CaseName}.log中，不需要修改 +echo "Network = ${Network}" > $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "RankSize = ${RANK_SIZE}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "BatchSize = ${batch_size}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "DeviceType = `uname -m`" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualFPS = ${FPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "TrainingTime = ${StepTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log \ No newline at end of file diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/utils.py b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/utils.py new file mode 100644 index 000000000..38d66a8da --- /dev/null +++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/utils.py @@ -0,0 +1,116 @@ +# coding=utf-8 +# Copyright 2021 The Google Research Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Utility functions.""" +import numpy as np +from scipy.stats import special_ortho_group +import tensorflow as tf +from npu_bridge.npu_init import * + + +def relative_angle(r1, r2): + """Relative angle (radians) between 3D rotation matrices.""" + rel_rot = tf.matmul(tf.transpose(r1, perm=[0, 2, 1]), r2) + trace = rel_rot[:, 0, 0] + rel_rot[:, 1, 1] + rel_rot[:, 2, 2] + cos_theta = (trace - 1.0) / 2.0 + cos_theta = tf.minimum(cos_theta, tf.ones_like(cos_theta)) + cos_theta = tf.maximum(cos_theta, (-1.0) * tf.ones_like(cos_theta)) + theta = tf.acos(cos_theta) + return theta + + +def random_rotation_benchmark_np(n): + """Sample a random 3D rotation by method used in Zhou et al, CVPR19. + + This numpy function is a copy of the PyTorch function + get_sampled_rotation_matrices_by_axisAngle() in the code made available + for Zhou et al, CVPR19, at https://github.com/papagina/RotationContinuity/. + + Args: + n: the number of rotation matrices to return. + + Returns: + [n, 3, 3] np array. + """ + theta = np.random.uniform(-1, 1, n) * np.pi + sin = np.sin(theta) + axis = np.random.randn(n, 3) + axis = axis / np.maximum(np.linalg.norm(axis, axis=-1, keepdims=True), 1e-7) + qw = np.cos(theta) + qx = axis[:, 0] * sin + qy = axis[:, 1] * sin + qz = axis[:, 2] * sin + + xx = qx*qx + yy = qy*qy + zz = qz*qz + xy = qx*qy + xz = qx*qz + yz = qy*qz + xw = qx*qw + yw = qy*qw + zw = qz*qw + + row0 = np.stack((1-2*yy-2*zz, 2*xy-2*zw, 2*xz+2*yw), axis=-1) + row1 = np.stack((2*xy+2*zw, 1-2*xx-2*zz, 2*yz-2*xw), axis=-1) + row2 = np.stack((2*xz-2*yw, 2*yz+2*xw, 1-2*xx-2*yy), axis=-1) + matrix = np.stack((row0, row1, row2), axis=1) + + return matrix + + +def random_rotation_benchmark(n): + """A TF wrapper for random_rotation_benchmark_np().""" + mat = tf.py_func( + func=lambda t: np.float32(random_rotation_benchmark_np(t)), + inp=[n], + Tout=tf.float32, + stateful=True) + return tf.reshape(mat, (n, 3, 3)) + + +def random_rotation(n): + """Sample rotations from a uniform distribution on SO(3).""" + mat = tf.py_func( + func=lambda t: np.float32(special_ortho_group.rvs(3, size=t)), + inp=[n], + Tout=tf.float32, + stateful=True) + return tf.reshape(mat, (n, 3, 3)) + + +def symmetric_orthogonalization(x): + """Maps 9D input vectors onto SO(3) via symmetric orthogonalization.""" + # Innner dimensions of the input should be 3x3 matrices. + m = tf.reshape(x, (-1, 3, 3)) + _, u, v = tf.svd(m) + det = tf.linalg.det(tf.matmul(u, v, transpose_b=True)) + r = tf.matmul( + tf.concat([u[:, :, :-1], u[:, :, -1:] * tf.reshape(det, [-1, 1, 1])], 2), + v, transpose_b=True) + return r + + +def gs_orthogonalization(p6): + """Gram-Schmidt orthogonalization from 6D input.""" + # Input should be [batch_size, 6] + x = p6[:, 0:3] + y = p6[:, 3:6] + xn = tf.math.l2_normalize(x, axis=-1) + z = tf.linalg.cross(xn, y) + zn = tf.math.l2_normalize(z, axis=-1) + y = tf.linalg.cross(zn, xn) + r = tf.stack([xn, y, zn], -1) + return r -- Gitee From e284d9e2dfbd65c9dd720d8bb8f8fe666d5004a5 Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Sun, 8 May 2022 14:36:31 +0000 Subject: [PATCH 48/88] =?UTF-8?q?=E5=88=A0=E9=99=A4=E6=96=87=E4=BB=B6=20Te?= =?UTF-8?q?nsorFlow/contrib/cv/SVD=5FID2019=5Ffor=5FTensorflow/NPU?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../cv/SVD_ID2019_for_Tensorflow/NPU/.keep | 0 .../NPU/NPU\347\262\276\345\272\246.txt" | 106 ----- .../NPU/genStatistical.sh | 203 --------- .../NPU/genTestData.sh | 203 --------- .../NPU/gen_pt_test_data.py | 72 --- .../NPU/main_point_cloud.py | 419 ------------------ .../NPU/modelarts_entry_stat.py | 40 -- .../NPU/train_full_1p.sh | 209 --------- .../cv/SVD_ID2019_for_Tensorflow/NPU/utils.py | 116 ----- 9 files changed, 1368 deletions(-) delete mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU/.keep delete mode 100644 "TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU/NPU\347\262\276\345\272\246.txt" delete mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU/genStatistical.sh delete mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU/genTestData.sh delete mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU/gen_pt_test_data.py delete mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU/main_point_cloud.py delete mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU/modelarts_entry_stat.py delete mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU/train_full_1p.sh delete mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU/utils.py diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU/.keep b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU/.keep deleted file mode 100644 index e69de29bb..000000000 diff --git "a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU/NPU\347\262\276\345\272\246.txt" "b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU/NPU\347\262\276\345\272\246.txt" deleted file mode 100644 index 954bca432..000000000 --- "a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU/NPU\347\262\276\345\272\246.txt" +++ /dev/null @@ -1,106 +0,0 @@ -Evaluated 39900 examples -Mean error: %f degrees 2.9278567 -Median error: %f degrees 1.7031833 -Std: %f degrees 8.454575 - -Percentiles: - 1%: 0.322641 - 2%: 0.414959 - 3%: 0.483760 - 4%: 0.539592 - 5%: 0.581482 - 6%: 0.623068 - 7%: 0.659679 - 8%: 0.691538 - 9%: 0.723617 - 10%: 0.751734 - 11%: 0.778837 - 12%: 0.803569 - 13%: 0.829923 - 14%: 0.853175 - 15%: 0.875810 - 16%: 0.898746 - 17%: 0.920686 - 18%: 0.941284 - 19%: 0.963068 - 20%: 0.985562 - 21%: 1.006582 - 22%: 1.029266 - 23%: 1.050157 - 24%: 1.071371 - 25%: 1.092174 - 26%: 1.113466 - 27%: 1.135911 - 28%: 1.157751 - 29%: 1.178524 - 30%: 1.201056 - 31%: 1.223973 - 32%: 1.247724 - 33%: 1.270570 - 34%: 1.294524 - 35%: 1.317301 - 36%: 1.340275 - 37%: 1.363005 - 38%: 1.388467 - 39%: 1.411946 - 40%: 1.436132 - 41%: 1.460185 - 42%: 1.484376 - 43%: 1.511031 - 44%: 1.535951 - 45%: 1.562352 - 46%: 1.589054 - 47%: 1.618583 - 48%: 1.644610 - 49%: 1.671404 - 50%: 1.703183 - 51%: 1.733704 - 52%: 1.763252 - 53%: 1.791003 - 54%: 1.822308 - 55%: 1.854563 - 56%: 1.886785 - 57%: 1.922339 - 58%: 1.957048 - 59%: 1.992330 - 60%: 2.030376 - 61%: 2.073393 - 62%: 2.113592 - 63%: 2.152860 - 64%: 2.195707 - 65%: 2.241841 - 66%: 2.286874 - 67%: 2.330366 - 68%: 2.379650 - 69%: 2.427613 - 70%: 2.480492 - 71%: 2.534432 - 72%: 2.593293 - 73%: 2.657559 - 74%: 2.726347 - 75%: 2.795964 - 76%: 2.867306 - 77%: 2.933115 - 78%: 3.011294 - 79%: 3.097182 - 80%: 3.192182 - 81%: 3.284318 - 82%: 3.385412 - 83%: 3.501069 - 84%: 3.619581 - 85%: 3.753794 - 86%: 3.906153 - 87%: 4.080823 - 88%: 4.264343 - 89%: 4.474133 - 90%: 4.726048 - 91%: 5.013212 - 92%: 5.333844 - 93%: 5.743374 - 94%: 6.223213 - 95%: 6.846590 - 96%: 7.720470 - 97%: 9.113199 - 98%: 11.549816 - 99%: 18.274883 -100%: 179.585983 \ No newline at end of file diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU/genStatistical.sh b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU/genStatistical.sh deleted file mode 100644 index 990ca29a5..000000000 --- a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU/genStatistical.sh +++ /dev/null @@ -1,203 +0,0 @@ -#!/bin/bash - -########################################################## -#########第3行 至 100行，请一定不要、不要、不要修改########## -#########第3行 至 100行，请一定不要、不要、不要修改########## -#########第3行 至 100行，请一定不要、不要、不要修改########## -########################################################## -# shell脚本所在路径 -cur_path=`echo $(cd $(dirname $0);pwd)` - -# 判断当前shell是否是performance -perf_flag=`echo $0 | grep performance | wc -l` - -# 当前执行网络的名称 -Network=`echo $(cd $(dirname $0);pwd) | awk -F"/" '{print $(NF-1)}'` - -export RANK_SIZE=1 -export RANK_ID=0 -export JOB_ID=10087 - -# 路径参数初始化 -data_path="" -output_path="" - -# 帮助信息，不需要修改 -if [[ $1 == --help || $1 == -h ]];then - echo"usage:./train_performance_1P.sh " - echo " " - echo "parameter explain: - --data_path # dataset of training - --output_path # output of training - --train_steps # max_step for training - --train_epochs # max_epoch for training - --batch_size # batch size - -h/--help show help message - " - exit 1 -fi - -# 参数校验，不需要修改 -for para in $* -do - if [[ $para == --data_path* ]];then - data_path=`echo ${para#*=}` - elif [[ $para == --output_path* ]];then - output_path=`echo ${para#*=}` - elif [[ $para == --train_steps* ]];then - train_steps=`echo ${para#*=}` - elif [[ $para == --train_epochs* ]];then - train_epochs=`echo ${para#*=}` - elif [[ $para == --batch_size* ]];then - batch_size=`echo ${para#*=}` - fi -done - -# 校验是否传入data_path,不需要修改 -if [[ $data_path == "" ]];then - echo "[Error] para \"data_path\" must be config" - exit 1 -fi - -# 校验是否传入output_path,不需要修改 -if [[ $output_path == "" ]];then - output_path="./test/output/${ASCEND_DEVICE_ID}" -fi - -# 设置打屏日志文件名，请保留，文件名为${print_log} -print_log="./test/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log" -etp_flag=${etp_running_flag} -if [ x"${etp_flag}" != xtrue ]; -then - echo "running without etp..." - print_log_name=`ls /home/ma-user/modelarts/log/ | grep proc-rank` - print_log="/home/ma-user/modelarts/log/${print_log_name}" -fi -echo ${print_log} - -CaseName="" -function get_casename() -{ - if [ x"${perf_flag}" = x1 ]; - then - CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'perf' - else - CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'acc' - fi -} - -# 跳转到code目录 -cd ${cur_path}/../ -rm -rf ./test/output/${ASCEND_DEVICE_ID} -mkdir -p ./test/output/${ASCEND_DEVICE_ID} - -# 训练开始时间记录，不需要修改 -start_time=$(date +%s) -########################################################## -#########第3行 至 100行，请一定不要、不要、不要修改########## -#########第3行 至 100行，请一定不要、不要、不要修改########## -#########第3行 至 100行，请一定不要、不要、不要修改########## -########################################################## - -#========================================================= -#========================================================= -#========训练执行命令，需要根据您的网络进行修改============== -#========================================================= -#========================================================= -# 您的训练数据集在${data_path}路径下，请直接使用这个变量获取 -# 您的训练输出目录在${output_path}路径下，请直接使用这个变量获取 -# 您的其他基础参数，可以自定义增加，但是batch_size请保留，并且设置正确的值 -batch_size=1 - -echo "------------------***PRINT DATA PATH***-----------------" - -docuClass="test_points_modified/*.pts" -modelPath="output" - -testData_Path="$data_path$docuClass" -outputPath="$data_path$modelPath" - -echo "$outputPath" -echo "$testData_Path" - - -if [ x"${modelarts_flag}" != x ]; -then - python /home/ma-user/modelarts/user-job-dir/code/main_point_cloud.py \ - --method=svd \ - --checkpoint_dir=${outputPath} \ - --pt_cloud_test_files=${testData_Path} \ - --predict_all_test=True -else - python /home/ma-user/modelarts/user-job-dir/code/main_point_cloud.py \ - --method=svd \ - --checkpoint_dir=${outputPath} \ - --pt_cloud_test_files=${testData_Path} \ - --predict_all_test=True -fi - - -# 性能相关数据计算 -StepTime=`grep "sec/step :" ${print_log} | tail -n 10 | awk '{print $NF}' | awk '{sum+=$1} END {print sum/NR}'` -FPS=`awk 'BEGIN{ "%.2f\n", '${batch_size}'/'${StepTime}'}'` - -# 精度相关数据计算 -train_accuracy=`grep "Final Accuracy accuracy" ${print_log} | awk '{print $NF}'` -# 提取所有loss打印信息 -grep "loss :" ${print_log} | awk -F ":" '{print $4}' | awk -F "-" '{print $1}' > ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt - - -########################################################### -#########后面的所有内容请不要修改########################### -#########后面的所有内容请不要修改########################### -#########后面的所有内容请不要修改########################### -########################################################### - -# 判断本次执行是否正确使用Ascend NPU -use_npu_flag=`grep "The model has been compiled on the Ascend AI processor" ${print_log} | wc -l` -if [ x"${use_npu_flag}" == x0 ]; -then - echo "------------------ ERROR NOTICE START ------------------" - echo "ERROR, your task haven't used Ascend NPU, please check your npu Migration." - echo "------------------ ERROR NOTICE END------------------" -else - echo "------------------ INFO NOTICE START------------------" - echo "INFO, your task have used Ascend NPU, please check your result." - echo "------------------ INFO NOTICE END------------------" -fi - -# 获取最终的casename，请保留，case文件名为${CaseName} -get_casename - -# 重命名loss文件 -if [ -f ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ]; -then - mv ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ./test/output/${ASCEND_DEVICE_ID}/${CaseName}_loss.txt -fi - -# 训练端到端耗时 -end_time=$(date +%s) -e2e_time=$(( $end_time - $start_time )) - -echo "------------------ Final result ------------------" -# 输出性能FPS/单step耗时/端到端耗时 -echo "Final Performance images/sec : $FPS" -echo "Final Performance sec/step : $StepTime" -echo "E2E Training Duration sec : $e2e_time" - -# 输出训练精度 -echo "Final Train Accuracy : ${train_accuracy}" - -# 最后一个迭代loss值，不需要修改 -ActualLoss=(`awk 'END {print $NF}' $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}_loss.txt`) - -#关键信息打印到${CaseName}.log中，不需要修改 -echo "Network = ${Network}" > $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "RankSize = ${RANK_SIZE}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "BatchSize = ${batch_size}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "DeviceType = `uname -m`" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "ActualFPS = ${FPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "TrainingTime = ${StepTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log \ No newline at end of file diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU/genTestData.sh b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU/genTestData.sh deleted file mode 100644 index 416b71b04..000000000 --- a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU/genTestData.sh +++ /dev/null @@ -1,203 +0,0 @@ -#!/bin/bash - -########################################################## -#########第3行 至 100行，请一定不要、不要、不要修改########## -#########第3行 至 100行，请一定不要、不要、不要修改########## -#########第3行 至 100行，请一定不要、不要、不要修改########## -########################################################## -# shell脚本所在路径 -cur_path=`echo $(cd $(dirname $0);pwd)` - -# 判断当前shell是否是performance -perf_flag=`echo $0 | grep performance | wc -l` - -# 当前执行网络的名称 -Network=`echo $(cd $(dirname $0);pwd) | awk -F"/" '{print $(NF-1)}'` - -export RANK_SIZE=1 -export RANK_ID=0 -export JOB_ID=10087 - -# 路径参数初始化 -data_path="" -output_path="" - -# 帮助信息，不需要修改 -if [[ $1 == --help || $1 == -h ]];then - echo"usage:./train_performance_1P.sh " - echo " " - echo "parameter explain: - --data_path # dataset of training - --output_path # output of training - --train_steps # max_step for training - --train_epochs # max_epoch for training - --batch_size # batch size - -h/--help show help message - " - exit 1 -fi - -# 参数校验，不需要修改 -for para in $* -do - if [[ $para == --data_path* ]];then - data_path=`echo ${para#*=}` - elif [[ $para == --output_path* ]];then - output_path=`echo ${para#*=}` - elif [[ $para == --train_steps* ]];then - train_steps=`echo ${para#*=}` - elif [[ $para == --train_epochs* ]];then - train_epochs=`echo ${para#*=}` - elif [[ $para == --batch_size* ]];then - batch_size=`echo ${para#*=}` - fi -done - -# 校验是否传入data_path,不需要修改 -if [[ $data_path == "" ]];then - echo "[Error] para \"data_path\" must be config" - exit 1 -fi - -# 校验是否传入output_path,不需要修改 -if [[ $output_path == "" ]];then - output_path="./test/output/${ASCEND_DEVICE_ID}" -fi - -# 设置打屏日志文件名，请保留，文件名为${print_log} -print_log="./test/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log" -etp_flag=${etp_running_flag} -if [ x"${etp_flag}" != xtrue ]; -then - echo "running without etp..." - print_log_name=`ls /home/ma-user/modelarts/log/ | grep proc-rank` - print_log="/home/ma-user/modelarts/log/${print_log_name}" -fi -echo ${print_log} - -CaseName="" -function get_casename() -{ - if [ x"${perf_flag}" = x1 ]; - then - CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'perf' - else - CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'acc' - fi -} - -# 跳转到code目录 -cd ${cur_path}/../ -rm -rf ./test/output/${ASCEND_DEVICE_ID} -mkdir -p ./test/output/${ASCEND_DEVICE_ID} - -# 训练开始时间记录，不需要修改 -start_time=$(date +%s) -########################################################## -#########第3行 至 100行，请一定不要、不要、不要修改########## -#########第3行 至 100行，请一定不要、不要、不要修改########## -#########第3行 至 100行，请一定不要、不要、不要修改########## -########################################################## - -#========================================================= -#========================================================= -#========训练执行命令，需要根据您的网络进行修改============== -#========================================================= -#========================================================= -# 您的训练数据集在${data_path}路径下，请直接使用这个变量获取 -# 您的训练输出目录在${output_path}路径下，请直接使用这个变量获取 -# 您的其他基础参数，可以自定义增加，但是batch_size请保留，并且设置正确的值 -batch_size=1 - -echo "------------------***PRINT DATA PATH***-----------------" - -inputDataPath="points_test/*.pts" -TestdataPath="test_points_modified" - -Data_Path="$data_path$inputDataPath" -outputPath="$output_path$TestdataPath" -AXANG_SAMPLIE="True" - - -echo "$outputPath" -echo "$testData_Path" - - -if [ x"${modelarts_flag}" != x ]; -then - python /home/ma-user/modelarts/user-job-dir/code/gen_pt_test_data.py\ - --input_test_files=${Data_Path} \ - --output_directory=${outputPath} \ - --random_rotation_axang=${AXANG_SAMPLIE} -else - python /home/ma-user/modelarts/user-job-dir/code/gen_pt_test_data.py\ - --input_test_files=${Data_Path} \ - --output_directory=${outputPath} \ - --random_rotation_axang=${AXANG_SAMPLIE} -fi - - -# 性能相关数据计算 -StepTime=`grep "sec/step :" ${print_log} | tail -n 10 | awk '{print $NF}' | awk '{sum+=$1} END {print sum/NR}'` -FPS=`awk 'BEGIN{ "%.2f\n", '${batch_size}'/'${StepTime}'}'` - -# 精度相关数据计算 -train_accuracy=`grep "Final Accuracy accuracy" ${print_log} | awk '{print $NF}'` -# 提取所有loss打印信息 -grep "loss :" ${print_log} | awk -F ":" '{print $4}' | awk -F "-" '{print $1}' > ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt - - -########################################################### -#########后面的所有内容请不要修改########################### -#########后面的所有内容请不要修改########################### -#########后面的所有内容请不要修改########################### -########################################################### - -# 判断本次执行是否正确使用Ascend NPU -use_npu_flag=`grep "The model has been compiled on the Ascend AI processor" ${print_log} | wc -l` -if [ x"${use_npu_flag}" == x0 ]; -then - echo "------------------ ERROR NOTICE START ------------------" - echo "ERROR, your task haven't used Ascend NPU, please check your npu Migration." - echo "------------------ ERROR NOTICE END------------------" -else - echo "------------------ INFO NOTICE START------------------" - echo "INFO, your task have used Ascend NPU, please check your result." - echo "------------------ INFO NOTICE END------------------" -fi - -# 获取最终的casename，请保留，case文件名为${CaseName} -get_casename - -# 重命名loss文件 -if [ -f ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ]; -then - mv ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ./test/output/${ASCEND_DEVICE_ID}/${CaseName}_loss.txt -fi - -# 训练端到端耗时 -end_time=$(date +%s) -e2e_time=$(( $end_time - $start_time )) - -echo "------------------ Final result ------------------" -# 输出性能FPS/单step耗时/端到端耗时 -echo "Final Performance images/sec : $FPS" -echo "Final Performance sec/step : $StepTime" -echo "E2E Training Duration sec : $e2e_time" - -# 输出训练精度 -echo "Final Train Accuracy : ${train_accuracy}" - -# 最后一个迭代loss值，不需要修改 -ActualLoss=(`awk 'END {print $NF}' $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}_loss.txt`) - -#关键信息打印到${CaseName}.log中，不需要修改 -echo "Network = ${Network}" > $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "RankSize = ${RANK_SIZE}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "BatchSize = ${batch_size}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "DeviceType = `uname -m`" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "ActualFPS = ${FPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "TrainingTime = ${StepTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log \ No newline at end of file diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU/gen_pt_test_data.py b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU/gen_pt_test_data.py deleted file mode 100644 index c1c299409..000000000 --- a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU/gen_pt_test_data.py +++ /dev/null @@ -1,72 +0,0 @@ -# coding=utf-8 -# Copyright 2021 The Google Research Authors. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""Generate modified point cloud test data with fixed random rotations.""" -import glob -import os -import pathlib - -import utils -from absl import app -from absl import flags -import numpy as np -from scipy.stats import special_ortho_group -from npu_bridge.npu_init import * - -FLAGS = flags.FLAGS -flags.DEFINE_string('input_test_files', '', - 'Regular expression for the original input test files.') -flags.DEFINE_string('output_directory', '', - 'Output directory where new test files will be stored.') -flags.DEFINE_integer('num_rotations_per_file', 100, - 'Number of random rotation augmentations per test point ' - 'cloud.') -flags.DEFINE_boolean('random_rotation_axang', True, - 'If true, samples random rotations using the method ' - 'from the original benchmark code. Otherwise samples ' - 'by Haar measure.') - - -def gen_test_data(): - """Generate the new (modified) test data.""" - # Create output directory. - os.makedirs(FLAGS.output_directory, exist_ok=True) - - # Get all test point cloud files in the original dataset. - input_test_files = glob.glob(FLAGS.input_test_files) - - for in_file in input_test_files: - out_file_prefix = pathlib.Path(in_file).stem - pts = np.loadtxt(in_file) # N x 3 - num_pts_to_keep = pts.shape[0] // 2 - pts = pts[:num_pts_to_keep, :] # N//2 x 3. - - for k in range(FLAGS.num_rotations_per_file): - if FLAGS.random_rotation_axang: - r = utils.random_rotation_benchmark_np(1) - r = r[0] - else: - r = special_ortho_group.rvs(3) - joined = np.float32(np.concatenate((r, pts), axis=0)) # (N//2+3) x 3. - out_file = os.path.join( - FLAGS.output_directory, '%s_r%03d.pts'%(out_file_prefix, k)) - np.savetxt(out_file, joined) - - -def main(unused_argv): - gen_test_data() - -if __name__ == '__main__': - app.run(main) diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU/main_point_cloud.py b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU/main_point_cloud.py deleted file mode 100644 index a3b4e9a22..000000000 --- a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU/main_point_cloud.py +++ /dev/null @@ -1,419 +0,0 @@ -# coding=utf-8 -# Copyright 2021 The Google Research Authors. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""Training and evaluation for the point cloud alignment experiment.""" -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import utils -from absl import flags -import numpy as np -import tensorflow as tf -import glob -import pathlib -import datetime -from npu_bridge.npu_init import * -from npu_bridge.estimator.npu.npu_estimator import NPUEstimatorSpec -from npu_bridge.estimator.npu.npu_estimator import NPUEstimator -from npu_bridge.estimator.npu.npu_config import NPURunConfig -#from npu_bridge.estimator.npu.npu_config import ProfilingConfig -import os -FLAGS = tf.app.flags.FLAGS - -# General flags. -flags.DEFINE_string('method', 'svd', - 'Specifies the method to use for predicting rotations. ' - 'Choices are "svd", "svd-inf", or "gs".') -flags.DEFINE_string('checkpoint_dir', '', - 'Locations for checkpoints, summaries, etc.') -flags.DEFINE_integer('train_steps', 2600000, 'Number of training iterations.') -flags.DEFINE_integer('save_checkpoints_steps', 10000, - 'How often to save checkpoints') -flags.DEFINE_integer('log_step_count', 500, 'How often to log the step count') -flags.DEFINE_integer('save_summaries_steps', 5000, - 'How often to save summaries.') -flags.DEFINE_float('learning_rate', 1e-5, 'Learning rate') -flags.DEFINE_boolean('lr_decay', False, 'Decay the learning rate if True.') -flags.DEFINE_integer('lr_decay_steps', 35000, - 'Learning rate decays steps.') -flags.DEFINE_float('lr_decay_rate', 0.95, - 'Learning rate decays rate.') -flags.DEFINE_boolean('predict_all_test', False, - 'If true, runs an eval job on latest checkpoint and ' - 'prints the error for each input.') -flags.DEFINE_integer('eval_examples', 39900, 'Number of test examples.') -flags.DEFINE_boolean('print_variable_names', False, - 'Print model variable names.') - -# Flags only used in the point cloud alignment experiment. -flags.DEFINE_integer('num_train_augmentations', 10, - 'Number of random rotations for augmenting each input ' - 'point cloud.') -flags.DEFINE_string('pt_cloud_train_files', '', - 'Expression matching all training point files, e.g. ' - '/path/to/files/pc_plane/points/*.pts') -flags.DEFINE_string('pt_cloud_test_files','', - 'Expression matching all modified test point files, e.g. ' - '/path/to/files/pc_plane/points_test/*.pts') -flags.DEFINE_boolean('random_rotation_axang', True, - 'If true, samples random rotations using the method ' - 'from the original benchmark code. Otherwise samples ' - 'by Haar measure.') - - -def pt_features(batch_pts): - """Input shape: [B, N, 3], output shape: [B, 1024].""" - with tf.variable_scope('ptenc', reuse=tf.AUTO_REUSE): - f1 = tf.layers.conv1d(inputs=batch_pts, filters=64, kernel_size=1) - f1 = tf.nn.leaky_relu(f1) - f2 = tf.layers.conv1d(inputs=f1, filters=128, kernel_size=1) - f2 = tf.nn.leaky_relu(f2) - f3 = tf.layers.conv1d(inputs=f2, filters=1024, kernel_size=1) - f = tf.reduce_max(f3, axis=1, keep_dims=False) - return f - - -def regress_from_features(batch_features, out_dim): - """Regress to a rotation representation from point cloud encodings. - - In Zhou et al, CVPR19, the paper describes this regression network as an MLP - mapping 2048->512->512->out_dim, but the associated code implements it with - one less layer: 2048->512->out_dim. We mimic the code. - - Args: - batch_features: [batch_size, in_dim]. - out_dim: desired output dimensionality. - - Returns: - A [batch_size, out_dim] tensor. - """ - f1 = tf.layers.dense(batch_features, 512) - f1 = tf.nn.leaky_relu(f1) - f2 = tf.layers.dense(f1, out_dim) - return f2 - - -def net_point_cloud(points1, points2, mode): - """Predict a relative rotation given two point clouds. - - Args: - points1: [batch_size, N, 3] float tensor. - points2: [batch_size, N, 3] float tensor. - mode: tf.estimator.ModeKeys. - - Returns: - [batch_size, 3, 3] matrices. - """ - f1 = pt_features(points1) - f2 = pt_features(points2) - f = tf.concat([f1, f2], axis=-1) - - if FLAGS.method == 'svd': - p = regress_from_features(f, 9) - return utils.symmetric_orthogonalization(p) - - if FLAGS.method == 'svd-inf': - p = regress_from_features(f, 9) - if mode == tf.estimator.ModeKeys.TRAIN: - return tf.reshape(p, (-1, 3, 3)) - else: - return utils.symmetric_orthogonalization(p) - - if FLAGS.method == 'gs': - p = regress_from_features(f, 6) - return utils.gs_orthogonalization(p) - - -def model_fn(features, labels, mode, params): - """The model_fn used to construct the tf.Estimator.""" - del labels, params # Unused. - if mode == tf.estimator.ModeKeys.TRAIN: - # Training data has point cloud of size [1, N, 3] and random rotations - # of size [1, FLAGS.num_train_augmentations, 3, 3] - rot = features['rot'][0] - num_rot = FLAGS.num_train_augmentations - batch_pts1 = tf.tile(features['data'], [num_rot, 1, 1]) - #batch_pts1 = tf.reshape(batch_pts1,(num_rot,-1,3)) - # In this experiment it does not matter if we pre or post-multiply the - # rotation as long as we are consistent between training and eval. - batch_pts2 = tf.matmul(batch_pts1, rot) # post-multiplying! - else: - # Test data has point cloud of size [1, N, 3] and a single random - # rotation of size [1, 3, 3] - batch_pts1 = features['data'] - rot = features['rot'] - batch_pts2 = tf.matmul(batch_pts1, rot) - rot = tf.reshape(rot, (-1, 3, 3)) - - # Predict the rotation. - r = net_point_cloud(batch_pts1, batch_pts2, mode) - - # Compute the loss. - loss = tf.nn.l2_loss(rot - r) - - # Compute the relative angle in radians. - theta = utils.relative_angle(r, rot) - - # Mean angle error over the batch. - mean_theta = tf.reduce_mean(theta) - mean_theta_deg = mean_theta * 180.0 / np.pi - - # Train, eval, or predict depending on mode. - if mode == tf.estimator.ModeKeys.TRAIN: - tf.summary.scalar('train/loss', loss) - tf.summary.scalar('train/theta', mean_theta_deg) - global_step = tf.train.get_or_create_global_step() - - if FLAGS.lr_decay: - learning_rate = tf.train.exponential_decay( - FLAGS.learning_rate, - global_step, - FLAGS.lr_decay_steps, - FLAGS.lr_decay_rate) - else: - learning_rate = FLAGS.learning_rate - - tf.summary.scalar('train/learning_rate', learning_rate) - optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate) - update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) - with tf.control_dependencies(update_ops): - train_op = optimizer.minimize(loss, global_step=global_step) - #train_op = util.set_graph_exec_config(train_op, dynamic_input=True,dynamic_graph_execute_mode="lazy_recompile") - - return NPUEstimatorSpec( - mode=mode, - loss=loss, - train_op=train_op) - - if mode == tf.estimator.ModeKeys.EVAL: - if FLAGS.predict_all_test: - print_error_op = tf.print('error:', mean_theta_deg) - with tf.control_dependencies([print_error_op]): - eval_metric_ops = { - 'mean_degree_err': tf.metrics.mean(mean_theta_deg), - } - else: - eval_metric_ops = { - 'mean_degree_err': tf.metrics.mean(mean_theta_deg), - } - - return NPUEstimatorSpec( - mode=mode, - loss=loss, - eval_metric_ops=eval_metric_ops) - - if mode == tf.estimator.ModeKeys.PREDICT: - pred = {'error': mean_theta_deg} - return NPUEstimatorSpec( - mode=mode, - predictions=pred) - - -def train_input_fn(): - """Generate training data iterator from the .pts files.""" - def _file_to_matrix(pts_path): - """Read Nx3 point cloud from a .pts file.""" - file_buffer = tf.read_file(pts_path) - lines = tf.string_split([file_buffer], delimiter='\n') - lines1 = tf.string_split(lines.values, delimiter='\r') - values = tf.stack(tf.decode_csv(lines1.values, - record_defaults=[[0.0], [0.0], [0.0]], - field_delim=' ')) - values = tf.transpose(values) # 3xN --> Nx3. - values = values[:tf.shape(values)[0] // 2, :] - - # modified by Lyoung - diff_num = 1500-tf.shape(values)[0] - repeat_pts = tf.tile(tf.reshape(values[1, :], (1, -1)), [diff_num, 1]) - values = tf.concat([values,repeat_pts], axis=0) - # The experiment code in - # github.com/papagina/RotationContinuity/.../shapenet/code/train_pointnet.py - # only used the first half of the points in each file. - return values - - def _random_rotation(pts): - """Attach N random rotations to a point cloud.""" - if FLAGS.random_rotation_axang: - rotations = utils.random_rotation_benchmark(FLAGS.num_train_augmentations) - else: - rotations = utils.random_rotation(FLAGS.num_train_augmentations) - return pts,rotations - - pts_paths = tf.gfile.Glob(FLAGS.pt_cloud_train_files) - dataset = tf.data.Dataset.from_tensor_slices(pts_paths) - dataset = dataset.map(_file_to_matrix) - dataset = dataset.cache() # Comment out if memory cannot hold all the data. - dataset = dataset.shuffle(buffer_size=50, reshuffle_each_iteration=True) - dataset = dataset.repeat() - dataset = dataset.map(_random_rotation) - dataset = dataset.batch(1) - #dataset = dataset.prefetch(buffer_size=buffer_size) - iterator = tf.data.make_one_shot_iterator(dataset) - batch_data, batch_rot = iterator.get_next() - features_dict = {'data': batch_data, 'rot': batch_rot} - batch_size = tf.shape(batch_data)[0] - batch_labels_dummy = tf.zeros(shape=(batch_size, 1)) - return (features_dict, batch_labels_dummy) - - -def eval_input_fn(): - """Generate test data from *modified* .pts files. - - See README and comments below for details on how the data is modified. - - Returns: - A tuple of features and associated labels. - """ - def _file_to_matrix(pts_path): - """Read Nx3 point cloud and 3x3 rotation matrix from a .pts file. - - The test data is a modified version of the original files. For each .pts - file we have (1) added a 3x3 rotation matrix for testing, and (2) removed - the second half of the point cloud since it is not used at all. - - Args: - pts_path: path to a .pts file. - - Returns: - A Nx3 point cloud. - A 3x3 rotation matrix. - """ - file_buffer = tf.read_file(pts_path) - lines = tf.string_split([file_buffer], delimiter='\n') - lines1 = tf.string_split(lines.values,delimiter='\r') - values = tf.stack(tf.decode_csv(lines1.values, - record_defaults=[[0.0], [0.0], [0.0]],field_delim=' ')) - values = tf.transpose(values)# 3xN --> Nx3. - - #modified by Lyoung - diff_num = 1414-tf.shape(values)[0] - repeat_pts = tf.tile(tf.reshape(values[4, :], (1, -1)), [diff_num, 1]) - values = tf.concat([values, repeat_pts], axis=0) - # First three rows are the rotation matrix, remaining rows the point cloud. - rot = values[:3, :] - - return values[4:, :], rot - - pts_paths = tf.io.gfile.glob(FLAGS.pt_cloud_test_files) - dataset = tf.data.Dataset.from_tensor_slices(pts_paths) - dataset = dataset.map(_file_to_matrix) - dataset = dataset.batch(1) - iterator = tf.data.make_one_shot_iterator(dataset) - batch_data, batch_rot = iterator.get_next() - - features_dict = {'data': batch_data, 'rot': batch_rot} - batch_size = tf.shape(batch_data)[0] - batch_labels_dummy = tf.zeros(shape=(batch_size, 1)) - return (features_dict, batch_labels_dummy) - - -def print_variable_names(): - """Print variable names in a model.""" - params = {'dummy': 0} - estimator = NPUEstimator( - model_fn=model_fn, - model_dir=FLAGS.checkpoint_dir, - params=params) - - names = estimator.get_variable_names() - for name in names: - print(name) - - -def predict_all_test(): - """Print error statistics for the test dataset.""" - - config = NPURunConfig( - save_summary_steps=None, - save_checkpoints_steps=None, - log_step_count_steps=None, - keep_checkpoint_max=None) - - params = {'dummy': 0} - estimator = NPUEstimator( - model_fn=model_fn, - model_dir=FLAGS.checkpoint_dir, - params=params, - config=config) - evals = estimator.predict(input_fn=eval_input_fn, yield_single_examples=True) - - # Print error statistics. - - all_errors = [x['error'] for x in evals] - errors = np.array(all_errors) - print('Evaluated %d examples'%np.size(errors)) - print('Mean error: %f degrees',np.mean(errors)) - print('Median error: %f degrees', np.median(errors)) - print('Std: %f degrees', np.std(errors)) - sorted_errors = np.sort(errors) - n = np.size(sorted_errors) - print('\nPercentiles:') - for perc in range(1, 101): - index = np.int32(np.float32(n * perc) / 100.0) - 1 - print('%3d%%: %f'%(perc, sorted_errors[index])) - - -def train_and_eval(): - """Train and evaluate a model.""" - save_summary_steps = FLAGS.save_summaries_steps - save_checkpoints_steps = FLAGS.save_checkpoints_steps - log_step_count = FLAGS.log_step_count - - config = NPURunConfig( - save_summary_steps=save_summary_steps, - save_checkpoints_steps=save_checkpoints_steps, - log_step_count_steps=log_step_count, - keep_checkpoint_max=None, - precision_mode = "all_mix_precision") - - params = {'dummy': 0} - estimator = NPUEstimator( - model_fn=model_fn, - model_dir=FLAGS.checkpoint_dir, - config=config, - params=params) - - - train_spec = tf.estimator.TrainSpec( - input_fn=train_input_fn, - max_steps=FLAGS.train_steps) - - - eval_spec = tf.estimator.EvalSpec(input_fn=eval_input_fn, - start_delay_secs=60, - steps=FLAGS.eval_examples, - throttle_secs=60) - - - - tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec) - - -def main(argv=None): # pylint: disable=unused-argument - - if FLAGS.print_variable_names: - print_variable_names() - return - - if FLAGS.predict_all_test: - predict_all_test() - else: - train_and_eval() - - -if __name__ == '__main__': - - tf.app.run() diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU/modelarts_entry_stat.py b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU/modelarts_entry_stat.py deleted file mode 100644 index 78debea21..000000000 --- a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU/modelarts_entry_stat.py +++ /dev/null @@ -1,40 +0,0 @@ -import os -import argparse -import sys - -# 解析输入参数data_url -parser = argparse.ArgumentParser() -parser.add_argument("--data_url", type=str, default="/home/ma-user/modelarts/inputs/data_url_0") -parser.add_argument("--train_url", type=str, default="/home/ma-user/modelarts/outputs/train_url_0/") -config = parser.parse_args() - -print("[CANN-Modelzoo] code_dir path is [%s]" % (sys.path[0])) -code_dir = sys.path[0] -os.chdir(code_dir) -print("[CANN-Modelzoo] work_dir path is [%s]" % (os.getcwd())) - -print("[CANN-Modelzoo] before train - list my run files:") -os.system("ls -al /usr/local/Ascend/ascend-toolkit/") - -print("[CANN-Modelzoo] before train - list my dataset files:") -os.system("ls -al %s" % config.data_url) - -files = os.listdir(config.data_url) -print("===>>>Files:", files) - - - -print("[CANN-Modelzoo] start run train shell") -# 设置sh文件格式为linux可执行 -os.system("dos2unix ./test/*") - -# 执行train_full_1p.sh或者train_performance_1p.sh，需要用户自己指定 -# full和performance的差异，performance只需要执行很少的step，控制在15分钟以内，主要关注性能FPS -os.system("bash /home/ma-user/modelarts/user-job-dir/code/genStatistical.sh --data_path=%s --output_path=%s " % (config.data_url, config.train_url)) - -print("[CANN-Modelzoo] finish run train shell") - -# 将当前执行目录所有文件拷贝到obs的output进行备份 -print("[CANN-Modelzoo] after train - list my output files:") -os.system("cp -r %s %s " % (code_dir, config.train_url)) -os.system("ls -al %s" % config.train_url) \ No newline at end of file diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU/train_full_1p.sh b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU/train_full_1p.sh deleted file mode 100644 index edd5eed9a..000000000 --- a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU/train_full_1p.sh +++ /dev/null @@ -1,209 +0,0 @@ -#!/bin/bash - -########################################################## -#########第3行 至 100行，请一定不要、不要、不要修改########## -#########第3行 至 100行，请一定不要、不要、不要修改########## -#########第3行 至 100行，请一定不要、不要、不要修改########## -########################################################## -# shell脚本所在路径 -cur_path=`echo $(cd $(dirname $0);pwd)` - -# 判断当前shell是否是performance -perf_flag=`echo $0 | grep performance | wc -l` - -# 当前执行网络的名称 -Network=`echo $(cd $(dirname $0);pwd) | awk -F"/" '{print $(NF-1)}'` - -export RANK_SIZE=1 -export RANK_ID=0 -export JOB_ID=10087 - -# 路径参数初始化 -data_path="" -output_path="" - -# 帮助信息，不需要修改 -if [[ $1 == --help || $1 == -h ]];then - echo"usage:./train_performance_1P.sh " - echo " " - echo "parameter explain: - --data_path # dataset of training - --output_path # output of training - --train_steps # max_step for training - --train_epochs # max_epoch for training - --batch_size # batch size - -h/--help show help message - " - exit 1 -fi - -# 参数校验，不需要修改 -for para in $* -do - if [[ $para == --data_path* ]];then - data_path=`echo ${para#*=}` - elif [[ $para == --output_path* ]];then - output_path=`echo ${para#*=}` - elif [[ $para == --train_steps* ]];then - train_steps=`echo ${para#*=}` - elif [[ $para == --train_epochs* ]];then - train_epochs=`echo ${para#*=}` - elif [[ $para == --batch_size* ]];then - batch_size=`echo ${para#*=}` - fi -done - -# 校验是否传入data_path,不需要修改 -if [[ $data_path == "" ]];then - echo "[Error] para \"data_path\" must be config" - exit 1 -fi - -# 校验是否传入output_path,不需要修改 -if [[ $output_path == "" ]];then - output_path="./test/output/${ASCEND_DEVICE_ID}" -fi - -# 设置打屏日志文件名，请保留，文件名为${print_log} -print_log="./test/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log" -etp_flag=${etp_running_flag} -if [ x"${etp_flag}" != xtrue ]; -then - echo "running without etp..." - print_log_name=`ls /home/ma-user/modelarts/log/ | grep proc-rank` - print_log="/home/ma-user/modelarts/log/${print_log_name}" -fi -echo ${print_log} - -CaseName="" -function get_casename() -{ - if [ x"${perf_flag}" = x1 ]; - then - CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'perf' - else - CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'acc' - fi -} - -# 跳转到code目录 -cd ${cur_path}/../ -rm -rf ./test/output/${ASCEND_DEVICE_ID} -mkdir -p ./test/output/${ASCEND_DEVICE_ID} - -# 训练开始时间记录，不需要修改 -start_time=$(date +%s) -########################################################## -#########第3行 至 100行，请一定不要、不要、不要修改########## -#########第3行 至 100行，请一定不要、不要、不要修改########## -#########第3行 至 100行，请一定不要、不要、不要修改########## -########################################################## - -#========================================================= -#========================================================= -#========训练执行命令，需要根据您的网络进行修改============== -#========================================================= -#========================================================= -# 您的训练数据集在${data_path}路径下，请直接使用这个变量获取 -# 您的训练输出目录在${output_path}路径下，请直接使用这个变量获取 -# 您的其他基础参数，可以自定义增加，但是batch_size请保留，并且设置正确的值 -batch_size=1 - -echo "------------------***PRINT DATA PATH***-----------------" -train_data="points/*.pts" -test_data="test_points_modified/*.pts" - -trainData_Path="$data_path$train_data" -testData_Path="$data_path$test_data" - - -if [ x"${modelarts_flag}" != x ]; -then - python3.7 /home/ma-user/modelarts/user-job-dir/code/main_point_cloud_boostPerf.py \ - --method=svd \ - --checkpoint_dir=${output_path} \ - --log_step_count=200 \ - --save_summaries_steps=25000 \ - --pt_cloud_train_files=${trainData_Path} \ - --pt_cloud_test_files=${testData_Path} \ - --train_steps=2600000 \ - --save_checkpoints_steps=100000 \ - --eval_examples=39900 -else - python3.7 /home/ma-user/modelarts/user-job-dir/code/main_point_cloud_boostPerf.py \ - --method=svd \ - --checkpoint_dir=${output_path} \ - --log_step_count=200 \ - --save_summaries_steps=25000 \ - --pt_cloud_train_files=${trainData_Path}\ - --pt_cloud_test_files=${testData_Path} \ - --train_steps=2600000 \ - --save_checkpoints_steps=100000 \ - --eval_examples=39900 > ${print_log} -fi - - -# 性能相关数据计算 -StepTime=`grep "sec/step :" ${print_log} | tail -n 10 | awk '{print $NF}' | awk '{sum+=$1} END {print sum/NR}'` -FPS=`awk 'BEGIN{ "%.2f\n", '${batch_size}'/'${StepTime}'}'` - -# 精度相关数据计算 -train_accuracy=`grep "Final Accuracy accuracy" ${print_log} | awk '{print $NF}'` -# 提取所有loss打印信息 -grep "loss :" ${print_log} | awk -F ":" '{print $4}' | awk -F "-" '{print $1}' > ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt - - -########################################################### -#########后面的所有内容请不要修改########################### -#########后面的所有内容请不要修改########################### -#########后面的所有内容请不要修改########################### -########################################################### - -# 判断本次执行是否正确使用Ascend NPU -use_npu_flag=`grep "The model has been compiled on the Ascend AI processor" ${print_log} | wc -l` -if [ x"${use_npu_flag}" == x0 ]; -then - echo "------------------ ERROR NOTICE START ------------------" - echo "ERROR, your task haven't used Ascend NPU, please check your npu Migration." - echo "------------------ ERROR NOTICE END------------------" -else - echo "------------------ INFO NOTICE START------------------" - echo "INFO, your task have used Ascend NPU, please check your result." - echo "------------------ INFO NOTICE END------------------" -fi - -# 获取最终的casename，请保留，case文件名为${CaseName} -get_casename - -# 重命名loss文件 -if [ -f ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ]; -then - mv ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ./test/output/${ASCEND_DEVICE_ID}/${CaseName}_loss.txt -fi - -# 训练端到端耗时 -end_time=$(date +%s) -e2e_time=$(( $end_time - $start_time )) - -echo "------------------ Final result ------------------" -# 输出性能FPS/单step耗时/端到端耗时 -echo "Final Performance images/sec : $FPS" -echo "Final Performance sec/step : $StepTime" -echo "E2E Training Duration sec : $e2e_time" - -# 输出训练精度 -echo "Final Train Accuracy : ${train_accuracy}" - -# 最后一个迭代loss值，不需要修改 -ActualLoss=(`awk 'END {print $NF}' $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}_loss.txt`) - -#关键信息打印到${CaseName}.log中，不需要修改 -echo "Network = ${Network}" > $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "RankSize = ${RANK_SIZE}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "BatchSize = ${batch_size}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "DeviceType = `uname -m`" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "ActualFPS = ${FPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "TrainingTime = ${StepTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log \ No newline at end of file diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU/utils.py b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU/utils.py deleted file mode 100644 index 38d66a8da..000000000 --- a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU/utils.py +++ /dev/null @@ -1,116 +0,0 @@ -# coding=utf-8 -# Copyright 2021 The Google Research Authors. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""Utility functions.""" -import numpy as np -from scipy.stats import special_ortho_group -import tensorflow as tf -from npu_bridge.npu_init import * - - -def relative_angle(r1, r2): - """Relative angle (radians) between 3D rotation matrices.""" - rel_rot = tf.matmul(tf.transpose(r1, perm=[0, 2, 1]), r2) - trace = rel_rot[:, 0, 0] + rel_rot[:, 1, 1] + rel_rot[:, 2, 2] - cos_theta = (trace - 1.0) / 2.0 - cos_theta = tf.minimum(cos_theta, tf.ones_like(cos_theta)) - cos_theta = tf.maximum(cos_theta, (-1.0) * tf.ones_like(cos_theta)) - theta = tf.acos(cos_theta) - return theta - - -def random_rotation_benchmark_np(n): - """Sample a random 3D rotation by method used in Zhou et al, CVPR19. - - This numpy function is a copy of the PyTorch function - get_sampled_rotation_matrices_by_axisAngle() in the code made available - for Zhou et al, CVPR19, at https://github.com/papagina/RotationContinuity/. - - Args: - n: the number of rotation matrices to return. - - Returns: - [n, 3, 3] np array. - """ - theta = np.random.uniform(-1, 1, n) * np.pi - sin = np.sin(theta) - axis = np.random.randn(n, 3) - axis = axis / np.maximum(np.linalg.norm(axis, axis=-1, keepdims=True), 1e-7) - qw = np.cos(theta) - qx = axis[:, 0] * sin - qy = axis[:, 1] * sin - qz = axis[:, 2] * sin - - xx = qx*qx - yy = qy*qy - zz = qz*qz - xy = qx*qy - xz = qx*qz - yz = qy*qz - xw = qx*qw - yw = qy*qw - zw = qz*qw - - row0 = np.stack((1-2*yy-2*zz, 2*xy-2*zw, 2*xz+2*yw), axis=-1) - row1 = np.stack((2*xy+2*zw, 1-2*xx-2*zz, 2*yz-2*xw), axis=-1) - row2 = np.stack((2*xz-2*yw, 2*yz+2*xw, 1-2*xx-2*yy), axis=-1) - matrix = np.stack((row0, row1, row2), axis=1) - - return matrix - - -def random_rotation_benchmark(n): - """A TF wrapper for random_rotation_benchmark_np().""" - mat = tf.py_func( - func=lambda t: np.float32(random_rotation_benchmark_np(t)), - inp=[n], - Tout=tf.float32, - stateful=True) - return tf.reshape(mat, (n, 3, 3)) - - -def random_rotation(n): - """Sample rotations from a uniform distribution on SO(3).""" - mat = tf.py_func( - func=lambda t: np.float32(special_ortho_group.rvs(3, size=t)), - inp=[n], - Tout=tf.float32, - stateful=True) - return tf.reshape(mat, (n, 3, 3)) - - -def symmetric_orthogonalization(x): - """Maps 9D input vectors onto SO(3) via symmetric orthogonalization.""" - # Innner dimensions of the input should be 3x3 matrices. - m = tf.reshape(x, (-1, 3, 3)) - _, u, v = tf.svd(m) - det = tf.linalg.det(tf.matmul(u, v, transpose_b=True)) - r = tf.matmul( - tf.concat([u[:, :, :-1], u[:, :, -1:] * tf.reshape(det, [-1, 1, 1])], 2), - v, transpose_b=True) - return r - - -def gs_orthogonalization(p6): - """Gram-Schmidt orthogonalization from 6D input.""" - # Input should be [batch_size, 6] - x = p6[:, 0:3] - y = p6[:, 3:6] - xn = tf.math.l2_normalize(x, axis=-1) - z = tf.linalg.cross(xn, y) - zn = tf.math.l2_normalize(z, axis=-1) - y = tf.linalg.cross(zn, xn) - r = tf.stack([xn, y, zn], -1) - return r -- Gitee From a20b2d2f19adf9608fa734bb8637573e6d055e45 Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Mon, 16 May 2022 15:16:09 +0000 Subject: [PATCH 49/88] =?UTF-8?q?=E5=88=A0=E9=99=A4=E6=96=87=E4=BB=B6=20Te?= =?UTF-8?q?nsorFlow/contrib/cv/SVD=5FID2019=5Ffor=5FTensorflow/main=5Fpoin?= =?UTF-8?q?t=5Fcloud.py?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../main_point_cloud.py | 419 ------------------ 1 file changed, 419 deletions(-) delete mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/main_point_cloud.py diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/main_point_cloud.py b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/main_point_cloud.py deleted file mode 100644 index a3b4e9a22..000000000 --- a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/main_point_cloud.py +++ /dev/null @@ -1,419 +0,0 @@ -# coding=utf-8 -# Copyright 2021 The Google Research Authors. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""Training and evaluation for the point cloud alignment experiment.""" -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import utils -from absl import flags -import numpy as np -import tensorflow as tf -import glob -import pathlib -import datetime -from npu_bridge.npu_init import * -from npu_bridge.estimator.npu.npu_estimator import NPUEstimatorSpec -from npu_bridge.estimator.npu.npu_estimator import NPUEstimator -from npu_bridge.estimator.npu.npu_config import NPURunConfig -#from npu_bridge.estimator.npu.npu_config import ProfilingConfig -import os -FLAGS = tf.app.flags.FLAGS - -# General flags. -flags.DEFINE_string('method', 'svd', - 'Specifies the method to use for predicting rotations. ' - 'Choices are "svd", "svd-inf", or "gs".') -flags.DEFINE_string('checkpoint_dir', '', - 'Locations for checkpoints, summaries, etc.') -flags.DEFINE_integer('train_steps', 2600000, 'Number of training iterations.') -flags.DEFINE_integer('save_checkpoints_steps', 10000, - 'How often to save checkpoints') -flags.DEFINE_integer('log_step_count', 500, 'How often to log the step count') -flags.DEFINE_integer('save_summaries_steps', 5000, - 'How often to save summaries.') -flags.DEFINE_float('learning_rate', 1e-5, 'Learning rate') -flags.DEFINE_boolean('lr_decay', False, 'Decay the learning rate if True.') -flags.DEFINE_integer('lr_decay_steps', 35000, - 'Learning rate decays steps.') -flags.DEFINE_float('lr_decay_rate', 0.95, - 'Learning rate decays rate.') -flags.DEFINE_boolean('predict_all_test', False, - 'If true, runs an eval job on latest checkpoint and ' - 'prints the error for each input.') -flags.DEFINE_integer('eval_examples', 39900, 'Number of test examples.') -flags.DEFINE_boolean('print_variable_names', False, - 'Print model variable names.') - -# Flags only used in the point cloud alignment experiment. -flags.DEFINE_integer('num_train_augmentations', 10, - 'Number of random rotations for augmenting each input ' - 'point cloud.') -flags.DEFINE_string('pt_cloud_train_files', '', - 'Expression matching all training point files, e.g. ' - '/path/to/files/pc_plane/points/*.pts') -flags.DEFINE_string('pt_cloud_test_files','', - 'Expression matching all modified test point files, e.g. ' - '/path/to/files/pc_plane/points_test/*.pts') -flags.DEFINE_boolean('random_rotation_axang', True, - 'If true, samples random rotations using the method ' - 'from the original benchmark code. Otherwise samples ' - 'by Haar measure.') - - -def pt_features(batch_pts): - """Input shape: [B, N, 3], output shape: [B, 1024].""" - with tf.variable_scope('ptenc', reuse=tf.AUTO_REUSE): - f1 = tf.layers.conv1d(inputs=batch_pts, filters=64, kernel_size=1) - f1 = tf.nn.leaky_relu(f1) - f2 = tf.layers.conv1d(inputs=f1, filters=128, kernel_size=1) - f2 = tf.nn.leaky_relu(f2) - f3 = tf.layers.conv1d(inputs=f2, filters=1024, kernel_size=1) - f = tf.reduce_max(f3, axis=1, keep_dims=False) - return f - - -def regress_from_features(batch_features, out_dim): - """Regress to a rotation representation from point cloud encodings. - - In Zhou et al, CVPR19, the paper describes this regression network as an MLP - mapping 2048->512->512->out_dim, but the associated code implements it with - one less layer: 2048->512->out_dim. We mimic the code. - - Args: - batch_features: [batch_size, in_dim]. - out_dim: desired output dimensionality. - - Returns: - A [batch_size, out_dim] tensor. - """ - f1 = tf.layers.dense(batch_features, 512) - f1 = tf.nn.leaky_relu(f1) - f2 = tf.layers.dense(f1, out_dim) - return f2 - - -def net_point_cloud(points1, points2, mode): - """Predict a relative rotation given two point clouds. - - Args: - points1: [batch_size, N, 3] float tensor. - points2: [batch_size, N, 3] float tensor. - mode: tf.estimator.ModeKeys. - - Returns: - [batch_size, 3, 3] matrices. - """ - f1 = pt_features(points1) - f2 = pt_features(points2) - f = tf.concat([f1, f2], axis=-1) - - if FLAGS.method == 'svd': - p = regress_from_features(f, 9) - return utils.symmetric_orthogonalization(p) - - if FLAGS.method == 'svd-inf': - p = regress_from_features(f, 9) - if mode == tf.estimator.ModeKeys.TRAIN: - return tf.reshape(p, (-1, 3, 3)) - else: - return utils.symmetric_orthogonalization(p) - - if FLAGS.method == 'gs': - p = regress_from_features(f, 6) - return utils.gs_orthogonalization(p) - - -def model_fn(features, labels, mode, params): - """The model_fn used to construct the tf.Estimator.""" - del labels, params # Unused. - if mode == tf.estimator.ModeKeys.TRAIN: - # Training data has point cloud of size [1, N, 3] and random rotations - # of size [1, FLAGS.num_train_augmentations, 3, 3] - rot = features['rot'][0] - num_rot = FLAGS.num_train_augmentations - batch_pts1 = tf.tile(features['data'], [num_rot, 1, 1]) - #batch_pts1 = tf.reshape(batch_pts1,(num_rot,-1,3)) - # In this experiment it does not matter if we pre or post-multiply the - # rotation as long as we are consistent between training and eval. - batch_pts2 = tf.matmul(batch_pts1, rot) # post-multiplying! - else: - # Test data has point cloud of size [1, N, 3] and a single random - # rotation of size [1, 3, 3] - batch_pts1 = features['data'] - rot = features['rot'] - batch_pts2 = tf.matmul(batch_pts1, rot) - rot = tf.reshape(rot, (-1, 3, 3)) - - # Predict the rotation. - r = net_point_cloud(batch_pts1, batch_pts2, mode) - - # Compute the loss. - loss = tf.nn.l2_loss(rot - r) - - # Compute the relative angle in radians. - theta = utils.relative_angle(r, rot) - - # Mean angle error over the batch. - mean_theta = tf.reduce_mean(theta) - mean_theta_deg = mean_theta * 180.0 / np.pi - - # Train, eval, or predict depending on mode. - if mode == tf.estimator.ModeKeys.TRAIN: - tf.summary.scalar('train/loss', loss) - tf.summary.scalar('train/theta', mean_theta_deg) - global_step = tf.train.get_or_create_global_step() - - if FLAGS.lr_decay: - learning_rate = tf.train.exponential_decay( - FLAGS.learning_rate, - global_step, - FLAGS.lr_decay_steps, - FLAGS.lr_decay_rate) - else: - learning_rate = FLAGS.learning_rate - - tf.summary.scalar('train/learning_rate', learning_rate) - optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate) - update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) - with tf.control_dependencies(update_ops): - train_op = optimizer.minimize(loss, global_step=global_step) - #train_op = util.set_graph_exec_config(train_op, dynamic_input=True,dynamic_graph_execute_mode="lazy_recompile") - - return NPUEstimatorSpec( - mode=mode, - loss=loss, - train_op=train_op) - - if mode == tf.estimator.ModeKeys.EVAL: - if FLAGS.predict_all_test: - print_error_op = tf.print('error:', mean_theta_deg) - with tf.control_dependencies([print_error_op]): - eval_metric_ops = { - 'mean_degree_err': tf.metrics.mean(mean_theta_deg), - } - else: - eval_metric_ops = { - 'mean_degree_err': tf.metrics.mean(mean_theta_deg), - } - - return NPUEstimatorSpec( - mode=mode, - loss=loss, - eval_metric_ops=eval_metric_ops) - - if mode == tf.estimator.ModeKeys.PREDICT: - pred = {'error': mean_theta_deg} - return NPUEstimatorSpec( - mode=mode, - predictions=pred) - - -def train_input_fn(): - """Generate training data iterator from the .pts files.""" - def _file_to_matrix(pts_path): - """Read Nx3 point cloud from a .pts file.""" - file_buffer = tf.read_file(pts_path) - lines = tf.string_split([file_buffer], delimiter='\n') - lines1 = tf.string_split(lines.values, delimiter='\r') - values = tf.stack(tf.decode_csv(lines1.values, - record_defaults=[[0.0], [0.0], [0.0]], - field_delim=' ')) - values = tf.transpose(values) # 3xN --> Nx3. - values = values[:tf.shape(values)[0] // 2, :] - - # modified by Lyoung - diff_num = 1500-tf.shape(values)[0] - repeat_pts = tf.tile(tf.reshape(values[1, :], (1, -1)), [diff_num, 1]) - values = tf.concat([values,repeat_pts], axis=0) - # The experiment code in - # github.com/papagina/RotationContinuity/.../shapenet/code/train_pointnet.py - # only used the first half of the points in each file. - return values - - def _random_rotation(pts): - """Attach N random rotations to a point cloud.""" - if FLAGS.random_rotation_axang: - rotations = utils.random_rotation_benchmark(FLAGS.num_train_augmentations) - else: - rotations = utils.random_rotation(FLAGS.num_train_augmentations) - return pts,rotations - - pts_paths = tf.gfile.Glob(FLAGS.pt_cloud_train_files) - dataset = tf.data.Dataset.from_tensor_slices(pts_paths) - dataset = dataset.map(_file_to_matrix) - dataset = dataset.cache() # Comment out if memory cannot hold all the data. - dataset = dataset.shuffle(buffer_size=50, reshuffle_each_iteration=True) - dataset = dataset.repeat() - dataset = dataset.map(_random_rotation) - dataset = dataset.batch(1) - #dataset = dataset.prefetch(buffer_size=buffer_size) - iterator = tf.data.make_one_shot_iterator(dataset) - batch_data, batch_rot = iterator.get_next() - features_dict = {'data': batch_data, 'rot': batch_rot} - batch_size = tf.shape(batch_data)[0] - batch_labels_dummy = tf.zeros(shape=(batch_size, 1)) - return (features_dict, batch_labels_dummy) - - -def eval_input_fn(): - """Generate test data from *modified* .pts files. - - See README and comments below for details on how the data is modified. - - Returns: - A tuple of features and associated labels. - """ - def _file_to_matrix(pts_path): - """Read Nx3 point cloud and 3x3 rotation matrix from a .pts file. - - The test data is a modified version of the original files. For each .pts - file we have (1) added a 3x3 rotation matrix for testing, and (2) removed - the second half of the point cloud since it is not used at all. - - Args: - pts_path: path to a .pts file. - - Returns: - A Nx3 point cloud. - A 3x3 rotation matrix. - """ - file_buffer = tf.read_file(pts_path) - lines = tf.string_split([file_buffer], delimiter='\n') - lines1 = tf.string_split(lines.values,delimiter='\r') - values = tf.stack(tf.decode_csv(lines1.values, - record_defaults=[[0.0], [0.0], [0.0]],field_delim=' ')) - values = tf.transpose(values)# 3xN --> Nx3. - - #modified by Lyoung - diff_num = 1414-tf.shape(values)[0] - repeat_pts = tf.tile(tf.reshape(values[4, :], (1, -1)), [diff_num, 1]) - values = tf.concat([values, repeat_pts], axis=0) - # First three rows are the rotation matrix, remaining rows the point cloud. - rot = values[:3, :] - - return values[4:, :], rot - - pts_paths = tf.io.gfile.glob(FLAGS.pt_cloud_test_files) - dataset = tf.data.Dataset.from_tensor_slices(pts_paths) - dataset = dataset.map(_file_to_matrix) - dataset = dataset.batch(1) - iterator = tf.data.make_one_shot_iterator(dataset) - batch_data, batch_rot = iterator.get_next() - - features_dict = {'data': batch_data, 'rot': batch_rot} - batch_size = tf.shape(batch_data)[0] - batch_labels_dummy = tf.zeros(shape=(batch_size, 1)) - return (features_dict, batch_labels_dummy) - - -def print_variable_names(): - """Print variable names in a model.""" - params = {'dummy': 0} - estimator = NPUEstimator( - model_fn=model_fn, - model_dir=FLAGS.checkpoint_dir, - params=params) - - names = estimator.get_variable_names() - for name in names: - print(name) - - -def predict_all_test(): - """Print error statistics for the test dataset.""" - - config = NPURunConfig( - save_summary_steps=None, - save_checkpoints_steps=None, - log_step_count_steps=None, - keep_checkpoint_max=None) - - params = {'dummy': 0} - estimator = NPUEstimator( - model_fn=model_fn, - model_dir=FLAGS.checkpoint_dir, - params=params, - config=config) - evals = estimator.predict(input_fn=eval_input_fn, yield_single_examples=True) - - # Print error statistics. - - all_errors = [x['error'] for x in evals] - errors = np.array(all_errors) - print('Evaluated %d examples'%np.size(errors)) - print('Mean error: %f degrees',np.mean(errors)) - print('Median error: %f degrees', np.median(errors)) - print('Std: %f degrees', np.std(errors)) - sorted_errors = np.sort(errors) - n = np.size(sorted_errors) - print('\nPercentiles:') - for perc in range(1, 101): - index = np.int32(np.float32(n * perc) / 100.0) - 1 - print('%3d%%: %f'%(perc, sorted_errors[index])) - - -def train_and_eval(): - """Train and evaluate a model.""" - save_summary_steps = FLAGS.save_summaries_steps - save_checkpoints_steps = FLAGS.save_checkpoints_steps - log_step_count = FLAGS.log_step_count - - config = NPURunConfig( - save_summary_steps=save_summary_steps, - save_checkpoints_steps=save_checkpoints_steps, - log_step_count_steps=log_step_count, - keep_checkpoint_max=None, - precision_mode = "all_mix_precision") - - params = {'dummy': 0} - estimator = NPUEstimator( - model_fn=model_fn, - model_dir=FLAGS.checkpoint_dir, - config=config, - params=params) - - - train_spec = tf.estimator.TrainSpec( - input_fn=train_input_fn, - max_steps=FLAGS.train_steps) - - - eval_spec = tf.estimator.EvalSpec(input_fn=eval_input_fn, - start_delay_secs=60, - steps=FLAGS.eval_examples, - throttle_secs=60) - - - - tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec) - - -def main(argv=None): # pylint: disable=unused-argument - - if FLAGS.print_variable_names: - print_variable_names() - return - - if FLAGS.predict_all_test: - predict_all_test() - else: - train_and_eval() - - -if __name__ == '__main__': - - tf.app.run() -- Gitee From f8049838e84b8cdb3f067905e183a57fbd61f77e Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Mon, 16 May 2022 15:16:12 +0000 Subject: [PATCH 50/88] =?UTF-8?q?=E5=88=A0=E9=99=A4=E6=96=87=E4=BB=B6=20Te?= =?UTF-8?q?nsorFlow/contrib/cv/SVD=5FID2019=5Ffor=5FTensorflow/train=5Fful?= =?UTF-8?q?l=5F1p.sh?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../train_full_1p.sh | 209 ------------------ 1 file changed, 209 deletions(-) delete mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/train_full_1p.sh diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/train_full_1p.sh b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/train_full_1p.sh deleted file mode 100644 index edd5eed9a..000000000 --- a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/train_full_1p.sh +++ /dev/null @@ -1,209 +0,0 @@ -#!/bin/bash - -########################################################## -#########第3行 至 100行，请一定不要、不要、不要修改########## -#########第3行 至 100行，请一定不要、不要、不要修改########## -#########第3行 至 100行，请一定不要、不要、不要修改########## -########################################################## -# shell脚本所在路径 -cur_path=`echo $(cd $(dirname $0);pwd)` - -# 判断当前shell是否是performance -perf_flag=`echo $0 | grep performance | wc -l` - -# 当前执行网络的名称 -Network=`echo $(cd $(dirname $0);pwd) | awk -F"/" '{print $(NF-1)}'` - -export RANK_SIZE=1 -export RANK_ID=0 -export JOB_ID=10087 - -# 路径参数初始化 -data_path="" -output_path="" - -# 帮助信息，不需要修改 -if [[ $1 == --help || $1 == -h ]];then - echo"usage:./train_performance_1P.sh " - echo " " - echo "parameter explain: - --data_path # dataset of training - --output_path # output of training - --train_steps # max_step for training - --train_epochs # max_epoch for training - --batch_size # batch size - -h/--help show help message - " - exit 1 -fi - -# 参数校验，不需要修改 -for para in $* -do - if [[ $para == --data_path* ]];then - data_path=`echo ${para#*=}` - elif [[ $para == --output_path* ]];then - output_path=`echo ${para#*=}` - elif [[ $para == --train_steps* ]];then - train_steps=`echo ${para#*=}` - elif [[ $para == --train_epochs* ]];then - train_epochs=`echo ${para#*=}` - elif [[ $para == --batch_size* ]];then - batch_size=`echo ${para#*=}` - fi -done - -# 校验是否传入data_path,不需要修改 -if [[ $data_path == "" ]];then - echo "[Error] para \"data_path\" must be config" - exit 1 -fi - -# 校验是否传入output_path,不需要修改 -if [[ $output_path == "" ]];then - output_path="./test/output/${ASCEND_DEVICE_ID}" -fi - -# 设置打屏日志文件名，请保留，文件名为${print_log} -print_log="./test/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log" -etp_flag=${etp_running_flag} -if [ x"${etp_flag}" != xtrue ]; -then - echo "running without etp..." - print_log_name=`ls /home/ma-user/modelarts/log/ | grep proc-rank` - print_log="/home/ma-user/modelarts/log/${print_log_name}" -fi -echo ${print_log} - -CaseName="" -function get_casename() -{ - if [ x"${perf_flag}" = x1 ]; - then - CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'perf' - else - CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'acc' - fi -} - -# 跳转到code目录 -cd ${cur_path}/../ -rm -rf ./test/output/${ASCEND_DEVICE_ID} -mkdir -p ./test/output/${ASCEND_DEVICE_ID} - -# 训练开始时间记录，不需要修改 -start_time=$(date +%s) -########################################################## -#########第3行 至 100行，请一定不要、不要、不要修改########## -#########第3行 至 100行，请一定不要、不要、不要修改########## -#########第3行 至 100行，请一定不要、不要、不要修改########## -########################################################## - -#========================================================= -#========================================================= -#========训练执行命令，需要根据您的网络进行修改============== -#========================================================= -#========================================================= -# 您的训练数据集在${data_path}路径下，请直接使用这个变量获取 -# 您的训练输出目录在${output_path}路径下，请直接使用这个变量获取 -# 您的其他基础参数，可以自定义增加，但是batch_size请保留，并且设置正确的值 -batch_size=1 - -echo "------------------***PRINT DATA PATH***-----------------" -train_data="points/*.pts" -test_data="test_points_modified/*.pts" - -trainData_Path="$data_path$train_data" -testData_Path="$data_path$test_data" - - -if [ x"${modelarts_flag}" != x ]; -then - python3.7 /home/ma-user/modelarts/user-job-dir/code/main_point_cloud_boostPerf.py \ - --method=svd \ - --checkpoint_dir=${output_path} \ - --log_step_count=200 \ - --save_summaries_steps=25000 \ - --pt_cloud_train_files=${trainData_Path} \ - --pt_cloud_test_files=${testData_Path} \ - --train_steps=2600000 \ - --save_checkpoints_steps=100000 \ - --eval_examples=39900 -else - python3.7 /home/ma-user/modelarts/user-job-dir/code/main_point_cloud_boostPerf.py \ - --method=svd \ - --checkpoint_dir=${output_path} \ - --log_step_count=200 \ - --save_summaries_steps=25000 \ - --pt_cloud_train_files=${trainData_Path}\ - --pt_cloud_test_files=${testData_Path} \ - --train_steps=2600000 \ - --save_checkpoints_steps=100000 \ - --eval_examples=39900 > ${print_log} -fi - - -# 性能相关数据计算 -StepTime=`grep "sec/step :" ${print_log} | tail -n 10 | awk '{print $NF}' | awk '{sum+=$1} END {print sum/NR}'` -FPS=`awk 'BEGIN{ "%.2f\n", '${batch_size}'/'${StepTime}'}'` - -# 精度相关数据计算 -train_accuracy=`grep "Final Accuracy accuracy" ${print_log} | awk '{print $NF}'` -# 提取所有loss打印信息 -grep "loss :" ${print_log} | awk -F ":" '{print $4}' | awk -F "-" '{print $1}' > ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt - - -########################################################### -#########后面的所有内容请不要修改########################### -#########后面的所有内容请不要修改########################### -#########后面的所有内容请不要修改########################### -########################################################### - -# 判断本次执行是否正确使用Ascend NPU -use_npu_flag=`grep "The model has been compiled on the Ascend AI processor" ${print_log} | wc -l` -if [ x"${use_npu_flag}" == x0 ]; -then - echo "------------------ ERROR NOTICE START ------------------" - echo "ERROR, your task haven't used Ascend NPU, please check your npu Migration." - echo "------------------ ERROR NOTICE END------------------" -else - echo "------------------ INFO NOTICE START------------------" - echo "INFO, your task have used Ascend NPU, please check your result." - echo "------------------ INFO NOTICE END------------------" -fi - -# 获取最终的casename，请保留，case文件名为${CaseName} -get_casename - -# 重命名loss文件 -if [ -f ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ]; -then - mv ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ./test/output/${ASCEND_DEVICE_ID}/${CaseName}_loss.txt -fi - -# 训练端到端耗时 -end_time=$(date +%s) -e2e_time=$(( $end_time - $start_time )) - -echo "------------------ Final result ------------------" -# 输出性能FPS/单step耗时/端到端耗时 -echo "Final Performance images/sec : $FPS" -echo "Final Performance sec/step : $StepTime" -echo "E2E Training Duration sec : $e2e_time" - -# 输出训练精度 -echo "Final Train Accuracy : ${train_accuracy}" - -# 最后一个迭代loss值，不需要修改 -ActualLoss=(`awk 'END {print $NF}' $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}_loss.txt`) - -#关键信息打印到${CaseName}.log中，不需要修改 -echo "Network = ${Network}" > $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "RankSize = ${RANK_SIZE}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "BatchSize = ${batch_size}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "DeviceType = `uname -m`" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "ActualFPS = ${FPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "TrainingTime = ${StepTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log \ No newline at end of file -- Gitee From f744fab6700d5594b5c03787d0709021318c513b Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Mon, 16 May 2022 15:18:58 +0000 Subject: [PATCH 51/88] =?UTF-8?q?=E6=9B=B4=E6=96=B0=E4=BA=86=E7=9B=B8?= =?UTF-8?q?=E5=85=B3=E9=94=99=E8=AF=AF=EF=BC=8C=E5=B9=B6=E4=B8=94=E4=B8=8A?= =?UTF-8?q?=E4=BC=A0=E4=BA=86=E5=85=B3=E4=BA=8E=E8=BF=90=E8=A1=8C=E6=80=A7?= =?UTF-8?q?=E8=83=BD=E6=96=B9=E9=9D=A2=E7=9A=84=E4=BB=A3=E7=A0=81?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../main_point_cloud_boostPerf.py | 418 ++++++++++++++++++ .../modelarts_entry_perf.py | 36 ++ .../train_full_1p.sh | 209 +++++++++ .../train_performance_1p.sh | 209 +++++++++ 4 files changed, 872 insertions(+) create mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/main_point_cloud_boostPerf.py create mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/modelarts_entry_perf.py create mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/train_full_1p.sh create mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/train_performance_1p.sh diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/main_point_cloud_boostPerf.py b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/main_point_cloud_boostPerf.py new file mode 100644 index 000000000..ff8e5563b --- /dev/null +++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/main_point_cloud_boostPerf.py @@ -0,0 +1,418 @@ +# coding=utf-8 +# Copyright 2021 The Google Research Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Training and evaluation for the point cloud alignment experiment.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import utils +from absl import flags +import numpy as np +import tensorflow as tf +import glob +import pathlib +import datetime +from npu_bridge.npu_init import * +from npu_bridge.estimator.npu.npu_estimator import NPUEstimatorSpec +from npu_bridge.estimator.npu.npu_estimator import NPUEstimator +from npu_bridge.estimator.npu.npu_config import NPURunConfig +import os +FLAGS = tf.app.flags.FLAGS + +# General flags. +flags.DEFINE_string('method', 'svd', + 'Specifies the method to use for predicting rotations. ' + 'Choices are "svd", "svd-inf", or "gs".') +flags.DEFINE_string('checkpoint_dir', '', + 'Locations for checkpoints, summaries, etc.') +flags.DEFINE_integer('train_steps', 2600000, 'Number of training iterations.') +flags.DEFINE_integer('save_checkpoints_steps', 10000, + 'How often to save checkpoints') +flags.DEFINE_integer('log_step_count', 500, 'How often to log the step count') +flags.DEFINE_integer('save_summaries_steps', 5000, + 'How often to save summaries.') +flags.DEFINE_float('learning_rate', 1e-5, 'Learning rate') +flags.DEFINE_boolean('lr_decay', False, 'Decay the learning rate if True.') +flags.DEFINE_integer('lr_decay_steps', 35000, + 'Learning rate decays steps.') +flags.DEFINE_float('lr_decay_rate', 0.95, + 'Learning rate decays rate.') +flags.DEFINE_boolean('predict_all_test', False, + 'If true, runs an eval job on latest checkpoint and ' + 'prints the error for each input.') +flags.DEFINE_integer('eval_examples', 39900, 'Number of test examples.') +flags.DEFINE_boolean('print_variable_names', False, + 'Print model variable names.') + +# Flags only used in the point cloud alignment experiment. +flags.DEFINE_integer('num_train_augmentations', 10, + 'Number of random rotations for augmenting each input ' + 'point cloud.') +flags.DEFINE_string('pt_cloud_train_files', '', + 'Expression matching all training point files, e.g. ' + '/path/to/files/pc_plane/points/*.pts') +flags.DEFINE_string('pt_cloud_test_files','', + 'Expression matching all modified test point files, e.g. ' + '/path/to/files/pc_plane/points_test/*.pts') +flags.DEFINE_boolean('random_rotation_axang', True, + 'If true, samples random rotations using the method ' + 'from the original benchmark code. Otherwise samples ' + 'by Haar measure.') + + +def pt_features(batch_pts): + """Input shape: [B, N, 3], output shape: [B, 1024].""" + with tf.variable_scope('ptenc', reuse=tf.AUTO_REUSE): + f1 = tf.layers.conv1d(inputs=batch_pts, filters=64, kernel_size=1) + f1 = tf.nn.leaky_relu(f1) + f2 = tf.layers.conv1d(inputs=f1, filters=128, kernel_size=1) + f2 = tf.nn.leaky_relu(f2) + f3 = tf.layers.conv1d(inputs=f2, filters=1024, kernel_size=1) + f = tf.reduce_max(f3, axis=1, keep_dims=False) + return f + + +def regress_from_features(batch_features, out_dim): + """Regress to a rotation representation from point cloud encodings. + + In Zhou et al, CVPR19, the paper describes this regression network as an MLP + mapping 2048->512->512->out_dim, but the associated code implements it with + one less layer: 2048->512->out_dim. We mimic the code. + + Args: + batch_features: [batch_size, in_dim]. + out_dim: desired output dimensionality. + + Returns: + A [batch_size, out_dim] tensor. + """ + f1 = tf.layers.dense(batch_features, 512) + f1 = tf.nn.leaky_relu(f1) + f2 = tf.layers.dense(f1, out_dim) + return f2 + + +def net_point_cloud(points1, points2, mode): + """Predict a relative rotation given two point clouds. + + Args: + points1: [batch_size, N, 3] float tensor. + points2: [batch_size, N, 3] float tensor. + mode: tf.estimator.ModeKeys. + + Returns: + [batch_size, 3, 3] matrices. + """ + f1 = pt_features(points1) + f2 = pt_features(points2) + f = tf.concat([f1, f2], axis=-1) + + if FLAGS.method == 'svd': + p = regress_from_features(f, 9) + return utils.symmetric_orthogonalization(p) + + if FLAGS.method == 'svd-inf': + p = regress_from_features(f, 9) + if mode == tf.estimator.ModeKeys.TRAIN: + return tf.reshape(p, (-1, 3, 3)) + else: + return utils.symmetric_orthogonalization(p) + + if FLAGS.method == 'gs': + p = regress_from_features(f, 6) + return utils.gs_orthogonalization(p) + + +def model_fn(features, labels, mode, params): + """The model_fn used to construct the tf.Estimator.""" + del labels, params # Unused. + if mode == tf.estimator.ModeKeys.TRAIN: + # Training data has point cloud of size [1, N, 3] and random rotations + # of size [1, FLAGS.num_train_augmentations, 3, 3] + rot = features['rot'][0] + num_rot = FLAGS.num_train_augmentations + batch_pts1 = tf.tile(features['data'], [num_rot, 1, 1]) + # In this experiment it does not matter if we pre or post-multiply the + # rotation as long as we are consistent between training and eval. + batch_pts2 = tf.matmul(batch_pts1, rot) # post-multiplying! + else: + # Test data has point cloud of size [1, N, 3] and a single random + # rotation of size [1, 3, 3] + batch_pts1 = features['data'] + rot = features['rot'] + batch_pts2 = tf.matmul(batch_pts1, rot) + rot = tf.reshape(rot, (-1, 3, 3)) + + # Predict the rotation. + r = net_point_cloud(batch_pts1, batch_pts2, mode) + + # Compute the loss. + loss = tf.nn.l2_loss(rot - r) + + # Compute the relative angle in radians. + theta = utils.relative_angle(r, rot) + + # Mean angle error over the batch. + mean_theta = tf.reduce_mean(theta) + mean_theta_deg = mean_theta * 180.0 / np.pi + + # Train, eval, or predict depending on mode. + if mode == tf.estimator.ModeKeys.TRAIN: + tf.summary.scalar('train/loss', loss) + tf.summary.scalar('train/theta', mean_theta_deg) + global_step = tf.train.get_or_create_global_step() + + if FLAGS.lr_decay: + learning_rate = tf.train.exponential_decay( + FLAGS.learning_rate, + global_step, + FLAGS.lr_decay_steps, + FLAGS.lr_decay_rate) + else: + learning_rate = FLAGS.learning_rate + + tf.summary.scalar('train/learning_rate', learning_rate) + optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate) + update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) + with tf.control_dependencies(update_ops): + train_op = optimizer.minimize(loss, global_step=global_step) + #train_op = util.set_graph_exec_config(train_op, dynamic_input=True,dynamic_graph_execute_mode="lazy_recompile") + + return NPUEstimatorSpec( + mode=mode, + loss=loss, + train_op=train_op) + + if mode == tf.estimator.ModeKeys.EVAL: + if FLAGS.predict_all_test: + print_error_op = tf.print('error:', mean_theta_deg) + with tf.control_dependencies([print_error_op]): + eval_metric_ops = { + 'mean_degree_err': tf.metrics.mean(mean_theta_deg), + } + else: + eval_metric_ops = { + 'mean_degree_err': tf.metrics.mean(mean_theta_deg), + } + + return NPUEstimatorSpec( + mode=mode, + loss=loss, + eval_metric_ops=eval_metric_ops) + + if mode == tf.estimator.ModeKeys.PREDICT: + pred = {'error': mean_theta_deg} + return NPUEstimatorSpec( + mode=mode, + predictions=pred) + + +def train_input_fn(): + """Generate training data iterator from the .pts files.""" + def _file_to_matrix(pts_path): + """Read Nx3 point cloud from a .pts file.""" + file_buffer = tf.read_file(pts_path) + lines = tf.string_split([file_buffer], delimiter='\n') + lines1 = tf.string_split(lines.values, delimiter='\r') + values = tf.stack(tf.decode_csv(lines1.values, + record_defaults=[[0.0], [0.0], [0.0]], + field_delim=' ')) + values = tf.transpose(values) # 3xN --> Nx3. + values = values[:tf.shape(values)[0] // 2, :] + + # modified by Lyoung + diff_num = 1500-tf.shape(values)[0] + repeat_pts = tf.tile(tf.reshape(values[1, :], (1, -1)), [diff_num, 1]) + values = tf.concat([values,repeat_pts], axis=0) + # The experiment code in + # github.com/papagina/RotationContinuity/.../shapenet/code/train_pointnet.py + # only used the first half of the points in each file. + return values + + def _random_rotation(pts): + """Attach N random rotations to a point cloud.""" + if FLAGS.random_rotation_axang: + rotations = utils.random_rotation_benchmark(FLAGS.num_train_augmentations) + else: + rotations = utils.random_rotation(FLAGS.num_train_augmentations) + return pts,rotations + + pts_paths = tf.gfile.Glob(FLAGS.pt_cloud_train_files) + dataset = tf.data.Dataset.from_tensor_slices(pts_paths) + dataset = dataset.map(_file_to_matrix) + dataset = dataset.cache() # Comment out if memory cannot hold all the data. + dataset = dataset.shuffle(buffer_size=50, reshuffle_each_iteration=True) + dataset = dataset.repeat() + dataset = dataset.map(_random_rotation) + dataset = dataset.batch(1) + + iterator = tf.data.make_one_shot_iterator(dataset) + batch_data, batch_rot = iterator.get_next() + features_dict = {'data': batch_data, 'rot': batch_rot} + batch_size = tf.shape(batch_data)[0] + batch_labels_dummy = tf.zeros(shape=(batch_size, 1)) + return (features_dict, batch_labels_dummy) + + +def eval_input_fn(): + """Generate test data from *modified* .pts files. + + See README and comments below for details on how the data is modified. + + Returns: + A tuple of features and associated labels. + """ + def _file_to_matrix(pts_path): + """Read Nx3 point cloud and 3x3 rotation matrix from a .pts file. + + The test data is a modified version of the original files. For each .pts + file we have (1) added a 3x3 rotation matrix for testing, and (2) removed + the second half of the point cloud since it is not used at all. + + Args: + pts_path: path to a .pts file. + + Returns: + A Nx3 point cloud. + A 3x3 rotation matrix. + """ + file_buffer = tf.read_file(pts_path) + lines = tf.string_split([file_buffer], delimiter='\n') + lines1 = tf.string_split(lines.values,delimiter='\r') + values = tf.stack(tf.decode_csv(lines1.values, + record_defaults=[[0.0], [0.0], [0.0]],field_delim=' ')) + values = tf.transpose(values)# 3xN --> Nx3. + + #modified by Lyoung + diff_num = 1414-tf.shape(values)[0] + repeat_pts = tf.tile(tf.reshape(values[4, :], (1, -1)), [diff_num, 1]) + values = tf.concat([values, repeat_pts], axis=0) + # First three rows are the rotation matrix, remaining rows the point cloud. + rot = values[:3, :] + + return values[4:, :], rot + + pts_paths = tf.io.gfile.glob(FLAGS.pt_cloud_test_files) + dataset = tf.data.Dataset.from_tensor_slices(pts_paths) + dataset = dataset.map(_file_to_matrix) + dataset = dataset.batch(1) + iterator = tf.data.make_one_shot_iterator(dataset) + batch_data, batch_rot = iterator.get_next() + + features_dict = {'data': batch_data, 'rot': batch_rot} + batch_size = tf.shape(batch_data)[0] + batch_labels_dummy = tf.zeros(shape=(batch_size, 1)) + return (features_dict, batch_labels_dummy) + + +def print_variable_names(): + """Print variable names in a model.""" + params = {'dummy': 0} + estimator = NPUEstimator( + model_fn=model_fn, + model_dir=FLAGS.checkpoint_dir, + params=params) + + names = estimator.get_variable_names() + for name in names: + print(name) + + +def predict_all_test(): + """Print error statistics for the test dataset.""" + + config = NPURunConfig( + save_summary_steps=None, + save_checkpoints_steps=None, + log_step_count_steps=None, + keep_checkpoint_max=None) + + params = {'dummy': 0} + estimator = NPUEstimator( + model_fn=model_fn, + model_dir=FLAGS.checkpoint_dir, + params=params, + config=config) + evals = estimator.predict(input_fn=eval_input_fn, yield_single_examples=True) + + # Print error statistics. + + all_errors = [x['error'] for x in evals] + errors = np.array(all_errors) + print('Evaluated %d examples'%np.size(errors)) + print('Mean error: %f degrees',np.mean(errors)) + print('Median error: %f degrees', np.median(errors)) + print('Std: %f degrees', np.std(errors)) + sorted_errors = np.sort(errors) + n = np.size(sorted_errors) + print('\nPercentiles:') + for perc in range(1, 101): + index = np.int32(np.float32(n * perc) / 100.0) - 1 + print('%3d%%: %f'%(perc, sorted_errors[index])) + + +def train_and_eval(): + """Train and evaluate a model.""" + save_summary_steps = FLAGS.save_summaries_steps + save_checkpoints_steps = FLAGS.save_checkpoints_steps + log_step_count = FLAGS.log_step_count + + config = NPURunConfig( + save_summary_steps=save_summary_steps, + save_checkpoints_steps=save_checkpoints_steps, + log_step_count_steps=log_step_count, + keep_checkpoint_max=None, + precision_mode = "allow_mix_precision") + + + params = {'dummy': 0} + estimator = NPUEstimator( + model_fn=model_fn, + model_dir=FLAGS.checkpoint_dir, + config=config, + params=params) + + + train_spec = tf.estimator.TrainSpec( + input_fn=train_input_fn, + max_steps=FLAGS.train_steps) + + + eval_spec = tf.estimator.EvalSpec(input_fn=eval_input_fn, + start_delay_secs=60, + steps=FLAGS.eval_examples, + throttle_secs=60) + + + + tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec) + + +def main(argv=None): # pylint: disable=unused-argument + + if FLAGS.print_variable_names: + print_variable_names() + return + + if FLAGS.predict_all_test: + predict_all_test() + else: + train_and_eval() + + +if __name__ == '__main__': + + tf.app.run() diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/modelarts_entry_perf.py b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/modelarts_entry_perf.py new file mode 100644 index 000000000..eb613e0f4 --- /dev/null +++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/modelarts_entry_perf.py @@ -0,0 +1,36 @@ +import os +import argparse +import sys + +# 解析输入参数data_url +parser = argparse.ArgumentParser() +parser.add_argument("--data_url", type=str, default="/home/ma-user/modelarts/inputs/data_url_0") +parser.add_argument("--train_url", type=str, default="/home/ma-user/modelarts/outputs/train_url_0/") +config = parser.parse_args() + +print("[CANN-Modelzoo] code_dir path is [%s]" % (sys.path[0])) +code_dir = sys.path[0] +os.chdir(code_dir) +print("[CANN-Modelzoo] work_dir path is [%s]" % (os.getcwd())) + +print("[CANN-Modelzoo] before train - list my run files:") +os.system("ls -al /usr/local/Ascend/ascend-toolkit/") + +print("[CANN-Modelzoo] before train - list my dataset files:") +os.system("ls -al %s" % config.data_url) + + +print("[CANN-Modelzoo] start run train shell") +# 设置sh文件格式为linux可执行 +os.system("dos2unix ./test/*") + +# 执行train_full_1p.sh或者train_performance_1p.sh，需要用户自己指定 +# full和performance的差异，performance只需要执行很少的step，控制在15分钟以内，主要关注性能FPS +os.system("bash /home/ma-user/modelarts/user-job-dir/code/train_performance_1p.sh --data_path=%s --output_path=%s " % (config.data_url, config.train_url)) + +print("[CANN-Modelzoo] finish run train shell") + +# 将当前执行目录所有文件拷贝到obs的output进行备份 +print("[CANN-Modelzoo] after train - list my output files:") +os.system("cp -r %s %s " % (code_dir, config.train_url)) +os.system("ls -al %s" % config.train_url) \ No newline at end of file diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/train_full_1p.sh b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/train_full_1p.sh new file mode 100644 index 000000000..edd5eed9a --- /dev/null +++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/train_full_1p.sh @@ -0,0 +1,209 @@ +#!/bin/bash + +########################################################## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +########################################################## +# shell脚本所在路径 +cur_path=`echo $(cd $(dirname $0);pwd)` + +# 判断当前shell是否是performance +perf_flag=`echo $0 | grep performance | wc -l` + +# 当前执行网络的名称 +Network=`echo $(cd $(dirname $0);pwd) | awk -F"/" '{print $(NF-1)}'` + +export RANK_SIZE=1 +export RANK_ID=0 +export JOB_ID=10087 + +# 路径参数初始化 +data_path="" +output_path="" + +# 帮助信息，不需要修改 +if [[ $1 == --help || $1 == -h ]];then + echo"usage:./train_performance_1P.sh " + echo " " + echo "parameter explain: + --data_path # dataset of training + --output_path # output of training + --train_steps # max_step for training + --train_epochs # max_epoch for training + --batch_size # batch size + -h/--help show help message + " + exit 1 +fi + +# 参数校验，不需要修改 +for para in $* +do + if [[ $para == --data_path* ]];then + data_path=`echo ${para#*=}` + elif [[ $para == --output_path* ]];then + output_path=`echo ${para#*=}` + elif [[ $para == --train_steps* ]];then + train_steps=`echo ${para#*=}` + elif [[ $para == --train_epochs* ]];then + train_epochs=`echo ${para#*=}` + elif [[ $para == --batch_size* ]];then + batch_size=`echo ${para#*=}` + fi +done + +# 校验是否传入data_path,不需要修改 +if [[ $data_path == "" ]];then + echo "[Error] para \"data_path\" must be config" + exit 1 +fi + +# 校验是否传入output_path,不需要修改 +if [[ $output_path == "" ]];then + output_path="./test/output/${ASCEND_DEVICE_ID}" +fi + +# 设置打屏日志文件名，请保留，文件名为${print_log} +print_log="./test/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log" +etp_flag=${etp_running_flag} +if [ x"${etp_flag}" != xtrue ]; +then + echo "running without etp..." + print_log_name=`ls /home/ma-user/modelarts/log/ | grep proc-rank` + print_log="/home/ma-user/modelarts/log/${print_log_name}" +fi +echo ${print_log} + +CaseName="" +function get_casename() +{ + if [ x"${perf_flag}" = x1 ]; + then + CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'perf' + else + CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'acc' + fi +} + +# 跳转到code目录 +cd ${cur_path}/../ +rm -rf ./test/output/${ASCEND_DEVICE_ID} +mkdir -p ./test/output/${ASCEND_DEVICE_ID} + +# 训练开始时间记录，不需要修改 +start_time=$(date +%s) +########################################################## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +########################################################## + +#========================================================= +#========================================================= +#========训练执行命令，需要根据您的网络进行修改============== +#========================================================= +#========================================================= +# 您的训练数据集在${data_path}路径下，请直接使用这个变量获取 +# 您的训练输出目录在${output_path}路径下，请直接使用这个变量获取 +# 您的其他基础参数，可以自定义增加，但是batch_size请保留，并且设置正确的值 +batch_size=1 + +echo "------------------***PRINT DATA PATH***-----------------" +train_data="points/*.pts" +test_data="test_points_modified/*.pts" + +trainData_Path="$data_path$train_data" +testData_Path="$data_path$test_data" + + +if [ x"${modelarts_flag}" != x ]; +then + python3.7 /home/ma-user/modelarts/user-job-dir/code/main_point_cloud_boostPerf.py \ + --method=svd \ + --checkpoint_dir=${output_path} \ + --log_step_count=200 \ + --save_summaries_steps=25000 \ + --pt_cloud_train_files=${trainData_Path} \ + --pt_cloud_test_files=${testData_Path} \ + --train_steps=2600000 \ + --save_checkpoints_steps=100000 \ + --eval_examples=39900 +else + python3.7 /home/ma-user/modelarts/user-job-dir/code/main_point_cloud_boostPerf.py \ + --method=svd \ + --checkpoint_dir=${output_path} \ + --log_step_count=200 \ + --save_summaries_steps=25000 \ + --pt_cloud_train_files=${trainData_Path}\ + --pt_cloud_test_files=${testData_Path} \ + --train_steps=2600000 \ + --save_checkpoints_steps=100000 \ + --eval_examples=39900 > ${print_log} +fi + + +# 性能相关数据计算 +StepTime=`grep "sec/step :" ${print_log} | tail -n 10 | awk '{print $NF}' | awk '{sum+=$1} END {print sum/NR}'` +FPS=`awk 'BEGIN{ "%.2f\n", '${batch_size}'/'${StepTime}'}'` + +# 精度相关数据计算 +train_accuracy=`grep "Final Accuracy accuracy" ${print_log} | awk '{print $NF}'` +# 提取所有loss打印信息 +grep "loss :" ${print_log} | awk -F ":" '{print $4}' | awk -F "-" '{print $1}' > ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt + + +########################################################### +#########后面的所有内容请不要修改########################### +#########后面的所有内容请不要修改########################### +#########后面的所有内容请不要修改########################### +########################################################### + +# 判断本次执行是否正确使用Ascend NPU +use_npu_flag=`grep "The model has been compiled on the Ascend AI processor" ${print_log} | wc -l` +if [ x"${use_npu_flag}" == x0 ]; +then + echo "------------------ ERROR NOTICE START ------------------" + echo "ERROR, your task haven't used Ascend NPU, please check your npu Migration." + echo "------------------ ERROR NOTICE END------------------" +else + echo "------------------ INFO NOTICE START------------------" + echo "INFO, your task have used Ascend NPU, please check your result." + echo "------------------ INFO NOTICE END------------------" +fi + +# 获取最终的casename，请保留，case文件名为${CaseName} +get_casename + +# 重命名loss文件 +if [ -f ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ]; +then + mv ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ./test/output/${ASCEND_DEVICE_ID}/${CaseName}_loss.txt +fi + +# 训练端到端耗时 +end_time=$(date +%s) +e2e_time=$(( $end_time - $start_time )) + +echo "------------------ Final result ------------------" +# 输出性能FPS/单step耗时/端到端耗时 +echo "Final Performance images/sec : $FPS" +echo "Final Performance sec/step : $StepTime" +echo "E2E Training Duration sec : $e2e_time" + +# 输出训练精度 +echo "Final Train Accuracy : ${train_accuracy}" + +# 最后一个迭代loss值，不需要修改 +ActualLoss=(`awk 'END {print $NF}' $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}_loss.txt`) + +#关键信息打印到${CaseName}.log中，不需要修改 +echo "Network = ${Network}" > $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "RankSize = ${RANK_SIZE}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "BatchSize = ${batch_size}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "DeviceType = `uname -m`" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualFPS = ${FPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "TrainingTime = ${StepTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log \ No newline at end of file diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/train_performance_1p.sh b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/train_performance_1p.sh new file mode 100644 index 000000000..b50b78dde --- /dev/null +++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/train_performance_1p.sh @@ -0,0 +1,209 @@ +#!/bin/bash + +########################################################## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +########################################################## +# shell脚本所在路径 +cur_path=`echo $(cd $(dirname $0);pwd)` + +# 判断当前shell是否是performance +perf_flag=`echo $0 | grep performance | wc -l` + +# 当前执行网络的名称 +Network=`echo $(cd $(dirname $0);pwd) | awk -F"/" '{print $(NF-1)}'` + +export RANK_SIZE=1 +export RANK_ID=0 +export JOB_ID=10087 + +# 路径参数初始化 +data_path="" +output_path="" + +# 帮助信息，不需要修改 +if [[ $1 == --help || $1 == -h ]];then + echo"usage:./train_performance_1P.sh " + echo " " + echo "parameter explain: + --data_path # dataset of training + --output_path # output of training + --train_steps # max_step for training + --train_epochs # max_epoch for training + --batch_size # batch size + -h/--help show help message + " + exit 1 +fi + +# 参数校验，不需要修改 +for para in $* +do + if [[ $para == --data_path* ]];then + data_path=`echo ${para#*=}` + elif [[ $para == --output_path* ]];then + output_path=`echo ${para#*=}` + elif [[ $para == --train_steps* ]];then + train_steps=`echo ${para#*=}` + elif [[ $para == --train_epochs* ]];then + train_epochs=`echo ${para#*=}` + elif [[ $para == --batch_size* ]];then + batch_size=`echo ${para#*=}` + fi +done + +# 校验是否传入data_path,不需要修改 +if [[ $data_path == "" ]];then + echo "[Error] para \"data_path\" must be config" + exit 1 +fi + +# 校验是否传入output_path,不需要修改 +if [[ $output_path == "" ]];then + output_path="./test/output/${ASCEND_DEVICE_ID}" +fi + +# 设置打屏日志文件名，请保留，文件名为${print_log} +print_log="./test/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log" +etp_flag=${etp_running_flag} +if [ x"${etp_flag}" != xtrue ]; +then + echo "running without etp..." + print_log_name=`ls /home/ma-user/modelarts/log/ | grep proc-rank` + print_log="/home/ma-user/modelarts/log/${print_log_name}" +fi +echo ${print_log} + +CaseName="" +function get_casename() +{ + if [ x"${perf_flag}" = x1 ]; + then + CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'perf' + else + CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'acc' + fi +} + +# 跳转到code目录 +cd ${cur_path}/../ +rm -rf ./test/output/${ASCEND_DEVICE_ID} +mkdir -p ./test/output/${ASCEND_DEVICE_ID} + +# 训练开始时间记录，不需要修改 +start_time=$(date +%s) +########################################################## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +########################################################## + +#========================================================= +#========================================================= +#========训练执行命令，需要根据您的网络进行修改============== +#========================================================= +#========================================================= +# 您的训练数据集在${data_path}路径下，请直接使用这个变量获取 +# 您的训练输出目录在${output_path}路径下，请直接使用这个变量获取 +# 您的其他基础参数，可以自定义增加，但是batch_size请保留，并且设置正确的值 +batch_size=1 + +echo "------------------***PRINT DATA PATH***-----------------" +train_data="points/*.pts" +test_data="points_test/*.pts" + +trainData_Path="$data_path$train_data" +testData_Path="$data_path$test_data" + + +if [ x"${modelarts_flag}" != x ]; +then + python3.7 /home/ma-user/modelarts/user-job-dir/code/main_point_cloud_boostPerf.py \ + --method=svd \ + --checkpoint_dir=${output_path} \ + --log_step_count=200 \ + --save_summaries_steps=2500 \ + --pt_cloud_train_files=${trainData_Path} \ + --pt_cloud_test_files=${testData_Path} \ + --train_steps=20000 \ + --save_checkpoints_steps=20000 \ + --eval_examples=399 +else + python3.7 /home/ma-user/modelarts/user-job-dir/code/main_point_cloud_boostPerf.py \ + --method=svd \ + --checkpoint_dir=${output_path} \ + --log_step_count=200 \ + --save_summaries_steps=2500 \ + --pt_cloud_train_files=${trainData_Path}\ + --pt_cloud_test_files=${testData_Path} \ + --train_steps=10000 \ + --save_checkpoints_steps=10000 \ + --eval_examples=399> ${print_log} +fi + + +# 性能相关数据计算 +StepTime=`grep "sec/step :" ${print_log} | tail -n 10 | awk '{print $NF}' | awk '{sum+=$1} END {print sum/NR}'` +FPS=`awk 'BEGIN{ "%.2f\n", '${batch_size}'/'${StepTime}'}'` + +# 精度相关数据计算 +train_accuracy=`grep "Final Accuracy accuracy" ${print_log} | awk '{print $NF}'` +# 提取所有loss打印信息 +grep "loss :" ${print_log} | awk -F ":" '{print $4}' | awk -F "-" '{print $1}' > ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt + + +########################################################### +#########后面的所有内容请不要修改########################### +#########后面的所有内容请不要修改########################### +#########后面的所有内容请不要修改########################### +########################################################### + +# 判断本次执行是否正确使用Ascend NPU +use_npu_flag=`grep "The model has been compiled on the Ascend AI processor" ${print_log} | wc -l` +if [ x"${use_npu_flag}" == x0 ]; +then + echo "------------------ ERROR NOTICE START ------------------" + echo "ERROR, your task haven't used Ascend NPU, please check your npu Migration." + echo "------------------ ERROR NOTICE END------------------" +else + echo "------------------ INFO NOTICE START------------------" + echo "INFO, your task have used Ascend NPU, please check your result." + echo "------------------ INFO NOTICE END------------------" +fi + +# 获取最终的casename，请保留，case文件名为${CaseName} +get_casename + +# 重命名loss文件 +if [ -f ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ]; +then + mv ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ./test/output/${ASCEND_DEVICE_ID}/${CaseName}_loss.txt +fi + +# 训练端到端耗时 +end_time=$(date +%s) +e2e_time=$(( $end_time - $start_time )) + +echo "------------------ Final result ------------------" +# 输出性能FPS/单step耗时/端到端耗时 +echo "Final Performance images/sec : $FPS" +echo "Final Performance sec/step : $StepTime" +echo "E2E Training Duration sec : $e2e_time" + +# 输出训练精度 +echo "Final Train Accuracy : ${train_accuracy}" + +# 最后一个迭代loss值，不需要修改 +ActualLoss=(`awk 'END {print $NF}' $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}_loss.txt`) + +#关键信息打印到${CaseName}.log中，不需要修改 +echo "Network = ${Network}" > $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "RankSize = ${RANK_SIZE}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "BatchSize = ${batch_size}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "DeviceType = `uname -m`" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualFPS = ${FPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "TrainingTime = ${StepTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "E2ETrainingTime = ${e2e_ti_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log \ No newline at end of file -- Gitee From 261d49a37b3d93ca953440fba17b02c13e482231 Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Mon, 16 May 2022 15:32:31 +0000 Subject: [PATCH 52/88] =?UTF-8?q?=E5=88=A0=E9=99=A4=E6=96=87=E4=BB=B6=20Te?= =?UTF-8?q?nsorFlow/contrib/cv/SVD=5FID2019=5Ffor=5FTensorflow/README.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../cv/SVD_ID2019_for_Tensorflow/README.md | 248 ------------------ 1 file changed, 248 deletions(-) delete mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md deleted file mode 100644 index 0c004e315..000000000 --- a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md +++ /dev/null @@ -1,248 +0,0 @@ -

基本信息

- -**发布者（Publisher）：Huawei** - -**应用领域（Application Domain）：CV** - -**版本（Version）：** - -**修改时间（Modified） ：2022.05.05** - -**框架（Framework）：TensorFlow 1.15.0** - -**处理器（Processor）：昇腾910** - -**描述（Description）：使用训练好的SVD模型，评估对称正交化在点云对准中的应用效果。** - -

概述

- -给定两个三维点云图，利用SVD正交化过程SVDO+(M)将其投射到SO(3)上，要求网络预测最佳对齐它们的3D旋转。代码的训练逻辑是每训练10w步保存一个模型，并且在测试集上检验该模型的精度，最后比较的都是260w步的模型精度 - -- 开源代码： - - https://github.com/google-research/google-research/tree/master/special_orthogonalization。 - -- 参考论文： - - [An Analysis of SVD for Deep Rotation Estimation](https://arxiv.org/abs/2006.14616) - -- 参考实现： - gpu最后训练出来的模型在 - - obs://cann-id2019/gpu - - 数据在 - obs://cann-id2019/dataset/ - - 共有3个数据集 - - 训练数据集points - - 测试数据集 points_test - - 第一步旋转后的数据集 test_points_modified - - npu训练出来的模型在 - - obs://cann-id2019/dataset/output - - - 相关代码均以上传，在NPU和GPU文件夹中可以找到 - -- 相关迁移的工作： - 在进行代码迁移到NPU上时，输入的训练数据为点云数据，点云数据的shape为(N,3)，其中N并不是固定的，因此在NPU上存在动态shape的问题，导致模型训练无法正常进行。我们为此想了三个解决方法：1、找出所有点云数据中最小的N，对于大于N的点云数据，仅取前N行的数据输入训练。2、找到所有点云数据中最大的N，对于小于N的点云进行补0操作，将所有数据固定为最大的N后，输入网络进行训练。3、找到所有点云数据中最大的N，对小于N的点云数据，从原数据中选择一个点云进行填补至行数为N，再将数据输入网络进行训练。该三种方法均成功解决了NPU上的动态shape问题，但是第一种方法删除了样本点，因此导致最后训练出的模型精度很差；第二种方法虽然并没有丢失样本信息，但是向数据中填入大量的0，改变了本来的代码逻辑，导致最后训练出的模型精度也并不高。对于第三种方法，即没有丢失样本信息，对每个点云数据中的某一个点云样本点进行重复操作，没有改变原始的代码逻辑，最后也获得了不错的精度表现。 - -- 通过Git获取对应commit\_id的代码方法如下： - - ``` - git clone {repository_url} # 克隆仓库的代码 - cd {repository_name} # 切换到模型的代码仓目录 - git checkout {branch} # 切换到对应分支 - git reset --hard ｛commit_id｝ # 代码设置到对应的commit_id - cd ｛code_path｝ # 切换到模型代码所在路径，若仓库下只有该模型，则无需切换 - ``` - -## 默认配置 - -- 数据集获取：obs://cann-id2019/dataset/ - -- 训练超参 - - - log_step_count=200 - - save_summaries_steps=25000 - - train_steps=2600000 - - save_checkpoints_steps=100000 - - eval_examples=39900 - - - -

训练环境准备

- -1. 硬件环境准备请参见各硬件产品文档"[驱动和固件安装升级指南]( https://support.huawei.com/enterprise/zh/category/ai-computing-platform-pid-1557196528909)"。需要在硬件设备上安装与CANN版本配套的固件与驱动。 -2. 宿主机上需要安装Docker并登录[Ascend Hub中心](https://ascendhub.huawei.com/#/detail?name=ascend-tensorflow-arm)获取镜像。 - - 当前模型支持的镜像列表如[表1](#zh-cn_topic_0000001074498056_table1519011227314)所示。 - - **表 1** 镜像列表 - - - - - - - - - - - - -

镜像名称

-

镜像版本

-

配套CANN版本

-
-

20.2.0

-

20.2

-
- - - -## 脚本参数 - -``` -gen_pt_test_data_gpu.py 中的参数 - ---input_test_files 传入需要进行旋转的点云数据集 ---output_directory 存储旋转后点云集的路径 ---random_rotation_axang boole型，若为真将会对传入的数据集采用指定方法旋转，默认为真 ---num_rotations_per_file 每个测试点云的随机旋转增加数。默认为100 - - -main_point_cloud_gpu.py 中的参数 - ---pt_cloud_test_files 测试数据集路径 ---pt_cloud_train_files 熟练数据集路径 ---method 指定用于预测旋转的方式。选项为"svd", "svd-inf", or "gs"。默认为“svd” ---checkpoint_dir 训练模型的存放位置 ---train_steps 训练迭代的次数。默认为2600000 ---save_checkpoints_steps 保存检查点的频率。默认为10000 ---log_step_count 日志记录一次的步数。默认为200 ---save_summaries_steps 保存一次summary的步数。默认为5000 ---learning_rate 默认为1e-5 ---lr_decay 如果为真，则衰减learning rate。默认为假 ---lr_decay_steps learning rate衰减步数。默认为35000 ---lr_decay_rate learning rate衰减速率。默认为0.95 ---predict_all_test 如果为真，则在最新的检查点上运行eval作业，并打印每个输入的误差信息。默认为假 ---eval_examples 测试样本的数量。默认为0 ---print_variable_names 打印模型变量名。默认为假 ---num_train_augmentations 增加每个输入点云的随机旋转数。默认为10 - -``` - - - -## 运行 - -GPU运行命令如下： - -**生成测试数据** - -注：生成的文件test_points_modified、points已包含在dataset文件夹中。 -```bash -# 将路径设置到训练点云图文件 -IN_FILES=/points_test/*.pts - -NEW_TEST_FILES_DIR=/test_points_modified - -AXANG_SAMPLING=True - -# 决定旋转轴角的分布 -AXANG_SAMPLING=True - -python -m special_orthogonalization.gen_pt_test_data_gpu --input_test_files=$IN_FILES --output_directory=$NEW_TEST_FILES_DIR --random_rotation_axang=$AXANG_SAMPLING -``` - -**训练与评价** -```bash -# 将路径设置到原始训练数据 -TRAIN_FILES=/points/*.pts - -#将路径设置到旋转后的训练数据 -TEST_FILES=$NEW_TEST_FILES_DIR/*.pts - -# 指定旋转预测方式 -METHOD=svd - -# 指定ckpt、summaries、评价结果等的存储路径 -OUT_DIR=/path/to/model - -python -m special_orthogonalization.main_point_cloud_gpu --method=$METHOD --checkpoint_dir=$OUT_DIR --log_step_count=200 --save_summaries_steps=25000 --pt_cloud_train_files=$TRAIN_FILES --pt_cloud_test_files=$TEST_FILES --train_steps=2600000 --save_checkpoints_steps=100000 --eval_examples=39900 -``` - -**从所有训练样本中生成统计数据** -```bash -# 打印均值、中位数、标准差和分位数 -python -m special_orthogonalization.main_point_cloud_gpu --method=$METHOD --checkpoint_dir=$OUT_DIR --pt_cloud_test_files=$TEST_FILES --predict_all_test=True -``` -## 运行 - -NPU运行命令方式如下: - -对于所有的三个步骤程序来说，modelarts插件obs桶中的数据路径均要写到真正包含数据的那一个路径 -如在dataset文件夹中含有points、points_test等包含数据的文件夹 -modelarts插件中的数据路径写为 obs://cann-id2019/dataset/ - -**生成测试数据** - -运行这一步我们需要的程序文件为gen_pt_test_data.py、modelarts_entry_Axang.py、genTestData.sh -这三个文件中的代码均不需要修改 -最后生成的旋转后的数据文件存放在obs桶当次程序文件的output路径中，文件名为test_points_modified， -为进行第二步模型训练，需要将生成旋转后的文件转移至obs桶中存放data的路径 - -注：需要确保的是存在obs桶里的data文件名为points_test - -**训练与评价** - -运行这一步我们需要的程序文件为main_point_cloud.py、modelarts_entry_acc_train.py、train_full_1p.sh -这三个文件中的代码均不需要修改 -最后生成的旋转后的数据文件存放在obs桶当次程序文件的output路径中，文件名为test_points_modified， -为进行第三步，需要将生成的output文件转移至obs桶中存放data的路径 - -注意：该次训练的模型保存在该次的obs文件夹中，进行第三步时又需要重启一次新的modelarts，因此我们需要将output文件中的 -checkpoint文件中最新模型的路径修改 -"/home/ma-user/modelarts/inputs/data_url_0/output" -这样第三步才能跑出正确的精度指标 - -**从所有训练样本中生成统计数据** - -运行这一步我们需要的程序文件为main_point_cloud.py、modelarts_entry_stat.py、genStatistical.sh -这三个文件的代码均不需要修改 -运行成功后将会在屏幕上打印出关于精度相应的统计量值 - -注意：由于加入了混合精度提高训练性能，导致该步出现未知错误，输出的误差均为0，因此建议将训练后的结果下载到本地 -修改checkpoint文件中模型的路径后，利用本地的GPU进行运行，实际上该步骤在运行时NPU成功启动但也未曾利用。 - -## 训练结果 -**精度对比：** - - - -| 测地线误差（°） | 论文发布 | GPU(初始代码未改动版本) | GPU实测|NPU实测 | -| ------------------------ | ------- | ----- | --------- |----------| -| 平均值 | 1.63 | 2.58 | 3.98 | 2.92 | -| 中值 | 0.89 | 1.68 | 2.6 | 1.7 | -| 标准差 | 6.70 | 6.93 | 9.36 | 8.45 | - -相比于论文中的精度，我们NPU迁移后实测差距依然较大，但是与我们未对代码任何改动初始的版本在GPU上跑出来的精度相差较小， -且对于相同的代码的代码改动，NPU上的精度优于GPU上的精度。需要注意的是，在NPU上运行程序时，我们采用混合精度来提升训练 -的性能，但是其中产生了未知的错误，导致代表的精度指标mean_degree_err在整个训练过程中始终为0，因此我们无法得知在NPU训练的 -260w步的过程中，精度指标是下降的过程是怎样的。值得庆幸的是通过NPU训练出的模型，能够在GPU上计算出精度，并且精度还不错。 - -**性能对比：** - -取华为v100上GPU运行的前2w步的global_step/sec平均值和NPU运行的前2w步的global_step/sec平均值进行对比，以达到性能对比的目的。 - -| 性能指标项 | 论文发布 | GPU实测 | NPU实测 | -| ------------------- | ------- | ------ | ------ | -| global_step/sec| 无 | 87.64 | 101.24 | - - -- Gitee From 6a238e3379c3dc77e69bdd366bfffcbba5f91fd4 Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Mon, 16 May 2022 15:33:19 +0000 Subject: [PATCH 53/88] =?UTF-8?q?=E6=9B=B4=E6=96=B0?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../cv/SVD_ID2019_for_Tensorflow/README.md | 250 ++++++++++++++++++ .../cv/SVD_ID2019_for_Tensorflow/calc_perf.py | 15 ++ 2 files changed, 265 insertions(+) create mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md create mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/calc_perf.py diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md new file mode 100644 index 000000000..60121bef4 --- /dev/null +++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md @@ -0,0 +1,250 @@ +

基本信息

+ +**发布者（Publisher）：Huawei** + +**应用领域（Application Domain）：CV** + +**版本（Version）：** + +**修改时间（Modified） ：2022.05.05** + +**框架（Framework）：TensorFlow 1.15.0** + +**处理器（Processor）：昇腾910** + +**描述（Description）：使用训练好的SVD模型，评估对称正交化在点云对准中的应用效果。** + +

概述

+ +给定两个三维点云图，利用SVD正交化过程SVDO+(M)将其投射到SO(3)上，要求网络预测最佳对齐它们的3D旋转。代码的训练逻辑是每训练10w步保存一个模型，并且在测试集上检验该模型的精度，最后比较的都是260w步的模型精度 + +- 开源代码： + + https://github.com/google-research/google-research/tree/master/special_orthogonalization。 + +- 参考论文： + + [An Analysis of SVD for Deep Rotation Estimation](https://arxiv.org/abs/2006.14616) + +- 参考实现： + gpu最后训练出来的模型在 + + obs://cann-id2019/gpu + + 数据在 + obs://cann-id2019/dataset/ + + 共有3个数据集 + + 训练数据集points + + 测试数据集 points_test + + 第一步旋转后的数据集 test_points_modified + + npu训练出来的模型在 + + obs://cann-id2019/dataset/output + + + 相关代码均以上传，在NPU和GPU文件夹中可以找到 + +- 相关迁移的工作： + 在进行代码迁移到NPU上时，输入的训练数据为点云数据，点云数据的shape为(N,3)，其中N并不是固定的，因此在NPU上存在动态shape的问题，导致模型训练无法正常进行。我们为此想了三个解决方法：1、找出所有点云数据中最小的N，对于大于N的点云数据，仅取前N行的数据输入训练。2、找到所有点云数据中最大的N，对于小于N的点云进行补0操作，将所有数据固定为最大的N后，输入网络进行训练。3、找到所有点云数据中最大的N，对小于N的点云数据，从原数据中选择一个点云进行填补至行数为N，再将数据输入网络进行训练。该三种方法均成功解决了NPU上的动态shape问题，但是第一种方法删除了样本点，因此导致最后训练出的模型精度很差；第二种方法虽然并没有丢失样本信息，但是向数据中填入大量的0，改变了本来的代码逻辑，导致最后训练出的模型精度也并不高。对于第三种方法，即没有丢失样本信息，对每个点云数据中的某一个点云样本点进行重复操作，没有改变原始的代码逻辑，最后也获得了不错的精度表现。 + +- 通过Git获取对应commit\_id的代码方法如下： + + ``` + git clone {repository_url} # 克隆仓库的代码 + cd {repository_name} # 切换到模型的代码仓目录 + git checkout {branch} # 切换到对应分支 + git reset --hard ｛commit_id｝ # 代码设置到对应的commit_id + cd ｛code_path｝ # 切换到模型代码所在路径，若仓库下只有该模型，则无需切换 + ``` + +## 默认配置 + +- 数据集获取：obs://cann-id2019/dataset/ + +- 训练超参 + + - log_step_count=200 + - save_summaries_steps=25000 + - train_steps=2600000 + - save_checkpoints_steps=100000 + - eval_examples=39900 + + + +

训练环境准备

+ +1. 硬件环境准备请参见各硬件产品文档"[驱动和固件安装升级指南]( https://support.huawei.com/enterprise/zh/category/ai-computing-platform-pid-1557196528909)"。需要在硬件设备上安装与CANN版本配套的固件与驱动。 +2. 宿主机上需要安装Docker并登录[Ascend Hub中心](https://ascendhub.huawei.com/#/detail?name=ascend-tensorflow-arm)获取镜像。 + + 当前模型支持的镜像列表如[表1](#zh-cn_topic_0000001074498056_table1519011227314)所示。 + + **表 1** 镜像列表 + + + + + + + + + + + + +

镜像名称

+

镜像版本

+

配套CANN版本

+
+

20.2.0

+

20.2

+
+ + + +## 脚本参数 + +``` +gen_pt_test_data_gpu.py 中的参数 + +--input_test_files 传入需要进行旋转的点云数据集 +--output_directory 存储旋转后点云集的路径 +--random_rotation_axang boole型，若为真将会对传入的数据集采用指定方法旋转，默认为真 +--num_rotations_per_file 每个测试点云的随机旋转增加数。默认为100 + + +main_point_cloud_gpu.py 中的参数 + +--pt_cloud_test_files 测试数据集路径 +--pt_cloud_train_files 熟练数据集路径 +--method 指定用于预测旋转的方式。选项为"svd", "svd-inf", or "gs"。默认为“svd” +--checkpoint_dir 训练模型的存放位置 +--train_steps 训练迭代的次数。默认为2600000 +--save_checkpoints_steps 保存检查点的频率。默认为10000 +--log_step_count 日志记录一次的步数。默认为200 +--save_summaries_steps 保存一次summary的步数。默认为5000 +--learning_rate 默认为1e-5 +--lr_decay 如果为真，则衰减learning rate。默认为假 +--lr_decay_steps learning rate衰减步数。默认为35000 +--lr_decay_rate learning rate衰减速率。默认为0.95 +--predict_all_test 如果为真，则在最新的检查点上运行eval作业，并打印每个输入的误差信息。默认为假 +--eval_examples 测试样本的数量。默认为0 +--print_variable_names 打印模型变量名。默认为假 +--num_train_augmentations 增加每个输入点云的随机旋转数。默认为10 + +``` + + + +## 运行 + +GPU运行命令如下： + +**生成测试数据** + +注：生成的文件test_points_modified、points已包含在dataset文件夹中。 +```bash +# 将路径设置到训练点云图文件 +IN_FILES=/points_test/*.pts + +NEW_TEST_FILES_DIR=/test_points_modified + +AXANG_SAMPLING=True + +# 决定旋转轴角的分布 +AXANG_SAMPLING=True + +python -m special_orthogonalization.gen_pt_test_data_gpu --input_test_files=$IN_FILES --output_directory=$NEW_TEST_FILES_DIR --random_rotation_axang=$AXANG_SAMPLING +``` + +**训练与评价** +```bash +# 将路径设置到原始训练数据 +TRAIN_FILES=/points/*.pts + +#将路径设置到旋转后的训练数据 +TEST_FILES=$NEW_TEST_FILES_DIR/*.pts + +# 指定旋转预测方式 +METHOD=svd + +# 指定ckpt、summaries、评价结果等的存储路径 +OUT_DIR=/path/to/model + +python -m special_orthogonalization.main_point_cloud_gpu --method=$METHOD --checkpoint_dir=$OUT_DIR --log_step_count=200 --save_summaries_steps=25000 --pt_cloud_train_files=$TRAIN_FILES --pt_cloud_test_files=$TEST_FILES --train_steps=2600000 --save_checkpoints_steps=100000 --eval_examples=39900 +``` + +**从所有训练样本中生成统计数据** +```bash +# 打印均值、中位数、标准差和分位数 +python -m special_orthogonalization.main_point_cloud_gpu --method=$METHOD --checkpoint_dir=$OUT_DIR --pt_cloud_test_files=$TEST_FILES --predict_all_test=True +``` +## 运行 + +NPU运行命令方式如下: + +对于所有的三个步骤程序来说，modelarts插件obs桶中的数据路径均要写到真正包含数据的那一个路径 +如在dataset文件夹中含有points、points_test等包含数据的文件夹 +modelarts插件中的数据路径写为 obs://cann-id2019/dataset/ + +**生成测试数据** + +运行这一步我们需要的程序文件为gen_pt_test_data.py、modelarts_entry_Axang.py、genTestData.sh +这三个文件中的代码均不需要修改 +最后生成的旋转后的数据文件存放在obs桶当次程序文件的output路径中，文件名为test_points_modified， +为进行第二步模型训练，需要将生成旋转后的文件转移至obs桶中存放data的路径 + +注：需要确保的是存在obs桶里的data文件名为points_test + +**训练与评价** + +运行这一步我们需要的程序文件为main_point_cloud_boostPerf.py、modelarts_entry_acc_train.py、train_full_1p.sh +这三个文件中的代码均不需要修改 +最后生成的旋转后的数据文件存放在obs桶当次程序文件的output路径中，文件名为test_points_modified， +为进行第三步，需要将生成的output文件转移至obs桶中存放data的路径 + +注意：该次训练的模型保存在该次的obs文件夹中，进行第三步时又需要重启一次新的modelarts，因此我们需要将output文件中的 +checkpoint文件中最新模型的路径修改 +"/home/ma-user/modelarts/inputs/data_url_0/output" +这样第三步才能跑出正确的精度指标 + + +**从所有训练样本中生成统计数据** + +运行这一步我们需要的程序文件为main_point_cloud_boostPerf.py、modelarts_entry_stat.py、genStatistical.sh +这三个文件的代码均不需要修改 +运行成功后将会在屏幕上打印出关于精度相应的统计量值 + +注意：由于加入了混合精度提高训练性能，导致该步出现未知错误，输出的误差均为0，因此建议将训练后的结果下载到本地 +修改checkpoint文件中模型的路径后，利用本地的GPU进行运行，实际上该步骤在运行时NPU成功启动但也未曾利用。 + +## 训练结果 +**精度对比：** + + + +| 测地线误差（°） | 论文发布 | GPU(初始代码未改动版本) | GPU实测|NPU实测 | +| ------------------------ | ------- | ----- | --------- |----------| +| 平均值 | 1.63 | 2.58 | 3.98 | 2.92 | +| 中值 | 0.89 | 1.68 | 2.6 | 1.7 | +| 标准差 | 6.70 | 6.93 | 9.36 | 8.45 | + +相比于论文中的精度，我们NPU迁移后实测差距依然较大，但是与我们未对代码任何改动初始的版本在GPU上跑出来的精度相差较小， +且对于相同的代码的代码改动，NPU上的精度优于GPU上的精度。需要注意的是，在NPU上运行程序时，我们采用混合精度来提升训练 +的性能，但是其中产生了未知的错误，导致代表的精度指标mean_degree_err在整个训练过程中始终为0，因此我们无法得知在NPU训练的 +260w步的过程中，精度指标是下降的过程是怎样的。值得庆幸的是通过NPU训练出的模型，能够在GPU上计算出精度，并且精度还不错。 + +**性能对比：** + +取华为v100上GPU运行的前2w步的global_step/sec平均值和NPU运行的前1w步的global_step/sec平均值进行对比，以达到性能对比的目的。 +对于NPU上的性能计算 需要的程序为main_point_cloud_boostPerf.py、modelarts_entry_perf.py、train_performance_1p.sh，其中参数已经 +设置完毕，无需更改。同时我们上传了我们计算性能的代码calc_perf.py，运行该代码需要将产生的日志文件从obs上下载下来，传入obs文件在本地的路径即可 +| 性能指标项 | 论文发布 | GPU实测 | NPU实测 | +| ------------------- | ------- | ------ | ------ | +| global_step/sec| 无 | 87.64 | 116.77 | + + diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/calc_perf.py b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/calc_perf.py new file mode 100644 index 000000000..ce34e8801 --- /dev/null +++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/calc_perf.py @@ -0,0 +1,15 @@ +import re +import numpy as np + +#NPU上运行的日志文件下载到本地的路径 +path = "" + +with open(path, mode='r') as f: + NpulogInfo = f.readlines() + +NpulogInfo = str(NpulogInfo) +valuesNPU = re.findall(r"global_step/sec:(.*?)\\n", NpulogInfo) +valuesNPU = [float(x) for x in valuesNPU] + +perf_npu = np.mean(valuesNPU) +print(perf_npu) -- Gitee From a1ad2719ae8d793e6adb471585deeb8f58886ffa Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Mon, 16 May 2022 15:41:38 +0000 Subject: [PATCH 54/88] =?UTF-8?q?=E6=9B=B4=E6=96=B0=E7=B2=BE=E5=BA=A6?= =?UTF-8?q?=E4=BB=A3=E7=A0=81?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../modelarts_entry_acc_train.py | 36 +++++++++++++++++++ 1 file changed, 36 insertions(+) create mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/modelarts_entry_acc_train.py diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/modelarts_entry_acc_train.py b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/modelarts_entry_acc_train.py new file mode 100644 index 000000000..b86164d74 --- /dev/null +++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/modelarts_entry_acc_train.py @@ -0,0 +1,36 @@ +import os +import argparse +import sys + +# 解析输入参数data_url +parser = argparse.ArgumentParser() +parser.add_argument("--data_url", type=str, default="/home/ma-user/modelarts/inputs/data_url_0") +parser.add_argument("--train_url", type=str, default="/home/ma-user/modelarts/outputs/train_url_0/") +config = parser.parse_args() + +print("[CANN-Modelzoo] code_dir path is [%s]" % (sys.path[0])) +code_dir = sys.path[0] +os.chdir(code_dir) +print("[CANN-Modelzoo] work_dir path is [%s]" % (os.getcwd())) + +print("[CANN-Modelzoo] before train - list my run files:") +os.system("ls -al /usr/local/Ascend/ascend-toolkit/") + +print("[CANN-Modelzoo] before train - list my dataset files:") +os.system("ls -al %s" % config.data_url) + + +print("[CANN-Modelzoo] start run train shell") +# 设置sh文件格式为linux可执行 +os.system("dos2unix ./test/*") + +# 执行train_full_1p.sh或者train_performance_1p.sh，需要用户自己指定 +# full和performance的差异，performance只需要执行很少的step，控制在15分钟以内，主要关注性能FPS +os.system("bash /home/ma-user/modelarts/user-job-dir/code/train_full_1p.sh --data_path=%s --output_path=%s " % (config.data_url, config.train_url)) + +print("[CANN-Modelzoo] finish run train shell") + +# 将当前执行目录所有文件拷贝到obs的output进行备份 +print("[CANN-Modelzoo] after train - list my output files:") +os.system("cp -r %s %s " % (code_dir, config.train_url)) +os.system("ls -al %s" % config.train_url) \ No newline at end of file -- Gitee From 2c54ac13f67cdef2ac22c8e30f056693c0439076 Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Mon, 16 May 2022 15:51:32 +0000 Subject: [PATCH 55/88] =?UTF-8?q?=E6=9B=B4=E6=96=B0?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../modelarts_entry_Axang.py | 36 +++++++++++++++++++ 1 file changed, 36 insertions(+) create mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/modelarts_entry_Axang.py diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/modelarts_entry_Axang.py b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/modelarts_entry_Axang.py new file mode 100644 index 000000000..40f00a348 --- /dev/null +++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/modelarts_entry_Axang.py @@ -0,0 +1,36 @@ +import os +import argparse +import sys + +# 解析输入参数data_url +parser = argparse.ArgumentParser() +parser.add_argument("--data_url", type=str, default="/home/ma-user/modelarts/inputs/data_url_0") +parser.add_argument("--train_url", type=str, default="/home/ma-user/modelarts/outputs/train_url_0/") +config = parser.parse_args() + +print("[CANN-Modelzoo] code_dir path is [%s]" % (sys.path[0])) +code_dir = sys.path[0] +os.chdir(code_dir) +print("[CANN-Modelzoo] work_dir path is [%s]" % (os.getcwd())) + +print("[CANN-Modelzoo] before train - list my run files:") +os.system("ls -al /usr/local/Ascend/ascend-toolkit/") + +print("[CANN-Modelzoo] before train - list my dataset files:") +os.system("ls -al %s" % config.data_url) + + +print("[CANN-Modelzoo] start run train shell") +# 设置sh文件格式为linux可执行 +os.system("dos2unix ./test/*") + +# 执行train_full_1p.sh或者train_performance_1p.sh，需要用户自己指定 +# full和performance的差异，performance只需要执行很少的step，控制在15分钟以内，主要关注性能FPS +os.system("bash /home/ma-user/modelarts/user-job-dir/code/genTestData.sh --data_path=%s --output_path=%s " % (config.data_url, config.train_url)) + +print("[CANN-Modelzoo] finish run train shell") + +# 将当前执行目录所有文件拷贝到obs的output进行备份 +print("[CANN-Modelzoo] after train - list my output files:") +os.system("cp -r %s %s " % (code_dir, config.train_url)) +os.system("ls -al %s" % config.train_url) \ No newline at end of file -- Gitee From f313504b6e117ce276453687b91289745e45a3b8 Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Tue, 17 May 2022 07:51:43 +0000 Subject: [PATCH 56/88] =?UTF-8?q?=E9=9C=80=E8=A6=81=E5=8A=A0=E8=BD=BD?= =?UTF-8?q?=E7=9A=84=E5=BA=93?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../requirements.txt | 35 +++++++++++++++++++ 1 file changed, 35 insertions(+) create mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/requirements.txt diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/requirements.txt b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/requirements.txt new file mode 100644 index 000000000..1f2a5e41d --- /dev/null +++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/requirements.txt @@ -0,0 +1,35 @@ +absl-py==0.11.0 +astunparse==1.6.3 +cachetools==4.1.1 +certifi==2020.6.20 +chardet==3.0.4 +gast==0.3.3 +google-auth==1.23.0 +google-auth-oauthlib==0.4.2 +google-pasta==0.2.0 +grpcio==1.33.2 +h5py==2.10.0 +idna==2.10 +importlib-metadata==2.0.0 +Keras-Preprocessing==1.1.2 +Markdown==3.3.3 +numpy==1.18.5 +oauthlib==3.1.0 +opt-einsum==3.3.0 +protobuf==3.13.0 +pyasn1==0.4.8 +pyasn1-modules==0.2.8 +requests==2.24.0 +requests-oauthlib==1.3.0 +rsa==4.6 +scipy==1.5.3 +six==1.15.0 +tensorboard==2.3.0 +tensorboard-plugin-wit==1.7.0 +tensorflow==2.3.1 +tensorflow-estimator==2.3.0 +termcolor==1.1.0 +urllib3==1.25.11 +Werkzeug==1.0.1 +wrapt==1.12.1 +zipp==3.4.0 -- Gitee From 3b9537e6d1dccbb0129575e3e1634a13564e9c63 Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Mon, 30 May 2022 14:21:49 +0000 Subject: [PATCH 57/88] =?UTF-8?q?=E5=88=A0=E9=99=A4=E6=96=87=E4=BB=B6=20Te?= =?UTF-8?q?nsorFlow/contrib/cv/SVD=5FID2019=5Ffor=5FTensorflow/train=5Fper?= =?UTF-8?q?formance=5F1p.sh?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../train_performance_1p.sh | 209 ------------------ 1 file changed, 209 deletions(-) delete mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/train_performance_1p.sh diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/train_performance_1p.sh b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/train_performance_1p.sh deleted file mode 100644 index b50b78dde..000000000 --- a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/train_performance_1p.sh +++ /dev/null @@ -1,209 +0,0 @@ -#!/bin/bash - -########################################################## -#########第3行 至 100行，请一定不要、不要、不要修改########## -#########第3行 至 100行，请一定不要、不要、不要修改########## -#########第3行 至 100行，请一定不要、不要、不要修改########## -########################################################## -# shell脚本所在路径 -cur_path=`echo $(cd $(dirname $0);pwd)` - -# 判断当前shell是否是performance -perf_flag=`echo $0 | grep performance | wc -l` - -# 当前执行网络的名称 -Network=`echo $(cd $(dirname $0);pwd) | awk -F"/" '{print $(NF-1)}'` - -export RANK_SIZE=1 -export RANK_ID=0 -export JOB_ID=10087 - -# 路径参数初始化 -data_path="" -output_path="" - -# 帮助信息，不需要修改 -if [[ $1 == --help || $1 == -h ]];then - echo"usage:./train_performance_1P.sh " - echo " " - echo "parameter explain: - --data_path # dataset of training - --output_path # output of training - --train_steps # max_step for training - --train_epochs # max_epoch for training - --batch_size # batch size - -h/--help show help message - " - exit 1 -fi - -# 参数校验，不需要修改 -for para in $* -do - if [[ $para == --data_path* ]];then - data_path=`echo ${para#*=}` - elif [[ $para == --output_path* ]];then - output_path=`echo ${para#*=}` - elif [[ $para == --train_steps* ]];then - train_steps=`echo ${para#*=}` - elif [[ $para == --train_epochs* ]];then - train_epochs=`echo ${para#*=}` - elif [[ $para == --batch_size* ]];then - batch_size=`echo ${para#*=}` - fi -done - -# 校验是否传入data_path,不需要修改 -if [[ $data_path == "" ]];then - echo "[Error] para \"data_path\" must be config" - exit 1 -fi - -# 校验是否传入output_path,不需要修改 -if [[ $output_path == "" ]];then - output_path="./test/output/${ASCEND_DEVICE_ID}" -fi - -# 设置打屏日志文件名，请保留，文件名为${print_log} -print_log="./test/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log" -etp_flag=${etp_running_flag} -if [ x"${etp_flag}" != xtrue ]; -then - echo "running without etp..." - print_log_name=`ls /home/ma-user/modelarts/log/ | grep proc-rank` - print_log="/home/ma-user/modelarts/log/${print_log_name}" -fi -echo ${print_log} - -CaseName="" -function get_casename() -{ - if [ x"${perf_flag}" = x1 ]; - then - CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'perf' - else - CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'acc' - fi -} - -# 跳转到code目录 -cd ${cur_path}/../ -rm -rf ./test/output/${ASCEND_DEVICE_ID} -mkdir -p ./test/output/${ASCEND_DEVICE_ID} - -# 训练开始时间记录，不需要修改 -start_time=$(date +%s) -########################################################## -#########第3行 至 100行，请一定不要、不要、不要修改########## -#########第3行 至 100行，请一定不要、不要、不要修改########## -#########第3行 至 100行，请一定不要、不要、不要修改########## -########################################################## - -#========================================================= -#========================================================= -#========训练执行命令，需要根据您的网络进行修改============== -#========================================================= -#========================================================= -# 您的训练数据集在${data_path}路径下，请直接使用这个变量获取 -# 您的训练输出目录在${output_path}路径下，请直接使用这个变量获取 -# 您的其他基础参数，可以自定义增加，但是batch_size请保留，并且设置正确的值 -batch_size=1 - -echo "------------------***PRINT DATA PATH***-----------------" -train_data="points/*.pts" -test_data="points_test/*.pts" - -trainData_Path="$data_path$train_data" -testData_Path="$data_path$test_data" - - -if [ x"${modelarts_flag}" != x ]; -then - python3.7 /home/ma-user/modelarts/user-job-dir/code/main_point_cloud_boostPerf.py \ - --method=svd \ - --checkpoint_dir=${output_path} \ - --log_step_count=200 \ - --save_summaries_steps=2500 \ - --pt_cloud_train_files=${trainData_Path} \ - --pt_cloud_test_files=${testData_Path} \ - --train_steps=20000 \ - --save_checkpoints_steps=20000 \ - --eval_examples=399 -else - python3.7 /home/ma-user/modelarts/user-job-dir/code/main_point_cloud_boostPerf.py \ - --method=svd \ - --checkpoint_dir=${output_path} \ - --log_step_count=200 \ - --save_summaries_steps=2500 \ - --pt_cloud_train_files=${trainData_Path}\ - --pt_cloud_test_files=${testData_Path} \ - --train_steps=10000 \ - --save_checkpoints_steps=10000 \ - --eval_examples=399> ${print_log} -fi - - -# 性能相关数据计算 -StepTime=`grep "sec/step :" ${print_log} | tail -n 10 | awk '{print $NF}' | awk '{sum+=$1} END {print sum/NR}'` -FPS=`awk 'BEGIN{ "%.2f\n", '${batch_size}'/'${StepTime}'}'` - -# 精度相关数据计算 -train_accuracy=`grep "Final Accuracy accuracy" ${print_log} | awk '{print $NF}'` -# 提取所有loss打印信息 -grep "loss :" ${print_log} | awk -F ":" '{print $4}' | awk -F "-" '{print $1}' > ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt - - -########################################################### -#########后面的所有内容请不要修改########################### -#########后面的所有内容请不要修改########################### -#########后面的所有内容请不要修改########################### -########################################################### - -# 判断本次执行是否正确使用Ascend NPU -use_npu_flag=`grep "The model has been compiled on the Ascend AI processor" ${print_log} | wc -l` -if [ x"${use_npu_flag}" == x0 ]; -then - echo "------------------ ERROR NOTICE START ------------------" - echo "ERROR, your task haven't used Ascend NPU, please check your npu Migration." - echo "------------------ ERROR NOTICE END------------------" -else - echo "------------------ INFO NOTICE START------------------" - echo "INFO, your task have used Ascend NPU, please check your result." - echo "------------------ INFO NOTICE END------------------" -fi - -# 获取最终的casename，请保留，case文件名为${CaseName} -get_casename - -# 重命名loss文件 -if [ -f ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ]; -then - mv ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ./test/output/${ASCEND_DEVICE_ID}/${CaseName}_loss.txt -fi - -# 训练端到端耗时 -end_time=$(date +%s) -e2e_time=$(( $end_time - $start_time )) - -echo "------------------ Final result ------------------" -# 输出性能FPS/单step耗时/端到端耗时 -echo "Final Performance images/sec : $FPS" -echo "Final Performance sec/step : $StepTime" -echo "E2E Training Duration sec : $e2e_time" - -# 输出训练精度 -echo "Final Train Accuracy : ${train_accuracy}" - -# 最后一个迭代loss值，不需要修改 -ActualLoss=(`awk 'END {print $NF}' $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}_loss.txt`) - -#关键信息打印到${CaseName}.log中，不需要修改 -echo "Network = ${Network}" > $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "RankSize = ${RANK_SIZE}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "BatchSize = ${batch_size}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "DeviceType = `uname -m`" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "ActualFPS = ${FPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "TrainingTime = ${StepTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "E2ETrainingTime = ${e2e_ti_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log \ No newline at end of file -- Gitee From 702fc34b21a967208418730315fca635bf899087 Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Mon, 30 May 2022 14:22:23 +0000 Subject: [PATCH 58/88] =?UTF-8?q?=E6=9B=B4=E6=96=B0?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../train_performance_1p.sh | 209 ++++++++++++++++++ 1 file changed, 209 insertions(+) create mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/train_performance_1p.sh diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/train_performance_1p.sh b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/train_performance_1p.sh new file mode 100644 index 000000000..a2b63e3af --- /dev/null +++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/train_performance_1p.sh @@ -0,0 +1,209 @@ +#!/bin/bash + +########################################################## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +########################################################## +# shell脚本所在路径 +cur_path=`echo $(cd $(dirname $0);pwd)` + +# 判断当前shell是否是performance +perf_flag=`echo $0 | grep performance | wc -l` + +# 当前执行网络的名称 +Network=`echo $(cd $(dirname $0);pwd) | awk -F"/" '{print $(NF-1)}'` + +export RANK_SIZE=1 +export RANK_ID=0 +export JOB_ID=10087 + +# 路径参数初始化 +data_path="" +output_path="" + +# 帮助信息，不需要修改 +if [[ $1 == --help || $1 == -h ]];then + echo"usage:./train_performance_1P.sh " + echo " " + echo "parameter explain: + --data_path # dataset of training + --output_path # output of training + --train_steps # max_step for training + --train_epochs # max_epoch for training + --batch_size # batch size + -h/--help show help message + " + exit 1 +fi + +# 参数校验，不需要修改 +for para in $* +do + if [[ $para == --data_path* ]];then + data_path=`echo ${para#*=}` + elif [[ $para == --output_path* ]];then + output_path=`echo ${para#*=}` + elif [[ $para == --train_steps* ]];then + train_steps=`echo ${para#*=}` + elif [[ $para == --train_epochs* ]];then + train_epochs=`echo ${para#*=}` + elif [[ $para == --batch_size* ]];then + batch_size=`echo ${para#*=}` + fi +done + +# 校验是否传入data_path,不需要修改 +if [[ $data_path == "" ]];then + echo "[Error] para \"data_path\" must be config" + exit 1 +fi + +# 校验是否传入output_path,不需要修改 +if [[ $output_path == "" ]];then + output_path="./test/output/${ASCEND_DEVICE_ID}" +fi + +# 设置打屏日志文件名，请保留，文件名为${print_log} +print_log="./test/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log" +etp_flag=${etp_running_flag} +if [ x"${etp_flag}" != xtrue ]; +then + echo "running without etp..." + print_log_name=`ls /home/ma-user/modelarts/log/ | grep proc-rank` + print_log="/home/ma-user/modelarts/log/${print_log_name}" +fi +echo ${print_log} + +CaseName="" +function get_casename() +{ + if [ x"${perf_flag}" = x1 ]; + then + CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'perf' + else + CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'acc' + fi +} + +# 跳转到code目录 +cd ${cur_path}/../ +rm -rf ./test/output/${ASCEND_DEVICE_ID} +mkdir -p ./test/output/${ASCEND_DEVICE_ID} + +# 训练开始时间记录，不需要修改 +start_time=$(date +%s) +########################################################## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +########################################################## + +#========================================================= +#========================================================= +#========训练执行命令，需要根据您的网络进行修改============== +#========================================================= +#========================================================= +# 您的训练数据集在${data_path}路径下，请直接使用这个变量获取 +# 您的训练输出目录在${output_path}路径下，请直接使用这个变量获取 +# 您的其他基础参数，可以自定义增加，但是batch_size请保留，并且设置正确的值 +batch_size=1 + +echo "------------------***PRINT DATA PATH***-----------------" +train_data="points/*.pts" +test_data="points_test/*.pts" + +trainData_Path="$data_path$train_data" +testData_Path="$sub_test$test_data" + + +if [ x"${modelarts_flag}" != x ]; +then + python3.7 /home/ma-user/modelarts/user-job-dir/code/main_point_cloud_boostPerf.py \ + --method=svd \ + --checkpoint_dir=${output_path} \ + --log_step_count=200 \ + --save_summaries_steps=250 \ + --pt_cloud_train_files=${trainData_Path} \ + --pt_cloud_test_files=${testData_Path} \ + --train_steps=10000 \ + --save_checkpoints_steps=10000 \ + --eval_examples=399 +else + python3.7 /home/ma-user/modelarts/user-job-dir/code/main_point_cloud_boostPerf.py \ + --method=svd \ + --checkpoint_dir=${output_path} \ + --log_step_count=200 \ + --save_summaries_steps=250 \ + --pt_cloud_train_files=${trainData_Path}\ + --pt_cloud_test_files=${testData_Path} \ + --train_steps=10000 \ + --save_checkpoints_steps=10000 \ + --eval_examples=399> ${print_log} +fi + + +# 性能相关数据计算 +StepTime=`grep "sec/step :" ${print_log} | tail -n 10 | awk '{print $NF}' | awk '{sum+=$1} END {print sum/NR}'` +FPS=`awk 'BEGIN{ "%.2f\n", '${batch_size}'/'${StepTime}'}'` + +# 精度相关数据计算 +train_accuracy=`grep "Final Accuracy accuracy" ${print_log} | awk '{print $NF}'` +# 提取所有loss打印信息 +grep "loss :" ${print_log} | awk -F ":" '{print $4}' | awk -F "-" '{print $1}' > ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt + + +########################################################### +#########后面的所有内容请不要修改########################### +#########后面的所有内容请不要修改########################### +#########后面的所有内容请不要修改########################### +########################################################### + +# 判断本次执行是否正确使用Ascend NPU +use_npu_flag=`grep "The model has been compiled on the Ascend AI processor" ${print_log} | wc -l` +if [ x"${use_npu_flag}" == x0 ]; +then + echo "------------------ ERROR NOTICE START ------------------" + echo "ERROR, your task haven't used Ascend NPU, please check your npu Migration." + echo "------------------ ERROR NOTICE END------------------" +else + echo "------------------ INFO NOTICE START------------------" + echo "INFO, your task have used Ascend NPU, please check your result." + echo "------------------ INFO NOTICE END------------------" +fi + +# 获取最终的casename，请保留，case文件名为${CaseName} +get_casename + +# 重命名loss文件 +if [ -f ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ]; +then + mv ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ./test/output/${ASCEND_DEVICE_ID}/${CaseName}_loss.txt +fi + +# 训练端到端耗时 +end_time=$(date +%s) +e2e_time=$(( $end_time - $start_time )) + +echo "------------------ Final result ------------------" +# 输出性能FPS/单step耗时/端到端耗时 +echo "Final Performance images/sec : $FPS" +echo "Final Performance sec/step : $StepTime" +echo "E2E Training Duration sec : $e2e_time" + +# 输出训练精度 +echo "Final Train Accuracy : ${train_accuracy}" + +# 最后一个迭代loss值，不需要修改 +ActualLoss=(`awk 'END {print $NF}' $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}_loss.txt`) + +#关键信息打印到${CaseName}.log中，不需要修改 +echo "Network = ${Network}" > $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "RankSize = ${RANK_SIZE}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "BatchSize = ${batch_size}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "DeviceType = `uname -m`" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualFPS = ${FPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "TrainingTime = ${StepTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "E2ETrainingTime = ${e2e_ti_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log \ No newline at end of file -- Gitee From 4a4ae597819679cb0fcd00ab48cdeeed96835314 Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Mon, 30 May 2022 14:25:42 +0000 Subject: [PATCH 59/88] =?UTF-8?q?=E5=88=A0=E9=99=A4=E6=96=87=E4=BB=B6=20Te?= =?UTF-8?q?nsorFlow/contrib/cv/SVD=5FID2019=5Ffor=5FTensorflow/train=5Fper?= =?UTF-8?q?formance=5F1p.sh?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../train_performance_1p.sh | 209 ------------------ 1 file changed, 209 deletions(-) delete mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/train_performance_1p.sh diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/train_performance_1p.sh b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/train_performance_1p.sh deleted file mode 100644 index a2b63e3af..000000000 --- a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/train_performance_1p.sh +++ /dev/null @@ -1,209 +0,0 @@ -#!/bin/bash - -########################################################## -#########第3行 至 100行，请一定不要、不要、不要修改########## -#########第3行 至 100行，请一定不要、不要、不要修改########## -#########第3行 至 100行，请一定不要、不要、不要修改########## -########################################################## -# shell脚本所在路径 -cur_path=`echo $(cd $(dirname $0);pwd)` - -# 判断当前shell是否是performance -perf_flag=`echo $0 | grep performance | wc -l` - -# 当前执行网络的名称 -Network=`echo $(cd $(dirname $0);pwd) | awk -F"/" '{print $(NF-1)}'` - -export RANK_SIZE=1 -export RANK_ID=0 -export JOB_ID=10087 - -# 路径参数初始化 -data_path="" -output_path="" - -# 帮助信息，不需要修改 -if [[ $1 == --help || $1 == -h ]];then - echo"usage:./train_performance_1P.sh " - echo " " - echo "parameter explain: - --data_path # dataset of training - --output_path # output of training - --train_steps # max_step for training - --train_epochs # max_epoch for training - --batch_size # batch size - -h/--help show help message - " - exit 1 -fi - -# 参数校验，不需要修改 -for para in $* -do - if [[ $para == --data_path* ]];then - data_path=`echo ${para#*=}` - elif [[ $para == --output_path* ]];then - output_path=`echo ${para#*=}` - elif [[ $para == --train_steps* ]];then - train_steps=`echo ${para#*=}` - elif [[ $para == --train_epochs* ]];then - train_epochs=`echo ${para#*=}` - elif [[ $para == --batch_size* ]];then - batch_size=`echo ${para#*=}` - fi -done - -# 校验是否传入data_path,不需要修改 -if [[ $data_path == "" ]];then - echo "[Error] para \"data_path\" must be config" - exit 1 -fi - -# 校验是否传入output_path,不需要修改 -if [[ $output_path == "" ]];then - output_path="./test/output/${ASCEND_DEVICE_ID}" -fi - -# 设置打屏日志文件名，请保留，文件名为${print_log} -print_log="./test/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log" -etp_flag=${etp_running_flag} -if [ x"${etp_flag}" != xtrue ]; -then - echo "running without etp..." - print_log_name=`ls /home/ma-user/modelarts/log/ | grep proc-rank` - print_log="/home/ma-user/modelarts/log/${print_log_name}" -fi -echo ${print_log} - -CaseName="" -function get_casename() -{ - if [ x"${perf_flag}" = x1 ]; - then - CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'perf' - else - CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'acc' - fi -} - -# 跳转到code目录 -cd ${cur_path}/../ -rm -rf ./test/output/${ASCEND_DEVICE_ID} -mkdir -p ./test/output/${ASCEND_DEVICE_ID} - -# 训练开始时间记录，不需要修改 -start_time=$(date +%s) -########################################################## -#########第3行 至 100行，请一定不要、不要、不要修改########## -#########第3行 至 100行，请一定不要、不要、不要修改########## -#########第3行 至 100行，请一定不要、不要、不要修改########## -########################################################## - -#========================================================= -#========================================================= -#========训练执行命令，需要根据您的网络进行修改============== -#========================================================= -#========================================================= -# 您的训练数据集在${data_path}路径下，请直接使用这个变量获取 -# 您的训练输出目录在${output_path}路径下，请直接使用这个变量获取 -# 您的其他基础参数，可以自定义增加，但是batch_size请保留，并且设置正确的值 -batch_size=1 - -echo "------------------***PRINT DATA PATH***-----------------" -train_data="points/*.pts" -test_data="points_test/*.pts" - -trainData_Path="$data_path$train_data" -testData_Path="$sub_test$test_data" - - -if [ x"${modelarts_flag}" != x ]; -then - python3.7 /home/ma-user/modelarts/user-job-dir/code/main_point_cloud_boostPerf.py \ - --method=svd \ - --checkpoint_dir=${output_path} \ - --log_step_count=200 \ - --save_summaries_steps=250 \ - --pt_cloud_train_files=${trainData_Path} \ - --pt_cloud_test_files=${testData_Path} \ - --train_steps=10000 \ - --save_checkpoints_steps=10000 \ - --eval_examples=399 -else - python3.7 /home/ma-user/modelarts/user-job-dir/code/main_point_cloud_boostPerf.py \ - --method=svd \ - --checkpoint_dir=${output_path} \ - --log_step_count=200 \ - --save_summaries_steps=250 \ - --pt_cloud_train_files=${trainData_Path}\ - --pt_cloud_test_files=${testData_Path} \ - --train_steps=10000 \ - --save_checkpoints_steps=10000 \ - --eval_examples=399> ${print_log} -fi - - -# 性能相关数据计算 -StepTime=`grep "sec/step :" ${print_log} | tail -n 10 | awk '{print $NF}' | awk '{sum+=$1} END {print sum/NR}'` -FPS=`awk 'BEGIN{ "%.2f\n", '${batch_size}'/'${StepTime}'}'` - -# 精度相关数据计算 -train_accuracy=`grep "Final Accuracy accuracy" ${print_log} | awk '{print $NF}'` -# 提取所有loss打印信息 -grep "loss :" ${print_log} | awk -F ":" '{print $4}' | awk -F "-" '{print $1}' > ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt - - -########################################################### -#########后面的所有内容请不要修改########################### -#########后面的所有内容请不要修改########################### -#########后面的所有内容请不要修改########################### -########################################################### - -# 判断本次执行是否正确使用Ascend NPU -use_npu_flag=`grep "The model has been compiled on the Ascend AI processor" ${print_log} | wc -l` -if [ x"${use_npu_flag}" == x0 ]; -then - echo "------------------ ERROR NOTICE START ------------------" - echo "ERROR, your task haven't used Ascend NPU, please check your npu Migration." - echo "------------------ ERROR NOTICE END------------------" -else - echo "------------------ INFO NOTICE START------------------" - echo "INFO, your task have used Ascend NPU, please check your result." - echo "------------------ INFO NOTICE END------------------" -fi - -# 获取最终的casename，请保留，case文件名为${CaseName} -get_casename - -# 重命名loss文件 -if [ -f ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ]; -then - mv ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ./test/output/${ASCEND_DEVICE_ID}/${CaseName}_loss.txt -fi - -# 训练端到端耗时 -end_time=$(date +%s) -e2e_time=$(( $end_time - $start_time )) - -echo "------------------ Final result ------------------" -# 输出性能FPS/单step耗时/端到端耗时 -echo "Final Performance images/sec : $FPS" -echo "Final Performance sec/step : $StepTime" -echo "E2E Training Duration sec : $e2e_time" - -# 输出训练精度 -echo "Final Train Accuracy : ${train_accuracy}" - -# 最后一个迭代loss值，不需要修改 -ActualLoss=(`awk 'END {print $NF}' $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}_loss.txt`) - -#关键信息打印到${CaseName}.log中，不需要修改 -echo "Network = ${Network}" > $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "RankSize = ${RANK_SIZE}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "BatchSize = ${batch_size}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "DeviceType = `uname -m`" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "ActualFPS = ${FPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "TrainingTime = ${StepTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "E2ETrainingTime = ${e2e_ti_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log \ No newline at end of file -- Gitee From 79e6804355398b954d181861292fa20dd099a693 Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Mon, 30 May 2022 14:25:59 +0000 Subject: [PATCH 60/88] =?UTF-8?q?=E6=9B=B4=E6=96=B0?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../train_performance_1p.sh | 209 ++++++++++++++++++ 1 file changed, 209 insertions(+) create mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/train_performance_1p.sh diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/train_performance_1p.sh b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/train_performance_1p.sh new file mode 100644 index 000000000..ab8be4ed0 --- /dev/null +++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/train_performance_1p.sh @@ -0,0 +1,209 @@ +#!/bin/bash + +########################################################## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +########################################################## +# shell脚本所在路径 +cur_path=`echo $(cd $(dirname $0);pwd)` + +# 判断当前shell是否是performance +perf_flag=`echo $0 | grep performance | wc -l` + +# 当前执行网络的名称 +Network=`echo $(cd $(dirname $0);pwd) | awk -F"/" '{print $(NF-1)}'` + +export RANK_SIZE=1 +export RANK_ID=0 +export JOB_ID=10087 + +# 路径参数初始化 +data_path="" +output_path="" + +# 帮助信息，不需要修改 +if [[ $1 == --help || $1 == -h ]];then + echo"usage:./train_performance_1P.sh " + echo " " + echo "parameter explain: + --data_path # dataset of training + --output_path # output of training + --train_steps # max_step for training + --train_epochs # max_epoch for training + --batch_size # batch size + -h/--help show help message + " + exit 1 +fi + +# 参数校验，不需要修改 +for para in $* +do + if [[ $para == --data_path* ]];then + data_path=`echo ${para#*=}` + elif [[ $para == --output_path* ]];then + output_path=`echo ${para#*=}` + elif [[ $para == --train_steps* ]];then + train_steps=`echo ${para#*=}` + elif [[ $para == --train_epochs* ]];then + train_epochs=`echo ${para#*=}` + elif [[ $para == --batch_size* ]];then + batch_size=`echo ${para#*=}` + fi +done + +# 校验是否传入data_path,不需要修改 +if [[ $data_path == "" ]];then + echo "[Error] para \"data_path\" must be config" + exit 1 +fi + +# 校验是否传入output_path,不需要修改 +if [[ $output_path == "" ]];then + output_path="./test/output/${ASCEND_DEVICE_ID}" +fi + +# 设置打屏日志文件名，请保留，文件名为${print_log} +print_log="./test/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log" +etp_flag=${etp_running_flag} +if [ x"${etp_flag}" != xtrue ]; +then + echo "running without etp..." + print_log_name=`ls /home/ma-user/modelarts/log/ | grep proc-rank` + print_log="/home/ma-user/modelarts/log/${print_log_name}" +fi +echo ${print_log} + +CaseName="" +function get_casename() +{ + if [ x"${perf_flag}" = x1 ]; + then + CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'perf' + else + CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'acc' + fi +} + +# 跳转到code目录 +cd ${cur_path}/../ +rm -rf ./test/output/${ASCEND_DEVICE_ID} +mkdir -p ./test/output/${ASCEND_DEVICE_ID} + +# 训练开始时间记录，不需要修改 +start_time=$(date +%s) +########################################################## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +########################################################## + +#========================================================= +#========================================================= +#========训练执行命令，需要根据您的网络进行修改============== +#========================================================= +#========================================================= +# 您的训练数据集在${data_path}路径下，请直接使用这个变量获取 +# 您的训练输出目录在${output_path}路径下，请直接使用这个变量获取 +# 您的其他基础参数，可以自定义增加，但是batch_size请保留，并且设置正确的值 +batch_size=1 + +echo "------------------***PRINT DATA PATH***-----------------" +train_data="points/*.pts" +test_data="sub_test/*.pts" + +trainData_Path="$data_path$train_data" +testData_Path="$data_path$test_data" + + +if [ x"${modelarts_flag}" != x ]; +then + python3.7 /home/ma-user/modelarts/user-job-dir/code/main_point_cloud_boostPerf.py \ + --method=svd \ + --checkpoint_dir=${output_path} \ + --log_step_count=200 \ + --save_summaries_steps=250 \ + --pt_cloud_train_files=${trainData_Path} \ + --pt_cloud_test_files=${testData_Path} \ + --train_steps=10000 \ + --save_checkpoints_steps=10000 \ + --eval_examples=399 +else + python3.7 /home/ma-user/modelarts/user-job-dir/code/main_point_cloud_boostPerf.py \ + --method=svd \ + --checkpoint_dir=${output_path} \ + --log_step_count=200 \ + --save_summaries_steps=250 \ + --pt_cloud_train_files=${trainData_Path}\ + --pt_cloud_test_files=${testData_Path} \ + --train_steps=10000 \ + --save_checkpoints_steps=10000 \ + --eval_examples=399> ${print_log} +fi + + +# 性能相关数据计算 +StepTime=`grep "sec/step :" ${print_log} | tail -n 10 | awk '{print $NF}' | awk '{sum+=$1} END {print sum/NR}'` +FPS=`awk 'BEGIN{ "%.2f\n", '${batch_size}'/'${StepTime}'}'` + +# 精度相关数据计算 +train_accuracy=`grep "Final Accuracy accuracy" ${print_log} | awk '{print $NF}'` +# 提取所有loss打印信息 +grep "loss :" ${print_log} | awk -F ":" '{print $4}' | awk -F "-" '{print $1}' > ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt + + +########################################################### +#########后面的所有内容请不要修改########################### +#########后面的所有内容请不要修改########################### +#########后面的所有内容请不要修改########################### +########################################################### + +# 判断本次执行是否正确使用Ascend NPU +use_npu_flag=`grep "The model has been compiled on the Ascend AI processor" ${print_log} | wc -l` +if [ x"${use_npu_flag}" == x0 ]; +then + echo "------------------ ERROR NOTICE START ------------------" + echo "ERROR, your task haven't used Ascend NPU, please check your npu Migration." + echo "------------------ ERROR NOTICE END------------------" +else + echo "------------------ INFO NOTICE START------------------" + echo "INFO, your task have used Ascend NPU, please check your result." + echo "------------------ INFO NOTICE END------------------" +fi + +# 获取最终的casename，请保留，case文件名为${CaseName} +get_casename + +# 重命名loss文件 +if [ -f ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ]; +then + mv ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ./test/output/${ASCEND_DEVICE_ID}/${CaseName}_loss.txt +fi + +# 训练端到端耗时 +end_time=$(date +%s) +e2e_time=$(( $end_time - $start_time )) + +echo "------------------ Final result ------------------" +# 输出性能FPS/单step耗时/端到端耗时 +echo "Final Performance images/sec : $FPS" +echo "Final Performance sec/step : $StepTime" +echo "E2E Training Duration sec : $e2e_time" + +# 输出训练精度 +echo "Final Train Accuracy : ${train_accuracy}" + +# 最后一个迭代loss值，不需要修改 +ActualLoss=(`awk 'END {print $NF}' $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}_loss.txt`) + +#关键信息打印到${CaseName}.log中，不需要修改 +echo "Network = ${Network}" > $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "RankSize = ${RANK_SIZE}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "BatchSize = ${batch_size}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "DeviceType = `uname -m`" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualFPS = ${FPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "TrainingTime = ${StepTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "E2ETrainingTime = ${e2e_ti_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log \ No newline at end of file -- Gitee From 4a4721a8d021fa09c199e001d03884f14b63086a Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Mon, 11 Jul 2022 13:36:37 +0000 Subject: [PATCH 61/88] =?UTF-8?q?=E5=88=A0=E9=99=A4=E6=96=87=E4=BB=B6=20Te?= =?UTF-8?q?nsorFlow/contrib/cv/SVD=5FID2019=5Ffor=5FTensorflow/README.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../cv/SVD_ID2019_for_Tensorflow/README.md | 250 ------------------ 1 file changed, 250 deletions(-) delete mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md deleted file mode 100644 index 60121bef4..000000000 --- a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md +++ /dev/null @@ -1,250 +0,0 @@ -

基本信息

- -**发布者（Publisher）：Huawei** - -**应用领域（Application Domain）：CV** - -**版本（Version）：** - -**修改时间（Modified） ：2022.05.05** - -**框架（Framework）：TensorFlow 1.15.0** - -**处理器（Processor）：昇腾910** - -**描述（Description）：使用训练好的SVD模型，评估对称正交化在点云对准中的应用效果。** - -

概述

- -给定两个三维点云图，利用SVD正交化过程SVDO+(M)将其投射到SO(3)上，要求网络预测最佳对齐它们的3D旋转。代码的训练逻辑是每训练10w步保存一个模型，并且在测试集上检验该模型的精度，最后比较的都是260w步的模型精度 - -- 开源代码： - - https://github.com/google-research/google-research/tree/master/special_orthogonalization。 - -- 参考论文： - - [An Analysis of SVD for Deep Rotation Estimation](https://arxiv.org/abs/2006.14616) - -- 参考实现： - gpu最后训练出来的模型在 - - obs://cann-id2019/gpu - - 数据在 - obs://cann-id2019/dataset/ - - 共有3个数据集 - - 训练数据集points - - 测试数据集 points_test - - 第一步旋转后的数据集 test_points_modified - - npu训练出来的模型在 - - obs://cann-id2019/dataset/output - - - 相关代码均以上传，在NPU和GPU文件夹中可以找到 - -- 相关迁移的工作： - 在进行代码迁移到NPU上时，输入的训练数据为点云数据，点云数据的shape为(N,3)，其中N并不是固定的，因此在NPU上存在动态shape的问题，导致模型训练无法正常进行。我们为此想了三个解决方法：1、找出所有点云数据中最小的N，对于大于N的点云数据，仅取前N行的数据输入训练。2、找到所有点云数据中最大的N，对于小于N的点云进行补0操作，将所有数据固定为最大的N后，输入网络进行训练。3、找到所有点云数据中最大的N，对小于N的点云数据，从原数据中选择一个点云进行填补至行数为N，再将数据输入网络进行训练。该三种方法均成功解决了NPU上的动态shape问题，但是第一种方法删除了样本点，因此导致最后训练出的模型精度很差；第二种方法虽然并没有丢失样本信息，但是向数据中填入大量的0，改变了本来的代码逻辑，导致最后训练出的模型精度也并不高。对于第三种方法，即没有丢失样本信息，对每个点云数据中的某一个点云样本点进行重复操作，没有改变原始的代码逻辑，最后也获得了不错的精度表现。 - -- 通过Git获取对应commit\_id的代码方法如下： - - ``` - git clone {repository_url} # 克隆仓库的代码 - cd {repository_name} # 切换到模型的代码仓目录 - git checkout {branch} # 切换到对应分支 - git reset --hard ｛commit_id｝ # 代码设置到对应的commit_id - cd ｛code_path｝ # 切换到模型代码所在路径，若仓库下只有该模型，则无需切换 - ``` - -## 默认配置 - -- 数据集获取：obs://cann-id2019/dataset/ - -- 训练超参 - - - log_step_count=200 - - save_summaries_steps=25000 - - train_steps=2600000 - - save_checkpoints_steps=100000 - - eval_examples=39900 - - - -

训练环境准备

- -1. 硬件环境准备请参见各硬件产品文档"[驱动和固件安装升级指南]( https://support.huawei.com/enterprise/zh/category/ai-computing-platform-pid-1557196528909)"。需要在硬件设备上安装与CANN版本配套的固件与驱动。 -2. 宿主机上需要安装Docker并登录[Ascend Hub中心](https://ascendhub.huawei.com/#/detail?name=ascend-tensorflow-arm)获取镜像。 - - 当前模型支持的镜像列表如[表1](#zh-cn_topic_0000001074498056_table1519011227314)所示。 - - **表 1** 镜像列表 - - - - - - - - - - - - -

镜像名称

-

镜像版本

-

配套CANN版本

-
-

20.2.0

-

20.2

-
- - - -## 脚本参数 - -``` -gen_pt_test_data_gpu.py 中的参数 - ---input_test_files 传入需要进行旋转的点云数据集 ---output_directory 存储旋转后点云集的路径 ---random_rotation_axang boole型，若为真将会对传入的数据集采用指定方法旋转，默认为真 ---num_rotations_per_file 每个测试点云的随机旋转增加数。默认为100 - - -main_point_cloud_gpu.py 中的参数 - ---pt_cloud_test_files 测试数据集路径 ---pt_cloud_train_files 熟练数据集路径 ---method 指定用于预测旋转的方式。选项为"svd", "svd-inf", or "gs"。默认为“svd” ---checkpoint_dir 训练模型的存放位置 ---train_steps 训练迭代的次数。默认为2600000 ---save_checkpoints_steps 保存检查点的频率。默认为10000 ---log_step_count 日志记录一次的步数。默认为200 ---save_summaries_steps 保存一次summary的步数。默认为5000 ---learning_rate 默认为1e-5 ---lr_decay 如果为真，则衰减learning rate。默认为假 ---lr_decay_steps learning rate衰减步数。默认为35000 ---lr_decay_rate learning rate衰减速率。默认为0.95 ---predict_all_test 如果为真，则在最新的检查点上运行eval作业，并打印每个输入的误差信息。默认为假 ---eval_examples 测试样本的数量。默认为0 ---print_variable_names 打印模型变量名。默认为假 ---num_train_augmentations 增加每个输入点云的随机旋转数。默认为10 - -``` - - - -## 运行 - -GPU运行命令如下： - -**生成测试数据** - -注：生成的文件test_points_modified、points已包含在dataset文件夹中。 -```bash -# 将路径设置到训练点云图文件 -IN_FILES=/points_test/*.pts - -NEW_TEST_FILES_DIR=/test_points_modified - -AXANG_SAMPLING=True - -# 决定旋转轴角的分布 -AXANG_SAMPLING=True - -python -m special_orthogonalization.gen_pt_test_data_gpu --input_test_files=$IN_FILES --output_directory=$NEW_TEST_FILES_DIR --random_rotation_axang=$AXANG_SAMPLING -``` - -**训练与评价** -```bash -# 将路径设置到原始训练数据 -TRAIN_FILES=/points/*.pts - -#将路径设置到旋转后的训练数据 -TEST_FILES=$NEW_TEST_FILES_DIR/*.pts - -# 指定旋转预测方式 -METHOD=svd - -# 指定ckpt、summaries、评价结果等的存储路径 -OUT_DIR=/path/to/model - -python -m special_orthogonalization.main_point_cloud_gpu --method=$METHOD --checkpoint_dir=$OUT_DIR --log_step_count=200 --save_summaries_steps=25000 --pt_cloud_train_files=$TRAIN_FILES --pt_cloud_test_files=$TEST_FILES --train_steps=2600000 --save_checkpoints_steps=100000 --eval_examples=39900 -``` - -**从所有训练样本中生成统计数据** -```bash -# 打印均值、中位数、标准差和分位数 -python -m special_orthogonalization.main_point_cloud_gpu --method=$METHOD --checkpoint_dir=$OUT_DIR --pt_cloud_test_files=$TEST_FILES --predict_all_test=True -``` -## 运行 - -NPU运行命令方式如下: - -对于所有的三个步骤程序来说，modelarts插件obs桶中的数据路径均要写到真正包含数据的那一个路径 -如在dataset文件夹中含有points、points_test等包含数据的文件夹 -modelarts插件中的数据路径写为 obs://cann-id2019/dataset/ - -**生成测试数据** - -运行这一步我们需要的程序文件为gen_pt_test_data.py、modelarts_entry_Axang.py、genTestData.sh -这三个文件中的代码均不需要修改 -最后生成的旋转后的数据文件存放在obs桶当次程序文件的output路径中，文件名为test_points_modified， -为进行第二步模型训练，需要将生成旋转后的文件转移至obs桶中存放data的路径 - -注：需要确保的是存在obs桶里的data文件名为points_test - -**训练与评价** - -运行这一步我们需要的程序文件为main_point_cloud_boostPerf.py、modelarts_entry_acc_train.py、train_full_1p.sh -这三个文件中的代码均不需要修改 -最后生成的旋转后的数据文件存放在obs桶当次程序文件的output路径中，文件名为test_points_modified， -为进行第三步，需要将生成的output文件转移至obs桶中存放data的路径 - -注意：该次训练的模型保存在该次的obs文件夹中，进行第三步时又需要重启一次新的modelarts，因此我们需要将output文件中的 -checkpoint文件中最新模型的路径修改 -"/home/ma-user/modelarts/inputs/data_url_0/output" -这样第三步才能跑出正确的精度指标 - - -**从所有训练样本中生成统计数据** - -运行这一步我们需要的程序文件为main_point_cloud_boostPerf.py、modelarts_entry_stat.py、genStatistical.sh -这三个文件的代码均不需要修改 -运行成功后将会在屏幕上打印出关于精度相应的统计量值 - -注意：由于加入了混合精度提高训练性能，导致该步出现未知错误，输出的误差均为0，因此建议将训练后的结果下载到本地 -修改checkpoint文件中模型的路径后，利用本地的GPU进行运行，实际上该步骤在运行时NPU成功启动但也未曾利用。 - -## 训练结果 -**精度对比：** - - - -| 测地线误差（°） | 论文发布 | GPU(初始代码未改动版本) | GPU实测|NPU实测 | -| ------------------------ | ------- | ----- | --------- |----------| -| 平均值 | 1.63 | 2.58 | 3.98 | 2.92 | -| 中值 | 0.89 | 1.68 | 2.6 | 1.7 | -| 标准差 | 6.70 | 6.93 | 9.36 | 8.45 | - -相比于论文中的精度，我们NPU迁移后实测差距依然较大，但是与我们未对代码任何改动初始的版本在GPU上跑出来的精度相差较小， -且对于相同的代码的代码改动，NPU上的精度优于GPU上的精度。需要注意的是，在NPU上运行程序时，我们采用混合精度来提升训练 -的性能，但是其中产生了未知的错误，导致代表的精度指标mean_degree_err在整个训练过程中始终为0，因此我们无法得知在NPU训练的 -260w步的过程中，精度指标是下降的过程是怎样的。值得庆幸的是通过NPU训练出的模型，能够在GPU上计算出精度，并且精度还不错。 - -**性能对比：** - -取华为v100上GPU运行的前2w步的global_step/sec平均值和NPU运行的前1w步的global_step/sec平均值进行对比，以达到性能对比的目的。 -对于NPU上的性能计算 需要的程序为main_point_cloud_boostPerf.py、modelarts_entry_perf.py、train_performance_1p.sh，其中参数已经 -设置完毕，无需更改。同时我们上传了我们计算性能的代码calc_perf.py，运行该代码需要将产生的日志文件从obs上下载下来，传入obs文件在本地的路径即可 -| 性能指标项 | 论文发布 | GPU实测 | NPU实测 | -| ------------------- | ------- | ------ | ------ | -| global_step/sec| 无 | 87.64 | 116.77 | - - -- Gitee From cc82c1d5f4edb52cc90ebdb02ad1ce8384f309c9 Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Mon, 11 Jul 2022 13:43:55 +0000 Subject: [PATCH 62/88] =?UTF-8?q?=E5=88=A0=E9=99=A4=E6=96=87=E4=BB=B6=20Te?= =?UTF-8?q?nsorFlow/contrib/cv/SVD=5FID2019=5Ffor=5FTensorflow/main=5Fpoin?= =?UTF-8?q?t=5Fcloud=5FboostPerf.py?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../main_point_cloud_boostPerf.py | 418 ------------------ 1 file changed, 418 deletions(-) delete mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/main_point_cloud_boostPerf.py diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/main_point_cloud_boostPerf.py b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/main_point_cloud_boostPerf.py deleted file mode 100644 index ff8e5563b..000000000 --- a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/main_point_cloud_boostPerf.py +++ /dev/null @@ -1,418 +0,0 @@ -# coding=utf-8 -# Copyright 2021 The Google Research Authors. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""Training and evaluation for the point cloud alignment experiment.""" -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import utils -from absl import flags -import numpy as np -import tensorflow as tf -import glob -import pathlib -import datetime -from npu_bridge.npu_init import * -from npu_bridge.estimator.npu.npu_estimator import NPUEstimatorSpec -from npu_bridge.estimator.npu.npu_estimator import NPUEstimator -from npu_bridge.estimator.npu.npu_config import NPURunConfig -import os -FLAGS = tf.app.flags.FLAGS - -# General flags. -flags.DEFINE_string('method', 'svd', - 'Specifies the method to use for predicting rotations. ' - 'Choices are "svd", "svd-inf", or "gs".') -flags.DEFINE_string('checkpoint_dir', '', - 'Locations for checkpoints, summaries, etc.') -flags.DEFINE_integer('train_steps', 2600000, 'Number of training iterations.') -flags.DEFINE_integer('save_checkpoints_steps', 10000, - 'How often to save checkpoints') -flags.DEFINE_integer('log_step_count', 500, 'How often to log the step count') -flags.DEFINE_integer('save_summaries_steps', 5000, - 'How often to save summaries.') -flags.DEFINE_float('learning_rate', 1e-5, 'Learning rate') -flags.DEFINE_boolean('lr_decay', False, 'Decay the learning rate if True.') -flags.DEFINE_integer('lr_decay_steps', 35000, - 'Learning rate decays steps.') -flags.DEFINE_float('lr_decay_rate', 0.95, - 'Learning rate decays rate.') -flags.DEFINE_boolean('predict_all_test', False, - 'If true, runs an eval job on latest checkpoint and ' - 'prints the error for each input.') -flags.DEFINE_integer('eval_examples', 39900, 'Number of test examples.') -flags.DEFINE_boolean('print_variable_names', False, - 'Print model variable names.') - -# Flags only used in the point cloud alignment experiment. -flags.DEFINE_integer('num_train_augmentations', 10, - 'Number of random rotations for augmenting each input ' - 'point cloud.') -flags.DEFINE_string('pt_cloud_train_files', '', - 'Expression matching all training point files, e.g. ' - '/path/to/files/pc_plane/points/*.pts') -flags.DEFINE_string('pt_cloud_test_files','', - 'Expression matching all modified test point files, e.g. ' - '/path/to/files/pc_plane/points_test/*.pts') -flags.DEFINE_boolean('random_rotation_axang', True, - 'If true, samples random rotations using the method ' - 'from the original benchmark code. Otherwise samples ' - 'by Haar measure.') - - -def pt_features(batch_pts): - """Input shape: [B, N, 3], output shape: [B, 1024].""" - with tf.variable_scope('ptenc', reuse=tf.AUTO_REUSE): - f1 = tf.layers.conv1d(inputs=batch_pts, filters=64, kernel_size=1) - f1 = tf.nn.leaky_relu(f1) - f2 = tf.layers.conv1d(inputs=f1, filters=128, kernel_size=1) - f2 = tf.nn.leaky_relu(f2) - f3 = tf.layers.conv1d(inputs=f2, filters=1024, kernel_size=1) - f = tf.reduce_max(f3, axis=1, keep_dims=False) - return f - - -def regress_from_features(batch_features, out_dim): - """Regress to a rotation representation from point cloud encodings. - - In Zhou et al, CVPR19, the paper describes this regression network as an MLP - mapping 2048->512->512->out_dim, but the associated code implements it with - one less layer: 2048->512->out_dim. We mimic the code. - - Args: - batch_features: [batch_size, in_dim]. - out_dim: desired output dimensionality. - - Returns: - A [batch_size, out_dim] tensor. - """ - f1 = tf.layers.dense(batch_features, 512) - f1 = tf.nn.leaky_relu(f1) - f2 = tf.layers.dense(f1, out_dim) - return f2 - - -def net_point_cloud(points1, points2, mode): - """Predict a relative rotation given two point clouds. - - Args: - points1: [batch_size, N, 3] float tensor. - points2: [batch_size, N, 3] float tensor. - mode: tf.estimator.ModeKeys. - - Returns: - [batch_size, 3, 3] matrices. - """ - f1 = pt_features(points1) - f2 = pt_features(points2) - f = tf.concat([f1, f2], axis=-1) - - if FLAGS.method == 'svd': - p = regress_from_features(f, 9) - return utils.symmetric_orthogonalization(p) - - if FLAGS.method == 'svd-inf': - p = regress_from_features(f, 9) - if mode == tf.estimator.ModeKeys.TRAIN: - return tf.reshape(p, (-1, 3, 3)) - else: - return utils.symmetric_orthogonalization(p) - - if FLAGS.method == 'gs': - p = regress_from_features(f, 6) - return utils.gs_orthogonalization(p) - - -def model_fn(features, labels, mode, params): - """The model_fn used to construct the tf.Estimator.""" - del labels, params # Unused. - if mode == tf.estimator.ModeKeys.TRAIN: - # Training data has point cloud of size [1, N, 3] and random rotations - # of size [1, FLAGS.num_train_augmentations, 3, 3] - rot = features['rot'][0] - num_rot = FLAGS.num_train_augmentations - batch_pts1 = tf.tile(features['data'], [num_rot, 1, 1]) - # In this experiment it does not matter if we pre or post-multiply the - # rotation as long as we are consistent between training and eval. - batch_pts2 = tf.matmul(batch_pts1, rot) # post-multiplying! - else: - # Test data has point cloud of size [1, N, 3] and a single random - # rotation of size [1, 3, 3] - batch_pts1 = features['data'] - rot = features['rot'] - batch_pts2 = tf.matmul(batch_pts1, rot) - rot = tf.reshape(rot, (-1, 3, 3)) - - # Predict the rotation. - r = net_point_cloud(batch_pts1, batch_pts2, mode) - - # Compute the loss. - loss = tf.nn.l2_loss(rot - r) - - # Compute the relative angle in radians. - theta = utils.relative_angle(r, rot) - - # Mean angle error over the batch. - mean_theta = tf.reduce_mean(theta) - mean_theta_deg = mean_theta * 180.0 / np.pi - - # Train, eval, or predict depending on mode. - if mode == tf.estimator.ModeKeys.TRAIN: - tf.summary.scalar('train/loss', loss) - tf.summary.scalar('train/theta', mean_theta_deg) - global_step = tf.train.get_or_create_global_step() - - if FLAGS.lr_decay: - learning_rate = tf.train.exponential_decay( - FLAGS.learning_rate, - global_step, - FLAGS.lr_decay_steps, - FLAGS.lr_decay_rate) - else: - learning_rate = FLAGS.learning_rate - - tf.summary.scalar('train/learning_rate', learning_rate) - optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate) - update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) - with tf.control_dependencies(update_ops): - train_op = optimizer.minimize(loss, global_step=global_step) - #train_op = util.set_graph_exec_config(train_op, dynamic_input=True,dynamic_graph_execute_mode="lazy_recompile") - - return NPUEstimatorSpec( - mode=mode, - loss=loss, - train_op=train_op) - - if mode == tf.estimator.ModeKeys.EVAL: - if FLAGS.predict_all_test: - print_error_op = tf.print('error:', mean_theta_deg) - with tf.control_dependencies([print_error_op]): - eval_metric_ops = { - 'mean_degree_err': tf.metrics.mean(mean_theta_deg), - } - else: - eval_metric_ops = { - 'mean_degree_err': tf.metrics.mean(mean_theta_deg), - } - - return NPUEstimatorSpec( - mode=mode, - loss=loss, - eval_metric_ops=eval_metric_ops) - - if mode == tf.estimator.ModeKeys.PREDICT: - pred = {'error': mean_theta_deg} - return NPUEstimatorSpec( - mode=mode, - predictions=pred) - - -def train_input_fn(): - """Generate training data iterator from the .pts files.""" - def _file_to_matrix(pts_path): - """Read Nx3 point cloud from a .pts file.""" - file_buffer = tf.read_file(pts_path) - lines = tf.string_split([file_buffer], delimiter='\n') - lines1 = tf.string_split(lines.values, delimiter='\r') - values = tf.stack(tf.decode_csv(lines1.values, - record_defaults=[[0.0], [0.0], [0.0]], - field_delim=' ')) - values = tf.transpose(values) # 3xN --> Nx3. - values = values[:tf.shape(values)[0] // 2, :] - - # modified by Lyoung - diff_num = 1500-tf.shape(values)[0] - repeat_pts = tf.tile(tf.reshape(values[1, :], (1, -1)), [diff_num, 1]) - values = tf.concat([values,repeat_pts], axis=0) - # The experiment code in - # github.com/papagina/RotationContinuity/.../shapenet/code/train_pointnet.py - # only used the first half of the points in each file. - return values - - def _random_rotation(pts): - """Attach N random rotations to a point cloud.""" - if FLAGS.random_rotation_axang: - rotations = utils.random_rotation_benchmark(FLAGS.num_train_augmentations) - else: - rotations = utils.random_rotation(FLAGS.num_train_augmentations) - return pts,rotations - - pts_paths = tf.gfile.Glob(FLAGS.pt_cloud_train_files) - dataset = tf.data.Dataset.from_tensor_slices(pts_paths) - dataset = dataset.map(_file_to_matrix) - dataset = dataset.cache() # Comment out if memory cannot hold all the data. - dataset = dataset.shuffle(buffer_size=50, reshuffle_each_iteration=True) - dataset = dataset.repeat() - dataset = dataset.map(_random_rotation) - dataset = dataset.batch(1) - - iterator = tf.data.make_one_shot_iterator(dataset) - batch_data, batch_rot = iterator.get_next() - features_dict = {'data': batch_data, 'rot': batch_rot} - batch_size = tf.shape(batch_data)[0] - batch_labels_dummy = tf.zeros(shape=(batch_size, 1)) - return (features_dict, batch_labels_dummy) - - -def eval_input_fn(): - """Generate test data from *modified* .pts files. - - See README and comments below for details on how the data is modified. - - Returns: - A tuple of features and associated labels. - """ - def _file_to_matrix(pts_path): - """Read Nx3 point cloud and 3x3 rotation matrix from a .pts file. - - The test data is a modified version of the original files. For each .pts - file we have (1) added a 3x3 rotation matrix for testing, and (2) removed - the second half of the point cloud since it is not used at all. - - Args: - pts_path: path to a .pts file. - - Returns: - A Nx3 point cloud. - A 3x3 rotation matrix. - """ - file_buffer = tf.read_file(pts_path) - lines = tf.string_split([file_buffer], delimiter='\n') - lines1 = tf.string_split(lines.values,delimiter='\r') - values = tf.stack(tf.decode_csv(lines1.values, - record_defaults=[[0.0], [0.0], [0.0]],field_delim=' ')) - values = tf.transpose(values)# 3xN --> Nx3. - - #modified by Lyoung - diff_num = 1414-tf.shape(values)[0] - repeat_pts = tf.tile(tf.reshape(values[4, :], (1, -1)), [diff_num, 1]) - values = tf.concat([values, repeat_pts], axis=0) - # First three rows are the rotation matrix, remaining rows the point cloud. - rot = values[:3, :] - - return values[4:, :], rot - - pts_paths = tf.io.gfile.glob(FLAGS.pt_cloud_test_files) - dataset = tf.data.Dataset.from_tensor_slices(pts_paths) - dataset = dataset.map(_file_to_matrix) - dataset = dataset.batch(1) - iterator = tf.data.make_one_shot_iterator(dataset) - batch_data, batch_rot = iterator.get_next() - - features_dict = {'data': batch_data, 'rot': batch_rot} - batch_size = tf.shape(batch_data)[0] - batch_labels_dummy = tf.zeros(shape=(batch_size, 1)) - return (features_dict, batch_labels_dummy) - - -def print_variable_names(): - """Print variable names in a model.""" - params = {'dummy': 0} - estimator = NPUEstimator( - model_fn=model_fn, - model_dir=FLAGS.checkpoint_dir, - params=params) - - names = estimator.get_variable_names() - for name in names: - print(name) - - -def predict_all_test(): - """Print error statistics for the test dataset.""" - - config = NPURunConfig( - save_summary_steps=None, - save_checkpoints_steps=None, - log_step_count_steps=None, - keep_checkpoint_max=None) - - params = {'dummy': 0} - estimator = NPUEstimator( - model_fn=model_fn, - model_dir=FLAGS.checkpoint_dir, - params=params, - config=config) - evals = estimator.predict(input_fn=eval_input_fn, yield_single_examples=True) - - # Print error statistics. - - all_errors = [x['error'] for x in evals] - errors = np.array(all_errors) - print('Evaluated %d examples'%np.size(errors)) - print('Mean error: %f degrees',np.mean(errors)) - print('Median error: %f degrees', np.median(errors)) - print('Std: %f degrees', np.std(errors)) - sorted_errors = np.sort(errors) - n = np.size(sorted_errors) - print('\nPercentiles:') - for perc in range(1, 101): - index = np.int32(np.float32(n * perc) / 100.0) - 1 - print('%3d%%: %f'%(perc, sorted_errors[index])) - - -def train_and_eval(): - """Train and evaluate a model.""" - save_summary_steps = FLAGS.save_summaries_steps - save_checkpoints_steps = FLAGS.save_checkpoints_steps - log_step_count = FLAGS.log_step_count - - config = NPURunConfig( - save_summary_steps=save_summary_steps, - save_checkpoints_steps=save_checkpoints_steps, - log_step_count_steps=log_step_count, - keep_checkpoint_max=None, - precision_mode = "allow_mix_precision") - - - params = {'dummy': 0} - estimator = NPUEstimator( - model_fn=model_fn, - model_dir=FLAGS.checkpoint_dir, - config=config, - params=params) - - - train_spec = tf.estimator.TrainSpec( - input_fn=train_input_fn, - max_steps=FLAGS.train_steps) - - - eval_spec = tf.estimator.EvalSpec(input_fn=eval_input_fn, - start_delay_secs=60, - steps=FLAGS.eval_examples, - throttle_secs=60) - - - - tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec) - - -def main(argv=None): # pylint: disable=unused-argument - - if FLAGS.print_variable_names: - print_variable_names() - return - - if FLAGS.predict_all_test: - predict_all_test() - else: - train_and_eval() - - -if __name__ == '__main__': - - tf.app.run() -- Gitee From a527b855a8b383e34690172f6d96808607a557dc Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Mon, 11 Jul 2022 13:44:25 +0000 Subject: [PATCH 63/88] =?UTF-8?q?=E6=9B=B4=E6=96=B0=E4=BB=A3=E7=A0=81=20?= =?UTF-8?q?=E8=83=BD=E5=A4=9F=E6=AD=A3=E5=B8=B8=E6=89=93=E5=8D=B0=E7=B2=BE?= =?UTF-8?q?=E5=BA=A6=E5=80=BC?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../cv/SVD_ID2019_for_Tensorflow/README.md | 253 ++++++++++ .../main_point_cloud_boostPerf.py | 461 ++++++++++++++++++ 2 files changed, 714 insertions(+) create mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md create mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/main_point_cloud_boostPerf.py diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md new file mode 100644 index 000000000..4a337f831 --- /dev/null +++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md @@ -0,0 +1,253 @@ +

基本信息

+ +**发布者（Publisher）：Huawei** + +**应用领域（Application Domain）：CV** + +**版本（Version）：** + +**修改时间（Modified） ：2022.05.05** + +**框架（Framework）：TensorFlow 1.15.0** + +**处理器（Processor）：昇腾910** + +**描述（Description）：使用训练好的SVD模型，评估对称正交化在点云对准中的应用效果。** + +

概述

+ +给定两个三维点云图，利用SVD正交化过程SVDO+(M)将其投射到SO(3)上，要求网络预测最佳对齐它们的3D旋转。代码的训练逻辑是每训练10w步保存一个模型，并且在测试集上检验该模型的精度，最后比较的都是260w步的模型精度 + +- 开源代码： + + https://github.com/google-research/google-research/tree/master/special_orthogonalization。 + +- 参考论文： + + [An Analysis of SVD for Deep Rotation Estimation](https://arxiv.org/abs/2006.14616) + +- 参考实现： + gpu最后训练出来的模型在 + + obs://cann-id2019/gpu + + 数据在 + obs://cann-id2019/dataset/ + + 共有3个数据集 + + 训练数据集points + + 测试数据集 points_test + + 第一步旋转后的数据集 test_points_modified + + npu训练出来的模型在 + + obs://cann-id2019/dataset/output + + + 相关代码均以上传，在NPU和GPU文件夹中可以找到 + +- 相关迁移的工作： + 在进行代码迁移到NPU上时，输入的训练数据为点云数据，点云数据的shape为(N,3)，其中N并不是固定的，因此在NPU上存在动态shape的问题，导致模型训练无法正常进行。我们为此想了三个解决方法：1、找出所有点云数据中最小的N，对于大于N的点云数据，仅取前N行的数据输入训练。2、找到所有点云数据中最大的N，对于小于N的点云进行补0操作，将所有数据固定为最大的N后，输入网络进行训练。3、找到所有点云数据中最大的N，对小于N的点云数据，从原数据中选择一个点云进行填补至行数为N，再将数据输入网络进行训练。该三种方法均成功解决了NPU上的动态shape问题，但是第一种方法删除了样本点，因此导致最后训练出的模型精度很差；第二种方法虽然并没有丢失样本信息，但是向数据中填入大量的0，改变了本来的代码逻辑，导致最后训练出的模型精度也并不高。对于第三种方法，即没有丢失样本信息，对每个点云数据中的某一个点云样本点进行重复操作，没有改变原始的代码逻辑，最后也获得了不错的精度表现。 + +- 通过Git获取对应commit\_id的代码方法如下： + + ``` + git clone {repository_url} # 克隆仓库的代码 + cd {repository_name} # 切换到模型的代码仓目录 + git checkout {branch} # 切换到对应分支 + git reset --hard ｛commit_id｝ # 代码设置到对应的commit_id + cd ｛code_path｝ # 切换到模型代码所在路径，若仓库下只有该模型，则无需切换 + ``` + +## 默认配置 + +- 数据集获取：obs://cann-id2019/dataset/ + +- 训练超参 + + - log_step_count=200 + - save_summaries_steps=25000 + - train_steps=2600000 + - save_checkpoints_steps=100000 + - eval_examples=39900 + + + +

训练环境准备

+ +1. 硬件环境准备请参见各硬件产品文档"[驱动和固件安装升级指南]( https://support.huawei.com/enterprise/zh/category/ai-computing-platform-pid-1557196528909)"。需要在硬件设备上安装与CANN版本配套的固件与驱动。 +2. 宿主机上需要安装Docker并登录[Ascend Hub中心](https://ascendhub.huawei.com/#/detail?name=ascend-tensorflow-arm)获取镜像。 + + 当前模型支持的镜像列表如[表1](#zh-cn_topic_0000001074498056_table1519011227314)所示。 + + **表 1** 镜像列表 + + + + + + + + + + + + +

镜像名称

+

镜像版本

+

配套CANN版本

+
+

20.2.0

+

20.2

+
+ + + +## 脚本参数 + +``` +gen_pt_test_data_gpu.py 中的参数 + +--input_test_files 传入需要进行旋转的点云数据集 +--output_directory 存储旋转后点云集的路径 +--random_rotation_axang boole型，若为真将会对传入的数据集采用指定方法旋转，默认为真 +--num_rotations_per_file 每个测试点云的随机旋转增加数。默认为100 + + +main_point_cloud_gpu.py 中的参数 + +--pt_cloud_test_files 测试数据集路径 +--pt_cloud_train_files 熟练数据集路径 +--method 指定用于预测旋转的方式。选项为"svd", "svd-inf", or "gs"。默认为“svd” +--checkpoint_dir 训练模型的存放位置 +--train_steps 训练迭代的次数。默认为2600000 +--save_checkpoints_steps 保存检查点的频率。默认为10000 +--log_step_count 日志记录一次的步数。默认为200 +--save_summaries_steps 保存一次summary的步数。默认为5000 +--learning_rate 默认为1e-5 +--lr_decay 如果为真，则衰减learning rate。默认为假 +--lr_decay_steps learning rate衰减步数。默认为35000 +--lr_decay_rate learning rate衰减速率。默认为0.95 +--predict_all_test 如果为真，则在最新的检查点上运行eval作业，并打印每个输入的误差信息。默认为假 +--eval_examples 测试样本的数量。默认为0 +--print_variable_names 打印模型变量名。默认为假 +--num_train_augmentations 增加每个输入点云的随机旋转数。默认为10 + +``` + + + +## 运行 + +GPU运行命令如下： + +**生成测试数据** + +注：生成的文件test_points_modified、points已包含在dataset文件夹中。 +```bash +# 将路径设置到训练点云图文件 +IN_FILES=/points_test/*.pts + +NEW_TEST_FILES_DIR=/test_points_modified + +AXANG_SAMPLING=True + +# 决定旋转轴角的分布 +AXANG_SAMPLING=True + +python -m special_orthogonalization.gen_pt_test_data_gpu --input_test_files=$IN_FILES --output_directory=$NEW_TEST_FILES_DIR --random_rotation_axang=$AXANG_SAMPLING +``` + +**训练与评价** +```bash +# 将路径设置到原始训练数据 +TRAIN_FILES=/points/*.pts + +#将路径设置到旋转后的训练数据 +TEST_FILES=$NEW_TEST_FILES_DIR/*.pts + +# 指定旋转预测方式 +METHOD=svd + +# 指定ckpt、summaries、评价结果等的存储路径 +OUT_DIR=/path/to/model + +python -m special_orthogonalization.main_point_cloud_gpu --method=$METHOD --checkpoint_dir=$OUT_DIR --log_step_count=200 --save_summaries_steps=25000 --pt_cloud_train_files=$TRAIN_FILES --pt_cloud_test_files=$TEST_FILES --train_steps=2600000 --save_checkpoints_steps=100000 --eval_examples=39900 +``` + +**从所有训练样本中生成统计数据** +```bash +# 打印均值、中位数、标准差和分位数 +python -m special_orthogonalization.main_point_cloud_gpu --method=$METHOD --checkpoint_dir=$OUT_DIR --pt_cloud_test_files=$TEST_FILES --predict_all_test=True +``` +## 运行 + +NPU运行命令方式如下: + +对于所有的三个步骤程序来说，modelarts插件obs桶中的数据路径均要写到真正包含数据的那一个路径 +如在dataset文件夹中含有points、points_test等包含数据的文件夹 +modelarts插件中的数据路径写为 obs://cann-id2019/dataset/ + +**生成测试数据** + +运行这一步我们需要的程序文件为gen_pt_test_data.py、modelarts_entry_Axang.py、genTestData.sh +这三个文件中的代码均不需要修改 +最后生成的旋转后的数据文件存放在obs桶当次程序文件的output路径中，文件名为test_points_modified， +为进行第二步模型训练，需要将生成旋转后的文件转移至obs桶中存放data的路径 + +注：需要确保的是存在obs桶里的data文件名为points_test + +**训练与评价** + +运行这一步我们需要的程序文件为main_point_cloud_boostPerf.py、modelarts_entry_acc_train.py、train_full_1p.sh +这三个文件中的代码均不需要修改 + +由于采用混合精度提高训练性能，一些算子计算溢出，为此增添switch_config.txt文件，该文件应该和代码所在目录一致。 + +最后生成的旋转后的数据文件存放在obs桶当次程序文件的output路径中，文件名为test_points_modified， +为进行第三步，需要将生成的output文件转移至obs桶中存放data的路径 + +注意：该次训练的模型保存在该次的obs文件夹中，进行第三步时又需要重启一次新的modelarts，因此我们需要将output文件中的 +checkpoint文件中最新模型的路径修改 +"/home/ma-user/modelarts/inputs/data_url_0/output" +这样第三步才能跑出正确的精度指标 + + +**从所有训练样本中生成统计数据** + +运行这一步我们需要的程序文件为main_point_cloud_boostPerf.py、modelarts_entry_stat.py、genStatistical.sh +这三个文件的代码均不需要修改 + +运行成功后将会在屏幕上打印出关于精度相应的统计量值 + + + +## 训练结果 +**精度对比：** + + + +| 测地线误差（°） | 论文发布 | GPU(初始代码未改动版本) | GPU实测|NPU实测 | +| ------------------------ | ------- | ----- | --------- |----------| +| 平均值 | 1.63 | 2.58 | 3.98 | 2.92 | +| 中值 | 0.89 | 1.68 | 2.6 | 1.7 | +| 标准差 | 6.70 | 6.93 | 9.36 | 8.45 | + +相比于论文中的精度，我们NPU迁移后实测差距依然较大，但是与我们未对代码任何改动初始的版本在GPU上跑出来的精度相差较小， +且对于相同的代码的代码改动，NPU上的精度优于GPU上的精度。需要注意的是，在NPU上运行程序时，我们采用混合精度来提升训练 +的性能，但是其中产生了未知的错误，导致代表的精度指标mean_degree_err在整个训练过程中始终为0，因此我们无法得知在NPU训练的 +260w步的过程中，精度指标是下降的过程是怎样的。值得庆幸的是通过NPU训练出的模型，能够在GPU上计算出精度，并且精度还不错。 + +**性能对比：** + +取华为v100上GPU运行的前2w步的global_step/sec平均值和NPU运行的前1w步的global_step/sec平均值进行对比，以达到性能对比的目的。 +对于NPU上的性能计算 需要的程序为main_point_cloud_boostPerf.py、modelarts_entry_perf.py、train_performance_1p.sh，其中参数已经 +设置完毕，无需更改。同时我们上传了我们计算性能的代码calc_perf.py，运行该代码需要将产生的日志文件从obs上下载下来，传入obs文件在本地的路径即可 +| 性能指标项 | 论文发布 | GPU实测 | NPU实测 | +| ------------------- | ------- | ------ | ------ | +| global_step/sec| 无 | 87.64 | 116.77 | + + diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/main_point_cloud_boostPerf.py b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/main_point_cloud_boostPerf.py new file mode 100644 index 000000000..ccccb007e --- /dev/null +++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/main_point_cloud_boostPerf.py @@ -0,0 +1,461 @@ +# coding=utf-8 +# Copyright 2021 The Google Research Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Training and evaluation for the point cloud alignment experiment.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import utils +from absl import flags +import numpy as np +import tensorflow as tf +import glob +import pathlib +import datetime +from npu_bridge.npu_init import * +from npu_bridge.estimator.npu.npu_estimator import NPUEstimatorSpec +from npu_bridge.estimator.npu.npu_estimator import NPUEstimator +from npu_bridge.estimator.npu.npu_config import NPURunConfig +from npu_bridge.estimator.npu.npu_config import ProfilingConfig +import precision_tool.tf_config as npu_tf_config + +import precision_tool.config.config as CONFIG + +import os +FLAGS = tf.app.flags.FLAGS + +# General flags. +flags.DEFINE_string('method', 'svd', + 'Specifies the method to use for predicting rotations. ' + 'Choices are "svd", "svd-inf", or "gs".') +flags.DEFINE_string('checkpoint_dir', '', + 'Locations for checkpoints, summaries, etc.') +flags.DEFINE_integer('train_steps', 2600000, 'Number of training iterations.') +flags.DEFINE_integer('save_checkpoints_steps', 10000, + 'How often to save checkpoints') +flags.DEFINE_integer('log_step_count', 500, 'How often to log the step count') +flags.DEFINE_integer('save_summaries_steps', 5000, + 'How often to save summaries.') +flags.DEFINE_float('learning_rate', 1e-5, 'Learning rate') +flags.DEFINE_boolean('lr_decay', False, 'Decay the learning rate if True.') +flags.DEFINE_integer('lr_decay_steps', 35000, + 'Learning rate decays steps.') +flags.DEFINE_float('lr_decay_rate', 0.95, + 'Learning rate decays rate.') +flags.DEFINE_boolean('predict_all_test', False, + 'If true, runs an eval job on latest checkpoint and ' + 'prints the error for each input.') +flags.DEFINE_integer('eval_examples', 39900, 'Number of test examples.') +flags.DEFINE_boolean('print_variable_names', False, + 'Print model variable names.') + +# Flags only used in the point cloud alignment experiment. +flags.DEFINE_integer('num_train_augmentations', 10, + 'Number of random rotations for augmenting each input ' + 'point cloud.') +flags.DEFINE_string('pt_cloud_train_files', '', + 'Expression matching all training point files, e.g. ' + '/path/to/files/pc_plane/points/*.pts') +flags.DEFINE_string('pt_cloud_test_files','', + 'Expression matching all modified test point files, e.g. ' + '/path/to/files/pc_plane/points_test/*.pts') +flags.DEFINE_boolean('random_rotation_axang', True, + 'If true, samples random rotations using the method ' + 'from the original benchmark code. Otherwise samples ' + 'by Haar measure.') +flags.DEFINE_boolean('Profiling',False, + 'parse NPU operator performance') + +flags.DEFINE_boolean('Dump',False, + 'overflow test') + + +def pt_features(batch_pts): + """Input shape: [B, N, 3], output shape: [B, 1024].""" + with tf.variable_scope('ptenc', reuse=tf.AUTO_REUSE): + f1 = tf.layers.conv1d(inputs=batch_pts, filters=64, kernel_size=1) + f1 = tf.nn.leaky_relu(f1) + f2 = tf.layers.conv1d(inputs=f1, filters=128, kernel_size=1) + f2 = tf.nn.leaky_relu(f2) + f3 = tf.layers.conv1d(inputs=f2, filters=1024, kernel_size=1) + f = tf.reduce_max(f3, axis=1, keep_dims=False) + return f + + +def regress_from_features(batch_features, out_dim): + """Regress to a rotation representation from point cloud encodings. + + In Zhou et al, CVPR19, the paper describes this regression network as an MLP + mapping 2048->512->512->out_dim, but the associated code implements it with + one less layer: 2048->512->out_dim. We mimic the code. + + Args: + batch_features: [batch_size, in_dim]. + out_dim: desired output dimensionality. + + Returns: + A [batch_size, out_dim] tensor. + """ + f1 = tf.layers.dense(batch_features, 512) + f1 = tf.nn.leaky_relu(f1) + f2 = tf.layers.dense(f1, out_dim) + return f2 + + +def net_point_cloud(points1, points2, mode): + """Predict a relative rotation given two point clouds. + + Args: + points1: [batch_size, N, 3] float tensor. + points2: [batch_size, N, 3] float tensor. + mode: tf.estimator.ModeKeys. + + Returns: + [batch_size, 3, 3] matrices. + """ + f1 = pt_features(points1) + f2 = pt_features(points2) + f = tf.concat([f1, f2], axis=-1) + + if FLAGS.method == 'svd': + p = regress_from_features(f, 9) + return utils.symmetric_orthogonalization(p) + + if FLAGS.method == 'svd-inf': + p = regress_from_features(f, 9) + if mode == tf.estimator.ModeKeys.TRAIN: + return tf.reshape(p, (-1, 3, 3)) + else: + return utils.symmetric_orthogonalization(p) + + if FLAGS.method == 'gs': + p = regress_from_features(f, 6) + return utils.gs_orthogonalization(p) + + +def model_fn(features, labels, mode, params): + """The model_fn used to construct the tf.Estimator.""" + del labels, params # Unused. + if mode == tf.estimator.ModeKeys.TRAIN: + # Training data has point cloud of size [1, N, 3] and random rotations + # of size [1, FLAGS.num_train_augmentations, 3, 3] + rot = features['rot'][0] + num_rot = FLAGS.num_train_augmentations + batch_pts1 = tf.tile(features['data'], [num_rot, 1, 1]) + # In this experiment it does not matter if we pre or post-multiply the + # rotation as long as we are consistent between training and eval. + batch_pts2 = tf.matmul(batch_pts1, rot) # post-multiplying! + else: + # Test data has point cloud of size [1, N, 3] and a single random + # rotation of size [1, 3, 3] + batch_pts1 = features['data'] + rot = features['rot'] + batch_pts2 = tf.matmul(batch_pts1, rot) + rot = tf.reshape(rot, (-1, 3, 3)) + + # Predict the rotation. + r = net_point_cloud(batch_pts1, batch_pts2, mode) + + # Compute the loss. + loss = tf.nn.l2_loss(rot - r) + + # Compute the relative angle in radians. + theta = utils.relative_angle(r, rot) + + # Mean angle error over the batch. + mean_theta = tf.reduce_mean(theta) + mean_theta_deg = mean_theta * 180.0 / np.pi + + # Train, eval, or predict depending on mode. + if mode == tf.estimator.ModeKeys.TRAIN: + tf.summary.scalar('train/loss', loss) + tf.summary.scalar('train/theta', mean_theta_deg) + global_step = tf.train.get_or_create_global_step() + + if FLAGS.lr_decay: + learning_rate = tf.train.exponential_decay( + FLAGS.learning_rate, + global_step, + FLAGS.lr_decay_steps, + FLAGS.lr_decay_rate) + else: + learning_rate = FLAGS.learning_rate + + tf.summary.scalar('train/learning_rate', learning_rate) + optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate) + update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) + with tf.control_dependencies(update_ops): + train_op = optimizer.minimize(loss, global_step=global_step) + #train_op = util.set_graph_exec_config(train_op, dynamic_input=True,dynamic_graph_execute_mode="lazy_recompile") + + return NPUEstimatorSpec( + mode=mode, + loss=loss, + train_op=train_op) + + if mode == tf.estimator.ModeKeys.EVAL: + if FLAGS.predict_all_test: + print_error_op = tf.print('error:', mean_theta_deg) + with tf.control_dependencies([print_error_op]): + eval_metric_ops = { + 'mean_degree_err': tf.metrics.mean(mean_theta_deg), + } + else: + eval_metric_ops = { + 'mean_degree_err': tf.metrics.mean(mean_theta_deg), + } + + return NPUEstimatorSpec( + mode=mode, + loss=loss, + eval_metric_ops=eval_metric_ops) + + if mode == tf.estimator.ModeKeys.PREDICT: + pred = {'error': mean_theta_deg} + return NPUEstimatorSpec( + mode=mode, + predictions=pred) + + +def train_input_fn(): + """Generate training data iterator from the .pts files.""" + def _file_to_matrix(pts_path): + """Read Nx3 point cloud from a .pts file.""" + file_buffer = tf.read_file(pts_path) + lines = tf.string_split([file_buffer], delimiter='\n') + lines1 = tf.string_split(lines.values, delimiter='\r') + values = tf.stack(tf.decode_csv(lines1.values, + record_defaults=[[0.0], [0.0], [0.0]], + field_delim=' ')) + values = tf.transpose(values) # 3xN --> Nx3. + values = values[:tf.shape(values)[0] // 2, :] + + # modified by Lyoung + diff_num = 1500-tf.shape(values)[0] + repeat_pts = tf.tile(tf.reshape(values[1, :], (1, -1)), [diff_num, 1]) + values = tf.concat([values,repeat_pts], axis=0) + # The experiment code in + # github.com/papagina/RotationContinuity/.../shapenet/code/train_pointnet.py + # only used the first half of the points in each file. + return values + + def _random_rotation(pts): + """Attach N random rotations to a point cloud.""" + if FLAGS.random_rotation_axang: + rotations = utils.random_rotation_benchmark(FLAGS.num_train_augmentations) + else: + rotations = utils.random_rotation(FLAGS.num_train_augmentations) + return pts,rotations + + pts_paths = tf.gfile.Glob(FLAGS.pt_cloud_train_files) + dataset = tf.data.Dataset.from_tensor_slices(pts_paths) + dataset = dataset.map(_file_to_matrix) + dataset = dataset.cache() # Comment out if memory cannot hold all the data. + dataset = dataset.shuffle(buffer_size=50, reshuffle_each_iteration=True) + dataset = dataset.repeat() + dataset = dataset.map(_random_rotation) + dataset = dataset.batch(1) + + iterator = tf.data.make_one_shot_iterator(dataset) + batch_data, batch_rot = iterator.get_next() + features_dict = {'data': batch_data, 'rot': batch_rot} + batch_size = tf.shape(batch_data)[0] + batch_labels_dummy = tf.zeros(shape=(batch_size, 1)) + return (features_dict, batch_labels_dummy) + + +def eval_input_fn(): + """Generate test data from *modified* .pts files. + + See README and comments below for details on how the data is modified. + + Returns: + A tuple of features and associated labels. + """ + def _file_to_matrix(pts_path): + """Read Nx3 point cloud and 3x3 rotation matrix from a .pts file. + + The test data is a modified version of the original files. For each .pts + file we have (1) added a 3x3 rotation matrix for testing, and (2) removed + the second half of the point cloud since it is not used at all. + + Args: + pts_path: path to a .pts file. + + Returns: + A Nx3 point cloud. + A 3x3 rotation matrix. + """ + file_buffer = tf.read_file(pts_path) + lines = tf.string_split([file_buffer], delimiter='\n') + lines1 = tf.string_split(lines.values,delimiter='\r') + values = tf.stack(tf.decode_csv(lines1.values, + record_defaults=[[0.0], [0.0], [0.0]],field_delim=' ')) + values = tf.transpose(values)# 3xN --> Nx3. + + #modified by Lyoung + diff_num = 1414-tf.shape(values)[0] + repeat_pts = tf.tile(tf.reshape(values[4, :], (1, -1)), [diff_num, 1]) + values = tf.concat([values, repeat_pts], axis=0) + # First three rows are the rotation matrix, remaining rows the point cloud. + rot = values[:3, :] + + return values[4:, :], rot + + pts_paths = tf.io.gfile.glob(FLAGS.pt_cloud_test_files) + dataset = tf.data.Dataset.from_tensor_slices(pts_paths) + dataset = dataset.map(_file_to_matrix) + dataset = dataset.batch(1) + iterator = tf.data.make_one_shot_iterator(dataset) + batch_data, batch_rot = iterator.get_next() + + features_dict = {'data': batch_data, 'rot': batch_rot} + batch_size = tf.shape(batch_data)[0] + batch_labels_dummy = tf.zeros(shape=(batch_size, 1)) + return (features_dict, batch_labels_dummy) + + +def print_variable_names(): + """Print variable names in a model.""" + params = {'dummy': 0} + estimator = NPUEstimator( + model_fn=model_fn, + model_dir=FLAGS.checkpoint_dir, + params=params) + + names = estimator.get_variable_names() + for name in names: + print(name) + + +def predict_all_test(): + """Print error statistics for the test dataset.""" + + config = NPURunConfig( + save_summary_steps=None, + save_checkpoints_steps=None, + log_step_count_steps=None, + keep_checkpoint_max=None) + + params = {'dummy': 0} + estimator = NPUEstimator( + model_fn=model_fn, + model_dir=FLAGS.checkpoint_dir, + params=params, + config=config) + evals = estimator.predict(input_fn=eval_input_fn, yield_single_examples=True) + + # Print error statistics. + + all_errors = [x['error'] for x in evals] + errors = np.array(all_errors) + print('Evaluated %d examples'%np.size(errors)) + print('Mean error: %f degrees',np.mean(errors)) + print('Median error: %f degrees', np.median(errors)) + print('Std: %f degrees', np.std(errors)) + sorted_errors = np.sort(errors) + n = np.size(sorted_errors) + print('\nPercentiles:') + for perc in range(1, 101): + index = np.int32(np.float32(n * perc) / 100.0) - 1 + print('%3d%%: %f'%(perc, sorted_errors[index])) + + +def train_and_eval(): + """Train and evaluate a model.""" + save_summary_steps = FLAGS.save_summaries_steps + save_checkpoints_steps = FLAGS.save_checkpoints_steps + log_step_count = FLAGS.log_step_count + + + dump_config = npu_tf_config.estimator_dump_config(action='overflow') + + profilingPath=os.path.join(FLAGS.checkpoint_dir,'npu_profiling') + if not os.path.exists(profilingPath): + os.makedirs(profilingPath) + + profiling_options= '{"output":"%s",\ + "task_trace":"on",\ + "aicpu":"on"}'%(profilingPath) + + profiling_config=ProfilingConfig(enable_profiling=True,profiling_options=profiling_options) + session_config=tf.ConfigProto() + + if FLAGS.Profiling: + config = NPURunConfig( + save_summary_steps=save_summary_steps, + save_checkpoints_steps=save_checkpoints_steps, + log_step_count_steps=log_step_count, + keep_checkpoint_max=None, + precision_mode = "allow_mix_precision", + profiling_config=profiling_config, + session_config=session_config) + if FLAGS.Dump == True: + config = NPURunConfig( + save_summary_steps=save_summary_steps, + save_checkpoints_steps=save_checkpoints_steps, + log_step_count_steps=log_step_count, + keep_checkpoint_max=None, + precision_mode="allow_mix_precision", + dump_config=dump_config) + + config = NPURunConfig( + save_summary_steps=save_summary_steps, + save_checkpoints_steps=save_checkpoints_steps, + log_step_count_steps=log_step_count, + keep_checkpoint_max=None, + precision_mode="allow_mix_precision", + customize_dtypes="/home/ma-user/modelarts/user-job-dir/code/switch_config.txt") + + params = {'dummy': 0} + estimator = NPUEstimator( + model_fn=model_fn, + model_dir=FLAGS.checkpoint_dir, + config=config, + params=params) + + + train_spec = tf.estimator.TrainSpec( + input_fn=train_input_fn, + max_steps=FLAGS.train_steps) + + + eval_spec = tf.estimator.EvalSpec(input_fn=eval_input_fn, + start_delay_secs=60, + steps=FLAGS.eval_examples, + throttle_secs=60) + + + + tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec) + + + +def main(argv=None): # pylint: disable=unused-argument + + if FLAGS.print_variable_names: + print_variable_names() + return + + if FLAGS.predict_all_test: + predict_all_test() + else: + train_and_eval() + + +if __name__ == '__main__': + + tf.app.run() -- Gitee From 7b7c8e5d4383f1b1a28b3b1985338b8252eb38dc Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Tue, 12 Jul 2022 02:35:36 +0000 Subject: [PATCH 64/88] =?UTF-8?q?=E5=85=B3=E9=97=AD=E6=BA=A2=E5=87=BA?= =?UTF-8?q?=E7=AE=97=E5=AD=90=E6=96=87=E4=BB=B6?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../contrib/cv/SVD_ID2019_for_Tensorflow/switch_config.txt | 3 +++ 1 file changed, 3 insertions(+) create mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/switch_config.txt diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/switch_config.txt b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/switch_config.txt new file mode 100644 index 000000000..995f4cea2 --- /dev/null +++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/switch_config.txt @@ -0,0 +1,3 @@ +OpType::MatMulV2:InputDtype:float16,float16,float32,OutputDtype:float32 +OpType::BatchMatMul:InputDtype:float16,float16,OutputDtype:float32 +OpType::BatchMatMulV2:InputDtype:float16,float16,OutputDtype:float32 \ No newline at end of file -- Gitee From 07e2f8e5de8bc67aa30a133c8bc1e23932eb544a Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Tue, 12 Jul 2022 02:39:46 +0000 Subject: [PATCH 65/88] =?UTF-8?q?=E5=88=A0=E9=99=A4=E6=96=87=E4=BB=B6=20Te?= =?UTF-8?q?nsorFlow/contrib/cv/SVD=5FID2019=5Ffor=5FTensorflow/main=5Fpoin?= =?UTF-8?q?t=5Fcloud=5FboostPerf.py?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../main_point_cloud_boostPerf.py | 461 ------------------ 1 file changed, 461 deletions(-) delete mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/main_point_cloud_boostPerf.py diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/main_point_cloud_boostPerf.py b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/main_point_cloud_boostPerf.py deleted file mode 100644 index ccccb007e..000000000 --- a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/main_point_cloud_boostPerf.py +++ /dev/null @@ -1,461 +0,0 @@ -# coding=utf-8 -# Copyright 2021 The Google Research Authors. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""Training and evaluation for the point cloud alignment experiment.""" -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import utils -from absl import flags -import numpy as np -import tensorflow as tf -import glob -import pathlib -import datetime -from npu_bridge.npu_init import * -from npu_bridge.estimator.npu.npu_estimator import NPUEstimatorSpec -from npu_bridge.estimator.npu.npu_estimator import NPUEstimator -from npu_bridge.estimator.npu.npu_config import NPURunConfig -from npu_bridge.estimator.npu.npu_config import ProfilingConfig -import precision_tool.tf_config as npu_tf_config - -import precision_tool.config.config as CONFIG - -import os -FLAGS = tf.app.flags.FLAGS - -# General flags. -flags.DEFINE_string('method', 'svd', - 'Specifies the method to use for predicting rotations. ' - 'Choices are "svd", "svd-inf", or "gs".') -flags.DEFINE_string('checkpoint_dir', '', - 'Locations for checkpoints, summaries, etc.') -flags.DEFINE_integer('train_steps', 2600000, 'Number of training iterations.') -flags.DEFINE_integer('save_checkpoints_steps', 10000, - 'How often to save checkpoints') -flags.DEFINE_integer('log_step_count', 500, 'How often to log the step count') -flags.DEFINE_integer('save_summaries_steps', 5000, - 'How often to save summaries.') -flags.DEFINE_float('learning_rate', 1e-5, 'Learning rate') -flags.DEFINE_boolean('lr_decay', False, 'Decay the learning rate if True.') -flags.DEFINE_integer('lr_decay_steps', 35000, - 'Learning rate decays steps.') -flags.DEFINE_float('lr_decay_rate', 0.95, - 'Learning rate decays rate.') -flags.DEFINE_boolean('predict_all_test', False, - 'If true, runs an eval job on latest checkpoint and ' - 'prints the error for each input.') -flags.DEFINE_integer('eval_examples', 39900, 'Number of test examples.') -flags.DEFINE_boolean('print_variable_names', False, - 'Print model variable names.') - -# Flags only used in the point cloud alignment experiment. -flags.DEFINE_integer('num_train_augmentations', 10, - 'Number of random rotations for augmenting each input ' - 'point cloud.') -flags.DEFINE_string('pt_cloud_train_files', '', - 'Expression matching all training point files, e.g. ' - '/path/to/files/pc_plane/points/*.pts') -flags.DEFINE_string('pt_cloud_test_files','', - 'Expression matching all modified test point files, e.g. ' - '/path/to/files/pc_plane/points_test/*.pts') -flags.DEFINE_boolean('random_rotation_axang', True, - 'If true, samples random rotations using the method ' - 'from the original benchmark code. Otherwise samples ' - 'by Haar measure.') -flags.DEFINE_boolean('Profiling',False, - 'parse NPU operator performance') - -flags.DEFINE_boolean('Dump',False, - 'overflow test') - - -def pt_features(batch_pts): - """Input shape: [B, N, 3], output shape: [B, 1024].""" - with tf.variable_scope('ptenc', reuse=tf.AUTO_REUSE): - f1 = tf.layers.conv1d(inputs=batch_pts, filters=64, kernel_size=1) - f1 = tf.nn.leaky_relu(f1) - f2 = tf.layers.conv1d(inputs=f1, filters=128, kernel_size=1) - f2 = tf.nn.leaky_relu(f2) - f3 = tf.layers.conv1d(inputs=f2, filters=1024, kernel_size=1) - f = tf.reduce_max(f3, axis=1, keep_dims=False) - return f - - -def regress_from_features(batch_features, out_dim): - """Regress to a rotation representation from point cloud encodings. - - In Zhou et al, CVPR19, the paper describes this regression network as an MLP - mapping 2048->512->512->out_dim, but the associated code implements it with - one less layer: 2048->512->out_dim. We mimic the code. - - Args: - batch_features: [batch_size, in_dim]. - out_dim: desired output dimensionality. - - Returns: - A [batch_size, out_dim] tensor. - """ - f1 = tf.layers.dense(batch_features, 512) - f1 = tf.nn.leaky_relu(f1) - f2 = tf.layers.dense(f1, out_dim) - return f2 - - -def net_point_cloud(points1, points2, mode): - """Predict a relative rotation given two point clouds. - - Args: - points1: [batch_size, N, 3] float tensor. - points2: [batch_size, N, 3] float tensor. - mode: tf.estimator.ModeKeys. - - Returns: - [batch_size, 3, 3] matrices. - """ - f1 = pt_features(points1) - f2 = pt_features(points2) - f = tf.concat([f1, f2], axis=-1) - - if FLAGS.method == 'svd': - p = regress_from_features(f, 9) - return utils.symmetric_orthogonalization(p) - - if FLAGS.method == 'svd-inf': - p = regress_from_features(f, 9) - if mode == tf.estimator.ModeKeys.TRAIN: - return tf.reshape(p, (-1, 3, 3)) - else: - return utils.symmetric_orthogonalization(p) - - if FLAGS.method == 'gs': - p = regress_from_features(f, 6) - return utils.gs_orthogonalization(p) - - -def model_fn(features, labels, mode, params): - """The model_fn used to construct the tf.Estimator.""" - del labels, params # Unused. - if mode == tf.estimator.ModeKeys.TRAIN: - # Training data has point cloud of size [1, N, 3] and random rotations - # of size [1, FLAGS.num_train_augmentations, 3, 3] - rot = features['rot'][0] - num_rot = FLAGS.num_train_augmentations - batch_pts1 = tf.tile(features['data'], [num_rot, 1, 1]) - # In this experiment it does not matter if we pre or post-multiply the - # rotation as long as we are consistent between training and eval. - batch_pts2 = tf.matmul(batch_pts1, rot) # post-multiplying! - else: - # Test data has point cloud of size [1, N, 3] and a single random - # rotation of size [1, 3, 3] - batch_pts1 = features['data'] - rot = features['rot'] - batch_pts2 = tf.matmul(batch_pts1, rot) - rot = tf.reshape(rot, (-1, 3, 3)) - - # Predict the rotation. - r = net_point_cloud(batch_pts1, batch_pts2, mode) - - # Compute the loss. - loss = tf.nn.l2_loss(rot - r) - - # Compute the relative angle in radians. - theta = utils.relative_angle(r, rot) - - # Mean angle error over the batch. - mean_theta = tf.reduce_mean(theta) - mean_theta_deg = mean_theta * 180.0 / np.pi - - # Train, eval, or predict depending on mode. - if mode == tf.estimator.ModeKeys.TRAIN: - tf.summary.scalar('train/loss', loss) - tf.summary.scalar('train/theta', mean_theta_deg) - global_step = tf.train.get_or_create_global_step() - - if FLAGS.lr_decay: - learning_rate = tf.train.exponential_decay( - FLAGS.learning_rate, - global_step, - FLAGS.lr_decay_steps, - FLAGS.lr_decay_rate) - else: - learning_rate = FLAGS.learning_rate - - tf.summary.scalar('train/learning_rate', learning_rate) - optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate) - update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) - with tf.control_dependencies(update_ops): - train_op = optimizer.minimize(loss, global_step=global_step) - #train_op = util.set_graph_exec_config(train_op, dynamic_input=True,dynamic_graph_execute_mode="lazy_recompile") - - return NPUEstimatorSpec( - mode=mode, - loss=loss, - train_op=train_op) - - if mode == tf.estimator.ModeKeys.EVAL: - if FLAGS.predict_all_test: - print_error_op = tf.print('error:', mean_theta_deg) - with tf.control_dependencies([print_error_op]): - eval_metric_ops = { - 'mean_degree_err': tf.metrics.mean(mean_theta_deg), - } - else: - eval_metric_ops = { - 'mean_degree_err': tf.metrics.mean(mean_theta_deg), - } - - return NPUEstimatorSpec( - mode=mode, - loss=loss, - eval_metric_ops=eval_metric_ops) - - if mode == tf.estimator.ModeKeys.PREDICT: - pred = {'error': mean_theta_deg} - return NPUEstimatorSpec( - mode=mode, - predictions=pred) - - -def train_input_fn(): - """Generate training data iterator from the .pts files.""" - def _file_to_matrix(pts_path): - """Read Nx3 point cloud from a .pts file.""" - file_buffer = tf.read_file(pts_path) - lines = tf.string_split([file_buffer], delimiter='\n') - lines1 = tf.string_split(lines.values, delimiter='\r') - values = tf.stack(tf.decode_csv(lines1.values, - record_defaults=[[0.0], [0.0], [0.0]], - field_delim=' ')) - values = tf.transpose(values) # 3xN --> Nx3. - values = values[:tf.shape(values)[0] // 2, :] - - # modified by Lyoung - diff_num = 1500-tf.shape(values)[0] - repeat_pts = tf.tile(tf.reshape(values[1, :], (1, -1)), [diff_num, 1]) - values = tf.concat([values,repeat_pts], axis=0) - # The experiment code in - # github.com/papagina/RotationContinuity/.../shapenet/code/train_pointnet.py - # only used the first half of the points in each file. - return values - - def _random_rotation(pts): - """Attach N random rotations to a point cloud.""" - if FLAGS.random_rotation_axang: - rotations = utils.random_rotation_benchmark(FLAGS.num_train_augmentations) - else: - rotations = utils.random_rotation(FLAGS.num_train_augmentations) - return pts,rotations - - pts_paths = tf.gfile.Glob(FLAGS.pt_cloud_train_files) - dataset = tf.data.Dataset.from_tensor_slices(pts_paths) - dataset = dataset.map(_file_to_matrix) - dataset = dataset.cache() # Comment out if memory cannot hold all the data. - dataset = dataset.shuffle(buffer_size=50, reshuffle_each_iteration=True) - dataset = dataset.repeat() - dataset = dataset.map(_random_rotation) - dataset = dataset.batch(1) - - iterator = tf.data.make_one_shot_iterator(dataset) - batch_data, batch_rot = iterator.get_next() - features_dict = {'data': batch_data, 'rot': batch_rot} - batch_size = tf.shape(batch_data)[0] - batch_labels_dummy = tf.zeros(shape=(batch_size, 1)) - return (features_dict, batch_labels_dummy) - - -def eval_input_fn(): - """Generate test data from *modified* .pts files. - - See README and comments below for details on how the data is modified. - - Returns: - A tuple of features and associated labels. - """ - def _file_to_matrix(pts_path): - """Read Nx3 point cloud and 3x3 rotation matrix from a .pts file. - - The test data is a modified version of the original files. For each .pts - file we have (1) added a 3x3 rotation matrix for testing, and (2) removed - the second half of the point cloud since it is not used at all. - - Args: - pts_path: path to a .pts file. - - Returns: - A Nx3 point cloud. - A 3x3 rotation matrix. - """ - file_buffer = tf.read_file(pts_path) - lines = tf.string_split([file_buffer], delimiter='\n') - lines1 = tf.string_split(lines.values,delimiter='\r') - values = tf.stack(tf.decode_csv(lines1.values, - record_defaults=[[0.0], [0.0], [0.0]],field_delim=' ')) - values = tf.transpose(values)# 3xN --> Nx3. - - #modified by Lyoung - diff_num = 1414-tf.shape(values)[0] - repeat_pts = tf.tile(tf.reshape(values[4, :], (1, -1)), [diff_num, 1]) - values = tf.concat([values, repeat_pts], axis=0) - # First three rows are the rotation matrix, remaining rows the point cloud. - rot = values[:3, :] - - return values[4:, :], rot - - pts_paths = tf.io.gfile.glob(FLAGS.pt_cloud_test_files) - dataset = tf.data.Dataset.from_tensor_slices(pts_paths) - dataset = dataset.map(_file_to_matrix) - dataset = dataset.batch(1) - iterator = tf.data.make_one_shot_iterator(dataset) - batch_data, batch_rot = iterator.get_next() - - features_dict = {'data': batch_data, 'rot': batch_rot} - batch_size = tf.shape(batch_data)[0] - batch_labels_dummy = tf.zeros(shape=(batch_size, 1)) - return (features_dict, batch_labels_dummy) - - -def print_variable_names(): - """Print variable names in a model.""" - params = {'dummy': 0} - estimator = NPUEstimator( - model_fn=model_fn, - model_dir=FLAGS.checkpoint_dir, - params=params) - - names = estimator.get_variable_names() - for name in names: - print(name) - - -def predict_all_test(): - """Print error statistics for the test dataset.""" - - config = NPURunConfig( - save_summary_steps=None, - save_checkpoints_steps=None, - log_step_count_steps=None, - keep_checkpoint_max=None) - - params = {'dummy': 0} - estimator = NPUEstimator( - model_fn=model_fn, - model_dir=FLAGS.checkpoint_dir, - params=params, - config=config) - evals = estimator.predict(input_fn=eval_input_fn, yield_single_examples=True) - - # Print error statistics. - - all_errors = [x['error'] for x in evals] - errors = np.array(all_errors) - print('Evaluated %d examples'%np.size(errors)) - print('Mean error: %f degrees',np.mean(errors)) - print('Median error: %f degrees', np.median(errors)) - print('Std: %f degrees', np.std(errors)) - sorted_errors = np.sort(errors) - n = np.size(sorted_errors) - print('\nPercentiles:') - for perc in range(1, 101): - index = np.int32(np.float32(n * perc) / 100.0) - 1 - print('%3d%%: %f'%(perc, sorted_errors[index])) - - -def train_and_eval(): - """Train and evaluate a model.""" - save_summary_steps = FLAGS.save_summaries_steps - save_checkpoints_steps = FLAGS.save_checkpoints_steps - log_step_count = FLAGS.log_step_count - - - dump_config = npu_tf_config.estimator_dump_config(action='overflow') - - profilingPath=os.path.join(FLAGS.checkpoint_dir,'npu_profiling') - if not os.path.exists(profilingPath): - os.makedirs(profilingPath) - - profiling_options= '{"output":"%s",\ - "task_trace":"on",\ - "aicpu":"on"}'%(profilingPath) - - profiling_config=ProfilingConfig(enable_profiling=True,profiling_options=profiling_options) - session_config=tf.ConfigProto() - - if FLAGS.Profiling: - config = NPURunConfig( - save_summary_steps=save_summary_steps, - save_checkpoints_steps=save_checkpoints_steps, - log_step_count_steps=log_step_count, - keep_checkpoint_max=None, - precision_mode = "allow_mix_precision", - profiling_config=profiling_config, - session_config=session_config) - if FLAGS.Dump == True: - config = NPURunConfig( - save_summary_steps=save_summary_steps, - save_checkpoints_steps=save_checkpoints_steps, - log_step_count_steps=log_step_count, - keep_checkpoint_max=None, - precision_mode="allow_mix_precision", - dump_config=dump_config) - - config = NPURunConfig( - save_summary_steps=save_summary_steps, - save_checkpoints_steps=save_checkpoints_steps, - log_step_count_steps=log_step_count, - keep_checkpoint_max=None, - precision_mode="allow_mix_precision", - customize_dtypes="/home/ma-user/modelarts/user-job-dir/code/switch_config.txt") - - params = {'dummy': 0} - estimator = NPUEstimator( - model_fn=model_fn, - model_dir=FLAGS.checkpoint_dir, - config=config, - params=params) - - - train_spec = tf.estimator.TrainSpec( - input_fn=train_input_fn, - max_steps=FLAGS.train_steps) - - - eval_spec = tf.estimator.EvalSpec(input_fn=eval_input_fn, - start_delay_secs=60, - steps=FLAGS.eval_examples, - throttle_secs=60) - - - - tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec) - - - -def main(argv=None): # pylint: disable=unused-argument - - if FLAGS.print_variable_names: - print_variable_names() - return - - if FLAGS.predict_all_test: - predict_all_test() - else: - train_and_eval() - - -if __name__ == '__main__': - - tf.app.run() -- Gitee From c32f593dfbc6964d800316383f39bb007a5e75cb Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Tue, 12 Jul 2022 02:40:56 +0000 Subject: [PATCH 66/88] =?UTF-8?q?=E6=9B=B4=E6=96=B0?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../main_point_cloud_boostPerf.py | 461 ++++++++++++++++++ 1 file changed, 461 insertions(+) create mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/main_point_cloud_boostPerf.py diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/main_point_cloud_boostPerf.py b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/main_point_cloud_boostPerf.py new file mode 100644 index 000000000..de8c8723a --- /dev/null +++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/main_point_cloud_boostPerf.py @@ -0,0 +1,461 @@ +# coding=utf-8 +# Copyright 2021 The Google Research Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Training and evaluation for the point cloud alignment experiment.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import utils +from absl import flags +import numpy as np +import tensorflow as tf +import glob +import pathlib +import datetime +from npu_bridge.npu_init import * +from npu_bridge.estimator.npu.npu_estimator import NPUEstimatorSpec +from npu_bridge.estimator.npu.npu_estimator import NPUEstimator +from npu_bridge.estimator.npu.npu_config import NPURunConfig +from npu_bridge.estimator.npu.npu_config import ProfilingConfig +#import precision_tool.tf_config as npu_tf_config + +#import precision_tool.config.config as CONFIG + +import os +FLAGS = tf.app.flags.FLAGS + +# General flags. +flags.DEFINE_string('method', 'svd', + 'Specifies the method to use for predicting rotations. ' + 'Choices are "svd", "svd-inf", or "gs".') +flags.DEFINE_string('checkpoint_dir', '', + 'Locations for checkpoints, summaries, etc.') +flags.DEFINE_integer('train_steps', 2600000, 'Number of training iterations.') +flags.DEFINE_integer('save_checkpoints_steps', 10000, + 'How often to save checkpoints') +flags.DEFINE_integer('log_step_count', 500, 'How often to log the step count') +flags.DEFINE_integer('save_summaries_steps', 5000, + 'How often to save summaries.') +flags.DEFINE_float('learning_rate', 1e-5, 'Learning rate') +flags.DEFINE_boolean('lr_decay', False, 'Decay the learning rate if True.') +flags.DEFINE_integer('lr_decay_steps', 35000, + 'Learning rate decays steps.') +flags.DEFINE_float('lr_decay_rate', 0.95, + 'Learning rate decays rate.') +flags.DEFINE_boolean('predict_all_test', False, + 'If true, runs an eval job on latest checkpoint and ' + 'prints the error for each input.') +flags.DEFINE_integer('eval_examples', 39900, 'Number of test examples.') +flags.DEFINE_boolean('print_variable_names', False, + 'Print model variable names.') + +# Flags only used in the point cloud alignment experiment. +flags.DEFINE_integer('num_train_augmentations', 10, + 'Number of random rotations for augmenting each input ' + 'point cloud.') +flags.DEFINE_string('pt_cloud_train_files', '', + 'Expression matching all training point files, e.g. ' + '/path/to/files/pc_plane/points/*.pts') +flags.DEFINE_string('pt_cloud_test_files','', + 'Expression matching all modified test point files, e.g. ' + '/path/to/files/pc_plane/points_test/*.pts') +flags.DEFINE_boolean('random_rotation_axang', True, + 'If true, samples random rotations using the method ' + 'from the original benchmark code. Otherwise samples ' + 'by Haar measure.') +flags.DEFINE_boolean('Profiling',False, + 'parse NPU operator performance') + +flags.DEFINE_boolean('Dump',False, + 'overflow test') + + +def pt_features(batch_pts): + """Input shape: [B, N, 3], output shape: [B, 1024].""" + with tf.variable_scope('ptenc', reuse=tf.AUTO_REUSE): + f1 = tf.layers.conv1d(inputs=batch_pts, filters=64, kernel_size=1) + f1 = tf.nn.leaky_relu(f1) + f2 = tf.layers.conv1d(inputs=f1, filters=128, kernel_size=1) + f2 = tf.nn.leaky_relu(f2) + f3 = tf.layers.conv1d(inputs=f2, filters=1024, kernel_size=1) + f = tf.reduce_max(f3, axis=1, keep_dims=False) + return f + + +def regress_from_features(batch_features, out_dim): + """Regress to a rotation representation from point cloud encodings. + + In Zhou et al, CVPR19, the paper describes this regression network as an MLP + mapping 2048->512->512->out_dim, but the associated code implements it with + one less layer: 2048->512->out_dim. We mimic the code. + + Args: + batch_features: [batch_size, in_dim]. + out_dim: desired output dimensionality. + + Returns: + A [batch_size, out_dim] tensor. + """ + f1 = tf.layers.dense(batch_features, 512) + f1 = tf.nn.leaky_relu(f1) + f2 = tf.layers.dense(f1, out_dim) + return f2 + + +def net_point_cloud(points1, points2, mode): + """Predict a relative rotation given two point clouds. + + Args: + points1: [batch_size, N, 3] float tensor. + points2: [batch_size, N, 3] float tensor. + mode: tf.estimator.ModeKeys. + + Returns: + [batch_size, 3, 3] matrices. + """ + f1 = pt_features(points1) + f2 = pt_features(points2) + f = tf.concat([f1, f2], axis=-1) + + if FLAGS.method == 'svd': + p = regress_from_features(f, 9) + return utils.symmetric_orthogonalization(p) + + if FLAGS.method == 'svd-inf': + p = regress_from_features(f, 9) + if mode == tf.estimator.ModeKeys.TRAIN: + return tf.reshape(p, (-1, 3, 3)) + else: + return utils.symmetric_orthogonalization(p) + + if FLAGS.method == 'gs': + p = regress_from_features(f, 6) + return utils.gs_orthogonalization(p) + + +def model_fn(features, labels, mode, params): + """The model_fn used to construct the tf.Estimator.""" + del labels, params # Unused. + if mode == tf.estimator.ModeKeys.TRAIN: + # Training data has point cloud of size [1, N, 3] and random rotations + # of size [1, FLAGS.num_train_augmentations, 3, 3] + rot = features['rot'][0] + num_rot = FLAGS.num_train_augmentations + batch_pts1 = tf.tile(features['data'], [num_rot, 1, 1]) + # In this experiment it does not matter if we pre or post-multiply the + # rotation as long as we are consistent between training and eval. + batch_pts2 = tf.matmul(batch_pts1, rot) # post-multiplying! + else: + # Test data has point cloud of size [1, N, 3] and a single random + # rotation of size [1, 3, 3] + batch_pts1 = features['data'] + rot = features['rot'] + batch_pts2 = tf.matmul(batch_pts1, rot) + rot = tf.reshape(rot, (-1, 3, 3)) + + # Predict the rotation. + r = net_point_cloud(batch_pts1, batch_pts2, mode) + + # Compute the loss. + loss = tf.nn.l2_loss(rot - r) + + # Compute the relative angle in radians. + theta = utils.relative_angle(r, rot) + + # Mean angle error over the batch. + mean_theta = tf.reduce_mean(theta) + mean_theta_deg = mean_theta * 180.0 / np.pi + + # Train, eval, or predict depending on mode. + if mode == tf.estimator.ModeKeys.TRAIN: + tf.summary.scalar('train/loss', loss) + tf.summary.scalar('train/theta', mean_theta_deg) + global_step = tf.train.get_or_create_global_step() + + if FLAGS.lr_decay: + learning_rate = tf.train.exponential_decay( + FLAGS.learning_rate, + global_step, + FLAGS.lr_decay_steps, + FLAGS.lr_decay_rate) + else: + learning_rate = FLAGS.learning_rate + + tf.summary.scalar('train/learning_rate', learning_rate) + optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate) + update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) + with tf.control_dependencies(update_ops): + train_op = optimizer.minimize(loss, global_step=global_step) + #train_op = util.set_graph_exec_config(train_op, dynamic_input=True,dynamic_graph_execute_mode="lazy_recompile") + + return NPUEstimatorSpec( + mode=mode, + loss=loss, + train_op=train_op) + + if mode == tf.estimator.ModeKeys.EVAL: + if FLAGS.predict_all_test: + print_error_op = tf.print('error:', mean_theta_deg) + with tf.control_dependencies([print_error_op]): + eval_metric_ops = { + 'mean_degree_err': tf.metrics.mean(mean_theta_deg), + } + else: + eval_metric_ops = { + 'mean_degree_err': tf.metrics.mean(mean_theta_deg), + } + + return NPUEstimatorSpec( + mode=mode, + loss=loss, + eval_metric_ops=eval_metric_ops) + + if mode == tf.estimator.ModeKeys.PREDICT: + pred = {'error': mean_theta_deg} + return NPUEstimatorSpec( + mode=mode, + predictions=pred) + + +def train_input_fn(): + """Generate training data iterator from the .pts files.""" + def _file_to_matrix(pts_path): + """Read Nx3 point cloud from a .pts file.""" + file_buffer = tf.read_file(pts_path) + lines = tf.string_split([file_buffer], delimiter='\n') + lines1 = tf.string_split(lines.values, delimiter='\r') + values = tf.stack(tf.decode_csv(lines1.values, + record_defaults=[[0.0], [0.0], [0.0]], + field_delim=' ')) + values = tf.transpose(values) # 3xN --> Nx3. + values = values[:tf.shape(values)[0] // 2, :] + + # modified by Lyoung + diff_num = 1500-tf.shape(values)[0] + repeat_pts = tf.tile(tf.reshape(values[1, :], (1, -1)), [diff_num, 1]) + values = tf.concat([values,repeat_pts], axis=0) + # The experiment code in + # github.com/papagina/RotationContinuity/.../shapenet/code/train_pointnet.py + # only used the first half of the points in each file. + return values + + def _random_rotation(pts): + """Attach N random rotations to a point cloud.""" + if FLAGS.random_rotation_axang: + rotations = utils.random_rotation_benchmark(FLAGS.num_train_augmentations) + else: + rotations = utils.random_rotation(FLAGS.num_train_augmentations) + return pts,rotations + + pts_paths = tf.gfile.Glob(FLAGS.pt_cloud_train_files) + dataset = tf.data.Dataset.from_tensor_slices(pts_paths) + dataset = dataset.map(_file_to_matrix) + dataset = dataset.cache() # Comment out if memory cannot hold all the data. + dataset = dataset.shuffle(buffer_size=50, reshuffle_each_iteration=True) + dataset = dataset.repeat() + dataset = dataset.map(_random_rotation) + dataset = dataset.batch(1) + + iterator = tf.data.make_one_shot_iterator(dataset) + batch_data, batch_rot = iterator.get_next() + features_dict = {'data': batch_data, 'rot': batch_rot} + batch_size = tf.shape(batch_data)[0] + batch_labels_dummy = tf.zeros(shape=(batch_size, 1)) + return (features_dict, batch_labels_dummy) + + +def eval_input_fn(): + """Generate test data from *modified* .pts files. + + See README and comments below for details on how the data is modified. + + Returns: + A tuple of features and associated labels. + """ + def _file_to_matrix(pts_path): + """Read Nx3 point cloud and 3x3 rotation matrix from a .pts file. + + The test data is a modified version of the original files. For each .pts + file we have (1) added a 3x3 rotation matrix for testing, and (2) removed + the second half of the point cloud since it is not used at all. + + Args: + pts_path: path to a .pts file. + + Returns: + A Nx3 point cloud. + A 3x3 rotation matrix. + """ + file_buffer = tf.read_file(pts_path) + lines = tf.string_split([file_buffer], delimiter='\n') + lines1 = tf.string_split(lines.values,delimiter='\r') + values = tf.stack(tf.decode_csv(lines1.values, + record_defaults=[[0.0], [0.0], [0.0]],field_delim=' ')) + values = tf.transpose(values)# 3xN --> Nx3. + + #modified by Lyoung + diff_num = 1414-tf.shape(values)[0] + repeat_pts = tf.tile(tf.reshape(values[4, :], (1, -1)), [diff_num, 1]) + values = tf.concat([values, repeat_pts], axis=0) + # First three rows are the rotation matrix, remaining rows the point cloud. + rot = values[:3, :] + + return values[4:, :], rot + + pts_paths = tf.io.gfile.glob(FLAGS.pt_cloud_test_files) + dataset = tf.data.Dataset.from_tensor_slices(pts_paths) + dataset = dataset.map(_file_to_matrix) + dataset = dataset.batch(1) + iterator = tf.data.make_one_shot_iterator(dataset) + batch_data, batch_rot = iterator.get_next() + + features_dict = {'data': batch_data, 'rot': batch_rot} + batch_size = tf.shape(batch_data)[0] + batch_labels_dummy = tf.zeros(shape=(batch_size, 1)) + return (features_dict, batch_labels_dummy) + + +def print_variable_names(): + """Print variable names in a model.""" + params = {'dummy': 0} + estimator = NPUEstimator( + model_fn=model_fn, + model_dir=FLAGS.checkpoint_dir, + params=params) + + names = estimator.get_variable_names() + for name in names: + print(name) + + +def predict_all_test(): + """Print error statistics for the test dataset.""" + + config = NPURunConfig( + save_summary_steps=None, + save_checkpoints_steps=None, + log_step_count_steps=None, + keep_checkpoint_max=None) + + params = {'dummy': 0} + estimator = NPUEstimator( + model_fn=model_fn, + model_dir=FLAGS.checkpoint_dir, + params=params, + config=config) + evals = estimator.predict(input_fn=eval_input_fn, yield_single_examples=True) + + # Print error statistics. + + all_errors = [x['error'] for x in evals] + errors = np.array(all_errors) + print('Evaluated %d examples'%np.size(errors)) + print('Mean error: %f degrees',np.mean(errors)) + print('Median error: %f degrees', np.median(errors)) + print('Std: %f degrees', np.std(errors)) + sorted_errors = np.sort(errors) + n = np.size(sorted_errors) + print('\nPercentiles:') + for perc in range(1, 101): + index = np.int32(np.float32(n * perc) / 100.0) - 1 + print('%3d%%: %f'%(perc, sorted_errors[index])) + + +def train_and_eval(): + """Train and evaluate a model.""" + save_summary_steps = FLAGS.save_summaries_steps + save_checkpoints_steps = FLAGS.save_checkpoints_steps + log_step_count = FLAGS.log_step_count + + + # dump_config = npu_tf_config.estimator_dump_config(action='overflow') + # + # profilingPath=os.path.join(FLAGS.checkpoint_dir,'npu_profiling') + # if not os.path.exists(profilingPath): + # os.makedirs(profilingPath) + # + # profiling_options= '{"output":"%s",\ + # "task_trace":"on",\ + # "aicpu":"on"}'%(profilingPath) + # + # profiling_config=ProfilingConfig(enable_profiling=True,profiling_options=profiling_options) + # session_config=tf.ConfigProto() + # + # if FLAGS.Profiling: + # config = NPURunConfig( + # save_summary_steps=save_summary_steps, + # save_checkpoints_steps=save_checkpoints_steps, + # log_step_count_steps=log_step_count, + # keep_checkpoint_max=None, + # precision_mode = "allow_mix_precision", + # profiling_config=profiling_config, + # session_config=session_config) + # if FLAGS.Dump == True: + # config = NPURunConfig( + # save_summary_steps=save_summary_steps, + # save_checkpoints_steps=save_checkpoints_steps, + # log_step_count_steps=log_step_count, + # keep_checkpoint_max=None, + # precision_mode="allow_mix_precision", + # dump_config=dump_config) + + config = NPURunConfig( + save_summary_steps=save_summary_steps, + save_checkpoints_steps=save_checkpoints_steps, + log_step_count_steps=log_step_count, + keep_checkpoint_max=None, + precision_mode="allow_mix_precision", + customize_dtypes="/home/ma-user/modelarts/user-job-dir/code/switch_config.txt") + + params = {'dummy': 0} + estimator = NPUEstimator( + model_fn=model_fn, + model_dir=FLAGS.checkpoint_dir, + config=config, + params=params) + + + train_spec = tf.estimator.TrainSpec( + input_fn=train_input_fn, + max_steps=FLAGS.train_steps) + + + eval_spec = tf.estimator.EvalSpec(input_fn=eval_input_fn, + start_delay_secs=60, + steps=FLAGS.eval_examples, + throttle_secs=60) + + + + tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec) + + + +def main(argv=None): # pylint: disable=unused-argument + + if FLAGS.print_variable_names: + print_variable_names() + return + + if FLAGS.predict_all_test: + predict_all_test() + else: + train_and_eval() + + +if __name__ == '__main__': + + tf.app.run() -- Gitee From 26523534da30c8b32127e4fc67e8dfc27859445c Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Tue, 12 Jul 2022 08:13:46 +0000 Subject: [PATCH 67/88] =?UTF-8?q?=E5=88=A0=E9=99=A4=E6=96=87=E4=BB=B6=20Te?= =?UTF-8?q?nsorFlow/contrib/cv/SVD=5FID2019=5Ffor=5FTensorflow/main=5Fpoin?= =?UTF-8?q?t=5Fcloud=5FboostPerf.py?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../main_point_cloud_boostPerf.py | 461 ------------------ 1 file changed, 461 deletions(-) delete mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/main_point_cloud_boostPerf.py diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/main_point_cloud_boostPerf.py b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/main_point_cloud_boostPerf.py deleted file mode 100644 index de8c8723a..000000000 --- a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/main_point_cloud_boostPerf.py +++ /dev/null @@ -1,461 +0,0 @@ -# coding=utf-8 -# Copyright 2021 The Google Research Authors. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""Training and evaluation for the point cloud alignment experiment.""" -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import utils -from absl import flags -import numpy as np -import tensorflow as tf -import glob -import pathlib -import datetime -from npu_bridge.npu_init import * -from npu_bridge.estimator.npu.npu_estimator import NPUEstimatorSpec -from npu_bridge.estimator.npu.npu_estimator import NPUEstimator -from npu_bridge.estimator.npu.npu_config import NPURunConfig -from npu_bridge.estimator.npu.npu_config import ProfilingConfig -#import precision_tool.tf_config as npu_tf_config - -#import precision_tool.config.config as CONFIG - -import os -FLAGS = tf.app.flags.FLAGS - -# General flags. -flags.DEFINE_string('method', 'svd', - 'Specifies the method to use for predicting rotations. ' - 'Choices are "svd", "svd-inf", or "gs".') -flags.DEFINE_string('checkpoint_dir', '', - 'Locations for checkpoints, summaries, etc.') -flags.DEFINE_integer('train_steps', 2600000, 'Number of training iterations.') -flags.DEFINE_integer('save_checkpoints_steps', 10000, - 'How often to save checkpoints') -flags.DEFINE_integer('log_step_count', 500, 'How often to log the step count') -flags.DEFINE_integer('save_summaries_steps', 5000, - 'How often to save summaries.') -flags.DEFINE_float('learning_rate', 1e-5, 'Learning rate') -flags.DEFINE_boolean('lr_decay', False, 'Decay the learning rate if True.') -flags.DEFINE_integer('lr_decay_steps', 35000, - 'Learning rate decays steps.') -flags.DEFINE_float('lr_decay_rate', 0.95, - 'Learning rate decays rate.') -flags.DEFINE_boolean('predict_all_test', False, - 'If true, runs an eval job on latest checkpoint and ' - 'prints the error for each input.') -flags.DEFINE_integer('eval_examples', 39900, 'Number of test examples.') -flags.DEFINE_boolean('print_variable_names', False, - 'Print model variable names.') - -# Flags only used in the point cloud alignment experiment. -flags.DEFINE_integer('num_train_augmentations', 10, - 'Number of random rotations for augmenting each input ' - 'point cloud.') -flags.DEFINE_string('pt_cloud_train_files', '', - 'Expression matching all training point files, e.g. ' - '/path/to/files/pc_plane/points/*.pts') -flags.DEFINE_string('pt_cloud_test_files','', - 'Expression matching all modified test point files, e.g. ' - '/path/to/files/pc_plane/points_test/*.pts') -flags.DEFINE_boolean('random_rotation_axang', True, - 'If true, samples random rotations using the method ' - 'from the original benchmark code. Otherwise samples ' - 'by Haar measure.') -flags.DEFINE_boolean('Profiling',False, - 'parse NPU operator performance') - -flags.DEFINE_boolean('Dump',False, - 'overflow test') - - -def pt_features(batch_pts): - """Input shape: [B, N, 3], output shape: [B, 1024].""" - with tf.variable_scope('ptenc', reuse=tf.AUTO_REUSE): - f1 = tf.layers.conv1d(inputs=batch_pts, filters=64, kernel_size=1) - f1 = tf.nn.leaky_relu(f1) - f2 = tf.layers.conv1d(inputs=f1, filters=128, kernel_size=1) - f2 = tf.nn.leaky_relu(f2) - f3 = tf.layers.conv1d(inputs=f2, filters=1024, kernel_size=1) - f = tf.reduce_max(f3, axis=1, keep_dims=False) - return f - - -def regress_from_features(batch_features, out_dim): - """Regress to a rotation representation from point cloud encodings. - - In Zhou et al, CVPR19, the paper describes this regression network as an MLP - mapping 2048->512->512->out_dim, but the associated code implements it with - one less layer: 2048->512->out_dim. We mimic the code. - - Args: - batch_features: [batch_size, in_dim]. - out_dim: desired output dimensionality. - - Returns: - A [batch_size, out_dim] tensor. - """ - f1 = tf.layers.dense(batch_features, 512) - f1 = tf.nn.leaky_relu(f1) - f2 = tf.layers.dense(f1, out_dim) - return f2 - - -def net_point_cloud(points1, points2, mode): - """Predict a relative rotation given two point clouds. - - Args: - points1: [batch_size, N, 3] float tensor. - points2: [batch_size, N, 3] float tensor. - mode: tf.estimator.ModeKeys. - - Returns: - [batch_size, 3, 3] matrices. - """ - f1 = pt_features(points1) - f2 = pt_features(points2) - f = tf.concat([f1, f2], axis=-1) - - if FLAGS.method == 'svd': - p = regress_from_features(f, 9) - return utils.symmetric_orthogonalization(p) - - if FLAGS.method == 'svd-inf': - p = regress_from_features(f, 9) - if mode == tf.estimator.ModeKeys.TRAIN: - return tf.reshape(p, (-1, 3, 3)) - else: - return utils.symmetric_orthogonalization(p) - - if FLAGS.method == 'gs': - p = regress_from_features(f, 6) - return utils.gs_orthogonalization(p) - - -def model_fn(features, labels, mode, params): - """The model_fn used to construct the tf.Estimator.""" - del labels, params # Unused. - if mode == tf.estimator.ModeKeys.TRAIN: - # Training data has point cloud of size [1, N, 3] and random rotations - # of size [1, FLAGS.num_train_augmentations, 3, 3] - rot = features['rot'][0] - num_rot = FLAGS.num_train_augmentations - batch_pts1 = tf.tile(features['data'], [num_rot, 1, 1]) - # In this experiment it does not matter if we pre or post-multiply the - # rotation as long as we are consistent between training and eval. - batch_pts2 = tf.matmul(batch_pts1, rot) # post-multiplying! - else: - # Test data has point cloud of size [1, N, 3] and a single random - # rotation of size [1, 3, 3] - batch_pts1 = features['data'] - rot = features['rot'] - batch_pts2 = tf.matmul(batch_pts1, rot) - rot = tf.reshape(rot, (-1, 3, 3)) - - # Predict the rotation. - r = net_point_cloud(batch_pts1, batch_pts2, mode) - - # Compute the loss. - loss = tf.nn.l2_loss(rot - r) - - # Compute the relative angle in radians. - theta = utils.relative_angle(r, rot) - - # Mean angle error over the batch. - mean_theta = tf.reduce_mean(theta) - mean_theta_deg = mean_theta * 180.0 / np.pi - - # Train, eval, or predict depending on mode. - if mode == tf.estimator.ModeKeys.TRAIN: - tf.summary.scalar('train/loss', loss) - tf.summary.scalar('train/theta', mean_theta_deg) - global_step = tf.train.get_or_create_global_step() - - if FLAGS.lr_decay: - learning_rate = tf.train.exponential_decay( - FLAGS.learning_rate, - global_step, - FLAGS.lr_decay_steps, - FLAGS.lr_decay_rate) - else: - learning_rate = FLAGS.learning_rate - - tf.summary.scalar('train/learning_rate', learning_rate) - optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate) - update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) - with tf.control_dependencies(update_ops): - train_op = optimizer.minimize(loss, global_step=global_step) - #train_op = util.set_graph_exec_config(train_op, dynamic_input=True,dynamic_graph_execute_mode="lazy_recompile") - - return NPUEstimatorSpec( - mode=mode, - loss=loss, - train_op=train_op) - - if mode == tf.estimator.ModeKeys.EVAL: - if FLAGS.predict_all_test: - print_error_op = tf.print('error:', mean_theta_deg) - with tf.control_dependencies([print_error_op]): - eval_metric_ops = { - 'mean_degree_err': tf.metrics.mean(mean_theta_deg), - } - else: - eval_metric_ops = { - 'mean_degree_err': tf.metrics.mean(mean_theta_deg), - } - - return NPUEstimatorSpec( - mode=mode, - loss=loss, - eval_metric_ops=eval_metric_ops) - - if mode == tf.estimator.ModeKeys.PREDICT: - pred = {'error': mean_theta_deg} - return NPUEstimatorSpec( - mode=mode, - predictions=pred) - - -def train_input_fn(): - """Generate training data iterator from the .pts files.""" - def _file_to_matrix(pts_path): - """Read Nx3 point cloud from a .pts file.""" - file_buffer = tf.read_file(pts_path) - lines = tf.string_split([file_buffer], delimiter='\n') - lines1 = tf.string_split(lines.values, delimiter='\r') - values = tf.stack(tf.decode_csv(lines1.values, - record_defaults=[[0.0], [0.0], [0.0]], - field_delim=' ')) - values = tf.transpose(values) # 3xN --> Nx3. - values = values[:tf.shape(values)[0] // 2, :] - - # modified by Lyoung - diff_num = 1500-tf.shape(values)[0] - repeat_pts = tf.tile(tf.reshape(values[1, :], (1, -1)), [diff_num, 1]) - values = tf.concat([values,repeat_pts], axis=0) - # The experiment code in - # github.com/papagina/RotationContinuity/.../shapenet/code/train_pointnet.py - # only used the first half of the points in each file. - return values - - def _random_rotation(pts): - """Attach N random rotations to a point cloud.""" - if FLAGS.random_rotation_axang: - rotations = utils.random_rotation_benchmark(FLAGS.num_train_augmentations) - else: - rotations = utils.random_rotation(FLAGS.num_train_augmentations) - return pts,rotations - - pts_paths = tf.gfile.Glob(FLAGS.pt_cloud_train_files) - dataset = tf.data.Dataset.from_tensor_slices(pts_paths) - dataset = dataset.map(_file_to_matrix) - dataset = dataset.cache() # Comment out if memory cannot hold all the data. - dataset = dataset.shuffle(buffer_size=50, reshuffle_each_iteration=True) - dataset = dataset.repeat() - dataset = dataset.map(_random_rotation) - dataset = dataset.batch(1) - - iterator = tf.data.make_one_shot_iterator(dataset) - batch_data, batch_rot = iterator.get_next() - features_dict = {'data': batch_data, 'rot': batch_rot} - batch_size = tf.shape(batch_data)[0] - batch_labels_dummy = tf.zeros(shape=(batch_size, 1)) - return (features_dict, batch_labels_dummy) - - -def eval_input_fn(): - """Generate test data from *modified* .pts files. - - See README and comments below for details on how the data is modified. - - Returns: - A tuple of features and associated labels. - """ - def _file_to_matrix(pts_path): - """Read Nx3 point cloud and 3x3 rotation matrix from a .pts file. - - The test data is a modified version of the original files. For each .pts - file we have (1) added a 3x3 rotation matrix for testing, and (2) removed - the second half of the point cloud since it is not used at all. - - Args: - pts_path: path to a .pts file. - - Returns: - A Nx3 point cloud. - A 3x3 rotation matrix. - """ - file_buffer = tf.read_file(pts_path) - lines = tf.string_split([file_buffer], delimiter='\n') - lines1 = tf.string_split(lines.values,delimiter='\r') - values = tf.stack(tf.decode_csv(lines1.values, - record_defaults=[[0.0], [0.0], [0.0]],field_delim=' ')) - values = tf.transpose(values)# 3xN --> Nx3. - - #modified by Lyoung - diff_num = 1414-tf.shape(values)[0] - repeat_pts = tf.tile(tf.reshape(values[4, :], (1, -1)), [diff_num, 1]) - values = tf.concat([values, repeat_pts], axis=0) - # First three rows are the rotation matrix, remaining rows the point cloud. - rot = values[:3, :] - - return values[4:, :], rot - - pts_paths = tf.io.gfile.glob(FLAGS.pt_cloud_test_files) - dataset = tf.data.Dataset.from_tensor_slices(pts_paths) - dataset = dataset.map(_file_to_matrix) - dataset = dataset.batch(1) - iterator = tf.data.make_one_shot_iterator(dataset) - batch_data, batch_rot = iterator.get_next() - - features_dict = {'data': batch_data, 'rot': batch_rot} - batch_size = tf.shape(batch_data)[0] - batch_labels_dummy = tf.zeros(shape=(batch_size, 1)) - return (features_dict, batch_labels_dummy) - - -def print_variable_names(): - """Print variable names in a model.""" - params = {'dummy': 0} - estimator = NPUEstimator( - model_fn=model_fn, - model_dir=FLAGS.checkpoint_dir, - params=params) - - names = estimator.get_variable_names() - for name in names: - print(name) - - -def predict_all_test(): - """Print error statistics for the test dataset.""" - - config = NPURunConfig( - save_summary_steps=None, - save_checkpoints_steps=None, - log_step_count_steps=None, - keep_checkpoint_max=None) - - params = {'dummy': 0} - estimator = NPUEstimator( - model_fn=model_fn, - model_dir=FLAGS.checkpoint_dir, - params=params, - config=config) - evals = estimator.predict(input_fn=eval_input_fn, yield_single_examples=True) - - # Print error statistics. - - all_errors = [x['error'] for x in evals] - errors = np.array(all_errors) - print('Evaluated %d examples'%np.size(errors)) - print('Mean error: %f degrees',np.mean(errors)) - print('Median error: %f degrees', np.median(errors)) - print('Std: %f degrees', np.std(errors)) - sorted_errors = np.sort(errors) - n = np.size(sorted_errors) - print('\nPercentiles:') - for perc in range(1, 101): - index = np.int32(np.float32(n * perc) / 100.0) - 1 - print('%3d%%: %f'%(perc, sorted_errors[index])) - - -def train_and_eval(): - """Train and evaluate a model.""" - save_summary_steps = FLAGS.save_summaries_steps - save_checkpoints_steps = FLAGS.save_checkpoints_steps - log_step_count = FLAGS.log_step_count - - - # dump_config = npu_tf_config.estimator_dump_config(action='overflow') - # - # profilingPath=os.path.join(FLAGS.checkpoint_dir,'npu_profiling') - # if not os.path.exists(profilingPath): - # os.makedirs(profilingPath) - # - # profiling_options= '{"output":"%s",\ - # "task_trace":"on",\ - # "aicpu":"on"}'%(profilingPath) - # - # profiling_config=ProfilingConfig(enable_profiling=True,profiling_options=profiling_options) - # session_config=tf.ConfigProto() - # - # if FLAGS.Profiling: - # config = NPURunConfig( - # save_summary_steps=save_summary_steps, - # save_checkpoints_steps=save_checkpoints_steps, - # log_step_count_steps=log_step_count, - # keep_checkpoint_max=None, - # precision_mode = "allow_mix_precision", - # profiling_config=profiling_config, - # session_config=session_config) - # if FLAGS.Dump == True: - # config = NPURunConfig( - # save_summary_steps=save_summary_steps, - # save_checkpoints_steps=save_checkpoints_steps, - # log_step_count_steps=log_step_count, - # keep_checkpoint_max=None, - # precision_mode="allow_mix_precision", - # dump_config=dump_config) - - config = NPURunConfig( - save_summary_steps=save_summary_steps, - save_checkpoints_steps=save_checkpoints_steps, - log_step_count_steps=log_step_count, - keep_checkpoint_max=None, - precision_mode="allow_mix_precision", - customize_dtypes="/home/ma-user/modelarts/user-job-dir/code/switch_config.txt") - - params = {'dummy': 0} - estimator = NPUEstimator( - model_fn=model_fn, - model_dir=FLAGS.checkpoint_dir, - config=config, - params=params) - - - train_spec = tf.estimator.TrainSpec( - input_fn=train_input_fn, - max_steps=FLAGS.train_steps) - - - eval_spec = tf.estimator.EvalSpec(input_fn=eval_input_fn, - start_delay_secs=60, - steps=FLAGS.eval_examples, - throttle_secs=60) - - - - tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec) - - - -def main(argv=None): # pylint: disable=unused-argument - - if FLAGS.print_variable_names: - print_variable_names() - return - - if FLAGS.predict_all_test: - predict_all_test() - else: - train_and_eval() - - -if __name__ == '__main__': - - tf.app.run() -- Gitee From 39bf0a1dc002c023f31d4635c01a47f5bde5ae3b Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Tue, 12 Jul 2022 08:14:02 +0000 Subject: [PATCH 68/88] =?UTF-8?q?=E6=9B=B4=E6=96=B0=E4=BB=A3=E7=A0=81=20?= =?UTF-8?q?=E8=83=BD=E5=A4=9F=E6=AD=A3=E5=B8=B8=E6=89=93=E5=8D=B0=E7=B2=BE?= =?UTF-8?q?=E5=BA=A6=E5=80=BC?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../main_point_cloud_boostPerf.py | 462 ++++++++++++++++++ 1 file changed, 462 insertions(+) create mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/main_point_cloud_boostPerf.py diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/main_point_cloud_boostPerf.py b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/main_point_cloud_boostPerf.py new file mode 100644 index 000000000..54f2fffa0 --- /dev/null +++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/main_point_cloud_boostPerf.py @@ -0,0 +1,462 @@ +# coding=utf-8 +# Copyright 2021 The Google Research Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Training and evaluation for the point cloud alignment experiment.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import utils +from absl import flags +import numpy as np +import tensorflow as tf +import glob +import pathlib +import datetime +from npu_bridge.npu_init import * +from npu_bridge.estimator.npu.npu_estimator import NPUEstimatorSpec +from npu_bridge.estimator.npu.npu_estimator import NPUEstimator +from npu_bridge.estimator.npu.npu_config import NPURunConfig +from npu_bridge.estimator.npu.npu_config import ProfilingConfig +#import precision_tool.tf_config as npu_tf_config + +#import precision_tool.config.config as CONFIG + +import os +FLAGS = tf.app.flags.FLAGS + +# General flags. +flags.DEFINE_string('method', 'svd', + 'Specifies the method to use for predicting rotations. ' + 'Choices are "svd", "svd-inf", or "gs".') +flags.DEFINE_string('checkpoint_dir', '', + 'Locations for checkpoints, summaries, etc.') +flags.DEFINE_integer('train_steps', 2600000, 'Number of training iterations.') +flags.DEFINE_integer('save_checkpoints_steps', 10000, + 'How often to save checkpoints') +flags.DEFINE_integer('log_step_count', 500, 'How often to log the step count') +flags.DEFINE_integer('save_summaries_steps', 5000, + 'How often to save summaries.') +flags.DEFINE_float('learning_rate', 1e-5, 'Learning rate') +flags.DEFINE_boolean('lr_decay', False, 'Decay the learning rate if True.') +flags.DEFINE_integer('lr_decay_steps', 35000, + 'Learning rate decays steps.') +flags.DEFINE_float('lr_decay_rate', 0.95, + 'Learning rate decays rate.') +flags.DEFINE_boolean('predict_all_test', False, + 'If true, runs an eval job on latest checkpoint and ' + 'prints the error for each input.') +flags.DEFINE_integer('eval_examples', 39900, 'Number of test examples.') +flags.DEFINE_boolean('print_variable_names', False, + 'Print model variable names.') + +# Flags only used in the point cloud alignment experiment. +flags.DEFINE_integer('num_train_augmentations', 10, + 'Number of random rotations for augmenting each input ' + 'point cloud.') +flags.DEFINE_string('pt_cloud_train_files', '', + 'Expression matching all training point files, e.g. ' + '/path/to/files/pc_plane/points/*.pts') +flags.DEFINE_string('pt_cloud_test_files','', + 'Expression matching all modified test point files, e.g. ' + '/path/to/files/pc_plane/points_test/*.pts') +flags.DEFINE_boolean('random_rotation_axang', True, + 'If true, samples random rotations using the method ' + 'from the original benchmark code. Otherwise samples ' + 'by Haar measure.') +flags.DEFINE_boolean('Profiling',True, + 'parse NPU operator performance') + +flags.DEFINE_boolean('Dump',False, + 'overflow test') + + +def pt_features(batch_pts): + """Input shape: [B, N, 3], output shape: [B, 1024].""" + with tf.variable_scope('ptenc', reuse=tf.AUTO_REUSE): + f1 = tf.layers.conv1d(inputs=batch_pts, filters=64, kernel_size=1) + f1 = tf.nn.leaky_relu(f1) + f2 = tf.layers.conv1d(inputs=f1, filters=128, kernel_size=1) + f2 = tf.nn.leaky_relu(f2) + f3 = tf.layers.conv1d(inputs=f2, filters=1024, kernel_size=1) + f = tf.reduce_max(f3, axis=1, keep_dims=False) + return f + + +def regress_from_features(batch_features, out_dim): + """Regress to a rotation representation from point cloud encodings. + + In Zhou et al, CVPR19, the paper describes this regression network as an MLP + mapping 2048->512->512->out_dim, but the associated code implements it with + one less layer: 2048->512->out_dim. We mimic the code. + + Args: + batch_features: [batch_size, in_dim]. + out_dim: desired output dimensionality. + + Returns: + A [batch_size, out_dim] tensor. + """ + f1 = tf.layers.dense(batch_features, 512) + f1 = tf.nn.leaky_relu(f1) + f2 = tf.layers.dense(f1, out_dim) + return f2 + + +def net_point_cloud(points1, points2, mode): + """Predict a relative rotation given two point clouds. + + Args: + points1: [batch_size, N, 3] float tensor. + points2: [batch_size, N, 3] float tensor. + mode: tf.estimator.ModeKeys. + + Returns: + [batch_size, 3, 3] matrices. + """ + f1 = pt_features(points1) + f2 = pt_features(points2) + f = tf.concat([f1, f2], axis=-1) + + if FLAGS.method == 'svd': + p = regress_from_features(f, 9) + return utils.symmetric_orthogonalization(p) + + if FLAGS.method == 'svd-inf': + p = regress_from_features(f, 9) + if mode == tf.estimator.ModeKeys.TRAIN: + return tf.reshape(p, (-1, 3, 3)) + else: + return utils.symmetric_orthogonalization(p) + + if FLAGS.method == 'gs': + p = regress_from_features(f, 6) + return utils.gs_orthogonalization(p) + + +def model_fn(features, labels, mode, params): + """The model_fn used to construct the tf.Estimator.""" + del labels, params # Unused. + if mode == tf.estimator.ModeKeys.TRAIN: + # Training data has point cloud of size [1, N, 3] and random rotations + # of size [1, FLAGS.num_train_augmentations, 3, 3] + rot = features['rot'][0] + num_rot = FLAGS.num_train_augmentations + batch_pts1 = tf.tile(features['data'], [num_rot, 1, 1]) + # In this experiment it does not matter if we pre or post-multiply the + # rotation as long as we are consistent between training and eval. + batch_pts2 = tf.matmul(batch_pts1, rot) # post-multiplying! + else: + # Test data has point cloud of size [1, N, 3] and a single random + # rotation of size [1, 3, 3] + batch_pts1 = features['data'] + rot = features['rot'] + batch_pts2 = tf.matmul(batch_pts1, rot) + rot = tf.reshape(rot, (-1, 3, 3)) + + # Predict the rotation. + r = net_point_cloud(batch_pts1, batch_pts2, mode) + + # Compute the loss. + loss = tf.nn.l2_loss(rot - r) + + # Compute the relative angle in radians. + theta = utils.relative_angle(r, rot) + + # Mean angle error over the batch. + mean_theta = tf.reduce_mean(theta) + mean_theta_deg = mean_theta * 180.0 / np.pi + + # Train, eval, or predict depending on mode. + if mode == tf.estimator.ModeKeys.TRAIN: + tf.summary.scalar('train/loss', loss) + tf.summary.scalar('train/theta', mean_theta_deg) + global_step = tf.train.get_or_create_global_step() + + if FLAGS.lr_decay: + learning_rate = tf.train.exponential_decay( + FLAGS.learning_rate, + global_step, + FLAGS.lr_decay_steps, + FLAGS.lr_decay_rate) + else: + learning_rate = FLAGS.learning_rate + + tf.summary.scalar('train/learning_rate', learning_rate) + optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate) + update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) + with tf.control_dependencies(update_ops): + train_op = optimizer.minimize(loss, global_step=global_step) + #train_op = util.set_graph_exec_config(train_op, dynamic_input=True,dynamic_graph_execute_mode="lazy_recompile") + + return NPUEstimatorSpec( + mode=mode, + loss=loss, + train_op=train_op) + + if mode == tf.estimator.ModeKeys.EVAL: + if FLAGS.predict_all_test: + print_error_op = tf.print('error:', mean_theta_deg) + with tf.control_dependencies([print_error_op]): + eval_metric_ops = { + 'mean_degree_err': tf.metrics.mean(mean_theta_deg), + } + else: + eval_metric_ops = { + 'mean_degree_err': tf.metrics.mean(mean_theta_deg), + } + + return NPUEstimatorSpec( + mode=mode, + loss=loss, + eval_metric_ops=eval_metric_ops) + + if mode == tf.estimator.ModeKeys.PREDICT: + pred = {'error': mean_theta_deg} + return NPUEstimatorSpec( + mode=mode, + predictions=pred) + + +def train_input_fn(): + """Generate training data iterator from the .pts files.""" + def _file_to_matrix(pts_path): + """Read Nx3 point cloud from a .pts file.""" + file_buffer = tf.read_file(pts_path) + lines = tf.string_split([file_buffer], delimiter='\n') + lines1 = tf.string_split(lines.values, delimiter='\r') + values = tf.stack(tf.decode_csv(lines1.values, + record_defaults=[[0.0], [0.0], [0.0]], + field_delim=' ')) + values = tf.transpose(values) # 3xN --> Nx3. + values = values[:tf.shape(values)[0] // 2, :] + + # modified by Lyoung + diff_num = 1500-tf.shape(values)[0] + repeat_pts = tf.tile(tf.reshape(values[1, :], (1, -1)), [diff_num, 1]) + values = tf.concat([values,repeat_pts], axis=0) + # The experiment code in + # github.com/papagina/RotationContinuity/.../shapenet/code/train_pointnet.py + # only used the first half of the points in each file. + return values + + def _random_rotation(pts): + """Attach N random rotations to a point cloud.""" + if FLAGS.random_rotation_axang: + rotations = utils.random_rotation_benchmark(FLAGS.num_train_augmentations) + else: + rotations = utils.random_rotation(FLAGS.num_train_augmentations) + return pts,rotations + + pts_paths = tf.gfile.Glob(FLAGS.pt_cloud_train_files) + dataset = tf.data.Dataset.from_tensor_slices(pts_paths) + dataset = dataset.map(_file_to_matrix) + dataset = dataset.cache() # Comment out if memory cannot hold all the data. + dataset = dataset.shuffle(buffer_size=50, reshuffle_each_iteration=True) + dataset = dataset.repeat() + dataset = dataset.map(_random_rotation) + dataset = dataset.batch(1) + + iterator = tf.data.make_one_shot_iterator(dataset) + batch_data, batch_rot = iterator.get_next() + features_dict = {'data': batch_data, 'rot': batch_rot} + batch_size = tf.shape(batch_data)[0] + batch_labels_dummy = tf.zeros(shape=(batch_size, 1)) + return (features_dict, batch_labels_dummy) + + +def eval_input_fn(): + """Generate test data from *modified* .pts files. + + See README and comments below for details on how the data is modified. + + Returns: + A tuple of features and associated labels. + """ + def _file_to_matrix(pts_path): + """Read Nx3 point cloud and 3x3 rotation matrix from a .pts file. + + The test data is a modified version of the original files. For each .pts + file we have (1) added a 3x3 rotation matrix for testing, and (2) removed + the second half of the point cloud since it is not used at all. + + Args: + pts_path: path to a .pts file. + + Returns: + A Nx3 point cloud. + A 3x3 rotation matrix. + """ + file_buffer = tf.read_file(pts_path) + lines = tf.string_split([file_buffer], delimiter='\n') + lines1 = tf.string_split(lines.values,delimiter='\r') + values = tf.stack(tf.decode_csv(lines1.values, + record_defaults=[[0.0], [0.0], [0.0]],field_delim=' ')) + values = tf.transpose(values)# 3xN --> Nx3. + + #modified by Lyoung + diff_num = 1414-tf.shape(values)[0] + repeat_pts = tf.tile(tf.reshape(values[4, :], (1, -1)), [diff_num, 1]) + values = tf.concat([values, repeat_pts], axis=0) + # First three rows are the rotation matrix, remaining rows the point cloud. + rot = values[:3, :] + + return values[4:, :], rot + + pts_paths = tf.io.gfile.glob(FLAGS.pt_cloud_test_files) + dataset = tf.data.Dataset.from_tensor_slices(pts_paths) + dataset = dataset.map(_file_to_matrix) + dataset = dataset.batch(1) + iterator = tf.data.make_one_shot_iterator(dataset) + batch_data, batch_rot = iterator.get_next() + + features_dict = {'data': batch_data, 'rot': batch_rot} + batch_size = tf.shape(batch_data)[0] + batch_labels_dummy = tf.zeros(shape=(batch_size, 1)) + return (features_dict, batch_labels_dummy) + + +def print_variable_names(): + """Print variable names in a model.""" + params = {'dummy': 0} + estimator = NPUEstimator( + model_fn=model_fn, + model_dir=FLAGS.checkpoint_dir, + params=params) + + names = estimator.get_variable_names() + for name in names: + print(name) + + +def predict_all_test(): + """Print error statistics for the test dataset.""" + + config = NPURunConfig( + save_summary_steps=None, + save_checkpoints_steps=None, + log_step_count_steps=None, + keep_checkpoint_max=None) + + params = {'dummy': 0} + estimator = NPUEstimator( + model_fn=model_fn, + model_dir=FLAGS.checkpoint_dir, + params=params, + config=config) + evals = estimator.predict(input_fn=eval_input_fn, yield_single_examples=True) + + # Print error statistics. + + all_errors = [x['error'] for x in evals] + errors = np.array(all_errors) + print('Evaluated %d examples'%np.size(errors)) + print('Mean error: %f degrees',np.mean(errors)) + print('Median error: %f degrees', np.median(errors)) + print('Std: %f degrees', np.std(errors)) + sorted_errors = np.sort(errors) + n = np.size(sorted_errors) + print('\nPercentiles:') + for perc in range(1, 101): + index = np.int32(np.float32(n * perc) / 100.0) - 1 + print('%3d%%: %f'%(perc, sorted_errors[index])) + + +def train_and_eval(): + """Train and evaluate a model.""" + save_summary_steps = FLAGS.save_summaries_steps + save_checkpoints_steps = FLAGS.save_checkpoints_steps + log_step_count = FLAGS.log_step_count + + + # dump_config = npu_tf_config.estimator_dump_config(action='overflow') + # + profilingPath=os.path.join(FLAGS.checkpoint_dir,'npu_profiling') + if not os.path.exists(profilingPath): + os.makedirs(profilingPath) + + profiling_options= '{"output":"%s",\ + "task_trace":"on",\ + "aicpu":"on"}'%(profilingPath) + + profiling_config=ProfilingConfig(enable_profiling=True,profiling_options=profiling_options) + session_config=tf.ConfigProto() + + if FLAGS.Profiling: + config = NPURunConfig( + save_summary_steps=save_summary_steps, + save_checkpoints_steps=save_checkpoints_steps, + log_step_count_steps=log_step_count, + keep_checkpoint_max=None, + precision_mode = "allow_mix_precision", + profiling_config=profiling_config, + session_config=session_config, + customize_dtypes="/home/ma-user/modelarts/user-job-dir/code/switch_config.txt") + # if FLAGS.Dump == True: + # config = NPURunConfig( + # save_summary_steps=save_summary_steps, + # save_checkpoints_steps=save_checkpoints_steps, + # log_step_count_steps=log_step_count, + # keep_checkpoint_max=None, + # precision_mode="allow_mix_precision", + # dump_config=dump_config) + else: + config = NPURunConfig( + save_summary_steps=save_summary_steps, + save_checkpoints_steps=save_checkpoints_steps, + log_step_count_steps=log_step_count, + keep_checkpoint_max=None, + precision_mode="allow_mix_precision", + customize_dtypes="/home/ma-user/modelarts/user-job-dir/code/switch_config.txt") + + params = {'dummy': 0} + estimator = NPUEstimator( + model_fn=model_fn, + model_dir=FLAGS.checkpoint_dir, + config=config, + params=params) + + + train_spec = tf.estimator.TrainSpec( + input_fn=train_input_fn, + max_steps=FLAGS.train_steps) + + + eval_spec = tf.estimator.EvalSpec(input_fn=eval_input_fn, + start_delay_secs=60, + steps=FLAGS.eval_examples, + throttle_secs=60) + + + + tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec) + + + +def main(argv=None): # pylint: disable=unused-argument + + if FLAGS.print_variable_names: + print_variable_names() + return + + if FLAGS.predict_all_test: + predict_all_test() + else: + train_and_eval() + + +if __name__ == '__main__': + + tf.app.run() -- Gitee From 10df269066d8155402772eeb778a7c3392ad9683 Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Thu, 18 Aug 2022 07:18:27 +0000 Subject: [PATCH 69/88] =?UTF-8?q?save=5Fmodel=E6=A8=A1=E5=9E=8B=E5=9B=BA?= =?UTF-8?q?=E5=8C=96=E4=B8=BApb=E6=A8=A1=E5=9E=8B?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: ab_dx_z <> --- .../cv/SVD_ID2019_for_Tensorflow/frezze.py | 48 +++++++++++++++++++ 1 file changed, 48 insertions(+) create mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/frezze.py diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/frezze.py b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/frezze.py new file mode 100644 index 000000000..6aeb2e1aa --- /dev/null +++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/frezze.py @@ -0,0 +1,48 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import tensorflow as tf +from tensorflow.python.tools import freeze_graph +from npu_bridge.npu_init import * + +freeze_graph.freeze_graph( + input_saved_model_dir='save_model_path', + output_node_names='L2Loss', + output_graph='test.pb', + initializer_nodes='', + input_graph= None, + input_saver= False, + input_binary=False, + input_checkpoint=None, + restore_op_name=None, + filename_tensor_name=None, + clear_devices=False, + input_meta_graph=False) + +# if __name__ == '__main__': +# main() \ No newline at end of file -- Gitee From 05be221da8af1c987ab79e25d0148bf37d2560cc Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Thu, 18 Aug 2022 07:20:22 +0000 Subject: [PATCH 70/88] =?UTF-8?q?=E5=88=A0=E9=99=A4=E6=96=87=E4=BB=B6=20Te?= =?UTF-8?q?nsorFlow/contrib/cv/SVD=5FID2019=5Ffor=5FTensorflow/README.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../cv/SVD_ID2019_for_Tensorflow/README.md | 253 ------------------ 1 file changed, 253 deletions(-) delete mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md deleted file mode 100644 index 4a337f831..000000000 --- a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md +++ /dev/null @@ -1,253 +0,0 @@ -

基本信息

- -**发布者（Publisher）：Huawei** - -**应用领域（Application Domain）：CV** - -**版本（Version）：** - -**修改时间（Modified） ：2022.05.05** - -**框架（Framework）：TensorFlow 1.15.0** - -**处理器（Processor）：昇腾910** - -**描述（Description）：使用训练好的SVD模型，评估对称正交化在点云对准中的应用效果。** - -

概述

- -给定两个三维点云图，利用SVD正交化过程SVDO+(M)将其投射到SO(3)上，要求网络预测最佳对齐它们的3D旋转。代码的训练逻辑是每训练10w步保存一个模型，并且在测试集上检验该模型的精度，最后比较的都是260w步的模型精度 - -- 开源代码： - - https://github.com/google-research/google-research/tree/master/special_orthogonalization。 - -- 参考论文： - - [An Analysis of SVD for Deep Rotation Estimation](https://arxiv.org/abs/2006.14616) - -- 参考实现： - gpu最后训练出来的模型在 - - obs://cann-id2019/gpu - - 数据在 - obs://cann-id2019/dataset/ - - 共有3个数据集 - - 训练数据集points - - 测试数据集 points_test - - 第一步旋转后的数据集 test_points_modified - - npu训练出来的模型在 - - obs://cann-id2019/dataset/output - - - 相关代码均以上传，在NPU和GPU文件夹中可以找到 - -- 相关迁移的工作： - 在进行代码迁移到NPU上时，输入的训练数据为点云数据，点云数据的shape为(N,3)，其中N并不是固定的，因此在NPU上存在动态shape的问题，导致模型训练无法正常进行。我们为此想了三个解决方法：1、找出所有点云数据中最小的N，对于大于N的点云数据，仅取前N行的数据输入训练。2、找到所有点云数据中最大的N，对于小于N的点云进行补0操作，将所有数据固定为最大的N后，输入网络进行训练。3、找到所有点云数据中最大的N，对小于N的点云数据，从原数据中选择一个点云进行填补至行数为N，再将数据输入网络进行训练。该三种方法均成功解决了NPU上的动态shape问题，但是第一种方法删除了样本点，因此导致最后训练出的模型精度很差；第二种方法虽然并没有丢失样本信息，但是向数据中填入大量的0，改变了本来的代码逻辑，导致最后训练出的模型精度也并不高。对于第三种方法，即没有丢失样本信息，对每个点云数据中的某一个点云样本点进行重复操作，没有改变原始的代码逻辑，最后也获得了不错的精度表现。 - -- 通过Git获取对应commit\_id的代码方法如下： - - ``` - git clone {repository_url} # 克隆仓库的代码 - cd {repository_name} # 切换到模型的代码仓目录 - git checkout {branch} # 切换到对应分支 - git reset --hard ｛commit_id｝ # 代码设置到对应的commit_id - cd ｛code_path｝ # 切换到模型代码所在路径，若仓库下只有该模型，则无需切换 - ``` - -## 默认配置 - -- 数据集获取：obs://cann-id2019/dataset/ - -- 训练超参 - - - log_step_count=200 - - save_summaries_steps=25000 - - train_steps=2600000 - - save_checkpoints_steps=100000 - - eval_examples=39900 - - - -

训练环境准备

- -1. 硬件环境准备请参见各硬件产品文档"[驱动和固件安装升级指南]( https://support.huawei.com/enterprise/zh/category/ai-computing-platform-pid-1557196528909)"。需要在硬件设备上安装与CANN版本配套的固件与驱动。 -2. 宿主机上需要安装Docker并登录[Ascend Hub中心](https://ascendhub.huawei.com/#/detail?name=ascend-tensorflow-arm)获取镜像。 - - 当前模型支持的镜像列表如[表1](#zh-cn_topic_0000001074498056_table1519011227314)所示。 - - **表 1** 镜像列表 - - - - - - - - - - - - -

镜像名称

-

镜像版本

-

配套CANN版本

-
-

20.2.0

-

20.2

-
- - - -## 脚本参数 - -``` -gen_pt_test_data_gpu.py 中的参数 - ---input_test_files 传入需要进行旋转的点云数据集 ---output_directory 存储旋转后点云集的路径 ---random_rotation_axang boole型，若为真将会对传入的数据集采用指定方法旋转，默认为真 ---num_rotations_per_file 每个测试点云的随机旋转增加数。默认为100 - - -main_point_cloud_gpu.py 中的参数 - ---pt_cloud_test_files 测试数据集路径 ---pt_cloud_train_files 熟练数据集路径 ---method 指定用于预测旋转的方式。选项为"svd", "svd-inf", or "gs"。默认为“svd” ---checkpoint_dir 训练模型的存放位置 ---train_steps 训练迭代的次数。默认为2600000 ---save_checkpoints_steps 保存检查点的频率。默认为10000 ---log_step_count 日志记录一次的步数。默认为200 ---save_summaries_steps 保存一次summary的步数。默认为5000 ---learning_rate 默认为1e-5 ---lr_decay 如果为真，则衰减learning rate。默认为假 ---lr_decay_steps learning rate衰减步数。默认为35000 ---lr_decay_rate learning rate衰减速率。默认为0.95 ---predict_all_test 如果为真，则在最新的检查点上运行eval作业，并打印每个输入的误差信息。默认为假 ---eval_examples 测试样本的数量。默认为0 ---print_variable_names 打印模型变量名。默认为假 ---num_train_augmentations 增加每个输入点云的随机旋转数。默认为10 - -``` - - - -## 运行 - -GPU运行命令如下： - -**生成测试数据** - -注：生成的文件test_points_modified、points已包含在dataset文件夹中。 -```bash -# 将路径设置到训练点云图文件 -IN_FILES=/points_test/*.pts - -NEW_TEST_FILES_DIR=/test_points_modified - -AXANG_SAMPLING=True - -# 决定旋转轴角的分布 -AXANG_SAMPLING=True - -python -m special_orthogonalization.gen_pt_test_data_gpu --input_test_files=$IN_FILES --output_directory=$NEW_TEST_FILES_DIR --random_rotation_axang=$AXANG_SAMPLING -``` - -**训练与评价** -```bash -# 将路径设置到原始训练数据 -TRAIN_FILES=/points/*.pts - -#将路径设置到旋转后的训练数据 -TEST_FILES=$NEW_TEST_FILES_DIR/*.pts - -# 指定旋转预测方式 -METHOD=svd - -# 指定ckpt、summaries、评价结果等的存储路径 -OUT_DIR=/path/to/model - -python -m special_orthogonalization.main_point_cloud_gpu --method=$METHOD --checkpoint_dir=$OUT_DIR --log_step_count=200 --save_summaries_steps=25000 --pt_cloud_train_files=$TRAIN_FILES --pt_cloud_test_files=$TEST_FILES --train_steps=2600000 --save_checkpoints_steps=100000 --eval_examples=39900 -``` - -**从所有训练样本中生成统计数据** -```bash -# 打印均值、中位数、标准差和分位数 -python -m special_orthogonalization.main_point_cloud_gpu --method=$METHOD --checkpoint_dir=$OUT_DIR --pt_cloud_test_files=$TEST_FILES --predict_all_test=True -``` -## 运行 - -NPU运行命令方式如下: - -对于所有的三个步骤程序来说，modelarts插件obs桶中的数据路径均要写到真正包含数据的那一个路径 -如在dataset文件夹中含有points、points_test等包含数据的文件夹 -modelarts插件中的数据路径写为 obs://cann-id2019/dataset/ - -**生成测试数据** - -运行这一步我们需要的程序文件为gen_pt_test_data.py、modelarts_entry_Axang.py、genTestData.sh -这三个文件中的代码均不需要修改 -最后生成的旋转后的数据文件存放在obs桶当次程序文件的output路径中，文件名为test_points_modified， -为进行第二步模型训练，需要将生成旋转后的文件转移至obs桶中存放data的路径 - -注：需要确保的是存在obs桶里的data文件名为points_test - -**训练与评价** - -运行这一步我们需要的程序文件为main_point_cloud_boostPerf.py、modelarts_entry_acc_train.py、train_full_1p.sh -这三个文件中的代码均不需要修改 - -由于采用混合精度提高训练性能，一些算子计算溢出，为此增添switch_config.txt文件，该文件应该和代码所在目录一致。 - -最后生成的旋转后的数据文件存放在obs桶当次程序文件的output路径中，文件名为test_points_modified， -为进行第三步，需要将生成的output文件转移至obs桶中存放data的路径 - -注意：该次训练的模型保存在该次的obs文件夹中，进行第三步时又需要重启一次新的modelarts，因此我们需要将output文件中的 -checkpoint文件中最新模型的路径修改 -"/home/ma-user/modelarts/inputs/data_url_0/output" -这样第三步才能跑出正确的精度指标 - - -**从所有训练样本中生成统计数据** - -运行这一步我们需要的程序文件为main_point_cloud_boostPerf.py、modelarts_entry_stat.py、genStatistical.sh -这三个文件的代码均不需要修改 - -运行成功后将会在屏幕上打印出关于精度相应的统计量值 - - - -## 训练结果 -**精度对比：** - - - -| 测地线误差（°） | 论文发布 | GPU(初始代码未改动版本) | GPU实测|NPU实测 | -| ------------------------ | ------- | ----- | --------- |----------| -| 平均值 | 1.63 | 2.58 | 3.98 | 2.92 | -| 中值 | 0.89 | 1.68 | 2.6 | 1.7 | -| 标准差 | 6.70 | 6.93 | 9.36 | 8.45 | - -相比于论文中的精度，我们NPU迁移后实测差距依然较大，但是与我们未对代码任何改动初始的版本在GPU上跑出来的精度相差较小， -且对于相同的代码的代码改动，NPU上的精度优于GPU上的精度。需要注意的是，在NPU上运行程序时，我们采用混合精度来提升训练 -的性能，但是其中产生了未知的错误，导致代表的精度指标mean_degree_err在整个训练过程中始终为0，因此我们无法得知在NPU训练的 -260w步的过程中，精度指标是下降的过程是怎样的。值得庆幸的是通过NPU训练出的模型，能够在GPU上计算出精度，并且精度还不错。 - -**性能对比：** - -取华为v100上GPU运行的前2w步的global_step/sec平均值和NPU运行的前1w步的global_step/sec平均值进行对比，以达到性能对比的目的。 -对于NPU上的性能计算 需要的程序为main_point_cloud_boostPerf.py、modelarts_entry_perf.py、train_performance_1p.sh，其中参数已经 -设置完毕，无需更改。同时我们上传了我们计算性能的代码calc_perf.py，运行该代码需要将产生的日志文件从obs上下载下来，传入obs文件在本地的路径即可 -| 性能指标项 | 论文发布 | GPU实测 | NPU实测 | -| ------------------- | ------- | ------ | ------ | -| global_step/sec| 无 | 87.64 | 116.77 | - - -- Gitee From cb719d0c6fa03d147c5ffa3a2ef2c56b89d7fff4 Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Thu, 18 Aug 2022 07:20:54 +0000 Subject: [PATCH 71/88] =?UTF-8?q?=E6=9B=B4=E6=96=B0?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: ab_dx_z <> --- .../cv/SVD_ID2019_for_Tensorflow/README.md | 256 ++++++++++++++++++ 1 file changed, 256 insertions(+) create mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md new file mode 100644 index 000000000..5de256f0f --- /dev/null +++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md @@ -0,0 +1,256 @@ +

基本信息

+ +**发布者（Publisher）：Huawei** + +**应用领域（Application Domain）：CV** + +**版本（Version）：** + +**修改时间（Modified） ：2022.05.05** + +**框架（Framework）：TensorFlow 1.15.0** + +**处理器（Processor）：昇腾910** + +**描述（Description）：使用训练好的SVD模型，评估对称正交化在点云对准中的应用效果。** + +

概述

+ +给定两个三维点云图，利用SVD正交化过程SVDO+(M)将其投射到SO(3)上，要求网络预测最佳对齐它们的3D旋转。代码的训练逻辑是每训练10w步保存一个模型，并且在测试集上检验该模型的精度，最后比较的都是260w步的模型精度 + +- 开源代码： + + https://github.com/google-research/google-research/tree/master/special_orthogonalization。 + +- 参考论文： + + [An Analysis of SVD for Deep Rotation Estimation](https://arxiv.org/abs/2006.14616) + +- 参考实现： + gpu最后训练出来的模型在 + + obs://cann-id2019/gpu + + 数据在 + obs://cann-id2019/dataset/ + + 共有3个数据集 + + 训练数据集points + + 测试数据集 points_test + + 第一步旋转后的数据集 test_points_modified + + npu训练出来的模型在 + + obs://cann-id2019/dataset/output + + + 相关代码均以上传，在NPU和GPU文件夹中可以找到 + +- 相关迁移的工作： + 在进行代码迁移到NPU上时，输入的训练数据为点云数据，点云数据的shape为(N,3)，其中N并不是固定的，因此在NPU上存在动态shape的问题，导致模型训练无法正常进行。我们为此想了三个解决方法：1、找出所有点云数据中最小的N，对于大于N的点云数据，仅取前N行的数据输入训练。2、找到所有点云数据中最大的N，对于小于N的点云进行补0操作，将所有数据固定为最大的N后，输入网络进行训练。3、找到所有点云数据中最大的N，对小于N的点云数据，从原数据中选择一个点云进行填补至行数为N，再将数据输入网络进行训练。该三种方法均成功解决了NPU上的动态shape问题，但是第一种方法删除了样本点，因此导致最后训练出的模型精度很差；第二种方法虽然并没有丢失样本信息，但是向数据中填入大量的0，改变了本来的代码逻辑，导致最后训练出的模型精度也并不高。对于第三种方法，即没有丢失样本信息，对每个点云数据中的某一个点云样本点进行重复操作，没有改变原始的代码逻辑，最后也获得了不错的精度表现。 + +- 通过Git获取对应commit\_id的代码方法如下： + + ``` + git clone {repository_url} # 克隆仓库的代码 + cd {repository_name} # 切换到模型的代码仓目录 + git checkout {branch} # 切换到对应分支 + git reset --hard ｛commit_id｝ # 代码设置到对应的commit_id + cd ｛code_path｝ # 切换到模型代码所在路径，若仓库下只有该模型，则无需切换 + ``` + +## 默认配置 + +- 数据集获取：obs://cann-id2019/dataset/ + +- 训练超参 + + - log_step_count=200 + - save_summaries_steps=25000 + - train_steps=2600000 + - save_checkpoints_steps=100000 + - eval_examples=39900 + + + +

训练环境准备

+ +1. 硬件环境准备请参见各硬件产品文档"[驱动和固件安装升级指南]( https://support.huawei.com/enterprise/zh/category/ai-computing-platform-pid-1557196528909)"。需要在硬件设备上安装与CANN版本配套的固件与驱动。 +2. 宿主机上需要安装Docker并登录[Ascend Hub中心](https://ascendhub.huawei.com/#/detail?name=ascend-tensorflow-arm)获取镜像。 + + 当前模型支持的镜像列表如[表1](#zh-cn_topic_0000001074498056_table1519011227314)所示。 + + **表 1** 镜像列表 + + + + + + + + + + + + +

镜像名称

+

镜像版本

+

配套CANN版本

+
+

20.2.0

+

20.2

+
+ + + +## 脚本参数 + +``` +gen_pt_test_data_gpu.py 中的参数 + +--input_test_files 传入需要进行旋转的点云数据集 +--output_directory 存储旋转后点云集的路径 +--random_rotation_axang boole型，若为真将会对传入的数据集采用指定方法旋转，默认为真 +--num_rotations_per_file 每个测试点云的随机旋转增加数。默认为100 + + +main_point_cloud_gpu.py 中的参数 + +--pt_cloud_test_files 测试数据集路径 +--pt_cloud_train_files 熟练数据集路径 +--method 指定用于预测旋转的方式。选项为"svd", "svd-inf", or "gs"。默认为“svd” +--checkpoint_dir 训练模型的存放位置 +--train_steps 训练迭代的次数。默认为2600000 +--save_checkpoints_steps 保存检查点的频率。默认为10000 +--log_step_count 日志记录一次的步数。默认为200 +--save_summaries_steps 保存一次summary的步数。默认为5000 +--learning_rate 默认为1e-5 +--lr_decay 如果为真，则衰减learning rate。默认为假 +--lr_decay_steps learning rate衰减步数。默认为35000 +--lr_decay_rate learning rate衰减速率。默认为0.95 +--predict_all_test 如果为真，则在最新的检查点上运行eval作业，并打印每个输入的误差信息。默认为假 +--eval_examples 测试样本的数量。默认为0 +--print_variable_names 打印模型变量名。默认为假 +--num_train_augmentations 增加每个输入点云的随机旋转数。默认为10 + +``` + + + +## 运行 + +GPU运行命令如下： + +**生成测试数据** + +注：生成的文件test_points_modified、points已包含在dataset文件夹中。 +```bash +# 将路径设置到训练点云图文件 +IN_FILES=/points_test/*.pts + +NEW_TEST_FILES_DIR=/test_points_modified + +AXANG_SAMPLING=True + +# 决定旋转轴角的分布 +AXANG_SAMPLING=True + +python -m special_orthogonalization.gen_pt_test_data_gpu --input_test_files=$IN_FILES --output_directory=$NEW_TEST_FILES_DIR --random_rotation_axang=$AXANG_SAMPLING +``` + +**训练与评价** +```bash +# 将路径设置到原始训练数据 +TRAIN_FILES=/points/*.pts + +#将路径设置到旋转后的训练数据 +TEST_FILES=$NEW_TEST_FILES_DIR/*.pts + +# 指定旋转预测方式 +METHOD=svd + +# 指定ckpt、summaries、评价结果等的存储路径 +OUT_DIR=/path/to/model + +python -m special_orthogonalization.main_point_cloud_gpu --method=$METHOD --checkpoint_dir=$OUT_DIR --log_step_count=200 --save_summaries_steps=25000 --pt_cloud_train_files=$TRAIN_FILES --pt_cloud_test_files=$TEST_FILES --train_steps=2600000 --save_checkpoints_steps=100000 --eval_examples=39900 +``` + +**从所有训练样本中生成统计数据** +```bash +# 打印均值、中位数、标准差和分位数 +python -m special_orthogonalization.main_point_cloud_gpu --method=$METHOD --checkpoint_dir=$OUT_DIR --pt_cloud_test_files=$TEST_FILES --predict_all_test=True +``` +## 运行 + +NPU运行命令方式如下: + +对于所有的三个步骤程序来说，modelarts插件obs桶中的数据路径均要写到真正包含数据的那一个路径 +如在dataset文件夹中含有points、points_test等包含数据的文件夹 +modelarts插件中的数据路径写为 obs://cann-id2019/dataset/ + +**生成测试数据** + +运行这一步我们需要的程序文件为gen_pt_test_data.py、modelarts_entry_Axang.py、genTestData.sh +这三个文件中的代码均不需要修改 +最后生成的旋转后的数据文件存放在obs桶当次程序文件的output路径中，文件名为test_points_modified， +为进行第二步模型训练，需要将生成旋转后的文件转移至obs桶中存放data的路径 + +注：需要确保的是存在obs桶里的data文件名为points_test + +**训练与评价** + +运行这一步我们需要的程序文件为main_point_cloud_boostPerf.py、modelarts_entry_acc_train.py、train_full_1p.sh +这三个文件中的代码均不需要修改 + +由于采用混合精度提高训练性能，一些算子计算溢出，为此增添switch_config.txt文件，该文件应该和代码所在目录一致。 + +最后生成的旋转后的数据文件存放在obs桶当次程序文件的output路径中，文件名为test_points_modified， +为进行第三步，需要将生成的output文件转移至obs桶中存放data的路径 + +注意：该次训练的模型保存在该次的obs文件夹中，进行第三步时又需要重启一次新的modelarts，因此我们需要将output文件中的 +checkpoint文件中最新模型的路径修改 +"/home/ma-user/modelarts/inputs/data_url_0/output" +这样第三步才能跑出正确的精度指标 + + +**从所有训练样本中生成统计数据** + +运行这一步我们需要的程序文件为main_point_cloud_boostPerf.py、modelarts_entry_stat.py、genStatistical.sh +这三个文件的代码均不需要修改 + +运行成功后将会在屏幕上打印出关于精度相应的统计量值 + + + +## 训练结果 +**精度对比：** + + + +| 测地线误差（°） | 论文发布 | GPU(初始代码未改动版本) | GPU实测|NPU实测 | +| ------------------------ | ------- | ----- | --------- |----------| +| 平均值 | 1.63 | 2.58 | 3.98 | 2.92 | +| 中值 | 0.89 | 1.68 | 2.6 | 1.7 | +| 标准差 | 6.70 | 6.93 | 9.36 | 8.45 | + +相比于论文中的精度，我们NPU迁移后实测差距依然较大，但是与我们未对代码任何改动初始的版本在GPU上跑出来的精度相差较小， +且对于相同的代码的代码改动，NPU上的精度优于GPU上的精度。需要注意的是，在NPU上运行程序时，我们采用混合精度来提升训练 +的性能，但是其中产生了未知的错误，导致代表的精度指标mean_degree_err在整个训练过程中始终为0，因此我们无法得知在NPU训练的 +260w步的过程中，精度指标是下降的过程是怎样的。值得庆幸的是通过NPU训练出的模型，能够在GPU上计算出精度，并且精度还不错。 + +**性能对比：** + +取华为v100上GPU运行的前2w步的global_step/sec平均值和NPU运行的前1w步的global_step/sec平均值进行对比，以达到性能对比的目的。 +对于NPU上的性能计算 需要的程序为main_point_cloud_boostPerf.py、modelarts_entry_perf.py、train_performance_1p.sh，其中参数已经 +设置完毕，无需更改。同时我们上传了我们计算性能的代码calc_perf.py，运行该代码需要将产生的日志文件从obs上下载下来，传入obs文件在本地的路径即可 +| 性能指标项 | 论文发布 | GPU实测 | NPU实测 | +| ------------------- | ------- | ------ | ------ | +| global_step/sec| 无 | 87.64 | 116.77 | + +##离线推理 +利用frezze.py文件将save_model模型保存为pb模型 + + -- Gitee From 3044deedba1022cd70ec21a2b224a46445c62f6c Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Thu, 18 Aug 2022 07:21:29 +0000 Subject: [PATCH 72/88] =?UTF-8?q?=E5=88=A0=E9=99=A4=E6=96=87=E4=BB=B6=20Te?= =?UTF-8?q?nsorFlow/contrib/cv/SVD=5FID2019=5Ffor=5FTensorflow/README.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../cv/SVD_ID2019_for_Tensorflow/README.md | 256 ------------------ 1 file changed, 256 deletions(-) delete mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md deleted file mode 100644 index 5de256f0f..000000000 --- a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md +++ /dev/null @@ -1,256 +0,0 @@ -

基本信息

- -**发布者（Publisher）：Huawei** - -**应用领域（Application Domain）：CV** - -**版本（Version）：** - -**修改时间（Modified） ：2022.05.05** - -**框架（Framework）：TensorFlow 1.15.0** - -**处理器（Processor）：昇腾910** - -**描述（Description）：使用训练好的SVD模型，评估对称正交化在点云对准中的应用效果。** - -

概述

- -给定两个三维点云图，利用SVD正交化过程SVDO+(M)将其投射到SO(3)上，要求网络预测最佳对齐它们的3D旋转。代码的训练逻辑是每训练10w步保存一个模型，并且在测试集上检验该模型的精度，最后比较的都是260w步的模型精度 - -- 开源代码： - - https://github.com/google-research/google-research/tree/master/special_orthogonalization。 - -- 参考论文： - - [An Analysis of SVD for Deep Rotation Estimation](https://arxiv.org/abs/2006.14616) - -- 参考实现： - gpu最后训练出来的模型在 - - obs://cann-id2019/gpu - - 数据在 - obs://cann-id2019/dataset/ - - 共有3个数据集 - - 训练数据集points - - 测试数据集 points_test - - 第一步旋转后的数据集 test_points_modified - - npu训练出来的模型在 - - obs://cann-id2019/dataset/output - - - 相关代码均以上传，在NPU和GPU文件夹中可以找到 - -- 相关迁移的工作： - 在进行代码迁移到NPU上时，输入的训练数据为点云数据，点云数据的shape为(N,3)，其中N并不是固定的，因此在NPU上存在动态shape的问题，导致模型训练无法正常进行。我们为此想了三个解决方法：1、找出所有点云数据中最小的N，对于大于N的点云数据，仅取前N行的数据输入训练。2、找到所有点云数据中最大的N，对于小于N的点云进行补0操作，将所有数据固定为最大的N后，输入网络进行训练。3、找到所有点云数据中最大的N，对小于N的点云数据，从原数据中选择一个点云进行填补至行数为N，再将数据输入网络进行训练。该三种方法均成功解决了NPU上的动态shape问题，但是第一种方法删除了样本点，因此导致最后训练出的模型精度很差；第二种方法虽然并没有丢失样本信息，但是向数据中填入大量的0，改变了本来的代码逻辑，导致最后训练出的模型精度也并不高。对于第三种方法，即没有丢失样本信息，对每个点云数据中的某一个点云样本点进行重复操作，没有改变原始的代码逻辑，最后也获得了不错的精度表现。 - -- 通过Git获取对应commit\_id的代码方法如下： - - ``` - git clone {repository_url} # 克隆仓库的代码 - cd {repository_name} # 切换到模型的代码仓目录 - git checkout {branch} # 切换到对应分支 - git reset --hard ｛commit_id｝ # 代码设置到对应的commit_id - cd ｛code_path｝ # 切换到模型代码所在路径，若仓库下只有该模型，则无需切换 - ``` - -## 默认配置 - -- 数据集获取：obs://cann-id2019/dataset/ - -- 训练超参 - - - log_step_count=200 - - save_summaries_steps=25000 - - train_steps=2600000 - - save_checkpoints_steps=100000 - - eval_examples=39900 - - - -

训练环境准备

- -1. 硬件环境准备请参见各硬件产品文档"[驱动和固件安装升级指南]( https://support.huawei.com/enterprise/zh/category/ai-computing-platform-pid-1557196528909)"。需要在硬件设备上安装与CANN版本配套的固件与驱动。 -2. 宿主机上需要安装Docker并登录[Ascend Hub中心](https://ascendhub.huawei.com/#/detail?name=ascend-tensorflow-arm)获取镜像。 - - 当前模型支持的镜像列表如[表1](#zh-cn_topic_0000001074498056_table1519011227314)所示。 - - **表 1** 镜像列表 - - - - - - - - - - - - -

镜像名称

-

镜像版本

-

配套CANN版本

-
-

20.2.0

-

20.2

-
- - - -## 脚本参数 - -``` -gen_pt_test_data_gpu.py 中的参数 - ---input_test_files 传入需要进行旋转的点云数据集 ---output_directory 存储旋转后点云集的路径 ---random_rotation_axang boole型，若为真将会对传入的数据集采用指定方法旋转，默认为真 ---num_rotations_per_file 每个测试点云的随机旋转增加数。默认为100 - - -main_point_cloud_gpu.py 中的参数 - ---pt_cloud_test_files 测试数据集路径 ---pt_cloud_train_files 熟练数据集路径 ---method 指定用于预测旋转的方式。选项为"svd", "svd-inf", or "gs"。默认为“svd” ---checkpoint_dir 训练模型的存放位置 ---train_steps 训练迭代的次数。默认为2600000 ---save_checkpoints_steps 保存检查点的频率。默认为10000 ---log_step_count 日志记录一次的步数。默认为200 ---save_summaries_steps 保存一次summary的步数。默认为5000 ---learning_rate 默认为1e-5 ---lr_decay 如果为真，则衰减learning rate。默认为假 ---lr_decay_steps learning rate衰减步数。默认为35000 ---lr_decay_rate learning rate衰减速率。默认为0.95 ---predict_all_test 如果为真，则在最新的检查点上运行eval作业，并打印每个输入的误差信息。默认为假 ---eval_examples 测试样本的数量。默认为0 ---print_variable_names 打印模型变量名。默认为假 ---num_train_augmentations 增加每个输入点云的随机旋转数。默认为10 - -``` - - - -## 运行 - -GPU运行命令如下： - -**生成测试数据** - -注：生成的文件test_points_modified、points已包含在dataset文件夹中。 -```bash -# 将路径设置到训练点云图文件 -IN_FILES=/points_test/*.pts - -NEW_TEST_FILES_DIR=/test_points_modified - -AXANG_SAMPLING=True - -# 决定旋转轴角的分布 -AXANG_SAMPLING=True - -python -m special_orthogonalization.gen_pt_test_data_gpu --input_test_files=$IN_FILES --output_directory=$NEW_TEST_FILES_DIR --random_rotation_axang=$AXANG_SAMPLING -``` - -**训练与评价** -```bash -# 将路径设置到原始训练数据 -TRAIN_FILES=/points/*.pts - -#将路径设置到旋转后的训练数据 -TEST_FILES=$NEW_TEST_FILES_DIR/*.pts - -# 指定旋转预测方式 -METHOD=svd - -# 指定ckpt、summaries、评价结果等的存储路径 -OUT_DIR=/path/to/model - -python -m special_orthogonalization.main_point_cloud_gpu --method=$METHOD --checkpoint_dir=$OUT_DIR --log_step_count=200 --save_summaries_steps=25000 --pt_cloud_train_files=$TRAIN_FILES --pt_cloud_test_files=$TEST_FILES --train_steps=2600000 --save_checkpoints_steps=100000 --eval_examples=39900 -``` - -**从所有训练样本中生成统计数据** -```bash -# 打印均值、中位数、标准差和分位数 -python -m special_orthogonalization.main_point_cloud_gpu --method=$METHOD --checkpoint_dir=$OUT_DIR --pt_cloud_test_files=$TEST_FILES --predict_all_test=True -``` -## 运行 - -NPU运行命令方式如下: - -对于所有的三个步骤程序来说，modelarts插件obs桶中的数据路径均要写到真正包含数据的那一个路径 -如在dataset文件夹中含有points、points_test等包含数据的文件夹 -modelarts插件中的数据路径写为 obs://cann-id2019/dataset/ - -**生成测试数据** - -运行这一步我们需要的程序文件为gen_pt_test_data.py、modelarts_entry_Axang.py、genTestData.sh -这三个文件中的代码均不需要修改 -最后生成的旋转后的数据文件存放在obs桶当次程序文件的output路径中，文件名为test_points_modified， -为进行第二步模型训练，需要将生成旋转后的文件转移至obs桶中存放data的路径 - -注：需要确保的是存在obs桶里的data文件名为points_test - -**训练与评价** - -运行这一步我们需要的程序文件为main_point_cloud_boostPerf.py、modelarts_entry_acc_train.py、train_full_1p.sh -这三个文件中的代码均不需要修改 - -由于采用混合精度提高训练性能，一些算子计算溢出，为此增添switch_config.txt文件，该文件应该和代码所在目录一致。 - -最后生成的旋转后的数据文件存放在obs桶当次程序文件的output路径中，文件名为test_points_modified， -为进行第三步，需要将生成的output文件转移至obs桶中存放data的路径 - -注意：该次训练的模型保存在该次的obs文件夹中，进行第三步时又需要重启一次新的modelarts，因此我们需要将output文件中的 -checkpoint文件中最新模型的路径修改 -"/home/ma-user/modelarts/inputs/data_url_0/output" -这样第三步才能跑出正确的精度指标 - - -**从所有训练样本中生成统计数据** - -运行这一步我们需要的程序文件为main_point_cloud_boostPerf.py、modelarts_entry_stat.py、genStatistical.sh -这三个文件的代码均不需要修改 - -运行成功后将会在屏幕上打印出关于精度相应的统计量值 - - - -## 训练结果 -**精度对比：** - - - -| 测地线误差（°） | 论文发布 | GPU(初始代码未改动版本) | GPU实测|NPU实测 | -| ------------------------ | ------- | ----- | --------- |----------| -| 平均值 | 1.63 | 2.58 | 3.98 | 2.92 | -| 中值 | 0.89 | 1.68 | 2.6 | 1.7 | -| 标准差 | 6.70 | 6.93 | 9.36 | 8.45 | - -相比于论文中的精度，我们NPU迁移后实测差距依然较大，但是与我们未对代码任何改动初始的版本在GPU上跑出来的精度相差较小， -且对于相同的代码的代码改动，NPU上的精度优于GPU上的精度。需要注意的是，在NPU上运行程序时，我们采用混合精度来提升训练 -的性能，但是其中产生了未知的错误，导致代表的精度指标mean_degree_err在整个训练过程中始终为0，因此我们无法得知在NPU训练的 -260w步的过程中，精度指标是下降的过程是怎样的。值得庆幸的是通过NPU训练出的模型，能够在GPU上计算出精度，并且精度还不错。 - -**性能对比：** - -取华为v100上GPU运行的前2w步的global_step/sec平均值和NPU运行的前1w步的global_step/sec平均值进行对比，以达到性能对比的目的。 -对于NPU上的性能计算 需要的程序为main_point_cloud_boostPerf.py、modelarts_entry_perf.py、train_performance_1p.sh，其中参数已经 -设置完毕，无需更改。同时我们上传了我们计算性能的代码calc_perf.py，运行该代码需要将产生的日志文件从obs上下载下来，传入obs文件在本地的路径即可 -| 性能指标项 | 论文发布 | GPU实测 | NPU实测 | -| ------------------- | ------- | ------ | ------ | -| global_step/sec| 无 | 87.64 | 116.77 | - -##离线推理 -利用frezze.py文件将save_model模型保存为pb模型 - - -- Gitee From 715137c0637952a53c2e7cb6f329c3a0559fd631 Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Thu, 18 Aug 2022 07:21:40 +0000 Subject: [PATCH 73/88] =?UTF-8?q?=E6=9B=B4=E6=96=B0?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: ab_dx_z <> --- .../cv/SVD_ID2019_for_Tensorflow/README.md | 256 ++++++++++++++++++ 1 file changed, 256 insertions(+) create mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md new file mode 100644 index 000000000..917ab7f44 --- /dev/null +++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md @@ -0,0 +1,256 @@ +

基本信息

+ +**发布者（Publisher）：Huawei** + +**应用领域（Application Domain）：CV** + +**版本（Version）：** + +**修改时间（Modified） ：2022.05.05** + +**框架（Framework）：TensorFlow 1.15.0** + +**处理器（Processor）：昇腾910** + +**描述（Description）：使用训练好的SVD模型，评估对称正交化在点云对准中的应用效果。** + +

概述

+ +给定两个三维点云图，利用SVD正交化过程SVDO+(M)将其投射到SO(3)上，要求网络预测最佳对齐它们的3D旋转。代码的训练逻辑是每训练10w步保存一个模型，并且在测试集上检验该模型的精度，最后比较的都是260w步的模型精度 + +- 开源代码： + + https://github.com/google-research/google-research/tree/master/special_orthogonalization。 + +- 参考论文： + + [An Analysis of SVD for Deep Rotation Estimation](https://arxiv.org/abs/2006.14616) + +- 参考实现： + gpu最后训练出来的模型在 + + obs://cann-id2019/gpu + + 数据在 + obs://cann-id2019/dataset/ + + 共有3个数据集 + + 训练数据集points + + 测试数据集 points_test + + 第一步旋转后的数据集 test_points_modified + + npu训练出来的模型在 + + obs://cann-id2019/dataset/output + + + 相关代码均以上传，在NPU和GPU文件夹中可以找到 + +- 相关迁移的工作： + 在进行代码迁移到NPU上时，输入的训练数据为点云数据，点云数据的shape为(N,3)，其中N并不是固定的，因此在NPU上存在动态shape的问题，导致模型训练无法正常进行。我们为此想了三个解决方法：1、找出所有点云数据中最小的N，对于大于N的点云数据，仅取前N行的数据输入训练。2、找到所有点云数据中最大的N，对于小于N的点云进行补0操作，将所有数据固定为最大的N后，输入网络进行训练。3、找到所有点云数据中最大的N，对小于N的点云数据，从原数据中选择一个点云进行填补至行数为N，再将数据输入网络进行训练。该三种方法均成功解决了NPU上的动态shape问题，但是第一种方法删除了样本点，因此导致最后训练出的模型精度很差；第二种方法虽然并没有丢失样本信息，但是向数据中填入大量的0，改变了本来的代码逻辑，导致最后训练出的模型精度也并不高。对于第三种方法，即没有丢失样本信息，对每个点云数据中的某一个点云样本点进行重复操作，没有改变原始的代码逻辑，最后也获得了不错的精度表现。 + +- 通过Git获取对应commit\_id的代码方法如下： + + ``` + git clone {repository_url} # 克隆仓库的代码 + cd {repository_name} # 切换到模型的代码仓目录 + git checkout {branch} # 切换到对应分支 + git reset --hard ｛commit_id｝ # 代码设置到对应的commit_id + cd ｛code_path｝ # 切换到模型代码所在路径，若仓库下只有该模型，则无需切换 + ``` + +## 默认配置 + +- 数据集获取：obs://cann-id2019/dataset/ + +- 训练超参 + + - log_step_count=200 + - save_summaries_steps=25000 + - train_steps=2600000 + - save_checkpoints_steps=100000 + - eval_examples=39900 + + + +

训练环境准备

+ +1. 硬件环境准备请参见各硬件产品文档"[驱动和固件安装升级指南]( https://support.huawei.com/enterprise/zh/category/ai-computing-platform-pid-1557196528909)"。需要在硬件设备上安装与CANN版本配套的固件与驱动。 +2. 宿主机上需要安装Docker并登录[Ascend Hub中心](https://ascendhub.huawei.com/#/detail?name=ascend-tensorflow-arm)获取镜像。 + + 当前模型支持的镜像列表如[表1](#zh-cn_topic_0000001074498056_table1519011227314)所示。 + + **表 1** 镜像列表 + + + + + + + + + + + + +

镜像名称

+

镜像版本

+

配套CANN版本

+
+

20.2.0

+

20.2

+
+ + + +## 脚本参数 + +``` +gen_pt_test_data_gpu.py 中的参数 + +--input_test_files 传入需要进行旋转的点云数据集 +--output_directory 存储旋转后点云集的路径 +--random_rotation_axang boole型，若为真将会对传入的数据集采用指定方法旋转，默认为真 +--num_rotations_per_file 每个测试点云的随机旋转增加数。默认为100 + + +main_point_cloud_gpu.py 中的参数 + +--pt_cloud_test_files 测试数据集路径 +--pt_cloud_train_files 熟练数据集路径 +--method 指定用于预测旋转的方式。选项为"svd", "svd-inf", or "gs"。默认为“svd” +--checkpoint_dir 训练模型的存放位置 +--train_steps 训练迭代的次数。默认为2600000 +--save_checkpoints_steps 保存检查点的频率。默认为10000 +--log_step_count 日志记录一次的步数。默认为200 +--save_summaries_steps 保存一次summary的步数。默认为5000 +--learning_rate 默认为1e-5 +--lr_decay 如果为真，则衰减learning rate。默认为假 +--lr_decay_steps learning rate衰减步数。默认为35000 +--lr_decay_rate learning rate衰减速率。默认为0.95 +--predict_all_test 如果为真，则在最新的检查点上运行eval作业，并打印每个输入的误差信息。默认为假 +--eval_examples 测试样本的数量。默认为0 +--print_variable_names 打印模型变量名。默认为假 +--num_train_augmentations 增加每个输入点云的随机旋转数。默认为10 + +``` + + + +## 运行 + +GPU运行命令如下： + +**生成测试数据** + +注：生成的文件test_points_modified、points已包含在dataset文件夹中。 +```bash +# 将路径设置到训练点云图文件 +IN_FILES=/points_test/*.pts + +NEW_TEST_FILES_DIR=/test_points_modified + +AXANG_SAMPLING=True + +# 决定旋转轴角的分布 +AXANG_SAMPLING=True + +python -m special_orthogonalization.gen_pt_test_data_gpu --input_test_files=$IN_FILES --output_directory=$NEW_TEST_FILES_DIR --random_rotation_axang=$AXANG_SAMPLING +``` + +**训练与评价** +```bash +# 将路径设置到原始训练数据 +TRAIN_FILES=/points/*.pts + +#将路径设置到旋转后的训练数据 +TEST_FILES=$NEW_TEST_FILES_DIR/*.pts + +# 指定旋转预测方式 +METHOD=svd + +# 指定ckpt、summaries、评价结果等的存储路径 +OUT_DIR=/path/to/model + +python -m special_orthogonalization.main_point_cloud_gpu --method=$METHOD --checkpoint_dir=$OUT_DIR --log_step_count=200 --save_summaries_steps=25000 --pt_cloud_train_files=$TRAIN_FILES --pt_cloud_test_files=$TEST_FILES --train_steps=2600000 --save_checkpoints_steps=100000 --eval_examples=39900 +``` + +**从所有训练样本中生成统计数据** +```bash +# 打印均值、中位数、标准差和分位数 +python -m special_orthogonalization.main_point_cloud_gpu --method=$METHOD --checkpoint_dir=$OUT_DIR --pt_cloud_test_files=$TEST_FILES --predict_all_test=True +``` +## 运行 + +NPU运行命令方式如下: + +对于所有的三个步骤程序来说，modelarts插件obs桶中的数据路径均要写到真正包含数据的那一个路径 +如在dataset文件夹中含有points、points_test等包含数据的文件夹 +modelarts插件中的数据路径写为 obs://cann-id2019/dataset/ + +**生成测试数据** + +运行这一步我们需要的程序文件为gen_pt_test_data.py、modelarts_entry_Axang.py、genTestData.sh +这三个文件中的代码均不需要修改 +最后生成的旋转后的数据文件存放在obs桶当次程序文件的output路径中，文件名为test_points_modified， +为进行第二步模型训练，需要将生成旋转后的文件转移至obs桶中存放data的路径 + +注：需要确保的是存在obs桶里的data文件名为points_test + +**训练与评价** + +运行这一步我们需要的程序文件为main_point_cloud_boostPerf.py、modelarts_entry_acc_train.py、train_full_1p.sh +这三个文件中的代码均不需要修改 + +由于采用混合精度提高训练性能，一些算子计算溢出，为此增添switch_config.txt文件，该文件应该和代码所在目录一致。 + +最后生成的旋转后的数据文件存放在obs桶当次程序文件的output路径中，文件名为test_points_modified， +为进行第三步，需要将生成的output文件转移至obs桶中存放data的路径 + +注意：该次训练的模型保存在该次的obs文件夹中，进行第三步时又需要重启一次新的modelarts，因此我们需要将output文件中的 +checkpoint文件中最新模型的路径修改 +"/home/ma-user/modelarts/inputs/data_url_0/output" +这样第三步才能跑出正确的精度指标 + + +**从所有训练样本中生成统计数据** + +运行这一步我们需要的程序文件为main_point_cloud_boostPerf.py、modelarts_entry_stat.py、genStatistical.sh +这三个文件的代码均不需要修改 + +运行成功后将会在屏幕上打印出关于精度相应的统计量值 + + + +## 训练结果 +**精度对比：** + + + +| 测地线误差（°） | 论文发布 | GPU(初始代码未改动版本) | GPU实测|NPU实测 | +| ------------------------ | ------- | ----- | --------- |----------| +| 平均值 | 1.63 | 2.58 | 3.98 | 2.92 | +| 中值 | 0.89 | 1.68 | 2.6 | 1.7 | +| 标准差 | 6.70 | 6.93 | 9.36 | 8.45 | + +相比于论文中的精度，我们NPU迁移后实测差距依然较大，但是与我们未对代码任何改动初始的版本在GPU上跑出来的精度相差较小， +且对于相同的代码的代码改动，NPU上的精度优于GPU上的精度。需要注意的是，在NPU上运行程序时，我们采用混合精度来提升训练 +的性能，但是其中产生了未知的错误，导致代表的精度指标mean_degree_err在整个训练过程中始终为0，因此我们无法得知在NPU训练的 +260w步的过程中，精度指标是下降的过程是怎样的。值得庆幸的是通过NPU训练出的模型，能够在GPU上计算出精度，并且精度还不错。 + +**性能对比：** + +取华为v100上GPU运行的前2w步的global_step/sec平均值和NPU运行的前1w步的global_step/sec平均值进行对比，以达到性能对比的目的。 +对于NPU上的性能计算 需要的程序为main_point_cloud_boostPerf.py、modelarts_entry_perf.py、train_performance_1p.sh，其中参数已经 +设置完毕，无需更改。同时我们上传了我们计算性能的代码calc_perf.py，运行该代码需要将产生的日志文件从obs上下载下来，传入obs文件在本地的路径即可 +| 性能指标项 | 论文发布 | GPU实测 | NPU实测 | +| ------------------- | ------- | ------ | ------ | +| global_step/sec| 无 | 87.64 | 116.77 | + +## 离线推理 +利用frezze.py文件将save_model模型保存为pb模型 + + -- Gitee From 5dbedcf7f5a4eee30aea06a133c85fc1f3ff53d3 Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Sat, 22 Oct 2022 13:12:15 +0000 Subject: [PATCH 74/88] update TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md. Signed-off-by: ab_dx_z <> --- .../contrib/cv/SVD_ID2019_for_Tensorflow/README.md | 14 ++++---------- 1 file changed, 4 insertions(+), 10 deletions(-) diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md index 917ab7f44..e688a48f0 100644 --- a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md +++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md @@ -27,12 +27,8 @@ [An Analysis of SVD for Deep Rotation Estimation](https://arxiv.org/abs/2006.14616) - 参考实现： - gpu最后训练出来的模型在 - obs://cann-id2019/gpu - - 数据在 - obs://cann-id2019/dataset/ + 数据下载百度网盘链接: 共有3个数据集 @@ -42,12 +38,10 @@ 第一步旋转后的数据集 test_points_modified - npu训练出来的模型在 + npu训练出来的模型下载百度网盘链接: + - obs://cann-id2019/dataset/output - - 相关代码均以上传，在NPU和GPU文件夹中可以找到 - 相关迁移的工作： 在进行代码迁移到NPU上时，输入的训练数据为点云数据，点云数据的shape为(N,3)，其中N并不是固定的，因此在NPU上存在动态shape的问题，导致模型训练无法正常进行。我们为此想了三个解决方法：1、找出所有点云数据中最小的N，对于大于N的点云数据，仅取前N行的数据输入训练。2、找到所有点云数据中最大的N，对于小于N的点云进行补0操作，将所有数据固定为最大的N后，输入网络进行训练。3、找到所有点云数据中最大的N，对小于N的点云数据，从原数据中选择一个点云进行填补至行数为N，再将数据输入网络进行训练。该三种方法均成功解决了NPU上的动态shape问题，但是第一种方法删除了样本点，因此导致最后训练出的模型精度很差；第二种方法虽然并没有丢失样本信息，但是向数据中填入大量的0，改变了本来的代码逻辑，导致最后训练出的模型精度也并不高。对于第三种方法，即没有丢失样本信息，对每个点云数据中的某一个点云样本点进行重复操作，没有改变原始的代码逻辑，最后也获得了不错的精度表现。 @@ -64,7 +58,7 @@ ## 默认配置 -- 数据集获取：obs://cann-id2019/dataset/ +- 数据集获取百度网盘链接： - 训练超参 -- Gitee From ac0326c89065712c63ab23d94f5c233a83a66ff9 Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Sat, 22 Oct 2022 13:15:51 +0000 Subject: [PATCH 75/88] =?UTF-8?q?=E5=88=A0=E9=99=A4=E6=96=87=E4=BB=B6=20Te?= =?UTF-8?q?nsorFlow/contrib/cv/SVD=5FID2019=5Ffor=5FTensorflow/main=5Fpoin?= =?UTF-8?q?t=5Fcloud=5FboostPerf.py?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../main_point_cloud_boostPerf.py | 462 ------------------ 1 file changed, 462 deletions(-) delete mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/main_point_cloud_boostPerf.py diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/main_point_cloud_boostPerf.py b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/main_point_cloud_boostPerf.py deleted file mode 100644 index 54f2fffa0..000000000 --- a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/main_point_cloud_boostPerf.py +++ /dev/null @@ -1,462 +0,0 @@ -# coding=utf-8 -# Copyright 2021 The Google Research Authors. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""Training and evaluation for the point cloud alignment experiment.""" -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import utils -from absl import flags -import numpy as np -import tensorflow as tf -import glob -import pathlib -import datetime -from npu_bridge.npu_init import * -from npu_bridge.estimator.npu.npu_estimator import NPUEstimatorSpec -from npu_bridge.estimator.npu.npu_estimator import NPUEstimator -from npu_bridge.estimator.npu.npu_config import NPURunConfig -from npu_bridge.estimator.npu.npu_config import ProfilingConfig -#import precision_tool.tf_config as npu_tf_config - -#import precision_tool.config.config as CONFIG - -import os -FLAGS = tf.app.flags.FLAGS - -# General flags. -flags.DEFINE_string('method', 'svd', - 'Specifies the method to use for predicting rotations. ' - 'Choices are "svd", "svd-inf", or "gs".') -flags.DEFINE_string('checkpoint_dir', '', - 'Locations for checkpoints, summaries, etc.') -flags.DEFINE_integer('train_steps', 2600000, 'Number of training iterations.') -flags.DEFINE_integer('save_checkpoints_steps', 10000, - 'How often to save checkpoints') -flags.DEFINE_integer('log_step_count', 500, 'How often to log the step count') -flags.DEFINE_integer('save_summaries_steps', 5000, - 'How often to save summaries.') -flags.DEFINE_float('learning_rate', 1e-5, 'Learning rate') -flags.DEFINE_boolean('lr_decay', False, 'Decay the learning rate if True.') -flags.DEFINE_integer('lr_decay_steps', 35000, - 'Learning rate decays steps.') -flags.DEFINE_float('lr_decay_rate', 0.95, - 'Learning rate decays rate.') -flags.DEFINE_boolean('predict_all_test', False, - 'If true, runs an eval job on latest checkpoint and ' - 'prints the error for each input.') -flags.DEFINE_integer('eval_examples', 39900, 'Number of test examples.') -flags.DEFINE_boolean('print_variable_names', False, - 'Print model variable names.') - -# Flags only used in the point cloud alignment experiment. -flags.DEFINE_integer('num_train_augmentations', 10, - 'Number of random rotations for augmenting each input ' - 'point cloud.') -flags.DEFINE_string('pt_cloud_train_files', '', - 'Expression matching all training point files, e.g. ' - '/path/to/files/pc_plane/points/*.pts') -flags.DEFINE_string('pt_cloud_test_files','', - 'Expression matching all modified test point files, e.g. ' - '/path/to/files/pc_plane/points_test/*.pts') -flags.DEFINE_boolean('random_rotation_axang', True, - 'If true, samples random rotations using the method ' - 'from the original benchmark code. Otherwise samples ' - 'by Haar measure.') -flags.DEFINE_boolean('Profiling',True, - 'parse NPU operator performance') - -flags.DEFINE_boolean('Dump',False, - 'overflow test') - - -def pt_features(batch_pts): - """Input shape: [B, N, 3], output shape: [B, 1024].""" - with tf.variable_scope('ptenc', reuse=tf.AUTO_REUSE): - f1 = tf.layers.conv1d(inputs=batch_pts, filters=64, kernel_size=1) - f1 = tf.nn.leaky_relu(f1) - f2 = tf.layers.conv1d(inputs=f1, filters=128, kernel_size=1) - f2 = tf.nn.leaky_relu(f2) - f3 = tf.layers.conv1d(inputs=f2, filters=1024, kernel_size=1) - f = tf.reduce_max(f3, axis=1, keep_dims=False) - return f - - -def regress_from_features(batch_features, out_dim): - """Regress to a rotation representation from point cloud encodings. - - In Zhou et al, CVPR19, the paper describes this regression network as an MLP - mapping 2048->512->512->out_dim, but the associated code implements it with - one less layer: 2048->512->out_dim. We mimic the code. - - Args: - batch_features: [batch_size, in_dim]. - out_dim: desired output dimensionality. - - Returns: - A [batch_size, out_dim] tensor. - """ - f1 = tf.layers.dense(batch_features, 512) - f1 = tf.nn.leaky_relu(f1) - f2 = tf.layers.dense(f1, out_dim) - return f2 - - -def net_point_cloud(points1, points2, mode): - """Predict a relative rotation given two point clouds. - - Args: - points1: [batch_size, N, 3] float tensor. - points2: [batch_size, N, 3] float tensor. - mode: tf.estimator.ModeKeys. - - Returns: - [batch_size, 3, 3] matrices. - """ - f1 = pt_features(points1) - f2 = pt_features(points2) - f = tf.concat([f1, f2], axis=-1) - - if FLAGS.method == 'svd': - p = regress_from_features(f, 9) - return utils.symmetric_orthogonalization(p) - - if FLAGS.method == 'svd-inf': - p = regress_from_features(f, 9) - if mode == tf.estimator.ModeKeys.TRAIN: - return tf.reshape(p, (-1, 3, 3)) - else: - return utils.symmetric_orthogonalization(p) - - if FLAGS.method == 'gs': - p = regress_from_features(f, 6) - return utils.gs_orthogonalization(p) - - -def model_fn(features, labels, mode, params): - """The model_fn used to construct the tf.Estimator.""" - del labels, params # Unused. - if mode == tf.estimator.ModeKeys.TRAIN: - # Training data has point cloud of size [1, N, 3] and random rotations - # of size [1, FLAGS.num_train_augmentations, 3, 3] - rot = features['rot'][0] - num_rot = FLAGS.num_train_augmentations - batch_pts1 = tf.tile(features['data'], [num_rot, 1, 1]) - # In this experiment it does not matter if we pre or post-multiply the - # rotation as long as we are consistent between training and eval. - batch_pts2 = tf.matmul(batch_pts1, rot) # post-multiplying! - else: - # Test data has point cloud of size [1, N, 3] and a single random - # rotation of size [1, 3, 3] - batch_pts1 = features['data'] - rot = features['rot'] - batch_pts2 = tf.matmul(batch_pts1, rot) - rot = tf.reshape(rot, (-1, 3, 3)) - - # Predict the rotation. - r = net_point_cloud(batch_pts1, batch_pts2, mode) - - # Compute the loss. - loss = tf.nn.l2_loss(rot - r) - - # Compute the relative angle in radians. - theta = utils.relative_angle(r, rot) - - # Mean angle error over the batch. - mean_theta = tf.reduce_mean(theta) - mean_theta_deg = mean_theta * 180.0 / np.pi - - # Train, eval, or predict depending on mode. - if mode == tf.estimator.ModeKeys.TRAIN: - tf.summary.scalar('train/loss', loss) - tf.summary.scalar('train/theta', mean_theta_deg) - global_step = tf.train.get_or_create_global_step() - - if FLAGS.lr_decay: - learning_rate = tf.train.exponential_decay( - FLAGS.learning_rate, - global_step, - FLAGS.lr_decay_steps, - FLAGS.lr_decay_rate) - else: - learning_rate = FLAGS.learning_rate - - tf.summary.scalar('train/learning_rate', learning_rate) - optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate) - update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) - with tf.control_dependencies(update_ops): - train_op = optimizer.minimize(loss, global_step=global_step) - #train_op = util.set_graph_exec_config(train_op, dynamic_input=True,dynamic_graph_execute_mode="lazy_recompile") - - return NPUEstimatorSpec( - mode=mode, - loss=loss, - train_op=train_op) - - if mode == tf.estimator.ModeKeys.EVAL: - if FLAGS.predict_all_test: - print_error_op = tf.print('error:', mean_theta_deg) - with tf.control_dependencies([print_error_op]): - eval_metric_ops = { - 'mean_degree_err': tf.metrics.mean(mean_theta_deg), - } - else: - eval_metric_ops = { - 'mean_degree_err': tf.metrics.mean(mean_theta_deg), - } - - return NPUEstimatorSpec( - mode=mode, - loss=loss, - eval_metric_ops=eval_metric_ops) - - if mode == tf.estimator.ModeKeys.PREDICT: - pred = {'error': mean_theta_deg} - return NPUEstimatorSpec( - mode=mode, - predictions=pred) - - -def train_input_fn(): - """Generate training data iterator from the .pts files.""" - def _file_to_matrix(pts_path): - """Read Nx3 point cloud from a .pts file.""" - file_buffer = tf.read_file(pts_path) - lines = tf.string_split([file_buffer], delimiter='\n') - lines1 = tf.string_split(lines.values, delimiter='\r') - values = tf.stack(tf.decode_csv(lines1.values, - record_defaults=[[0.0], [0.0], [0.0]], - field_delim=' ')) - values = tf.transpose(values) # 3xN --> Nx3. - values = values[:tf.shape(values)[0] // 2, :] - - # modified by Lyoung - diff_num = 1500-tf.shape(values)[0] - repeat_pts = tf.tile(tf.reshape(values[1, :], (1, -1)), [diff_num, 1]) - values = tf.concat([values,repeat_pts], axis=0) - # The experiment code in - # github.com/papagina/RotationContinuity/.../shapenet/code/train_pointnet.py - # only used the first half of the points in each file. - return values - - def _random_rotation(pts): - """Attach N random rotations to a point cloud.""" - if FLAGS.random_rotation_axang: - rotations = utils.random_rotation_benchmark(FLAGS.num_train_augmentations) - else: - rotations = utils.random_rotation(FLAGS.num_train_augmentations) - return pts,rotations - - pts_paths = tf.gfile.Glob(FLAGS.pt_cloud_train_files) - dataset = tf.data.Dataset.from_tensor_slices(pts_paths) - dataset = dataset.map(_file_to_matrix) - dataset = dataset.cache() # Comment out if memory cannot hold all the data. - dataset = dataset.shuffle(buffer_size=50, reshuffle_each_iteration=True) - dataset = dataset.repeat() - dataset = dataset.map(_random_rotation) - dataset = dataset.batch(1) - - iterator = tf.data.make_one_shot_iterator(dataset) - batch_data, batch_rot = iterator.get_next() - features_dict = {'data': batch_data, 'rot': batch_rot} - batch_size = tf.shape(batch_data)[0] - batch_labels_dummy = tf.zeros(shape=(batch_size, 1)) - return (features_dict, batch_labels_dummy) - - -def eval_input_fn(): - """Generate test data from *modified* .pts files. - - See README and comments below for details on how the data is modified. - - Returns: - A tuple of features and associated labels. - """ - def _file_to_matrix(pts_path): - """Read Nx3 point cloud and 3x3 rotation matrix from a .pts file. - - The test data is a modified version of the original files. For each .pts - file we have (1) added a 3x3 rotation matrix for testing, and (2) removed - the second half of the point cloud since it is not used at all. - - Args: - pts_path: path to a .pts file. - - Returns: - A Nx3 point cloud. - A 3x3 rotation matrix. - """ - file_buffer = tf.read_file(pts_path) - lines = tf.string_split([file_buffer], delimiter='\n') - lines1 = tf.string_split(lines.values,delimiter='\r') - values = tf.stack(tf.decode_csv(lines1.values, - record_defaults=[[0.0], [0.0], [0.0]],field_delim=' ')) - values = tf.transpose(values)# 3xN --> Nx3. - - #modified by Lyoung - diff_num = 1414-tf.shape(values)[0] - repeat_pts = tf.tile(tf.reshape(values[4, :], (1, -1)), [diff_num, 1]) - values = tf.concat([values, repeat_pts], axis=0) - # First three rows are the rotation matrix, remaining rows the point cloud. - rot = values[:3, :] - - return values[4:, :], rot - - pts_paths = tf.io.gfile.glob(FLAGS.pt_cloud_test_files) - dataset = tf.data.Dataset.from_tensor_slices(pts_paths) - dataset = dataset.map(_file_to_matrix) - dataset = dataset.batch(1) - iterator = tf.data.make_one_shot_iterator(dataset) - batch_data, batch_rot = iterator.get_next() - - features_dict = {'data': batch_data, 'rot': batch_rot} - batch_size = tf.shape(batch_data)[0] - batch_labels_dummy = tf.zeros(shape=(batch_size, 1)) - return (features_dict, batch_labels_dummy) - - -def print_variable_names(): - """Print variable names in a model.""" - params = {'dummy': 0} - estimator = NPUEstimator( - model_fn=model_fn, - model_dir=FLAGS.checkpoint_dir, - params=params) - - names = estimator.get_variable_names() - for name in names: - print(name) - - -def predict_all_test(): - """Print error statistics for the test dataset.""" - - config = NPURunConfig( - save_summary_steps=None, - save_checkpoints_steps=None, - log_step_count_steps=None, - keep_checkpoint_max=None) - - params = {'dummy': 0} - estimator = NPUEstimator( - model_fn=model_fn, - model_dir=FLAGS.checkpoint_dir, - params=params, - config=config) - evals = estimator.predict(input_fn=eval_input_fn, yield_single_examples=True) - - # Print error statistics. - - all_errors = [x['error'] for x in evals] - errors = np.array(all_errors) - print('Evaluated %d examples'%np.size(errors)) - print('Mean error: %f degrees',np.mean(errors)) - print('Median error: %f degrees', np.median(errors)) - print('Std: %f degrees', np.std(errors)) - sorted_errors = np.sort(errors) - n = np.size(sorted_errors) - print('\nPercentiles:') - for perc in range(1, 101): - index = np.int32(np.float32(n * perc) / 100.0) - 1 - print('%3d%%: %f'%(perc, sorted_errors[index])) - - -def train_and_eval(): - """Train and evaluate a model.""" - save_summary_steps = FLAGS.save_summaries_steps - save_checkpoints_steps = FLAGS.save_checkpoints_steps - log_step_count = FLAGS.log_step_count - - - # dump_config = npu_tf_config.estimator_dump_config(action='overflow') - # - profilingPath=os.path.join(FLAGS.checkpoint_dir,'npu_profiling') - if not os.path.exists(profilingPath): - os.makedirs(profilingPath) - - profiling_options= '{"output":"%s",\ - "task_trace":"on",\ - "aicpu":"on"}'%(profilingPath) - - profiling_config=ProfilingConfig(enable_profiling=True,profiling_options=profiling_options) - session_config=tf.ConfigProto() - - if FLAGS.Profiling: - config = NPURunConfig( - save_summary_steps=save_summary_steps, - save_checkpoints_steps=save_checkpoints_steps, - log_step_count_steps=log_step_count, - keep_checkpoint_max=None, - precision_mode = "allow_mix_precision", - profiling_config=profiling_config, - session_config=session_config, - customize_dtypes="/home/ma-user/modelarts/user-job-dir/code/switch_config.txt") - # if FLAGS.Dump == True: - # config = NPURunConfig( - # save_summary_steps=save_summary_steps, - # save_checkpoints_steps=save_checkpoints_steps, - # log_step_count_steps=log_step_count, - # keep_checkpoint_max=None, - # precision_mode="allow_mix_precision", - # dump_config=dump_config) - else: - config = NPURunConfig( - save_summary_steps=save_summary_steps, - save_checkpoints_steps=save_checkpoints_steps, - log_step_count_steps=log_step_count, - keep_checkpoint_max=None, - precision_mode="allow_mix_precision", - customize_dtypes="/home/ma-user/modelarts/user-job-dir/code/switch_config.txt") - - params = {'dummy': 0} - estimator = NPUEstimator( - model_fn=model_fn, - model_dir=FLAGS.checkpoint_dir, - config=config, - params=params) - - - train_spec = tf.estimator.TrainSpec( - input_fn=train_input_fn, - max_steps=FLAGS.train_steps) - - - eval_spec = tf.estimator.EvalSpec(input_fn=eval_input_fn, - start_delay_secs=60, - steps=FLAGS.eval_examples, - throttle_secs=60) - - - - tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec) - - - -def main(argv=None): # pylint: disable=unused-argument - - if FLAGS.print_variable_names: - print_variable_names() - return - - if FLAGS.predict_all_test: - predict_all_test() - else: - train_and_eval() - - -if __name__ == '__main__': - - tf.app.run() -- Gitee From 5219bb67c323571b58c86b13448951cb51106800 Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Sat, 22 Oct 2022 13:17:50 +0000 Subject: [PATCH 76/88] =?UTF-8?q?=E5=88=A0=E9=99=A4=E6=96=87=E4=BB=B6=20Te?= =?UTF-8?q?nsorFlow/contrib/cv/SVD=5FID2019=5Ffor=5FTensorflow/frezze.py?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../cv/SVD_ID2019_for_Tensorflow/frezze.py | 48 ------------------- 1 file changed, 48 deletions(-) delete mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/frezze.py diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/frezze.py b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/frezze.py deleted file mode 100644 index 6aeb2e1aa..000000000 --- a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/frezze.py +++ /dev/null @@ -1,48 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import tensorflow as tf -from tensorflow.python.tools import freeze_graph -from npu_bridge.npu_init import * - -freeze_graph.freeze_graph( - input_saved_model_dir='save_model_path', - output_node_names='L2Loss', - output_graph='test.pb', - initializer_nodes='', - input_graph= None, - input_saver= False, - input_binary=False, - input_checkpoint=None, - restore_op_name=None, - filename_tensor_name=None, - clear_devices=False, - input_meta_graph=False) - -# if __name__ == '__main__': -# main() \ No newline at end of file -- Gitee From f22355ab77aca3ea177a263a2da6c559eb8530d5 Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Sat, 22 Oct 2022 13:18:35 +0000 Subject: [PATCH 77/88] update TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md. Signed-off-by: ab_dx_z <> --- TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md index e688a48f0..78822ea33 100644 --- a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md +++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md @@ -245,6 +245,6 @@ checkpoint文件中最新模型的路径修改 | global_step/sec| 无 | 87.64 | 116.77 | ## 离线推理 -利用frezze.py文件将save_model模型保存为pb模型 +参考 SVD_ID2019_for_ACL -- Gitee From b42c9e065d94dac38e49335f0c05a55ba3622d5c Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Sat, 22 Oct 2022 13:18:54 +0000 Subject: [PATCH 78/88] =?UTF-8?q?=E5=88=A0=E9=99=A4=E6=96=87=E4=BB=B6=20Te?= =?UTF-8?q?nsorFlow/contrib/cv/SVD=5FID2019=5Ffor=5FTensorflow/train=5Fful?= =?UTF-8?q?l=5F1p.sh?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../train_full_1p.sh | 209 ------------------ 1 file changed, 209 deletions(-) delete mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/train_full_1p.sh diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/train_full_1p.sh b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/train_full_1p.sh deleted file mode 100644 index edd5eed9a..000000000 --- a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/train_full_1p.sh +++ /dev/null @@ -1,209 +0,0 @@ -#!/bin/bash - -########################################################## -#########第3行 至 100行，请一定不要、不要、不要修改########## -#########第3行 至 100行，请一定不要、不要、不要修改########## -#########第3行 至 100行，请一定不要、不要、不要修改########## -########################################################## -# shell脚本所在路径 -cur_path=`echo $(cd $(dirname $0);pwd)` - -# 判断当前shell是否是performance -perf_flag=`echo $0 | grep performance | wc -l` - -# 当前执行网络的名称 -Network=`echo $(cd $(dirname $0);pwd) | awk -F"/" '{print $(NF-1)}'` - -export RANK_SIZE=1 -export RANK_ID=0 -export JOB_ID=10087 - -# 路径参数初始化 -data_path="" -output_path="" - -# 帮助信息，不需要修改 -if [[ $1 == --help || $1 == -h ]];then - echo"usage:./train_performance_1P.sh " - echo " " - echo "parameter explain: - --data_path # dataset of training - --output_path # output of training - --train_steps # max_step for training - --train_epochs # max_epoch for training - --batch_size # batch size - -h/--help show help message - " - exit 1 -fi - -# 参数校验，不需要修改 -for para in $* -do - if [[ $para == --data_path* ]];then - data_path=`echo ${para#*=}` - elif [[ $para == --output_path* ]];then - output_path=`echo ${para#*=}` - elif [[ $para == --train_steps* ]];then - train_steps=`echo ${para#*=}` - elif [[ $para == --train_epochs* ]];then - train_epochs=`echo ${para#*=}` - elif [[ $para == --batch_size* ]];then - batch_size=`echo ${para#*=}` - fi -done - -# 校验是否传入data_path,不需要修改 -if [[ $data_path == "" ]];then - echo "[Error] para \"data_path\" must be config" - exit 1 -fi - -# 校验是否传入output_path,不需要修改 -if [[ $output_path == "" ]];then - output_path="./test/output/${ASCEND_DEVICE_ID}" -fi - -# 设置打屏日志文件名，请保留，文件名为${print_log} -print_log="./test/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log" -etp_flag=${etp_running_flag} -if [ x"${etp_flag}" != xtrue ]; -then - echo "running without etp..." - print_log_name=`ls /home/ma-user/modelarts/log/ | grep proc-rank` - print_log="/home/ma-user/modelarts/log/${print_log_name}" -fi -echo ${print_log} - -CaseName="" -function get_casename() -{ - if [ x"${perf_flag}" = x1 ]; - then - CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'perf' - else - CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'acc' - fi -} - -# 跳转到code目录 -cd ${cur_path}/../ -rm -rf ./test/output/${ASCEND_DEVICE_ID} -mkdir -p ./test/output/${ASCEND_DEVICE_ID} - -# 训练开始时间记录，不需要修改 -start_time=$(date +%s) -########################################################## -#########第3行 至 100行，请一定不要、不要、不要修改########## -#########第3行 至 100行，请一定不要、不要、不要修改########## -#########第3行 至 100行，请一定不要、不要、不要修改########## -########################################################## - -#========================================================= -#========================================================= -#========训练执行命令，需要根据您的网络进行修改============== -#========================================================= -#========================================================= -# 您的训练数据集在${data_path}路径下，请直接使用这个变量获取 -# 您的训练输出目录在${output_path}路径下，请直接使用这个变量获取 -# 您的其他基础参数，可以自定义增加，但是batch_size请保留，并且设置正确的值 -batch_size=1 - -echo "------------------***PRINT DATA PATH***-----------------" -train_data="points/*.pts" -test_data="test_points_modified/*.pts" - -trainData_Path="$data_path$train_data" -testData_Path="$data_path$test_data" - - -if [ x"${modelarts_flag}" != x ]; -then - python3.7 /home/ma-user/modelarts/user-job-dir/code/main_point_cloud_boostPerf.py \ - --method=svd \ - --checkpoint_dir=${output_path} \ - --log_step_count=200 \ - --save_summaries_steps=25000 \ - --pt_cloud_train_files=${trainData_Path} \ - --pt_cloud_test_files=${testData_Path} \ - --train_steps=2600000 \ - --save_checkpoints_steps=100000 \ - --eval_examples=39900 -else - python3.7 /home/ma-user/modelarts/user-job-dir/code/main_point_cloud_boostPerf.py \ - --method=svd \ - --checkpoint_dir=${output_path} \ - --log_step_count=200 \ - --save_summaries_steps=25000 \ - --pt_cloud_train_files=${trainData_Path}\ - --pt_cloud_test_files=${testData_Path} \ - --train_steps=2600000 \ - --save_checkpoints_steps=100000 \ - --eval_examples=39900 > ${print_log} -fi - - -# 性能相关数据计算 -StepTime=`grep "sec/step :" ${print_log} | tail -n 10 | awk '{print $NF}' | awk '{sum+=$1} END {print sum/NR}'` -FPS=`awk 'BEGIN{ "%.2f\n", '${batch_size}'/'${StepTime}'}'` - -# 精度相关数据计算 -train_accuracy=`grep "Final Accuracy accuracy" ${print_log} | awk '{print $NF}'` -# 提取所有loss打印信息 -grep "loss :" ${print_log} | awk -F ":" '{print $4}' | awk -F "-" '{print $1}' > ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt - - -########################################################### -#########后面的所有内容请不要修改########################### -#########后面的所有内容请不要修改########################### -#########后面的所有内容请不要修改########################### -########################################################### - -# 判断本次执行是否正确使用Ascend NPU -use_npu_flag=`grep "The model has been compiled on the Ascend AI processor" ${print_log} | wc -l` -if [ x"${use_npu_flag}" == x0 ]; -then - echo "------------------ ERROR NOTICE START ------------------" - echo "ERROR, your task haven't used Ascend NPU, please check your npu Migration." - echo "------------------ ERROR NOTICE END------------------" -else - echo "------------------ INFO NOTICE START------------------" - echo "INFO, your task have used Ascend NPU, please check your result." - echo "------------------ INFO NOTICE END------------------" -fi - -# 获取最终的casename，请保留，case文件名为${CaseName} -get_casename - -# 重命名loss文件 -if [ -f ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ]; -then - mv ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ./test/output/${ASCEND_DEVICE_ID}/${CaseName}_loss.txt -fi - -# 训练端到端耗时 -end_time=$(date +%s) -e2e_time=$(( $end_time - $start_time )) - -echo "------------------ Final result ------------------" -# 输出性能FPS/单step耗时/端到端耗时 -echo "Final Performance images/sec : $FPS" -echo "Final Performance sec/step : $StepTime" -echo "E2E Training Duration sec : $e2e_time" - -# 输出训练精度 -echo "Final Train Accuracy : ${train_accuracy}" - -# 最后一个迭代loss值，不需要修改 -ActualLoss=(`awk 'END {print $NF}' $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}_loss.txt`) - -#关键信息打印到${CaseName}.log中，不需要修改 -echo "Network = ${Network}" > $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "RankSize = ${RANK_SIZE}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "BatchSize = ${batch_size}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "DeviceType = `uname -m`" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "ActualFPS = ${FPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "TrainingTime = ${StepTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log \ No newline at end of file -- Gitee From c3fb510bc15b3e29a795774f623b9cc692457cd8 Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Sat, 22 Oct 2022 13:19:59 +0000 Subject: [PATCH 79/88] =?UTF-8?q?=E6=A8=A1=E5=9E=8B=E4=BF=9D=E5=AD=98?= =?UTF-8?q?=E6=94=B9=E4=B8=BAsaved=5Fmodel=E5=BD=A2=E5=BC=8F?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: ab_dx_z <> --- .../main_point_cloud_perf.py | 441 ++++++++++++++++++ .../train_full_1p.sh | 209 +++++++++ 2 files changed, 650 insertions(+) create mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/main_point_cloud_perf.py create mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/train_full_1p.sh diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/main_point_cloud_perf.py b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/main_point_cloud_perf.py new file mode 100644 index 000000000..316d7da20 --- /dev/null +++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/main_point_cloud_perf.py @@ -0,0 +1,441 @@ +# coding=utf-8 +# Copyright 2021 The Google Research Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Training and evaluation for the point cloud alignment experiment.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import utils +from absl import flags +import numpy as np +import tensorflow as tf +import glob +import pathlib +import datetime +from npu_bridge.npu_init import * +from npu_bridge.estimator.npu.npu_estimator import NPUEstimatorSpec +from npu_bridge.estimator.npu.npu_estimator import NPUEstimator +from npu_bridge.estimator.npu.npu_config import NPURunConfig +import os +FLAGS = tf.app.flags.FLAGS + +# General flags. +flags.DEFINE_string('method', 'svd', + 'Specifies the method to use for predicting rotations. ' + 'Choices are "svd", "svd-inf", or "gs".') +flags.DEFINE_string('checkpoint_dir', '', + 'Locations for checkpoints, summaries, etc.') +flags.DEFINE_integer('train_steps', 2600000, 'Number of training iterations.') +flags.DEFINE_integer('save_checkpoints_steps', 10000, + 'How often to save checkpoints') +flags.DEFINE_integer('log_step_count', 500, 'How often to log the step count') +flags.DEFINE_integer('save_summaries_steps', 5000, + 'How often to save summaries.') +flags.DEFINE_float('learning_rate', 1e-5, 'Learning rate') +flags.DEFINE_boolean('lr_decay', False, 'Decay the learning rate if True.') +flags.DEFINE_integer('lr_decay_steps', 35000, + 'Learning rate decays steps.') +flags.DEFINE_float('lr_decay_rate', 0.95, + 'Learning rate decays rate.') +flags.DEFINE_boolean('predict_all_test', False, + 'If true, runs an eval job on latest checkpoint and ' + 'prints the error for each input.') +flags.DEFINE_integer('eval_examples', 39900, 'Number of test examples.') +flags.DEFINE_boolean('print_variable_names', False, + 'Print model variable names.') + +# Flags only used in the point cloud alignment experiment. +flags.DEFINE_integer('num_train_augmentations', 10, + 'Number of random rotations for augmenting each input ' + 'point cloud.') +flags.DEFINE_string('pt_cloud_train_files', '', + 'Expression matching all training point files, e.g. ' + '/path/to/files/pc_plane/points/*.pts') +flags.DEFINE_string('pt_cloud_test_files','', + 'Expression matching all modified test point files, e.g. ' + '/path/to/files/pc_plane/points_test/*.pts') +flags.DEFINE_boolean('random_rotation_axang', True, + 'If true, samples random rotations using the method ' + 'from the original benchmark code. Otherwise samples ' + 'by Haar measure.') + + +def pt_features(batch_pts): + """Input shape: [B, N, 3], output shape: [B, 1024].""" + with tf.variable_scope('ptenc', reuse=tf.AUTO_REUSE): + f1 = tf.layers.conv1d(inputs=batch_pts, filters=64, kernel_size=1) + f1 = tf.nn.leaky_relu(f1) + f2 = tf.layers.conv1d(inputs=f1, filters=128, kernel_size=1) + f2 = tf.nn.leaky_relu(f2) + f3 = tf.layers.conv1d(inputs=f2, filters=1024, kernel_size=1) + f = tf.reduce_max(f3, axis=1, keep_dims=False) + return f + + +def regress_from_features(batch_features, out_dim): + """Regress to a rotation representation from point cloud encodings. + + In Zhou et al, CVPR19, the paper describes this regression network as an MLP + mapping 2048->512->512->out_dim, but the associated code implements it with + one less layer: 2048->512->out_dim. We mimic the code. + + Args: + batch_features: [batch_size, in_dim]. + out_dim: desired output dimensionality. + + Returns: + A [batch_size, out_dim] tensor. + """ + f1 = tf.layers.dense(batch_features, 512) + f1 = tf.nn.leaky_relu(f1) + f2 = tf.layers.dense(f1, out_dim) + return f2 + + +def net_point_cloud(points1, points2, mode): + """Predict a relative rotation given two point clouds. + + Args: + points1: [batch_size, N, 3] float tensor. + points2: [batch_size, N, 3] float tensor. + mode: tf.estimator.ModeKeys. + + Returns: + [batch_size, 3, 3] matrices. + """ + f1 = pt_features(points1) + f2 = pt_features(points2) + f = tf.concat([f1, f2], axis=-1) + + if FLAGS.method == 'svd': + p = regress_from_features(f, 9) + return utils.symmetric_orthogonalization(p) + + if FLAGS.method == 'svd-inf': + p = regress_from_features(f, 9) + if mode == tf.estimator.ModeKeys.TRAIN: + return tf.reshape(p, (-1, 3, 3)) + else: + return utils.symmetric_orthogonalization(p) + + if FLAGS.method == 'gs': + p = regress_from_features(f, 6) + return utils.gs_orthogonalization(p) + + +def model_fn(features, labels, mode, params): + """The model_fn used to construct the tf.Estimator.""" + del params # Unused. + if mode == tf.estimator.ModeKeys.TRAIN: + # Training data has point cloud of size [1, N, 3] and random rotations + # of size [1, FLAGS.num_train_augmentations, 3, 3] + rot = labels[0] + data = features + num_rot = FLAGS.num_train_augmentations + batch_pts1 = tf.tile(data, [num_rot, 1, 1]) + # In this experiment it does not matter if we pre or post-multiply the + # rotation as long as we are consistent between training and eval. + batch_pts2 = tf.matmul(batch_pts1, rot) # post-multiplying! + else: + # Test data has point cloud of size [1, N, 3] and a single random + # rotation of size [1, 3, 3] + batch_pts1 = features['data'] + rot = features['rot'] + batch_pts2 = tf.matmul(batch_pts1, rot) + rot = tf.reshape(rot, (-1, 3, 3)) + + # Predict the rotation. + + + r = net_point_cloud(batch_pts1, batch_pts2, mode) + + unit_one = tf.constant(1.0,dtype=tf.float32) + + rotation_matrix = tf.multiply(r,unit_one,name='rotation_matrix') + # Compute the loss. + loss = tf.nn.l2_loss(rot - r) + + # Compute the relative angle in radians. + theta = utils.relative_angle(rot, r) + + # Mean angle error over the batch. + mean_theta = tf.reduce_mean(theta) + + mean_theta_deg = mean_theta * 180.0 / np.pi + + # Train, eval, or predict depending on mode. + if mode == tf.estimator.ModeKeys.TRAIN: + tf.summary.scalar('train/loss', loss) + tf.summary.scalar('train/theta', mean_theta_deg) + global_step = tf.train.get_or_create_global_step() + + if FLAGS.lr_decay: + learning_rate = tf.train.exponential_decay( + FLAGS.learning_rate, + global_step, + FLAGS.lr_decay_steps, + FLAGS.lr_decay_rate) + else: + learning_rate = FLAGS.learning_rate + + tf.summary.scalar('train/learning_rate', learning_rate) + optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate) + update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) + with tf.control_dependencies(update_ops): + train_op = optimizer.minimize(loss, global_step=global_step) + #train_op = util.set_graph_exec_config(train_op, dynamic_input=True,dynamic_graph_execute_mode="lazy_recompile") + + return NPUEstimatorSpec( + mode=mode, + loss=loss, + train_op=train_op, + predictions=rotation_matrix) + + if mode == tf.estimator.ModeKeys.EVAL: + if FLAGS.predict_all_test: + print_error_op = tf.print('error:', mean_theta_deg) + with tf.control_dependencies([print_error_op]): + eval_metric_ops = { + 'mean_degree_err': tf.metrics.mean(mean_theta_deg), + } + else: + eval_metric_ops = { + 'mean_degree_err': tf.metrics.mean(mean_theta_deg), + } + + return NPUEstimatorSpec( + mode=mode, + loss=loss, + eval_metric_ops=eval_metric_ops) + + if mode == tf.estimator.ModeKeys.PREDICT: + pred = {'error': mean_theta_deg} + return NPUEstimatorSpec( + mode=mode, + predictions=pred) + + +def train_input_fn(): + """Generate training data iterator from the .pts files.""" + def _file_to_matrix(pts_path): + """Read Nx3 point cloud from a .pts file.""" + file_buffer = tf.read_file(pts_path) + lines = tf.string_split([file_buffer], delimiter='\n') + lines1 = tf.string_split(lines.values, delimiter='\r') + values = tf.stack(tf.decode_csv(lines1.values, + record_defaults=[[0.0], [0.0], [0.0]], + field_delim=' ')) + values = tf.transpose(values) # 3xN --> Nx3. + values = values[:tf.shape(values)[0] // 2, :] + + # modified by Lyoung + diff_num = 1500-tf.shape(values)[0] + repeat_pts = tf.tile(tf.reshape(values[1, :], (1, -1)), [diff_num, 1]) + values = tf.concat([values,repeat_pts], axis=0) + # The experiment code in + # github.com/papagina/RotationContinuity/.../shapenet/code/train_pointnet.py + # only used the first half of the points in each file. + return values + + def _random_rotation(pts): + """Attach N random rotations to a point cloud.""" + if FLAGS.random_rotation_axang: + rotations = utils.random_rotation_benchmark(FLAGS.num_train_augmentations) + else: + rotations = utils.random_rotation(FLAGS.num_train_augmentations) + return pts,rotations + + pts_paths = tf.gfile.Glob(FLAGS.pt_cloud_train_files) + dataset = tf.data.Dataset.from_tensor_slices(pts_paths) + dataset = dataset.map(_file_to_matrix) + dataset = dataset.cache() # Comment out if memory cannot hold all the data. + dataset = dataset.shuffle(buffer_size=50, reshuffle_each_iteration=True) + dataset = dataset.repeat() + dataset = dataset.map(_random_rotation) + dataset = dataset.batch(1) + iterator = tf.data.make_one_shot_iterator(dataset) + batch_data, batch_rot = iterator.get_next() + #features_dict = {'data': batch_data, 'rot': batch_rot} + #batch_size = tf.shape(batch_data)[0] + #batch_labels_dummy = tf.zeros(shape=(batch_size, 1)) + return batch_data,batch_rot + + +def eval_input_fn(): + """Generate test data from *modified* .pts files. + + See README and comments below for details on how the data is modified. + + Returns: + A tuple of features and associated labels. + """ + def _file_to_matrix(pts_path): + """Read Nx3 point cloud and 3x3 rotation matrix from a .pts file. + + The test data is a modified version of the original files. For each .pts + file we have (1) added a 3x3 rotation matrix for testing, and (2) removed + the second half of the point cloud since it is not used at all. + + Args: + pts_path: path to a .pts file. + + Returns: + A Nx3 point cloud. + A 3x3 rotation matrix. + """ + file_buffer = tf.read_file(pts_path) + lines = tf.string_split([file_buffer], delimiter='\n') + lines1 = tf.string_split(lines.values,delimiter='\r') + values = tf.stack(tf.decode_csv(lines1.values, + record_defaults=[[0.0], [0.0], [0.0]],field_delim=' ')) + values = tf.transpose(values)# 3xN --> Nx3. + + #modified by Lyoung + diff_num = 1414-tf.shape(values)[0] + repeat_pts = tf.tile(tf.reshape(values[4, :], (1, -1)), [diff_num, 1]) + values = tf.concat([values, repeat_pts], axis=0) + # First three rows are the rotation matrix, remaining rows the point cloud. + rot = values[:3, :] + + return values[4:, :], rot + + pts_paths = tf.io.gfile.glob(FLAGS.pt_cloud_test_files) + dataset = tf.data.Dataset.from_tensor_slices(pts_paths) + dataset = dataset.map(_file_to_matrix) + dataset = dataset.batch(1) + iterator = tf.data.make_one_shot_iterator(dataset) + batch_data, batch_rot = iterator.get_next() + features_dict = {'data': batch_data, 'rot': batch_rot} + #batch_size = tf.shape(batch_data)[0] + #batch_labels_dummy = tf.zeros(shape=(batch_size, 1)) + return features_dict + + +def print_variable_names(): + """Print variable names in a model.""" + params = {'dummy': 0} + estimator = NPUEstimator( + model_fn=model_fn, + model_dir=FLAGS.checkpoint_dir, + params=params) + + names = estimator.get_variable_names() + for name in names: + print(name) + + +def predict_all_test(): + """Print error statistics for the test dataset.""" + + config = NPURunConfig( + save_summary_steps=None, + save_checkpoints_steps=None, + log_step_count_steps=None, + keep_checkpoint_max=None) + + params = {'dummy': 0} + estimator = NPUEstimator( + model_fn=model_fn, + model_dir=FLAGS.checkpoint_dir, + params=params, + config=config) + evals = estimator.predict(input_fn=eval_input_fn, yield_single_examples=True) + + # Print error statistics. + + all_errors = [x['error'] for x in evals] + errors = np.array(all_errors) + print('Evaluated %d examples'%np.size(errors)) + print('Mean error: %f degrees',np.mean(errors)) + print('Median error: %f degrees', np.median(errors)) + print('Std: %f degrees', np.std(errors)) + sorted_errors = np.sort(errors) + n = np.size(sorted_errors) + print('\nPercentiles:') + for perc in range(1, 101): + index = np.int32(np.float32(n * perc) / 100.0) - 1 + print('%3d%%: %f'%(perc, sorted_errors[index])) + +def serving_input_fn(): + input_data = tf.placeholder(tf.float32, [None, None, 3], name='data') + input_rot = tf.placeholder(tf.float32, [None, None, 3], name='rot') + input_fn = tf.estimator.export.build_raw_serving_input_receiver_fn({ + 'data': input_data, + 'rot': input_rot + })() + return input_fn + + + +def train_and_eval(): + """Train and evaluate a model.""" + save_summary_steps = FLAGS.save_summaries_steps + save_checkpoints_steps = FLAGS.save_checkpoints_steps + log_step_count = FLAGS.log_step_count + + config = NPURunConfig( + save_summary_steps=save_summary_steps, + save_checkpoints_steps=save_checkpoints_steps, + log_step_count_steps=log_step_count, + keep_checkpoint_max=None, + precision_mode = "allow_mix_precision") + + + + params = {'dummy': 0} + estimator = NPUEstimator( + model_fn=model_fn, + model_dir=FLAGS.checkpoint_dir, + config=config, + params=params) + + + # train_spec = tf.estimator.TrainSpec( + # input_fn=train_input_fn, + # max_steps=FLAGS.train_steps) + # + # eval_spec = tf.estimator.EvalSpec(input_fn=eval_input_fn, + # start_delay_secs=60, + # steps=FLAGS.eval_examples, + # throttle_secs=60) + + #tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec) + + estimator.train(input_fn=train_input_fn, + max_steps=FLAGS.train_steps) + + + estimator.export_savedmodel(FLAGS.checkpoint_dir, + serving_input_fn) + + estimator.evaluate(input_fn=eval_input_fn, steps=FLAGS.eval_examples) + + +def main(argv=None): # pylint: disable=unused-argument + + if FLAGS.print_variable_names: + print_variable_names() + return + + if FLAGS.predict_all_test: + predict_all_test() + else: + train_and_eval() + + +if __name__ == '__main__': + + tf.app.run() diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/train_full_1p.sh b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/train_full_1p.sh new file mode 100644 index 000000000..137538832 --- /dev/null +++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/train_full_1p.sh @@ -0,0 +1,209 @@ +#!/bin/bash + +########################################################## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +########################################################## +# shell脚本所在路径 +cur_path=`echo $(cd $(dirname $0);pwd)` + +# 判断当前shell是否是performance +perf_flag=`echo $0 | grep performance | wc -l` + +# 当前执行网络的名称 +Network=`echo $(cd $(dirname $0);pwd) | awk -F"/" '{print $(NF-1)}'` + +export RANK_SIZE=1 +export RANK_ID=0 +export JOB_ID=10087 + +# 路径参数初始化 +data_path="" +output_path="" + +# 帮助信息，不需要修改 +if [[ $1 == --help || $1 == -h ]];then + echo"usage:./train_performance_1P.sh " + echo " " + echo "parameter explain: + --data_path # dataset of training + --output_path # output of training + --train_steps # max_step for training + --train_epochs # max_epoch for training + --batch_size # batch size + -h/--help show help message + " + exit 1 +fi + +# 参数校验，不需要修改 +for para in $* +do + if [[ $para == --data_path* ]];then + data_path=`echo ${para#*=}` + elif [[ $para == --output_path* ]];then + output_path=`echo ${para#*=}` + elif [[ $para == --train_steps* ]];then + train_steps=`echo ${para#*=}` + elif [[ $para == --train_epochs* ]];then + train_epochs=`echo ${para#*=}` + elif [[ $para == --batch_size* ]];then + batch_size=`echo ${para#*=}` + fi +done + +# 校验是否传入data_path,不需要修改 +if [[ $data_path == "" ]];then + echo "[Error] para \"data_path\" must be config" + exit 1 +fi + +# 校验是否传入output_path,不需要修改 +if [[ $output_path == "" ]];then + output_path="./test/output/${ASCEND_DEVICE_ID}" +fi + +# 设置打屏日志文件名，请保留，文件名为${print_log} +print_log="./test/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log" +etp_flag=${etp_running_flag} +if [ x"${etp_flag}" != xtrue ]; +then + echo "running without etp..." + print_log_name=`ls /home/ma-user/modelarts/log/ | grep proc-rank` + print_log="/home/ma-user/modelarts/log/${print_log_name}" +fi +echo ${print_log} + +CaseName="" +function get_casename() +{ + if [ x"${perf_flag}" = x1 ]; + then + CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'perf' + else + CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'acc' + fi +} + +# 跳转到code目录 +cd ${cur_path}/../ +rm -rf ./test/output/${ASCEND_DEVICE_ID} +mkdir -p ./test/output/${ASCEND_DEVICE_ID} + +# 训练开始时间记录，不需要修改 +start_time=$(date +%s) +########################################################## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +########################################################## + +#========================================================= +#========================================================= +#========训练执行命令，需要根据您的网络进行修改============== +#========================================================= +#========================================================= +# 您的训练数据集在${data_path}路径下，请直接使用这个变量获取 +# 您的训练输出目录在${output_path}路径下，请直接使用这个变量获取 +# 您的其他基础参数，可以自定义增加，但是batch_size请保留，并且设置正确的值 +batch_size=1 + +echo "------------------***PRINT DATA PATH***-----------------" +train_data="points/*.pts" +test_data="test_points_modified/*.pts" + +trainData_Path="$data_path$train_data" +testData_Path="$data_path$test_data" + + +if [ x"${modelarts_flag}" != x ]; +then + python3.7 /home/ma-user/modelarts/user-job-dir/code/main_point_cloud_perf.py \ + --method=svd \ + --checkpoint_dir=${output_path} \ + --log_step_count=200 \ + --save_summaries_steps=25000 \ + --pt_cloud_train_files=${trainData_Path} \ + --pt_cloud_test_files=${testData_Path} \ + --train_steps=2600000 \ + --save_checkpoints_steps=100000 \ + --eval_examples=39900 +else + python3.7 /home/ma-user/modelarts/user-job-dir/code/main_point_cloud_perf.py \ + --method=svd \ + --checkpoint_dir=${output_path} \ + --log_step_count=200 \ + --save_summaries_steps=25000 \ + --pt_cloud_train_files=${trainData_Path}\ + --pt_cloud_test_files=${testData_Path} \ + --train_steps=2600000 \ + --save_checkpoints_steps=100000 \ + --eval_examples=39900 > ${print_log} +fi + + +# 性能相关数据计算 +StepTime=`grep "sec/step :" ${print_log} | tail -n 10 | awk '{print $NF}' | awk '{sum+=$1} END {print sum/NR}'` +FPS=`awk 'BEGIN{ "%.2f\n", '${batch_size}'/'${StepTime}'}'` + +# 精度相关数据计算 +train_accuracy=`grep "Final Accuracy accuracy" ${print_log} | awk '{print $NF}'` +# 提取所有loss打印信息 +grep "loss :" ${print_log} | awk -F ":" '{print $4}' | awk -F "-" '{print $1}' > ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt + + +########################################################### +#########后面的所有内容请不要修改########################### +#########后面的所有内容请不要修改########################### +#########后面的所有内容请不要修改########################### +########################################################### + +# 判断本次执行是否正确使用Ascend NPU +use_npu_flag=`grep "The model has been compiled on the Ascend AI processor" ${print_log} | wc -l` +if [ x"${use_npu_flag}" == x0 ]; +then + echo "------------------ ERROR NOTICE START ------------------" + echo "ERROR, your task haven't used Ascend NPU, please check your npu Migration." + echo "------------------ ERROR NOTICE END------------------" +else + echo "------------------ INFO NOTICE START------------------" + echo "INFO, your task have used Ascend NPU, please check your result." + echo "------------------ INFO NOTICE END------------------" +fi + +# 获取最终的casename，请保留，case文件名为${CaseName} +get_casename + +# 重命名loss文件 +if [ -f ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ]; +then + mv ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ./test/output/${ASCEND_DEVICE_ID}/${CaseName}_loss.txt +fi + +# 训练端到端耗时 +end_time=$(date +%s) +e2e_time=$(( $end_time - $start_time )) + +echo "------------------ Final result ------------------" +# 输出性能FPS/单step耗时/端到端耗时 +echo "Final Performance images/sec : $FPS" +echo "Final Performance sec/step : $StepTime" +echo "E2E Training Duration sec : $e2e_time" + +# 输出训练精度 +echo "Final Train Accuracy : ${train_accuracy}" + +# 最后一个迭代loss值，不需要修改 +ActualLoss=(`awk 'END {print $NF}' $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}_loss.txt`) + +#关键信息打印到${CaseName}.log中，不需要修改 +echo "Network = ${Network}" > $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "RankSize = ${RANK_SIZE}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "BatchSize = ${batch_size}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "DeviceType = `uname -m`" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualFPS = ${FPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "TrainingTime = ${StepTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log \ No newline at end of file -- Gitee From 0cc9ac9f5d7f548020e045066036dc66fa0dac9e Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Sat, 22 Oct 2022 13:22:44 +0000 Subject: [PATCH 80/88] =?UTF-8?q?=E5=88=A0=E9=99=A4=E6=96=87=E4=BB=B6=20Te?= =?UTF-8?q?nsorFlow/contrib/cv/SVD=5FID2019=5Ffor=5FTensorflow/genStatisti?= =?UTF-8?q?cal.sh?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../genStatistical.sh | 203 ------------------ 1 file changed, 203 deletions(-) delete mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/genStatistical.sh diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/genStatistical.sh b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/genStatistical.sh deleted file mode 100644 index 990ca29a5..000000000 --- a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/genStatistical.sh +++ /dev/null @@ -1,203 +0,0 @@ -#!/bin/bash - -########################################################## -#########第3行 至 100行，请一定不要、不要、不要修改########## -#########第3行 至 100行，请一定不要、不要、不要修改########## -#########第3行 至 100行，请一定不要、不要、不要修改########## -########################################################## -# shell脚本所在路径 -cur_path=`echo $(cd $(dirname $0);pwd)` - -# 判断当前shell是否是performance -perf_flag=`echo $0 | grep performance | wc -l` - -# 当前执行网络的名称 -Network=`echo $(cd $(dirname $0);pwd) | awk -F"/" '{print $(NF-1)}'` - -export RANK_SIZE=1 -export RANK_ID=0 -export JOB_ID=10087 - -# 路径参数初始化 -data_path="" -output_path="" - -# 帮助信息，不需要修改 -if [[ $1 == --help || $1 == -h ]];then - echo"usage:./train_performance_1P.sh " - echo " " - echo "parameter explain: - --data_path # dataset of training - --output_path # output of training - --train_steps # max_step for training - --train_epochs # max_epoch for training - --batch_size # batch size - -h/--help show help message - " - exit 1 -fi - -# 参数校验，不需要修改 -for para in $* -do - if [[ $para == --data_path* ]];then - data_path=`echo ${para#*=}` - elif [[ $para == --output_path* ]];then - output_path=`echo ${para#*=}` - elif [[ $para == --train_steps* ]];then - train_steps=`echo ${para#*=}` - elif [[ $para == --train_epochs* ]];then - train_epochs=`echo ${para#*=}` - elif [[ $para == --batch_size* ]];then - batch_size=`echo ${para#*=}` - fi -done - -# 校验是否传入data_path,不需要修改 -if [[ $data_path == "" ]];then - echo "[Error] para \"data_path\" must be config" - exit 1 -fi - -# 校验是否传入output_path,不需要修改 -if [[ $output_path == "" ]];then - output_path="./test/output/${ASCEND_DEVICE_ID}" -fi - -# 设置打屏日志文件名，请保留，文件名为${print_log} -print_log="./test/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log" -etp_flag=${etp_running_flag} -if [ x"${etp_flag}" != xtrue ]; -then - echo "running without etp..." - print_log_name=`ls /home/ma-user/modelarts/log/ | grep proc-rank` - print_log="/home/ma-user/modelarts/log/${print_log_name}" -fi -echo ${print_log} - -CaseName="" -function get_casename() -{ - if [ x"${perf_flag}" = x1 ]; - then - CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'perf' - else - CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'acc' - fi -} - -# 跳转到code目录 -cd ${cur_path}/../ -rm -rf ./test/output/${ASCEND_DEVICE_ID} -mkdir -p ./test/output/${ASCEND_DEVICE_ID} - -# 训练开始时间记录，不需要修改 -start_time=$(date +%s) -########################################################## -#########第3行 至 100行，请一定不要、不要、不要修改########## -#########第3行 至 100行，请一定不要、不要、不要修改########## -#########第3行 至 100行，请一定不要、不要、不要修改########## -########################################################## - -#========================================================= -#========================================================= -#========训练执行命令，需要根据您的网络进行修改============== -#========================================================= -#========================================================= -# 您的训练数据集在${data_path}路径下，请直接使用这个变量获取 -# 您的训练输出目录在${output_path}路径下，请直接使用这个变量获取 -# 您的其他基础参数，可以自定义增加，但是batch_size请保留，并且设置正确的值 -batch_size=1 - -echo "------------------***PRINT DATA PATH***-----------------" - -docuClass="test_points_modified/*.pts" -modelPath="output" - -testData_Path="$data_path$docuClass" -outputPath="$data_path$modelPath" - -echo "$outputPath" -echo "$testData_Path" - - -if [ x"${modelarts_flag}" != x ]; -then - python /home/ma-user/modelarts/user-job-dir/code/main_point_cloud.py \ - --method=svd \ - --checkpoint_dir=${outputPath} \ - --pt_cloud_test_files=${testData_Path} \ - --predict_all_test=True -else - python /home/ma-user/modelarts/user-job-dir/code/main_point_cloud.py \ - --method=svd \ - --checkpoint_dir=${outputPath} \ - --pt_cloud_test_files=${testData_Path} \ - --predict_all_test=True -fi - - -# 性能相关数据计算 -StepTime=`grep "sec/step :" ${print_log} | tail -n 10 | awk '{print $NF}' | awk '{sum+=$1} END {print sum/NR}'` -FPS=`awk 'BEGIN{ "%.2f\n", '${batch_size}'/'${StepTime}'}'` - -# 精度相关数据计算 -train_accuracy=`grep "Final Accuracy accuracy" ${print_log} | awk '{print $NF}'` -# 提取所有loss打印信息 -grep "loss :" ${print_log} | awk -F ":" '{print $4}' | awk -F "-" '{print $1}' > ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt - - -########################################################### -#########后面的所有内容请不要修改########################### -#########后面的所有内容请不要修改########################### -#########后面的所有内容请不要修改########################### -########################################################### - -# 判断本次执行是否正确使用Ascend NPU -use_npu_flag=`grep "The model has been compiled on the Ascend AI processor" ${print_log} | wc -l` -if [ x"${use_npu_flag}" == x0 ]; -then - echo "------------------ ERROR NOTICE START ------------------" - echo "ERROR, your task haven't used Ascend NPU, please check your npu Migration." - echo "------------------ ERROR NOTICE END------------------" -else - echo "------------------ INFO NOTICE START------------------" - echo "INFO, your task have used Ascend NPU, please check your result." - echo "------------------ INFO NOTICE END------------------" -fi - -# 获取最终的casename，请保留，case文件名为${CaseName} -get_casename - -# 重命名loss文件 -if [ -f ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ]; -then - mv ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ./test/output/${ASCEND_DEVICE_ID}/${CaseName}_loss.txt -fi - -# 训练端到端耗时 -end_time=$(date +%s) -e2e_time=$(( $end_time - $start_time )) - -echo "------------------ Final result ------------------" -# 输出性能FPS/单step耗时/端到端耗时 -echo "Final Performance images/sec : $FPS" -echo "Final Performance sec/step : $StepTime" -echo "E2E Training Duration sec : $e2e_time" - -# 输出训练精度 -echo "Final Train Accuracy : ${train_accuracy}" - -# 最后一个迭代loss值，不需要修改 -ActualLoss=(`awk 'END {print $NF}' $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}_loss.txt`) - -#关键信息打印到${CaseName}.log中，不需要修改 -echo "Network = ${Network}" > $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "RankSize = ${RANK_SIZE}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "BatchSize = ${batch_size}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "DeviceType = `uname -m`" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "ActualFPS = ${FPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "TrainingTime = ${StepTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log \ No newline at end of file -- Gitee From 66d31493976ea8e3f7a54f163e24f3e181a5f5e4 Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Sat, 22 Oct 2022 13:22:53 +0000 Subject: [PATCH 81/88] =?UTF-8?q?=E5=88=A0=E9=99=A4=E6=96=87=E4=BB=B6=20Te?= =?UTF-8?q?nsorFlow/contrib/cv/SVD=5FID2019=5Ffor=5FTensorflow/train=5Fper?= =?UTF-8?q?formance=5F1p.sh?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../train_performance_1p.sh | 209 ------------------ 1 file changed, 209 deletions(-) delete mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/train_performance_1p.sh diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/train_performance_1p.sh b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/train_performance_1p.sh deleted file mode 100644 index ab8be4ed0..000000000 --- a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/train_performance_1p.sh +++ /dev/null @@ -1,209 +0,0 @@ -#!/bin/bash - -########################################################## -#########第3行 至 100行，请一定不要、不要、不要修改########## -#########第3行 至 100行，请一定不要、不要、不要修改########## -#########第3行 至 100行，请一定不要、不要、不要修改########## -########################################################## -# shell脚本所在路径 -cur_path=`echo $(cd $(dirname $0);pwd)` - -# 判断当前shell是否是performance -perf_flag=`echo $0 | grep performance | wc -l` - -# 当前执行网络的名称 -Network=`echo $(cd $(dirname $0);pwd) | awk -F"/" '{print $(NF-1)}'` - -export RANK_SIZE=1 -export RANK_ID=0 -export JOB_ID=10087 - -# 路径参数初始化 -data_path="" -output_path="" - -# 帮助信息，不需要修改 -if [[ $1 == --help || $1 == -h ]];then - echo"usage:./train_performance_1P.sh " - echo " " - echo "parameter explain: - --data_path # dataset of training - --output_path # output of training - --train_steps # max_step for training - --train_epochs # max_epoch for training - --batch_size # batch size - -h/--help show help message - " - exit 1 -fi - -# 参数校验，不需要修改 -for para in $* -do - if [[ $para == --data_path* ]];then - data_path=`echo ${para#*=}` - elif [[ $para == --output_path* ]];then - output_path=`echo ${para#*=}` - elif [[ $para == --train_steps* ]];then - train_steps=`echo ${para#*=}` - elif [[ $para == --train_epochs* ]];then - train_epochs=`echo ${para#*=}` - elif [[ $para == --batch_size* ]];then - batch_size=`echo ${para#*=}` - fi -done - -# 校验是否传入data_path,不需要修改 -if [[ $data_path == "" ]];then - echo "[Error] para \"data_path\" must be config" - exit 1 -fi - -# 校验是否传入output_path,不需要修改 -if [[ $output_path == "" ]];then - output_path="./test/output/${ASCEND_DEVICE_ID}" -fi - -# 设置打屏日志文件名，请保留，文件名为${print_log} -print_log="./test/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log" -etp_flag=${etp_running_flag} -if [ x"${etp_flag}" != xtrue ]; -then - echo "running without etp..." - print_log_name=`ls /home/ma-user/modelarts/log/ | grep proc-rank` - print_log="/home/ma-user/modelarts/log/${print_log_name}" -fi -echo ${print_log} - -CaseName="" -function get_casename() -{ - if [ x"${perf_flag}" = x1 ]; - then - CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'perf' - else - CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'acc' - fi -} - -# 跳转到code目录 -cd ${cur_path}/../ -rm -rf ./test/output/${ASCEND_DEVICE_ID} -mkdir -p ./test/output/${ASCEND_DEVICE_ID} - -# 训练开始时间记录，不需要修改 -start_time=$(date +%s) -########################################################## -#########第3行 至 100行，请一定不要、不要、不要修改########## -#########第3行 至 100行，请一定不要、不要、不要修改########## -#########第3行 至 100行，请一定不要、不要、不要修改########## -########################################################## - -#========================================================= -#========================================================= -#========训练执行命令，需要根据您的网络进行修改============== -#========================================================= -#========================================================= -# 您的训练数据集在${data_path}路径下，请直接使用这个变量获取 -# 您的训练输出目录在${output_path}路径下，请直接使用这个变量获取 -# 您的其他基础参数，可以自定义增加，但是batch_size请保留，并且设置正确的值 -batch_size=1 - -echo "------------------***PRINT DATA PATH***-----------------" -train_data="points/*.pts" -test_data="sub_test/*.pts" - -trainData_Path="$data_path$train_data" -testData_Path="$data_path$test_data" - - -if [ x"${modelarts_flag}" != x ]; -then - python3.7 /home/ma-user/modelarts/user-job-dir/code/main_point_cloud_boostPerf.py \ - --method=svd \ - --checkpoint_dir=${output_path} \ - --log_step_count=200 \ - --save_summaries_steps=250 \ - --pt_cloud_train_files=${trainData_Path} \ - --pt_cloud_test_files=${testData_Path} \ - --train_steps=10000 \ - --save_checkpoints_steps=10000 \ - --eval_examples=399 -else - python3.7 /home/ma-user/modelarts/user-job-dir/code/main_point_cloud_boostPerf.py \ - --method=svd \ - --checkpoint_dir=${output_path} \ - --log_step_count=200 \ - --save_summaries_steps=250 \ - --pt_cloud_train_files=${trainData_Path}\ - --pt_cloud_test_files=${testData_Path} \ - --train_steps=10000 \ - --save_checkpoints_steps=10000 \ - --eval_examples=399> ${print_log} -fi - - -# 性能相关数据计算 -StepTime=`grep "sec/step :" ${print_log} | tail -n 10 | awk '{print $NF}' | awk '{sum+=$1} END {print sum/NR}'` -FPS=`awk 'BEGIN{ "%.2f\n", '${batch_size}'/'${StepTime}'}'` - -# 精度相关数据计算 -train_accuracy=`grep "Final Accuracy accuracy" ${print_log} | awk '{print $NF}'` -# 提取所有loss打印信息 -grep "loss :" ${print_log} | awk -F ":" '{print $4}' | awk -F "-" '{print $1}' > ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt - - -########################################################### -#########后面的所有内容请不要修改########################### -#########后面的所有内容请不要修改########################### -#########后面的所有内容请不要修改########################### -########################################################### - -# 判断本次执行是否正确使用Ascend NPU -use_npu_flag=`grep "The model has been compiled on the Ascend AI processor" ${print_log} | wc -l` -if [ x"${use_npu_flag}" == x0 ]; -then - echo "------------------ ERROR NOTICE START ------------------" - echo "ERROR, your task haven't used Ascend NPU, please check your npu Migration." - echo "------------------ ERROR NOTICE END------------------" -else - echo "------------------ INFO NOTICE START------------------" - echo "INFO, your task have used Ascend NPU, please check your result." - echo "------------------ INFO NOTICE END------------------" -fi - -# 获取最终的casename，请保留，case文件名为${CaseName} -get_casename - -# 重命名loss文件 -if [ -f ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ]; -then - mv ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ./test/output/${ASCEND_DEVICE_ID}/${CaseName}_loss.txt -fi - -# 训练端到端耗时 -end_time=$(date +%s) -e2e_time=$(( $end_time - $start_time )) - -echo "------------------ Final result ------------------" -# 输出性能FPS/单step耗时/端到端耗时 -echo "Final Performance images/sec : $FPS" -echo "Final Performance sec/step : $StepTime" -echo "E2E Training Duration sec : $e2e_time" - -# 输出训练精度 -echo "Final Train Accuracy : ${train_accuracy}" - -# 最后一个迭代loss值，不需要修改 -ActualLoss=(`awk 'END {print $NF}' $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}_loss.txt`) - -#关键信息打印到${CaseName}.log中，不需要修改 -echo "Network = ${Network}" > $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "RankSize = ${RANK_SIZE}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "BatchSize = ${batch_size}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "DeviceType = `uname -m`" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "ActualFPS = ${FPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "TrainingTime = ${StepTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "E2ETrainingTime = ${e2e_ti_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log \ No newline at end of file -- Gitee From c0d20fee066cbf673d8b1d2df6129ca7f56cb5b5 Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Sat, 22 Oct 2022 13:23:36 +0000 Subject: [PATCH 82/88] =?UTF-8?q?=E6=9B=B4=E6=96=B0?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: ab_dx_z <> --- .../genStatistical.sh | 203 +++++++++++++++++ .../train_performance_1p.sh | 209 ++++++++++++++++++ 2 files changed, 412 insertions(+) create mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/genStatistical.sh create mode 100644 TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/train_performance_1p.sh diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/genStatistical.sh b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/genStatistical.sh new file mode 100644 index 000000000..f9a760ef8 --- /dev/null +++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/genStatistical.sh @@ -0,0 +1,203 @@ +#!/bin/bash + +########################################################## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +########################################################## +# shell脚本所在路径 +cur_path=`echo $(cd $(dirname $0);pwd)` + +# 判断当前shell是否是performance +perf_flag=`echo $0 | grep performance | wc -l` + +# 当前执行网络的名称 +Network=`echo $(cd $(dirname $0);pwd) | awk -F"/" '{print $(NF-1)}'` + +export RANK_SIZE=1 +export RANK_ID=0 +export JOB_ID=10087 + +# 路径参数初始化 +data_path="" +output_path="" + +# 帮助信息，不需要修改 +if [[ $1 == --help || $1 == -h ]];then + echo"usage:./train_performance_1P.sh " + echo " " + echo "parameter explain: + --data_path # dataset of training + --output_path # output of training + --train_steps # max_step for training + --train_epochs # max_epoch for training + --batch_size # batch size + -h/--help show help message + " + exit 1 +fi + +# 参数校验，不需要修改 +for para in $* +do + if [[ $para == --data_path* ]];then + data_path=`echo ${para#*=}` + elif [[ $para == --output_path* ]];then + output_path=`echo ${para#*=}` + elif [[ $para == --train_steps* ]];then + train_steps=`echo ${para#*=}` + elif [[ $para == --train_epochs* ]];then + train_epochs=`echo ${para#*=}` + elif [[ $para == --batch_size* ]];then + batch_size=`echo ${para#*=}` + fi +done + +# 校验是否传入data_path,不需要修改 +if [[ $data_path == "" ]];then + echo "[Error] para \"data_path\" must be config" + exit 1 +fi + +# 校验是否传入output_path,不需要修改 +if [[ $output_path == "" ]];then + output_path="./test/output/${ASCEND_DEVICE_ID}" +fi + +# 设置打屏日志文件名，请保留，文件名为${print_log} +print_log="./test/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log" +etp_flag=${etp_running_flag} +if [ x"${etp_flag}" != xtrue ]; +then + echo "running without etp..." + print_log_name=`ls /home/ma-user/modelarts/log/ | grep proc-rank` + print_log="/home/ma-user/modelarts/log/${print_log_name}" +fi +echo ${print_log} + +CaseName="" +function get_casename() +{ + if [ x"${perf_flag}" = x1 ]; + then + CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'perf' + else + CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'acc' + fi +} + +# 跳转到code目录 +cd ${cur_path}/../ +rm -rf ./test/output/${ASCEND_DEVICE_ID} +mkdir -p ./test/output/${ASCEND_DEVICE_ID} + +# 训练开始时间记录，不需要修改 +start_time=$(date +%s) +########################################################## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +########################################################## + +#========================================================= +#========================================================= +#========训练执行命令，需要根据您的网络进行修改============== +#========================================================= +#========================================================= +# 您的训练数据集在${data_path}路径下，请直接使用这个变量获取 +# 您的训练输出目录在${output_path}路径下，请直接使用这个变量获取 +# 您的其他基础参数，可以自定义增加，但是batch_size请保留，并且设置正确的值 +batch_size=1 + +echo "------------------***PRINT DATA PATH***-----------------" + +docuClass="test_points_modified/*.pts" +modelPath="output" + +testData_Path="$data_path$docuClass" +outputPath="$data_path$modelPath" + +echo "$outputPath" +echo "$testData_Path" + + +if [ x"${modelarts_flag}" != x ]; +then + python /home/ma-user/modelarts/user-job-dir/code/main_point_cloud_perf.py \ + --method=svd \ + --checkpoint_dir=${outputPath} \ + --pt_cloud_test_files=${testData_Path} \ + --predict_all_test=True +else + python /home/ma-user/modelarts/user-job-dir/code/main_point_cloud_perf.py \ + --method=svd \ + --checkpoint_dir=${outputPath} \ + --pt_cloud_test_files=${testData_Path} \ + --predict_all_test=True +fi + + +# 性能相关数据计算 +StepTime=`grep "sec/step :" ${print_log} | tail -n 10 | awk '{print $NF}' | awk '{sum+=$1} END {print sum/NR}'` +FPS=`awk 'BEGIN{ "%.2f\n", '${batch_size}'/'${StepTime}'}'` + +# 精度相关数据计算 +train_accuracy=`grep "Final Accuracy accuracy" ${print_log} | awk '{print $NF}'` +# 提取所有loss打印信息 +grep "loss :" ${print_log} | awk -F ":" '{print $4}' | awk -F "-" '{print $1}' > ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt + + +########################################################### +#########后面的所有内容请不要修改########################### +#########后面的所有内容请不要修改########################### +#########后面的所有内容请不要修改########################### +########################################################### + +# 判断本次执行是否正确使用Ascend NPU +use_npu_flag=`grep "The model has been compiled on the Ascend AI processor" ${print_log} | wc -l` +if [ x"${use_npu_flag}" == x0 ]; +then + echo "------------------ ERROR NOTICE START ------------------" + echo "ERROR, your task haven't used Ascend NPU, please check your npu Migration." + echo "------------------ ERROR NOTICE END------------------" +else + echo "------------------ INFO NOTICE START------------------" + echo "INFO, your task have used Ascend NPU, please check your result." + echo "------------------ INFO NOTICE END------------------" +fi + +# 获取最终的casename，请保留，case文件名为${CaseName} +get_casename + +# 重命名loss文件 +if [ -f ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ]; +then + mv ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ./test/output/${ASCEND_DEVICE_ID}/${CaseName}_loss.txt +fi + +# 训练端到端耗时 +end_time=$(date +%s) +e2e_time=$(( $end_time - $start_time )) + +echo "------------------ Final result ------------------" +# 输出性能FPS/单step耗时/端到端耗时 +echo "Final Performance images/sec : $FPS" +echo "Final Performance sec/step : $StepTime" +echo "E2E Training Duration sec : $e2e_time" + +# 输出训练精度 +echo "Final Train Accuracy : ${train_accuracy}" + +# 最后一个迭代loss值，不需要修改 +ActualLoss=(`awk 'END {print $NF}' $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}_loss.txt`) + +#关键信息打印到${CaseName}.log中，不需要修改 +echo "Network = ${Network}" > $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "RankSize = ${RANK_SIZE}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "BatchSize = ${batch_size}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "DeviceType = `uname -m`" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualFPS = ${FPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "TrainingTime = ${StepTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log \ No newline at end of file diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/train_performance_1p.sh b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/train_performance_1p.sh new file mode 100644 index 000000000..330245b56 --- /dev/null +++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/train_performance_1p.sh @@ -0,0 +1,209 @@ +#!/bin/bash + +########################################################## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +########################################################## +# shell脚本所在路径 +cur_path=`echo $(cd $(dirname $0);pwd)` + +# 判断当前shell是否是performance +perf_flag=`echo $0 | grep performance | wc -l` + +# 当前执行网络的名称 +Network=`echo $(cd $(dirname $0);pwd) | awk -F"/" '{print $(NF-1)}'` + +export RANK_SIZE=1 +export RANK_ID=0 +export JOB_ID=10087 + +# 路径参数初始化 +data_path="" +output_path="" + +# 帮助信息，不需要修改 +if [[ $1 == --help || $1 == -h ]];then + echo"usage:./train_performance_1P.sh " + echo " " + echo "parameter explain: + --data_path # dataset of training + --output_path # output of training + --train_steps # max_step for training + --train_epochs # max_epoch for training + --batch_size # batch size + -h/--help show help message + " + exit 1 +fi + +# 参数校验，不需要修改 +for para in $* +do + if [[ $para == --data_path* ]];then + data_path=`echo ${para#*=}` + elif [[ $para == --output_path* ]];then + output_path=`echo ${para#*=}` + elif [[ $para == --train_steps* ]];then + train_steps=`echo ${para#*=}` + elif [[ $para == --train_epochs* ]];then + train_epochs=`echo ${para#*=}` + elif [[ $para == --batch_size* ]];then + batch_size=`echo ${para#*=}` + fi +done + +# 校验是否传入data_path,不需要修改 +if [[ $data_path == "" ]];then + echo "[Error] para \"data_path\" must be config" + exit 1 +fi + +# 校验是否传入output_path,不需要修改 +if [[ $output_path == "" ]];then + output_path="./test/output/${ASCEND_DEVICE_ID}" +fi + +# 设置打屏日志文件名，请保留，文件名为${print_log} +print_log="./test/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log" +etp_flag=${etp_running_flag} +if [ x"${etp_flag}" != xtrue ]; +then + echo "running without etp..." + print_log_name=`ls /home/ma-user/modelarts/log/ | grep proc-rank` + print_log="/home/ma-user/modelarts/log/${print_log_name}" +fi +echo ${print_log} + +CaseName="" +function get_casename() +{ + if [ x"${perf_flag}" = x1 ]; + then + CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'perf' + else + CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'acc' + fi +} + +# 跳转到code目录 +cd ${cur_path}/../ +rm -rf ./test/output/${ASCEND_DEVICE_ID} +mkdir -p ./test/output/${ASCEND_DEVICE_ID} + +# 训练开始时间记录，不需要修改 +start_time=$(date +%s) +########################################################## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +########################################################## + +#========================================================= +#========================================================= +#========训练执行命令，需要根据您的网络进行修改============== +#========================================================= +#========================================================= +# 您的训练数据集在${data_path}路径下，请直接使用这个变量获取 +# 您的训练输出目录在${output_path}路径下，请直接使用这个变量获取 +# 您的其他基础参数，可以自定义增加，但是batch_size请保留，并且设置正确的值 +batch_size=1 + +echo "------------------***PRINT DATA PATH***-----------------" +train_data="points/*.pts" +test_data="sub_test/*.pts" + +trainData_Path="$data_path$train_data" +testData_Path="$data_path$test_data" + + +if [ x"${modelarts_flag}" != x ]; +then + python3.7 /home/ma-user/modelarts/user-job-dir/code/main_point_cloud_perf.py \ + --method=svd \ + --checkpoint_dir=${output_path} \ + --log_step_count=200 \ + --save_summaries_steps=250 \ + --pt_cloud_train_files=${trainData_Path} \ + --pt_cloud_test_files=${testData_Path} \ + --train_steps=10000 \ + --save_checkpoints_steps=10000 \ + --eval_examples=399 +else + python3.7 /home/ma-user/modelarts/user-job-dir/code/main_point_cloud_perf.py \ + --method=svd \ + --checkpoint_dir=${output_path} \ + --log_step_count=200 \ + --save_summaries_steps=250 \ + --pt_cloud_train_files=${trainData_Path}\ + --pt_cloud_test_files=${testData_Path} \ + --train_steps=10000 \ + --save_checkpoints_steps=10000 \ + --eval_examples=399> ${print_log} +fi + + +# 性能相关数据计算 +StepTime=`grep "sec/step :" ${print_log} | tail -n 10 | awk '{print $NF}' | awk '{sum+=$1} END {print sum/NR}'` +FPS=`awk 'BEGIN{ "%.2f\n", '${batch_size}'/'${StepTime}'}'` + +# 精度相关数据计算 +train_accuracy=`grep "Final Accuracy accuracy" ${print_log} | awk '{print $NF}'` +# 提取所有loss打印信息 +grep "loss :" ${print_log} | awk -F ":" '{print $4}' | awk -F "-" '{print $1}' > ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt + + +########################################################### +#########后面的所有内容请不要修改########################### +#########后面的所有内容请不要修改########################### +#########后面的所有内容请不要修改########################### +########################################################### + +# 判断本次执行是否正确使用Ascend NPU +use_npu_flag=`grep "The model has been compiled on the Ascend AI processor" ${print_log} | wc -l` +if [ x"${use_npu_flag}" == x0 ]; +then + echo "------------------ ERROR NOTICE START ------------------" + echo "ERROR, your task haven't used Ascend NPU, please check your npu Migration." + echo "------------------ ERROR NOTICE END------------------" +else + echo "------------------ INFO NOTICE START------------------" + echo "INFO, your task have used Ascend NPU, please check your result." + echo "------------------ INFO NOTICE END------------------" +fi + +# 获取最终的casename，请保留，case文件名为${CaseName} +get_casename + +# 重命名loss文件 +if [ -f ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ]; +then + mv ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ./test/output/${ASCEND_DEVICE_ID}/${CaseName}_loss.txt +fi + +# 训练端到端耗时 +end_time=$(date +%s) +e2e_time=$(( $end_time - $start_time )) + +echo "------------------ Final result ------------------" +# 输出性能FPS/单step耗时/端到端耗时 +echo "Final Performance images/sec : $FPS" +echo "Final Performance sec/step : $StepTime" +echo "E2E Training Duration sec : $e2e_time" + +# 输出训练精度 +echo "Final Train Accuracy : ${train_accuracy}" + +# 最后一个迭代loss值，不需要修改 +ActualLoss=(`awk 'END {print $NF}' $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}_loss.txt`) + +#关键信息打印到${CaseName}.log中，不需要修改 +echo "Network = ${Network}" > $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "RankSize = ${RANK_SIZE}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "BatchSize = ${batch_size}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "DeviceType = `uname -m`" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualFPS = ${FPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "TrainingTime = ${StepTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "E2ETrainingTime = ${e2e_ti_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log \ No newline at end of file -- Gitee From 075fa9c1b7001beac4b37a349f15be2fe370751f Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Sat, 22 Oct 2022 14:08:14 +0000 Subject: [PATCH 83/88] update TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md. Signed-off-by: ab_dx_z <> --- TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md index 78822ea33..cbb95c3f1 100644 --- a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md +++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md @@ -28,7 +28,8 @@ - 参考实现： - 数据下载百度网盘链接: + 数据下载百度网盘链接：https://pan.baidu.com/s/1up1HW6McgSor3JF0yqQZSA +提取码：2019 共有3个数据集 @@ -38,7 +39,8 @@ 第一步旋转后的数据集 test_points_modified - npu训练出来的模型下载百度网盘链接: + npu训练出来的模型下载百度网盘链接：https://pan.baidu.com/s/1JU1koZR7uGlkKfRYIk8tsw +提取码：2019 -- Gitee From 483f27ac20d25c7fe3ed550f930645437413942d Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Sat, 22 Oct 2022 14:11:54 +0000 Subject: [PATCH 84/88] =?UTF-8?q?=E6=96=B0=E5=BB=BA=20SVD-ID2019=5Ffor=5FA?= =?UTF-8?q?CL?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ACL_TensorFlow/contrib/cv/SVD-ID2019_for_ACL/.keep | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 ACL_TensorFlow/contrib/cv/SVD-ID2019_for_ACL/.keep diff --git a/ACL_TensorFlow/contrib/cv/SVD-ID2019_for_ACL/.keep b/ACL_TensorFlow/contrib/cv/SVD-ID2019_for_ACL/.keep new file mode 100644 index 000000000..e69de29bb -- Gitee From c16d449cbf1b7d915668af9923085549e0d425e1 Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Sat, 22 Oct 2022 14:12:33 +0000 Subject: [PATCH 85/88] =?UTF-8?q?=E7=A6=BB=E7=BA=BF=E6=8E=A8=E7=90=86?= =?UTF-8?q?=E6=96=87=E4=BB=B6?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: ab_dx_z <> --- .../cv/SVD-ID2019_for_ACL/utils_gpu.py | 115 ++++++++++++++++++ 1 file changed, 115 insertions(+) create mode 100644 ACL_TensorFlow/contrib/cv/SVD-ID2019_for_ACL/utils_gpu.py diff --git a/ACL_TensorFlow/contrib/cv/SVD-ID2019_for_ACL/utils_gpu.py b/ACL_TensorFlow/contrib/cv/SVD-ID2019_for_ACL/utils_gpu.py new file mode 100644 index 000000000..ff47d69c2 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SVD-ID2019_for_ACL/utils_gpu.py @@ -0,0 +1,115 @@ +# coding=utf-8 +# Copyright 2021 The Google Research Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Utility functions.""" +import numpy as np +from scipy.stats import special_ortho_group +import tensorflow as tf + + +def relative_angle(r1, r2): + """Relative angle (radians) between 3D rotation matrices.""" + rel_rot = tf.matmul(tf.transpose(r1, perm=[0, 2, 1]), r2) + trace = rel_rot[:, 0, 0] + rel_rot[:, 1, 1] + rel_rot[:, 2, 2] + cos_theta = (trace - 1.0) / 2.0 + cos_theta = tf.minimum(cos_theta, tf.ones_like(cos_theta)) + cos_theta = tf.maximum(cos_theta, (-1.0) * tf.ones_like(cos_theta)) + theta = tf.acos(cos_theta) + return theta + + +def random_rotation_benchmark_np(n): + """Sample a random 3D rotation by method used in Zhou et al, CVPR19. + + This numpy function is a copy of the PyTorch function + get_sampled_rotation_matrices_by_axisAngle() in the code made available + for Zhou et al, CVPR19, at https://github.com/papagina/RotationContinuity/. + + Args: + n: the number of rotation matrices to return. + + Returns: + [n, 3, 3] np array. + """ + theta = np.random.uniform(-1, 1, n) * np.pi + sin = np.sin(theta) + axis = np.random.randn(n, 3) + axis = axis / np.maximum(np.linalg.norm(axis, axis=-1, keepdims=True), 1e-7) + qw = np.cos(theta) + qx = axis[:, 0] * sin + qy = axis[:, 1] * sin + qz = axis[:, 2] * sin + + xx = qx*qx + yy = qy*qy + zz = qz*qz + xy = qx*qy + xz = qx*qz + yz = qy*qz + xw = qx*qw + yw = qy*qw + zw = qz*qw + + row0 = np.stack((1-2*yy-2*zz, 2*xy-2*zw, 2*xz+2*yw), axis=-1) + row1 = np.stack((2*xy+2*zw, 1-2*xx-2*zz, 2*yz-2*xw), axis=-1) + row2 = np.stack((2*xz-2*yw, 2*yz+2*xw, 1-2*xx-2*yy), axis=-1) + matrix = np.stack((row0, row1, row2), axis=1) + + return matrix + + +def random_rotation_benchmark(n): + """A TF wrapper for random_rotation_benchmark_np().""" + mat = tf.py_func( + func=lambda t: np.float32(random_rotation_benchmark_np(t)), + inp=[n], + Tout=tf.float32, + stateful=True) + return tf.reshape(mat, (n, 3, 3)) + + +def random_rotation(n): + """Sample rotations from a uniform distribution on SO(3).""" + mat = tf.py_func( + func=lambda t: np.float32(special_ortho_group.rvs(3, size=t)), + inp=[n], + Tout=tf.float32, + stateful=True) + return tf.reshape(mat, (n, 3, 3)) + + +def symmetric_orthogonalization(x): + """Maps 9D input vectors onto SO(3) via symmetric orthogonalization.""" + # Innner dimensions of the input should be 3x3 matrices. + m = tf.reshape(x, (-1, 3, 3)) + _, u, v = tf.svd(m) + det = tf.linalg.det(tf.matmul(u, v, transpose_b=True)) + r = tf.matmul( + tf.concat([u[:, :, :-1], u[:, :, -1:] * tf.reshape(det, [-1, 1, 1])], 2), + v, transpose_b=True) + return r + + +def gs_orthogonalization(p6): + """Gram-Schmidt orthogonalization from 6D input.""" + # Input should be [batch_size, 6] + x = p6[:, 0:3] + y = p6[:, 3:6] + xn = tf.math.l2_normalize(x, axis=-1) + z = tf.linalg.cross(xn, y) + zn = tf.math.l2_normalize(z, axis=-1) + y = tf.linalg.cross(zn, xn) + r = tf.stack([xn, y, zn], -1) + return r -- Gitee From 56a0181f45a11c1cc0c8be6502df82b5caa17fc7 Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Sat, 22 Oct 2022 14:13:56 +0000 Subject: [PATCH 86/88] add ACL_TensorFlow/contrib/cv/SVD-ID2019_for_ACL. Signed-off-by: ab_dx_z <> --- ACL_TensorFlow/contrib/cv/SVD-ID2019_for_ACL/README.md | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 ACL_TensorFlow/contrib/cv/SVD-ID2019_for_ACL/README.md diff --git a/ACL_TensorFlow/contrib/cv/SVD-ID2019_for_ACL/README.md b/ACL_TensorFlow/contrib/cv/SVD-ID2019_for_ACL/README.md new file mode 100644 index 000000000..e69de29bb -- Gitee From 805be11a12385b4d1d381096ef85e57f3c52331b Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Sat, 22 Oct 2022 14:14:32 +0000 Subject: [PATCH 87/88] update ACL_TensorFlow/contrib/cv/SVD-ID2019_for_ACL/README.md. Signed-off-by: ab_dx_z <> --- .../contrib/cv/SVD-ID2019_for_ACL/README.md | 89 +++++++++++++++++++ 1 file changed, 89 insertions(+) diff --git a/ACL_TensorFlow/contrib/cv/SVD-ID2019_for_ACL/README.md b/ACL_TensorFlow/contrib/cv/SVD-ID2019_for_ACL/README.md index e69de29bb..25ea1bb75 100644 --- a/ACL_TensorFlow/contrib/cv/SVD-ID2019_for_ACL/README.md +++ b/ACL_TensorFlow/contrib/cv/SVD-ID2019_for_ACL/README.md @@ -0,0 +1,89 @@ +# SVD_ID2019_for ACL + +#### 概述 +给定两个三维点云图，利用SVD正交化过程SVDO+(M)将其投射到SO(3)上，要求网络预测最佳对齐它们的3D旋转。 + +- 开源代码：训练获取 + + https://github.com/google-research/google-research/tree/master/special_orthogonalization。 + +- 参考论文： + + [An Analysis of SVD for Deep Rotation Estimation](https://arxiv.org/abs/2006.14616) + +#### 数据集 + +训练数据集 points + +测试数据集 points_test + +旋转后数据集 points_test_modified + +#### 模型固化 +-直接获取 + +直接下载获取，百度网盘 +链接：https://pan.baidu.com/s/17zKWq2aY06cF9IQW6htn_A +提取码：2019 +-训练获取 + +训练获取 +训练完成saved_model模型网盘链接：https://pan.baidu.com/s/1Y4ato6Ob-6-rcXr31AvgoA +提取码：2019 + +1.按照SVD_ID2019_for_Tensorflow中的流程训练，模型保存为saved_model格式 + +2.将saved_model格式文件冻结为pb文件（需要在freeze.py文件中修改路径） + +python freeze.py + +得到svd.pd + +#### 使用ATC工具将pb文件转换为om模型 +命令行代码示例 + +atc --model=/home/test_user04/svd.pb --framework=3 --output=/home/test_user04/svd --soc_version=Ascend310 --input_shape="data_1:1,1410,3;rot_1:1,3,3" + +注意所使用机器的Ascend的型号 + +模型直接下载百度网盘链接：https://pan.baidu.com/s/14-m0ZhPQyIr8enpUgVytpg +提取码：2019 + +得到svd.om +#### 制作数据集 +-直接下载,数据集在svd_inference/data_1 + + + +-自己制作 +原数据链接：链接：https://pan.baidu.com/s/1aGAO3os8ifDnYm1yXrxndQ +提取码：2019 + +使用pts2txt制作数据集(注意修改数据路径，数据路径为/xxx/points_test_modified/*.pts,注意修改产生数据集后的路径) + +python pts2txt.py + +#### 获取离线推理输出bin文件 + +推理文件在压缩包svd_inference中，下载百度网盘链接：https://pan.baidu.com/s/1OfCxHMUJcnyqp2IcvV3eWg +提取码：2019 + +脚本在src文件夹中，直接运行 + +python svdom_inference.py + +推理结果直接下载网盘链接：https://pan.baidu.com/s/1NFNfJkTUW4u7YJcaHK9mLw +提取码：2019 + +#### 使用输出的bin文件验证推理精度 + +运行脚本 + +python calc_acc.py + +得到推理精度:3.150504164928697 + +与在线推理精度近似 + +关于以上所有文件的百度网盘链接：https://pan.baidu.com/s/1sR8gYK8jM6xCZwbq7eK50A +提取码：2019 \ No newline at end of file -- Gitee From 7a0b464ef4805ea6bbbdd10245145e3eec0fed9a Mon Sep 17 00:00:00 2001 From: ab_dx_z <10158481+ab_dx_z@user.noreply.gitee.com> Date: Sat, 22 Oct 2022 14:15:37 +0000 Subject: [PATCH 88/88] =?UTF-8?q?=E7=A6=BB=E7=BA=BF=E6=8E=A8=E7=90=86?= =?UTF-8?q?=E8=84=9A=E6=9C=AC?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: ab_dx_z <> --- .../contrib/cv/SVD-ID2019_for_ACL/calc_acc.py | 33 ++++++++++++ .../contrib/cv/SVD-ID2019_for_ACL/frezze.py | 50 +++++++++++++++++++ .../contrib/cv/SVD-ID2019_for_ACL/pts2txt.py | 44 ++++++++++++++++ 3 files changed, 127 insertions(+) create mode 100644 ACL_TensorFlow/contrib/cv/SVD-ID2019_for_ACL/calc_acc.py create mode 100644 ACL_TensorFlow/contrib/cv/SVD-ID2019_for_ACL/frezze.py create mode 100644 ACL_TensorFlow/contrib/cv/SVD-ID2019_for_ACL/pts2txt.py diff --git a/ACL_TensorFlow/contrib/cv/SVD-ID2019_for_ACL/calc_acc.py b/ACL_TensorFlow/contrib/cv/SVD-ID2019_for_ACL/calc_acc.py new file mode 100644 index 000000000..b71c645e3 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SVD-ID2019_for_ACL/calc_acc.py @@ -0,0 +1,33 @@ +import numpy as np +import glob +import pathlib +import os +import utils_gpu +import tensorflow as tf + +INFERENCE_DIR = "C:/Users/1young/Desktop/svd_output/svd_output/*.bin" +TEST_DIR = "D:/svd_code2/data_1" +tf.enable_eager_execution() + + +def main(): + input_test_files = glob.glob(INFERENCE_DIR) + mean_err = [] + for in_file in input_test_files: + out_file_prefix = pathlib.Path(in_file).stem + rot_path = os.path.join(TEST_DIR,'%s.txt' % out_file_prefix) + rot = np.loadtxt(rot_path)[:3,:].reshape((-1,3,3)) + r = np.loadtxt(in_file).reshape((-1,3,3)) + theta = utils_gpu.relative_angle(rot, r) + mean_theta = tf.reduce_mean(theta) + mean_theta_deg = mean_theta * 180.0 / np.pi + mean_theta_deg = mean_theta_deg.numpy() + mean_err.append(mean_theta_deg) + print("the mean of error") + print(np.mean(np.array(mean_err))) + +if __name__=="__main__": + main() + + + diff --git a/ACL_TensorFlow/contrib/cv/SVD-ID2019_for_ACL/frezze.py b/ACL_TensorFlow/contrib/cv/SVD-ID2019_for_ACL/frezze.py new file mode 100644 index 000000000..70014c274 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SVD-ID2019_for_ACL/frezze.py @@ -0,0 +1,50 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import tensorflow as tf +from tensorflow.python.tools import freeze_graph +#from npu_bridge.npu_init import * + +saved_model_path = 'C:/Users/1young/Desktop/1666004668' +def main(): + freeze_graph.freeze_graph( + input_saved_model_dir=saved_model_path, + output_node_names='rotation_matrix', + output_graph='svd.pb', + initializer_nodes='', + input_graph= None, + input_saver= False, + input_binary=False, + input_checkpoint=None, + restore_op_name=None, + filename_tensor_name=None, + clear_devices=False, + input_meta_graph=False) + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/ACL_TensorFlow/contrib/cv/SVD-ID2019_for_ACL/pts2txt.py b/ACL_TensorFlow/contrib/cv/SVD-ID2019_for_ACL/pts2txt.py new file mode 100644 index 000000000..01f84fb3a --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SVD-ID2019_for_ACL/pts2txt.py @@ -0,0 +1,44 @@ +import os +import numpy as np +import tensorflow as tf +import glob +import pathlib + +inputpath = "D:\special_orthogonalization\points_test_modified\*.pts" +outputpath = "D:\svd_code2\data_1" + + +tf.enable_eager_execution() + +def data_processing(pts_path): + file_buffer = tf.read_file(pts_path) + lines = tf.string_split([file_buffer], delimiter='\n') + lines1 = tf.string_split(lines.values, delimiter='\r') + values = tf.stack(tf.decode_csv(lines1.values, + record_defaults=[[0.0], [0.0], [0.0]], field_delim=' ')) + values = tf.transpose(values) # 3xN --> Nx3. + diff_num = 1414-tf.shape(values)[0] + repeat_pts = tf.tile(tf.reshape(values[4,:],(1,-1)),[diff_num,1]) + + values = tf.concat([values,repeat_pts],axis=0) + # First three rows are the rotation matrix, remaining rows the point cloud. + values = tf.concat([values, repeat_pts], axis=0) + # First three rows are the rotation matrix, remaining rows the point cloud. + return values.numpy() + +def file_save(path,datapath): + input_test_files = glob.glob(path) + for in_file in input_test_files: + out_file_prefix = pathlib.Path(in_file).stem + values = data_processing(in_file) + out_file1 = os.path.join( + datapath, '%s.txt' % out_file_prefix) + np.savetxt(out_file1,values) + +def main(): + os.makedirs(outputpath, exist_ok=True) + file_save(inputpath,outputpath) + +if __name__ == '__main__': + main() + -- Gitee